diff --git a/eeg_ml_pipeline/EEGExtract.py b/eeg_ml_pipeline/EEGExtract.py index 00c46f5..88c5236 100644 --- a/eeg_ml_pipeline/EEGExtract.py +++ b/eeg_ml_pipeline/EEGExtract.py @@ -273,7 +273,6 @@ def hjorthParameters(xV): # in the matlab code for hjorth complexity subtraction by mob not division was used return mobility, complexity -########## # false nearest neighbor descriptor def falseNearestNeighbor(eegData, fast=True): # Average Mutual Information @@ -293,18 +292,18 @@ def falseNearestNeighbor(eegData, fast=True): else: cur_eegData = eegData[chan, :, epoch] lagidx = 0 # we are looking for the index of the lag that makes the signal maximally uncorrelated to the original - minNMI = 1 # normed_mutual_info is from 1 (perfectly correlated) to 0 (not at all correlated) - for lag in range(1, max_delay): - x = cur_eegData[:-lag] - xlag = cur_eegData[lag:] - convert float data into histogram bins - nbins = int(np.floor(1 + np.log2(len(x)) + 0.5)) - x_discrete = np.histogram(x, bins=nbins)[0] - xlag_discrete = np.histogram(xlag, bins=nbins)[0] - cNMI = normed_mutual_info(x_discrete, xlag_discrete) - if cNMI < minNMI: - minNMI = cNMI - lagidx = lag + # # minNMI = 1 # normed_mutual_info is from 1 (perfectly correlated) to 0 (not at all correlated) + # # for lag in range(1, max_delay): + # # x = cur_eegData[:-lag] + # # xlag = cur_eegData[lag:] + # # # convert float data into histogram bins + # # nbins = int(np.floor(1 + np.log2(len(x)) + 0.5)) + # # x_discrete = np.histogram(x, bins=nbins)[0] + # # xlag_discrete = np.histogram(xlag, bins=nbins)[0] + # # cNMI = normed_mutual_info(x_discrete, xlag_discrete) + # # if cNMI < minNMI: + # # minNMI = cNMI + # # lagidx = lag # nearest neighbors part knn = int(max(2, 6*lagidx)) # heuristic (number of nearest neighbors to look up) m = 1 # lagidx + 1 diff --git a/eeg_ml_pipeline/ImportUtils.py b/eeg_ml_pipeline/ImportUtils.py index 10c64b7..27b34c0 100644 --- a/eeg_ml_pipeline/ImportUtils.py +++ b/eeg_ml_pipeline/ImportUtils.py @@ -40,12 +40,18 @@ import copy from sklearn import feature_selection import argparse -import cuml -from cuml.svm import SVR -from cuml.ensemble import RandomForestRegressor -from cuml.svm import SVC -from cuml.ensemble import RandomForestClassifier -from cuml.metrics import accuracy_score +from sklearn.svm import SVR +from sklearn.svm import SVC +from sklearn.ensemble import RandomForestRegressor +from sklearn.ensemble import RandomForestClassifier +from sklearn.metrics import accuracy_score + +# import cuml +# from cuml.svm import SVR +# from cuml.ensemble import RandomForestRegressor +# from cuml.svm import SVC +# from cuml.ensemble import RandomForestClassifier +# from cuml.metrics import accuracy_score # In[ ]: diff --git a/eeg_ml_pipeline/TopNByClassifier.py b/eeg_ml_pipeline/TopNByClassifier.py deleted file mode 100644 index d83f9e8..0000000 --- a/eeg_ml_pipeline/TopNByClassifier.py +++ /dev/null @@ -1,735 +0,0 @@ -#!/usr/bin/env python -# coding: utf-8 - -# In[ ]: - - -from ImportUtils import * -from sklearn.model_selection import ParameterGrid - -from sklearn.model_selection import train_test_split - -from sklearn.preprocessing import StandardScaler -from sklearn.metrics import accuracy_score - -from sklearn.feature_selection import chi2 -from sklearn.feature_selection import SelectKBest, f_classif -from sklearn.model_selection import train_test_split -from sklearn.preprocessing import StandardScaler -from sklearn.metrics import accuracy_score - -from sklearn.ensemble import RandomForestRegressor as sklearnrfi - -import os -import glob -from scipy import io,signal -import numpy as np -import pandas as pd -from sklearn import preprocessing -import pickle -from sklearn.metrics import mean_squared_error -from sklearn.impute import SimpleImputer - - -import matplotlib.pyplot as plt -# %matplotlib inline -import seaborn as sns -import copy - -def topElectrodeRegressionRanking(dataset, window, stride, sfreq, clf, label, scale=False, pca=False): - ''' - Ranks of features according to rmse computed by regressor passed in clf - Plots electrode v/s rmse graph - - ''' - # parameters :- - # dataset - name of the dataset - # window - length of the sliding window in seconds - # stride - length of the stride of the sliding window in seconds - # sfreq - sampling frequency of the EEG data - # clf - name of the classifier to be used - # label - valence/arousal/dominance/liking label (shape depends upon the dataset) in an enumerated form (0- valence ; 1-arousal ; 2- like; 3-dominance) - # scale - sclaing of the EEG data if required - - # returns :- - # void - - pwd = os.getcwd() - - #load extracted features - ##################################################################################################################################################### - featurepath = os.getcwd() + '/' + dataset + '/data_extracted/featuresDict/' - ans = np.load((featurepath + "shannonEntropy_{}_{}.npz").format(window,stride), allow_pickle=True)['features'] - Y_epoch = np.load((featurepath + "shannonEntropy_{}_{}.npz").format(window,stride), allow_pickle=True)['Y'] - - rmseList = [] - electrodeList = ['AF3', 'F7', 'F3', 'FC5', 'T7', 'P7', 'O1', 'O2', 'P8', 'T8', 'FC6', 'F4', 'F8', 'AF4'] - fs = sfreq - pwd = os.getcwd() - featuresDict = loadFeaturesDict(dataset) - asm_features = ['dasm_delta', 'dasm_theta', 'dasm_alpha', 'dasm_beta', 'dasm_gamma', 'rasm_delta', 'rasm_theta', 'rasm_alpha', 'rasm_beta', 'rasm_gamma'] - for asm in asm_features: - featuresDict.pop(asm) - - common = [] - with open('intersection.pkl', 'rb') as f: - common = pickle.load(f) - - for k in list(featuresDict.keys()): - if k not in common: - # pop out common feature - featuresDict.pop(k) - - selectFeatures = list(featuresDict.keys()) - y = Y_epoch[:,label] #valence - ##################################################################################################################################################### - - for electrode in range(14): - # Load FeaturesDict from memory - - - print("Number of segments are: {}".format(ans.shape[1])) - - featureMatrix = np.empty((len(selectFeatures),ans.shape[1])) #[14*32 + 1,80640] - i=0 - for key,value in featuresDict.items(): - featureMatrix[i,:] = value[electrode,:] - i = i+1 - - print(featureMatrix.T.shape) - featureMatrix = featureMatrix.astype(np.float32) - - #Impute NaN values with zero - if np.isnan(featureMatrix).any(): - featureMatrix = np.nan_to_num(featureMatrix,nan=0) - - #Name Feature vector columns - feature_channel_index = [] - for feature in selectFeatures: - feature_channel_index.append(feature + str(electrode)) - - print("Number of Feature-Columns: {}\n".format(len(feature_channel_index))) #debug - - #Preparing dataset from feature matrix - X = pd.DataFrame(featureMatrix.T) - X.columns = feature_channel_index - X = X.replace([np.inf, -np.inf], np.nan) - X = X.fillna(0) - - - print("Features Ready for undergoing selection tests done ...\n") - - # Perform train_test_split to get training and test data - X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.2, random_state=42) - - # Normalise-scale data - # Feature Scaling - if(scale == True): - sc = StandardScaler() - X_train = sc.fit_transform(X_train) - X_test = sc.transform(X_test) - - # Apply classfier - clf.fit(X_train, y_train) - y_predict = clf.predict(X_test) - rmse = mean_squared_error(y_test, y_predict,squared=False) - print("window: {}, stide: {}, rmse: {}".format(window,stride,rmse)) - rmseList.append(rmse) - - - #rank electrodes based on RMSE computed by the classifier - electrode_df = pd.DataFrame(electrodeList) - rmse_df = pd.DataFrame(rmseList) - #concat two dataframes for better visualization - electrodeRanking = pd.concat([electrode_df, rmse_df],axis=1) - electrodeRanking.columns = ['Electrode','RMSE'] #naming the dataframe columns - features_result = electrodeRanking.sort_values('RMSE') - print(features_result) - # return features_result - - ################################################################################## - N = features_result.shape[0] - topRmseList = [] - topNList = ["{}".format(x) for x in range(1,N+1)] - - - for n in range(1,N+1): - - - topnelectrodes = features_result.head(n) - electrode_index = topnelectrodes.index - electrode_index = list(electrode_index)[:n] - - # X-Values - featureMatrix = np.empty((len(selectFeatures)*len(electrode_index),ans.shape[1])) - - i = 0 - for index in electrode_index: - for key,value in featuresDict.items(): - featureMatrix[i,:] = value[index,:] - i = i+1 - - featureMatrix = featureMatrix.astype(np.float32) - print(featureMatrix.T.shape) - - # Removing NaN Values - if np.isnan(featureMatrix).any(): - featureMatrix = np.nan_to_num(featureMatrix,nan=0) - - # Name Feature vector columns - feature_channel_index = [] - for index in electrode_index: - for feature in selectFeatures: - feature_channel_index.append(feature + str(index)) - - print("Number of Feature-Columns: {}\n".format(len(feature_channel_index))) - - X = pd.DataFrame(featureMatrix.T) - X.columns = feature_channel_index - X = X.replace([np.inf, -np.inf], np.nan) - X = X.fillna(0) - - - print("Features Ready for undergoing selection tests done ...\n") - - - X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.2, random_state=42) - - # Normalise-scale data - # Feature Scaling - if(scale == True): - sc = StandardScaler() - X_train = sc.fit_transform(X_train) - X_test = sc.transform(X_test) - - # Apply classfier - - search_method = "tpot" - best_clf = None - if(search_method == "bayes_sk_opt"): - - # BayesCV scikit opt - search_space = {"bootstrap": Categorical([True, False]), # values for boostrap can be either True or False - "max_depth": Integer(6, 20), # values of max_depth are integers from 6 to 20 - "max_features": Categorical(['auto', 'sqrt','log2']), - "min_samples_leaf": Integer(2, 10), - "min_samples_split": Integer(2, 10), - "n_estimators": Integer(100, 500) - } - - forest_bayes_search = BayesSearchCV(clf, search_space, n_iter=32, cv=5) - print(forest_bayes_search) - print(forest_bayes_search.fit(X_train, y_train)) - print("Best Parameters are: ", forest_bayes_search.best_params_) - best_clf = forest_bayes_search.best_estimator_ - - elif(search_method =="random_grid_search"): - print("Random Search followed by GridSearch initiated!\n"); - #RandomSearchCV followed by GridSearchCV - random_grid = {'n_estimators': [int(x) for x in np.linspace(start = 200, stop = 2000, num = 10)], - 'max_features': ['auto', 'sqrt','log2'], - 'max_depth': [int(x) for x in np.linspace(10, 1000,10)], - 'min_samples_split': [2, 5, 10,14], - 'min_samples_leaf': [1, 2, 4,6,8], - } - rf_randomcv=RandomizedSearchCV(estimator=clf,param_distributions=random_grid,n_iter=100,cv=5,verbose=2,random_state=100) - print(rf_randomcv.fit(X_train, y_train)) - print("Best Parameters for RandomSearchCV are: ", rf_randomcv.best_params_) - print("RMSE with RandomSearchCV is :",mean_squared_error(y_test, rf_randomcv.best_estimator_.predict(X_test),squared=False)); - - param_grid = { - 'max_depth': [rf_randomcv.best_params_['max_depth']], - 'max_features': [rf_randomcv.best_params_['max_features']], - 'min_samples_leaf': [rf_randomcv.best_params_['min_samples_leaf'], - rf_randomcv.best_params_['min_samples_leaf']+2, - rf_randomcv.best_params_['min_samples_leaf'] + 4], - 'min_samples_split': [rf_randomcv.best_params_['min_samples_split'] - 2, - rf_randomcv.best_params_['min_samples_split'] - 1, - rf_randomcv.best_params_['min_samples_split'], - rf_randomcv.best_params_['min_samples_split'] +1, - rf_randomcv.best_params_['min_samples_split'] + 2], - 'n_estimators': [rf_randomcv.best_params_['n_estimators'] - 200, rf_randomcv.best_params_['n_estimators'] - 100, - rf_randomcv.best_params_['n_estimators'], - rf_randomcv.best_params_['n_estimators'] + 100, rf_randomcv.best_params_['n_estimators'] + 200] - } - - grid_search=GridSearchCV(estimator=rf,param_grid=param_grid,cv=10, verbose=5) - grid_search.fit(X_train,y_train) - best_clf = rf_randomcv.best_estimator_ - elif search_method =="manual_search": - min_rmse = 1000 - best_clf = clf - min_params = None - # 2*3*3*3*3 - param_grid = {'n_estimators': [50, 100], - 'max_features': ['auto'], - 'max_depth': [2, 10, 100], - 'min_samples_split': [2, 5, 10], - 'min_samples_leaf': [1, 2, 8], - } - - param_grid = ParameterGrid(param_grid) - for params in param_grid: - print("Current Parameters : ", params) - temp_clf = RandomForestRegressor( max_features = params['max_features'], min_samples_leaf = params['min_samples_leaf'], min_samples_split = params['min_samples_split'], n_estimators = params['n_estimators'],max_depth = params['max_depth']); - temp_clf.fit(X_train,y_train) - y_predict = temp_clf.predict(X_test) - rmse = mean_squared_error(y_test, y_predict,squared=False) - print("Current RMSE with above params : ", rmse) - if(min_rmse > rmse): - min_rmse = rmse; - best_clf = temp_clf; - min_params = params; - - print("Best Params for parameter search are : \n", min_params) - print("window: {}, stide: {}, rmse: {}".format(window,stride,min_rmse)) - topRmseList.append(min_rmse) - elif search_method == "tpot": - from tpot import TPOTRegressor; - # TPOT setup - GENERATIONS = 5 - POP_SIZE = 100 - CV = 5 - SEED = 42 - - tpot = TPOTRegressor( - generations=GENERATIONS, - population_size=POP_SIZE, - random_state=SEED, - config_dict="TPOT cuML", - n_jobs=1, # cuML requires n_jobs=1 - cv=CV, - verbosity=2, - ) - - tpot.fit(X_train, y_train) - - y_predict = tpot.predict(X_test) - rmse = mean_squared_error(y_test, y_predict,squared=False) - print("window: {}, stide: {}, rmse: {}".format(window,stride,rmse)) - topRmseList.append(rmse) - - - else: - best_clf = clf - best_clf.fit(X_train,y_train) - - - if search_method != "manual_search" and search_method != "tpot": - y_predict = best_clf.predict(X_test) - rmse = mean_squared_error(y_test, y_predict,squared=False) - print("window: {}, stide: {}, rmse: {}".format(window,stride,rmse)) - topRmseList.append(rmse) - - - topNElectrode_df = pd.DataFrame(topNList) - topNRmse_df = pd.DataFrame(topRmseList) - #concat two dataframes for better visualization - topNElectrodeRanking = pd.concat([topNElectrode_df, topNRmse_df],axis=1) - topNElectrodeRanking.columns = ['Electrode','RMSE'] #naming the dataframe columns - print(topNElectrodeRanking) - - # Plotting - fig = plt.gcf() - fig.set_size_inches(20, 10) - plt.rcParams.update({'font.size': 30}) - plt.xlabel('Top N Electrodes') - plt.ylabel('RMSE') - plt.plot(topNElectrodeRanking.loc[:,"Electrode"], topNElectrodeRanking.loc[:,"RMSE"]) - plt.tight_layout() - - -# In[ ]: - - -def topFeaturesRegressionRanking(dataset, window, stride, sfreq, clf, label, scale=False, pca=False): - ''' - Ranks of features according to rmse computed by regressor passed in clf - Plots electrode v/s rmse graph - - ''' - # parameters :- - # dataset - name of the dataset - # window - length of the sliding window in seconds - # stride - length of the stride of the sliding window in seconds - # sfreq - sampling frequency of the EEG data - # clf - name of the classifier to be used - # label - valence/arousal/dominance/liking label (shape depends upon the dataset) - # scale - sclaing of the EEG data if required - - # returns :- - # void - fs = sfreq - pwd = os.getcwd() - featurepath = os.getcwd() + '/' + dataset + '/data_extracted/featuresDict/' - ans = np.load((featurepath + "shannonEntropy_{}_{}.npz").format(window,stride), allow_pickle=True)['features'] - Y_epoch = np.load((featurepath + "shannonEntropy_{}_{}.npz").format(window,stride), allow_pickle=True)['Y'] - print("Number of segments are: {}".format(ans.shape[1])) - - featuresDict = None - featuresDict = loadFeaturesDict(dataset) - - common = [] - with open('intersection.pkl', 'rb') as f: - common = pickle.load(f) - - for k in list(featuresDict.keys()): - if k not in common: - # pop out common feature - featuresDict.pop(k) - - featuresList = list(featuresDict.keys()) - - y = Y_epoch[:,label] #valence - - - rmseList = [] - - #################################################################### - #modify featuresList - featureMatrix = np.empty((0,ans.shape[1])) #[14*32 + 1,80640] - for key,value in featuresDict.items(): - featureMatrix = np.append(featureMatrix,value,axis=0) - - - if np.isnan(featureMatrix).any(): - featureMatrix = np.nan_to_num(featureMatrix,nan=0) - - featureMatrix = featureMatrix.astype('float64') - - - feature_channel_index = [] - for feature in featuresList: - for i in range(featuresDict[feature].shape[0]): - if(i>=10): - feature_channel_index.append(feature + str(i)) - else: - feature_channel_index.append(feature + '0' + str(i)) - - print(len(list(featuresDict.keys()))) - print("Number of Feature-Columns: {}\n".format(len(feature_channel_index))) - - X = pd.DataFrame(featureMatrix.T) - X = X.replace([np.inf, -np.inf], np.nan) - X = X.fillna(0) - X.columns = feature_channel_index - - #Remove Variance = 0 features - constant_filter = VarianceThreshold(threshold=0) - constant_filter.fit(X) - constant_columns = [column for column in X.columns - if column not in - X.columns[constant_filter.get_support()]] - X = constant_filter.transform(X) - - for column in constant_columns: - feature_channel_index.remove(column) - - print(len(feature_channel_index),feature_channel_index ) - - X = pd.DataFrame(X) - X.columns = feature_channel_index - - - filtered_featuresList = [] - print(type(X)) - for col in X.columns: - feature = col[:-2] - electrode = int(col[-2:]) - if(feature not in filtered_featuresList): - filtered_featuresList.append(feature) - - featuresList = filtered_featuresList - - for feature in featuresList: - # Load FeaturesDict from memory - - - - featureMatrix = featuresDict[feature] - featureMatrix = featureMatrix.astype(np.float32) - - if np.isnan(featureMatrix).any(): - featureMatrix = np.nan_to_num(featureMatrix,nan=0) - - - - feature_channel_index = [] - - for i in range(featuresDict[feature].shape[0]): - feature_channel_index.append(feature + str(i)) - - print("Number of Feature-Columns: {}\n".format(len(feature_channel_index))) - - X = pd.DataFrame(featureMatrix.T) - X = X.replace([np.inf, -np.inf], np.nan) - X = X.fillna(0) - X.columns = feature_channel_index - - - print("Features Ready for undergoing selection tests done ...\n") - - - X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.2, random_state=42) - - # Normalise-scale data - # Feature Scaling - if(scale == True): - sc = StandardScaler() - X_train = sc.fit_transform(X_train) - X_test = sc.transform(X_test) - - # Apply classfier - clf.fit(X_train, y_train) - y_predict = clf.predict(X_test) - rmse = mean_squared_error(y_test, y_predict,squared=False) - print("window: {}, stide: {}, rmse: {}".format(window,stride,rmse)) - rmseList.append(rmse) - - - - features_df = pd.DataFrame(featuresList) - rmse_df = pd.DataFrame(rmseList) - #concat two dataframes for better visualization - featureRanking = pd.concat([features_df, rmse_df],axis=1) - featureRanking.columns = ['Feature','RMSE'] #naming the dataframe columns - features_result = featureRanking.sort_values('RMSE') - features_result.to_csv(pwd + "/" + dataset + "/arousal_plots/" + "CommonFeaturesRegressionRanking" + str(window) + str(stride) + ".csv") - print(features_result) - - ########################################### - N = features_result.shape[0] - topNRmseList = [] - topNList = ["{}".format(x) for x in range(1,N+1)] - - - - for n in range(1,N+1): - - - topnfeatures = copy.deepcopy(features_result.head(n)) - topnfeatures = topnfeatures['Feature'].tolist() #list of feature-names - - # X-Values################################################ - - featureMatrix = np.empty((0,ans.shape[1])) - - for feature in topnfeatures: - featureMatrix = np.append(featureMatrix, featuresDict[feature], axis=0) - - featureMatrix = featureMatrix.astype(np.float32) - print(featureMatrix.T.shape) - - feature_channel_index = [] - for feature in topnfeatures: - i=0 - for i in range(featuresDict[feature].shape[0]): - feature_channel_index.append(feature + str(i)) - - - # Removing NaN Values - if np.isnan(featureMatrix).any(): - featureMatrix = np.nan_to_num(featureMatrix,nan=0) - - - print("Number of Feature-Columns: {}\n".format(len(feature_channel_index))) - - X = pd.DataFrame(featureMatrix.T) - X.columns = feature_channel_index - X = X.replace([np.inf, -np.inf], np.nan) - X = X.fillna(0) - - - print("Features Ready for undergoing selection tests done ...\n") - - - X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.2, random_state=42) - - # Normalise-scale data - # Feature Scaling - if(scale == True): - sc = StandardScaler() - X_train = sc.fit_transform(X_train) - X_test = sc.transform(X_test) - - clf.fit(X_train, y_train) - y_predict = clf.predict(X_test) - rmse = mean_squared_error(y_test, y_predict,squared=False) - print("window: {}, stide: {}, rmse: {}".format(window,stride,rmse)) - topNRmseList.append(rmse) - - - - topNFeatures_df = pd.DataFrame(topNList) - topNRmse_df = pd.DataFrame(topNRmseList) - - #concat two dataframes for better visualization - topNFeaturesRanking = pd.concat([topNFeatures_df, topNRmse_df],axis=1) - topNFeaturesRanking.columns = ['Feature','RMSE'] #naming the dataframe columns - print(topNFeaturesRanking) - topNFeaturesRanking.to_csv(pwd + "/" + dataset + "/arousal_plots/" + "topCommonFeaturesRegressionRanking" + str(window) + str(stride) + ".csv") - - # Plotting - fig = plt.gcf() - fig.set_size_inches(25, 10) - plt.rcParams.update({'font.size': 30}) - plt.xlabel('Top N Features') - plt.ylabel('RMSE') - plt.plot(topNFeaturesRanking.loc[:,"Feature"], topNFeaturesRanking.loc[:,"RMSE"]) - plt.tight_layout() - - -# In[ ]: - - -def topFeatureColumnsRegressionRanking(dataset, window, stride, sfreq, clf, label, scale=False, pca=False): - - # parameters :- - # dataset - name of the dataset - # window - length of the sliding window in seconds - # stride - length of the stride of the sliding window in seconds - # sfreq - sampling frequency of the EEG data - # clf - name of the classifier to be used - # label - valence/arousal/dominance/liking label (shape depends upon the dataset) - # scale - sclaing of the EEG data if required - - # returns :- - # void - - fs = sfreq - pwd = os.getcwd() - featurepath = os.getcwd() + '/' + dataset + '/data_extracted/featuresDict/' - - ans = np.load((featurepath + "shannonEntropy_{}_{}.npz").format(window,stride), allow_pickle=True)['features'] - Y_epoch = np.load((featurepath + "shannonEntropy_{}_{}.npz").format(window,stride), allow_pickle=True)['Y'] - electrodeList = ['AF3', 'F7', 'F3', 'FC5', 'T7', 'P7', 'O1', 'O2', 'P8', 'T8', 'FC6', 'F4', 'F8', 'AF4'] - - - print("Number of segments are: {}".format(ans.shape[1])) - - #X############################################################################################## - - featuresDict = None - featuresDict = loadFeaturesDict(dataset) - - common = [] - with open('intersection.pkl', 'rb') as f: - common = pickle.load(f) - - for k in list(featuresDict.keys()): - if k not in common: - # pop out common feature - featuresDict.pop(k) - - - featuresList = list(featuresDict.keys()) - - # defining column names - feature_channel_index = [] - - for feature in featuresList: - for i in range(featuresDict[feature].shape[0]): - feature_channel_index.append(feature + str(i)) - - #defining feature matrix - featureMatrix = np.empty((0,ans.shape[1])) #[14*32 + 1,80640] - for key,value in featuresDict.items(): - featureMatrix = np.append(featureMatrix,value,axis=0) - - - print("Shape of FeatureMatrix: {}\n".format(featureMatrix.T.shape)) - - #data-imputation and nan-removal - featureMatrix = featureMatrix.astype(np.float32) - - if np.isnan(featureMatrix).any(): - featureMatrix = np.nan_to_num(featureMatrix,nan=0) - - X = pd.DataFrame(featureMatrix.T) - X = X.replace([np.inf, -np.inf], np.nan) - X = X.fillna(0) - X.columns = feature_channel_index - - - #Y##################################################################### - - y = Y_epoch[:,label] #valence - - ######################################################################## - rmseList = [] - - for col in feature_channel_index: - input_df = pd.DataFrame(X[col]) - - X_train, X_test, y_train, y_test = train_test_split(input_df, y, test_size=0.2, random_state=42) - - # Normalise-scale data - # Feature Scaling - if(scale == True): - sc = StandardScaler() - X_train = sc.fit_transform(X_train) - X_test = sc.transform(X_test) - - # Apply classfier - clf.fit(X_train, y_train) - y_predict = clf.predict(X_test) - rmse = mean_squared_error(y_test, y_predict, squared=False) - rmseList.append(rmse) - - - - col_df = pd.DataFrame(feature_channel_index) - rmse_df = pd.DataFrame(rmseList) - #concat two dataframes for better visualization - colRanking = pd.concat([col_df, rmse_df],axis=1) - colRanking.columns = ['Column','RMSE'] #naming the dataframe columns - features_result = colRanking.sort_values('RMSE') - print(features_result) - - - N = len(feature_channel_index) - topNRmseList = [] - topNList = ["{}".format(x) for x in range(1,N+1)] - - for n in range(1, N+1): - ranking_df = features_result.head(n) - topncols = ranking_df['Column'].tolist() - - X_train, X_test, y_train, y_test = train_test_split(X[topncols], y, test_size=0.2, random_state=42) - - # Normalise-scale data - # Feature Scaling - if(scale == True): - sc = StandardScaler() - X_train = sc.fit_transform(X_train) - X_test = sc.transform(X_test) - - # Apply classfier - clf.fit(X_train, y_train) - y_predict = clf.predict(X_test) - rmse = mean_squared_error(y_test, y_predict, squared=False) - topNRmseList.append(rmse) - - - topcol_df = pd.DataFrame(topNList) - toprmse_df = pd.DataFrame(topNRmseList) - #concat two dataframes for better visualization - topcolRanking = pd.concat([topcol_df, toprmse_df],axis=1) - topcolRanking.columns = ['Column','RMSE'] #naming the dataframe columns - topfeatures_result = topcolRanking - print(topfeatures_result) - topfeatures_result.to_csv(pwd + "/" + dataset + "/arousal_plots/" + "ColumnsRegressionRanking" + str(window) + str(stride) + ".csv") - - - # Plotting - fig = plt.gcf() - fig.set_size_inches(60, 9) - plt.xlabel('Top N Columns') - plt.ylabel('RMSE') - plt.title("Top N Columns v/s RMSE Plot for Window:{} Stride:{} epoched data by varying N".format(window,stride)) - plt.plot(topfeatures_result.loc[:,"Column"], topfeatures_result.loc[:,"RMSE"]) - plt.tight_layout() - plt.savefig(pwd + "/" + dataset + "/arousal_plots/" + "topFeatureColumnsRegressionRanking" + str(window) + str(stride) + ".svg", bbox_inches='tight') - plt.show() - plt.clf() - diff --git a/eeg_ml_pipeline/TopNByFSMethods.py b/eeg_ml_pipeline/TopNByFSMethods.py index e903210..fe00392 100644 --- a/eeg_ml_pipeline/TopNByFSMethods.py +++ b/eeg_ml_pipeline/TopNByFSMethods.py @@ -1,17 +1,19 @@ #!/usr/bin/env python # coding: utf-8 +# DATE - 01/11/2022 -# In[ ]: +# AUTHOR - ROHIT GARG from ImportUtils import * from sklearn.ensemble import RandomForestRegressor as sklearnrfi from sklearn.feature_selection import VarianceThreshold +from sklearn.metrics import r2_score +from sklearn.metrics import mean_absolute_error +from sklearn.metrics import explained_variance_score -# In[ ]: - def topElectrodeFSRegressionRanking(dataset, window, stride, sfreq, clf, label, scale=False, pca=False, mutual_info = False, method='SelectKBest'): ''' @@ -129,7 +131,7 @@ def topElectrodeFSRegressionRanking(dataset, window, stride, sfreq, clf, label, featureScores.columns = ['Specs','Score'] #naming the dataframe columns features_result = featureScores.nlargest(X.shape[1],'Score') print(features_result) - features_result.to_csv(pwd + "/" + dataset + "/arousal_plots/" + "CommonElectrodeFSRegressionRanking"+ method + str(window) + str(stride) + ".csv") + features_result.to_csv(f"output/{dataset}_{label}_electrode_selection.csv") ################################################################### @@ -150,10 +152,14 @@ def topElectrodeFSRegressionRanking(dataset, window, stride, sfreq, clf, label, N = len(topelectrodes) topRmseList = [] + topR2List = [] + topMAEList = [] + topEVList = [] topNList = ["{}".format(x) for x in range(1,N+1)] for n in range(1,N+1): + electrode_index = topelectrodes[:n] print(topelectrodes) @@ -207,8 +213,17 @@ def topElectrodeFSRegressionRanking(dataset, window, stride, sfreq, clf, label, clf.fit(X_train, y_train) y_predict = clf.predict(X_test) rmse = mean_squared_error(y_test, y_predict,squared=False) + score_r2 = r2_score(y_test, y_predict) + score_mae = mean_absolute_error(y_test, y_predict) + score_ev = explained_variance_score(y_test, y_predict) print("window: {}, stide: {}, rmse: {}".format(window,stride,rmse)) + print(f"r2: {score_r2}") + print(f"mae: {score_mae}") + print(f"ev: {score_ev}") topRmseList.append(rmse) + topR2List.append(score_r2) + topMAEList.append(score_mae) + topEVList.append(score_ev) @@ -217,11 +232,19 @@ def topElectrodeFSRegressionRanking(dataset, window, stride, sfreq, clf, label, # features_result = features_result.reset_index() topNElectrode_df = pd.DataFrame(topNList) topNRmse_df = pd.DataFrame(topRmseList) + topNR2_df = pd.DataFrame(topR2List) + topNMAE_df = pd.DataFrame(topMAEList) + topNEV_df = pd.DataFrame(topEVList) + #concat two dataframes for better visualization - topNElectrodeRanking = pd.concat([topNElectrode_df, topNRmse_df],axis=1) - topNElectrodeRanking.columns = ['Electrode','RMSE'] #naming the dataframe columns - print(topNElectrodeRanking) - topNElectrodeRanking.to_csv(pwd + "/" + dataset + "/arousal_plots/" + "topCommonElectrodeFSRegressionRanking"+ method + str(window) + str(stride) + ".csv") + topNElectrode = pd.concat([topNElectrode_df, topNRmse_df, topNR2_df, topNMAE_df, topNEV_df],axis=1) + + topNElectrode.columns = ['Electrode','RMSE', 'R2', 'MAE', 'EV'] #naming the dataframe columns + + print(topNElectrode) + + topNElectrode.to_csv(f"output/{dataset}_{label}_electrode.csv") + # return features_result @@ -231,8 +254,10 @@ def topElectrodeFSRegressionRanking(dataset, window, stride, sfreq, clf, label, plt.rcParams.update({'font.size': 30}) plt.xlabel('Top N Electrodes') plt.ylabel('RMSE') - plt.plot(topNElectrodeRanking.loc[:,"Electrode"], topNElectrodeRanking.loc[:,"RMSE"]) + plt.plot(topNElectrode.loc[:,"Electrode"], topNElectrode.loc[:,"RMSE"]) plt.tight_layout() + plt.savefig(f"output/{dataset}_{label}_electrode_RMSE.svg") + plt.clf() # In[ ]: @@ -363,7 +388,7 @@ def topFeatureFSRegressionRanking(dataset, window, stride, sfreq, clf, label, sc featureScores.columns = ['Specs','Score'] #naming the dataframe columns features_result = featureScores.nlargest(X.shape[1],'Score') print(features_result) - features_result.to_csv(pwd + "/" + dataset + "/arousal_plots/" + "CommonFeatureFSRegressionRanking"+ method + str(window) + str(stride) + ".csv") + features_result.to_csv(f"output/{dataset}_{label}_feature_selection.csv") @@ -386,8 +411,11 @@ def topFeatureFSRegressionRanking(dataset, window, stride, sfreq, clf, label, sc # TOP-N-FEATURE-RANKING print(topfeatures) print(topelectrodes) - N = len(topfeatures) + N = len(topfeatures) topNRmseList = [] + topR2List = [] + topMAEList = [] + topEVList = [] topNList = ["{}".format(x) for x in range(1,N+1)] @@ -441,8 +469,17 @@ def topFeatureFSRegressionRanking(dataset, window, stride, sfreq, clf, label, sc clf.fit(X_train, y_train) y_predict = clf.predict(X_test) rmse = mean_squared_error(y_test, y_predict,squared=False) + score_r2 = r2_score(y_test, y_predict) + score_mae = mean_absolute_error(y_test, y_predict) + score_ev = explained_variance_score(y_test, y_predict) print("window: {}, stide: {}, rmse: {}".format(window,stride,rmse)) + print(f"r2: {score_r2}") + print(f"mae: {score_mae}") + print(f"ev: {score_ev}") + topEVList.append(score_ev) topNRmseList.append(rmse) + topR2List.append(score_r2) + topMAEList.append(score_mae) @@ -450,198 +487,32 @@ def topFeatureFSRegressionRanking(dataset, window, stride, sfreq, clf, label, sc topNFeatures_df = pd.DataFrame(topNList) topNRmse_df = pd.DataFrame(topNRmseList) + topNR2_df = pd.DataFrame(topR2List) + topNMAE_df = pd.DataFrame(topMAEList) + topNEV_df = pd.DataFrame(topEVList) + #concat two dataframes for better visualization - topNFeaturesRanking = pd.concat([topNFeatures_df, topNRmse_df],axis=1) - topNFeaturesRanking.columns = ['Feature','RMSE'] #naming the dataframe columns - print(topNFeaturesRanking) - topNFeaturesRanking.to_csv(pwd + "/" + dataset + "/arousal_plots/" + "topCommonFeatureFSRegressionRanking"+ method + str(window) + str(stride) + ".csv") - + topNFeatures = pd.concat([topNFeatures_df, topNRmse_df, topNR2_df, topNMAE_df, topNEV_df],axis=1) + + topNFeatures.columns = ['Feature', 'RMSE', 'R2', 'MAE', 'EV'] #naming the dataframe columns + + print(topNFeatures) + + topNFeatures.to_csv(f"output/{dataset}_{label}_features.csv") + # Plotting fig = plt.gcf() fig.set_size_inches(25, 10) plt.rcParams.update({'font.size': 30}) plt.xlabel('Top N Features') plt.ylabel('RMSE') - plt.plot(topNFeaturesRanking.loc[:,"Feature"], topNFeaturesRanking.loc[:,"RMSE"]) + plt.plot(topNFeatures.loc[:,"Feature"], topNFeatures.loc[:,"RMSE"]) plt.tight_layout() # In[ ]: - -def topFSColumnsRegressionRanking(dataset, window, stride, sfreq, clf, label, scale=False, pca=False, mutual_info = False, method='SelectKBest'): - # Method C - # parameters :- - # dataset - name of the dataset - # window - length of the sliding window in seconds - # stride - length of the stride of the sliding window in seconds - # sfreq - sampling frequency of the EEG data - # clf - name of the classifier to be used - # label - valence/arousal/dominance/liking label (shape depends upon the dataset) - # scale - sclaing of the EEG data if required - # mutual_info - Mutual ranking between features based on information theory - # method - 'RandomForest' 'RFE' 'SelectKBest' - - # returns :- - # void - fs = sfreq - pwd = os.getcwd() - - featurepath = os.getcwd() + '/' + dataset + '/data_extracted/featuresDict/' - ans = np.load((featurepath + "shannonEntropy_{}_{}.npz").format(window,stride), allow_pickle=True)['features'] - Y_epoch = np.load((featurepath + "shannonEntropy_{}_{}.npz").format(window,stride), allow_pickle=True)['Y'] - - print("Number of segments are: {}".format(ans.shape[1])) - - #X############################################################################################## - - featuresDict = None - featuresDict = loadFeaturesDict(dataset) - - common = [] - with open('intersection.pkl', 'rb') as f: - common = pickle.load(f) - - for k in list(featuresDict.keys()): - if k not in common: - # pop out common feature - featuresDict.pop(k) - - print("Number of Features:",len(list(featuresDict.keys()))) - featuresList = list(featuresDict.keys()) - - feature_channel_index = [] - - feature_channel_index = [] - for feature in featuresList: - for i in range(featuresDict[feature].shape[0]): - if(i>=10): - feature_channel_index.append(feature +'_'+ str(i)) - else: - feature_channel_index.append(feature + '_0' + str(i)) - - print(len(list(featuresDict.keys()))) - print("Number of Feature-Columns: {}\n".format(len(feature_channel_index))) - - #defining feature matrix - featureMatrix = np.empty((0,ans.shape[1])) #[14*32 + 1,80640] - for key,value in featuresDict.items(): - featureMatrix = np.append(featureMatrix,value,axis=0) - - - - print("Shape of FeatureMatrix: {}\n".format(featureMatrix.T.shape)) - - #data-imputation and nan-removal - featureMatrix = featureMatrix.astype(np.float32) - - if np.isnan(featureMatrix).any(): - featureMatrix = np.nan_to_num(featureMatrix,nan=0) - - X = pd.DataFrame(featureMatrix.T) - X = X.replace([np.inf, -np.inf], np.nan) - X = X.fillna(0) - X.columns = feature_channel_index - - - #Y##################################################################### - - y = Y_epoch[:,label] #valence - # y = pd.DataFrame(y) - - ######################################################################## - dfscores = None - - if(method == 'RandomForest'): - '''Random Forest Feature Importances''' - estimator = sklearnrfi() #RandomForestRegressor() - fit = estimator.fit(X,y) - dfscores = pd.DataFrame(fit.feature_importances_) - elif(method == 'RFE'): - ''' RFE''' - selector = RFE(clf, n_features_to_select=X.shape[1], step=1) - selector = selector.fit(X, y) - dfscores = pd.DataFrame(selector.ranking_) - - elif(method == 'SelectKBest'): - """SelecKBest""" - #apply SelectKBest class to extract top 10 best features - func = None - if mutual_info == False: - func = f_classif - else: - func = mutual_info_classif - - bestfeatures = SelectKBest(score_func=func, k=X.shape[1]) - fit = bestfeatures.fit(X,y) - - dfscores = pd.DataFrame(fit.scores_) - - - - - dfcolumns = pd.DataFrame(X.columns) - - #concat two dataframes for better visualization - featureScores = pd.concat([dfcolumns,dfscores],axis=1) - featureScores.columns = ['Column','Score'] #naming the dataframe columns - features_result = featureScores.nlargest(X.shape[1],'Score') - print(features_result) - - N = len(feature_channel_index) - topNRmseList = [] - topNList = ["{}".format(x) for x in range(1,N+1)] - - for n in range(1, N+1): - ranking_df = features_result.head(n) - topncols = ranking_df['Column'].tolist() - - input_df = pd.DataFrame(X[topncols]) - - X_train, X_test, y_train, y_test = train_test_split(input_df, y, test_size=0.2, random_state=42) - - # Normalise-scale data - # Feature Scaling - if(scale == True): - sc = StandardScaler() - X_train = sc.fit_transform(X_train) - X_test = sc.transform(X_test) - - # Apply classfier - clf.fit(X_train, y_train) - y_predict = clf.predict(X_test) - rmse = mean_squared_error(y_test, y_predict, squared=False) - print(n,rmse) - topNRmseList.append(rmse) - - - topcol_df = pd.DataFrame(topNList) - toprmse_df = pd.DataFrame(topNRmseList) - #concat two dataframes for better visualization - topcolRanking = pd.concat([topcol_df, toprmse_df],axis=1) - topcolRanking.columns = ['Column','RMSE'] #naming the dataframe columns - topfeatures_result = topcolRanking - print(topfeatures_result) - topfeatures_result.to_csv(pwd + "/" + dataset + "/arousal_plots/" + "topFSColumnsRegressionRanking"+method + str(window) + str(stride) + ".csv") - - # Plotting - fig = plt.gcf() - fig.set_size_inches(60, 9) - - plt.xlabel('Top N Columns') - plt.ylabel('RMSE') - plt.title("Top N Columns v/s RMSE Plot for Window:{} Stride:{} epoched data by varying N".format(window,stride)) - plt.plot(topfeatures_result.loc[:,"Column"], topfeatures_result.loc[:,"RMSE"]) - plt.tight_layout() - plt.savefig(pwd + "/" + dataset + "/arousal_plots/" + "topFSColumnsRegressionRanking"+method + str(window) + str(stride) + ".svg", bbox_inches='tight', dpi=500) - plt.show() - plt.clf() - - -# In[ ]: - - if __name__ == '__main__': pass diff --git a/eeg_ml_pipeline/feature_select_main.py b/eeg_ml_pipeline/feature_select_main.py index 389c38a..3700181 100644 --- a/eeg_ml_pipeline/feature_select_main.py +++ b/eeg_ml_pipeline/feature_select_main.py @@ -5,16 +5,17 @@ # Script to get the feature ranking and electrode ranking through - # Method A :- Random Forest Regressor - # Method B :- F score based Ranking - # Method C :- Random Forest Importances approach + +# Method :- F score based Ranking + # Main function from ImportUtils import * from TopNByFSMethods import * from TopNByClassifier import * from args_eeg import args as my_args - +# uncomment to extract features +# from EpochedFeatures import * if __name__ == '__main__': # args object to fetch command line inputs @@ -34,37 +35,12 @@ if __name__ == '__main__': fs_method = args.fs_method #feature extraction - getEpochedFeatures(dataset, window, stride, sfreq, label) + # uncomment to extract features + # getEpochedFeatures(dataset, window, stride, sfreq, label) if(top == "e"): clf = RandomForestRegressor() - topElectrodeRegressionRanking(dataset, window, stride, sfreq, clf, label, scale=False, pca=False) topElectrodeFSRegressionRanking(dataset, window, stride, sfreq, clf, label, scale=False, pca=False, mutual_info = False, method='SelectKBest') - topElectrodeFSRegressionRanking(dataset, window, stride, sfreq, clf, label, scale=False, pca=False, mutual_info = False, method='RandomForest') - plt.legend(["Method A","Method B", "Method C"]) - - if(label == 1): - plt.savefig(pwd + "/" + dataset + "/arousal_plots/" + "CorrectedElectrodewiseRanking" + str(window) + str(stride) + ".svg", bbox_inches='tight') - plt.show() - plt.clf() - - else: - plt.savefig(pwd + "/" + dataset + "/plots/" + "CorrectedElectrodewiseRanking" + str(window) + str(stride) + ".svg", bbox_inches='tight') - plt.show() - plt.clf() elif(top == "f"): clf = RandomForestRegressor() - topFeaturesRegressionRanking(dataset, window, stride, sfreq, clf, label, scale=False, pca=False) - topFeatureFSRegressionRanking(dataset, window, stride, sfreq, clf, label, scale=False, pca=False, mutual_info = False, method='SelectKBest') - topFeatureFSRegressionRanking(dataset, window, stride, sfreq, clf, label, scale=False, pca=False, mutual_info = False, method='RandomForest') - if(label == 1): - plt.legend(["Method A","Method B", "Method C"]) - plt.savefig(pwd + "/" + dataset + "/arousal_plots/" + "CorrectedFeaturewiseRanking" + str(window) + str(stride) + ".svg", bbox_inches='tight') - plt.show() - plt.clf() - else: - plt.legend(["Method A","Method B", "Method C"]) - plt.savefig(pwd + "/" + dataset + "/plots/" + "CorrectedFeaturewiseRanking" + str(window) + str(stride) + ".svg", bbox_inches='tight') - plt.show() - plt.clf() - + topFeatureFSRegressionRanking(dataset, window, stride, sfreq, clf, label, scale=False, pca=False, mutual_info = False, method='SelectKBest') \ No newline at end of file diff --git a/eeg_ml_pipeline/frontiers_revision.ipynb b/eeg_ml_pipeline/frontiers_revision.ipynb new file mode 100644 index 0000000..b22bbea --- /dev/null +++ b/eeg_ml_pipeline/frontiers_revision.ipynb @@ -0,0 +1 @@ +{"cells":[{"cell_type":"markdown","metadata":{"id":"kNsuYDUQ3d5G"},"source":["```\n","function ConnectButton(){\n"," console.log(\"Connect pushed\"); \n"," document.querySelector(\"#top-toolbar > colab-connect-button\").shadowRoot.querySelector(\"#connect\").click() \n","}\n","setInterval(ConnectButton,60000);\n","```"]},{"cell_type":"code","execution_count":null,"metadata":{"colab":{"base_uri":"https://localhost:8080/"},"executionInfo":{"elapsed":914,"status":"ok","timestamp":1635346518717,"user":{"displayName":"Rohit Garg","photoUrl":"https://lh3.googleusercontent.com/a-/AOh14Gj3Za6nmSY12sPvpCoWOtWQcVR5CMMSPBRGTexH_w=s64","userId":"11252576926243182044"},"user_tz":-330},"id":"0SIi0AD819OZ","outputId":"7c393eec-31fe-400b-c4b8-35696fa5f548"},"outputs":[{"name":"stdout","output_type":"stream","text":["NVIDIA-SMI has failed because it couldn't communicate with the NVIDIA driver. Make sure that the latest NVIDIA driver is installed and running.\n","\n"]}],"source":["!nvidia-smi -L"]},{"cell_type":"code","execution_count":null,"metadata":{"id":"cjIUtnIoKybD"},"outputs":[],"source":["# !pip uninstall scikit-learn\n","# !pip install scikit-learn"]},{"cell_type":"code","source":["import sys, os"],"metadata":{"id":"t63rrG7scB2b","executionInfo":{"status":"ok","timestamp":1667926419408,"user_tz":-330,"elapsed":518,"user":{"displayName":"Rohit Garg","userId":"11252576926243182044"}}},"execution_count":1,"outputs":[]},{"cell_type":"code","execution_count":2,"metadata":{"id":"nYAs8puMClpU","colab":{"base_uri":"https://localhost:8080/","height":1000},"executionInfo":{"status":"error","timestamp":1667926438703,"user_tz":-330,"elapsed":8643,"user":{"displayName":"Rohit Garg","userId":"11252576926243182044"}},"outputId":"3e295de9-141f-4e9e-c961-14e6fbc28641"},"outputs":[{"output_type":"stream","name":"stdout","text":["Cloning into 'rapidsai-csp-utils'...\n","remote: Enumerating objects: 300, done.\u001b[K\n","remote: Counting objects: 100% (129/129), done.\u001b[K\n","remote: Compressing objects: 100% (74/74), done.\u001b[K\n","remote: Total 300 (delta 74), reused 99 (delta 55), pack-reused 171\u001b[K\n","Receiving objects: 100% (300/300), 87.58 KiB | 1019.00 KiB/s, done.\n","Resolving deltas: 100% (136/136), done.\n","PLEASE READ FOR 21.06\n","********************************************************************************************************\n","Another release, another script change. We had to revise the script, which now:\n","1. Does a more comprehensive install\n","2. Includes BlazingSQL\n","3. is far easier for everyone to understand and maintain\n","\n","The script will require you to add these 5 cells to your notebook. We have also created a new startup template: \n","https://colab.research.google.com/drive/1TAAi_szMfWqRfHVfjGSqnGVLr_ztzUM9?usp=sharing\n","\n","CHANGES T\n","CELL 1:\n"," # This get the RAPIDS-Colab install files and test check your GPU. Run cells 1 and 2 only.\n"," # Please read the output of this cell. If your Colab Instance is not RAPIDS compatible, it will warn you and give you remediation steps.\n"," !git clone https://github.com/rapidsai/rapidsai-csp-utils.git\n"," !python rapidsai-csp-utils/colab/env-check.py\n","\n","CELL 2:\n"," # This will update the Colab environment and restart the kernel.\n"," !bash rapidsai-csp-utils/colab/update_gcc.sh\n"," import os\n"," os._exit(00)\n","\n","CELL 3:\n"," ## Installing CondaColab. This will restart your kernel again\n"," import condacolab\n"," condacolab.install()\n","\n","CELL 4:\n"," import condacolab\n"," condacolab.check()\n","\n","CELL 5:\n"," # Installing RAPIDS is now 'python rapidsai-csp-utils/colab/install_rapids.py '\n"," # The options are 'stable' and 'nightly'. Leaving it blank or adding any other words will default to stable.\n"," # The option are default blank or 'core'. By default, we install RAPIDSAI and BlazingSQL. The 'core' option will install only RAPIDSAI and not include BlazingSQL, \n"," !python rapidsai-csp-utils/colab/install_rapids.py nightly\n"," import os\n"," os.environ['NUMBAPRO_NVVM'] = '/usr/local/cuda/nvvm/lib64/libnvvm.so'\n"," os.environ['NUMBAPRO_LIBDEVICE'] = '/usr/local/cuda/nvvm/libdevice/'\n"," os.environ['CONDA_PREFIX'] = '/usr/local'\n","\n","********************************************************************************************************\n","\n","Enjoy using RAPIDS! If you have any issues with or suggestions for RAPIDSAI on Colab, please create a issue on https://github.com/rapidsai/rapidsai-csp-utils/issues/new.\n"]},{"output_type":"error","ename":"FileNotFoundError","evalue":"ignored","traceback":["\u001b[0;31m---------------------------------------------------------------------------\u001b[0m","\u001b[0;31mFileNotFoundError\u001b[0m Traceback (most recent call last)","\u001b[0;32m\u001b[0m in \u001b[0;36m\u001b[0;34m\u001b[0m\n\u001b[1;32m 8\u001b[0m \u001b[0msys\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mpath\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0msys\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mpath\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0mdist_package_index\u001b[0m\u001b[0;34m]\u001b[0m \u001b[0;34m+\u001b[0m \u001b[0;34m[\u001b[0m\u001b[0;34m'/usr/local/lib/python3.7/site-packages'\u001b[0m\u001b[0;34m]\u001b[0m \u001b[0;34m+\u001b[0m \u001b[0msys\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mpath\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0mdist_package_index\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 9\u001b[0m \u001b[0msys\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mpath\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m---> 10\u001b[0;31m \u001b[0mexec\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mopen\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m'rapidsai-csp-utils/colab/update_modules.py'\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mread\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mglobals\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m","\u001b[0;31mFileNotFoundError\u001b[0m: [Errno 2] No such file or directory: 'rapidsai-csp-utils/colab/update_modules.py'"]}],"source":["# # Install RAPIDS\n","!git clone https://github.com/rapidsai/rapidsai-csp-utils.git\n","!bash rapidsai-csp-utils/colab/rapids-colab.sh stable\n","\n","import sys, os\n","\n","dist_package_index = sys.path.index('/usr/local/lib/python3.7/dist-packages')\n","sys.path = sys.path[:dist_package_index] + ['/usr/local/lib/python3.7/site-packages'] + sys.path[dist_package_index:]\n","sys.path\n","exec(open('rapidsai-csp-utils/colab/update_modules.py').read(), globals())"]},{"cell_type":"code","execution_count":null,"metadata":{"colab":{"base_uri":"https://localhost:8080/"},"executionInfo":{"elapsed":1777,"status":"ok","timestamp":1631787287853,"user":{"displayName":"Rohit Garg","photoUrl":"https://lh3.googleusercontent.com/a-/AOh14Gj3Za6nmSY12sPvpCoWOtWQcVR5CMMSPBRGTexH_w=s64","userId":"11252576926243182044"},"user_tz":-330},"id":"B0C8IV5TQnjN","outputId":"336e51c2-c9d9-481a-ed1b-a6ce48ede80a"},"outputs":[{"name":"stdout","output_type":"stream","text":["fatal: destination path 'rapidsai-csp-utils' already exists and is not an empty directory.\n","Traceback (most recent call last):\n"," File \"rapidsai-csp-utils/colab/env-check.py\", line 24, in \n"," Please use 'Runtime -> Factory Reset Runtimes...', which will allocate you a different GPU instance, to try again.\"\"\"\n","Exception: \n"," Unfortunately Colab didn't give you a RAPIDS compatible GPU (P4, P100, T4, or V100), but gave you a Tesla K80.\n","\n"," Please use 'Runtime -> Factory Reset Runtimes...', which will allocate you a different GPU instance, to try again.\n"]}],"source":["# # This get the RAPIDS-Colab install files and test check your GPU. Run this and the next cell only.\n","# # Please read the output of this cell. If your Colab Instance is not RAPIDS compatible, it will warn you and give you remediation steps.\n","!git clone https://github.com/rapidsai/rapidsai-csp-utils.git\n","!python rapidsai-csp-utils/colab/env-check.py"]},{"cell_type":"code","execution_count":null,"metadata":{"id":"4sKA_9Dbg4s2"},"outputs":[],"source":["# # This will update the Colab environment and restart the kernel. Don't run the next cell until you see the session crash.\n","!bash rapidsai-csp-utils/colab/update_gcc.sh\n","import os\n","os._exit(00)"]},{"cell_type":"code","execution_count":null,"metadata":{"colab":{"base_uri":"https://localhost:8080/"},"executionInfo":{"elapsed":33900,"status":"ok","timestamp":1626697546976,"user":{"displayName":"Rohit Garg","photoUrl":"https://lh3.googleusercontent.com/a-/AOh14Gj3Za6nmSY12sPvpCoWOtWQcVR5CMMSPBRGTexH_w=s64","userId":"11252576926243182044"},"user_tz":-330},"id":"d1C4pNiBhYHX","outputId":"8cbf3898-ed3f-49a0-cb0c-df8a5bf30c0f"},"outputs":[{"name":"stdout","output_type":"stream","text":["⏬ Downloading https://github.com/jaimergp/miniforge/releases/latest/download/Mambaforge-colab-Linux-x86_64.sh...\n","📦 Installing...\n","📌 Adjusting configuration...\n","🩹 Patching environment...\n","⏲ Done in 0:00:34\n","🔁 Restarting kernel...\n"]}],"source":["# # This will install CondaColab. This will restart your kernel one last time. Run this cell by itself and only run the next cell once you see the session crash.\n","import condacolab\n","condacolab.install()"]},{"cell_type":"code","execution_count":null,"metadata":{"colab":{"base_uri":"https://localhost:8080/"},"executionInfo":{"elapsed":686,"status":"ok","timestamp":1626697580564,"user":{"displayName":"Rohit Garg","photoUrl":"https://lh3.googleusercontent.com/a-/AOh14Gj3Za6nmSY12sPvpCoWOtWQcVR5CMMSPBRGTexH_w=s64","userId":"11252576926243182044"},"user_tz":-330},"id":"-Ko_pJZbhZrM","outputId":"546992a3-582d-4b25-fd16-3db89052badc"},"outputs":[{"name":"stdout","output_type":"stream","text":["✨🍰✨ Everything looks OK!\n"]}],"source":["# # you can now run the rest of the cells as normal\n","import condacolab\n","condacolab.check()"]},{"cell_type":"code","execution_count":null,"metadata":{"colab":{"background_save":true},"id":"G_2dancFhf70"},"outputs":[],"source":["# # Installing RAPIDS is now 'python rapidsai-csp-utils/colab/install_rapids.py '\n","# # The options are 'stable' and 'nightly'. Leaving it blank or adding any other words will default to stable.\n","# # The option are default blank or 'core'. By default, we install RAPIDSAI and BlazingSQL. The 'core' option will install only RAPIDSAI and not include BlazingSQL, \n","!python rapidsai-csp-utils/colab/install_rapids.py stable\n","import os\n","os.environ['NUMBAPRO_NVVM'] = '/usr/local/cuda/nvvm/lib64/libnvvm.so'\n","os.environ['NUMBAPRO_LIBDEVICE'] = '/usr/local/cuda/nvvm/libdevice/'\n","os.environ['CONDA_PREFIX'] = '/usr/local'"]},{"cell_type":"code","execution_count":null,"metadata":{"id":"jxUyuZBQCl1z"},"outputs":[],"source":["import cuml"]},{"cell_type":"markdown","source":["
"],"metadata":{"id":"GuIGCq8DkznR"}},{"cell_type":"code","execution_count":null,"metadata":{"colab":{"base_uri":"https://localhost:8080/","height":35},"executionInfo":{"elapsed":978,"status":"ok","timestamp":1631793520242,"user":{"displayName":"Rohit Garg","photoUrl":"https://lh3.googleusercontent.com/a-/AOh14Gj3Za6nmSY12sPvpCoWOtWQcVR5CMMSPBRGTexH_w=s64","userId":"11252576926243182044"},"user_tz":-330},"id":"rR5iqa4TL5Kz","outputId":"a8d65275-9f7e-4f65-bafe-bb3d2d6b09cf"},"outputs":[{"data":{"application/vnd.google.colaboratory.intrinsic+json":{"type":"string"},"text/plain":["'0.22.2.post1'"]},"execution_count":1,"metadata":{},"output_type":"execute_result"}],"source":["# Check sklearn version\n","import sklearn\n","sklearn.__version__"]},{"cell_type":"code","execution_count":3,"metadata":{"id":"3tr1ZQ5JYrZh","colab":{"base_uri":"https://localhost:8080/"},"executionInfo":{"status":"ok","timestamp":1667926547041,"user_tz":-330,"elapsed":37236,"user":{"displayName":"Rohit Garg","userId":"11252576926243182044"}},"outputId":"74ef82a6-ab79-40a1-fe39-9da090a71fd6"},"outputs":[{"output_type":"stream","name":"stdout","text":["Mounted at /content/drive\n"]}],"source":["from google.colab import drive\n","drive.mount('/content/drive')"]},{"cell_type":"code","source":["%pwd"],"metadata":{"colab":{"base_uri":"https://localhost:8080/","height":36},"id":"BWFzeUJqvPUD","executionInfo":{"status":"ok","timestamp":1667281783848,"user_tz":-330,"elapsed":11,"user":{"displayName":"Rohit Garg","userId":"11252576926243182044"}},"outputId":"0c343e66-4faf-4432-be0c-76c2f6ede719"},"execution_count":null,"outputs":[{"output_type":"execute_result","data":{"text/plain":["'/content'"],"application/vnd.google.colaboratory.intrinsic+json":{"type":"string"}},"metadata":{},"execution_count":4}]},{"cell_type":"code","execution_count":4,"metadata":{"colab":{"base_uri":"https://localhost:8080/"},"executionInfo":{"elapsed":525,"status":"ok","timestamp":1667926560231,"user":{"displayName":"Rohit Garg","userId":"11252576926243182044"},"user_tz":-330},"id":"S24YyBV9CmGA","outputId":"5987fdb9-ed1a-47c0-ee75-1707afe6612e"},"outputs":[{"output_type":"stream","name":"stdout","text":["/content/drive/MyDrive/IEEE_EEG_Cluster_Files\n"]}],"source":["%cd drive/MyDrive/IEEE_EEG_Cluster_Files"]},{"cell_type":"code","execution_count":9,"metadata":{"colab":{"base_uri":"https://localhost:8080/"},"executionInfo":{"elapsed":47,"status":"ok","timestamp":1667926329077,"user":{"displayName":"Rohit Garg","userId":"11252576926243182044"},"user_tz":-330},"id":"CTDZv4qkvI7r","outputId":"ddf3af8a-a343-452d-b5b2-e4509ef2cfe3"},"outputs":[{"output_type":"stream","name":"stdout","text":["/content/drive/My Drive/IEEE_EEG_Cluster_Files\n"]}],"source":["!pwd"]},{"cell_type":"code","execution_count":5,"metadata":{"executionInfo":{"elapsed":1345,"status":"ok","timestamp":1667926566321,"user":{"displayName":"Rohit Garg","userId":"11252576926243182044"},"user_tz":-330},"id":"X0zudozFCmMD"},"outputs":[],"source":["import os\n","import glob\n","from scipy import io,signal\n","import numpy as np\n","import pandas as pd\n","from sklearn import preprocessing\n","import pickle\n","from sklearn.metrics import mean_squared_error\n","%matplotlib inline\n","import matplotlib.pyplot as plt\n"]},{"cell_type":"code","source":["!pip uninstall scikit-learn"],"metadata":{"colab":{"base_uri":"https://localhost:8080/"},"id":"6moxbS2dc4ts","executionInfo":{"status":"ok","timestamp":1641088341266,"user_tz":-330,"elapsed":6845,"user":{"displayName":"Rohit Garg","photoUrl":"https://lh3.googleusercontent.com/a-/AOh14Gj3Za6nmSY12sPvpCoWOtWQcVR5CMMSPBRGTexH_w=s64","userId":"11252576926243182044"}},"outputId":"18ca8a0f-75d1-454a-f259-6fc3882866d8"},"execution_count":null,"outputs":[{"output_type":"stream","name":"stdout","text":["Found existing installation: scikit-learn 0.24.2\n","Uninstalling scikit-learn-0.24.2:\n"," Would remove:\n"," /usr/local/lib/python3.7/dist-packages/scikit_learn-0.24.2.dist-info/*\n"," /usr/local/lib/python3.7/dist-packages/scikit_learn.libs/libgomp-f7e03b3e.so.1.0.0\n"," /usr/local/lib/python3.7/dist-packages/sklearn/*\n","Proceed (y/n)? y\n"," Successfully uninstalled scikit-learn-0.24.2\n"]}]},{"cell_type":"code","source":["!pip uninstall auto-sklearn"],"metadata":{"id":"l2vomgVumUrr"},"execution_count":null,"outputs":[]},{"cell_type":"code","source":["import sklearn\n","sklearn.__version__"],"metadata":{"colab":{"base_uri":"https://localhost:8080/","height":36},"id":"61ke59E3dRAM","executionInfo":{"status":"ok","timestamp":1667626680917,"user_tz":-330,"elapsed":395,"user":{"displayName":"Rohit Garg","userId":"11252576926243182044"}},"outputId":"13418095-cee9-4220-90bf-23b196e6e08e"},"execution_count":null,"outputs":[{"output_type":"execute_result","data":{"text/plain":["'1.0.2'"],"application/vnd.google.colaboratory.intrinsic+json":{"type":"string"}},"metadata":{},"execution_count":7}]},{"cell_type":"code","source":["!pip uninstall scikit-learn -y\n","!pip install -U scikit-learn"],"metadata":{"colab":{"base_uri":"https://localhost:8080/"},"id":"-S6tcsLGd58K","executionInfo":{"status":"ok","timestamp":1641088466553,"user_tz":-330,"elapsed":5579,"user":{"displayName":"Rohit Garg","photoUrl":"https://lh3.googleusercontent.com/a-/AOh14Gj3Za6nmSY12sPvpCoWOtWQcVR5CMMSPBRGTexH_w=s64","userId":"11252576926243182044"}},"outputId":"907c53f9-5089-4b70-b750-d0eafaad672f"},"execution_count":null,"outputs":[{"output_type":"stream","name":"stdout","text":["Found existing installation: scikit-learn 1.0.2\n","Uninstalling scikit-learn-1.0.2:\n"," Successfully uninstalled scikit-learn-1.0.2\n","Collecting scikit-learn\n"," Using cached scikit_learn-1.0.2-cp37-cp37m-manylinux_2_17_x86_64.manylinux2014_x86_64.whl (24.8 MB)\n","Requirement already satisfied: joblib>=0.11 in /usr/local/lib/python3.7/dist-packages (from scikit-learn) (1.1.0)\n","Requirement already satisfied: scipy>=1.1.0 in /usr/local/lib/python3.7/dist-packages (from scikit-learn) (1.7.3)\n","Requirement already satisfied: numpy>=1.14.6 in /usr/local/lib/python3.7/dist-packages (from scikit-learn) (1.19.5)\n","Requirement already satisfied: threadpoolctl>=2.0.0 in /usr/local/lib/python3.7/dist-packages (from scikit-learn) (3.0.0)\n","Installing collected packages: scikit-learn\n","\u001b[31mERROR: pip's dependency resolver does not currently take into account all the packages that are installed. This behaviour is the source of the following dependency conflicts.\n","auto-sklearn 0.14.2 requires scikit-learn<0.25.0,>=0.24.0, but you have scikit-learn 1.0.2 which is incompatible.\u001b[0m\n","Successfully installed scikit-learn-1.0.2\n"]}]},{"cell_type":"code","source":["a"],"metadata":{"id":"RkzbBjZJNEIJ"},"execution_count":null,"outputs":[]},{"cell_type":"code","execution_count":9,"metadata":{"colab":{"base_uri":"https://localhost:8080/","height":1000},"executionInfo":{"elapsed":69672,"status":"ok","timestamp":1667926948534,"user":{"displayName":"Rohit Garg","userId":"11252576926243182044"},"user_tz":-330},"id":"Hevmq4O3CmR4","outputId":"1b12b3f7-4a9f-4759-f8dd-70496ae73105"},"outputs":[{"output_type":"stream","name":"stdout","text":["Looking in indexes: https://pypi.org/simple, https://us-python.pkg.dev/colab-wheels/public/simple/\n","Collecting auto-sklearn\n"," Downloading auto-sklearn-0.15.0.tar.gz (6.5 MB)\n","\u001b[K |████████████████████████████████| 6.5 MB 9.6 MB/s 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six>=1.5 in /usr/local/lib/python3.7/dist-packages (from python-dateutil>=2.7.3->pandas>=1.0->auto-sklearn) (1.15.0)\n","Collecting emcee>=3.0.0\n"," Downloading emcee-3.1.3-py2.py3-none-any.whl (46 kB)\n","\u001b[K |████████████████████████████████| 46 kB 3.6 MB/s \n","\u001b[?25hRequirement already satisfied: heapdict in /usr/local/lib/python3.7/dist-packages (from zict>=0.1.3->distributed>=2012.12->auto-sklearn) (1.0.1)\n","Requirement already satisfied: MarkupSafe>=0.23 in /usr/local/lib/python3.7/dist-packages (from jinja2->distributed>=2012.12->auto-sklearn) (2.0.1)\n","Building wheels for collected packages: auto-sklearn, pynisher, smac, liac-arff\n"," Building wheel for auto-sklearn (PEP 517) ... \u001b[?25l\u001b[?25hdone\n"," Created wheel for auto-sklearn: filename=auto_sklearn-0.15.0-py3-none-any.whl size=6641946 sha256=3e201967b3071bf7a2121c29c6d8b3c11f3d47c0c64a74c0331aca6e54ac72a2\n"," Stored in directory: /root/.cache/pip/wheels/26/57/ce/ca63ad74b90273f9a682028d187645a42dce5c5255228d46c8\n"," Building wheel for pynisher (setup.py) ... \u001b[?25l\u001b[?25hdone\n"," Created wheel for pynisher: filename=pynisher-0.6.4-py3-none-any.whl size=7043 sha256=4970e58b48dbfa5b2fa57f125992765ee2967185cf3c2736ff1b921e01f3ffb6\n"," Stored in directory: /root/.cache/pip/wheels/42/71/95/7555ec3253e1ba8add72ae5febf1b015d297f3b73ba296d6f6\n"," Building wheel for smac (setup.py) ... \u001b[?25l\u001b[?25hdone\n"," Created wheel for smac: filename=smac-1.2-py3-none-any.whl size=215932 sha256=d47405e91ef0eb8f8013c18dcb14716b5d1e751318826a2ee2ee7cb28b207ddf\n"," Stored in directory: /root/.cache/pip/wheels/ad/95/67/6afc6b04d3715070c853d0a9d7c7b1fb822def38671dfbbb9f\n"," Building wheel for liac-arff (setup.py) ... \u001b[?25l\u001b[?25hdone\n"," Created wheel for liac-arff: filename=liac_arff-2.5.0-py3-none-any.whl size=11732 sha256=6c0b6557c5ca5f57095cfefc3ceae32e782495852ec2049d28143ce9bd2e620d\n"," Stored in directory: /root/.cache/pip/wheels/1f/0f/15/332ca86cbebf25ddf98518caaf887945fbe1712b97a0f2493b\n","Successfully built auto-sklearn pynisher smac liac-arff\n","Installing collected packages: scikit-learn, pyrfr, pynisher, emcee, ConfigSpace, smac, liac-arff, distro, auto-sklearn\n"," Attempting uninstall: scikit-learn\n"," Found existing installation: scikit-learn 1.0.2\n"," Uninstalling scikit-learn-1.0.2:\n"," Successfully uninstalled scikit-learn-1.0.2\n","\u001b[31mERROR: pip's dependency resolver does not currently take into account all the packages that are installed. This behaviour is the source of the following dependency conflicts.\n","yellowbrick 1.5 requires scikit-learn>=1.0.0, but you have scikit-learn 0.24.2 which is incompatible.\u001b[0m\n","Successfully installed ConfigSpace-0.4.21 auto-sklearn-0.15.0 distro-1.8.0 emcee-3.1.3 liac-arff-2.5.0 pynisher-0.6.4 pyrfr-0.8.3 scikit-learn-0.24.2 smac-1.2\n"]},{"output_type":"display_data","data":{"application/vnd.colab-display-data+json":{"pip_warning":{"packages":["sklearn"]}}},"metadata":{}},{"output_type":"stream","name":"stdout","text":["Looking in indexes: https://pypi.org/simple, https://us-python.pkg.dev/colab-wheels/public/simple/\n","Collecting dit\n"," Downloading dit-1.5-py3-none-any.whl (298 kB)\n","\u001b[K |████████████████████████████████| 298 kB 13.6 MB/s \n","\u001b[?25hCollecting pypoman\n"," Downloading pypoman-0.5.4.tar.gz (12 kB)\n","Collecting debtcollector\n"," Downloading debtcollector-2.5.0-py3-none-any.whl (23 kB)\n","Requirement already satisfied: scipy>=1.2.0 in /usr/local/lib/python3.7/dist-packages (from dit) (1.7.3)\n","Requirement already satisfied: networkx>=2.6 in /usr/local/lib/python3.7/dist-packages (from dit) (2.6.3)\n","Collecting boltons\n"," Downloading boltons-21.0.0-py2.py3-none-any.whl (193 kB)\n","\u001b[K |████████████████████████████████| 193 kB 72.5 MB/s \n","\u001b[?25hCollecting lattices>=0.3.5\n"," Downloading lattices-0.3.5-py2.py3-none-any.whl (26 kB)\n","Requirement already satisfied: numpy>=1.11 in /usr/local/lib/python3.7/dist-packages (from dit) (1.21.6)\n","Collecting PLTable\n"," Downloading PLTable-1.0.2.tar.gz (16 kB)\n","Requirement already satisfied: importlib-metadata>=1.7.0 in /usr/local/lib/python3.7/dist-packages (from debtcollector->dit) (4.13.0)\n","Requirement already satisfied: wrapt>=1.7.0 in /usr/local/lib/python3.7/dist-packages (from debtcollector->dit) (1.14.1)\n","Requirement already satisfied: typing-extensions>=3.6.4 in /usr/local/lib/python3.7/dist-packages (from importlib-metadata>=1.7.0->debtcollector->dit) (4.1.1)\n","Requirement already satisfied: zipp>=0.5 in /usr/local/lib/python3.7/dist-packages (from importlib-metadata>=1.7.0->debtcollector->dit) (3.10.0)\n","Requirement already satisfied: cvxopt in /usr/local/lib/python3.7/dist-packages (from pypoman->dit) (1.3.0)\n","Collecting pycddlib\n"," Downloading pycddlib-2.1.6.tar.gz (159 kB)\n","\u001b[K |████████████████████████████████| 159 kB 75.5 MB/s \n","\u001b[?25hBuilding wheels for collected packages: PLTable, pypoman, pycddlib\n"," Building wheel for PLTable (setup.py) ... \u001b[?25l\u001b[?25hdone\n"," Created wheel for PLTable: filename=PLTable-1.0.2-py3-none-any.whl size=16919 sha256=2dc46c4be5915fc6f8890b58d50af27b5858ac55f464b61946bf4830cb4d3fd3\n"," Stored in directory: /root/.cache/pip/wheels/cd/9c/56/b3869f39d16c51a19e7ff532e4ca5b3d875ecea0c08a54f403\n"," Building wheel for pypoman (setup.py) ... \u001b[?25l\u001b[?25hdone\n"," Created wheel for pypoman: filename=pypoman-0.5.4-py3-none-any.whl size=18170 sha256=54adb7f98d652e0d74abd0895c2a7816cea27963cadb06160c21712064ccb3a8\n"," Stored in directory: /root/.cache/pip/wheels/81/e8/97/fef2125285ec983f8d1a2e563b1c4e988c03af93f0d1d1972b\n"," Building wheel for pycddlib (setup.py) ... \u001b[?25lerror\n","\u001b[31m ERROR: Failed building wheel for pycddlib\u001b[0m\n","\u001b[?25h Running setup.py clean for pycddlib\n","Successfully built PLTable pypoman\n","Failed to build pycddlib\n","Installing collected packages: pycddlib, pypoman, PLTable, lattices, debtcollector, boltons, dit\n"," Running setup.py install for pycddlib ... \u001b[?25l\u001b[?25herror\n","\u001b[31mERROR: Command errored out with exit status 1: /usr/bin/python3 -u -c 'import io, os, sys, setuptools, tokenize; sys.argv[0] = '\"'\"'/tmp/pip-install-lawa8alf/pycddlib_da5648d95fc34a1dac1964a6ad58d0f4/setup.py'\"'\"'; __file__='\"'\"'/tmp/pip-install-lawa8alf/pycddlib_da5648d95fc34a1dac1964a6ad58d0f4/setup.py'\"'\"';f = getattr(tokenize, '\"'\"'open'\"'\"', open)(__file__) if os.path.exists(__file__) else io.StringIO('\"'\"'from setuptools import setup; setup()'\"'\"');code = f.read().replace('\"'\"'\\r\\n'\"'\"', '\"'\"'\\n'\"'\"');f.close();exec(compile(code, __file__, '\"'\"'exec'\"'\"'))' install --record /tmp/pip-record-cd0dco71/install-record.txt --single-version-externally-managed --compile --install-headers /usr/local/include/python3.7/pycddlib Check the logs for full command output.\u001b[0m\n","Looking in indexes: https://pypi.org/simple, https://us-python.pkg.dev/colab-wheels/public/simple/\n","Collecting pyinform\n"," Downloading pyinform-0.2.0-py3-none-any.whl (131 kB)\n","\u001b[K |████████████████████████████████| 131 kB 14.9 MB/s \n","\u001b[?25hRequirement already satisfied: numpy in /usr/local/lib/python3.7/dist-packages (from pyinform) (1.21.6)\n","Installing collected packages: pyinform\n","Successfully installed pyinform-0.2.0\n","Looking in indexes: https://pypi.org/simple, https://us-python.pkg.dev/colab-wheels/public/simple/\n","Requirement already satisfied: matplotlib in /usr/local/lib/python3.7/dist-packages (3.2.2)\n","Requirement already satisfied: numpy>=1.11 in /usr/local/lib/python3.7/dist-packages (from matplotlib) (1.21.6)\n","Requirement already satisfied: kiwisolver>=1.0.1 in /usr/local/lib/python3.7/dist-packages (from matplotlib) (1.4.4)\n","Requirement already satisfied: cycler>=0.10 in /usr/local/lib/python3.7/dist-packages (from matplotlib) (0.11.0)\n","Requirement already satisfied: python-dateutil>=2.1 in /usr/local/lib/python3.7/dist-packages (from matplotlib) (2.8.2)\n","Requirement already satisfied: pyparsing!=2.0.4,!=2.1.2,!=2.1.6,>=2.0.1 in /usr/local/lib/python3.7/dist-packages (from matplotlib) (3.0.9)\n","Requirement already satisfied: typing-extensions in /usr/local/lib/python3.7/dist-packages (from kiwisolver>=1.0.1->matplotlib) (4.1.1)\n","Requirement already satisfied: six>=1.5 in /usr/local/lib/python3.7/dist-packages (from python-dateutil>=2.1->matplotlib) (1.15.0)\n","Looking in indexes: https://pypi.org/simple, https://us-python.pkg.dev/colab-wheels/public/simple/\n","Requirement already satisfied: seaborn in /usr/local/lib/python3.7/dist-packages (0.11.2)\n","Requirement already satisfied: matplotlib>=2.2 in /usr/local/lib/python3.7/dist-packages (from seaborn) (3.2.2)\n","Requirement already satisfied: numpy>=1.15 in /usr/local/lib/python3.7/dist-packages (from seaborn) (1.21.6)\n","Requirement already satisfied: scipy>=1.0 in /usr/local/lib/python3.7/dist-packages (from seaborn) (1.7.3)\n","Requirement already satisfied: pandas>=0.23 in /usr/local/lib/python3.7/dist-packages (from seaborn) (1.3.5)\n","Requirement already satisfied: cycler>=0.10 in /usr/local/lib/python3.7/dist-packages (from matplotlib>=2.2->seaborn) (0.11.0)\n","Requirement already satisfied: kiwisolver>=1.0.1 in /usr/local/lib/python3.7/dist-packages (from matplotlib>=2.2->seaborn) (1.4.4)\n","Requirement already satisfied: pyparsing!=2.0.4,!=2.1.2,!=2.1.6,>=2.0.1 in /usr/local/lib/python3.7/dist-packages (from matplotlib>=2.2->seaborn) (3.0.9)\n","Requirement already satisfied: python-dateutil>=2.1 in /usr/local/lib/python3.7/dist-packages (from matplotlib>=2.2->seaborn) (2.8.2)\n","Requirement already satisfied: typing-extensions in /usr/local/lib/python3.7/dist-packages (from kiwisolver>=1.0.1->matplotlib>=2.2->seaborn) (4.1.1)\n","Requirement already satisfied: pytz>=2017.3 in /usr/local/lib/python3.7/dist-packages (from pandas>=0.23->seaborn) (2022.5)\n","Requirement already satisfied: six>=1.5 in /usr/local/lib/python3.7/dist-packages (from python-dateutil>=2.1->matplotlib>=2.2->seaborn) (1.15.0)\n","Looking in indexes: https://pypi.org/simple, https://us-python.pkg.dev/colab-wheels/public/simple/\n","Requirement already satisfied: hyperopt in /usr/local/lib/python3.7/dist-packages (0.1.2)\n","Requirement already satisfied: six in /usr/local/lib/python3.7/dist-packages (from hyperopt) (1.15.0)\n","Requirement already satisfied: pymongo in /usr/local/lib/python3.7/dist-packages (from hyperopt) (4.3.2)\n","Requirement already satisfied: future in /usr/local/lib/python3.7/dist-packages (from hyperopt) (0.16.0)\n","Requirement already satisfied: scipy in /usr/local/lib/python3.7/dist-packages (from hyperopt) (1.7.3)\n","Requirement already satisfied: tqdm in /usr/local/lib/python3.7/dist-packages (from hyperopt) (4.64.1)\n","Requirement already satisfied: networkx in /usr/local/lib/python3.7/dist-packages (from hyperopt) (2.6.3)\n","Requirement already satisfied: numpy in /usr/local/lib/python3.7/dist-packages (from hyperopt) (1.21.6)\n","Requirement already satisfied: dnspython<3.0.0,>=1.16.0 in /usr/local/lib/python3.7/dist-packages (from pymongo->hyperopt) (2.2.1)\n","Looking in indexes: https://pypi.org/simple, https://us-python.pkg.dev/colab-wheels/public/simple/\n","Collecting tpot\n"," Downloading TPOT-0.11.7-py3-none-any.whl (87 kB)\n","\u001b[K |████████████████████████████████| 87 kB 2.6 MB/s \n","\u001b[?25hRequirement already satisfied: joblib>=0.13.2 in /usr/local/lib/python3.7/dist-packages (from tpot) (1.2.0)\n","Requirement already satisfied: numpy>=1.16.3 in /usr/local/lib/python3.7/dist-packages (from tpot) (1.21.6)\n","Requirement already satisfied: scipy>=1.3.1 in /usr/local/lib/python3.7/dist-packages (from tpot) (1.7.3)\n","Requirement already satisfied: scikit-learn>=0.22.0 in /usr/local/lib/python3.7/dist-packages (from tpot) (0.24.2)\n","Collecting xgboost>=1.1.0\n"," Downloading xgboost-1.6.2-py3-none-manylinux2014_x86_64.whl (255.9 MB)\n","\u001b[K |████████████████████████████████| 255.9 MB 49 kB/s \n","\u001b[?25hCollecting stopit>=1.1.1\n"," Downloading stopit-1.1.2.tar.gz (18 kB)\n","Requirement already satisfied: tqdm>=4.36.1 in /usr/local/lib/python3.7/dist-packages (from tpot) (4.64.1)\n","Requirement already satisfied: pandas>=0.24.2 in /usr/local/lib/python3.7/dist-packages (from tpot) (1.3.5)\n","Collecting update-checker>=0.16\n"," Downloading update_checker-0.18.0-py3-none-any.whl (7.0 kB)\n","Collecting deap>=1.2\n"," Downloading deap-1.3.3-cp37-cp37m-manylinux_2_5_x86_64.manylinux1_x86_64.manylinux_2_17_x86_64.manylinux2014_x86_64.whl (139 kB)\n","\u001b[K |████████████████████████████████| 139 kB 27.2 MB/s \n","\u001b[?25hRequirement already satisfied: python-dateutil>=2.7.3 in /usr/local/lib/python3.7/dist-packages (from pandas>=0.24.2->tpot) (2.8.2)\n","Requirement already satisfied: pytz>=2017.3 in /usr/local/lib/python3.7/dist-packages (from pandas>=0.24.2->tpot) (2022.5)\n","Requirement already satisfied: six>=1.5 in /usr/local/lib/python3.7/dist-packages (from python-dateutil>=2.7.3->pandas>=0.24.2->tpot) (1.15.0)\n","Requirement already satisfied: threadpoolctl>=2.0.0 in /usr/local/lib/python3.7/dist-packages (from scikit-learn>=0.22.0->tpot) (3.1.0)\n","Requirement already satisfied: requests>=2.3.0 in /usr/local/lib/python3.7/dist-packages (from update-checker>=0.16->tpot) (2.23.0)\n","Requirement already satisfied: certifi>=2017.4.17 in /usr/local/lib/python3.7/dist-packages (from requests>=2.3.0->update-checker>=0.16->tpot) (2022.9.24)\n","Requirement already satisfied: urllib3!=1.25.0,!=1.25.1,<1.26,>=1.21.1 in /usr/local/lib/python3.7/dist-packages (from requests>=2.3.0->update-checker>=0.16->tpot) (1.24.3)\n","Requirement already satisfied: chardet<4,>=3.0.2 in /usr/local/lib/python3.7/dist-packages (from requests>=2.3.0->update-checker>=0.16->tpot) (3.0.4)\n","Requirement already satisfied: idna<3,>=2.5 in /usr/local/lib/python3.7/dist-packages (from requests>=2.3.0->update-checker>=0.16->tpot) (2.10)\n","Building wheels for collected packages: stopit\n"," Building wheel for stopit (setup.py) ... \u001b[?25l\u001b[?25hdone\n"," Created wheel for stopit: filename=stopit-1.1.2-py3-none-any.whl size=11956 sha256=51b0722095c81205b1a44ae50f66871f948f2c95896a19678f7dbe0a2d9a7a6e\n"," Stored in directory: /root/.cache/pip/wheels/e2/d2/79/eaf81edb391e27c87f51b8ef901ecc85a5363dc96b8b8d71e3\n","Successfully built stopit\n","Installing collected packages: xgboost, update-checker, stopit, deap, tpot\n"," Attempting uninstall: xgboost\n"," Found existing installation: xgboost 0.90\n"," Uninstalling xgboost-0.90:\n"," Successfully uninstalled xgboost-0.90\n","Successfully installed deap-1.3.3 stopit-1.1.2 tpot-0.11.7 update-checker-0.18.0 xgboost-1.6.2\n"]}],"source":["!pip install auto-sklearn\n","# !pip install scikit-optimize\n","!pip install dit\n","!pip install pyinform\n","!pip install matplotlib\n","!pip install seaborn\n","!pip install hyperopt\n","!pip install tpot"]},{"cell_type":"code","execution_count":6,"metadata":{"executionInfo":{"elapsed":525,"status":"ok","timestamp":1667926571585,"user":{"displayName":"Rohit Garg","userId":"11252576926243182044"},"user_tz":-330},"id":"GYIWZaMUHkIo"},"outputs":[],"source":["architecture = 'sklearn'\n","if architecture == 'sklearn':\n"," from sklearn.svm import SVR \n"," from sklearn.svm import SVC\n"," from sklearn.ensemble import RandomForestRegressor\n"," from sklearn.ensemble import RandomForestClassifier\n"," from sklearn.metrics import accuracy_score\n"," \n","else: \n"," from cuml.svm import SVR\n"," from cuml.ensemble import RandomForestRegressor\n"," from cuml.svm import SVC\n"," from cuml.ensemble import RandomForestClassifier\n"," from cuml.metrics import accuracy_score"]},{"cell_type":"markdown","metadata":{"id":"rUPxcrjinvWb"},"source":["TEST RUN 03/11/2021"]},{"cell_type":"code","source":["os.getcwd()"],"metadata":{"id":"Yl8KZqxWqDTe","executionInfo":{"status":"ok","timestamp":1667280425115,"user_tz":-330,"elapsed":745,"user":{"displayName":"Rohit Garg","userId":"11252576926243182044"}},"outputId":"f1bbf063-c5ee-494c-eefd-ebd68e5cb53b","colab":{"base_uri":"https://localhost:8080/","height":36}},"execution_count":null,"outputs":[{"output_type":"execute_result","data":{"text/plain":["'/content/drive/MyDrive/IEEE_EEG_Project/Decoding_EEG-main'"],"application/vnd.google.colaboratory.intrinsic+json":{"type":"string"}},"metadata":{},"execution_count":9}]},{"cell_type":"markdown","source":["DREAMER-TOP-N-FEATURES"],"metadata":{"id":"OiXfZa1Gd_kN"}},{"cell_type":"markdown","source":["Valence"],"metadata":{"id":"vLui4t7XeCpM"}},{"cell_type":"code","source":["!python3 feature_select_main.py --dataset OASIS --window 1 --stride 1 --sfreq 128 --model rfr --label 0 --approach byfs --ml_algo regression --top f --fs_method SelectKBest"],"metadata":{"colab":{"base_uri":"https://localhost:8080/"},"id":"bl7vb9emeIkR","executionInfo":{"status":"ok","timestamp":1667926978663,"user_tz":-330,"elapsed":2471,"user":{"displayName":"Rohit Garg","userId":"11252576926243182044"}},"outputId":"b6b30270-81e0-4143-a031-adf900d28b56"},"execution_count":10,"outputs":[{"output_type":"stream","name":"stdout","text":["Traceback (most recent call last):\n"," File \"feature_select_main.py\", line 17, in \n"," from EpochedFeatures import *\n"," File \"/content/drive/MyDrive/IEEE_EEG_Cluster_Files/EpochedFeatures.py\", line 9, in \n"," import EEGExtract as eeg\n"," File \"/content/drive/MyDrive/IEEE_EEG_Cluster_Files/EEGExtract.py\", line 12, in \n"," from dit.other import tsallis_entropy\n","ModuleNotFoundError: No module named 'dit'\n"]}]},{"cell_type":"markdown","source":["Arousal"],"metadata":{"id":"BqvxQ4_4eE0S"}},{"cell_type":"code","source":["!python3 feature_select_main.py --top_factor 9 --dataset DREAMER --window 1 --stride 1 --sfreq 128 --model rfr --label 1 --approach byfs --ml_algo regression --top f --fs_method SelectKBest"],"metadata":{"colab":{"base_uri":"https://localhost:8080/"},"id":"Y0FiC9GPqJ77","executionInfo":{"status":"ok","timestamp":1667669074325,"user_tz":-330,"elapsed":2722651,"user":{"displayName":"Rohit Garg","userId":"11252576926243182044"}},"outputId":"56759611-085e-4140-c130-cfec8ea80974"},"execution_count":null,"outputs":[{"output_type":"stream","name":"stdout","text":["{'top_factor': 9, 'dataset': 'DREAMER', 'window': 1, 'stride': 1, 'sfreq': 128, 'model': 'rfr', 'label': 1, 'approach': 'byfs', 'ml_algo': 'regression', 'top': 'f', 'fs_method': 'SelectKBest'}\n","Number of segments are: 188370\n","['ShannonRes_delta', 'ShannonRes_theta', 'ShannonRes_alpha', 'ShannonRes_beta', 'ShannonRes_gamma', 'HjorthComp', 'HjorthMob', 'medianFreq', 'bandPwr_delta', 'bandPwr_theta', 'bandPwr_alpha', 'bandPwr_beta', 'bandPwr_gamma', 'stdDev', 'shortSpikeNum', 'dasm_delta', 'dasm_theta', 'dasm_alpha', 'dasm_beta', 'dasm_gamma', 'rasm_delta', 'rasm_theta', 'rasm_alpha', 'rasm_beta', 'rasm_gamma']\n","25\n","Number of Feature-Columns: 280\n","\n","275 ['ShannonRes_delta00', 'ShannonRes_delta01', 'ShannonRes_delta02', 'ShannonRes_delta03', 'ShannonRes_delta04', 'ShannonRes_delta05', 'ShannonRes_delta06', 'ShannonRes_delta07', 'ShannonRes_delta08', 'ShannonRes_delta09', 'ShannonRes_delta10', 'ShannonRes_delta11', 'ShannonRes_delta12', 'ShannonRes_delta13', 'ShannonRes_theta00', 'ShannonRes_theta01', 'ShannonRes_theta02', 'ShannonRes_theta03', 'ShannonRes_theta04', 'ShannonRes_theta05', 'ShannonRes_theta06', 'ShannonRes_theta07', 'ShannonRes_theta08', 'ShannonRes_theta09', 'ShannonRes_theta10', 'ShannonRes_theta11', 'ShannonRes_theta12', 'ShannonRes_theta13', 'ShannonRes_alpha00', 'ShannonRes_alpha01', 'ShannonRes_alpha02', 'ShannonRes_alpha03', 'ShannonRes_alpha04', 'ShannonRes_alpha05', 'ShannonRes_alpha06', 'ShannonRes_alpha07', 'ShannonRes_alpha08', 'ShannonRes_alpha09', 'ShannonRes_alpha10', 'ShannonRes_alpha11', 'ShannonRes_alpha12', 'ShannonRes_alpha13', 'ShannonRes_beta00', 'ShannonRes_beta01', 'ShannonRes_beta02', 'ShannonRes_beta03', 'ShannonRes_beta04', 'ShannonRes_beta05', 'ShannonRes_beta06', 'ShannonRes_beta07', 'ShannonRes_beta08', 'ShannonRes_beta09', 'ShannonRes_beta10', 'ShannonRes_beta11', 'ShannonRes_beta12', 'ShannonRes_beta13', 'ShannonRes_gamma00', 'ShannonRes_gamma01', 'ShannonRes_gamma02', 'ShannonRes_gamma03', 'ShannonRes_gamma04', 'ShannonRes_gamma05', 'ShannonRes_gamma06', 'ShannonRes_gamma07', 'ShannonRes_gamma08', 'ShannonRes_gamma09', 'ShannonRes_gamma10', 'ShannonRes_gamma11', 'ShannonRes_gamma12', 'ShannonRes_gamma13', 'HjorthComp00', 'HjorthComp01', 'HjorthComp02', 'HjorthComp03', 'HjorthComp04', 'HjorthComp05', 'HjorthComp06', 'HjorthComp07', 'HjorthComp08', 'HjorthComp09', 'HjorthComp10', 'HjorthComp11', 'HjorthComp12', 'HjorthComp13', 'HjorthMob00', 'HjorthMob01', 'HjorthMob02', 'HjorthMob03', 'HjorthMob04', 'HjorthMob05', 'HjorthMob06', 'HjorthMob07', 'HjorthMob08', 'HjorthMob09', 'HjorthMob10', 'HjorthMob11', 'HjorthMob12', 'HjorthMob13', 'medianFreq00', 'medianFreq01', 'medianFreq02', 'medianFreq03', 'medianFreq04', 'medianFreq05', 'medianFreq06', 'medianFreq07', 'medianFreq08', 'medianFreq09', 'medianFreq10', 'medianFreq11', 'medianFreq12', 'medianFreq13', 'bandPwr_delta00', 'bandPwr_delta01', 'bandPwr_delta02', 'bandPwr_delta03', 'bandPwr_delta04', 'bandPwr_delta05', 'bandPwr_delta06', 'bandPwr_delta07', 'bandPwr_delta08', 'bandPwr_delta09', 'bandPwr_delta10', 'bandPwr_delta11', 'bandPwr_delta12', 'bandPwr_delta13', 'bandPwr_theta00', 'bandPwr_theta01', 'bandPwr_theta02', 'bandPwr_theta03', 'bandPwr_theta04', 'bandPwr_theta05', 'bandPwr_theta06', 'bandPwr_theta07', 'bandPwr_theta08', 'bandPwr_theta09', 'bandPwr_theta10', 'bandPwr_theta11', 'bandPwr_theta12', 'bandPwr_theta13', 'bandPwr_alpha00', 'bandPwr_alpha01', 'bandPwr_alpha02', 'bandPwr_alpha03', 'bandPwr_alpha04', 'bandPwr_alpha05', 'bandPwr_alpha06', 'bandPwr_alpha07', 'bandPwr_alpha08', 'bandPwr_alpha09', 'bandPwr_alpha10', 'bandPwr_alpha11', 'bandPwr_alpha12', 'bandPwr_alpha13', 'bandPwr_beta00', 'bandPwr_beta01', 'bandPwr_beta02', 'bandPwr_beta03', 'bandPwr_beta04', 'bandPwr_beta05', 'bandPwr_beta06', 'bandPwr_beta07', 'bandPwr_beta08', 'bandPwr_beta09', 'bandPwr_beta10', 'bandPwr_beta11', 'bandPwr_beta12', 'bandPwr_beta13', 'bandPwr_gamma00', 'bandPwr_gamma01', 'bandPwr_gamma02', 'bandPwr_gamma03', 'bandPwr_gamma04', 'bandPwr_gamma05', 'bandPwr_gamma06', 'bandPwr_gamma07', 'bandPwr_gamma08', 'bandPwr_gamma09', 'bandPwr_gamma10', 'bandPwr_gamma11', 'bandPwr_gamma12', 'bandPwr_gamma13', 'stdDev00', 'stdDev01', 'stdDev02', 'stdDev03', 'stdDev04', 'stdDev05', 'stdDev06', 'stdDev07', 'stdDev08', 'stdDev09', 'stdDev10', 'stdDev11', 'stdDev12', 'stdDev13', 'shortSpikeNum03', 'shortSpikeNum04', 'shortSpikeNum05', 'shortSpikeNum06', 'shortSpikeNum07', 'shortSpikeNum09', 'shortSpikeNum10', 'shortSpikeNum12', 'shortSpikeNum13', 'dasm_delta00', 'dasm_delta01', 'dasm_delta02', 'dasm_delta03', 'dasm_delta04', 'dasm_delta05', 'dasm_delta06', 'dasm_theta00', 'dasm_theta01', 'dasm_theta02', 'dasm_theta03', 'dasm_theta04', 'dasm_theta05', 'dasm_theta06', 'dasm_alpha00', 'dasm_alpha01', 'dasm_alpha02', 'dasm_alpha03', 'dasm_alpha04', 'dasm_alpha05', 'dasm_alpha06', 'dasm_beta00', 'dasm_beta01', 'dasm_beta02', 'dasm_beta03', 'dasm_beta04', 'dasm_beta05', 'dasm_beta06', 'dasm_gamma00', 'dasm_gamma01', 'dasm_gamma02', 'dasm_gamma03', 'dasm_gamma04', 'dasm_gamma05', 'dasm_gamma06', 'rasm_delta00', 'rasm_delta01', 'rasm_delta02', 'rasm_delta03', 'rasm_delta04', 'rasm_delta05', 'rasm_delta06', 'rasm_theta00', 'rasm_theta01', 'rasm_theta02', 'rasm_theta03', 'rasm_theta04', 'rasm_theta05', 'rasm_theta06', 'rasm_alpha00', 'rasm_alpha01', 'rasm_alpha02', 'rasm_alpha03', 'rasm_alpha04', 'rasm_alpha05', 'rasm_alpha06', 'rasm_beta00', 'rasm_beta01', 'rasm_beta02', 'rasm_beta03', 'rasm_beta04', 'rasm_beta05', 'rasm_beta06', 'rasm_gamma00', 'rasm_gamma01', 'rasm_gamma02', 'rasm_gamma03', 'rasm_gamma04', 'rasm_gamma05', 'rasm_gamma06']\n","y.shape: (188370,)\n"," Specs Score\n","60 ShannonRes_gamma04 1248.318358\n","79 HjorthComp09 1209.122459\n","65 ShannonRes_gamma09 1192.528633\n","68 ShannonRes_gamma12 1174.905391\n","61 ShannonRes_gamma05 1132.100627\n",".. ... ...\n","120 bandPwr_delta08 0.555948\n","242 rasm_delta02 0.460075\n","207 dasm_delta02 0.448493\n","124 bandPwr_delta12 0.394653\n","125 bandPwr_delta13 0.167670\n","\n","[275 rows x 2 columns]\n","['ShannonRes_gamma', 'HjorthComp', 'ShannonRes_beta', 'dasm_alpha', 'rasm_alpha', 'HjorthMob', 'bandPwr_alpha', 'stdDev', 'bandPwr_beta', 'ShannonRes_alpha', 'rasm_beta', 'dasm_beta', 'dasm_theta', 'medianFreq', 'rasm_theta', 'dasm_gamma', 'rasm_gamma', 'ShannonRes_delta', 'ShannonRes_theta', 'bandPwr_theta', 'bandPwr_gamma', 'bandPwr_delta', 'rasm_delta', 'dasm_delta', 'shortSpikeNum']\n","[4, 9, 12, 5, 3, 10, 8, 7, 13, 0, 2, 6, 1, 11]\n","(188370, 112)\n","Number of Feature-Columns: 112\n","\n","Features Ready for undergoing selection tests done ...\n","\n","window: 1, stide: 1, rmse: 0.6063212322198969\n","window: 1, stide: 1, rmse: 0.6063212322198969\n","r2: 0.6828816852132531\n","mae: 0.3990616871051654\n","ev: 0.682889296710527\n"," Feature ... EV\n","0 9 ... 0.682889\n","\n","[1 rows x 5 columns]\n"]}]},{"cell_type":"code","source":["state = np.random.RandomState(100)\n","ser = pd.Series(state.normal(10, 5, 25))\n","np.percentile(ser, q=[0, 25, 50, 75, 100])"],"metadata":{"id":"JVXgNwe13lD0","executionInfo":{"status":"ok","timestamp":1667669847565,"user_tz":-330,"elapsed":7,"user":{"displayName":"Rohit Garg","userId":"11252576926243182044"}},"outputId":"a92e4490-f58f-4abb-f13b-1cd2a24a7ecb","colab":{"base_uri":"https://localhost:8080/"}},"execution_count":null,"outputs":[{"output_type":"execute_result","data":{"text/plain":["array([ 1.25117263, 7.70986507, 10.92259345, 13.36360403, 18.0949083 ])"]},"metadata":{},"execution_count":15}]},{"cell_type":"code","source":["!python3 feature_select_main.py --top_factor 14 --dataset DREAMER --window 1 --stride 1 --sfreq 128 --model rfr --label 0 --approach byfs --ml_algo regression --top e --fs_method SelectKBest"],"metadata":{"colab":{"base_uri":"https://localhost:8080/"},"id":"VfFzfBI370P_","executionInfo":{"status":"ok","timestamp":1667658252181,"user_tz":-330,"elapsed":4062416,"user":{"displayName":"Rohit Garg","userId":"11252576926243182044"}},"outputId":"3827179f-28bf-4b9f-eb48-81091ce326b4"},"execution_count":null,"outputs":[{"output_type":"stream","name":"stdout","text":["{'top_factor': 14, 'dataset': 'DREAMER', 'window': 1, 'stride': 1, 'sfreq': 128, 'model': 'rfr', 'label': 0, 'approach': 'byfs', 'ml_algo': 'regression', 'top': 'e', 'fs_method': 'SelectKBest'}\n","Number of segments are: 188370\n","['ShannonRes_delta', 'ShannonRes_theta', 'ShannonRes_alpha', 'ShannonRes_beta', 'ShannonRes_gamma', 'HjorthComp', 'HjorthMob', 'medianFreq', 'bandPwr_delta', 'bandPwr_theta', 'bandPwr_alpha', 'bandPwr_beta', 'bandPwr_gamma', 'stdDev', 'shortSpikeNum']\n","Number of Feature-Columns: 210\n","\n","y.shape: (188370,)\n","/usr/local/lib/python3.7/dist-packages/sklearn/feature_selection/_univariate_selection.py:112: UserWarning: Features [196 197 198 204 207] are constant.\n"," warnings.warn(\"Features %s are constant.\" % constant_features_idx, UserWarning)\n","/usr/local/lib/python3.7/dist-packages/sklearn/feature_selection/_univariate_selection.py:113: RuntimeWarning: invalid value encountered in true_divide\n"," f = msb / msw\n"," Specs Score\n","65 ShannonRes_gamma09 1888.444463\n","61 ShannonRes_gamma05 1757.685620\n","63 ShannonRes_gamma07 1588.190675\n","66 ShannonRes_gamma10 1575.955842\n","68 ShannonRes_gamma12 1529.537735\n",".. ... ...\n","114 bandPwr_delta02 0.815222\n","117 bandPwr_delta05 0.758937\n","120 bandPwr_delta08 0.662457\n","124 bandPwr_delta12 0.210495\n","1 ShannonRes_delta01 0.169751\n","\n","[205 rows x 2 columns]\n","[9, 5, 7, 10, 12, 8, 4, 6, 3, 2, 13, 1, 0, 11]\n","[9, 5, 7, 10, 12, 8, 4, 6, 3, 2, 13, 1, 0, 11]\n","(188370, 210)\n","Number of Feature-Columns: 210\n","\n","Features Ready for undergoing selection tests done ...\n","\n","window: 1, stide: 1, rmse: 0.7216987912551506\n","r2: 0.7000523658593081\n","mae: 0.5004355789138397\n","ev: 0.7000529324849366\n"," Electrode ... EV\n","0 14 ... 0.700053\n","\n","[1 rows x 5 columns]\n"]}]},{"cell_type":"code","source":["!python3 feature_select_main.py --top_factor 14 --dataset DREAMER --window 1 --stride 1 --sfreq 128 --model rfr --label 1 --approach byfs --ml_algo regression --top e --fs_method SelectKBest"],"metadata":{"colab":{"base_uri":"https://localhost:8080/"},"id":"fu6PWohe746O","executionInfo":{"status":"ok","timestamp":1667662464328,"user_tz":-330,"elapsed":4212152,"user":{"displayName":"Rohit Garg","userId":"11252576926243182044"}},"outputId":"774e508a-e7b5-43c5-910b-ee11444540b8"},"execution_count":null,"outputs":[{"output_type":"stream","name":"stdout","text":["{'top_factor': 14, 'dataset': 'DREAMER', 'window': 1, 'stride': 1, 'sfreq': 128, 'model': 'rfr', 'label': 1, 'approach': 'byfs', 'ml_algo': 'regression', 'top': 'e', 'fs_method': 'SelectKBest'}\n","Number of segments are: 188370\n","['ShannonRes_delta', 'ShannonRes_theta', 'ShannonRes_alpha', 'ShannonRes_beta', 'ShannonRes_gamma', 'HjorthComp', 'HjorthMob', 'medianFreq', 'bandPwr_delta', 'bandPwr_theta', 'bandPwr_alpha', 'bandPwr_beta', 'bandPwr_gamma', 'stdDev', 'shortSpikeNum']\n","Number of Feature-Columns: 210\n","\n","y.shape: (188370,)\n","/usr/local/lib/python3.7/dist-packages/sklearn/feature_selection/_univariate_selection.py:112: UserWarning: Features [196 197 198 204 207] are constant.\n"," warnings.warn(\"Features %s are constant.\" % constant_features_idx, UserWarning)\n","/usr/local/lib/python3.7/dist-packages/sklearn/feature_selection/_univariate_selection.py:113: RuntimeWarning: invalid value encountered in true_divide\n"," f = msb / msw\n"," Specs Score\n","60 ShannonRes_gamma04 1248.318358\n","79 HjorthComp09 1209.122459\n","65 ShannonRes_gamma09 1192.528633\n","68 ShannonRes_gamma12 1174.905391\n","61 ShannonRes_gamma05 1132.100627\n",".. ... ...\n","123 bandPwr_delta11 0.820405\n","116 bandPwr_delta04 0.760073\n","120 bandPwr_delta08 0.555948\n","124 bandPwr_delta12 0.394653\n","125 bandPwr_delta13 0.167670\n","\n","[205 rows x 2 columns]\n","[4, 9, 12, 5, 3, 10, 8, 7, 13, 0, 2, 6, 11, 1]\n","[4, 9, 12, 5, 3, 10, 8, 7, 13, 0, 2, 6, 11, 1]\n","(188370, 210)\n","Number of Feature-Columns: 210\n","\n","Features Ready for undergoing selection tests done ...\n","\n","window: 1, stide: 1, rmse: 0.6142419434127833\n","r2: 0.6745421813741982\n","mae: 0.40563545150501673\n","ev: 0.6745485878535202\n"," Electrode ... EV\n","0 14 ... 0.674549\n","\n","[1 rows x 5 columns]\n"]}]},{"cell_type":"code","source":["!python3 feature_select_main.py --top_factor 14 --dataset DEAP --window 1 --stride 1 --sfreq 128 --model rfr --label 0 --approach byfs --ml_algo regression --top e --fs_method SelectKBest"],"metadata":{"colab":{"base_uri":"https://localhost:8080/"},"id":"iheAa-jgtSRA","executionInfo":{"status":"ok","timestamp":1667652169754,"user_tz":-330,"elapsed":1644227,"user":{"displayName":"Rohit Garg","userId":"11252576926243182044"}},"outputId":"db393965-1449-4d41-fa0f-21d9c1241584"},"execution_count":null,"outputs":[{"output_type":"stream","name":"stdout","text":["{'top_factor': 14, 'dataset': 'DEAP', 'window': 1, 'stride': 1, 'sfreq': 128, 'model': 'rfr', 'label': 0, 'approach': 'byfs', 'ml_algo': 'regression', 'top': 'e', 'fs_method': 'SelectKBest'}\n","Number of segments are: 80640\n","['ShannonRes_delta', 'ShannonRes_theta', 'ShannonRes_alpha', 'ShannonRes_beta', 'ShannonRes_gamma', 'HjorthComp', 'HjorthMob', 'medianFreq', 'bandPwr_delta', 'bandPwr_theta', 'bandPwr_alpha', 'bandPwr_beta', 'bandPwr_gamma', 'stdDev', 'shortSpikeNum']\n","Number of Feature-Columns: 210\n","\n","y.shape: (80640,)\n","/usr/local/lib/python3.7/dist-packages/sklearn/feature_selection/_univariate_selection.py:112: UserWarning: Features [196 199 203 207] are constant.\n"," warnings.warn(\"Features %s are constant.\" % constant_features_idx, UserWarning)\n","/usr/local/lib/python3.7/dist-packages/sklearn/feature_selection/_univariate_selection.py:113: RuntimeWarning: invalid value encountered in true_divide\n"," f = msb / msw\n"," Specs Score\n","183 stdDev01 119.015497\n","43 ShannonRes_beta01 109.396980\n","54 ShannonRes_beta12 96.831215\n","155 bandPwr_beta01 95.801674\n","57 ShannonRes_gamma01 95.727081\n",".. ... ...\n","198 shortSpikeNum02 1.578476\n","200 shortSpikeNum04 1.578476\n","205 shortSpikeNum09 1.578476\n","208 shortSpikeNum12 1.578476\n","209 shortSpikeNum13 1.578476\n","\n","[206 rows x 2 columns]\n","[1, 12, 5, 10, 8, 11, 6, 0, 7, 13, 2, 9, 4, 3]\n","[1, 12, 5, 10, 8, 11, 6, 0, 7, 13, 2, 9, 4, 3]\n","(80640, 210)\n","Number of Feature-Columns: 210\n","\n","Features Ready for undergoing selection tests done ...\n","\n","window: 1, stide: 1, rmse: 1.881620397407473\n","r2: 0.21937372309578296\n","mae: 1.539977424355159\n","ev: 0.21938836071063372\n"," Electrode ... EV\n","0 14 ... 0.219388\n","\n","[1 rows x 5 columns]\n"]}]},{"cell_type":"code","source":["!python3 feature_select_main.py --top_factor 14 --dataset DEAP --window 1 --stride 1 --sfreq 128 --model rfr --label 1 --approach byfs --ml_algo regression --top e --fs_method SelectKBest"],"metadata":{"colab":{"base_uri":"https://localhost:8080/"},"id":"VzaJF3wD0mZP","executionInfo":{"status":"ok","timestamp":1667654077488,"user_tz":-330,"elapsed":1784629,"user":{"displayName":"Rohit Garg","userId":"11252576926243182044"}},"outputId":"951db328-1832-41cc-ed8d-ad68d2eb5ca4"},"execution_count":null,"outputs":[{"output_type":"stream","name":"stdout","text":["{'top_factor': 14, 'dataset': 'DEAP', 'window': 1, 'stride': 1, 'sfreq': 128, 'model': 'rfr', 'label': 1, 'approach': 'byfs', 'ml_algo': 'regression', 'top': 'e', 'fs_method': 'SelectKBest'}\n","Number of segments are: 80640\n","['ShannonRes_delta', 'ShannonRes_theta', 'ShannonRes_alpha', 'ShannonRes_beta', 'ShannonRes_gamma', 'HjorthComp', 'HjorthMob', 'medianFreq', 'bandPwr_delta', 'bandPwr_theta', 'bandPwr_alpha', 'bandPwr_beta', 'bandPwr_gamma', 'stdDev', 'shortSpikeNum']\n","Number of Feature-Columns: 210\n","\n","y.shape: (80640,)\n","/usr/local/lib/python3.7/dist-packages/sklearn/feature_selection/_univariate_selection.py:112: UserWarning: Features [196 199 203 207] are constant.\n"," warnings.warn(\"Features %s are constant.\" % constant_features_idx, UserWarning)\n","/usr/local/lib/python3.7/dist-packages/sklearn/feature_selection/_univariate_selection.py:113: RuntimeWarning: invalid value encountered in true_divide\n"," f = msb / msw\n"," Specs Score\n","169 bandPwr_gamma01 119.183483\n","43 ShannonRes_beta01 103.470834\n","183 stdDev01 97.458312\n","54 ShannonRes_beta12 94.892195\n","155 bandPwr_beta01 94.679725\n",".. ... ...\n","100 medianFreq02 1.860896\n","122 bandPwr_delta10 1.859203\n","98 medianFreq00 1.849017\n","121 bandPwr_delta09 1.834850\n","110 medianFreq12 1.672528\n","\n","[206 rows x 2 columns]\n","[1, 12, 10, 8, 5, 7, 2, 11, 4, 0, 9, 13, 6, 3]\n","[1, 12, 10, 8, 5, 7, 2, 11, 4, 0, 9, 13, 6, 3]\n","(80640, 210)\n","Number of Feature-Columns: 210\n","\n","Features Ready for undergoing selection tests done ...\n","\n","window: 1, stide: 1, rmse: 1.7561622918940636\n","r2: 0.2372180169648268\n","mae: 1.4176366009424604\n","ev: 0.23723748069501904\n"," Electrode ... EV\n","0 14 ... 0.237237\n","\n","[1 rows x 5 columns]\n"]}]},{"cell_type":"code","source":["!python3 feature_select_main.py --top_factor 8 --dataset DEAP --window 1 --stride 1 --sfreq 128 --model rfr --label 0 --approach byfs --ml_algo regression --top f --fs_method SelectKBest"],"metadata":{"colab":{"base_uri":"https://localhost:8080/"},"id":"29YaQhWIS5E8","executionInfo":{"status":"ok","timestamp":1667649388245,"user_tz":-330,"elapsed":764912,"user":{"displayName":"Rohit Garg","userId":"11252576926243182044"}},"outputId":"eb6fb854-29e8-4bd3-abdb-35a4b989f4c8"},"execution_count":null,"outputs":[{"output_type":"stream","name":"stdout","text":["{'top_factor': 8, 'dataset': 'DEAP', 'window': 1, 'stride': 1, 'sfreq': 128, 'model': 'rfr', 'label': 0, 'approach': 'byfs', 'ml_algo': 'regression', 'top': 'f', 'fs_method': 'SelectKBest'}\n","Number of segments are: 80640\n","['ShannonRes_delta', 'ShannonRes_theta', 'ShannonRes_alpha', 'ShannonRes_beta', 'ShannonRes_gamma', 'HjorthComp', 'HjorthMob', 'medianFreq', 'bandPwr_delta', 'bandPwr_theta', 'bandPwr_alpha', 'bandPwr_beta', 'bandPwr_gamma', 'stdDev', 'shortSpikeNum', 'dasm_delta', 'dasm_theta', 'dasm_alpha', 'dasm_beta', 'dasm_gamma', 'rasm_delta', 'rasm_theta', 'rasm_alpha', 'rasm_beta', 'rasm_gamma']\n","25\n","Number of Feature-Columns: 280\n","\n","276 ['ShannonRes_delta00', 'ShannonRes_delta01', 'ShannonRes_delta02', 'ShannonRes_delta03', 'ShannonRes_delta04', 'ShannonRes_delta05', 'ShannonRes_delta06', 'ShannonRes_delta07', 'ShannonRes_delta08', 'ShannonRes_delta09', 'ShannonRes_delta10', 'ShannonRes_delta11', 'ShannonRes_delta12', 'ShannonRes_delta13', 'ShannonRes_theta00', 'ShannonRes_theta01', 'ShannonRes_theta02', 'ShannonRes_theta03', 'ShannonRes_theta04', 'ShannonRes_theta05', 'ShannonRes_theta06', 'ShannonRes_theta07', 'ShannonRes_theta08', 'ShannonRes_theta09', 'ShannonRes_theta10', 'ShannonRes_theta11', 'ShannonRes_theta12', 'ShannonRes_theta13', 'ShannonRes_alpha00', 'ShannonRes_alpha01', 'ShannonRes_alpha02', 'ShannonRes_alpha03', 'ShannonRes_alpha04', 'ShannonRes_alpha05', 'ShannonRes_alpha06', 'ShannonRes_alpha07', 'ShannonRes_alpha08', 'ShannonRes_alpha09', 'ShannonRes_alpha10', 'ShannonRes_alpha11', 'ShannonRes_alpha12', 'ShannonRes_alpha13', 'ShannonRes_beta00', 'ShannonRes_beta01', 'ShannonRes_beta02', 'ShannonRes_beta03', 'ShannonRes_beta04', 'ShannonRes_beta05', 'ShannonRes_beta06', 'ShannonRes_beta07', 'ShannonRes_beta08', 'ShannonRes_beta09', 'ShannonRes_beta10', 'ShannonRes_beta11', 'ShannonRes_beta12', 'ShannonRes_beta13', 'ShannonRes_gamma00', 'ShannonRes_gamma01', 'ShannonRes_gamma02', 'ShannonRes_gamma03', 'ShannonRes_gamma04', 'ShannonRes_gamma05', 'ShannonRes_gamma06', 'ShannonRes_gamma07', 'ShannonRes_gamma08', 'ShannonRes_gamma09', 'ShannonRes_gamma10', 'ShannonRes_gamma11', 'ShannonRes_gamma12', 'ShannonRes_gamma13', 'HjorthComp00', 'HjorthComp01', 'HjorthComp02', 'HjorthComp03', 'HjorthComp04', 'HjorthComp05', 'HjorthComp06', 'HjorthComp07', 'HjorthComp08', 'HjorthComp09', 'HjorthComp10', 'HjorthComp11', 'HjorthComp12', 'HjorthComp13', 'HjorthMob00', 'HjorthMob01', 'HjorthMob02', 'HjorthMob03', 'HjorthMob04', 'HjorthMob05', 'HjorthMob06', 'HjorthMob07', 'HjorthMob08', 'HjorthMob09', 'HjorthMob10', 'HjorthMob11', 'HjorthMob12', 'HjorthMob13', 'medianFreq00', 'medianFreq01', 'medianFreq02', 'medianFreq03', 'medianFreq04', 'medianFreq05', 'medianFreq06', 'medianFreq07', 'medianFreq08', 'medianFreq09', 'medianFreq10', 'medianFreq11', 'medianFreq12', 'medianFreq13', 'bandPwr_delta00', 'bandPwr_delta01', 'bandPwr_delta02', 'bandPwr_delta03', 'bandPwr_delta04', 'bandPwr_delta05', 'bandPwr_delta06', 'bandPwr_delta07', 'bandPwr_delta08', 'bandPwr_delta09', 'bandPwr_delta10', 'bandPwr_delta11', 'bandPwr_delta12', 'bandPwr_delta13', 'bandPwr_theta00', 'bandPwr_theta01', 'bandPwr_theta02', 'bandPwr_theta03', 'bandPwr_theta04', 'bandPwr_theta05', 'bandPwr_theta06', 'bandPwr_theta07', 'bandPwr_theta08', 'bandPwr_theta09', 'bandPwr_theta10', 'bandPwr_theta11', 'bandPwr_theta12', 'bandPwr_theta13', 'bandPwr_alpha00', 'bandPwr_alpha01', 'bandPwr_alpha02', 'bandPwr_alpha03', 'bandPwr_alpha04', 'bandPwr_alpha05', 'bandPwr_alpha06', 'bandPwr_alpha07', 'bandPwr_alpha08', 'bandPwr_alpha09', 'bandPwr_alpha10', 'bandPwr_alpha11', 'bandPwr_alpha12', 'bandPwr_alpha13', 'bandPwr_beta00', 'bandPwr_beta01', 'bandPwr_beta02', 'bandPwr_beta03', 'bandPwr_beta04', 'bandPwr_beta05', 'bandPwr_beta06', 'bandPwr_beta07', 'bandPwr_beta08', 'bandPwr_beta09', 'bandPwr_beta10', 'bandPwr_beta11', 'bandPwr_beta12', 'bandPwr_beta13', 'bandPwr_gamma00', 'bandPwr_gamma01', 'bandPwr_gamma02', 'bandPwr_gamma03', 'bandPwr_gamma04', 'bandPwr_gamma05', 'bandPwr_gamma06', 'bandPwr_gamma07', 'bandPwr_gamma08', 'bandPwr_gamma09', 'bandPwr_gamma10', 'bandPwr_gamma11', 'bandPwr_gamma12', 'bandPwr_gamma13', 'stdDev00', 'stdDev01', 'stdDev02', 'stdDev03', 'stdDev04', 'stdDev05', 'stdDev06', 'stdDev07', 'stdDev08', 'stdDev09', 'stdDev10', 'stdDev11', 'stdDev12', 'stdDev13', 'shortSpikeNum01', 'shortSpikeNum02', 'shortSpikeNum04', 'shortSpikeNum05', 'shortSpikeNum06', 'shortSpikeNum08', 'shortSpikeNum09', 'shortSpikeNum10', 'shortSpikeNum12', 'shortSpikeNum13', 'dasm_delta00', 'dasm_delta01', 'dasm_delta02', 'dasm_delta03', 'dasm_delta04', 'dasm_delta05', 'dasm_delta06', 'dasm_theta00', 'dasm_theta01', 'dasm_theta02', 'dasm_theta03', 'dasm_theta04', 'dasm_theta05', 'dasm_theta06', 'dasm_alpha00', 'dasm_alpha01', 'dasm_alpha02', 'dasm_alpha03', 'dasm_alpha04', 'dasm_alpha05', 'dasm_alpha06', 'dasm_beta00', 'dasm_beta01', 'dasm_beta02', 'dasm_beta03', 'dasm_beta04', 'dasm_beta05', 'dasm_beta06', 'dasm_gamma00', 'dasm_gamma01', 'dasm_gamma02', 'dasm_gamma03', 'dasm_gamma04', 'dasm_gamma05', 'dasm_gamma06', 'rasm_delta00', 'rasm_delta01', 'rasm_delta02', 'rasm_delta03', 'rasm_delta04', 'rasm_delta05', 'rasm_delta06', 'rasm_theta00', 'rasm_theta01', 'rasm_theta02', 'rasm_theta03', 'rasm_theta04', 'rasm_theta05', 'rasm_theta06', 'rasm_alpha00', 'rasm_alpha01', 'rasm_alpha02', 'rasm_alpha03', 'rasm_alpha04', 'rasm_alpha05', 'rasm_alpha06', 'rasm_beta00', 'rasm_beta01', 'rasm_beta02', 'rasm_beta03', 'rasm_beta04', 'rasm_beta05', 'rasm_beta06', 'rasm_gamma00', 'rasm_gamma01', 'rasm_gamma02', 'rasm_gamma03', 'rasm_gamma04', 'rasm_gamma05', 'rasm_gamma06']\n","y.shape: (80640,)\n"," Specs Score\n","270 rasm_gamma01 125.772623\n","263 rasm_beta01 122.623519\n","183 stdDev01 119.015497\n","228 dasm_beta01 115.666179\n","43 ShannonRes_beta01 109.396980\n",".. ... ...\n","244 rasm_delta03 0.579155\n","255 rasm_alpha00 0.554878\n","243 rasm_delta02 0.413400\n","246 rasm_delta05 0.345636\n","247 rasm_delta06 0.314725\n","\n","[276 rows x 2 columns]\n","['rasm_gamma', 'rasm_beta', 'stdDev', 'dasm_beta', 'ShannonRes_beta', 'dasm_gamma', 'bandPwr_beta', 'ShannonRes_gamma', 'dasm_theta', 'dasm_alpha', 'rasm_theta', 'ShannonRes_alpha', 'bandPwr_gamma', 'ShannonRes_theta', 'ShannonRes_delta', 'dasm_delta', 'bandPwr_alpha', 'bandPwr_theta', 'HjorthMob', 'HjorthComp', 'rasm_alpha', 'bandPwr_delta', 'shortSpikeNum', 'rasm_delta', 'medianFreq']\n","[1, 12, 5, 10, 8, 4, 0, 2, 11, 6, 7, 13, 9, 3]\n","(80640, 84)\n","Number of Feature-Columns: 84\n","\n","Features Ready for undergoing selection tests done ...\n","\n","window: 1, stide: 1, rmse: 1.8478133509827406\n","window: 1, stide: 1, rmse: 1.8478133509827406\n","r2: 0.24717272914165667\n","mae: 1.4984721664186509\n","ev: 0.24718309896979185\n"," Feature ... EV\n","0 8 ... 0.247183\n","\n","[1 rows x 5 columns]\n"]}]},{"cell_type":"code","source":["OASIS-AROUSAL-ELECTRODE"],"metadata":{"id":"TR6PwEZpYlLz"},"execution_count":null,"outputs":[]},{"cell_type":"code","source":["!python3 feature_select_main.py --dataset DEAP --window 1 --stride 1 --sfreq 128 --model rfr --label 1 --approach byfs --ml_algo regression --top f --fs_method SelectKBest"],"metadata":{"colab":{"base_uri":"https://localhost:8080/"},"id":"YndvSV-Ki4sL","outputId":"c25573ec-638b-435a-cfbd-81141c74a521"},"execution_count":null,"outputs":[{"metadata":{"tags":null},"name":"stdout","output_type":"stream","text":["{'dataset': 'DEAP', 'window': 1, 'stride': 1, 'sfreq': 128, 'model': 'rfr', 'label': 1, 'approach': 'byfs', 'ml_algo': 'regression', 'top': 'f', 'fs_method': 'SelectKBest'}\n","Number of segments are: 80640\n","['ShannonRes_delta', 'ShannonRes_theta', 'ShannonRes_alpha', 'ShannonRes_beta', 'ShannonRes_gamma', 'HjorthComp', 'HjorthMob', 'medianFreq', 'bandPwr_delta', 'bandPwr_theta', 'bandPwr_alpha', 'bandPwr_beta', 'bandPwr_gamma', 'stdDev', 'shortSpikeNum', 'dasm_delta', 'dasm_theta', 'dasm_alpha', 'dasm_beta', 'dasm_gamma', 'rasm_delta', 'rasm_theta', 'rasm_alpha', 'rasm_beta', 'rasm_gamma']\n","25\n","Number of Feature-Columns: 280\n","\n","276 ['ShannonRes_delta00', 'ShannonRes_delta01', 'ShannonRes_delta02', 'ShannonRes_delta03', 'ShannonRes_delta04', 'ShannonRes_delta05', 'ShannonRes_delta06', 'ShannonRes_delta07', 'ShannonRes_delta08', 'ShannonRes_delta09', 'ShannonRes_delta10', 'ShannonRes_delta11', 'ShannonRes_delta12', 'ShannonRes_delta13', 'ShannonRes_theta00', 'ShannonRes_theta01', 'ShannonRes_theta02', 'ShannonRes_theta03', 'ShannonRes_theta04', 'ShannonRes_theta05', 'ShannonRes_theta06', 'ShannonRes_theta07', 'ShannonRes_theta08', 'ShannonRes_theta09', 'ShannonRes_theta10', 'ShannonRes_theta11', 'ShannonRes_theta12', 'ShannonRes_theta13', 'ShannonRes_alpha00', 'ShannonRes_alpha01', 'ShannonRes_alpha02', 'ShannonRes_alpha03', 'ShannonRes_alpha04', 'ShannonRes_alpha05', 'ShannonRes_alpha06', 'ShannonRes_alpha07', 'ShannonRes_alpha08', 'ShannonRes_alpha09', 'ShannonRes_alpha10', 'ShannonRes_alpha11', 'ShannonRes_alpha12', 'ShannonRes_alpha13', 'ShannonRes_beta00', 'ShannonRes_beta01', 'ShannonRes_beta02', 'ShannonRes_beta03', 'ShannonRes_beta04', 'ShannonRes_beta05', 'ShannonRes_beta06', 'ShannonRes_beta07', 'ShannonRes_beta08', 'ShannonRes_beta09', 'ShannonRes_beta10', 'ShannonRes_beta11', 'ShannonRes_beta12', 'ShannonRes_beta13', 'ShannonRes_gamma00', 'ShannonRes_gamma01', 'ShannonRes_gamma02', 'ShannonRes_gamma03', 'ShannonRes_gamma04', 'ShannonRes_gamma05', 'ShannonRes_gamma06', 'ShannonRes_gamma07', 'ShannonRes_gamma08', 'ShannonRes_gamma09', 'ShannonRes_gamma10', 'ShannonRes_gamma11', 'ShannonRes_gamma12', 'ShannonRes_gamma13', 'HjorthComp00', 'HjorthComp01', 'HjorthComp02', 'HjorthComp03', 'HjorthComp04', 'HjorthComp05', 'HjorthComp06', 'HjorthComp07', 'HjorthComp08', 'HjorthComp09', 'HjorthComp10', 'HjorthComp11', 'HjorthComp12', 'HjorthComp13', 'HjorthMob00', 'HjorthMob01', 'HjorthMob02', 'HjorthMob03', 'HjorthMob04', 'HjorthMob05', 'HjorthMob06', 'HjorthMob07', 'HjorthMob08', 'HjorthMob09', 'HjorthMob10', 'HjorthMob11', 'HjorthMob12', 'HjorthMob13', 'medianFreq00', 'medianFreq01', 'medianFreq02', 'medianFreq03', 'medianFreq04', 'medianFreq05', 'medianFreq06', 'medianFreq07', 'medianFreq08', 'medianFreq09', 'medianFreq10', 'medianFreq11', 'medianFreq12', 'medianFreq13', 'bandPwr_delta00', 'bandPwr_delta01', 'bandPwr_delta02', 'bandPwr_delta03', 'bandPwr_delta04', 'bandPwr_delta05', 'bandPwr_delta06', 'bandPwr_delta07', 'bandPwr_delta08', 'bandPwr_delta09', 'bandPwr_delta10', 'bandPwr_delta11', 'bandPwr_delta12', 'bandPwr_delta13', 'bandPwr_theta00', 'bandPwr_theta01', 'bandPwr_theta02', 'bandPwr_theta03', 'bandPwr_theta04', 'bandPwr_theta05', 'bandPwr_theta06', 'bandPwr_theta07', 'bandPwr_theta08', 'bandPwr_theta09', 'bandPwr_theta10', 'bandPwr_theta11', 'bandPwr_theta12', 'bandPwr_theta13', 'bandPwr_alpha00', 'bandPwr_alpha01', 'bandPwr_alpha02', 'bandPwr_alpha03', 'bandPwr_alpha04', 'bandPwr_alpha05', 'bandPwr_alpha06', 'bandPwr_alpha07', 'bandPwr_alpha08', 'bandPwr_alpha09', 'bandPwr_alpha10', 'bandPwr_alpha11', 'bandPwr_alpha12', 'bandPwr_alpha13', 'bandPwr_beta00', 'bandPwr_beta01', 'bandPwr_beta02', 'bandPwr_beta03', 'bandPwr_beta04', 'bandPwr_beta05', 'bandPwr_beta06', 'bandPwr_beta07', 'bandPwr_beta08', 'bandPwr_beta09', 'bandPwr_beta10', 'bandPwr_beta11', 'bandPwr_beta12', 'bandPwr_beta13', 'bandPwr_gamma00', 'bandPwr_gamma01', 'bandPwr_gamma02', 'bandPwr_gamma03', 'bandPwr_gamma04', 'bandPwr_gamma05', 'bandPwr_gamma06', 'bandPwr_gamma07', 'bandPwr_gamma08', 'bandPwr_gamma09', 'bandPwr_gamma10', 'bandPwr_gamma11', 'bandPwr_gamma12', 'bandPwr_gamma13', 'stdDev00', 'stdDev01', 'stdDev02', 'stdDev03', 'stdDev04', 'stdDev05', 'stdDev06', 'stdDev07', 'stdDev08', 'stdDev09', 'stdDev10', 'stdDev11', 'stdDev12', 'stdDev13', 'shortSpikeNum01', 'shortSpikeNum02', 'shortSpikeNum04', 'shortSpikeNum05', 'shortSpikeNum06', 'shortSpikeNum08', 'shortSpikeNum09', 'shortSpikeNum10', 'shortSpikeNum12', 'shortSpikeNum13', 'dasm_delta00', 'dasm_delta01', 'dasm_delta02', 'dasm_delta03', 'dasm_delta04', 'dasm_delta05', 'dasm_delta06', 'dasm_theta00', 'dasm_theta01', 'dasm_theta02', 'dasm_theta03', 'dasm_theta04', 'dasm_theta05', 'dasm_theta06', 'dasm_alpha00', 'dasm_alpha01', 'dasm_alpha02', 'dasm_alpha03', 'dasm_alpha04', 'dasm_alpha05', 'dasm_alpha06', 'dasm_beta00', 'dasm_beta01', 'dasm_beta02', 'dasm_beta03', 'dasm_beta04', 'dasm_beta05', 'dasm_beta06', 'dasm_gamma00', 'dasm_gamma01', 'dasm_gamma02', 'dasm_gamma03', 'dasm_gamma04', 'dasm_gamma05', 'dasm_gamma06', 'rasm_delta00', 'rasm_delta01', 'rasm_delta02', 'rasm_delta03', 'rasm_delta04', 'rasm_delta05', 'rasm_delta06', 'rasm_theta00', 'rasm_theta01', 'rasm_theta02', 'rasm_theta03', 'rasm_theta04', 'rasm_theta05', 'rasm_theta06', 'rasm_alpha00', 'rasm_alpha01', 'rasm_alpha02', 'rasm_alpha03', 'rasm_alpha04', 'rasm_alpha05', 'rasm_alpha06', 'rasm_beta00', 'rasm_beta01', 'rasm_beta02', 'rasm_beta03', 'rasm_beta04', 'rasm_beta05', 'rasm_beta06', 'rasm_gamma00', 'rasm_gamma01', 'rasm_gamma02', 'rasm_gamma03', 'rasm_gamma04', 'rasm_gamma05', 'rasm_gamma06']\n","y.shape: (80640,)\n"," Specs Score\n","263 rasm_beta01 129.163653\n","270 rasm_gamma01 126.440712\n","169 bandPwr_gamma01 119.183483\n","228 dasm_beta01 115.248145\n","235 dasm_gamma01 108.891355\n",".. ... ...\n","244 rasm_delta03 0.930492\n","245 rasm_delta04 0.930192\n","241 rasm_delta00 0.895129\n","255 rasm_alpha00 0.733979\n","247 rasm_delta06 0.635068\n","\n","[276 rows x 2 columns]\n","['rasm_beta', 'rasm_gamma', 'bandPwr_gamma', 'dasm_beta', 'dasm_gamma', 'ShannonRes_beta', 'stdDev', 'bandPwr_beta', 'ShannonRes_gamma', 'dasm_theta', 'rasm_theta', 'dasm_alpha', 'ShannonRes_alpha', 'ShannonRes_theta', 'ShannonRes_delta', 'dasm_delta', 'HjorthMob', 'bandPwr_alpha', 'bandPwr_theta', 'HjorthComp', 'rasm_alpha', 'shortSpikeNum', 'bandPwr_delta', 'medianFreq', 'rasm_delta']\n","[1, 12, 10, 8, 0, 5, 7, 2, 11, 4, 9, 13, 6, 3]\n","(80640, 7)\n","Number of Feature-Columns: 7\n","\n","Features Ready for undergoing selection tests done ...\n","\n","window: 1, stide: 1, rmse: 1.889081532700148\n","window: 1, stide: 1, rmse: 1.889081532700148\n","r2: 0.11738249667766054\n","mae: 1.5318224826388889\n","ev: 0.11739014249315527\n","(80640, 14)\n","Number of Feature-Columns: 14\n","\n","Features Ready for undergoing selection tests done ...\n","\n","window: 1, stide: 1, rmse: 1.8080540915650651\n","window: 1, stide: 1, rmse: 1.8080540915650651\n","r2: 0.19147403931514106\n","mae: 1.4553402839781746\n","ev: 0.19147428244878106\n","(80640, 28)\n","Number of Feature-Columns: 28\n","\n","Features Ready for undergoing selection tests done ...\n","\n","window: 1, stide: 1, rmse: 1.7235226623098576\n","window: 1, stide: 1, rmse: 1.7235226623098576\n","r2: 0.26530831050805426\n","mae: 1.373863039434524\n","ev: 0.2653319908788119\n","(80640, 35)\n","Number of Feature-Columns: 35\n","\n","Features Ready for undergoing selection tests done ...\n","\n","window: 1, stide: 1, rmse: 1.7244290044355743\n","window: 1, stide: 1, rmse: 1.7244290044355743\n","r2: 0.26453540844583556\n","mae: 1.3747240265376983\n","ev: 0.2645606266348274\n","(80640, 42)\n","Number of Feature-Columns: 42\n","\n","Features Ready for undergoing selection tests done ...\n","\n","window: 1, stide: 1, rmse: 1.724740225488977\n","window: 1, stide: 1, rmse: 1.724740225488977\n","r2: 0.26426991451213744\n","mae: 1.3758601376488098\n","ev: 0.2642821830007759\n","(80640, 56)\n","Number of Feature-Columns: 56\n","\n","Features Ready for undergoing selection tests done ...\n","\n","window: 1, stide: 1, rmse: 1.7219123655761586\n","window: 1, stide: 1, rmse: 1.7219123655761586\n","r2: 0.2666805221749494\n","mae: 1.3748935949900796\n","ev: 0.2666966914765341\n","(80640, 70)\n","Number of Feature-Columns: 70\n","\n","Features Ready for undergoing selection tests done ...\n","\n","window: 1, stide: 1, rmse: 1.7159718021877275\n","window: 1, stide: 1, rmse: 1.7159718021877275\n","r2: 0.2717316691457039\n","mae: 1.3716557601686508\n","ev: 0.27175803648926333\n","(80640, 84)\n","Number of Feature-Columns: 84\n","\n","Features Ready for undergoing selection tests done ...\n","\n","window: 1, stide: 1, rmse: 1.7162350188320512\n","window: 1, stide: 1, rmse: 1.7162350188320512\n","r2: 0.27150823068590735\n","mae: 1.3726899367559524\n","ev: 0.2715343405052436\n","(80640, 98)\n","Number of Feature-Columns: 98\n","\n","Features Ready for undergoing selection tests done ...\n","\n","window: 1, stide: 1, rmse: 1.716895519654905\n","window: 1, stide: 1, rmse: 1.716895519654905\n","r2: 0.27094739607070883\n","mae: 1.372985844494048\n","ev: 0.270982390129633\n","(80640, 105)\n","Number of Feature-Columns: 105\n","\n","Features Ready for undergoing selection tests done ...\n","\n","window: 1, stide: 1, rmse: 1.7201159799059704\n","window: 1, stide: 1, rmse: 1.7201159799059704\n","r2: 0.2682097956481918\n","mae: 1.3765855034722223\n","ev: 0.26826818378581274\n","(80640, 112)\n","Number of Feature-Columns: 112\n","\n","Features Ready for undergoing selection tests done ...\n","\n","window: 1, stide: 1, rmse: 1.7249876320721\n","window: 1, stide: 1, rmse: 1.7249876320721\n","r2: 0.2640588247269332\n","mae: 1.3813283916170633\n","ev: 0.2640775932984847\n","(80640, 119)\n","Number of Feature-Columns: 119\n","\n","Features Ready for undergoing selection tests done ...\n","\n","window: 1, stide: 1, rmse: 1.721085847632969\n","window: 1, stide: 1, rmse: 1.721085847632969\n","r2: 0.26738433992342014\n","mae: 1.3797661396329366\n","ev: 0.2674021083526402\n","(80640, 133)\n","Number of Feature-Columns: 133\n","\n","Features Ready for undergoing selection tests done ...\n","\n","window: 1, stide: 1, rmse: 1.7263918433863623\n","window: 1, stide: 1, rmse: 1.7263918433863623\n","r2: 0.26286016390432965\n","mae: 1.3878154761904764\n","ev: 0.2629199554507665\n","(80640, 147)\n","Number of Feature-Columns: 147\n","\n","Features Ready for undergoing selection tests done ...\n","\n","window: 1, stide: 1, rmse: 1.7351512800370854\n","window: 1, stide: 1, rmse: 1.7351512800370854\n","r2: 0.255360927346738\n","mae: 1.3943447544642857\n","ev: 0.2554080725399943\n","(80640, 161)\n","Number of Feature-Columns: 161\n","\n","Features Ready for undergoing selection tests done ...\n","\n"]}]},{"cell_type":"code","source":["!python3 feature_select_main.py --dataset DEAP --window 1 --stride 1 --sfreq 128 --model rfr --label 1 --approach byfs --ml_algo regression --top e --fs_method SelectKBest"],"metadata":{"id":"zDo8sPGEi8lO"},"execution_count":null,"outputs":[]},{"cell_type":"code","source":["!python3 feature_select_main.py --dataset OASIS --window 1 --stride 1 --sfreq 128 --model rfr --label 1 --apaproach byfs --ml_algo regression --top f --fs_method SelectKBest"],"metadata":{"colab":{"base_uri":"https://localhost:8080/"},"id":"iHPYxwxJY_NQ","executionInfo":{"status":"ok","timestamp":1667629097978,"user_tz":-330,"elapsed":607802,"user":{"displayName":"Rohit Garg","userId":"11252576926243182044"}},"outputId":"cd593be0-8049-422b-b143-d2b9122338ef"},"execution_count":null,"outputs":[{"output_type":"stream","name":"stdout","text":["{'dataset': 'OASIS', 'window': 1, 'stride': 1, 'sfreq': 128, 'model': 'rfr', 'label': 1, 'approach': 'byfs', 'ml_algo': 'regression', 'top': 'f', 'fs_method': 'SelectKBest'}\n","Number of segments are: 3000\n","['ShannonRes_delta', 'ShannonRes_theta', 'ShannonRes_alpha', 'ShannonRes_beta', 'ShannonRes_gamma', 'HjorthComp', 'HjorthMob', 'medianFreq', 'bandPwr_delta', 'bandPwr_theta', 'bandPwr_alpha', 'bandPwr_beta', 'bandPwr_gamma', 'stdDev', 'shortSpikeNum', 'dasm_delta', 'dasm_theta', 'dasm_alpha', 'dasm_beta', 'dasm_gamma', 'rasm_delta', 'rasm_theta', 'rasm_alpha', 'rasm_beta', 'rasm_gamma']\n","25\n","Number of Feature-Columns: 280\n","\n","277 ['ShannonRes_delta00', 'ShannonRes_delta01', 'ShannonRes_delta02', 'ShannonRes_delta03', 'ShannonRes_delta04', 'ShannonRes_delta05', 'ShannonRes_delta06', 'ShannonRes_delta07', 'ShannonRes_delta08', 'ShannonRes_delta09', 'ShannonRes_delta10', 'ShannonRes_delta11', 'ShannonRes_delta12', 'ShannonRes_delta13', 'ShannonRes_theta00', 'ShannonRes_theta01', 'ShannonRes_theta02', 'ShannonRes_theta03', 'ShannonRes_theta04', 'ShannonRes_theta05', 'ShannonRes_theta06', 'ShannonRes_theta07', 'ShannonRes_theta08', 'ShannonRes_theta09', 'ShannonRes_theta10', 'ShannonRes_theta11', 'ShannonRes_theta12', 'ShannonRes_theta13', 'ShannonRes_alpha00', 'ShannonRes_alpha01', 'ShannonRes_alpha02', 'ShannonRes_alpha03', 'ShannonRes_alpha04', 'ShannonRes_alpha05', 'ShannonRes_alpha06', 'ShannonRes_alpha07', 'ShannonRes_alpha08', 'ShannonRes_alpha09', 'ShannonRes_alpha10', 'ShannonRes_alpha11', 'ShannonRes_alpha12', 'ShannonRes_alpha13', 'ShannonRes_beta00', 'ShannonRes_beta01', 'ShannonRes_beta02', 'ShannonRes_beta03', 'ShannonRes_beta04', 'ShannonRes_beta05', 'ShannonRes_beta06', 'ShannonRes_beta07', 'ShannonRes_beta08', 'ShannonRes_beta09', 'ShannonRes_beta10', 'ShannonRes_beta11', 'ShannonRes_beta12', 'ShannonRes_beta13', 'ShannonRes_gamma00', 'ShannonRes_gamma01', 'ShannonRes_gamma02', 'ShannonRes_gamma03', 'ShannonRes_gamma04', 'ShannonRes_gamma05', 'ShannonRes_gamma06', 'ShannonRes_gamma07', 'ShannonRes_gamma08', 'ShannonRes_gamma09', 'ShannonRes_gamma10', 'ShannonRes_gamma11', 'ShannonRes_gamma12', 'ShannonRes_gamma13', 'HjorthComp00', 'HjorthComp01', 'HjorthComp02', 'HjorthComp03', 'HjorthComp04', 'HjorthComp05', 'HjorthComp06', 'HjorthComp07', 'HjorthComp08', 'HjorthComp09', 'HjorthComp10', 'HjorthComp11', 'HjorthComp12', 'HjorthComp13', 'HjorthMob00', 'HjorthMob01', 'HjorthMob02', 'HjorthMob03', 'HjorthMob04', 'HjorthMob05', 'HjorthMob06', 'HjorthMob07', 'HjorthMob08', 'HjorthMob09', 'HjorthMob10', 'HjorthMob11', 'HjorthMob12', 'HjorthMob13', 'medianFreq00', 'medianFreq01', 'medianFreq02', 'medianFreq03', 'medianFreq04', 'medianFreq05', 'medianFreq06', 'medianFreq07', 'medianFreq08', 'medianFreq09', 'medianFreq10', 'medianFreq11', 'medianFreq12', 'medianFreq13', 'bandPwr_delta00', 'bandPwr_delta01', 'bandPwr_delta02', 'bandPwr_delta03', 'bandPwr_delta04', 'bandPwr_delta05', 'bandPwr_delta06', 'bandPwr_delta07', 'bandPwr_delta08', 'bandPwr_delta09', 'bandPwr_delta10', 'bandPwr_delta11', 'bandPwr_delta12', 'bandPwr_delta13', 'bandPwr_theta00', 'bandPwr_theta01', 'bandPwr_theta02', 'bandPwr_theta03', 'bandPwr_theta04', 'bandPwr_theta05', 'bandPwr_theta06', 'bandPwr_theta07', 'bandPwr_theta08', 'bandPwr_theta09', 'bandPwr_theta10', 'bandPwr_theta11', 'bandPwr_theta12', 'bandPwr_theta13', 'bandPwr_alpha00', 'bandPwr_alpha01', 'bandPwr_alpha02', 'bandPwr_alpha03', 'bandPwr_alpha04', 'bandPwr_alpha05', 'bandPwr_alpha06', 'bandPwr_alpha07', 'bandPwr_alpha08', 'bandPwr_alpha09', 'bandPwr_alpha10', 'bandPwr_alpha11', 'bandPwr_alpha12', 'bandPwr_alpha13', 'bandPwr_beta00', 'bandPwr_beta01', 'bandPwr_beta02', 'bandPwr_beta03', 'bandPwr_beta04', 'bandPwr_beta05', 'bandPwr_beta06', 'bandPwr_beta07', 'bandPwr_beta08', 'bandPwr_beta09', 'bandPwr_beta10', 'bandPwr_beta11', 'bandPwr_beta12', 'bandPwr_beta13', 'bandPwr_gamma00', 'bandPwr_gamma01', 'bandPwr_gamma02', 'bandPwr_gamma03', 'bandPwr_gamma04', 'bandPwr_gamma05', 'bandPwr_gamma06', 'bandPwr_gamma07', 'bandPwr_gamma08', 'bandPwr_gamma09', 'bandPwr_gamma10', 'bandPwr_gamma11', 'bandPwr_gamma12', 'bandPwr_gamma13', 'stdDev00', 'stdDev01', 'stdDev02', 'stdDev03', 'stdDev04', 'stdDev05', 'stdDev06', 'stdDev07', 'stdDev08', 'stdDev09', 'stdDev10', 'stdDev11', 'stdDev12', 'stdDev13', 'shortSpikeNum00', 'shortSpikeNum01', 'shortSpikeNum02', 'shortSpikeNum03', 'shortSpikeNum04', 'shortSpikeNum06', 'shortSpikeNum09', 'shortSpikeNum10', 'shortSpikeNum11', 'shortSpikeNum12', 'shortSpikeNum13', 'dasm_delta00', 'dasm_delta01', 'dasm_delta02', 'dasm_delta03', 'dasm_delta04', 'dasm_delta05', 'dasm_delta06', 'dasm_theta00', 'dasm_theta01', 'dasm_theta02', 'dasm_theta03', 'dasm_theta04', 'dasm_theta05', 'dasm_theta06', 'dasm_alpha00', 'dasm_alpha01', 'dasm_alpha02', 'dasm_alpha03', 'dasm_alpha04', 'dasm_alpha05', 'dasm_alpha06', 'dasm_beta00', 'dasm_beta01', 'dasm_beta02', 'dasm_beta03', 'dasm_beta04', 'dasm_beta05', 'dasm_beta06', 'dasm_gamma00', 'dasm_gamma01', 'dasm_gamma02', 'dasm_gamma03', 'dasm_gamma04', 'dasm_gamma05', 'dasm_gamma06', 'rasm_delta00', 'rasm_delta01', 'rasm_delta02', 'rasm_delta03', 'rasm_delta04', 'rasm_delta05', 'rasm_delta06', 'rasm_theta00', 'rasm_theta01', 'rasm_theta02', 'rasm_theta03', 'rasm_theta04', 'rasm_theta05', 'rasm_theta06', 'rasm_alpha00', 'rasm_alpha01', 'rasm_alpha02', 'rasm_alpha03', 'rasm_alpha04', 'rasm_alpha05', 'rasm_alpha06', 'rasm_beta00', 'rasm_beta01', 'rasm_beta02', 'rasm_beta03', 'rasm_beta04', 'rasm_beta05', 'rasm_beta06', 'rasm_gamma00', 'rasm_gamma01', 'rasm_gamma02', 'rasm_gamma03', 'rasm_gamma04', 'rasm_gamma05', 'rasm_gamma06']\n","y.shape: (3000,)\n"," Specs Score\n","85 HjorthMob01 97.061403\n","93 HjorthMob09 90.599666\n","88 HjorthMob04 86.246860\n","57 ShannonRes_gamma01 84.899912\n","65 ShannonRes_gamma09 83.423733\n",".. ... ...\n","259 rasm_alpha03 0.132157\n","238 dasm_gamma03 0.111871\n","273 rasm_gamma03 0.108946\n","231 dasm_beta03 0.075382\n","266 rasm_beta03 0.073473\n","\n","[277 rows x 2 columns]\n","['HjorthMob', 'ShannonRes_gamma', 'ShannonRes_beta', 'HjorthComp', 'bandPwr_gamma', 'stdDev', 'medianFreq', 'ShannonRes_delta', 'ShannonRes_theta', 'ShannonRes_alpha', 'bandPwr_beta', 'bandPwr_delta', 'rasm_gamma', 'dasm_gamma', 'rasm_alpha', 'dasm_alpha', 'shortSpikeNum', 'rasm_delta', 'rasm_beta', 'dasm_delta', 'dasm_beta', 'rasm_theta', 'dasm_theta', 'bandPwr_theta', 'bandPwr_alpha']\n","[1, 9, 4, 12, 2, 11, 0, 13, 10, 3, 6, 5, 8, 7]\n","(3000, 14)\n","Number of Feature-Columns: 14\n","\n","Features Ready for undergoing selection tests done ...\n","\n","window: 1, stide: 1, rmse: 2.270169193400938\n","window: 1, stide: 1, rmse: 2.270169193400938\n","r2: 0.23599055345083186\n","mae: 1.8145166666666668\n","ev: 0.23965475576511275\n","(3000, 28)\n","Number of Feature-Columns: 28\n","\n","Features Ready for undergoing selection tests done ...\n","\n","window: 1, stide: 1, rmse: 2.309011188077413\n","window: 1, stide: 1, rmse: 2.309011188077413\n","r2: 0.20962289573381643\n","mae: 1.8635999999999997\n","ev: 0.21274119601383612\n","(3000, 42)\n","Number of Feature-Columns: 42\n","\n","Features Ready for undergoing selection tests done ...\n","\n","window: 1, stide: 1, rmse: 2.3472648977054127\n","window: 1, stide: 1, rmse: 2.3472648977054127\n","r2: 0.18321738593312453\n","mae: 1.9003833333333333\n","ev: 0.18819737312633822\n","(3000, 56)\n","Number of Feature-Columns: 56\n","\n","Features Ready for undergoing selection tests done ...\n","\n","window: 1, stide: 1, rmse: 2.3301905859106604\n","window: 1, stide: 1, rmse: 2.3301905859106604\n","r2: 0.19505693460714868\n","mae: 1.8914166666666665\n","ev: 0.1986392492587712\n","(3000, 70)\n","Number of Feature-Columns: 70\n","\n","Features Ready for undergoing selection tests done ...\n","\n","window: 1, stide: 1, rmse: 2.335861475630208\n","window: 1, stide: 1, rmse: 2.335861475630208\n","r2: 0.19113425300609455\n","mae: 1.8882833333333333\n","ev: 0.1941961921017954\n","(3000, 84)\n","Number of Feature-Columns: 84\n","\n","Features Ready for undergoing selection tests done ...\n","\n","window: 1, stide: 1, rmse: 2.323665244106101\n","window: 1, stide: 1, rmse: 2.323665244106101\n","r2: 0.19955886180200944\n","mae: 1.8843500000000002\n","ev: 0.2034023357354635\n","(3000, 98)\n","Number of Feature-Columns: 98\n","\n","Features Ready for undergoing selection tests done ...\n","\n","window: 1, stide: 1, rmse: 2.3180569449433293\n","window: 1, stide: 1, rmse: 2.3180569449433293\n","r2: 0.2034180200955361\n","mae: 1.8760666666666665\n","ev: 0.20595301087135542\n","(3000, 112)\n","Number of Feature-Columns: 112\n","\n","Features Ready for undergoing selection tests done ...\n","\n","window: 1, stide: 1, rmse: 2.3263683070399668\n","window: 1, stide: 1, rmse: 2.3263683070399668\n","r2: 0.1976955114478668\n","mae: 1.9080833333333331\n","ev: 0.20257099164058634\n","(3000, 126)\n","Number of Feature-Columns: 126\n","\n","Features Ready for undergoing selection tests done ...\n","\n","window: 1, stide: 1, rmse: 2.3433164887967366\n","window: 1, stide: 1, rmse: 2.3433164887967366\n","r2: 0.18596294679624448\n","mae: 1.9069833333333335\n","ev: 0.1901878125926536\n","(3000, 140)\n","Number of Feature-Columns: 140\n","\n","Features Ready for undergoing selection tests done ...\n","\n","window: 1, stide: 1, rmse: 2.341323450529636\n","window: 1, stide: 1, rmse: 2.341323450529636\n","r2: 0.18734706802833134\n","mae: 1.9117833333333332\n","ev: 0.19031038910393672\n","(3000, 154)\n","Number of Feature-Columns: 154\n","\n","Features Ready for undergoing selection tests done ...\n","\n","window: 1, stide: 1, rmse: 2.339116392999715\n","window: 1, stide: 1, rmse: 2.339116392999715\n","r2: 0.18887844671388576\n","mae: 1.9043166666666669\n","ev: 0.19251783071157946\n","(3000, 168)\n","Number of Feature-Columns: 168\n","\n","Features Ready for undergoing selection tests done ...\n","\n","window: 1, stide: 1, rmse: 2.3605274128183025\n","window: 1, stide: 1, rmse: 2.3605274128183025\n","r2: 0.17396134080052694\n","mae: 1.9337333333333333\n","ev: 0.17790169346071483\n","(3000, 175)\n","Number of Feature-Columns: 175\n","\n","Features Ready for undergoing selection tests done ...\n","\n","window: 1, stide: 1, rmse: 2.376774950782397\n","window: 1, stide: 1, rmse: 2.376774950782397\n","r2: 0.16255093888980388\n","mae: 1.9418499999999999\n","ev: 0.16632155991599396\n","(3000, 182)\n","Number of Feature-Columns: 182\n","\n","Features Ready for undergoing selection tests done ...\n","\n","window: 1, stide: 1, rmse: 2.3436173962487987\n","window: 1, stide: 1, rmse: 2.3436173962487987\n","r2: 0.18575387086147255\n","mae: 1.9194166666666668\n","ev: 0.1889871652528411\n","(3000, 189)\n","Number of Feature-Columns: 189\n","\n","Features Ready for undergoing selection tests done ...\n","\n","window: 1, stide: 1, rmse: 2.3436403307675007\n","window: 1, stide: 1, rmse: 2.3436403307675007\n","r2: 0.18573793444243114\n","mae: 1.9109999999999998\n","ev: 0.18854614412782067\n","(3000, 196)\n","Number of Feature-Columns: 196\n","\n","Features Ready for undergoing selection tests done ...\n","\n","window: 1, stide: 1, rmse: 2.367693181136441\n","window: 1, stide: 1, rmse: 2.367693181136441\n","r2: 0.16893857684071822\n","mae: 1.924533333333333\n","ev: 0.17256635628397288\n","(3000, 210)\n","Number of Feature-Columns: 210\n","\n","Features Ready for undergoing selection tests done ...\n","\n","window: 1, stide: 1, rmse: 2.345765653256949\n","window: 1, stide: 1, rmse: 2.345765653256949\n","r2: 0.18426044309010048\n","mae: 1.9127833333333335\n","ev: 0.1879776643468949\n","(3000, 217)\n","Number of Feature-Columns: 217\n","\n","Features Ready for undergoing selection tests done ...\n","\n","window: 1, stide: 1, rmse: 2.3503775937779303\n","window: 1, stide: 1, rmse: 2.3503775937779303\n","r2: 0.18104968703673197\n","mae: 1.920216666666667\n","ev: 0.184380030019766\n","(3000, 224)\n","Number of Feature-Columns: 224\n","\n","Features Ready for undergoing selection tests done ...\n","\n","window: 1, stide: 1, rmse: 2.33869846282072\n","window: 1, stide: 1, rmse: 2.33869846282072\n","r2: 0.18916826717180035\n","mae: 1.8942500000000002\n","ev: 0.192048343312469\n","(3000, 231)\n","Number of Feature-Columns: 231\n","\n","Features Ready for undergoing selection tests done ...\n","\n","window: 1, stide: 1, rmse: 2.342570454294456\n","window: 1, stide: 1, rmse: 2.342570454294456\n","r2: 0.18648118926041835\n","mae: 1.9050333333333334\n","ev: 0.18932350782408158\n","(3000, 238)\n","Number of Feature-Columns: 238\n","\n","Features Ready for undergoing selection tests done ...\n","\n","window: 1, stide: 1, rmse: 2.343711408571172\n","window: 1, stide: 1, rmse: 2.343711408571172\n","r2: 0.18568854389721623\n","mae: 1.9091166666666664\n","ev: 0.18898932667600055\n","(3000, 245)\n","Number of Feature-Columns: 245\n","\n","Features Ready for undergoing selection tests done ...\n","\n","window: 1, stide: 1, rmse: 2.3257114682035116\n","window: 1, stide: 1, rmse: 2.3257114682035116\n","r2: 0.19814850107066373\n","mae: 1.8852166666666668\n","ev: 0.20015190351671874\n","(3000, 252)\n","Number of Feature-Columns: 252\n","\n","Features Ready for undergoing selection tests done ...\n","\n","window: 1, stide: 1, rmse: 2.3340669799015337\n","window: 1, stide: 1, rmse: 2.3340669799015337\n","r2: 0.1923765771701531\n","mae: 1.8967333333333334\n","ev: 0.19554524724098166\n","(3000, 266)\n","Number of Feature-Columns: 266\n","\n","Features Ready for undergoing selection tests done ...\n","\n","window: 1, stide: 1, rmse: 2.3142309017612455\n","window: 1, stide: 1, rmse: 2.3142309017612455\n","r2: 0.20604542908911216\n","mae: 1.8766666666666667\n","ev: 0.20951571405040348\n","(3000, 280)\n","Number of Feature-Columns: 280\n","\n","Features Ready for undergoing selection tests done ...\n","\n","window: 1, stide: 1, rmse: 2.3453174269879407\n","window: 1, stide: 1, rmse: 2.3453174269879407\n","r2: 0.18457215450502373\n","mae: 1.8995166666666667\n","ev: 0.18785379381485745\n"," Feature ... EV\n","0 1 ... 0.239655\n","1 2 ... 0.212741\n","2 3 ... 0.188197\n","3 4 ... 0.198639\n","4 5 ... 0.194196\n","5 6 ... 0.203402\n","6 7 ... 0.205953\n","7 8 ... 0.202571\n","8 9 ... 0.190188\n","9 10 ... 0.190310\n","10 11 ... 0.192518\n","11 12 ... 0.177902\n","12 13 ... 0.166322\n","13 14 ... 0.188987\n","14 15 ... 0.188546\n","15 16 ... 0.172566\n","16 17 ... 0.187978\n","17 18 ... 0.184380\n","18 19 ... 0.192048\n","19 20 ... 0.189324\n","20 21 ... 0.188989\n","21 22 ... 0.200152\n","22 23 ... 0.195545\n","23 24 ... 0.209516\n","24 25 ... 0.187854\n","25 NaN ... NaN\n","26 NaN ... NaN\n","27 NaN ... NaN\n","28 NaN ... NaN\n","29 NaN ... NaN\n","30 NaN ... NaN\n","31 NaN ... NaN\n","32 NaN ... NaN\n","33 NaN ... NaN\n","34 NaN ... NaN\n","35 NaN ... NaN\n","36 NaN ... NaN\n","37 NaN ... NaN\n","38 NaN ... NaN\n","39 NaN ... NaN\n","40 NaN ... NaN\n","41 NaN ... NaN\n","42 NaN ... NaN\n","43 NaN ... NaN\n","44 NaN ... NaN\n","45 NaN ... NaN\n","46 NaN ... NaN\n","47 NaN ... NaN\n","48 NaN ... NaN\n","49 NaN ... NaN\n","\n","[50 rows x 5 columns]\n","Traceback (most recent call last):\n"," File \"feature_select_main.py\", line 58, in \n"," topFeatureFSRegressionRanking(dataset, window, stride, sfreq, clf, label, scale=False, pca=False, mutual_info = False, method='SelectKBest')\n"," File \"/content/drive/MyDrive/IEEE_EEG_Cluster_Files/TopNByFSMethods.py\", line 513, in topFeatureFSRegressionRanking\n"," plt.plot(topNFeatures.loc[:,\"Feature\"], topNFeatures.loc[:,\"RMSE\"])\n"," File \"/usr/local/lib/python3.7/dist-packages/matplotlib/pyplot.py\", line 2763, in plot\n"," is not None else {}), **kwargs)\n"," File \"/usr/local/lib/python3.7/dist-packages/matplotlib/axes/_axes.py\", line 1647, in plot\n"," lines = [*self._get_lines(*args, data=data, **kwargs)]\n"," File \"/usr/local/lib/python3.7/dist-packages/matplotlib/axes/_base.py\", line 216, in __call__\n"," yield from self._plot_args(this, kwargs)\n"," File \"/usr/local/lib/python3.7/dist-packages/matplotlib/axes/_base.py\", line 337, in _plot_args\n"," self.axes.xaxis.update_units(x)\n"," File \"/usr/local/lib/python3.7/dist-packages/matplotlib/axis.py\", line 1516, in update_units\n"," default = self.converter.default_units(data, self)\n"," File \"/usr/local/lib/python3.7/dist-packages/matplotlib/category.py\", line 107, in default_units\n"," axis.set_units(UnitData(data))\n"," File \"/usr/local/lib/python3.7/dist-packages/matplotlib/category.py\", line 175, in __init__\n"," self.update(data)\n"," File \"/usr/local/lib/python3.7/dist-packages/matplotlib/category.py\", line 212, in update\n"," cbook._check_isinstance((str, bytes), value=val)\n"," File \"/usr/local/lib/python3.7/dist-packages/matplotlib/cbook/__init__.py\", line 2128, in _check_isinstance\n"," type_name(type(v))))\n","TypeError: 'value' must be an instance of str or bytes, not a float\n"]}]},{"cell_type":"code","source":["!python3 feature_select_main.py --dataset OASIS --window 1 --stride 1 --sfreq 128 --model rfr --label 1 --approach byfs --ml_algo regression --top e --fs_method SelectKBest"],"metadata":{"colab":{"base_uri":"https://localhost:8080/"},"id":"iudkxzXaSUJ9","executionInfo":{"status":"ok","timestamp":1667628138867,"user_tz":-330,"elapsed":238474,"user":{"displayName":"Rohit Garg","userId":"11252576926243182044"}},"outputId":"1bc9d963-8a1b-493a-e098-0752b103988d"},"execution_count":null,"outputs":[{"output_type":"stream","name":"stdout","text":["{'dataset': 'OASIS', 'window': 1, 'stride': 1, 'sfreq': 128, 'model': 'rfr', 'label': 1, 'approach': 'byfs', 'ml_algo': 'regression', 'top': 'e', 'fs_method': 'SelectKBest'}\n","Number of segments are: 3000\n","['ShannonRes_delta', 'ShannonRes_theta', 'ShannonRes_alpha', 'ShannonRes_beta', 'ShannonRes_gamma', 'HjorthComp', 'HjorthMob', 'medianFreq', 'bandPwr_delta', 'bandPwr_theta', 'bandPwr_alpha', 'bandPwr_beta', 'bandPwr_gamma', 'stdDev', 'shortSpikeNum']\n","Number of Feature-Columns: 210\n","\n","y.shape: (3000,)\n","/usr/local/lib/python3.7/dist-packages/sklearn/feature_selection/_univariate_selection.py:112: UserWarning: Features [201 203 204] are constant.\n"," warnings.warn(\"Features %s are constant.\" % constant_features_idx, UserWarning)\n","/usr/local/lib/python3.7/dist-packages/sklearn/feature_selection/_univariate_selection.py:113: RuntimeWarning: invalid value encountered in true_divide\n"," f = msb / msw\n"," Specs Score\n","85 HjorthMob01 97.061403\n","93 HjorthMob09 90.599666\n","88 HjorthMob04 86.246860\n","57 ShannonRes_gamma01 84.899912\n","65 ShannonRes_gamma09 83.423733\n",".. ... ...\n","156 bandPwr_beta02 0.190277\n","164 bandPwr_beta10 0.186123\n","41 ShannonRes_alpha13 0.161368\n","150 bandPwr_alpha10 0.157337\n","142 bandPwr_alpha02 0.132411\n","\n","[207 rows x 2 columns]\n","[1, 9, 4, 12, 2, 11, 0, 13, 10, 3, 6, 5, 8, 7]\n","[1]\n","(3000, 15)\n","Number of Feature-Columns: 15\n","\n","Features Ready for undergoing selection tests done ...\n","\n","window: 1, stide: 1, rmse: 2.5977206996390767\n","r2: -0.00038503541426471166\n","mae: 2.1283833333333333\n","ev: 0.004021941401745832\n","[1, 9, 4, 12, 2, 11, 0, 13, 10, 3, 6, 5, 8, 7]\n","[1, 9]\n","(3000, 30)\n","Number of Feature-Columns: 30\n","\n","Features Ready for undergoing selection tests done ...\n","\n","window: 1, stide: 1, rmse: 2.455529508408862\n","r2: 0.1061336435513095\n","mae: 2.012016666666667\n","ev: 0.10926126770713218\n","[1, 9, 4, 12, 2, 11, 0, 13, 10, 3, 6, 5, 8, 7]\n","[1, 9, 4]\n","(3000, 45)\n","Number of Feature-Columns: 45\n","\n","Features Ready for undergoing selection tests done ...\n","\n","window: 1, stide: 1, rmse: 2.4565285126237253\n","r2: 0.10540617690660514\n","mae: 2.009433333333333\n","ev: 0.10973089705155648\n","[1, 9, 4, 12, 2, 11, 0, 13, 10, 3, 6, 5, 8, 7]\n","[1, 9, 4, 12]\n","(3000, 60)\n","Number of Feature-Columns: 60\n","\n","Features Ready for undergoing selection tests done ...\n","\n","window: 1, stide: 1, rmse: 2.4630828920954597\n","r2: 0.10062599242299441\n","mae: 2.0191333333333334\n","ev: 0.10521109800691808\n","[1, 9, 4, 12, 2, 11, 0, 13, 10, 3, 6, 5, 8, 7]\n","[1, 9, 4, 12, 2]\n","(3000, 75)\n","Number of Feature-Columns: 75\n","\n","Features Ready for undergoing selection tests done ...\n","\n","window: 1, stide: 1, rmse: 2.4437417007531708\n","r2: 0.11469508318234223\n","mae: 1.98375\n","ev: 0.11793129735628394\n","[1, 9, 4, 12, 2, 11, 0, 13, 10, 3, 6, 5, 8, 7]\n","[1, 9, 4, 12, 2, 11]\n","(3000, 90)\n","Number of Feature-Columns: 90\n","\n","Features Ready for undergoing selection tests done ...\n","\n","window: 1, stide: 1, rmse: 2.428299954014468\n","r2: 0.12584803162576164\n","mae: 1.9736666666666667\n","ev: 0.12786928166694111\n","[1, 9, 4, 12, 2, 11, 0, 13, 10, 3, 6, 5, 8, 7]\n","[1, 9, 4, 12, 2, 11, 0]\n","(3000, 105)\n","Number of Feature-Columns: 105\n","\n","Features Ready for undergoing selection tests done ...\n","\n","window: 1, stide: 1, rmse: 2.3975726266371997\n","r2: 0.14783082688189741\n","mae: 1.95195\n","ev: 0.14972768773678113\n","[1, 9, 4, 12, 2, 11, 0, 13, 10, 3, 6, 5, 8, 7]\n","[1, 9, 4, 12, 2, 11, 0, 13]\n","(3000, 120)\n","Number of Feature-Columns: 120\n","\n","Features Ready for undergoing selection tests done ...\n","\n","window: 1, stide: 1, rmse: 2.4009670621092103\n","r2: 0.14541615055180357\n","mae: 1.9585166666666665\n","ev: 0.14805097187448524\n","[1, 9, 4, 12, 2, 11, 0, 13, 10, 3, 6, 5, 8, 7]\n","[1, 9, 4, 12, 2, 11, 0, 13, 10]\n","(3000, 135)\n","Number of Feature-Columns: 135\n","\n","Features Ready for undergoing selection tests done ...\n","\n","window: 1, stide: 1, rmse: 2.411583608337061\n","r2: 0.13784187942678316\n","mae: 1.9722166666666665\n","ev: 0.14033504204414426\n","[1, 9, 4, 12, 2, 11, 0, 13, 10, 3, 6, 5, 8, 7]\n","[1, 9, 4, 12, 2, 11, 0, 13, 10, 3]\n","(3000, 150)\n","Number of Feature-Columns: 150\n","\n","Features Ready for undergoing selection tests done ...\n","\n","window: 1, stide: 1, rmse: 2.35060066791448\n","r2: 0.18089422665129296\n","mae: 1.9267833333333328\n","ev: 0.18422901503047273\n","[1, 9, 4, 12, 2, 11, 0, 13, 10, 3, 6, 5, 8, 7]\n","[1, 9, 4, 12, 2, 11, 0, 13, 10, 3, 6]\n","(3000, 165)\n","Number of Feature-Columns: 165\n","\n","Features Ready for undergoing selection tests done ...\n","\n","window: 1, stide: 1, rmse: 2.3780155942858463\n","r2: 0.16167643716027003\n","mae: 1.9407166666666669\n","ev: 0.165309629756218\n","[1, 9, 4, 12, 2, 11, 0, 13, 10, 3, 6, 5, 8, 7]\n","[1, 9, 4, 12, 2, 11, 0, 13, 10, 3, 6, 5]\n","(3000, 180)\n","Number of Feature-Columns: 180\n","\n","Features Ready for undergoing selection tests done ...\n","\n","window: 1, stide: 1, rmse: 2.3978659956997874\n","r2: 0.14762226980728044\n","mae: 1.9455333333333336\n","ev: 0.1508242597595124\n","[1, 9, 4, 12, 2, 11, 0, 13, 10, 3, 6, 5, 8, 7]\n","[1, 9, 4, 12, 2, 11, 0, 13, 10, 3, 6, 5, 8]\n","(3000, 195)\n","Number of Feature-Columns: 195\n","\n","Features Ready for undergoing selection tests done ...\n","\n","window: 1, stide: 1, rmse: 2.3825054389584643\n","r2: 0.15850783231757537\n","mae: 1.9497833333333332\n","ev: 0.16264638548015153\n","[1, 9, 4, 12, 2, 11, 0, 13, 10, 3, 6, 5, 8, 7]\n","[1, 9, 4, 12, 2, 11, 0, 13, 10, 3, 6, 5, 8, 7]\n","(3000, 210)\n","Number of Feature-Columns: 210\n","\n","Features Ready for undergoing selection tests done ...\n","\n","window: 1, stide: 1, rmse: 2.3486811902285365\n","r2: 0.18223142810080706\n","mae: 1.9173000000000002\n","ev: 0.18536695256135727\n"," Electrode ... EV\n","0 1 ... 0.004022\n","1 2 ... 0.109261\n","2 3 ... 0.109731\n","3 4 ... 0.105211\n","4 5 ... 0.117931\n","5 6 ... 0.127869\n","6 7 ... 0.149728\n","7 8 ... 0.148051\n","8 9 ... 0.140335\n","9 10 ... 0.184229\n","10 11 ... 0.165310\n","11 12 ... 0.150824\n","12 13 ... 0.162646\n","13 14 ... 0.185367\n","\n","[14 rows x 5 columns]\n","Traceback (most recent call last):\n"," File \"feature_select_main.py\", line 46, in \n"," plt.savefig(pwd + \"/\" + dataset + \"/arousal_plots/\" + \"CorrectedElectrodewiseRanking\" + str(window) + str(stride) + \".svg\", bbox_inches='tight')\n"," File \"/usr/local/lib/python3.7/dist-packages/matplotlib/pyplot.py\", line 723, in savefig\n"," res = fig.savefig(*args, **kwargs)\n"," File \"/usr/local/lib/python3.7/dist-packages/matplotlib/figure.py\", line 2203, in savefig\n"," self.canvas.print_figure(fname, **kwargs)\n"," File \"/usr/local/lib/python3.7/dist-packages/matplotlib/backend_bases.py\", line 2126, in print_figure\n"," **kwargs)\n"," File \"/usr/local/lib/python3.7/dist-packages/matplotlib/backends/backend_svg.py\", line 1184, in print_svg\n"," with cbook.open_file_cm(filename, \"w\", encoding=\"utf-8\") as fh:\n"," File \"/usr/lib/python3.7/contextlib.py\", line 112, in __enter__\n"," return next(self.gen)\n"," File \"/usr/local/lib/python3.7/dist-packages/matplotlib/cbook/__init__.py\", line 418, in open_file_cm\n"," fh, opened = to_filehandle(path_or_file, mode, True, encoding)\n"," File \"/usr/local/lib/python3.7/dist-packages/matplotlib/cbook/__init__.py\", line 403, in to_filehandle\n"," fh = open(fname, flag, encoding=encoding)\n","FileNotFoundError: [Errno 2] No such file or directory: '/content/drive/MyDrive/IEEE_EEG_Cluster_Files/OASIS/arousal_plots/CorrectedElectrodewiseRanking11.svg'\n"]}]},{"cell_type":"code","source":["!python3 feature_select_main.py --dataset OASIS --window 1 --stride 1 --sfreq 128 --model rfr --label 0 --approach byfs --ml_algo regression --top f --fs_method SelectKBest"],"metadata":{"colab":{"base_uri":"https://localhost:8080/"},"id":"e2nGRCK0aAWd","executionInfo":{"status":"ok","timestamp":1667629696503,"user_tz":-330,"elapsed":597042,"user":{"displayName":"Rohit Garg","userId":"11252576926243182044"}},"outputId":"55fd679b-8744-40ff-e1ce-f66be1c8e389"},"execution_count":null,"outputs":[{"output_type":"stream","name":"stdout","text":["{'dataset': 'OASIS', 'window': 1, 'stride': 1, 'sfreq': 128, 'model': 'rfr', 'label': 0, 'approach': 'byfs', 'ml_algo': 'regression', 'top': 'f', 'fs_method': 'SelectKBest'}\n","Number of segments are: 3000\n","['ShannonRes_delta', 'ShannonRes_theta', 'ShannonRes_alpha', 'ShannonRes_beta', 'ShannonRes_gamma', 'HjorthComp', 'HjorthMob', 'medianFreq', 'bandPwr_delta', 'bandPwr_theta', 'bandPwr_alpha', 'bandPwr_beta', 'bandPwr_gamma', 'stdDev', 'shortSpikeNum', 'dasm_delta', 'dasm_theta', 'dasm_alpha', 'dasm_beta', 'dasm_gamma', 'rasm_delta', 'rasm_theta', 'rasm_alpha', 'rasm_beta', 'rasm_gamma']\n","25\n","Number of Feature-Columns: 280\n","\n","277 ['ShannonRes_delta00', 'ShannonRes_delta01', 'ShannonRes_delta02', 'ShannonRes_delta03', 'ShannonRes_delta04', 'ShannonRes_delta05', 'ShannonRes_delta06', 'ShannonRes_delta07', 'ShannonRes_delta08', 'ShannonRes_delta09', 'ShannonRes_delta10', 'ShannonRes_delta11', 'ShannonRes_delta12', 'ShannonRes_delta13', 'ShannonRes_theta00', 'ShannonRes_theta01', 'ShannonRes_theta02', 'ShannonRes_theta03', 'ShannonRes_theta04', 'ShannonRes_theta05', 'ShannonRes_theta06', 'ShannonRes_theta07', 'ShannonRes_theta08', 'ShannonRes_theta09', 'ShannonRes_theta10', 'ShannonRes_theta11', 'ShannonRes_theta12', 'ShannonRes_theta13', 'ShannonRes_alpha00', 'ShannonRes_alpha01', 'ShannonRes_alpha02', 'ShannonRes_alpha03', 'ShannonRes_alpha04', 'ShannonRes_alpha05', 'ShannonRes_alpha06', 'ShannonRes_alpha07', 'ShannonRes_alpha08', 'ShannonRes_alpha09', 'ShannonRes_alpha10', 'ShannonRes_alpha11', 'ShannonRes_alpha12', 'ShannonRes_alpha13', 'ShannonRes_beta00', 'ShannonRes_beta01', 'ShannonRes_beta02', 'ShannonRes_beta03', 'ShannonRes_beta04', 'ShannonRes_beta05', 'ShannonRes_beta06', 'ShannonRes_beta07', 'ShannonRes_beta08', 'ShannonRes_beta09', 'ShannonRes_beta10', 'ShannonRes_beta11', 'ShannonRes_beta12', 'ShannonRes_beta13', 'ShannonRes_gamma00', 'ShannonRes_gamma01', 'ShannonRes_gamma02', 'ShannonRes_gamma03', 'ShannonRes_gamma04', 'ShannonRes_gamma05', 'ShannonRes_gamma06', 'ShannonRes_gamma07', 'ShannonRes_gamma08', 'ShannonRes_gamma09', 'ShannonRes_gamma10', 'ShannonRes_gamma11', 'ShannonRes_gamma12', 'ShannonRes_gamma13', 'HjorthComp00', 'HjorthComp01', 'HjorthComp02', 'HjorthComp03', 'HjorthComp04', 'HjorthComp05', 'HjorthComp06', 'HjorthComp07', 'HjorthComp08', 'HjorthComp09', 'HjorthComp10', 'HjorthComp11', 'HjorthComp12', 'HjorthComp13', 'HjorthMob00', 'HjorthMob01', 'HjorthMob02', 'HjorthMob03', 'HjorthMob04', 'HjorthMob05', 'HjorthMob06', 'HjorthMob07', 'HjorthMob08', 'HjorthMob09', 'HjorthMob10', 'HjorthMob11', 'HjorthMob12', 'HjorthMob13', 'medianFreq00', 'medianFreq01', 'medianFreq02', 'medianFreq03', 'medianFreq04', 'medianFreq05', 'medianFreq06', 'medianFreq07', 'medianFreq08', 'medianFreq09', 'medianFreq10', 'medianFreq11', 'medianFreq12', 'medianFreq13', 'bandPwr_delta00', 'bandPwr_delta01', 'bandPwr_delta02', 'bandPwr_delta03', 'bandPwr_delta04', 'bandPwr_delta05', 'bandPwr_delta06', 'bandPwr_delta07', 'bandPwr_delta08', 'bandPwr_delta09', 'bandPwr_delta10', 'bandPwr_delta11', 'bandPwr_delta12', 'bandPwr_delta13', 'bandPwr_theta00', 'bandPwr_theta01', 'bandPwr_theta02', 'bandPwr_theta03', 'bandPwr_theta04', 'bandPwr_theta05', 'bandPwr_theta06', 'bandPwr_theta07', 'bandPwr_theta08', 'bandPwr_theta09', 'bandPwr_theta10', 'bandPwr_theta11', 'bandPwr_theta12', 'bandPwr_theta13', 'bandPwr_alpha00', 'bandPwr_alpha01', 'bandPwr_alpha02', 'bandPwr_alpha03', 'bandPwr_alpha04', 'bandPwr_alpha05', 'bandPwr_alpha06', 'bandPwr_alpha07', 'bandPwr_alpha08', 'bandPwr_alpha09', 'bandPwr_alpha10', 'bandPwr_alpha11', 'bandPwr_alpha12', 'bandPwr_alpha13', 'bandPwr_beta00', 'bandPwr_beta01', 'bandPwr_beta02', 'bandPwr_beta03', 'bandPwr_beta04', 'bandPwr_beta05', 'bandPwr_beta06', 'bandPwr_beta07', 'bandPwr_beta08', 'bandPwr_beta09', 'bandPwr_beta10', 'bandPwr_beta11', 'bandPwr_beta12', 'bandPwr_beta13', 'bandPwr_gamma00', 'bandPwr_gamma01', 'bandPwr_gamma02', 'bandPwr_gamma03', 'bandPwr_gamma04', 'bandPwr_gamma05', 'bandPwr_gamma06', 'bandPwr_gamma07', 'bandPwr_gamma08', 'bandPwr_gamma09', 'bandPwr_gamma10', 'bandPwr_gamma11', 'bandPwr_gamma12', 'bandPwr_gamma13', 'stdDev00', 'stdDev01', 'stdDev02', 'stdDev03', 'stdDev04', 'stdDev05', 'stdDev06', 'stdDev07', 'stdDev08', 'stdDev09', 'stdDev10', 'stdDev11', 'stdDev12', 'stdDev13', 'shortSpikeNum00', 'shortSpikeNum01', 'shortSpikeNum02', 'shortSpikeNum03', 'shortSpikeNum04', 'shortSpikeNum06', 'shortSpikeNum09', 'shortSpikeNum10', 'shortSpikeNum11', 'shortSpikeNum12', 'shortSpikeNum13', 'dasm_delta00', 'dasm_delta01', 'dasm_delta02', 'dasm_delta03', 'dasm_delta04', 'dasm_delta05', 'dasm_delta06', 'dasm_theta00', 'dasm_theta01', 'dasm_theta02', 'dasm_theta03', 'dasm_theta04', 'dasm_theta05', 'dasm_theta06', 'dasm_alpha00', 'dasm_alpha01', 'dasm_alpha02', 'dasm_alpha03', 'dasm_alpha04', 'dasm_alpha05', 'dasm_alpha06', 'dasm_beta00', 'dasm_beta01', 'dasm_beta02', 'dasm_beta03', 'dasm_beta04', 'dasm_beta05', 'dasm_beta06', 'dasm_gamma00', 'dasm_gamma01', 'dasm_gamma02', 'dasm_gamma03', 'dasm_gamma04', 'dasm_gamma05', 'dasm_gamma06', 'rasm_delta00', 'rasm_delta01', 'rasm_delta02', 'rasm_delta03', 'rasm_delta04', 'rasm_delta05', 'rasm_delta06', 'rasm_theta00', 'rasm_theta01', 'rasm_theta02', 'rasm_theta03', 'rasm_theta04', 'rasm_theta05', 'rasm_theta06', 'rasm_alpha00', 'rasm_alpha01', 'rasm_alpha02', 'rasm_alpha03', 'rasm_alpha04', 'rasm_alpha05', 'rasm_alpha06', 'rasm_beta00', 'rasm_beta01', 'rasm_beta02', 'rasm_beta03', 'rasm_beta04', 'rasm_beta05', 'rasm_beta06', 'rasm_gamma00', 'rasm_gamma01', 'rasm_gamma02', 'rasm_gamma03', 'rasm_gamma04', 'rasm_gamma05', 'rasm_gamma06']\n","y.shape: (3000,)\n"," Specs Score\n","57 ShannonRes_gamma01 25.060579\n","60 ShannonRes_gamma04 22.195534\n","65 ShannonRes_gamma09 22.167270\n","68 ShannonRes_gamma12 22.103202\n","85 HjorthMob01 21.810232\n",".. ... ...\n","261 rasm_alpha05 0.213991\n","266 rasm_beta03 0.211496\n","231 dasm_beta03 0.210966\n","240 dasm_gamma05 0.152615\n","275 rasm_gamma05 0.148320\n","\n","[277 rows x 2 columns]\n","['ShannonRes_gamma', 'HjorthMob', 'ShannonRes_beta', 'HjorthComp', 'medianFreq', 'stdDev', 'ShannonRes_delta', 'ShannonRes_theta', 'dasm_alpha', 'rasm_beta', 'dasm_beta', 'rasm_alpha', 'bandPwr_beta', 'rasm_theta', 'bandPwr_alpha', 'dasm_theta', 'bandPwr_theta', 'dasm_gamma', 'rasm_gamma', 'bandPwr_gamma', 'dasm_delta', 'rasm_delta', 'bandPwr_delta', 'ShannonRes_alpha', 'shortSpikeNum']\n","[1, 4, 9, 12, 2, 0, 3, 13, 11, 10, 6, 5, 7, 8]\n","(3000, 14)\n","Number of Feature-Columns: 14\n","\n","Features Ready for undergoing selection tests done ...\n","\n","window: 1, stide: 1, rmse: 2.7876847621876713\n","window: 1, stide: 1, rmse: 2.7876847621876713\n","r2: -0.005341121273668703\n","mae: 2.3694666666666664\n","ev: -0.0046263067942518\n","(3000, 28)\n","Number of Feature-Columns: 28\n","\n","Features Ready for undergoing selection tests done ...\n","\n","window: 1, stide: 1, rmse: 2.7216267500645026\n","window: 1, stide: 1, rmse: 2.7216267500645026\n","r2: 0.04174023381070047\n","mae: 2.3195833333333336\n","ev: 0.04199660628784885\n","(3000, 42)\n","Number of Feature-Columns: 42\n","\n","Features Ready for undergoing selection tests done ...\n","\n","window: 1, stide: 1, rmse: 2.734595277794016\n","window: 1, stide: 1, rmse: 2.734595277794016\n","r2: 0.03258627752838572\n","mae: 2.336033333333333\n","ev: 0.03304735593819674\n","(3000, 56)\n","Number of Feature-Columns: 56\n","\n","Features Ready for undergoing selection tests done ...\n","\n","window: 1, stide: 1, rmse: 2.7249147693093083\n","window: 1, stide: 1, rmse: 2.7249147693093083\n","r2: 0.03942347248994171\n","mae: 2.3239166666666664\n","ev: 0.03958330929248777\n","(3000, 70)\n","Number of Feature-Columns: 70\n","\n","Features Ready for undergoing selection tests done ...\n","\n","window: 1, stide: 1, rmse: 2.775903216372406\n","window: 1, stide: 1, rmse: 2.775903216372406\n","r2: 0.00313863482494392\n","mae: 2.370033333333333\n","ev: 0.003811763555946701\n","(3000, 84)\n","Number of Feature-Columns: 84\n","\n","Features Ready for undergoing selection tests done ...\n","\n","window: 1, stide: 1, rmse: 2.720274894197276\n","window: 1, stide: 1, rmse: 2.720274894197276\n","r2: 0.04269194944307175\n","mae: 2.3177166666666666\n","ev: 0.04293994492526154\n","(3000, 98)\n","Number of Feature-Columns: 98\n","\n","Features Ready for undergoing selection tests done ...\n","\n","window: 1, stide: 1, rmse: 2.735912370672716\n","window: 1, stide: 1, rmse: 2.735912370672716\n","r2: 0.03165416111463293\n","mae: 2.33865\n","ev: 0.03230906253638477\n","(3000, 112)\n","Number of Feature-Columns: 112\n","\n","Features Ready for undergoing selection tests done ...\n","\n","window: 1, stide: 1, rmse: 2.7469995449580984\n","window: 1, stide: 1, rmse: 2.7469995449580984\n","r2: 0.02378989378905294\n","mae: 2.353283333333333\n","ev: 0.024459605234220416\n","(3000, 119)\n","Number of Feature-Columns: 119\n","\n","Features Ready for undergoing selection tests done ...\n","\n","window: 1, stide: 1, rmse: 2.7417061294019094\n","window: 1, stide: 1, rmse: 2.7417061294019094\n","r2: 0.027548545259317714\n","mae: 2.3460166666666664\n","ev: 0.027829274239569046\n","(3000, 126)\n","Number of Feature-Columns: 126\n","\n","Features Ready for undergoing selection tests done ...\n","\n","window: 1, stide: 1, rmse: 2.7402140731458675\n","window: 1, stide: 1, rmse: 2.7402140731458675\n","r2: 0.028606687451756763\n","mae: 2.33655\n","ev: 0.028911201991976276\n","(3000, 133)\n","Number of Feature-Columns: 133\n","\n","Features Ready for undergoing selection tests done ...\n","\n","window: 1, stide: 1, rmse: 2.7563884885842924\n","window: 1, stide: 1, rmse: 2.7563884885842924\n","r2: 0.01710533124619995\n","mae: 2.35575\n","ev: 0.017537279086548674\n","(3000, 140)\n","Number of Feature-Columns: 140\n","\n","Features Ready for undergoing selection tests done ...\n","\n","window: 1, stide: 1, rmse: 2.7525962592917015\n","window: 1, stide: 1, rmse: 2.7525962592917015\n","r2: 0.01980799665368682\n","mae: 2.3609166666666663\n","ev: 0.019942269233036103\n","(3000, 154)\n","Number of Feature-Columns: 154\n","\n","Features Ready for undergoing selection tests done ...\n","\n","window: 1, stide: 1, rmse: 2.7373109980416914\n","window: 1, stide: 1, rmse: 2.7373109980416914\n","r2: 0.03066385076132938\n","mae: 2.3335166666666667\n","ev: 0.03092485115518251\n","(3000, 161)\n","Number of Feature-Columns: 161\n","\n","Features Ready for undergoing selection tests done ...\n","\n","window: 1, stide: 1, rmse: 2.7206787449703307\n","window: 1, stide: 1, rmse: 2.7206787449703307\n","r2: 0.04240768530856387\n","mae: 2.3182833333333335\n","ev: 0.0428961913047603\n","(3000, 175)\n","Number of Feature-Columns: 175\n","\n","Features Ready for undergoing selection tests done ...\n","\n","window: 1, stide: 1, rmse: 2.71350535900214\n","window: 1, stide: 1, rmse: 2.71350535900214\n","r2: 0.047450635411411035\n","mae: 2.3252666666666664\n","ev: 0.0476440546162018\n","(3000, 182)\n","Number of Feature-Columns: 182\n","\n","Features Ready for undergoing selection tests done ...\n","\n","window: 1, stide: 1, rmse: 2.732668750507459\n","window: 1, stide: 1, rmse: 2.732668750507459\n","r2: 0.033948886790256205\n","mae: 2.339516666666667\n","ev: 0.03426865120434219\n","(3000, 196)\n","Number of Feature-Columns: 196\n","\n","Features Ready for undergoing selection tests done ...\n","\n","window: 1, stide: 1, rmse: 2.724002722710338\n","window: 1, stide: 1, rmse: 2.724002722710338\n","r2: 0.040066387232262746\n","mae: 2.3280166666666666\n","ev: 0.0403844388169462\n","(3000, 203)\n","Number of Feature-Columns: 203\n","\n","Features Ready for undergoing selection tests done ...\n","\n","window: 1, stide: 1, rmse: 2.7178641736971825\n","window: 1, stide: 1, rmse: 2.7178641736971825\n","r2: 0.044387939473128135\n","mae: 2.3231\n","ev: 0.04461217710161547\n","(3000, 210)\n","Number of Feature-Columns: 210\n","\n","Features Ready for undergoing selection tests done ...\n","\n","window: 1, stide: 1, rmse: 2.7292849185577284\n","window: 1, stide: 1, rmse: 2.7292849185577284\n","r2: 0.03633990521654029\n","mae: 2.32895\n","ev: 0.03657633291935658\n","(3000, 224)\n","Number of Feature-Columns: 224\n","\n","Features Ready for undergoing selection tests done ...\n","\n","window: 1, stide: 1, rmse: 2.7413255005562545\n","window: 1, stide: 1, rmse: 2.7413255005562545\n","r2: 0.027818535815470002\n","mae: 2.34245\n","ev: 0.02795842295645612\n","(3000, 231)\n","Number of Feature-Columns: 231\n","\n","Features Ready for undergoing selection tests done ...\n","\n","window: 1, stide: 1, rmse: 2.747659337205639\n","window: 1, stide: 1, rmse: 2.747659337205639\n","r2: 0.023320892465189424\n","mae: 2.3557166666666665\n","ev: 0.02347711214461612\n","(3000, 238)\n","Number of Feature-Columns: 238\n","\n","Features Ready for undergoing selection tests done ...\n","\n","window: 1, stide: 1, rmse: 2.7513387347495644\n","window: 1, stide: 1, rmse: 2.7513387347495644\n","r2: 0.02070339417931233\n","mae: 2.35135\n","ev: 0.0207488750393493\n","(3000, 252)\n","Number of Feature-Columns: 252\n","\n","Features Ready for undergoing selection tests done ...\n","\n","window: 1, stide: 1, rmse: 2.7467785677043572\n","window: 1, stide: 1, rmse: 2.7467785677043572\n","r2: 0.023946946273561198\n","mae: 2.3608166666666666\n","ev: 0.024235360562376407\n","(3000, 266)\n","Number of Feature-Columns: 266\n","\n","Features Ready for undergoing selection tests done ...\n","\n","window: 1, stide: 1, rmse: 2.72808867768871\n","window: 1, stide: 1, rmse: 2.72808867768871\n","r2: 0.037184461204759045\n","mae: 2.3260500000000004\n","ev: 0.03763657316969771\n","(3000, 280)\n","Number of Feature-Columns: 280\n","\n","Features Ready for undergoing selection tests done ...\n","\n","window: 1, stide: 1, rmse: 2.7343061277040652\n","window: 1, stide: 1, rmse: 2.7343061277040652\n","r2: 0.03279085111062241\n","mae: 2.3380666666666667\n","ev: 0.03294216484042478\n"," Feature ... EV\n","0 1 ... -0.004626\n","1 2 ... 0.041997\n","2 3 ... 0.033047\n","3 4 ... 0.039583\n","4 5 ... 0.003812\n","5 6 ... 0.042940\n","6 7 ... 0.032309\n","7 8 ... 0.024460\n","8 9 ... 0.027829\n","9 10 ... 0.028911\n","10 11 ... 0.017537\n","11 12 ... 0.019942\n","12 13 ... 0.030925\n","13 14 ... 0.042896\n","14 15 ... 0.047644\n","15 16 ... 0.034269\n","16 17 ... 0.040384\n","17 18 ... 0.044612\n","18 19 ... 0.036576\n","19 20 ... 0.027958\n","20 21 ... 0.023477\n","21 22 ... 0.020749\n","22 23 ... 0.024235\n","23 24 ... 0.037637\n","24 25 ... 0.032942\n","25 NaN ... NaN\n","26 NaN ... NaN\n","27 NaN ... NaN\n","28 NaN ... NaN\n","29 NaN ... NaN\n","30 NaN ... NaN\n","31 NaN ... NaN\n","32 NaN ... NaN\n","33 NaN ... NaN\n","34 NaN ... NaN\n","35 NaN ... NaN\n","36 NaN ... NaN\n","37 NaN ... NaN\n","38 NaN ... NaN\n","39 NaN ... NaN\n","40 NaN ... NaN\n","41 NaN ... NaN\n","42 NaN ... NaN\n","43 NaN ... NaN\n","44 NaN ... NaN\n","45 NaN ... NaN\n","46 NaN ... NaN\n","47 NaN ... NaN\n","48 NaN ... NaN\n","49 NaN ... NaN\n","\n","[50 rows x 5 columns]\n","Traceback (most recent call last):\n"," File \"feature_select_main.py\", line 58, in \n"," topFeatureFSRegressionRanking(dataset, window, stride, sfreq, clf, label, scale=False, pca=False, mutual_info = False, method='SelectKBest')\n"," File \"/content/drive/MyDrive/IEEE_EEG_Cluster_Files/TopNByFSMethods.py\", line 513, in topFeatureFSRegressionRanking\n"," plt.tight_layout()\n"," File \"/usr/local/lib/python3.7/dist-packages/matplotlib/pyplot.py\", line 2763, in plot\n"," is not None else {}), **kwargs)\n"," File \"/usr/local/lib/python3.7/dist-packages/matplotlib/axes/_axes.py\", line 1647, in plot\n"," lines = [*self._get_lines(*args, data=data, **kwargs)]\n"," File \"/usr/local/lib/python3.7/dist-packages/matplotlib/axes/_base.py\", line 216, in __call__\n"," yield from self._plot_args(this, kwargs)\n"," File \"/usr/local/lib/python3.7/dist-packages/matplotlib/axes/_base.py\", line 337, in _plot_args\n"," self.axes.xaxis.update_units(x)\n"," File \"/usr/local/lib/python3.7/dist-packages/matplotlib/axis.py\", line 1516, in update_units\n"," default = self.converter.default_units(data, self)\n"," File \"/usr/local/lib/python3.7/dist-packages/matplotlib/category.py\", line 107, in default_units\n"," axis.set_units(UnitData(data))\n"," File \"/usr/local/lib/python3.7/dist-packages/matplotlib/category.py\", line 175, in __init__\n"," self.update(data)\n"," File \"/usr/local/lib/python3.7/dist-packages/matplotlib/category.py\", line 212, in update\n"," cbook._check_isinstance((str, bytes), value=val)\n"," File \"/usr/local/lib/python3.7/dist-packages/matplotlib/cbook/__init__.py\", line 2128, in _check_isinstance\n"," type_name(type(v))))\n","TypeError: 'value' must be an instance of str or bytes, not a float\n"]}]},{"cell_type":"code","source":["!python3 feature_select_main.py --dataset OASIS --window 1 --stride 1 --sfreq 128 --model rfr --label 0 --approach byfs --ml_algo regression --top e --fs_method SelectKBest"],"metadata":{"colab":{"base_uri":"https://localhost:8080/"},"id":"ZBexTeapaJQX","executionInfo":{"status":"ok","timestamp":1667629923639,"user_tz":-330,"elapsed":227143,"user":{"displayName":"Rohit Garg","userId":"11252576926243182044"}},"outputId":"37d2e016-583f-4aaa-80d2-ec086c7f5ee5"},"execution_count":null,"outputs":[{"output_type":"stream","name":"stdout","text":["{'dataset': 'OASIS', 'window': 1, 'stride': 1, 'sfreq': 128, 'model': 'rfr', 'label': 0, 'approach': 'byfs', 'ml_algo': 'regression', 'top': 'e', 'fs_method': 'SelectKBest'}\n","Number of segments are: 3000\n","['ShannonRes_delta', 'ShannonRes_theta', 'ShannonRes_alpha', 'ShannonRes_beta', 'ShannonRes_gamma', 'HjorthComp', 'HjorthMob', 'medianFreq', 'bandPwr_delta', 'bandPwr_theta', 'bandPwr_alpha', 'bandPwr_beta', 'bandPwr_gamma', 'stdDev', 'shortSpikeNum']\n","Number of Feature-Columns: 210\n","\n","y.shape: (3000,)\n","/usr/local/lib/python3.7/dist-packages/sklearn/feature_selection/_univariate_selection.py:112: UserWarning: Features [201 203 204] are constant.\n"," warnings.warn(\"Features %s are constant.\" % constant_features_idx, UserWarning)\n","/usr/local/lib/python3.7/dist-packages/sklearn/feature_selection/_univariate_selection.py:113: RuntimeWarning: invalid value encountered in true_divide\n"," f = msb / msw\n"," Specs Score\n","57 ShannonRes_gamma01 25.060579\n","60 ShannonRes_gamma04 22.195534\n","65 ShannonRes_gamma09 22.167270\n","68 ShannonRes_gamma12 22.103202\n","85 HjorthMob01 21.810232\n",".. ... ...\n","143 bandPwr_alpha03 0.451556\n","138 bandPwr_theta12 0.450174\n","171 bandPwr_gamma03 0.372456\n","104 medianFreq06 0.368649\n","38 ShannonRes_alpha10 0.276893\n","\n","[207 rows x 2 columns]\n","[1, 4, 9, 12, 2, 0, 3, 13, 11, 10, 6, 5, 7, 8]\n","[1]\n","(3000, 15)\n","Number of Feature-Columns: 15\n","\n","Features Ready for undergoing selection tests done ...\n","\n","window: 1, stide: 1, rmse: 2.8019186283687825\n","r2: -0.015633837436447973\n","mae: 2.400466666666667\n","ev: -0.015351297630700644\n","[1, 4, 9, 12, 2, 0, 3, 13, 11, 10, 6, 5, 7, 8]\n","[1, 4]\n","(3000, 30)\n","Number of Feature-Columns: 30\n","\n","Features Ready for undergoing selection tests done ...\n","\n","window: 1, stide: 1, rmse: 2.791533777931647\n","r2: -0.008119229657994609\n","mae: 2.4003833333333335\n","ev: -0.007681782537074522\n","[1, 4, 9, 12, 2, 0, 3, 13, 11, 10, 6, 5, 7, 8]\n","[1, 4, 9]\n","(3000, 45)\n","Number of Feature-Columns: 45\n","\n","Features Ready for undergoing selection tests done ...\n","\n","window: 1, stide: 1, rmse: 2.791597601135713\n","r2: -0.008165327710147219\n","mae: 2.3843833333333335\n","ev: -0.007951557480260663\n","[1, 4, 9, 12, 2, 0, 3, 13, 11, 10, 6, 5, 7, 8]\n","[1, 4, 9, 12]\n","(3000, 60)\n","Number of Feature-Columns: 60\n","\n","Features Ready for undergoing selection tests done ...\n","\n","window: 1, stide: 1, rmse: 2.7897076907805234\n","r2: -0.006800734808988462\n","mae: 2.3937333333333335\n","ev: -0.0064046394160626186\n","[1, 4, 9, 12, 2, 0, 3, 13, 11, 10, 6, 5, 7, 8]\n","[1, 4, 9, 12, 2]\n","(3000, 75)\n","Number of Feature-Columns: 75\n","\n","Features Ready for undergoing selection tests done ...\n","\n","window: 1, stide: 1, rmse: 2.7765491771861943\n","r2: 0.002674635721893859\n","mae: 2.370033333333333\n","ev: 0.002970632795307182\n","[1, 4, 9, 12, 2, 0, 3, 13, 11, 10, 6, 5, 7, 8]\n","[1, 4, 9, 12, 2, 0]\n","(3000, 90)\n","Number of Feature-Columns: 90\n","\n","Features Ready for undergoing selection tests done ...\n","\n","window: 1, stide: 1, rmse: 2.7781942576668994\n","r2: 0.001492472951353463\n","mae: 2.374766666666667\n","ev: 0.0017602965971962314\n","[1, 4, 9, 12, 2, 0, 3, 13, 11, 10, 6, 5, 7, 8]\n","[1, 4, 9, 12, 2, 0, 3]\n","(3000, 105)\n","Number of Feature-Columns: 105\n","\n","Features Ready for undergoing selection tests done ...\n","\n","window: 1, stide: 1, rmse: 2.8018623865802788\n","r2: -0.015593065024558372\n","mae: 2.39745\n","ev: -0.01538209028864812\n","[1, 4, 9, 12, 2, 0, 3, 13, 11, 10, 6, 5, 7, 8]\n","[1, 4, 9, 12, 2, 0, 3, 13]\n","(3000, 120)\n","Number of Feature-Columns: 120\n","\n","Features Ready for undergoing selection tests done ...\n","\n","window: 1, stide: 1, rmse: 2.774725241653066\n","r2: 0.003984506052255976\n","mae: 2.3720833333333333\n","ev: 0.004190977352014125\n","[1, 4, 9, 12, 2, 0, 3, 13, 11, 10, 6, 5, 7, 8]\n","[1, 4, 9, 12, 2, 0, 3, 13, 11]\n","(3000, 135)\n","Number of Feature-Columns: 135\n","\n","Features Ready for undergoing selection tests done ...\n","\n","window: 1, stide: 1, rmse: 2.7523765791281782\n","r2: 0.01996444542189002\n","mae: 2.3567500000000003\n","ev: 0.020351289617085988\n","[1, 4, 9, 12, 2, 0, 3, 13, 11, 10, 6, 5, 7, 8]\n","[1, 4, 9, 12, 2, 0, 3, 13, 11, 10]\n","(3000, 150)\n","Number of Feature-Columns: 150\n","\n","Features Ready for undergoing selection tests done ...\n","\n","window: 1, stide: 1, rmse: 2.774245543086144\n","r2: 0.004328861520416449\n","mae: 2.3863\n","ev: 0.004684526463616834\n","[1, 4, 9, 12, 2, 0, 3, 13, 11, 10, 6, 5, 7, 8]\n","[1, 4, 9, 12, 2, 0, 3, 13, 11, 10, 6]\n","(3000, 165)\n","Number of Feature-Columns: 165\n","\n","Features Ready for undergoing selection tests done ...\n","\n","window: 1, stide: 1, rmse: 2.782179780915197\n","r2: -0.0013744464137093182\n","mae: 2.372866666666667\n","ev: -0.0012569781699058868\n","[1, 4, 9, 12, 2, 0, 3, 13, 11, 10, 6, 5, 7, 8]\n","[1, 4, 9, 12, 2, 0, 3, 13, 11, 10, 6, 5]\n","(3000, 180)\n","Number of Feature-Columns: 180\n","\n","Features Ready for undergoing selection tests done ...\n","\n","window: 1, stide: 1, rmse: 2.7779576130675574\n","r2: 0.001662570020310783\n","mae: 2.3674166666666663\n","ev: 0.0018055941862120672\n","[1, 4, 9, 12, 2, 0, 3, 13, 11, 10, 6, 5, 7, 8]\n","[1, 4, 9, 12, 2, 0, 3, 13, 11, 10, 6, 5, 7]\n","(3000, 195)\n","Number of Feature-Columns: 195\n","\n","Features Ready for undergoing selection tests done ...\n","\n","window: 1, stide: 1, rmse: 2.7647974307954883\n","r2: 0.011099130217294806\n","mae: 2.3609500000000003\n","ev: 0.011424925757268944\n","[1, 4, 9, 12, 2, 0, 3, 13, 11, 10, 6, 5, 7, 8]\n","[1, 4, 9, 12, 2, 0, 3, 13, 11, 10, 6, 5, 7, 8]\n","(3000, 210)\n","Number of Feature-Columns: 210\n","\n","Features Ready for undergoing selection tests done ...\n","\n","window: 1, stide: 1, rmse: 2.7603488004235985\n","r2: 0.014278903996170822\n","mae: 2.3710500000000003\n","ev: 0.014588882456435526\n"," Electrode ... EV\n","0 1 ... -0.015351\n","1 2 ... -0.007682\n","2 3 ... -0.007952\n","3 4 ... -0.006405\n","4 5 ... 0.002971\n","5 6 ... 0.001760\n","6 7 ... -0.015382\n","7 8 ... 0.004191\n","8 9 ... 0.020351\n","9 10 ... 0.004685\n","10 11 ... -0.001257\n","11 12 ... 0.001806\n","12 13 ... 0.011425\n","13 14 ... 0.014589\n","\n","[14 rows x 5 columns]\n"]}]},{"cell_type":"code","source":["!python3 feature_select_main.py --dataset OASIS --window 1 --stride 1 --sfreq 128 --model rfr --label 1 --approach byfs --ml_algo regression --top f --fs_method SelectKBest"],"metadata":{"id":"lzjtbk9lSa1O"},"execution_count":null,"outputs":[]},{"cell_type":"code","execution_count":null,"metadata":{"colab":{"base_uri":"https://localhost:8080/"},"id":"f1vTtzyBn0we","outputId":"a1fa5ab2-32c2-4370-e214-46e536359ae8","executionInfo":{"status":"ok","timestamp":1667316422886,"user_tz":-330,"elapsed":890829,"user":{"displayName":"Rohit Garg","userId":"11252576926243182044"}}},"outputs":[{"output_type":"stream","name":"stdout","text":["{'dataset': 'DREAMER', 'window': 1, 'stride': 1, 'sfreq': 128, 'model': 'rfr', 'label': 0, 'approach': 'byfs', 'ml_algo': 'regression', 'top': 'e', 'fs_method': 'SelectKBest'}\n","Number of segments are: 188370\n","['ShannonRes_delta', 'ShannonRes_theta', 'ShannonRes_alpha', 'ShannonRes_beta', 'ShannonRes_gamma', 'HjorthComp', 'HjorthMob', 'medianFreq', 'bandPwr_delta', 'bandPwr_theta', 'bandPwr_alpha', 'bandPwr_beta', 'bandPwr_gamma', 'stdDev', 'shortSpikeNum']\n","Number of Feature-Columns: 210\n","\n","y.shape: (188370,)\n","/usr/local/lib/python3.7/dist-packages/sklearn/feature_selection/_univariate_selection.py:112: UserWarning: Features [196 197 198 204 207] are constant.\n"," warnings.warn(\"Features %s are constant.\" % constant_features_idx, UserWarning)\n","/usr/local/lib/python3.7/dist-packages/sklearn/feature_selection/_univariate_selection.py:113: RuntimeWarning: invalid value encountered in true_divide\n"," f = msb / msw\n"," Specs Score\n","65 ShannonRes_gamma09 1888.444463\n","61 ShannonRes_gamma05 1757.685620\n","63 ShannonRes_gamma07 1588.190675\n","66 ShannonRes_gamma10 1575.955842\n","68 ShannonRes_gamma12 1529.537735\n",".. ... ...\n","114 bandPwr_delta02 0.815222\n","117 bandPwr_delta05 0.758937\n","120 bandPwr_delta08 0.662457\n","124 bandPwr_delta12 0.210495\n","1 ShannonRes_delta01 0.169751\n","\n","[205 rows x 2 columns]\n","[9, 5, 7, 10, 12, 8, 4, 6, 3, 2, 13, 1, 0, 11]\n","[9]\n","(188370, 15)\n","Number of Feature-Columns: 15\n","\n","Features Ready for undergoing selection tests done ...\n","\n","window: 1, stide: 1, rmse: 1.1484326839937207\n","r2: 0.24047062904446193\n","mae: 0.9170305781175346\n","[9, 5, 7, 10, 12, 8, 4, 6, 3, 2, 13, 1, 0, 11]\n","[9, 5]\n","(188370, 30)\n","Number of Feature-Columns: 30\n","\n","Features Ready for undergoing selection tests done ...\n","\n","window: 1, stide: 1, rmse: 1.015971095070625\n","r2: 0.4055762005118162\n","mae: 0.7783245739767478\n","[9, 5, 7, 10, 12, 8, 4, 6, 3, 2, 13, 1, 0, 11]\n","[9, 5, 7]\n","(188370, 45)\n","Number of Feature-Columns: 45\n","\n","Features Ready for undergoing selection tests done ...\n","\n","window: 1, stide: 1, rmse: 0.9679195260166497\n","r2: 0.46047448617184983\n","mae: 0.7290462918723787\n","[9, 5, 7, 10, 12, 8, 4, 6, 3, 2, 13, 1, 0, 11]\n","[9, 5, 7, 10]\n","(188370, 60)\n","Number of Feature-Columns: 60\n","\n","Features Ready for undergoing selection tests done ...\n","\n","window: 1, stide: 1, rmse: 0.9269578909945255\n","r2: 0.505172876991702\n","mae: 0.689738811912725\n","[9, 5, 7, 10, 12, 8, 4, 6, 3, 2, 13, 1, 0, 11]\n","[9, 5, 7, 10, 12]\n","(188370, 75)\n","Number of Feature-Columns: 75\n","\n","Features Ready for undergoing selection tests done ...\n","\n","window: 1, stide: 1, rmse: 0.9247323704724097\n","r2: 0.5075460720252828\n","mae: 0.6894309072569941\n","[9, 5, 7, 10, 12, 8, 4, 6, 3, 2, 13, 1, 0, 11]\n","[9, 5, 7, 10, 12, 8]\n","(188370, 90)\n","Number of Feature-Columns: 90\n","\n","Features Ready for undergoing selection tests done ...\n","\n","window: 1, stide: 1, rmse: 0.9126174389067288\n","r2: 0.5203648403581516\n","mae: 0.6757832988267771\n","[9, 5, 7, 10, 12, 8, 4, 6, 3, 2, 13, 1, 0, 11]\n","[9, 5, 7, 10, 12, 8, 4]\n","(188370, 105)\n","Number of Feature-Columns: 105\n","\n","Features Ready for undergoing selection tests done ...\n","\n","window: 1, stide: 1, rmse: 0.8606997493329974\n","r2: 0.5733842961730445\n","mae: 0.6300108828369697\n","[9, 5, 7, 10, 12, 8, 4, 6, 3, 2, 13, 1, 0, 11]\n","[9, 5, 7, 10, 12, 8, 4, 6]\n","(188370, 120)\n","Number of Feature-Columns: 120\n","\n","Features Ready for undergoing selection tests done ...\n","\n","window: 1, stide: 1, rmse: 0.8550778286884855\n","r2: 0.5789392337649126\n","mae: 0.6251627116844508\n","[9, 5, 7, 10, 12, 8, 4, 6, 3, 2, 13, 1, 0, 11]\n","[9, 5, 7, 10, 12, 8, 4, 6, 3]\n","(188370, 135)\n","Number of Feature-Columns: 135\n","\n","Features Ready for undergoing selection tests done ...\n","\n","window: 1, stide: 1, rmse: 0.8184541057475057\n","r2: 0.6142355985214187\n","mae: 0.59004884004884\n","[9, 5, 7, 10, 12, 8, 4, 6, 3, 2, 13, 1, 0, 11]\n","[9, 5, 7, 10, 12, 8, 4, 6, 3, 2]\n","(188370, 150)\n","Number of Feature-Columns: 150\n","\n","Features Ready for undergoing selection tests done ...\n","\n","window: 1, stide: 1, rmse: 0.7986552275749412\n","r2: 0.6326735832933816\n","mae: 0.5714912140999097\n","[9, 5, 7, 10, 12, 8, 4, 6, 3, 2, 13, 1, 0, 11]\n","[9, 5, 7, 10, 12, 8, 4, 6, 3, 2, 13]\n","(188370, 165)\n","Number of Feature-Columns: 165\n","\n","Features Ready for undergoing selection tests done ...\n","\n","window: 1, stide: 1, rmse: 0.7945507360889976\n","r2: 0.6364394484687141\n","mae: 0.5624101502362372\n","[9, 5, 7, 10, 12, 8, 4, 6, 3, 2, 13, 1, 0, 11]\n","[9, 5, 7, 10, 12, 8, 4, 6, 3, 2, 13, 1]\n","(188370, 180)\n","Number of Feature-Columns: 180\n","\n","Features Ready for undergoing selection tests done ...\n","\n","window: 1, stide: 1, rmse: 0.7781461492760654\n","r2: 0.6512968824028613\n","mae: 0.546838137707703\n","[9, 5, 7, 10, 12, 8, 4, 6, 3, 2, 13, 1, 0, 11]\n","[9, 5, 7, 10, 12, 8, 4, 6, 3, 2, 13, 1, 0]\n","(188370, 195)\n","Number of Feature-Columns: 195\n","\n","Features Ready for undergoing selection tests done ...\n","\n","window: 1, stide: 1, rmse: 0.7623386147296946\n","r2: 0.6653203376172936\n","mae: 0.5341577745925572\n","[9, 5, 7, 10, 12, 8, 4, 6, 3, 2, 13, 1, 0, 11]\n","[9, 5, 7, 10, 12, 8, 4, 6, 3, 2, 13, 1, 0, 11]\n","(188370, 210)\n","Number of Feature-Columns: 210\n","\n","Features Ready for undergoing selection tests done ...\n","\n","window: 1, stide: 1, rmse: 0.722093985903788\n","r2: 0.6997237795950163\n","mae: 0.5007323352975527\n"," Electrode RMSE\n","0 1 1.148433\n","1 2 1.015971\n","2 3 0.967920\n","3 4 0.926958\n","4 5 0.924732\n","5 6 0.912617\n","6 7 0.860700\n","7 8 0.855078\n","8 9 0.818454\n","9 10 0.798655\n","10 11 0.794551\n","11 12 0.778146\n","12 13 0.762339\n","13 14 0.722094\n"," Electrode R2\n","0 1 0.240471\n","1 2 0.405576\n","2 3 0.460474\n","3 4 0.505173\n","4 5 0.507546\n","5 6 0.520365\n","6 7 0.573384\n","7 8 0.578939\n","8 9 0.614236\n","9 10 0.632674\n","10 11 0.636439\n","11 12 0.651297\n","12 13 0.665320\n","13 14 0.699724\n"," Electrode MAE\n","0 1 0.917031\n","1 2 0.778325\n","2 3 0.729046\n","3 4 0.689739\n","4 5 0.689431\n","5 6 0.675783\n","6 7 0.630011\n","7 8 0.625163\n","8 9 0.590049\n","9 10 0.571491\n","10 11 0.562410\n","11 12 0.546838\n","12 13 0.534158\n","13 14 0.500732\n","Traceback (most recent call last):\n"," File \"feature_select_main.py\", line 51, in \n"," plt.savefig(pwd + \"/\" + dataset + \"/plots/\" + \"CorrectedElectrodewiseRanking\" + str(window) + str(stride) + \".svg\", bbox_inches='tight')\n"," File \"/usr/local/lib/python3.7/dist-packages/matplotlib/pyplot.py\", line 723, in savefig\n"," res = fig.savefig(*args, **kwargs)\n"," File \"/usr/local/lib/python3.7/dist-packages/matplotlib/figure.py\", line 2203, in savefig\n"," self.canvas.print_figure(fname, **kwargs)\n"," File \"/usr/local/lib/python3.7/dist-packages/matplotlib/backend_bases.py\", line 2126, in print_figure\n"," **kwargs)\n"," File \"/usr/local/lib/python3.7/dist-packages/matplotlib/backends/backend_svg.py\", line 1184, in print_svg\n"," with cbook.open_file_cm(filename, \"w\", encoding=\"utf-8\") as fh:\n"," File \"/usr/lib/python3.7/contextlib.py\", line 112, in __enter__\n"," return next(self.gen)\n"," File \"/usr/local/lib/python3.7/dist-packages/matplotlib/cbook/__init__.py\", line 418, in open_file_cm\n"," fh, opened = to_filehandle(path_or_file, mode, True, encoding)\n"," File \"/usr/local/lib/python3.7/dist-packages/matplotlib/cbook/__init__.py\", line 403, in to_filehandle\n"," fh = open(fname, flag, encoding=encoding)\n","FileNotFoundError: [Errno 2] No such file or directory: '/content/drive/MyDrive/IEEE_EEG_Cluster_Files/DREAMER/plots/CorrectedElectrodewiseRanking11.svg'\n"]}],"source":["!python3 feature_select_main.py --dataset DREAMER --window 1 --stride 1 --sfreq 128 --model rfr --label 0 --approach byfs --ml_algo regression --top e --fs_method SelectKBest"]},{"cell_type":"code","execution_count":null,"metadata":{"colab":{"base_uri":"https://localhost:8080/"},"executionInfo":{"elapsed":7104,"status":"ok","timestamp":1667277513982,"user":{"displayName":"Rohit Garg","userId":"11252576926243182044"},"user_tz":-330},"id":"c2ginRNnrZan","outputId":"d0c9189f-fa4a-410a-9ee6-41f5c90eaf91"},"outputs":[{"output_type":"stream","name":"stdout","text":["{'dataset': 'OASIS', 'window': 1, 'stride': 1, 'sfreq': 128, 'model': 'knn', 'label': 0, 'approach': 'byclassifier', 'ml_algo': 'regression', 'top': 'f', 'fs_method': 'None'}\n","Number of segments are: 3000\n","Traceback (most recent call last):\n"," File \"feature_select_main.py\", line 58, in \n"," topFeaturesRegressionRanking(dataset, window, stride, sfreq, clf, label, scale=False, pca=False, ht = True)\n"," File \"/content/drive/MyDrive/IEEE_EEG_Project/TopNByClassifier.py\", line 302, in topFeaturesRegressionRanking\n"," featuresDict = loadFeaturesDict(dataset)\n"," File \"/content/drive/MyDrive/IEEE_EEG_Project/ImportUtils.py\", line 172, in loadFeaturesDict\n"," featuresDict['dasm_delta'] = np.load(featurepath + \"dasm_delta_1_1.npz\",allow_pickle=True)['features']\n"," File \"/usr/local/lib/python3.7/dist-packages/numpy/lib/npyio.py\", line 424, in load\n"," magic = fid.read(N)\n","KeyboardInterrupt\n"]}],"source":["!python3 feature_select_main.py --dataset OASIS --window 1 --stride 1 --sfreq 128 --model knn --label 0 --approach byclassifier --ml_algo regression --top f --fs_method None"]},{"cell_type":"code","execution_count":null,"metadata":{"colab":{"base_uri":"https://localhost:8080/"},"executionInfo":{"elapsed":215576,"status":"ok","timestamp":1635942847223,"user":{"displayName":"Rohit Garg","photoUrl":"https://lh3.googleusercontent.com/a-/AOh14Gj3Za6nmSY12sPvpCoWOtWQcVR5CMMSPBRGTexH_w=s64","userId":"11252576926243182044"},"user_tz":-330},"id":"TBmTNYwBrmin","outputId":"f1c87456-0303-41fa-85a6-45efb25fce38"},"outputs":[{"name":"stdout","output_type":"stream","text":["\u001b[1;30;43mStreaming output truncated to the last 5000 lines.\u001b[0m\n","[CV] .... n_neighbors=3, weights=distance, score=-0.388, total= 0.0s\n","[CV] n_neighbors=3, weights=distance .................................\n","[CV] .... n_neighbors=3, weights=distance, score=-0.252, total= 0.0s\n","[CV] n_neighbors=3, weights=distance .................................\n","[CV] .... n_neighbors=3, weights=distance, score=-0.147, total= 0.0s\n","[CV] n_neighbors=3, weights=distance .................................\n","[CV] .... n_neighbors=3, weights=distance, score=-0.380, total= 0.0s\n","[CV] n_neighbors=3, weights=distance .................................\n","[CV] .... n_neighbors=3, weights=distance, score=-0.331, total= 0.0s\n","[CV] n_neighbors=3, weights=distance .................................\n","[CV] .... n_neighbors=3, weights=distance, score=-0.397, total= 0.0s\n","[CV] n_neighbors=3, weights=distance .................................\n","[CV] .... n_neighbors=3, weights=distance, score=-0.395, total= 0.0s\n","[CV] n_neighbors=3, weights=distance .................................\n","[CV] .... n_neighbors=3, weights=distance, score=-0.368, total= 0.0s\n","[CV] n_neighbors=3, weights=distance .................................\n","[CV] .... n_neighbors=3, weights=distance, score=-0.175, total= 0.0s\n","[CV] n_neighbors=3, weights=distance .................................\n","[CV] .... n_neighbors=3, weights=distance, score=-0.453, total= 0.0s\n","[CV] n_neighbors=3, weights=distance .................................\n","[CV] .... n_neighbors=3, weights=distance, score=-0.405, total= 0.0s\n","[CV] n_neighbors=3, weights=distance .................................\n","[CV] .... n_neighbors=3, weights=distance, score=-0.322, total= 0.0s\n","[CV] n_neighbors=3, weights=distance .................................\n","[CV] .... n_neighbors=3, weights=distance, score=-0.213, total= 0.0s\n","[CV] n_neighbors=3, weights=distance .................................\n","[CV] .... n_neighbors=3, weights=distance, score=-0.316, total= 0.0s\n","[CV] n_neighbors=3, weights=distance .................................\n","[CV] .... n_neighbors=3, weights=distance, score=-0.189, total= 0.0s\n","[CV] n_neighbors=3, weights=distance .................................\n","[CV] .... n_neighbors=3, weights=distance, score=-0.327, total= 0.0s\n","[CV] n_neighbors=3, weights=distance .................................\n","[CV] .... n_neighbors=3, weights=distance, score=-0.256, total= 0.0s\n","[CV] n_neighbors=3, weights=distance .................................\n","[CV] .... n_neighbors=3, weights=distance, score=-0.495, total= 0.0s\n","[CV] n_neighbors=3, weights=distance .................................\n","[CV] .... n_neighbors=3, weights=distance, score=-0.359, total= 0.0s\n","[CV] n_neighbors=3, weights=distance .................................\n","[CV] .... n_neighbors=3, weights=distance, score=-0.316, total= 0.0s\n","[CV] n_neighbors=4, weights=uniform ..................................\n","[CV] ..... n_neighbors=4, weights=uniform, score=-0.271, total= 0.0s\n","[CV] n_neighbors=4, weights=uniform ..................................\n","[CV] ..... n_neighbors=4, weights=uniform, score=-0.257, total= 0.0s\n","[CV] n_neighbors=4, weights=uniform ..................................\n","[CV] ..... n_neighbors=4, weights=uniform, score=-0.225, total= 0.0s\n","[CV] n_neighbors=4, weights=uniform ..................................\n","[CV] ..... n_neighbors=4, weights=uniform, score=-0.402, total= 0.0s\n","[CV] n_neighbors=4, weights=uniform ..................................\n","[CV] ..... n_neighbors=4, weights=uniform, score=-0.192, total= 0.0s\n","[CV] n_neighbors=4, weights=uniform ..................................\n","[CV] ..... n_neighbors=4, weights=uniform, score=-0.189, total= 0.0s\n","[CV] n_neighbors=4, weights=uniform ..................................\n","[CV] ..... n_neighbors=4, weights=uniform, score=-0.231, total= 0.0s\n","[CV] n_neighbors=4, weights=uniform ..................................\n","[CV] ..... n_neighbors=4, weights=uniform, score=-0.318, total= 0.0s\n","[CV] n_neighbors=4, weights=uniform ..................................\n","[CV] ..... n_neighbors=4, weights=uniform, score=-0.210, total= 0.0s\n","[CV] n_neighbors=4, weights=uniform ..................................\n","[CV] ..... n_neighbors=4, weights=uniform, score=-0.158, total= 0.0s\n","[CV] n_neighbors=4, weights=uniform ..................................\n","[CV] ..... n_neighbors=4, weights=uniform, score=-0.224, total= 0.0s\n","[CV] n_neighbors=4, weights=uniform ..................................\n","[CV] ..... n_neighbors=4, weights=uniform, score=-0.195, total= 0.0s\n","[CV] n_neighbors=4, weights=uniform ..................................\n","[CV] ..... n_neighbors=4, weights=uniform, score=-0.123, total= 0.0s\n","[CV] n_neighbors=4, weights=uniform ..................................\n","[CV] ..... n_neighbors=4, weights=uniform, score=-0.237, total= 0.0s\n","[CV] n_neighbors=4, weights=uniform ..................................\n","[CV] ..... n_neighbors=4, weights=uniform, score=-0.198, total= 0.0s\n","[CV] n_neighbors=4, weights=uniform ..................................\n","[CV] ..... n_neighbors=4, weights=uniform, score=-0.317, total= 0.0s\n","[CV] n_neighbors=4, weights=uniform ..................................\n","[CV] ..... n_neighbors=4, weights=uniform, score=-0.276, total= 0.0s\n","[CV] n_neighbors=4, weights=uniform ..................................\n","[CV] ..... n_neighbors=4, weights=uniform, score=-0.230, total= 0.0s\n","[CV] n_neighbors=4, weights=uniform ..................................\n","[CV] ..... n_neighbors=4, weights=uniform, score=-0.135, total= 0.0s\n","[CV] n_neighbors=4, weights=uniform ..................................\n","[CV] ..... n_neighbors=4, weights=uniform, score=-0.330, total= 0.0s\n","[CV] n_neighbors=4, weights=uniform ..................................\n","[CV] ..... n_neighbors=4, weights=uniform, score=-0.229, total= 0.0s\n","[CV] n_neighbors=4, weights=uniform ..................................\n","[CV] ..... n_neighbors=4, weights=uniform, score=-0.277, total= 0.0s\n","[CV] n_neighbors=4, weights=uniform ..................................\n","[CV] ..... n_neighbors=4, weights=uniform, score=-0.111, total= 0.0s\n","[CV] n_neighbors=4, weights=uniform ..................................\n","[CV] ..... n_neighbors=4, weights=uniform, score=-0.236, total= 0.0s\n","[CV] n_neighbors=4, weights=uniform ..................................\n","[CV] ..... n_neighbors=4, weights=uniform, score=-0.135, total= 0.0s\n","[CV] n_neighbors=4, weights=uniform ..................................\n","[CV] ..... n_neighbors=4, weights=uniform, score=-0.223, total= 0.0s\n","[CV] n_neighbors=4, weights=uniform ..................................\n","[CV] ..... n_neighbors=4, weights=uniform, score=-0.222, total= 0.0s\n","[CV] n_neighbors=4, weights=uniform ..................................\n","[CV] ..... n_neighbors=4, weights=uniform, score=-0.317, total= 0.0s\n","[CV] n_neighbors=4, weights=uniform ..................................\n","[CV] ..... n_neighbors=4, weights=uniform, score=-0.297, total= 0.0s\n","[CV] n_neighbors=4, weights=uniform ..................................\n","[CV] ..... n_neighbors=4, weights=uniform, score=-0.222, total= 0.0s\n","[CV] n_neighbors=4, weights=distance .................................\n","[CV] .... n_neighbors=4, weights=distance, score=-0.297, total= 0.0s\n","[CV] n_neighbors=4, weights=distance .................................\n","[CV] .... n_neighbors=4, weights=distance, score=-0.319, total= 0.0s\n","[CV] n_neighbors=4, weights=distance .................................\n","[CV] .... n_neighbors=4, weights=distance, score=-0.241, total= 0.0s\n","[CV] n_neighbors=4, weights=distance .................................\n","[CV] .... n_neighbors=4, weights=distance, score=-0.461, total= 0.0s\n","[CV] n_neighbors=4, weights=distance .................................\n","[CV] .... n_neighbors=4, weights=distance, score=-0.197, total= 0.0s\n","[CV] n_neighbors=4, weights=distance .................................\n","[CV] .... n_neighbors=4, weights=distance, score=-0.214, total= 0.0s\n","[CV] n_neighbors=4, weights=distance .................................\n","[CV] .... n_neighbors=4, weights=distance, score=-0.253, total= 0.0s\n","[CV] n_neighbors=4, weights=distance .................................\n","[CV] .... n_neighbors=4, weights=distance, score=-0.331, total= 0.0s\n","[CV] n_neighbors=4, weights=distance .................................\n","[CV] .... n_neighbors=4, weights=distance, score=-0.200, total= 0.0s\n","[CV] n_neighbors=4, weights=distance .................................\n","[CV] .... n_neighbors=4, weights=distance, score=-0.182, total= 0.0s\n","[CV] n_neighbors=4, weights=distance .................................\n","[CV] .... n_neighbors=4, weights=distance, score=-0.251, total= 0.0s\n","[CV] n_neighbors=4, weights=distance .................................\n","[CV] .... n_neighbors=4, weights=distance, score=-0.211, total= 0.0s\n","[CV] n_neighbors=4, weights=distance .................................\n","[CV] .... n_neighbors=4, weights=distance, score=-0.111, total= 0.0s\n","[CV] n_neighbors=4, weights=distance .................................\n","[CV] .... n_neighbors=4, weights=distance, score=-0.287, total= 0.0s\n","[CV] n_neighbors=4, weights=distance .................................\n","[CV] .... n_neighbors=4, weights=distance, score=-0.234, total= 0.0s\n","[CV] n_neighbors=4, weights=distance .................................\n","[CV] .... n_neighbors=4, weights=distance, score=-0.324, total= 0.0s\n","[CV] n_neighbors=4, weights=distance .................................\n","[CV] .... n_neighbors=4, weights=distance, score=-0.308, total= 0.0s\n","[CV] n_neighbors=4, weights=distance .................................\n","[CV] .... n_neighbors=4, weights=distance, score=-0.263, total= 0.0s\n","[CV] n_neighbors=4, weights=distance .................................\n","[CV] .... n_neighbors=4, weights=distance, score=-0.164, total= 0.0s\n","[CV] n_neighbors=4, weights=distance .................................\n","[CV] .... n_neighbors=4, weights=distance, score=-0.354, total= 0.0s\n","[CV] n_neighbors=4, weights=distance .................................\n","[CV] .... n_neighbors=4, weights=distance, score=-0.273, total= 0.0s\n","[CV] n_neighbors=4, weights=distance .................................\n","[CV] .... n_neighbors=4, weights=distance, score=-0.277, total= 0.0s\n","[CV] n_neighbors=4, weights=distance .................................\n","[CV] .... n_neighbors=4, weights=distance, score=-0.145, total= 0.0s\n","[CV] n_neighbors=4, weights=distance .................................\n","[CV] .... n_neighbors=4, weights=distance, score=-0.258, total= 0.0s\n","[CV] n_neighbors=4, weights=distance .................................\n","[CV] .... n_neighbors=4, weights=distance, score=-0.136, total= 0.0s\n","[CV] n_neighbors=4, weights=distance .................................\n","[CV] .... n_neighbors=4, weights=distance, score=-0.252, total= 0.0s\n","[CV] n_neighbors=4, weights=distance .................................\n","[CV] .... n_neighbors=4, weights=distance, score=-0.225, total= 0.0s\n","[CV] n_neighbors=4, weights=distance .................................\n","[CV] .... n_neighbors=4, weights=distance, score=-0.374, total= 0.0s\n","[CV] n_neighbors=4, weights=distance .................................\n","[CV] .... n_neighbors=4, weights=distance, score=-0.307, total= 0.0s\n","[CV] n_neighbors=4, weights=distance .................................\n","[CV] .... n_neighbors=4, weights=distance, score=-0.255, total= 0.0s\n","[CV] n_neighbors=5, weights=uniform ..................................\n","[CV] ..... n_neighbors=5, weights=uniform, score=-0.225, total= 0.0s\n","[CV] n_neighbors=5, weights=uniform ..................................\n","[CV] ..... n_neighbors=5, weights=uniform, score=-0.174, total= 0.0s\n","[CV] n_neighbors=5, weights=uniform ..................................\n","[CV] ..... n_neighbors=5, weights=uniform, score=-0.159, total= 0.0s\n","[CV] n_neighbors=5, weights=uniform ..................................\n","[CV] ..... n_neighbors=5, weights=uniform, score=-0.314, total= 0.0s\n","[CV] n_neighbors=5, weights=uniform ..................................\n","[CV] ..... n_neighbors=5, weights=uniform, score=-0.151, total= 0.0s\n","[CV] n_neighbors=5, weights=uniform ..................................\n","[CV] ..... n_neighbors=5, weights=uniform, score=-0.118, total= 0.0s\n","[CV] n_neighbors=5, weights=uniform ..................................\n","[CV] ..... n_neighbors=5, weights=uniform, score=-0.193, total= 0.0s\n","[CV] n_neighbors=5, weights=uniform ..................................\n","[CV] ..... n_neighbors=5, weights=uniform, score=-0.253, total= 0.0s\n","[CV] n_neighbors=5, weights=uniform ..................................\n","[CV] ..... n_neighbors=5, weights=uniform, score=-0.170, total= 0.0s\n","[CV] n_neighbors=5, weights=uniform ..................................\n","[CV] ..... n_neighbors=5, weights=uniform, score=-0.106, total= 0.0s\n","[CV] n_neighbors=5, weights=uniform ..................................\n","[CV] ..... n_neighbors=5, weights=uniform, score=-0.150, total= 0.0s\n","[CV] n_neighbors=5, weights=uniform ..................................\n","[CV] ..... n_neighbors=5, weights=uniform, score=-0.172, total= 0.0s\n","[CV] n_neighbors=5, weights=uniform ..................................\n","[CV] ..... n_neighbors=5, weights=uniform, score=-0.085, total= 0.0s\n","[CV] n_neighbors=5, weights=uniform ..................................\n","[CV] ..... n_neighbors=5, weights=uniform, score=-0.169, total= 0.0s\n","[CV] n_neighbors=5, weights=uniform ..................................\n","[CV] ..... n_neighbors=5, weights=uniform, score=-0.167, total= 0.0s\n","[CV] n_neighbors=5, weights=uniform ..................................\n","[CV] ..... n_neighbors=5, weights=uniform, score=-0.256, total= 0.0s\n","[CV] n_neighbors=5, weights=uniform ..................................\n","[CV] ..... n_neighbors=5, weights=uniform, score=-0.218, total= 0.0s\n","[CV] n_neighbors=5, weights=uniform ..................................\n","[CV] ..... n_neighbors=5, weights=uniform, score=-0.221, total= 0.0s\n","[CV] n_neighbors=5, weights=uniform ..................................\n","[CV] ..... n_neighbors=5, weights=uniform, score=-0.108, total= 0.0s\n","[CV] n_neighbors=5, weights=uniform ..................................\n","[CV] ..... n_neighbors=5, weights=uniform, score=-0.245, total= 0.0s\n","[CV] n_neighbors=5, weights=uniform ..................................\n","[CV] ..... n_neighbors=5, weights=uniform, score=-0.134, total= 0.0s\n","[CV] n_neighbors=5, weights=uniform ..................................\n","[CV] ..... n_neighbors=5, weights=uniform, score=-0.199, total= 0.0s\n","[CV] n_neighbors=5, weights=uniform ..................................\n","[CV] ..... n_neighbors=5, weights=uniform, score=-0.079, total= 0.0s\n","[CV] n_neighbors=5, weights=uniform ..................................\n","[CV] ..... n_neighbors=5, weights=uniform, score=-0.139, total= 0.0s\n","[CV] n_neighbors=5, weights=uniform ..................................\n","[CV] ..... n_neighbors=5, weights=uniform, score=-0.122, total= 0.0s\n","[CV] n_neighbors=5, weights=uniform ..................................\n","[CV] ..... n_neighbors=5, weights=uniform, score=-0.234, total= 0.0s\n","[CV] n_neighbors=5, weights=uniform ..................................\n","[CV] ..... n_neighbors=5, weights=uniform, score=-0.149, total= 0.0s\n","[CV] n_neighbors=5, weights=uniform ..................................\n","[CV] ..... n_neighbors=5, weights=uniform, score=-0.228, total= 0.0s\n","[CV] n_neighbors=5, weights=uniform ..................................\n","[CV] ..... n_neighbors=5, weights=uniform, score=-0.254, total= 0.0s\n","[CV] n_neighbors=5, weights=uniform ..................................\n","[CV] ..... n_neighbors=5, weights=uniform, score=-0.142, total= 0.0s\n","[CV] n_neighbors=5, weights=distance .................................\n","[CV] .... n_neighbors=5, weights=distance, score=-0.244, total= 0.0s\n","[CV] n_neighbors=5, weights=distance .................................\n","[CV] .... n_neighbors=5, weights=distance, score=-0.239, total= 0.0s\n","[CV] n_neighbors=5, weights=distance .................................\n","[CV] .... n_neighbors=5, weights=distance, score=-0.180, total= 0.0s\n","[CV] n_neighbors=5, weights=distance .................................\n","[CV] .... n_neighbors=5, weights=distance, score=-0.375, total= 0.0s\n","[CV] n_neighbors=5, weights=distance .................................\n","[CV] .... n_neighbors=5, weights=distance, score=-0.165, total= 0.0s\n","[CV] n_neighbors=5, weights=distance .................................\n","[CV] .... n_neighbors=5, weights=distance, score=-0.144, total= 0.0s\n","[CV] n_neighbors=5, weights=distance .................................\n","[CV] .... n_neighbors=5, weights=distance, score=-0.210, total= 0.0s\n","[CV] n_neighbors=5, weights=distance .................................\n","[CV] .... n_neighbors=5, weights=distance, score=-0.270, total= 0.0s\n","[CV] n_neighbors=5, weights=distance .................................\n","[CV] .... n_neighbors=5, weights=distance, score=-0.159, total= 0.0s\n","[CV] n_neighbors=5, weights=distance .................................\n","[CV] .... n_neighbors=5, weights=distance, score=-0.131, total= 0.0s\n","[CV] n_neighbors=5, weights=distance .................................\n","[CV] .... n_neighbors=5, weights=distance, score=-0.186, total= 0.0s\n","[CV] n_neighbors=5, weights=distance .................................\n","[CV] .... n_neighbors=5, weights=distance, score=-0.182, total= 0.0s\n","[CV] n_neighbors=5, weights=distance .................................\n","[CV] .... n_neighbors=5, weights=distance, score=-0.059, total= 0.0s\n","[CV] n_neighbors=5, weights=distance .................................\n","[CV] .... n_neighbors=5, weights=distance, score=-0.212, total= 0.0s\n","[CV] n_neighbors=5, weights=distance .................................\n","[CV] .... n_neighbors=5, weights=distance, score=-0.203, total= 0.0s\n","[CV] n_neighbors=5, weights=distance .................................\n","[CV] .... n_neighbors=5, weights=distance, score=-0.265, total= 0.0s\n","[CV] n_neighbors=5, weights=distance .................................\n","[CV] .... n_neighbors=5, weights=distance, score=-0.247, total= 0.0s\n","[CV] n_neighbors=5, weights=distance .................................\n","[CV] .... n_neighbors=5, weights=distance, score=-0.237, total= 0.0s\n","[CV] n_neighbors=5, weights=distance .................................\n","[CV] .... n_neighbors=5, weights=distance, score=-0.135, total= 0.0s\n","[CV] n_neighbors=5, weights=distance .................................\n","[CV] .... n_neighbors=5, weights=distance, score=-0.272, total= 0.0s\n","[CV] n_neighbors=5, weights=distance .................................\n","[CV] .... n_neighbors=5, weights=distance, score=-0.182, total= 0.0s\n","[CV] n_neighbors=5, weights=distance .................................\n","[CV] .... n_neighbors=5, weights=distance, score=-0.204, total= 0.0s\n","[CV] n_neighbors=5, weights=distance .................................\n","[CV] .... n_neighbors=5, weights=distance, score=-0.107, total= 0.0s\n","[CV] n_neighbors=5, weights=distance .................................\n","[CV] .... n_neighbors=5, weights=distance, score=-0.175, total= 0.0s\n","[CV] n_neighbors=5, weights=distance .................................\n","[CV] .... n_neighbors=5, weights=distance, score=-0.117, total= 0.0s\n","[CV] n_neighbors=5, weights=distance .................................\n","[CV] .... n_neighbors=5, weights=distance, score=-0.242, total= 0.0s\n","[CV] n_neighbors=5, weights=distance .................................\n","[CV] .... n_neighbors=5, weights=distance, score=-0.164, total= 0.0s\n","[CV] n_neighbors=5, weights=distance .................................\n","[CV] .... n_neighbors=5, weights=distance, score=-0.291, total= 0.0s\n","[CV] n_neighbors=5, weights=distance .................................\n","[CV] .... n_neighbors=5, weights=distance, score=-0.271, total= 0.0s\n","[CV] n_neighbors=5, weights=distance .................................\n","[CV] .... n_neighbors=5, weights=distance, score=-0.187, total= 0.0s\n","[CV] n_neighbors=6, weights=uniform ..................................\n","[CV] ..... n_neighbors=6, weights=uniform, score=-0.176, total= 0.0s\n","[CV] n_neighbors=6, weights=uniform ..................................\n","[CV] ..... n_neighbors=6, weights=uniform, score=-0.169, total= 0.0s\n","[CV] n_neighbors=6, weights=uniform ..................................\n","[CV] ..... n_neighbors=6, weights=uniform, score=-0.145, total= 0.0s\n","[CV] n_neighbors=6, weights=uniform ..................................\n","[CV] ..... n_neighbors=6, weights=uniform, score=-0.245, total= 0.0s\n","[CV] n_neighbors=6, weights=uniform ..................................\n","[CV] ..... n_neighbors=6, weights=uniform, score=-0.127, total= 0.0s\n","[CV] n_neighbors=6, weights=uniform ..................................\n","[CV] ..... n_neighbors=6, weights=uniform, score=-0.099, total= 0.0s\n","[CV] n_neighbors=6, weights=uniform ..................................\n","[CV] ..... n_neighbors=6, weights=uniform, score=-0.220, total= 0.0s\n","[CV] n_neighbors=6, weights=uniform ..................................\n","[CV] ..... n_neighbors=6, weights=uniform, score=-0.198, total= 0.0s\n","[CV] n_neighbors=6, weights=uniform ..................................\n","[CV] ..... n_neighbors=6, weights=uniform, score=-0.118, total= 0.0s\n","[CV] n_neighbors=6, weights=uniform ..................................\n","[CV] ..... n_neighbors=6, weights=uniform, score=-0.115, total= 0.0s\n","[CV] n_neighbors=6, weights=uniform ..................................\n","[CV] ..... n_neighbors=6, weights=uniform, score=-0.097, total= 0.0s\n","[CV] n_neighbors=6, weights=uniform ..................................\n","[CV] ..... n_neighbors=6, weights=uniform, score=-0.148, total= 0.0s\n","[CV] n_neighbors=6, weights=uniform ..................................\n","[CV] ..... n_neighbors=6, weights=uniform, score=-0.054, total= 0.0s\n","[CV] n_neighbors=6, weights=uniform ..................................\n","[CV] ..... n_neighbors=6, weights=uniform, score=-0.122, total= 0.0s\n","[CV] n_neighbors=6, weights=uniform ..................................\n","[CV] ..... n_neighbors=6, weights=uniform, score=-0.164, total= 0.0s\n","[CV] n_neighbors=6, weights=uniform ..................................\n","[CV] ..... n_neighbors=6, weights=uniform, score=-0.278, total= 0.0s\n","[CV] n_neighbors=6, weights=uniform ..................................\n","[CV] ..... n_neighbors=6, weights=uniform, score=-0.208, total= 0.0s\n","[CV] n_neighbors=6, weights=uniform ..................................\n","[CV] ..... n_neighbors=6, weights=uniform, score=-0.212, total= 0.0s\n","[CV] n_neighbors=6, weights=uniform ..................................\n","[CV] ..... n_neighbors=6, weights=uniform, score=-0.094, total= 0.0s\n","[CV] n_neighbors=6, weights=uniform ..................................\n","[CV] ..... n_neighbors=6, weights=uniform, score=-0.220, total= 0.0s\n","[CV] n_neighbors=6, weights=uniform ..................................\n","[CV] ..... n_neighbors=6, weights=uniform, score=-0.106, total= 0.0s\n","[CV] n_neighbors=6, weights=uniform ..................................\n","[CV] ..... n_neighbors=6, weights=uniform, score=-0.155, total= 0.0s\n","[CV] n_neighbors=6, weights=uniform ..................................\n","[CV] ..... n_neighbors=6, weights=uniform, score=-0.083, total= 0.0s\n","[CV] n_neighbors=6, weights=uniform ..................................\n","[CV] ..... n_neighbors=6, weights=uniform, score=-0.122, total= 0.0s\n","[CV] n_neighbors=6, weights=uniform ..................................\n","[CV] ..... n_neighbors=6, weights=uniform, score=-0.047, total= 0.0s\n","[CV] n_neighbors=6, weights=uniform ..................................\n","[CV] ..... n_neighbors=6, weights=uniform, score=-0.175, total= 0.0s\n","[CV] n_neighbors=6, weights=uniform ..................................\n","[CV] ..... n_neighbors=6, weights=uniform, score=-0.105, total= 0.0s\n","[CV] n_neighbors=6, weights=uniform ..................................\n","[CV] ..... n_neighbors=6, weights=uniform, score=-0.213, total= 0.0s\n","[CV] n_neighbors=6, weights=uniform ..................................\n","[CV] ..... n_neighbors=6, weights=uniform, score=-0.183, total= 0.0s\n","[CV] n_neighbors=6, weights=uniform ..................................\n","[CV] ..... n_neighbors=6, weights=uniform, score=-0.137, total= 0.0s\n","[CV] n_neighbors=6, weights=distance .................................\n","[CV] .... n_neighbors=6, weights=distance, score=-0.197, total= 0.0s\n","[CV] n_neighbors=6, weights=distance .................................\n","[CV] .... n_neighbors=6, weights=distance, score=-0.220, total= 0.0s\n","[CV] n_neighbors=6, weights=distance .................................\n","[CV] .... n_neighbors=6, weights=distance, score=-0.160, total= 0.0s\n","[CV] n_neighbors=6, weights=distance .................................\n","[CV] .... n_neighbors=6, weights=distance, score=-0.311, total= 0.0s\n","[CV] n_neighbors=6, weights=distance .................................\n","[CV] .... n_neighbors=6, weights=distance, score=-0.138, total= 0.0s\n","[CV] n_neighbors=6, weights=distance .................................\n","[CV] .... n_neighbors=6, weights=distance, score=-0.113, total= 0.0s\n","[CV] n_neighbors=6, weights=distance .................................\n","[CV] .... n_neighbors=6, weights=distance, score=-0.222, total= 0.0s\n","[CV] n_neighbors=6, weights=distance .................................\n","[CV] .... n_neighbors=6, weights=distance, score=-0.215, total= 0.0s\n","[CV] n_neighbors=6, weights=distance .................................\n","[CV] .... n_neighbors=6, weights=distance, score=-0.114, total= 0.0s\n","[CV] n_neighbors=6, weights=distance .................................\n","[CV] .... n_neighbors=6, weights=distance, score=-0.131, total= 0.0s\n","[CV] n_neighbors=6, weights=distance .................................\n","[CV] .... n_neighbors=6, weights=distance, score=-0.130, total= 0.0s\n","[CV] n_neighbors=6, weights=distance .................................\n","[CV] .... n_neighbors=6, weights=distance, score=-0.157, total= 0.0s\n","[CV] n_neighbors=6, weights=distance .................................\n","[CV] .... n_neighbors=6, weights=distance, score=-0.033, total= 0.0s\n","[CV] n_neighbors=6, weights=distance .................................\n","[CV] .... n_neighbors=6, weights=distance, score=-0.165, total= 0.0s\n","[CV] n_neighbors=6, weights=distance .................................\n","[CV] .... n_neighbors=6, weights=distance, score=-0.190, total= 0.0s\n","[CV] n_neighbors=6, weights=distance .................................\n","[CV] .... n_neighbors=6, weights=distance, score=-0.273, total= 0.0s\n","[CV] n_neighbors=6, weights=distance .................................\n","[CV] .... n_neighbors=6, weights=distance, score=-0.231, total= 0.0s\n","[CV] n_neighbors=6, weights=distance .................................\n","[CV] .... n_neighbors=6, weights=distance, score=-0.223, total= 0.0s\n","[CV] n_neighbors=6, weights=distance .................................\n","[CV] .... n_neighbors=6, weights=distance, score=-0.116, total= 0.0s\n","[CV] n_neighbors=6, weights=distance .................................\n","[CV] .... n_neighbors=6, weights=distance, score=-0.243, total= 0.0s\n","[CV] n_neighbors=6, weights=distance .................................\n","[CV] .... n_neighbors=6, weights=distance, score=-0.144, total= 0.0s\n","[CV] n_neighbors=6, weights=distance .................................\n","[CV] .... n_neighbors=6, weights=distance, score=-0.163, total= 0.0s\n","[CV] n_neighbors=6, weights=distance .................................\n","[CV] .... n_neighbors=6, weights=distance, score=-0.102, total= 0.0s\n","[CV] n_neighbors=6, weights=distance .................................\n","[CV] .... n_neighbors=6, weights=distance, score=-0.147, total= 0.0s\n","[CV] n_neighbors=6, weights=distance .................................\n","[CV] .... n_neighbors=6, weights=distance, score=-0.053, total= 0.0s\n","[CV] n_neighbors=6, weights=distance .................................\n","[CV] .... n_neighbors=6, weights=distance, score=-0.191, total= 0.0s\n","[CV] n_neighbors=6, weights=distance .................................\n","[CV] .... n_neighbors=6, weights=distance, score=-0.124, total= 0.0s\n","[CV] n_neighbors=6, weights=distance .................................\n","[CV] .... n_neighbors=6, weights=distance, score=-0.266, total= 0.0s\n","[CV] n_neighbors=6, weights=distance .................................\n","[CV] .... n_neighbors=6, weights=distance, score=-0.205, total= 0.0s\n","[CV] n_neighbors=6, weights=distance .................................\n","[CV] .... n_neighbors=6, weights=distance, score=-0.170, total= 0.0s\n","[Parallel(n_jobs=1)]: Done 300 out of 300 | elapsed: 5.3s finished\n","KNeighborsRegressor(algorithm='auto', leaf_size=30, metric='minkowski',\n"," metric_params=None, n_jobs=None, n_neighbors=6, p=2,\n"," weights='uniform')\n","window: 1, stide: 1, rmse: 3.003493336470421\n","[1, 4, 9, 12, 2, 0, 3, 13, 11, 10, 6, 5, 7, 8]\n","[1, 4, 9, 12]\n","(3000, 60)\n","Number of Feature-Columns: 60\n","\n","Features Ready for undergoing selection tests done ...\n","\n","Fitting 30 folds for each of 10 candidates, totalling 300 fits\n","[Parallel(n_jobs=1)]: Using backend SequentialBackend with 1 concurrent workers.\n","[CV] n_neighbors=2, weights=uniform ..................................\n","[CV] ..... n_neighbors=2, weights=uniform, score=-0.467, total= 0.0s\n","[Parallel(n_jobs=1)]: Done 1 out of 1 | elapsed: 0.0s remaining: 0.0s\n","[CV] n_neighbors=2, weights=uniform ..................................\n","[CV] ..... n_neighbors=2, weights=uniform, score=-0.406, total= 0.0s\n","[Parallel(n_jobs=1)]: Done 2 out of 2 | elapsed: 0.1s remaining: 0.0s\n","[CV] n_neighbors=2, weights=uniform ..................................\n","[CV] ..... n_neighbors=2, weights=uniform, score=-0.526, total= 0.0s\n","[Parallel(n_jobs=1)]: Done 3 out of 3 | elapsed: 0.1s remaining: 0.0s\n","[CV] n_neighbors=2, weights=uniform ..................................\n","[CV] ..... n_neighbors=2, weights=uniform, score=-0.616, total= 0.0s\n","[Parallel(n_jobs=1)]: Done 4 out of 4 | elapsed: 0.1s remaining: 0.0s\n","[CV] n_neighbors=2, weights=uniform ..................................\n","[CV] ..... n_neighbors=2, weights=uniform, score=-0.488, total= 0.0s\n","[CV] n_neighbors=2, weights=uniform ..................................\n","[CV] ..... n_neighbors=2, weights=uniform, score=-0.520, total= 0.0s\n","[CV] n_neighbors=2, weights=uniform ..................................\n","[CV] ..... n_neighbors=2, weights=uniform, score=-0.581, total= 0.0s\n","[CV] n_neighbors=2, weights=uniform ..................................\n","[CV] ..... n_neighbors=2, weights=uniform, score=-0.515, total= 0.0s\n","[CV] n_neighbors=2, weights=uniform ..................................\n","[CV] ..... n_neighbors=2, weights=uniform, score=-0.560, total= 0.0s\n","[CV] n_neighbors=2, weights=uniform ..................................\n","[CV] ..... n_neighbors=2, weights=uniform, score=-0.511, total= 0.0s\n","[CV] n_neighbors=2, weights=uniform ..................................\n","[CV] ..... n_neighbors=2, weights=uniform, score=-0.564, total= 0.0s\n","[CV] n_neighbors=2, weights=uniform ..................................\n","[CV] ..... n_neighbors=2, weights=uniform, score=-0.541, total= 0.0s\n","[CV] n_neighbors=2, weights=uniform ..................................\n","[CV] ..... n_neighbors=2, weights=uniform, score=-0.409, total= 0.0s\n","[CV] n_neighbors=2, weights=uniform ..................................\n","[CV] ..... n_neighbors=2, weights=uniform, score=-0.424, total= 0.0s\n","[CV] n_neighbors=2, weights=uniform ..................................\n","[CV] ..... n_neighbors=2, weights=uniform, score=-0.679, total= 0.0s\n","[CV] n_neighbors=2, weights=uniform ..................................\n","[CV] ..... n_neighbors=2, weights=uniform, score=-0.703, total= 0.0s\n","[CV] n_neighbors=2, weights=uniform ..................................\n","[CV] ..... n_neighbors=2, weights=uniform, score=-0.567, total= 0.0s\n","[CV] n_neighbors=2, weights=uniform ..................................\n","[CV] ..... n_neighbors=2, weights=uniform, score=-0.472, total= 0.0s\n","[CV] n_neighbors=2, weights=uniform ..................................\n","[CV] ..... n_neighbors=2, weights=uniform, score=-0.543, total= 0.0s\n","[CV] n_neighbors=2, weights=uniform ..................................\n","[CV] ..... n_neighbors=2, weights=uniform, score=-0.503, total= 0.0s\n","[CV] n_neighbors=2, weights=uniform ..................................\n","[CV] ..... n_neighbors=2, weights=uniform, score=-0.397, total= 0.0s\n","[CV] n_neighbors=2, weights=uniform ..................................\n","[CV] ..... n_neighbors=2, weights=uniform, score=-0.450, total= 0.0s\n","[CV] n_neighbors=2, weights=uniform ..................................\n","[CV] ..... n_neighbors=2, weights=uniform, score=-0.443, total= 0.0s\n","[CV] n_neighbors=2, weights=uniform ..................................\n","[CV] ..... n_neighbors=2, weights=uniform, score=-0.543, total= 0.0s\n","[CV] n_neighbors=2, weights=uniform ..................................\n","[CV] ..... n_neighbors=2, weights=uniform, score=-0.484, total= 0.0s\n","[CV] n_neighbors=2, weights=uniform ..................................\n","[CV] ..... n_neighbors=2, weights=uniform, score=-0.528, total= 0.0s\n","[CV] n_neighbors=2, weights=uniform ..................................\n","[CV] ..... n_neighbors=2, weights=uniform, score=-0.448, total= 0.0s\n","[CV] n_neighbors=2, weights=uniform ..................................\n","[CV] ..... n_neighbors=2, weights=uniform, score=-0.482, total= 0.0s\n","[CV] n_neighbors=2, weights=uniform ..................................\n","[CV] ..... n_neighbors=2, weights=uniform, score=-0.620, total= 0.0s\n","[CV] n_neighbors=2, weights=uniform ..................................\n","[CV] ..... n_neighbors=2, weights=uniform, score=-0.451, total= 0.0s\n","[CV] n_neighbors=2, weights=distance .................................\n","[CV] .... n_neighbors=2, weights=distance, score=-0.499, total= 0.0s\n","[CV] n_neighbors=2, weights=distance .................................\n","[CV] .... n_neighbors=2, weights=distance, score=-0.405, total= 0.0s\n","[CV] n_neighbors=2, weights=distance .................................\n","[CV] .... n_neighbors=2, weights=distance, score=-0.554, total= 0.0s\n","[CV] n_neighbors=2, weights=distance .................................\n","[CV] .... n_neighbors=2, weights=distance, score=-0.624, total= 0.0s\n","[CV] n_neighbors=2, weights=distance .................................\n","[CV] .... n_neighbors=2, weights=distance, score=-0.509, total= 0.0s\n","[CV] n_neighbors=2, weights=distance .................................\n","[CV] .... n_neighbors=2, weights=distance, score=-0.556, total= 0.0s\n","[CV] n_neighbors=2, weights=distance .................................\n","[CV] .... n_neighbors=2, weights=distance, score=-0.585, total= 0.0s\n","[CV] n_neighbors=2, weights=distance .................................\n","[CV] .... n_neighbors=2, weights=distance, score=-0.546, total= 0.0s\n","[CV] n_neighbors=2, weights=distance .................................\n","[CV] .... n_neighbors=2, weights=distance, score=-0.594, total= 0.0s\n","[CV] n_neighbors=2, weights=distance .................................\n","[CV] .... n_neighbors=2, weights=distance, score=-0.531, total= 0.0s\n","[CV] n_neighbors=2, weights=distance .................................\n","[CV] .... n_neighbors=2, weights=distance, score=-0.588, total= 0.0s\n","[CV] n_neighbors=2, weights=distance .................................\n","[CV] .... n_neighbors=2, weights=distance, score=-0.570, total= 0.0s\n","[CV] n_neighbors=2, weights=distance .................................\n","[CV] .... n_neighbors=2, weights=distance, score=-0.431, total= 0.0s\n","[CV] n_neighbors=2, weights=distance .................................\n","[CV] .... n_neighbors=2, weights=distance, score=-0.484, total= 0.0s\n","[CV] n_neighbors=2, weights=distance .................................\n","[CV] .... n_neighbors=2, weights=distance, score=-0.708, total= 0.0s\n","[CV] n_neighbors=2, weights=distance .................................\n","[CV] .... n_neighbors=2, weights=distance, score=-0.717, total= 0.0s\n","[CV] n_neighbors=2, weights=distance .................................\n","[CV] .... n_neighbors=2, weights=distance, score=-0.586, total= 0.0s\n","[CV] n_neighbors=2, weights=distance .................................\n","[CV] .... n_neighbors=2, weights=distance, score=-0.483, total= 0.0s\n","[CV] n_neighbors=2, weights=distance .................................\n","[CV] .... n_neighbors=2, weights=distance, score=-0.552, total= 0.0s\n","[CV] n_neighbors=2, weights=distance .................................\n","[CV] .... n_neighbors=2, weights=distance, score=-0.534, total= 0.0s\n","[CV] n_neighbors=2, weights=distance .................................\n","[CV] .... n_neighbors=2, weights=distance, score=-0.424, total= 0.0s\n","[CV] n_neighbors=2, weights=distance .................................\n","[CV] .... n_neighbors=2, weights=distance, score=-0.468, total= 0.0s\n","[CV] n_neighbors=2, weights=distance .................................\n","[CV] .... n_neighbors=2, weights=distance, score=-0.470, total= 0.0s\n","[CV] n_neighbors=2, weights=distance .................................\n","[CV] .... n_neighbors=2, weights=distance, score=-0.546, total= 0.0s\n","[CV] n_neighbors=2, weights=distance .................................\n","[CV] .... n_neighbors=2, weights=distance, score=-0.517, total= 0.0s\n","[CV] n_neighbors=2, weights=distance .................................\n","[CV] .... n_neighbors=2, weights=distance, score=-0.540, total= 0.0s\n","[CV] n_neighbors=2, weights=distance .................................\n","[CV] .... n_neighbors=2, weights=distance, score=-0.442, total= 0.0s\n","[CV] n_neighbors=2, weights=distance .................................\n","[CV] .... n_neighbors=2, weights=distance, score=-0.525, total= 0.0s\n","[CV] n_neighbors=2, weights=distance .................................\n","[CV] .... n_neighbors=2, weights=distance, score=-0.645, total= 0.0s\n","[CV] n_neighbors=2, weights=distance .................................\n","[CV] .... n_neighbors=2, weights=distance, score=-0.469, total= 0.0s\n","[CV] n_neighbors=3, weights=uniform ..................................\n","[CV] ..... n_neighbors=3, weights=uniform, score=-0.281, total= 0.0s\n","[CV] n_neighbors=3, weights=uniform ..................................\n","[CV] ..... n_neighbors=3, weights=uniform, score=-0.247, total= 0.0s\n","[CV] n_neighbors=3, weights=uniform ..................................\n","[CV] ..... n_neighbors=3, weights=uniform, score=-0.329, total= 0.0s\n","[CV] n_neighbors=3, weights=uniform ..................................\n","[CV] ..... n_neighbors=3, weights=uniform, score=-0.368, total= 0.0s\n","[CV] n_neighbors=3, weights=uniform ..................................\n","[CV] ..... n_neighbors=3, weights=uniform, score=-0.288, total= 0.0s\n","[CV] n_neighbors=3, weights=uniform ..................................\n","[CV] ..... n_neighbors=3, weights=uniform, score=-0.280, total= 0.0s\n","[CV] n_neighbors=3, weights=uniform ..................................\n","[CV] ..... n_neighbors=3, weights=uniform, score=-0.296, total= 0.0s\n","[CV] n_neighbors=3, weights=uniform ..................................\n","[CV] ..... n_neighbors=3, weights=uniform, score=-0.392, total= 0.0s\n","[CV] n_neighbors=3, weights=uniform ..................................\n","[CV] ..... n_neighbors=3, weights=uniform, score=-0.279, total= 0.0s\n","[CV] n_neighbors=3, weights=uniform ..................................\n","[CV] ..... n_neighbors=3, weights=uniform, score=-0.351, total= 0.0s\n","[CV] n_neighbors=3, weights=uniform ..................................\n","[CV] ..... n_neighbors=3, weights=uniform, score=-0.213, total= 0.0s\n","[CV] n_neighbors=3, weights=uniform ..................................\n","[CV] ..... n_neighbors=3, weights=uniform, score=-0.308, total= 0.0s\n","[CV] n_neighbors=3, weights=uniform ..................................\n","[CV] ..... n_neighbors=3, weights=uniform, score=-0.244, total= 0.0s\n","[CV] n_neighbors=3, weights=uniform ..................................\n","[CV] ..... n_neighbors=3, weights=uniform, score=-0.296, total= 0.0s\n","[CV] n_neighbors=3, weights=uniform ..................................\n","[CV] ..... n_neighbors=3, weights=uniform, score=-0.455, total= 0.0s\n","[CV] n_neighbors=3, weights=uniform ..................................\n","[CV] ..... n_neighbors=3, weights=uniform, score=-0.374, total= 0.0s\n","[CV] n_neighbors=3, weights=uniform ..................................\n","[CV] ..... n_neighbors=3, weights=uniform, score=-0.345, total= 0.0s\n","[CV] n_neighbors=3, weights=uniform ..................................\n","[CV] ..... n_neighbors=3, weights=uniform, score=-0.383, total= 0.0s\n","[CV] n_neighbors=3, weights=uniform ..................................\n","[CV] ..... n_neighbors=3, weights=uniform, score=-0.308, total= 0.0s\n","[CV] n_neighbors=3, weights=uniform ..................................\n","[CV] ..... n_neighbors=3, weights=uniform, score=-0.196, total= 0.0s\n","[CV] n_neighbors=3, weights=uniform ..................................\n","[CV] ..... n_neighbors=3, weights=uniform, score=-0.214, total= 0.0s\n","[CV] n_neighbors=3, weights=uniform ..................................\n","[CV] ..... n_neighbors=3, weights=uniform, score=-0.303, total= 0.0s\n","[CV] n_neighbors=3, weights=uniform ..................................\n","[CV] ..... n_neighbors=3, weights=uniform, score=-0.249, total= 0.0s\n","[CV] n_neighbors=3, weights=uniform ..................................\n","[CV] ..... n_neighbors=3, weights=uniform, score=-0.350, total= 0.0s\n","[CV] n_neighbors=3, weights=uniform ..................................\n","[CV] ..... n_neighbors=3, weights=uniform, score=-0.328, total= 0.0s\n","[CV] n_neighbors=3, weights=uniform ..................................\n","[CV] ..... n_neighbors=3, weights=uniform, score=-0.365, total= 0.0s\n","[CV] n_neighbors=3, weights=uniform ..................................\n","[CV] ..... n_neighbors=3, weights=uniform, score=-0.196, total= 0.0s\n","[CV] n_neighbors=3, weights=uniform ..................................\n","[CV] ..... n_neighbors=3, weights=uniform, score=-0.271, total= 0.0s\n","[CV] n_neighbors=3, weights=uniform ..................................\n","[CV] ..... n_neighbors=3, weights=uniform, score=-0.436, total= 0.0s\n","[CV] n_neighbors=3, weights=uniform ..................................\n","[CV] ..... n_neighbors=3, weights=uniform, score=-0.287, total= 0.0s\n","[CV] n_neighbors=3, weights=distance .................................\n","[CV] .... n_neighbors=3, weights=distance, score=-0.313, total= 0.0s\n","[CV] n_neighbors=3, weights=distance .................................\n","[CV] .... n_neighbors=3, weights=distance, score=-0.263, total= 0.0s\n","[CV] n_neighbors=3, weights=distance .................................\n","[CV] .... n_neighbors=3, weights=distance, score=-0.368, total= 0.0s\n","[CV] n_neighbors=3, weights=distance .................................\n","[CV] .... n_neighbors=3, weights=distance, score=-0.393, total= 0.0s\n","[CV] n_neighbors=3, weights=distance .................................\n","[CV] .... n_neighbors=3, weights=distance, score=-0.312, total= 0.0s\n","[CV] n_neighbors=3, weights=distance .................................\n","[CV] .... n_neighbors=3, weights=distance, score=-0.310, total= 0.0s\n","[CV] n_neighbors=3, weights=distance .................................\n","[CV] .... n_neighbors=3, weights=distance, score=-0.320, total= 0.0s\n","[CV] n_neighbors=3, weights=distance .................................\n","[CV] .... n_neighbors=3, weights=distance, score=-0.416, total= 0.0s\n","[CV] n_neighbors=3, weights=distance .................................\n","[CV] .... n_neighbors=3, weights=distance, score=-0.317, total= 0.0s\n","[CV] n_neighbors=3, weights=distance .................................\n","[CV] .... n_neighbors=3, weights=distance, score=-0.372, total= 0.0s\n","[CV] n_neighbors=3, weights=distance .................................\n","[CV] .... n_neighbors=3, weights=distance, score=-0.268, total= 0.0s\n","[CV] n_neighbors=3, weights=distance .................................\n","[CV] .... n_neighbors=3, weights=distance, score=-0.347, total= 0.0s\n","[CV] n_neighbors=3, weights=distance .................................\n","[CV] .... n_neighbors=3, weights=distance, score=-0.276, total= 0.0s\n","[CV] n_neighbors=3, weights=distance .................................\n","[CV] .... n_neighbors=3, weights=distance, score=-0.345, total= 0.0s\n","[CV] n_neighbors=3, weights=distance .................................\n","[CV] .... n_neighbors=3, weights=distance, score=-0.481, total= 0.0s\n","[CV] n_neighbors=3, weights=distance .................................\n","[CV] .... n_neighbors=3, weights=distance, score=-0.409, total= 0.0s\n","[CV] n_neighbors=3, weights=distance .................................\n","[CV] .... n_neighbors=3, weights=distance, score=-0.372, total= 0.0s\n","[CV] n_neighbors=3, weights=distance .................................\n","[CV] .... n_neighbors=3, weights=distance, score=-0.386, total= 0.0s\n","[CV] n_neighbors=3, weights=distance .................................\n","[CV] .... n_neighbors=3, weights=distance, score=-0.328, total= 0.0s\n","[CV] n_neighbors=3, weights=distance .................................\n","[CV] .... n_neighbors=3, weights=distance, score=-0.247, total= 0.0s\n","[CV] n_neighbors=3, weights=distance .................................\n","[CV] .... n_neighbors=3, weights=distance, score=-0.241, total= 0.0s\n","[CV] n_neighbors=3, weights=distance .................................\n","[CV] .... n_neighbors=3, weights=distance, score=-0.318, total= 0.0s\n","[CV] n_neighbors=3, weights=distance .................................\n","[CV] .... n_neighbors=3, weights=distance, score=-0.273, total= 0.0s\n","[CV] n_neighbors=3, weights=distance .................................\n","[CV] .... n_neighbors=3, weights=distance, score=-0.362, total= 0.0s\n","[CV] n_neighbors=3, weights=distance .................................\n","[CV] .... n_neighbors=3, weights=distance, score=-0.372, total= 0.0s\n","[CV] n_neighbors=3, weights=distance .................................\n","[CV] .... n_neighbors=3, weights=distance, score=-0.388, total= 0.0s\n","[CV] n_neighbors=3, weights=distance .................................\n","[CV] .... n_neighbors=3, weights=distance, score=-0.216, total= 0.0s\n","[CV] n_neighbors=3, weights=distance .................................\n","[CV] .... n_neighbors=3, weights=distance, score=-0.317, total= 0.0s\n","[CV] n_neighbors=3, weights=distance .................................\n","[CV] .... n_neighbors=3, weights=distance, score=-0.467, total= 0.0s\n","[CV] n_neighbors=3, weights=distance .................................\n","[CV] .... n_neighbors=3, weights=distance, score=-0.314, total= 0.0s\n","[CV] n_neighbors=4, weights=uniform ..................................\n","[CV] ..... n_neighbors=4, weights=uniform, score=-0.165, total= 0.0s\n","[CV] n_neighbors=4, weights=uniform ..................................\n","[CV] ..... n_neighbors=4, weights=uniform, score=-0.117, total= 0.0s\n","[CV] n_neighbors=4, weights=uniform ..................................\n","[CV] ..... n_neighbors=4, weights=uniform, score=-0.312, total= 0.0s\n","[CV] n_neighbors=4, weights=uniform ..................................\n","[CV] ..... n_neighbors=4, weights=uniform, score=-0.316, total= 0.0s\n","[CV] n_neighbors=4, weights=uniform ..................................\n","[CV] ..... n_neighbors=4, weights=uniform, score=-0.185, total= 0.0s\n","[CV] n_neighbors=4, weights=uniform ..................................\n","[CV] ..... n_neighbors=4, weights=uniform, score=-0.262, total= 0.0s\n","[CV] n_neighbors=4, weights=uniform ..................................\n","[CV] ..... n_neighbors=4, weights=uniform, score=-0.219, total= 0.0s\n","[CV] n_neighbors=4, weights=uniform ..................................\n","[CV] ..... n_neighbors=4, weights=uniform, score=-0.207, total= 0.0s\n","[CV] n_neighbors=4, weights=uniform ..................................\n","[CV] ..... n_neighbors=4, weights=uniform, score=-0.232, total= 0.0s\n","[CV] n_neighbors=4, weights=uniform ..................................\n","[CV] ..... n_neighbors=4, weights=uniform, score=-0.247, total= 0.0s\n","[CV] n_neighbors=4, weights=uniform ..................................\n","[CV] ..... n_neighbors=4, weights=uniform, score=-0.121, total= 0.0s\n","[CV] n_neighbors=4, weights=uniform ..................................\n","[CV] ..... n_neighbors=4, weights=uniform, score=-0.249, total= 0.0s\n","[CV] n_neighbors=4, weights=uniform ..................................\n","[CV] ..... n_neighbors=4, weights=uniform, score=-0.162, total= 0.0s\n","[CV] n_neighbors=4, weights=uniform ..................................\n","[CV] ..... n_neighbors=4, weights=uniform, score=-0.183, total= 0.0s\n","[CV] n_neighbors=4, weights=uniform ..................................\n","[CV] ..... n_neighbors=4, weights=uniform, score=-0.292, total= 0.0s\n","[CV] n_neighbors=4, weights=uniform ..................................\n","[CV] ..... n_neighbors=4, weights=uniform, score=-0.268, total= 0.0s\n","[CV] n_neighbors=4, weights=uniform ..................................\n","[CV] ..... n_neighbors=4, weights=uniform, score=-0.236, total= 0.0s\n","[CV] n_neighbors=4, weights=uniform ..................................\n","[CV] ..... n_neighbors=4, weights=uniform, score=-0.284, total= 0.0s\n","[CV] n_neighbors=4, weights=uniform ..................................\n","[CV] ..... n_neighbors=4, weights=uniform, score=-0.221, total= 0.0s\n","[CV] n_neighbors=4, weights=uniform ..................................\n","[CV] ..... n_neighbors=4, weights=uniform, score=-0.164, total= 0.0s\n","[CV] n_neighbors=4, weights=uniform ..................................\n","[CV] ..... n_neighbors=4, weights=uniform, score=-0.194, total= 0.0s\n","[CV] n_neighbors=4, weights=uniform ..................................\n","[CV] ..... n_neighbors=4, weights=uniform, score=-0.274, total= 0.0s\n","[CV] n_neighbors=4, weights=uniform ..................................\n","[CV] ..... n_neighbors=4, weights=uniform, score=-0.201, total= 0.0s\n","[CV] n_neighbors=4, weights=uniform ..................................\n","[CV] ..... n_neighbors=4, weights=uniform, score=-0.163, total= 0.0s\n","[CV] n_neighbors=4, weights=uniform 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0.0s\n","[CV] n_neighbors=4, weights=distance .................................\n","[CV] .... n_neighbors=4, weights=distance, score=-0.154, total= 0.0s\n","[CV] n_neighbors=4, weights=distance .................................\n","[CV] .... n_neighbors=4, weights=distance, score=-0.346, total= 0.0s\n","[CV] n_neighbors=4, weights=distance .................................\n","[CV] .... n_neighbors=4, weights=distance, score=-0.333, total= 0.0s\n","[CV] n_neighbors=4, weights=distance .................................\n","[CV] .... n_neighbors=4, weights=distance, score=-0.220, total= 0.0s\n","[CV] n_neighbors=4, weights=distance .................................\n","[CV] .... n_neighbors=4, weights=distance, score=-0.279, total= 0.0s\n","[CV] n_neighbors=4, weights=distance .................................\n","[CV] .... n_neighbors=4, weights=distance, score=-0.249, total= 0.0s\n","[CV] n_neighbors=4, weights=distance .................................\n","[CV] .... n_neighbors=4, weights=distance, score=-0.243, total= 0.0s\n","[CV] n_neighbors=4, weights=distance .................................\n","[CV] .... n_neighbors=4, weights=distance, score=-0.264, total= 0.0s\n","[CV] n_neighbors=4, weights=distance .................................\n","[CV] .... n_neighbors=4, weights=distance, score=-0.264, total= 0.0s\n","[CV] n_neighbors=4, weights=distance .................................\n","[CV] .... n_neighbors=4, weights=distance, score=-0.164, total= 0.0s\n","[CV] n_neighbors=4, weights=distance .................................\n","[CV] .... n_neighbors=4, weights=distance, score=-0.284, total= 0.0s\n","[CV] n_neighbors=4, weights=distance .................................\n","[CV] .... n_neighbors=4, weights=distance, score=-0.189, total= 0.0s\n","[CV] n_neighbors=4, weights=distance .................................\n","[CV] .... n_neighbors=4, weights=distance, score=-0.230, total= 0.0s\n","[CV] n_neighbors=4, weights=distance .................................\n","[CV] .... n_neighbors=4, weights=distance, score=-0.331, total= 0.0s\n","[CV] n_neighbors=4, weights=distance .................................\n","[CV] .... n_neighbors=4, weights=distance, score=-0.303, total= 0.0s\n","[CV] n_neighbors=4, weights=distance .................................\n","[CV] .... n_neighbors=4, weights=distance, score=-0.263, total= 0.0s\n","[CV] n_neighbors=4, weights=distance .................................\n","[CV] .... n_neighbors=4, weights=distance, score=-0.293, total= 0.0s\n","[CV] n_neighbors=4, weights=distance .................................\n","[CV] .... n_neighbors=4, weights=distance, score=-0.241, total= 0.0s\n","[CV] n_neighbors=4, weights=distance .................................\n","[CV] .... n_neighbors=4, weights=distance, score=-0.206, total= 0.0s\n","[CV] n_neighbors=4, weights=distance .................................\n","[CV] .... n_neighbors=4, weights=distance, score=-0.214, total= 0.0s\n","[CV] n_neighbors=4, weights=distance .................................\n","[CV] .... n_neighbors=4, weights=distance, score=-0.284, total= 0.0s\n","[CV] n_neighbors=4, weights=distance .................................\n","[CV] .... n_neighbors=4, weights=distance, score=-0.223, total= 0.0s\n","[CV] n_neighbors=4, weights=distance .................................\n","[CV] .... n_neighbors=4, weights=distance, score=-0.195, total= 0.0s\n","[CV] n_neighbors=4, weights=distance .................................\n","[CV] .... n_neighbors=4, weights=distance, score=-0.275, total= 0.0s\n","[CV] n_neighbors=4, weights=distance .................................\n","[CV] .... n_neighbors=4, weights=distance, score=-0.315, total= 0.0s\n","[CV] n_neighbors=4, weights=distance .................................\n","[CV] .... n_neighbors=4, weights=distance, score=-0.161, total= 0.0s\n","[CV] n_neighbors=4, weights=distance .................................\n","[CV] .... n_neighbors=4, weights=distance, score=-0.215, total= 0.0s\n","[CV] n_neighbors=4, weights=distance .................................\n","[CV] .... n_neighbors=4, weights=distance, score=-0.329, total= 0.0s\n","[CV] n_neighbors=4, weights=distance .................................\n","[CV] .... n_neighbors=4, weights=distance, score=-0.141, total= 0.0s\n","[CV] n_neighbors=5, weights=uniform ..................................\n","[CV] ..... n_neighbors=5, weights=uniform, score=-0.192, total= 0.0s\n","[CV] n_neighbors=5, weights=uniform ..................................\n","[CV] ..... n_neighbors=5, weights=uniform, score=-0.114, total= 0.0s\n","[CV] n_neighbors=5, weights=uniform ..................................\n","[CV] ..... n_neighbors=5, weights=uniform, score=-0.222, total= 0.0s\n","[CV] n_neighbors=5, weights=uniform ..................................\n","[CV] ..... n_neighbors=5, weights=uniform, score=-0.251, total= 0.0s\n","[CV] n_neighbors=5, weights=uniform 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0.0s\n","[CV] n_neighbors=5, weights=uniform ..................................\n","[CV] ..... n_neighbors=5, weights=uniform, score=-0.206, total= 0.0s\n","[CV] n_neighbors=5, weights=uniform ..................................\n","[CV] ..... n_neighbors=5, weights=uniform, score=-0.089, total= 0.0s\n","[CV] n_neighbors=5, weights=uniform ..................................\n","[CV] ..... n_neighbors=5, weights=uniform, score=-0.117, total= 0.0s\n","[CV] n_neighbors=5, weights=uniform ..................................\n","[CV] ..... n_neighbors=5, weights=uniform, score=-0.216, total= 0.0s\n","[CV] n_neighbors=5, weights=uniform ..................................\n","[CV] ..... n_neighbors=5, weights=uniform, score=-0.240, total= 0.0s\n","[CV] n_neighbors=5, weights=uniform ..................................\n","[CV] ..... n_neighbors=5, weights=uniform, score=-0.201, total= 0.0s\n","[CV] n_neighbors=5, weights=uniform ..................................\n","[CV] ..... n_neighbors=5, weights=uniform, score=-0.225, total= 0.0s\n","[CV] n_neighbors=5, weights=uniform ..................................\n","[CV] ..... n_neighbors=5, weights=uniform, score=-0.174, total= 0.0s\n","[CV] n_neighbors=5, weights=uniform ..................................\n","[CV] ..... n_neighbors=5, weights=uniform, score=-0.095, total= 0.0s\n","[CV] n_neighbors=5, weights=uniform ..................................\n","[CV] ..... n_neighbors=5, weights=uniform, score=-0.150, total= 0.0s\n","[CV] n_neighbors=5, weights=uniform ..................................\n","[CV] ..... n_neighbors=5, weights=uniform, score=-0.204, total= 0.0s\n","[CV] n_neighbors=5, weights=uniform ..................................\n","[CV] ..... n_neighbors=5, weights=uniform, score=-0.125, total= 0.0s\n","[CV] n_neighbors=5, weights=uniform ..................................\n","[CV] ..... n_neighbors=5, weights=uniform, score=-0.155, total= 0.0s\n","[CV] n_neighbors=5, weights=uniform 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0.0s\n","[CV] n_neighbors=5, weights=distance .................................\n","[CV] .... n_neighbors=5, weights=distance, score=-0.139, total= 0.0s\n","[CV] n_neighbors=5, weights=distance .................................\n","[CV] .... n_neighbors=5, weights=distance, score=-0.258, total= 0.0s\n","[CV] n_neighbors=5, weights=distance .................................\n","[CV] .... n_neighbors=5, weights=distance, score=-0.274, total= 0.0s\n","[CV] n_neighbors=5, weights=distance .................................\n","[CV] .... n_neighbors=5, weights=distance, score=-0.178, total= 0.0s\n","[CV] n_neighbors=5, weights=distance .................................\n","[CV] .... n_neighbors=5, weights=distance, score=-0.201, total= 0.0s\n","[CV] n_neighbors=5, weights=distance .................................\n","[CV] .... n_neighbors=5, weights=distance, score=-0.217, total= 0.0s\n","[CV] n_neighbors=5, weights=distance .................................\n","[CV] .... n_neighbors=5, weights=distance, score=-0.225, total= 0.0s\n","[CV] n_neighbors=5, weights=distance .................................\n","[CV] .... n_neighbors=5, weights=distance, score=-0.227, total= 0.0s\n","[CV] n_neighbors=5, weights=distance .................................\n","[CV] .... n_neighbors=5, weights=distance, score=-0.228, total= 0.0s\n","[CV] n_neighbors=5, weights=distance .................................\n","[CV] .... n_neighbors=5, weights=distance, score=-0.159, total= 0.0s\n","[CV] n_neighbors=5, weights=distance .................................\n","[CV] .... n_neighbors=5, weights=distance, score=-0.238, total= 0.0s\n","[CV] n_neighbors=5, weights=distance .................................\n","[CV] .... n_neighbors=5, weights=distance, score=-0.117, total= 0.0s\n","[CV] n_neighbors=5, weights=distance .................................\n","[CV] .... n_neighbors=5, weights=distance, score=-0.163, total= 0.0s\n","[CV] n_neighbors=5, weights=distance 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0.0s\n","[CV] n_neighbors=5, weights=distance .................................\n","[CV] .... n_neighbors=5, weights=distance, score=-0.218, total= 0.0s\n","[CV] n_neighbors=5, weights=distance .................................\n","[CV] .... n_neighbors=5, weights=distance, score=-0.151, total= 0.0s\n","[CV] n_neighbors=5, weights=distance .................................\n","[CV] .... n_neighbors=5, weights=distance, score=-0.171, total= 0.0s\n","[CV] n_neighbors=5, weights=distance .................................\n","[CV] .... n_neighbors=5, weights=distance, score=-0.207, total= 0.0s\n","[CV] n_neighbors=5, weights=distance .................................\n","[CV] .... n_neighbors=5, weights=distance, score=-0.227, total= 0.0s\n","[CV] n_neighbors=5, weights=distance .................................\n","[CV] .... n_neighbors=5, weights=distance, score=-0.098, total= 0.0s\n","[CV] n_neighbors=5, weights=distance .................................\n","[CV] .... n_neighbors=5, weights=distance, score=-0.196, total= 0.0s\n","[CV] n_neighbors=5, weights=distance .................................\n","[CV] .... n_neighbors=5, weights=distance, score=-0.298, total= 0.0s\n","[CV] n_neighbors=5, weights=distance .................................\n","[CV] .... n_neighbors=5, weights=distance, score=-0.088, total= 0.0s\n","[CV] n_neighbors=6, weights=uniform ..................................\n","[CV] ..... n_neighbors=6, weights=uniform, score=-0.156, total= 0.0s\n","[CV] n_neighbors=6, weights=uniform ..................................\n","[CV] ..... n_neighbors=6, weights=uniform, score=-0.082, total= 0.0s\n","[CV] n_neighbors=6, weights=uniform ..................................\n","[CV] ..... n_neighbors=6, weights=uniform, score=-0.169, total= 0.0s\n","[CV] n_neighbors=6, weights=uniform ..................................\n","[CV] ..... n_neighbors=6, weights=uniform, score=-0.202, total= 0.0s\n","[CV] n_neighbors=6, weights=uniform 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0.0s\n","[CV] n_neighbors=6, weights=uniform ..................................\n","[CV] ..... n_neighbors=6, weights=uniform, score=-0.151, total= 0.0s\n","[CV] n_neighbors=6, weights=uniform ..................................\n","[CV] ..... n_neighbors=6, weights=uniform, score=-0.082, total= 0.0s\n","[CV] n_neighbors=6, weights=uniform ..................................\n","[CV] ..... n_neighbors=6, weights=uniform, score=-0.135, total= 0.0s\n","[CV] n_neighbors=6, weights=uniform ..................................\n","[CV] ..... n_neighbors=6, weights=uniform, score=-0.172, total= 0.0s\n","[CV] n_neighbors=6, weights=uniform ..................................\n","[CV] ..... n_neighbors=6, weights=uniform, score=-0.199, total= 0.0s\n","[CV] n_neighbors=6, weights=uniform ..................................\n","[CV] ..... n_neighbors=6, weights=uniform, score=-0.200, total= 0.0s\n","[CV] n_neighbors=6, weights=uniform ..................................\n","[CV] ..... n_neighbors=6, weights=uniform, score=-0.174, total= 0.0s\n","[CV] n_neighbors=6, weights=uniform ..................................\n","[CV] ..... n_neighbors=6, weights=uniform, score=-0.143, total= 0.0s\n","[CV] n_neighbors=6, weights=uniform ..................................\n","[CV] ..... n_neighbors=6, weights=uniform, score=-0.096, total= 0.0s\n","[CV] n_neighbors=6, weights=uniform ..................................\n","[CV] ..... n_neighbors=6, weights=uniform, score=-0.120, total= 0.0s\n","[CV] n_neighbors=6, weights=uniform ..................................\n","[CV] ..... n_neighbors=6, weights=uniform, score=-0.157, total= 0.0s\n","[CV] n_neighbors=6, weights=uniform ..................................\n","[CV] ..... n_neighbors=6, weights=uniform, score=-0.094, total= 0.0s\n","[CV] n_neighbors=6, weights=uniform ..................................\n","[CV] ..... n_neighbors=6, weights=uniform, score=-0.146, total= 0.0s\n","[CV] n_neighbors=6, weights=uniform ..................................\n","[CV] ..... n_neighbors=6, weights=uniform, score=-0.113, total= 0.0s\n","[CV] n_neighbors=6, weights=uniform ..................................\n","[CV] ..... n_neighbors=6, weights=uniform, score=-0.193, total= 0.0s\n","[CV] n_neighbors=6, weights=uniform ..................................\n","[CV] ..... n_neighbors=6, weights=uniform, score=-0.084, total= 0.0s\n","[CV] n_neighbors=6, weights=uniform ..................................\n","[CV] ..... n_neighbors=6, weights=uniform, score=-0.132, total= 0.0s\n","[CV] n_neighbors=6, weights=uniform ..................................\n","[CV] ..... n_neighbors=6, weights=uniform, score=-0.278, total= 0.0s\n","[CV] n_neighbors=6, weights=uniform ..................................\n","[CV] ..... n_neighbors=6, weights=uniform, score=-0.033, total= 0.0s\n","[CV] n_neighbors=6, weights=distance .................................\n","[CV] .... n_neighbors=6, weights=distance, score=-0.174, total= 0.0s\n","[CV] n_neighbors=6, weights=distance .................................\n","[CV] .... n_neighbors=6, weights=distance, score=-0.101, total= 0.0s\n","[CV] n_neighbors=6, weights=distance .................................\n","[CV] .... n_neighbors=6, weights=distance, score=-0.204, total= 0.0s\n","[CV] n_neighbors=6, weights=distance .................................\n","[CV] .... n_neighbors=6, weights=distance, score=-0.225, total= 0.0s\n","[CV] n_neighbors=6, weights=distance .................................\n","[CV] .... n_neighbors=6, weights=distance, score=-0.159, total= 0.0s\n","[CV] n_neighbors=6, weights=distance .................................\n","[CV] .... n_neighbors=6, weights=distance, score=-0.162, total= 0.0s\n","[CV] n_neighbors=6, weights=distance .................................\n","[CV] .... n_neighbors=6, weights=distance, score=-0.198, total= 0.0s\n","[CV] n_neighbors=6, weights=distance .................................\n","[CV] .... n_neighbors=6, weights=distance, score=-0.181, total= 0.0s\n","[CV] n_neighbors=6, weights=distance .................................\n","[CV] .... n_neighbors=6, weights=distance, score=-0.187, total= 0.0s\n","[CV] n_neighbors=6, weights=distance .................................\n","[CV] .... n_neighbors=6, weights=distance, score=-0.161, total= 0.0s\n","[CV] n_neighbors=6, weights=distance .................................\n","[CV] .... n_neighbors=6, weights=distance, score=-0.151, total= 0.0s\n","[CV] n_neighbors=6, weights=distance .................................\n","[CV] .... n_neighbors=6, weights=distance, score=-0.184, total= 0.0s\n","[CV] n_neighbors=6, weights=distance .................................\n","[CV] .... n_neighbors=6, weights=distance, score=-0.100, total= 0.0s\n","[CV] n_neighbors=6, weights=distance .................................\n","[CV] .... n_neighbors=6, weights=distance, score=-0.171, total= 0.0s\n","[CV] n_neighbors=6, weights=distance .................................\n","[CV] .... n_neighbors=6, weights=distance, score=-0.210, total= 0.0s\n","[CV] n_neighbors=6, weights=distance .................................\n","[CV] .... n_neighbors=6, weights=distance, score=-0.227, total= 0.0s\n","[CV] n_neighbors=6, weights=distance .................................\n","[CV] .... n_neighbors=6, weights=distance, score=-0.216, total= 0.0s\n","[CV] n_neighbors=6, weights=distance .................................\n","[CV] .... n_neighbors=6, weights=distance, score=-0.198, total= 0.0s\n","[CV] n_neighbors=6, weights=distance .................................\n","[CV] .... n_neighbors=6, weights=distance, score=-0.153, total= 0.0s\n","[CV] n_neighbors=6, weights=distance .................................\n","[CV] .... n_neighbors=6, weights=distance, score=-0.121, total= 0.0s\n","[CV] n_neighbors=6, weights=distance .................................\n","[CV] .... n_neighbors=6, weights=distance, score=-0.134, total= 0.0s\n","[CV] n_neighbors=6, weights=distance .................................\n","[CV] .... n_neighbors=6, weights=distance, score=-0.173, total= 0.0s\n","[CV] n_neighbors=6, weights=distance .................................\n","[CV] .... n_neighbors=6, weights=distance, score=-0.119, total= 0.0s\n","[CV] n_neighbors=6, weights=distance .................................\n","[CV] .... n_neighbors=6, weights=distance, score=-0.156, total= 0.0s\n","[CV] n_neighbors=6, weights=distance .................................\n","[CV] .... n_neighbors=6, weights=distance, score=-0.154, total= 0.0s\n","[CV] n_neighbors=6, weights=distance .................................\n","[CV] .... n_neighbors=6, weights=distance, score=-0.207, total= 0.0s\n","[CV] n_neighbors=6, weights=distance .................................\n","[CV] .... n_neighbors=6, weights=distance, score=-0.091, total= 0.0s\n","[CV] n_neighbors=6, weights=distance .................................\n","[CV] .... n_neighbors=6, weights=distance, score=-0.159, total= 0.0s\n","[CV] n_neighbors=6, weights=distance .................................\n","[CV] .... n_neighbors=6, weights=distance, score=-0.300, total= 0.0s\n","[CV] n_neighbors=6, weights=distance .................................\n","[CV] .... n_neighbors=6, weights=distance, score=-0.064, total= 0.0s\n","[Parallel(n_jobs=1)]: Done 300 out of 300 | elapsed: 7.8s finished\n","KNeighborsRegressor(algorithm='auto', leaf_size=30, metric='minkowski',\n"," metric_params=None, n_jobs=None, n_neighbors=6, p=2,\n"," weights='uniform')\n","window: 1, stide: 1, rmse: 3.0015042525003657\n","[1, 4, 9, 12, 2, 0, 3, 13, 11, 10, 6, 5, 7, 8]\n","[1, 4, 9, 12, 2]\n","(3000, 75)\n","Number of Feature-Columns: 75\n","\n","Features Ready for undergoing selection tests done ...\n","\n","Fitting 30 folds for each of 10 candidates, totalling 300 fits\n","[Parallel(n_jobs=1)]: Using backend SequentialBackend with 1 concurrent workers.\n","[CV] n_neighbors=2, weights=uniform ..................................\n","[CV] ..... n_neighbors=2, weights=uniform, score=-0.610, total= 0.0s\n","[Parallel(n_jobs=1)]: Done 1 out of 1 | elapsed: 0.0s remaining: 0.0s\n","[CV] n_neighbors=2, weights=uniform ..................................\n","[CV] ..... n_neighbors=2, weights=uniform, score=-0.562, total= 0.0s\n","[Parallel(n_jobs=1)]: Done 2 out of 2 | elapsed: 0.1s remaining: 0.0s\n","[CV] n_neighbors=2, weights=uniform ..................................\n","[CV] ..... n_neighbors=2, weights=uniform, score=-0.564, total= 0.0s\n","[Parallel(n_jobs=1)]: Done 3 out of 3 | elapsed: 0.1s remaining: 0.0s\n","[CV] n_neighbors=2, weights=uniform ..................................\n","[CV] ..... n_neighbors=2, weights=uniform, score=-0.584, total= 0.0s\n","[Parallel(n_jobs=1)]: Done 4 out of 4 | elapsed: 0.1s remaining: 0.0s\n","[CV] n_neighbors=2, weights=uniform ..................................\n","[CV] ..... n_neighbors=2, weights=uniform, score=-0.502, total= 0.0s\n","[CV] n_neighbors=2, weights=uniform ..................................\n","[CV] ..... n_neighbors=2, weights=uniform, score=-0.384, total= 0.0s\n","[CV] n_neighbors=2, weights=uniform ..................................\n","[CV] ..... n_neighbors=2, weights=uniform, score=-0.603, total= 0.0s\n","[CV] n_neighbors=2, weights=uniform ..................................\n","[CV] ..... n_neighbors=2, weights=uniform, score=-0.415, total= 0.0s\n","[CV] n_neighbors=2, weights=uniform ..................................\n","[CV] ..... n_neighbors=2, weights=uniform, score=-0.318, total= 0.0s\n","[CV] n_neighbors=2, weights=uniform ..................................\n","[CV] ..... n_neighbors=2, weights=uniform, score=-0.558, total= 0.0s\n","[CV] n_neighbors=2, weights=uniform ..................................\n","[CV] ..... n_neighbors=2, weights=uniform, score=-0.437, total= 0.0s\n","[CV] n_neighbors=2, weights=uniform ..................................\n","[CV] ..... n_neighbors=2, weights=uniform, score=-0.617, total= 0.0s\n","[CV] n_neighbors=2, weights=uniform ..................................\n","[CV] ..... n_neighbors=2, weights=uniform, score=-0.451, total= 0.0s\n","[CV] n_neighbors=2, weights=uniform ..................................\n","[CV] ..... n_neighbors=2, weights=uniform, score=-0.400, total= 0.0s\n","[CV] n_neighbors=2, weights=uniform ..................................\n","[CV] ..... n_neighbors=2, weights=uniform, score=-0.443, total= 0.0s\n","[CV] n_neighbors=2, weights=uniform ..................................\n","[CV] ..... n_neighbors=2, weights=uniform, score=-0.593, total= 0.0s\n","[CV] n_neighbors=2, weights=uniform ..................................\n","[CV] ..... n_neighbors=2, weights=uniform, score=-0.432, total= 0.0s\n","[CV] n_neighbors=2, weights=uniform ..................................\n","[CV] ..... n_neighbors=2, weights=uniform, score=-0.600, total= 0.0s\n","[CV] n_neighbors=2, weights=uniform ..................................\n","[CV] ..... n_neighbors=2, weights=uniform, score=-0.611, total= 0.0s\n","[CV] n_neighbors=2, weights=uniform ..................................\n","[CV] ..... n_neighbors=2, weights=uniform, score=-0.524, total= 0.0s\n","[CV] n_neighbors=2, weights=uniform ..................................\n","[CV] ..... n_neighbors=2, weights=uniform, score=-0.455, total= 0.0s\n","[CV] n_neighbors=2, weights=uniform ..................................\n","[CV] ..... n_neighbors=2, weights=uniform, score=-0.661, total= 0.0s\n","[CV] n_neighbors=2, weights=uniform ..................................\n","[CV] ..... n_neighbors=2, weights=uniform, score=-0.438, total= 0.0s\n","[CV] n_neighbors=2, weights=uniform ..................................\n","[CV] ..... n_neighbors=2, weights=uniform, score=-0.579, total= 0.0s\n","[CV] n_neighbors=2, weights=uniform ..................................\n","[CV] ..... n_neighbors=2, weights=uniform, score=-0.439, total= 0.0s\n","[CV] n_neighbors=2, weights=uniform ..................................\n","[CV] ..... n_neighbors=2, weights=uniform, score=-0.470, total= 0.0s\n","[CV] n_neighbors=2, weights=uniform ..................................\n","[CV] ..... n_neighbors=2, weights=uniform, score=-0.494, total= 0.0s\n","[CV] n_neighbors=2, weights=uniform ..................................\n","[CV] ..... n_neighbors=2, weights=uniform, score=-0.608, total= 0.0s\n","[CV] n_neighbors=2, weights=uniform ..................................\n","[CV] ..... n_neighbors=2, weights=uniform, score=-0.693, total= 0.0s\n","[CV] n_neighbors=2, weights=uniform ..................................\n","[CV] ..... n_neighbors=2, weights=uniform, score=-0.389, total= 0.0s\n","[CV] n_neighbors=2, weights=distance .................................\n","[CV] .... n_neighbors=2, weights=distance, score=-0.613, total= 0.0s\n","[CV] n_neighbors=2, weights=distance .................................\n","[CV] .... n_neighbors=2, weights=distance, score=-0.567, total= 0.0s\n","[CV] n_neighbors=2, weights=distance .................................\n","[CV] .... n_neighbors=2, weights=distance, score=-0.586, total= 0.0s\n","[CV] n_neighbors=2, weights=distance .................................\n","[CV] .... n_neighbors=2, weights=distance, score=-0.610, total= 0.0s\n","[CV] n_neighbors=2, weights=distance .................................\n","[CV] .... n_neighbors=2, weights=distance, score=-0.527, total= 0.0s\n","[CV] n_neighbors=2, weights=distance .................................\n","[CV] .... n_neighbors=2, weights=distance, score=-0.404, total= 0.0s\n","[CV] n_neighbors=2, weights=distance .................................\n","[CV] .... n_neighbors=2, weights=distance, score=-0.611, total= 0.0s\n","[CV] n_neighbors=2, weights=distance .................................\n","[CV] .... n_neighbors=2, weights=distance, score=-0.433, total= 0.0s\n","[CV] n_neighbors=2, weights=distance .................................\n","[CV] .... n_neighbors=2, weights=distance, score=-0.327, total= 0.0s\n","[CV] n_neighbors=2, weights=distance .................................\n","[CV] .... n_neighbors=2, weights=distance, score=-0.562, total= 0.0s\n","[CV] n_neighbors=2, weights=distance .................................\n","[CV] .... n_neighbors=2, weights=distance, score=-0.455, total= 0.0s\n","[CV] n_neighbors=2, weights=distance .................................\n","[CV] .... n_neighbors=2, weights=distance, score=-0.603, total= 0.0s\n","[CV] n_neighbors=2, weights=distance .................................\n","[CV] .... n_neighbors=2, weights=distance, score=-0.453, total= 0.0s\n","[CV] n_neighbors=2, weights=distance .................................\n","[CV] .... n_neighbors=2, weights=distance, score=-0.398, total= 0.0s\n","[CV] n_neighbors=2, weights=distance .................................\n","[CV] .... n_neighbors=2, weights=distance, score=-0.461, total= 0.0s\n","[CV] n_neighbors=2, weights=distance .................................\n","[CV] .... n_neighbors=2, weights=distance, score=-0.594, total= 0.0s\n","[CV] n_neighbors=2, weights=distance .................................\n","[CV] .... n_neighbors=2, weights=distance, score=-0.435, total= 0.0s\n","[CV] n_neighbors=2, weights=distance .................................\n","[CV] .... n_neighbors=2, weights=distance, score=-0.627, total= 0.0s\n","[CV] n_neighbors=2, weights=distance .................................\n","[CV] .... n_neighbors=2, weights=distance, score=-0.610, total= 0.0s\n","[CV] n_neighbors=2, weights=distance .................................\n","[CV] .... n_neighbors=2, weights=distance, score=-0.573, total= 0.0s\n","[CV] n_neighbors=2, weights=distance .................................\n","[CV] .... n_neighbors=2, weights=distance, score=-0.481, total= 0.0s\n","[CV] n_neighbors=2, weights=distance .................................\n","[CV] .... n_neighbors=2, weights=distance, score=-0.676, total= 0.0s\n","[CV] n_neighbors=2, weights=distance .................................\n","[CV] .... n_neighbors=2, weights=distance, score=-0.432, total= 0.0s\n","[CV] n_neighbors=2, weights=distance .................................\n","[CV] .... n_neighbors=2, weights=distance, score=-0.600, total= 0.0s\n","[CV] n_neighbors=2, weights=distance .................................\n","[CV] .... n_neighbors=2, weights=distance, score=-0.438, total= 0.0s\n","[CV] n_neighbors=2, weights=distance .................................\n","[CV] .... n_neighbors=2, weights=distance, score=-0.474, total= 0.0s\n","[CV] n_neighbors=2, weights=distance .................................\n","[CV] .... n_neighbors=2, weights=distance, score=-0.491, total= 0.0s\n","[CV] n_neighbors=2, weights=distance .................................\n","[CV] .... n_neighbors=2, weights=distance, score=-0.616, total= 0.0s\n","[CV] n_neighbors=2, weights=distance .................................\n","[CV] .... n_neighbors=2, weights=distance, score=-0.714, total= 0.0s\n","[CV] n_neighbors=2, weights=distance .................................\n","[CV] .... n_neighbors=2, weights=distance, score=-0.408, total= 0.0s\n","[CV] n_neighbors=3, weights=uniform ..................................\n","[CV] ..... n_neighbors=3, weights=uniform, score=-0.407, total= 0.0s\n","[CV] n_neighbors=3, weights=uniform ..................................\n","[CV] ..... n_neighbors=3, weights=uniform, score=-0.345, total= 0.0s\n","[CV] n_neighbors=3, weights=uniform ..................................\n","[CV] ..... n_neighbors=3, weights=uniform, score=-0.324, total= 0.0s\n","[CV] n_neighbors=3, weights=uniform ..................................\n","[CV] ..... n_neighbors=3, weights=uniform, score=-0.486, total= 0.0s\n","[CV] n_neighbors=3, weights=uniform ..................................\n","[CV] ..... n_neighbors=3, weights=uniform, score=-0.322, total= 0.0s\n","[CV] n_neighbors=3, weights=uniform ..................................\n","[CV] ..... n_neighbors=3, weights=uniform, score=-0.261, total= 0.0s\n","[CV] n_neighbors=3, weights=uniform ..................................\n","[CV] ..... n_neighbors=3, weights=uniform, score=-0.422, total= 0.0s\n","[CV] n_neighbors=3, weights=uniform ..................................\n","[CV] ..... n_neighbors=3, weights=uniform, score=-0.308, total= 0.0s\n","[CV] n_neighbors=3, weights=uniform ..................................\n","[CV] ..... n_neighbors=3, weights=uniform, score=-0.268, total= 0.0s\n","[CV] n_neighbors=3, weights=uniform ..................................\n","[CV] ..... n_neighbors=3, weights=uniform, score=-0.342, total= 0.0s\n","[CV] n_neighbors=3, weights=uniform ..................................\n","[CV] ..... n_neighbors=3, weights=uniform, score=-0.278, total= 0.0s\n","[CV] n_neighbors=3, weights=uniform ..................................\n","[CV] ..... n_neighbors=3, weights=uniform, score=-0.448, total= 0.0s\n","[CV] n_neighbors=3, weights=uniform ..................................\n","[CV] ..... n_neighbors=3, weights=uniform, score=-0.277, total= 0.0s\n","[CV] n_neighbors=3, weights=uniform ..................................\n","[CV] ..... n_neighbors=3, weights=uniform, score=-0.197, total= 0.0s\n","[CV] n_neighbors=3, weights=uniform ..................................\n","[CV] ..... n_neighbors=3, weights=uniform, score=-0.303, total= 0.0s\n","[CV] n_neighbors=3, weights=uniform ..................................\n","[CV] ..... n_neighbors=3, weights=uniform, score=-0.310, total= 0.0s\n","[CV] n_neighbors=3, weights=uniform ..................................\n","[CV] ..... n_neighbors=3, weights=uniform, score=-0.302, total= 0.0s\n","[CV] n_neighbors=3, weights=uniform ..................................\n","[CV] ..... n_neighbors=3, weights=uniform, score=-0.388, total= 0.0s\n","[CV] n_neighbors=3, weights=uniform ..................................\n","[CV] ..... n_neighbors=3, weights=uniform, score=-0.431, total= 0.0s\n","[CV] n_neighbors=3, weights=uniform ..................................\n","[CV] ..... n_neighbors=3, weights=uniform, score=-0.404, total= 0.0s\n","[CV] n_neighbors=3, weights=uniform ..................................\n","[CV] ..... n_neighbors=3, weights=uniform, score=-0.361, total= 0.0s\n","[CV] n_neighbors=3, weights=uniform ..................................\n","[CV] ..... n_neighbors=3, weights=uniform, score=-0.413, total= 0.0s\n","[CV] n_neighbors=3, weights=uniform ..................................\n","[CV] ..... n_neighbors=3, weights=uniform, score=-0.276, total= 0.0s\n","[CV] n_neighbors=3, weights=uniform ..................................\n","[CV] ..... n_neighbors=3, weights=uniform, score=-0.416, total= 0.0s\n","[CV] n_neighbors=3, weights=uniform ..................................\n","[CV] ..... n_neighbors=3, weights=uniform, score=-0.304, total= 0.0s\n","[CV] n_neighbors=3, weights=uniform ..................................\n","[CV] ..... n_neighbors=3, weights=uniform, score=-0.389, total= 0.0s\n","[CV] n_neighbors=3, weights=uniform ..................................\n","[CV] ..... n_neighbors=3, weights=uniform, score=-0.251, total= 0.0s\n","[CV] n_neighbors=3, weights=uniform ..................................\n","[CV] ..... n_neighbors=3, weights=uniform, score=-0.332, total= 0.0s\n","[CV] n_neighbors=3, weights=uniform ..................................\n","[CV] ..... n_neighbors=3, weights=uniform, score=-0.427, total= 0.0s\n","[CV] n_neighbors=3, weights=uniform ..................................\n","[CV] ..... n_neighbors=3, weights=uniform, score=-0.317, total= 0.0s\n","[CV] n_neighbors=3, weights=distance .................................\n","[CV] .... n_neighbors=3, weights=distance, score=-0.416, total= 0.0s\n","[CV] n_neighbors=3, weights=distance .................................\n","[CV] .... n_neighbors=3, weights=distance, score=-0.341, total= 0.0s\n","[CV] n_neighbors=3, weights=distance .................................\n","[CV] .... n_neighbors=3, weights=distance, score=-0.347, total= 0.0s\n","[CV] n_neighbors=3, weights=distance .................................\n","[CV] .... n_neighbors=3, weights=distance, score=-0.491, total= 0.0s\n","[CV] n_neighbors=3, weights=distance .................................\n","[CV] .... n_neighbors=3, weights=distance, score=-0.330, total= 0.0s\n","[CV] n_neighbors=3, weights=distance .................................\n","[CV] .... n_neighbors=3, weights=distance, score=-0.269, total= 0.0s\n","[CV] n_neighbors=3, weights=distance .................................\n","[CV] .... n_neighbors=3, weights=distance, score=-0.421, total= 0.0s\n","[CV] n_neighbors=3, weights=distance .................................\n","[CV] .... n_neighbors=3, weights=distance, score=-0.319, total= 0.0s\n","[CV] n_neighbors=3, weights=distance .................................\n","[CV] .... n_neighbors=3, weights=distance, score=-0.261, total= 0.0s\n","[CV] n_neighbors=3, weights=distance .................................\n","[CV] .... n_neighbors=3, weights=distance, score=-0.354, total= 0.0s\n","[CV] n_neighbors=3, weights=distance .................................\n","[CV] .... n_neighbors=3, weights=distance, score=-0.286, total= 0.0s\n","[CV] n_neighbors=3, weights=distance .................................\n","[CV] .... n_neighbors=3, weights=distance, score=-0.444, total= 0.0s\n","[CV] n_neighbors=3, weights=distance .................................\n","[CV] .... n_neighbors=3, weights=distance, score=-0.277, total= 0.0s\n","[CV] n_neighbors=3, weights=distance .................................\n","[CV] .... n_neighbors=3, weights=distance, score=-0.201, total= 0.0s\n","[CV] n_neighbors=3, weights=distance .................................\n","[CV] .... n_neighbors=3, weights=distance, score=-0.313, total= 0.0s\n","[CV] n_neighbors=3, weights=distance .................................\n","[CV] .... n_neighbors=3, weights=distance, score=-0.328, total= 0.0s\n","[CV] n_neighbors=3, weights=distance .................................\n","[CV] .... n_neighbors=3, weights=distance, score=-0.296, total= 0.0s\n","[CV] n_neighbors=3, weights=distance .................................\n","[CV] .... n_neighbors=3, weights=distance, score=-0.405, total= 0.0s\n","[CV] n_neighbors=3, weights=distance .................................\n","[CV] .... n_neighbors=3, weights=distance, score=-0.434, total= 0.0s\n","[CV] n_neighbors=3, weights=distance .................................\n","[CV] .... n_neighbors=3, weights=distance, score=-0.431, total= 0.0s\n","[CV] n_neighbors=3, weights=distance .................................\n","[CV] .... n_neighbors=3, weights=distance, score=-0.373, total= 0.0s\n","[CV] n_neighbors=3, weights=distance .................................\n","[CV] .... n_neighbors=3, weights=distance, score=-0.428, total= 0.0s\n","[CV] n_neighbors=3, weights=distance .................................\n","[CV] .... n_neighbors=3, weights=distance, score=-0.269, total= 0.0s\n","[CV] n_neighbors=3, weights=distance .................................\n","[CV] .... n_neighbors=3, weights=distance, score=-0.433, total= 0.0s\n","[CV] n_neighbors=3, weights=distance .................................\n","[CV] .... n_neighbors=3, weights=distance, score=-0.298, total= 0.0s\n","[CV] n_neighbors=3, weights=distance .................................\n","[CV] .... n_neighbors=3, weights=distance, score=-0.388, total= 0.0s\n","[CV] n_neighbors=3, weights=distance .................................\n","[CV] .... n_neighbors=3, weights=distance, score=-0.251, total= 0.0s\n","[CV] n_neighbors=3, weights=distance .................................\n","[CV] .... n_neighbors=3, weights=distance, score=-0.357, total= 0.0s\n","[CV] n_neighbors=3, weights=distance .................................\n","[CV] .... n_neighbors=3, weights=distance, score=-0.459, total= 0.0s\n","[CV] n_neighbors=3, weights=distance .................................\n","[CV] .... n_neighbors=3, weights=distance, score=-0.324, total= 0.0s\n","[CV] n_neighbors=4, weights=uniform ..................................\n","[CV] ..... n_neighbors=4, weights=uniform, score=-0.347, total= 0.0s\n","[CV] n_neighbors=4, weights=uniform ..................................\n","[CV] ..... n_neighbors=4, weights=uniform, score=-0.281, total= 0.0s\n","[CV] n_neighbors=4, weights=uniform ..................................\n","[CV] ..... n_neighbors=4, weights=uniform, score=-0.275, total= 0.0s\n","[CV] n_neighbors=4, weights=uniform ..................................\n","[CV] ..... n_neighbors=4, weights=uniform, score=-0.337, total= 0.0s\n","[CV] n_neighbors=4, weights=uniform ..................................\n","[CV] ..... n_neighbors=4, weights=uniform, score=-0.303, total= 0.0s\n","[CV] n_neighbors=4, weights=uniform ..................................\n","[CV] ..... n_neighbors=4, weights=uniform, score=-0.172, total= 0.0s\n","[CV] n_neighbors=4, weights=uniform ..................................\n","[CV] ..... n_neighbors=4, weights=uniform, score=-0.384, total= 0.0s\n","[CV] n_neighbors=4, weights=uniform ..................................\n","[CV] ..... n_neighbors=4, weights=uniform, score=-0.205, total= 0.0s\n","[CV] n_neighbors=4, weights=uniform ..................................\n","[CV] ..... n_neighbors=4, weights=uniform, score=-0.197, total= 0.0s\n","[CV] n_neighbors=4, weights=uniform ..................................\n","[CV] ..... n_neighbors=4, weights=uniform, score=-0.235, total= 0.0s\n","[CV] n_neighbors=4, weights=uniform ..................................\n","[CV] ..... n_neighbors=4, weights=uniform, score=-0.192, total= 0.0s\n","[CV] n_neighbors=4, weights=uniform ..................................\n","[CV] ..... n_neighbors=4, weights=uniform, score=-0.356, total= 0.0s\n","[CV] n_neighbors=4, weights=uniform ..................................\n","[CV] ..... n_neighbors=4, weights=uniform, score=-0.230, total= 0.0s\n","[CV] n_neighbors=4, weights=uniform ..................................\n","[CV] ..... n_neighbors=4, weights=uniform, score=-0.182, total= 0.0s\n","[CV] n_neighbors=4, weights=uniform ..................................\n","[CV] ..... n_neighbors=4, weights=uniform, score=-0.203, total= 0.0s\n","[CV] n_neighbors=4, weights=uniform ..................................\n","[CV] ..... n_neighbors=4, weights=uniform, score=-0.227, total= 0.0s\n","[CV] n_neighbors=4, weights=uniform ..................................\n","[CV] ..... n_neighbors=4, weights=uniform, score=-0.261, total= 0.0s\n","[CV] n_neighbors=4, weights=uniform ..................................\n","[CV] ..... n_neighbors=4, weights=uniform, score=-0.280, total= 0.0s\n","[CV] n_neighbors=4, weights=uniform ..................................\n","[CV] ..... n_neighbors=4, weights=uniform, score=-0.318, total= 0.0s\n","[CV] n_neighbors=4, weights=uniform ..................................\n","[CV] ..... n_neighbors=4, weights=uniform, score=-0.239, total= 0.0s\n","[CV] n_neighbors=4, weights=uniform ..................................\n","[CV] ..... n_neighbors=4, weights=uniform, score=-0.169, total= 0.0s\n","[CV] n_neighbors=4, weights=uniform ..................................\n","[CV] ..... n_neighbors=4, weights=uniform, score=-0.272, total= 0.0s\n","[CV] n_neighbors=4, weights=uniform ..................................\n","[CV] ..... n_neighbors=4, weights=uniform, score=-0.251, total= 0.0s\n","[CV] n_neighbors=4, weights=uniform ..................................\n","[CV] ..... n_neighbors=4, weights=uniform, score=-0.361, total= 0.0s\n","[CV] n_neighbors=4, weights=uniform ..................................\n","[CV] ..... n_neighbors=4, weights=uniform, score=-0.190, total= 0.0s\n","[CV] n_neighbors=4, weights=uniform ..................................\n","[CV] ..... n_neighbors=4, weights=uniform, score=-0.259, total= 0.0s\n","[CV] n_neighbors=4, weights=uniform ..................................\n","[CV] ..... n_neighbors=4, weights=uniform, score=-0.234, total= 0.0s\n","[CV] n_neighbors=4, weights=uniform ..................................\n","[CV] ..... n_neighbors=4, weights=uniform, score=-0.320, total= 0.0s\n","[CV] n_neighbors=4, weights=uniform ..................................\n","[CV] ..... n_neighbors=4, weights=uniform, score=-0.398, total= 0.0s\n","[CV] n_neighbors=4, weights=uniform ..................................\n","[CV] ..... n_neighbors=4, weights=uniform, score=-0.269, total= 0.0s\n","[CV] n_neighbors=4, weights=distance .................................\n","[CV] .... n_neighbors=4, weights=distance, score=-0.354, total= 0.0s\n","[CV] n_neighbors=4, weights=distance .................................\n","[CV] .... n_neighbors=4, weights=distance, score=-0.279, total= 0.0s\n","[CV] n_neighbors=4, weights=distance .................................\n","[CV] .... n_neighbors=4, weights=distance, score=-0.290, total= 0.0s\n","[CV] n_neighbors=4, weights=distance .................................\n","[CV] .... n_neighbors=4, weights=distance, score=-0.346, total= 0.0s\n","[CV] n_neighbors=4, weights=distance .................................\n","[CV] .... n_neighbors=4, weights=distance, score=-0.304, total= 0.0s\n","[CV] n_neighbors=4, weights=distance .................................\n","[CV] .... n_neighbors=4, weights=distance, score=-0.174, total= 0.0s\n","[CV] n_neighbors=4, weights=distance .................................\n","[CV] .... n_neighbors=4, weights=distance, score=-0.380, total= 0.0s\n","[CV] n_neighbors=4, weights=distance .................................\n","[CV] .... n_neighbors=4, weights=distance, score=-0.216, total= 0.0s\n","[CV] n_neighbors=4, weights=distance .................................\n","[CV] .... n_neighbors=4, weights=distance, score=-0.195, total= 0.0s\n","[CV] n_neighbors=4, weights=distance .................................\n","[CV] .... n_neighbors=4, weights=distance, score=-0.244, total= 0.0s\n","[CV] n_neighbors=4, weights=distance .................................\n","[CV] .... n_neighbors=4, weights=distance, score=-0.206, total= 0.0s\n","[CV] n_neighbors=4, weights=distance .................................\n","[CV] .... n_neighbors=4, weights=distance, score=-0.351, total= 0.0s\n","[CV] n_neighbors=4, weights=distance .................................\n","[CV] .... n_neighbors=4, weights=distance, score=-0.223, total= 0.0s\n","[CV] n_neighbors=4, weights=distance .................................\n","[CV] .... n_neighbors=4, weights=distance, score=-0.178, total= 0.0s\n","[CV] n_neighbors=4, weights=distance .................................\n","[CV] .... n_neighbors=4, weights=distance, score=-0.212, total= 0.0s\n","[CV] n_neighbors=4, weights=distance .................................\n","[CV] .... n_neighbors=4, weights=distance, score=-0.243, total= 0.0s\n","[CV] n_neighbors=4, weights=distance .................................\n","[CV] .... n_neighbors=4, weights=distance, score=-0.256, total= 0.0s\n","[CV] n_neighbors=4, weights=distance .................................\n","[CV] .... n_neighbors=4, weights=distance, score=-0.294, total= 0.0s\n","[CV] n_neighbors=4, weights=distance .................................\n","[CV] .... n_neighbors=4, weights=distance, score=-0.331, total= 0.0s\n","[CV] n_neighbors=4, weights=distance .................................\n","[CV] .... n_neighbors=4, weights=distance, score=-0.267, total= 0.0s\n","[CV] n_neighbors=4, weights=distance .................................\n","[CV] .... n_neighbors=4, weights=distance, score=-0.196, total= 0.0s\n","[CV] n_neighbors=4, weights=distance .................................\n","[CV] .... n_neighbors=4, weights=distance, score=-0.294, total= 0.0s\n","[CV] n_neighbors=4, weights=distance .................................\n","[CV] .... n_neighbors=4, weights=distance, score=-0.233, total= 0.0s\n","[CV] n_neighbors=4, weights=distance .................................\n","[CV] .... n_neighbors=4, weights=distance, score=-0.370, total= 0.0s\n","[CV] n_neighbors=4, weights=distance .................................\n","[CV] .... n_neighbors=4, weights=distance, score=-0.190, total= 0.0s\n","[CV] n_neighbors=4, weights=distance .................................\n","[CV] .... n_neighbors=4, weights=distance, score=-0.267, total= 0.0s\n","[CV] n_neighbors=4, weights=distance .................................\n","[CV] .... n_neighbors=4, weights=distance, score=-0.220, total= 0.0s\n","[CV] n_neighbors=4, weights=distance .................................\n","[CV] .... n_neighbors=4, weights=distance, score=-0.331, total= 0.0s\n","[CV] n_neighbors=4, weights=distance .................................\n","[CV] .... n_neighbors=4, weights=distance, score=-0.421, total= 0.0s\n","[CV] n_neighbors=4, weights=distance .................................\n","[CV] .... n_neighbors=4, weights=distance, score=-0.273, total= 0.0s\n","[CV] n_neighbors=5, weights=uniform ..................................\n","[CV] ..... n_neighbors=5, weights=uniform, score=-0.282, total= 0.0s\n","[CV] n_neighbors=5, weights=uniform ..................................\n","[CV] ..... n_neighbors=5, weights=uniform, score=-0.207, total= 0.0s\n","[CV] n_neighbors=5, weights=uniform ..................................\n","[CV] ..... n_neighbors=5, weights=uniform, score=-0.174, total= 0.0s\n","[CV] n_neighbors=5, weights=uniform ..................................\n","[CV] ..... n_neighbors=5, weights=uniform, score=-0.287, total= 0.0s\n","[CV] n_neighbors=5, weights=uniform ..................................\n","[CV] ..... n_neighbors=5, weights=uniform, score=-0.288, total= 0.0s\n","[CV] n_neighbors=5, weights=uniform ..................................\n","[CV] ..... n_neighbors=5, weights=uniform, score=-0.156, total= 0.0s\n","[CV] n_neighbors=5, weights=uniform ..................................\n","[CV] ..... n_neighbors=5, weights=uniform, score=-0.345, total= 0.0s\n","[CV] n_neighbors=5, weights=uniform ..................................\n","[CV] ..... n_neighbors=5, weights=uniform, score=-0.194, total= 0.0s\n","[CV] n_neighbors=5, weights=uniform ..................................\n","[CV] ..... n_neighbors=5, weights=uniform, score=-0.146, total= 0.0s\n","[CV] n_neighbors=5, weights=uniform ..................................\n","[CV] ..... n_neighbors=5, weights=uniform, score=-0.166, total= 0.0s\n","[CV] n_neighbors=5, weights=uniform ..................................\n","[CV] ..... n_neighbors=5, weights=uniform, score=-0.115, total= 0.0s\n","[CV] n_neighbors=5, weights=uniform ..................................\n","[CV] ..... n_neighbors=5, weights=uniform, score=-0.292, total= 0.0s\n","[CV] n_neighbors=5, weights=uniform ..................................\n","[CV] ..... n_neighbors=5, weights=uniform, score=-0.223, total= 0.0s\n","[CV] n_neighbors=5, weights=uniform ..................................\n","[CV] ..... n_neighbors=5, weights=uniform, score=-0.170, total= 0.0s\n","[CV] n_neighbors=5, weights=uniform ..................................\n","[CV] ..... n_neighbors=5, weights=uniform, score=-0.140, total= 0.0s\n","[CV] n_neighbors=5, weights=uniform ..................................\n","[CV] ..... n_neighbors=5, weights=uniform, score=-0.218, total= 0.0s\n","[CV] n_neighbors=5, weights=uniform ..................................\n","[CV] ..... n_neighbors=5, weights=uniform, score=-0.202, total= 0.0s\n","[CV] n_neighbors=5, weights=uniform ..................................\n","[CV] ..... n_neighbors=5, weights=uniform, score=-0.227, total= 0.0s\n","[CV] n_neighbors=5, weights=uniform ..................................\n","[CV] ..... n_neighbors=5, weights=uniform, score=-0.293, total= 0.0s\n","[CV] n_neighbors=5, weights=uniform ..................................\n","[CV] ..... n_neighbors=5, weights=uniform, score=-0.199, total= 0.0s\n","[CV] n_neighbors=5, weights=uniform ..................................\n","[CV] ..... n_neighbors=5, weights=uniform, score=-0.174, total= 0.0s\n","[CV] n_neighbors=5, weights=uniform ..................................\n","[CV] ..... n_neighbors=5, weights=uniform, score=-0.185, total= 0.0s\n","[CV] n_neighbors=5, weights=uniform ..................................\n","[CV] ..... n_neighbors=5, weights=uniform, score=-0.256, total= 0.0s\n","[CV] n_neighbors=5, weights=uniform ..................................\n","[CV] ..... n_neighbors=5, weights=uniform, score=-0.271, total= 0.0s\n","[CV] n_neighbors=5, weights=uniform ..................................\n","[CV] ..... n_neighbors=5, weights=uniform, score=-0.141, total= 0.0s\n","[CV] n_neighbors=5, weights=uniform ..................................\n","[CV] ..... n_neighbors=5, weights=uniform, score=-0.205, total= 0.0s\n","[CV] n_neighbors=5, weights=uniform ..................................\n","[CV] ..... n_neighbors=5, weights=uniform, score=-0.198, total= 0.0s\n","[CV] n_neighbors=5, weights=uniform ..................................\n","[CV] ..... n_neighbors=5, weights=uniform, score=-0.258, total= 0.0s\n","[CV] n_neighbors=5, weights=uniform ..................................\n","[CV] ..... n_neighbors=5, weights=uniform, score=-0.322, total= 0.0s\n","[CV] n_neighbors=5, weights=uniform ..................................\n","[CV] ..... n_neighbors=5, weights=uniform, score=-0.237, total= 0.0s\n","[CV] n_neighbors=5, weights=distance .................................\n","[CV] .... n_neighbors=5, weights=distance, score=-0.293, total= 0.0s\n","[CV] n_neighbors=5, weights=distance .................................\n","[CV] .... n_neighbors=5, weights=distance, score=-0.206, total= 0.0s\n","[CV] n_neighbors=5, weights=distance .................................\n","[CV] .... n_neighbors=5, weights=distance, score=-0.192, total= 0.0s\n","[CV] n_neighbors=5, weights=distance .................................\n","[CV] .... n_neighbors=5, weights=distance, score=-0.291, total= 0.0s\n","[CV] n_neighbors=5, weights=distance .................................\n","[CV] .... n_neighbors=5, weights=distance, score=-0.286, total= 0.0s\n","[CV] n_neighbors=5, weights=distance .................................\n","[CV] .... n_neighbors=5, weights=distance, score=-0.154, total= 0.0s\n","[CV] n_neighbors=5, weights=distance .................................\n","[CV] .... n_neighbors=5, weights=distance, score=-0.340, total= 0.0s\n","[CV] n_neighbors=5, weights=distance .................................\n","[CV] .... n_neighbors=5, weights=distance, score=-0.195, total= 0.0s\n","[CV] n_neighbors=5, weights=distance .................................\n","[CV] .... n_neighbors=5, weights=distance, score=-0.144, total= 0.0s\n","[CV] n_neighbors=5, weights=distance .................................\n","[CV] .... n_neighbors=5, weights=distance, score=-0.180, total= 0.0s\n","[CV] n_neighbors=5, weights=distance .................................\n","[CV] .... n_neighbors=5, weights=distance, score=-0.131, total= 0.0s\n","[CV] n_neighbors=5, weights=distance .................................\n","[CV] .... n_neighbors=5, weights=distance, score=-0.291, total= 0.0s\n","[CV] n_neighbors=5, weights=distance .................................\n","[CV] .... n_neighbors=5, weights=distance, score=-0.208, total= 0.0s\n","[CV] n_neighbors=5, weights=distance .................................\n","[CV] .... n_neighbors=5, weights=distance, score=-0.160, total= 0.0s\n","[CV] n_neighbors=5, weights=distance .................................\n","[CV] .... n_neighbors=5, weights=distance, score=-0.152, total= 0.0s\n","[CV] n_neighbors=5, weights=distance .................................\n","[CV] .... n_neighbors=5, weights=distance, score=-0.225, total= 0.0s\n","[CV] n_neighbors=5, weights=distance .................................\n","[CV] .... n_neighbors=5, weights=distance, score=-0.202, total= 0.0s\n","[CV] n_neighbors=5, weights=distance .................................\n","[CV] .... n_neighbors=5, weights=distance, score=-0.244, total= 0.0s\n","[CV] n_neighbors=5, weights=distance .................................\n","[CV] .... n_neighbors=5, weights=distance, score=-0.296, total= 0.0s\n","[CV] n_neighbors=5, weights=distance .................................\n","[CV] .... n_neighbors=5, weights=distance, score=-0.222, total= 0.0s\n","[CV] n_neighbors=5, weights=distance .................................\n","[CV] .... n_neighbors=5, weights=distance, score=-0.188, total= 0.0s\n","[CV] n_neighbors=5, weights=distance .................................\n","[CV] .... n_neighbors=5, weights=distance, score=-0.209, total= 0.0s\n","[CV] n_neighbors=5, weights=distance .................................\n","[CV] .... n_neighbors=5, weights=distance, score=-0.230, total= 0.0s\n","[CV] n_neighbors=5, weights=distance .................................\n","[CV] .... n_neighbors=5, weights=distance, score=-0.283, total= 0.0s\n","[CV] n_neighbors=5, weights=distance .................................\n","[CV] .... n_neighbors=5, weights=distance, score=-0.144, total= 0.0s\n","[CV] n_neighbors=5, weights=distance .................................\n","[CV] .... n_neighbors=5, weights=distance, score=-0.214, total= 0.0s\n","[CV] n_neighbors=5, weights=distance .................................\n","[CV] .... n_neighbors=5, weights=distance, score=-0.183, total= 0.0s\n","[CV] n_neighbors=5, weights=distance .................................\n","[CV] .... n_neighbors=5, weights=distance, score=-0.271, total= 0.0s\n","[CV] n_neighbors=5, weights=distance .................................\n","[CV] .... n_neighbors=5, weights=distance, score=-0.344, total= 0.0s\n","[CV] n_neighbors=5, weights=distance .................................\n","[CV] .... n_neighbors=5, weights=distance, score=-0.235, total= 0.0s\n","[CV] n_neighbors=6, weights=uniform ..................................\n","[CV] ..... n_neighbors=6, weights=uniform, score=-0.252, total= 0.0s\n","[CV] n_neighbors=6, weights=uniform ..................................\n","[CV] ..... n_neighbors=6, weights=uniform, score=-0.146, total= 0.0s\n","[CV] n_neighbors=6, weights=uniform ..................................\n","[CV] ..... n_neighbors=6, weights=uniform, score=-0.215, total= 0.0s\n","[CV] n_neighbors=6, weights=uniform ..................................\n","[CV] ..... n_neighbors=6, weights=uniform, score=-0.226, total= 0.0s\n","[CV] n_neighbors=6, weights=uniform ..................................\n","[CV] ..... n_neighbors=6, weights=uniform, score=-0.225, total= 0.0s\n","[CV] n_neighbors=6, weights=uniform ..................................\n","[CV] ..... n_neighbors=6, weights=uniform, score=-0.114, total= 0.0s\n","[CV] n_neighbors=6, weights=uniform ..................................\n","[CV] ..... n_neighbors=6, weights=uniform, score=-0.275, total= 0.0s\n","[CV] n_neighbors=6, weights=uniform ..................................\n","[CV] ..... n_neighbors=6, weights=uniform, score=-0.174, total= 0.0s\n","[CV] n_neighbors=6, weights=uniform ..................................\n","[CV] ..... n_neighbors=6, weights=uniform, score=-0.127, total= 0.0s\n","[CV] n_neighbors=6, weights=uniform ..................................\n","[CV] ..... n_neighbors=6, weights=uniform, score=-0.134, total= 0.0s\n","[CV] n_neighbors=6, weights=uniform ..................................\n","[CV] ..... n_neighbors=6, weights=uniform, score=-0.130, total= 0.0s\n","[CV] n_neighbors=6, weights=uniform ..................................\n","[CV] ..... n_neighbors=6, weights=uniform, score=-0.255, total= 0.0s\n","[CV] n_neighbors=6, weights=uniform ..................................\n","[CV] ..... n_neighbors=6, weights=uniform, score=-0.162, total= 0.0s\n","[CV] n_neighbors=6, weights=uniform ..................................\n","[CV] ..... n_neighbors=6, weights=uniform, score=-0.146, total= 0.0s\n","[CV] n_neighbors=6, weights=uniform ..................................\n","[CV] ..... n_neighbors=6, weights=uniform, score=-0.128, total= 0.0s\n","[CV] n_neighbors=6, weights=uniform ..................................\n","[CV] ..... n_neighbors=6, weights=uniform, score=-0.201, total= 0.0s\n","[CV] n_neighbors=6, weights=uniform ..................................\n","[CV] ..... n_neighbors=6, weights=uniform, score=-0.143, total= 0.0s\n","[CV] n_neighbors=6, weights=uniform ..................................\n","[CV] ..... n_neighbors=6, weights=uniform, score=-0.214, total= 0.0s\n","[CV] n_neighbors=6, weights=uniform ..................................\n","[CV] ..... n_neighbors=6, weights=uniform, score=-0.238, total= 0.0s\n","[CV] n_neighbors=6, weights=uniform ..................................\n","[CV] ..... n_neighbors=6, weights=uniform, score=-0.194, total= 0.0s\n","[CV] n_neighbors=6, weights=uniform ..................................\n","[CV] ..... n_neighbors=6, weights=uniform, score=-0.154, total= 0.0s\n","[CV] n_neighbors=6, weights=uniform ..................................\n","[CV] ..... n_neighbors=6, weights=uniform, score=-0.152, total= 0.0s\n","[CV] n_neighbors=6, weights=uniform ..................................\n","[CV] ..... n_neighbors=6, weights=uniform, score=-0.207, total= 0.0s\n","[CV] n_neighbors=6, weights=uniform ..................................\n","[CV] ..... n_neighbors=6, weights=uniform, score=-0.191, total= 0.0s\n","[CV] n_neighbors=6, weights=uniform ..................................\n","[CV] ..... n_neighbors=6, weights=uniform, score=-0.095, total= 0.0s\n","[CV] n_neighbors=6, weights=uniform ..................................\n","[CV] ..... n_neighbors=6, weights=uniform, score=-0.212, total= 0.0s\n","[CV] n_neighbors=6, weights=uniform ..................................\n","[CV] ..... n_neighbors=6, weights=uniform, score=-0.150, total= 0.0s\n","[CV] n_neighbors=6, weights=uniform ..................................\n","[CV] ..... n_neighbors=6, weights=uniform, score=-0.187, total= 0.0s\n","[CV] n_neighbors=6, weights=uniform ..................................\n","[CV] ..... n_neighbors=6, weights=uniform, score=-0.286, total= 0.0s\n","[CV] n_neighbors=6, weights=uniform ..................................\n","[CV] ..... n_neighbors=6, weights=uniform, score=-0.188, total= 0.0s\n","[CV] n_neighbors=6, weights=distance .................................\n","[CV] .... n_neighbors=6, weights=distance, score=-0.260, total= 0.0s\n","[CV] n_neighbors=6, weights=distance .................................\n","[CV] .... n_neighbors=6, weights=distance, score=-0.150, total= 0.0s\n","[CV] n_neighbors=6, weights=distance .................................\n","[CV] .... n_neighbors=6, weights=distance, score=-0.218, total= 0.0s\n","[CV] n_neighbors=6, weights=distance .................................\n","[CV] .... n_neighbors=6, weights=distance, score=-0.236, total= 0.0s\n","[CV] n_neighbors=6, weights=distance .................................\n","[CV] .... n_neighbors=6, weights=distance, score=-0.229, total= 0.0s\n","[CV] n_neighbors=6, weights=distance .................................\n","[CV] .... n_neighbors=6, weights=distance, score=-0.115, total= 0.0s\n","[CV] n_neighbors=6, weights=distance .................................\n","[CV] .... n_neighbors=6, weights=distance, score=-0.275, total= 0.0s\n","[CV] n_neighbors=6, weights=distance .................................\n","[CV] .... n_neighbors=6, weights=distance, score=-0.172, total= 0.0s\n","[CV] n_neighbors=6, weights=distance .................................\n","[CV] .... n_neighbors=6, weights=distance, score=-0.123, total= 0.0s\n","[CV] n_neighbors=6, weights=distance .................................\n","[CV] .... n_neighbors=6, weights=distance, score=-0.144, total= 0.0s\n","[CV] n_neighbors=6, weights=distance .................................\n","[CV] .... n_neighbors=6, weights=distance, score=-0.134, total= 0.0s\n","[CV] n_neighbors=6, weights=distance .................................\n","[CV] .... n_neighbors=6, weights=distance, score=-0.255, total= 0.0s\n","[CV] n_neighbors=6, weights=distance .................................\n","[CV] .... n_neighbors=6, weights=distance, score=-0.155, total= 0.0s\n","[CV] n_neighbors=6, weights=distance .................................\n","[CV] .... n_neighbors=6, weights=distance, score=-0.139, total= 0.0s\n","[CV] n_neighbors=6, weights=distance .................................\n","[CV] .... n_neighbors=6, weights=distance, score=-0.132, total= 0.0s\n","[CV] n_neighbors=6, weights=distance .................................\n","[CV] .... n_neighbors=6, weights=distance, score=-0.207, total= 0.0s\n","[CV] n_neighbors=6, weights=distance .................................\n","[CV] .... n_neighbors=6, weights=distance, score=-0.146, total= 0.0s\n","[CV] n_neighbors=6, weights=distance .................................\n","[CV] .... n_neighbors=6, weights=distance, score=-0.230, total= 0.0s\n","[CV] n_neighbors=6, weights=distance .................................\n","[CV] .... n_neighbors=6, weights=distance, score=-0.244, total= 0.0s\n","[CV] n_neighbors=6, weights=distance .................................\n","[CV] .... n_neighbors=6, weights=distance, score=-0.212, total= 0.0s\n","[CV] n_neighbors=6, weights=distance .................................\n","[CV] .... n_neighbors=6, weights=distance, score=-0.164, total= 0.0s\n","[CV] n_neighbors=6, weights=distance .................................\n","[CV] .... n_neighbors=6, weights=distance, score=-0.168, total= 0.0s\n","[CV] n_neighbors=6, weights=distance .................................\n","[CV] .... n_neighbors=6, weights=distance, score=-0.186, total= 0.0s\n","[CV] n_neighbors=6, weights=distance .................................\n","[CV] .... n_neighbors=6, weights=distance, score=-0.205, total= 0.0s\n","[CV] n_neighbors=6, weights=distance .................................\n","[CV] .... n_neighbors=6, weights=distance, score=-0.099, total= 0.0s\n","[CV] n_neighbors=6, weights=distance .................................\n","[CV] .... n_neighbors=6, weights=distance, score=-0.211, total= 0.0s\n","[CV] n_neighbors=6, weights=distance .................................\n","[CV] .... n_neighbors=6, weights=distance, score=-0.139, total= 0.0s\n","[CV] n_neighbors=6, weights=distance .................................\n","[CV] .... n_neighbors=6, weights=distance, score=-0.201, total= 0.0s\n","[CV] n_neighbors=6, weights=distance .................................\n","[CV] .... n_neighbors=6, weights=distance, score=-0.307, total= 0.0s\n","[CV] n_neighbors=6, weights=distance .................................\n","[CV] .... n_neighbors=6, weights=distance, score=-0.193, total= 0.0s\n","[Parallel(n_jobs=1)]: Done 300 out of 300 | elapsed: 11.2s finished\n","KNeighborsRegressor(algorithm='auto', leaf_size=30, metric='minkowski',\n"," metric_params=None, n_jobs=None, n_neighbors=6, p=2,\n"," weights='uniform')\n","window: 1, stide: 1, rmse: 2.9783244730328944\n","[1, 4, 9, 12, 2, 0, 3, 13, 11, 10, 6, 5, 7, 8]\n","[1, 4, 9, 12, 2, 0]\n","(3000, 90)\n","Number of Feature-Columns: 90\n","\n","Features Ready for undergoing selection tests done ...\n","\n","Fitting 30 folds for each of 10 candidates, totalling 300 fits\n","[Parallel(n_jobs=1)]: Using backend SequentialBackend with 1 concurrent workers.\n","[CV] n_neighbors=2, weights=uniform ..................................\n","[CV] ..... n_neighbors=2, weights=uniform, score=-0.436, total= 0.1s\n","[Parallel(n_jobs=1)]: Done 1 out of 1 | elapsed: 0.1s remaining: 0.0s\n","[CV] n_neighbors=2, weights=uniform ..................................\n","[CV] ..... n_neighbors=2, weights=uniform, score=-0.545, total= 0.0s\n","[Parallel(n_jobs=1)]: Done 2 out of 2 | elapsed: 0.1s remaining: 0.0s\n","[CV] n_neighbors=2, weights=uniform ..................................\n","[CV] ..... n_neighbors=2, weights=uniform, score=-0.615, total= 0.0s\n","[Parallel(n_jobs=1)]: Done 3 out of 3 | elapsed: 0.2s remaining: 0.0s\n","[CV] n_neighbors=2, weights=uniform ..................................\n","[CV] ..... n_neighbors=2, weights=uniform, score=-0.588, total= 0.0s\n","[Parallel(n_jobs=1)]: Done 4 out of 4 | elapsed: 0.2s remaining: 0.0s\n","[CV] n_neighbors=2, weights=uniform ..................................\n","[CV] ..... n_neighbors=2, weights=uniform, score=-0.517, total= 0.0s\n","[CV] n_neighbors=2, weights=uniform ..................................\n","[CV] ..... n_neighbors=2, weights=uniform, score=-0.539, total= 0.0s\n","[CV] n_neighbors=2, weights=uniform ..................................\n","[CV] ..... n_neighbors=2, weights=uniform, score=-0.613, total= 0.0s\n","[CV] n_neighbors=2, weights=uniform ..................................\n","[CV] ..... n_neighbors=2, weights=uniform, score=-0.329, total= 0.0s\n","[CV] n_neighbors=2, weights=uniform ..................................\n","[CV] ..... n_neighbors=2, weights=uniform, score=-0.426, total= 0.0s\n","[CV] n_neighbors=2, weights=uniform ..................................\n","[CV] ..... n_neighbors=2, weights=uniform, score=-0.475, total= 0.0s\n","[CV] n_neighbors=2, weights=uniform ..................................\n","[CV] ..... n_neighbors=2, weights=uniform, score=-0.472, total= 0.0s\n","[CV] n_neighbors=2, weights=uniform ..................................\n","[CV] ..... n_neighbors=2, weights=uniform, score=-0.599, total= 0.0s\n","[CV] n_neighbors=2, weights=uniform ..................................\n","[CV] ..... n_neighbors=2, weights=uniform, score=-0.476, total= 0.0s\n","[CV] n_neighbors=2, weights=uniform ..................................\n","[CV] ..... n_neighbors=2, weights=uniform, score=-0.489, total= 0.0s\n","[CV] n_neighbors=2, weights=uniform ..................................\n","[CV] ..... n_neighbors=2, weights=uniform, score=-0.401, total= 0.0s\n","[CV] n_neighbors=2, weights=uniform ..................................\n","[CV] ..... n_neighbors=2, weights=uniform, score=-0.472, total= 0.0s\n","[CV] n_neighbors=2, weights=uniform ..................................\n","[CV] ..... n_neighbors=2, weights=uniform, score=-0.414, total= 0.0s\n","[CV] n_neighbors=2, weights=uniform ..................................\n","[CV] ..... n_neighbors=2, weights=uniform, score=-0.590, total= 0.0s\n","[CV] n_neighbors=2, weights=uniform ..................................\n","[CV] ..... n_neighbors=2, weights=uniform, score=-0.506, total= 0.0s\n","[CV] n_neighbors=2, weights=uniform ..................................\n","[CV] ..... n_neighbors=2, weights=uniform, score=-0.552, total= 0.1s\n","[CV] n_neighbors=2, weights=uniform ..................................\n","[CV] ..... n_neighbors=2, weights=uniform, score=-0.598, total= 0.0s\n","[CV] n_neighbors=2, weights=uniform ..................................\n","[CV] ..... n_neighbors=2, weights=uniform, score=-0.505, total= 0.1s\n","[CV] n_neighbors=2, weights=uniform ..................................\n","[CV] ..... n_neighbors=2, weights=uniform, score=-0.521, total= 0.0s\n","[CV] n_neighbors=2, weights=uniform ..................................\n","[CV] ..... n_neighbors=2, weights=uniform, score=-0.660, total= 0.0s\n","[CV] n_neighbors=2, weights=uniform ..................................\n","[CV] ..... n_neighbors=2, weights=uniform, score=-0.450, total= 0.0s\n","[CV] n_neighbors=2, weights=uniform ..................................\n","[CV] ..... n_neighbors=2, weights=uniform, score=-0.607, total= 0.0s\n","[CV] n_neighbors=2, weights=uniform ..................................\n","[CV] ..... n_neighbors=2, weights=uniform, score=-0.491, total= 0.1s\n","[CV] n_neighbors=2, weights=uniform ..................................\n","[CV] ..... n_neighbors=2, weights=uniform, score=-0.518, total= 0.0s\n","[CV] n_neighbors=2, weights=uniform ..................................\n","[CV] ..... n_neighbors=2, weights=uniform, score=-0.584, total= 0.0s\n","[CV] n_neighbors=2, weights=uniform ..................................\n","[CV] ..... n_neighbors=2, weights=uniform, score=-0.339, total= 0.0s\n","[CV] n_neighbors=2, weights=distance .................................\n","[CV] .... n_neighbors=2, weights=distance, score=-0.443, total= 0.0s\n","[CV] n_neighbors=2, weights=distance .................................\n","[CV] .... n_neighbors=2, weights=distance, score=-0.541, total= 0.0s\n","[CV] n_neighbors=2, weights=distance .................................\n","[CV] .... n_neighbors=2, weights=distance, score=-0.622, total= 0.0s\n","[CV] n_neighbors=2, weights=distance .................................\n","[CV] .... n_neighbors=2, weights=distance, score=-0.581, total= 0.1s\n","[CV] n_neighbors=2, weights=distance .................................\n","[CV] .... n_neighbors=2, weights=distance, score=-0.515, total= 0.0s\n","[CV] n_neighbors=2, weights=distance .................................\n","[CV] .... n_neighbors=2, weights=distance, score=-0.549, total= 0.0s\n","[CV] n_neighbors=2, weights=distance .................................\n","[CV] .... n_neighbors=2, weights=distance, score=-0.603, total= 0.0s\n","[CV] n_neighbors=2, weights=distance .................................\n","[CV] .... n_neighbors=2, weights=distance, score=-0.340, total= 0.0s\n","[CV] n_neighbors=2, weights=distance .................................\n","[CV] .... n_neighbors=2, weights=distance, score=-0.432, total= 0.0s\n","[CV] n_neighbors=2, weights=distance .................................\n","[CV] .... n_neighbors=2, weights=distance, score=-0.485, total= 0.0s\n","[CV] n_neighbors=2, weights=distance .................................\n","[CV] .... n_neighbors=2, weights=distance, score=-0.468, total= 0.0s\n","[CV] n_neighbors=2, weights=distance .................................\n","[CV] .... n_neighbors=2, weights=distance, score=-0.599, total= 0.0s\n","[CV] n_neighbors=2, weights=distance .................................\n","[CV] .... n_neighbors=2, weights=distance, score=-0.489, total= 0.0s\n","[CV] n_neighbors=2, weights=distance .................................\n","[CV] .... n_neighbors=2, weights=distance, score=-0.497, total= 0.0s\n","[CV] n_neighbors=2, weights=distance .................................\n","[CV] .... n_neighbors=2, weights=distance, score=-0.407, total= 0.0s\n","[CV] n_neighbors=2, weights=distance .................................\n","[CV] .... n_neighbors=2, weights=distance, score=-0.481, total= 0.0s\n","[CV] n_neighbors=2, weights=distance .................................\n","[CV] .... n_neighbors=2, weights=distance, score=-0.408, total= 0.0s\n","[CV] n_neighbors=2, weights=distance .................................\n","[CV] .... n_neighbors=2, weights=distance, score=-0.602, total= 0.0s\n","[CV] n_neighbors=2, weights=distance .................................\n","[CV] .... n_neighbors=2, weights=distance, score=-0.503, total= 0.0s\n","[CV] n_neighbors=2, weights=distance .................................\n","[CV] .... n_neighbors=2, weights=distance, score=-0.546, total= 0.0s\n","[CV] n_neighbors=2, weights=distance .................................\n","[CV] .... n_neighbors=2, weights=distance, score=-0.599, total= 0.0s\n","[CV] n_neighbors=2, weights=distance .................................\n","[CV] .... n_neighbors=2, weights=distance, score=-0.527, total= 0.0s\n","[CV] n_neighbors=2, weights=distance .................................\n","[CV] .... n_neighbors=2, weights=distance, score=-0.531, total= 0.0s\n","[CV] n_neighbors=2, weights=distance .................................\n","[CV] .... n_neighbors=2, weights=distance, score=-0.652, total= 0.0s\n","[CV] n_neighbors=2, weights=distance .................................\n","[CV] .... n_neighbors=2, weights=distance, score=-0.438, total= 0.0s\n","[CV] n_neighbors=2, weights=distance .................................\n","[CV] .... n_neighbors=2, weights=distance, score=-0.594, total= 0.0s\n","[CV] n_neighbors=2, weights=distance .................................\n","[CV] .... n_neighbors=2, weights=distance, score=-0.492, total= 0.0s\n","[CV] n_neighbors=2, weights=distance .................................\n","[CV] .... n_neighbors=2, weights=distance, score=-0.543, total= 0.0s\n","[CV] n_neighbors=2, weights=distance .................................\n","[CV] .... n_neighbors=2, weights=distance, score=-0.596, total= 0.0s\n","[CV] n_neighbors=2, weights=distance .................................\n","[CV] .... n_neighbors=2, weights=distance, score=-0.342, total= 0.0s\n","[CV] n_neighbors=3, weights=uniform ..................................\n","[CV] ..... n_neighbors=3, weights=uniform, score=-0.353, total= 0.1s\n","[CV] n_neighbors=3, weights=uniform ..................................\n","[CV] ..... n_neighbors=3, weights=uniform, score=-0.384, total= 0.0s\n","[CV] n_neighbors=3, weights=uniform ..................................\n","[CV] ..... n_neighbors=3, weights=uniform, score=-0.378, total= 0.1s\n","[CV] n_neighbors=3, weights=uniform ..................................\n","[CV] ..... n_neighbors=3, weights=uniform, score=-0.426, total= 0.1s\n","[CV] n_neighbors=3, weights=uniform ..................................\n","[CV] ..... n_neighbors=3, weights=uniform, score=-0.299, total= 0.1s\n","[CV] n_neighbors=3, weights=uniform ..................................\n","[CV] ..... n_neighbors=3, weights=uniform, score=-0.303, total= 0.1s\n","[CV] n_neighbors=3, weights=uniform ..................................\n","[CV] ..... n_neighbors=3, weights=uniform, score=-0.575, total= 0.1s\n","[CV] n_neighbors=3, weights=uniform ..................................\n","[CV] ..... n_neighbors=3, weights=uniform, score=-0.273, total= 0.1s\n","[CV] n_neighbors=3, weights=uniform ..................................\n","[CV] ..... n_neighbors=3, weights=uniform, score=-0.256, total= 0.1s\n","[CV] n_neighbors=3, weights=uniform ..................................\n","[CV] ..... n_neighbors=3, weights=uniform, score=-0.305, total= 0.1s\n","[CV] n_neighbors=3, weights=uniform ..................................\n","[CV] ..... n_neighbors=3, weights=uniform, score=-0.254, total= 0.1s\n","[CV] n_neighbors=3, weights=uniform ..................................\n","[CV] ..... n_neighbors=3, weights=uniform, score=-0.402, total= 0.1s\n","[CV] n_neighbors=3, weights=uniform ..................................\n","[CV] ..... n_neighbors=3, weights=uniform, score=-0.338, total= 0.1s\n","[CV] n_neighbors=3, weights=uniform ..................................\n","[CV] ..... n_neighbors=3, weights=uniform, score=-0.393, total= 0.1s\n","[CV] n_neighbors=3, weights=uniform ..................................\n","[CV] ..... n_neighbors=3, weights=uniform, score=-0.213, total= 0.1s\n","[CV] n_neighbors=3, weights=uniform ..................................\n","[CV] ..... n_neighbors=3, weights=uniform, score=-0.338, total= 0.1s\n","[CV] n_neighbors=3, weights=uniform ..................................\n","[CV] ..... n_neighbors=3, weights=uniform, score=-0.278, total= 0.1s\n","[CV] n_neighbors=3, weights=uniform ..................................\n","[CV] ..... n_neighbors=3, weights=uniform, score=-0.411, total= 0.1s\n","[CV] n_neighbors=3, weights=uniform ..................................\n","[CV] ..... n_neighbors=3, weights=uniform, score=-0.429, total= 0.1s\n","[CV] n_neighbors=3, weights=uniform ..................................\n","[CV] ..... n_neighbors=3, weights=uniform, score=-0.338, total= 0.1s\n","[CV] n_neighbors=3, weights=uniform ..................................\n","[CV] ..... n_neighbors=3, weights=uniform, score=-0.417, total= 0.1s\n","[CV] n_neighbors=3, weights=uniform ..................................\n","[CV] ..... n_neighbors=3, weights=uniform, score=-0.425, total= 0.1s\n","[CV] n_neighbors=3, weights=uniform ..................................\n","[CV] ..... n_neighbors=3, weights=uniform, score=-0.264, total= 0.0s\n","[CV] n_neighbors=3, weights=uniform ..................................\n","[CV] ..... n_neighbors=3, weights=uniform, score=-0.376, total= 0.1s\n","[CV] n_neighbors=3, weights=uniform ..................................\n","[CV] ..... n_neighbors=3, weights=uniform, score=-0.318, total= 0.1s\n","[CV] n_neighbors=3, weights=uniform ..................................\n","[CV] ..... n_neighbors=3, weights=uniform, score=-0.404, total= 0.1s\n","[CV] n_neighbors=3, weights=uniform ..................................\n","[CV] ..... n_neighbors=3, weights=uniform, score=-0.409, total= 0.1s\n","[CV] n_neighbors=3, weights=uniform ..................................\n","[CV] ..... n_neighbors=3, weights=uniform, score=-0.347, total= 0.1s\n","[CV] n_neighbors=3, weights=uniform ..................................\n","[CV] ..... n_neighbors=3, weights=uniform, score=-0.390, total= 0.1s\n","[CV] n_neighbors=3, weights=uniform ..................................\n","[CV] ..... n_neighbors=3, weights=uniform, score=-0.177, total= 0.1s\n","[CV] n_neighbors=3, weights=distance .................................\n","[CV] .... n_neighbors=3, weights=distance, score=-0.351, total= 0.1s\n","[CV] n_neighbors=3, weights=distance .................................\n","[CV] .... n_neighbors=3, weights=distance, score=-0.387, total= 0.1s\n","[CV] n_neighbors=3, weights=distance .................................\n","[CV] .... n_neighbors=3, weights=distance, score=-0.390, total= 0.1s\n","[CV] n_neighbors=3, weights=distance .................................\n","[CV] .... n_neighbors=3, weights=distance, score=-0.425, total= 0.1s\n","[CV] n_neighbors=3, weights=distance .................................\n","[CV] .... n_neighbors=3, weights=distance, score=-0.302, total= 0.1s\n","[CV] n_neighbors=3, weights=distance .................................\n","[CV] .... n_neighbors=3, weights=distance, score=-0.314, total= 0.1s\n","[CV] n_neighbors=3, weights=distance .................................\n","[CV] .... n_neighbors=3, weights=distance, score=-0.555, total= 0.1s\n","[CV] n_neighbors=3, weights=distance .................................\n","[CV] .... n_neighbors=3, weights=distance, score=-0.275, total= 0.1s\n","[CV] n_neighbors=3, weights=distance .................................\n","[CV] .... n_neighbors=3, weights=distance, score=-0.258, total= 0.1s\n","[CV] n_neighbors=3, weights=distance .................................\n","[CV] .... n_neighbors=3, weights=distance, score=-0.315, total= 0.1s\n","[CV] n_neighbors=3, weights=distance .................................\n","[CV] .... n_neighbors=3, weights=distance, score=-0.258, total= 0.0s\n","[CV] n_neighbors=3, weights=distance .................................\n","[CV] .... n_neighbors=3, weights=distance, score=-0.402, total= 0.1s\n","[CV] n_neighbors=3, weights=distance .................................\n","[CV] .... n_neighbors=3, weights=distance, score=-0.348, total= 0.1s\n","[CV] n_neighbors=3, weights=distance .................................\n","[CV] .... n_neighbors=3, weights=distance, score=-0.396, total= 0.1s\n","[CV] n_neighbors=3, weights=distance .................................\n","[CV] .... n_neighbors=3, weights=distance, score=-0.225, total= 0.1s\n","[CV] n_neighbors=3, weights=distance .................................\n","[CV] .... n_neighbors=3, weights=distance, score=-0.343, total= 0.1s\n","[CV] n_neighbors=3, weights=distance .................................\n","[CV] .... n_neighbors=3, weights=distance, score=-0.271, total= 0.1s\n","[CV] n_neighbors=3, weights=distance .................................\n","[CV] .... n_neighbors=3, weights=distance, score=-0.420, total= 0.1s\n","[CV] n_neighbors=3, weights=distance .................................\n","[CV] .... n_neighbors=3, weights=distance, score=-0.424, total= 0.1s\n","[CV] n_neighbors=3, weights=distance .................................\n","[CV] .... n_neighbors=3, weights=distance, score=-0.346, total= 0.1s\n","[CV] n_neighbors=3, weights=distance .................................\n","[CV] .... n_neighbors=3, weights=distance, score=-0.430, total= 0.1s\n","[CV] n_neighbors=3, weights=distance .................................\n","[CV] .... n_neighbors=3, weights=distance, score=-0.429, total= 0.1s\n","[CV] n_neighbors=3, weights=distance .................................\n","[CV] .... n_neighbors=3, weights=distance, score=-0.277, total= 0.1s\n","[CV] n_neighbors=3, weights=distance .................................\n","[CV] .... n_neighbors=3, weights=distance, score=-0.383, total= 0.1s\n","[CV] n_neighbors=3, weights=distance .................................\n","[CV] .... n_neighbors=3, weights=distance, score=-0.317, total= 0.0s\n","[CV] n_neighbors=3, weights=distance .................................\n","[CV] .... n_neighbors=3, weights=distance, score=-0.401, total= 0.0s\n","[CV] n_neighbors=3, weights=distance .................................\n","[CV] .... n_neighbors=3, weights=distance, score=-0.396, total= 0.1s\n","[CV] n_neighbors=3, weights=distance .................................\n","[CV] .... n_neighbors=3, weights=distance, score=-0.365, total= 0.1s\n","[CV] n_neighbors=3, weights=distance .................................\n","[CV] .... n_neighbors=3, weights=distance, score=-0.402, total= 0.1s\n","[CV] n_neighbors=3, weights=distance .................................\n","[CV] .... n_neighbors=3, weights=distance, score=-0.184, total= 0.1s\n","[CV] n_neighbors=4, weights=uniform ..................................\n","[CV] ..... n_neighbors=4, weights=uniform, score=-0.237, total= 0.1s\n","[CV] n_neighbors=4, weights=uniform ..................................\n","[CV] ..... n_neighbors=4, weights=uniform, score=-0.193, total= 0.1s\n","[CV] n_neighbors=4, weights=uniform ..................................\n","[CV] ..... n_neighbors=4, weights=uniform, score=-0.284, total= 0.1s\n","[CV] n_neighbors=4, weights=uniform ..................................\n","[CV] ..... n_neighbors=4, weights=uniform, score=-0.235, total= 0.1s\n","[CV] n_neighbors=4, weights=uniform ..................................\n","[CV] ..... n_neighbors=4, weights=uniform, score=-0.221, total= 0.1s\n","[CV] n_neighbors=4, weights=uniform ..................................\n","[CV] ..... n_neighbors=4, weights=uniform, score=-0.195, total= 0.1s\n","[CV] n_neighbors=4, weights=uniform ..................................\n","[CV] ..... n_neighbors=4, weights=uniform, score=-0.394, total= 0.1s\n","[CV] n_neighbors=4, weights=uniform ..................................\n","[CV] ..... n_neighbors=4, weights=uniform, score=-0.189, total= 0.1s\n","[CV] n_neighbors=4, weights=uniform ..................................\n","[CV] ..... n_neighbors=4, weights=uniform, score=-0.176, total= 0.1s\n","[CV] n_neighbors=4, weights=uniform ..................................\n","[CV] ..... n_neighbors=4, weights=uniform, score=-0.183, total= 0.1s\n","[CV] n_neighbors=4, weights=uniform ..................................\n","[CV] ..... n_neighbors=4, weights=uniform, score=-0.152, total= 0.1s\n","[CV] n_neighbors=4, weights=uniform ..................................\n","[CV] ..... n_neighbors=4, weights=uniform, score=-0.332, total= 0.1s\n","[CV] n_neighbors=4, weights=uniform ..................................\n","[CV] ..... n_neighbors=4, weights=uniform, score=-0.188, total= 0.1s\n","[CV] n_neighbors=4, weights=uniform ..................................\n","[CV] ..... n_neighbors=4, weights=uniform, score=-0.338, total= 0.1s\n","[CV] n_neighbors=4, weights=uniform ..................................\n","[CV] ..... n_neighbors=4, weights=uniform, score=-0.135, total= 0.1s\n","[CV] n_neighbors=4, weights=uniform ..................................\n","[CV] ..... n_neighbors=4, weights=uniform, score=-0.247, total= 0.1s\n","[CV] n_neighbors=4, weights=uniform ..................................\n","[CV] ..... n_neighbors=4, weights=uniform, score=-0.160, total= 0.1s\n","[CV] n_neighbors=4, weights=uniform ..................................\n","[CV] ..... n_neighbors=4, weights=uniform, score=-0.289, total= 0.1s\n","[CV] n_neighbors=4, weights=uniform ..................................\n","[CV] ..... n_neighbors=4, weights=uniform, score=-0.312, total= 0.1s\n","[CV] n_neighbors=4, weights=uniform ..................................\n","[CV] ..... n_neighbors=4, weights=uniform, score=-0.235, total= 0.1s\n","[CV] n_neighbors=4, weights=uniform ..................................\n","[CV] ..... n_neighbors=4, weights=uniform, score=-0.261, total= 0.1s\n","[CV] n_neighbors=4, weights=uniform ..................................\n","[CV] ..... n_neighbors=4, weights=uniform, score=-0.329, total= 0.1s\n","[CV] n_neighbors=4, weights=uniform ..................................\n","[CV] ..... n_neighbors=4, weights=uniform, score=-0.120, total= 0.1s\n","[CV] n_neighbors=4, weights=uniform ..................................\n","[CV] ..... n_neighbors=4, weights=uniform, score=-0.230, total= 0.1s\n","[CV] n_neighbors=4, weights=uniform ..................................\n","[CV] ..... n_neighbors=4, weights=uniform, score=-0.269, total= 0.1s\n","[CV] n_neighbors=4, weights=uniform ..................................\n","[CV] ..... n_neighbors=4, weights=uniform, score=-0.297, total= 0.1s\n","[CV] n_neighbors=4, weights=uniform ..................................\n","[CV] ..... n_neighbors=4, weights=uniform, score=-0.268, total= 0.1s\n","[CV] n_neighbors=4, weights=uniform ..................................\n","[CV] ..... n_neighbors=4, weights=uniform, score=-0.285, total= 0.1s\n","[CV] n_neighbors=4, weights=uniform ..................................\n","[CV] ..... n_neighbors=4, weights=uniform, score=-0.323, total= 0.1s\n","[CV] n_neighbors=4, weights=uniform ..................................\n","[CV] ..... n_neighbors=4, weights=uniform, score=-0.161, total= 0.1s\n","[CV] n_neighbors=4, weights=distance .................................\n","[CV] .... n_neighbors=4, weights=distance, score=-0.239, total= 0.1s\n","[CV] n_neighbors=4, weights=distance .................................\n","[CV] .... n_neighbors=4, weights=distance, score=-0.206, total= 0.1s\n","[CV] n_neighbors=4, weights=distance .................................\n","[CV] .... n_neighbors=4, weights=distance, score=-0.296, total= 0.1s\n","[CV] n_neighbors=4, weights=distance .................................\n","[CV] .... n_neighbors=4, weights=distance, score=-0.246, total= 0.1s\n","[CV] n_neighbors=4, weights=distance .................................\n","[CV] .... n_neighbors=4, weights=distance, score=-0.221, total= 0.1s\n","[CV] n_neighbors=4, weights=distance .................................\n","[CV] .... n_neighbors=4, weights=distance, score=-0.205, total= 0.1s\n","[CV] n_neighbors=4, weights=distance .................................\n","[CV] .... n_neighbors=4, weights=distance, score=-0.395, total= 0.1s\n","[CV] n_neighbors=4, weights=distance .................................\n","[CV] .... n_neighbors=4, weights=distance, score=-0.186, total= 0.1s\n","[CV] n_neighbors=4, weights=distance .................................\n","[CV] .... n_neighbors=4, weights=distance, score=-0.173, total= 0.1s\n","[CV] n_neighbors=4, weights=distance .................................\n","[CV] .... n_neighbors=4, weights=distance, score=-0.197, total= 0.1s\n","[CV] n_neighbors=4, weights=distance .................................\n","[CV] .... n_neighbors=4, weights=distance, score=-0.159, total= 0.1s\n","[CV] n_neighbors=4, weights=distance .................................\n","[CV] .... n_neighbors=4, weights=distance, score=-0.333, total= 0.1s\n","[CV] n_neighbors=4, weights=distance .................................\n","[CV] .... n_neighbors=4, weights=distance, score=-0.203, total= 0.1s\n","[CV] n_neighbors=4, weights=distance .................................\n","[CV] .... n_neighbors=4, weights=distance, score=-0.340, total= 0.1s\n","[CV] n_neighbors=4, weights=distance .................................\n","[CV] .... n_neighbors=4, weights=distance, score=-0.140, total= 0.1s\n","[CV] n_neighbors=4, weights=distance .................................\n","[CV] .... n_neighbors=4, weights=distance, score=-0.250, total= 0.1s\n","[CV] n_neighbors=4, weights=distance .................................\n","[CV] .... n_neighbors=4, weights=distance, score=-0.160, total= 0.1s\n","[CV] n_neighbors=4, weights=distance .................................\n","[CV] .... n_neighbors=4, weights=distance, score=-0.296, total= 0.1s\n","[CV] n_neighbors=4, weights=distance .................................\n","[CV] .... n_neighbors=4, weights=distance, score=-0.313, total= 0.1s\n","[CV] n_neighbors=4, weights=distance .................................\n","[CV] .... n_neighbors=4, weights=distance, score=-0.245, total= 0.1s\n","[CV] n_neighbors=4, weights=distance .................................\n","[CV] .... n_neighbors=4, weights=distance, score=-0.279, total= 0.1s\n","[CV] n_neighbors=4, weights=distance .................................\n","[CV] .... n_neighbors=4, weights=distance, score=-0.340, total= 0.1s\n","[CV] n_neighbors=4, weights=distance .................................\n","[CV] .... n_neighbors=4, weights=distance, score=-0.133, total= 0.1s\n","[CV] n_neighbors=4, weights=distance .................................\n","[CV] .... n_neighbors=4, weights=distance, score=-0.240, total= 0.1s\n","[CV] n_neighbors=4, weights=distance .................................\n","[CV] .... n_neighbors=4, weights=distance, score=-0.264, total= 0.1s\n","[CV] n_neighbors=4, weights=distance .................................\n","[CV] .... n_neighbors=4, weights=distance, score=-0.298, total= 0.1s\n","[CV] n_neighbors=4, weights=distance .................................\n","[CV] .... n_neighbors=4, weights=distance, score=-0.260, total= 0.1s\n","[CV] n_neighbors=4, weights=distance .................................\n","[CV] .... n_neighbors=4, weights=distance, score=-0.299, total= 0.1s\n","[CV] n_neighbors=4, weights=distance .................................\n","[CV] .... n_neighbors=4, weights=distance, score=-0.336, total= 0.1s\n","[CV] n_neighbors=4, weights=distance .................................\n","[CV] .... n_neighbors=4, weights=distance, score=-0.163, total= 0.1s\n","[CV] n_neighbors=5, weights=uniform ..................................\n","[CV] ..... n_neighbors=5, weights=uniform, score=-0.227, total= 0.1s\n","[CV] n_neighbors=5, weights=uniform ..................................\n","[CV] ..... n_neighbors=5, weights=uniform, score=-0.122, total= 0.1s\n","[CV] n_neighbors=5, weights=uniform ..................................\n","[CV] ..... n_neighbors=5, weights=uniform, score=-0.273, total= 0.1s\n","[CV] n_neighbors=5, weights=uniform ..................................\n","[CV] ..... n_neighbors=5, weights=uniform, score=-0.164, total= 0.1s\n","[CV] n_neighbors=5, weights=uniform ..................................\n","[CV] ..... n_neighbors=5, weights=uniform, score=-0.209, total= 0.1s\n","[CV] n_neighbors=5, weights=uniform ..................................\n","[CV] ..... n_neighbors=5, weights=uniform, score=-0.130, total= 0.1s\n","[CV] n_neighbors=5, weights=uniform ..................................\n","[CV] ..... n_neighbors=5, weights=uniform, score=-0.327, total= 0.1s\n","[CV] n_neighbors=5, weights=uniform ..................................\n","[CV] ..... n_neighbors=5, weights=uniform, score=-0.201, total= 0.1s\n","[CV] n_neighbors=5, weights=uniform ..................................\n","[CV] ..... n_neighbors=5, weights=uniform, score=-0.163, total= 0.1s\n","[CV] n_neighbors=5, weights=uniform ..................................\n","[CV] ..... n_neighbors=5, weights=uniform, score=-0.142, total= 0.1s\n","[CV] n_neighbors=5, weights=uniform ..................................\n","[CV] ..... n_neighbors=5, weights=uniform, score=-0.144, total= 0.1s\n","[CV] n_neighbors=5, weights=uniform ..................................\n","[CV] ..... n_neighbors=5, weights=uniform, score=-0.276, total= 0.1s\n","[CV] n_neighbors=5, weights=uniform ..................................\n","[CV] ..... n_neighbors=5, weights=uniform, score=-0.155, total= 0.1s\n","[CV] n_neighbors=5, weights=uniform ..................................\n","[CV] ..... n_neighbors=5, weights=uniform, score=-0.262, total= 0.1s\n","[CV] n_neighbors=5, weights=uniform ..................................\n","[CV] ..... n_neighbors=5, weights=uniform, score=-0.153, total= 0.1s\n","[CV] n_neighbors=5, weights=uniform ..................................\n","[CV] ..... n_neighbors=5, weights=uniform, score=-0.204, total= 0.1s\n","[CV] n_neighbors=5, weights=uniform ..................................\n","[CV] ..... n_neighbors=5, weights=uniform, score=-0.144, total= 0.1s\n","[CV] n_neighbors=5, weights=uniform ..................................\n","[CV] ..... n_neighbors=5, weights=uniform, score=-0.226, total= 0.1s\n","[CV] n_neighbors=5, weights=uniform ..................................\n","[CV] ..... n_neighbors=5, weights=uniform, score=-0.218, total= 0.1s\n","[CV] n_neighbors=5, weights=uniform ..................................\n","[CV] ..... n_neighbors=5, weights=uniform, score=-0.163, total= 0.1s\n","[CV] n_neighbors=5, weights=uniform ..................................\n","[CV] ..... n_neighbors=5, weights=uniform, score=-0.150, total= 0.1s\n","[CV] n_neighbors=5, weights=uniform ..................................\n","[CV] ..... n_neighbors=5, weights=uniform, score=-0.298, total= 0.1s\n","[CV] n_neighbors=5, weights=uniform ..................................\n","[CV] ..... n_neighbors=5, weights=uniform, score=-0.099, total= 0.1s\n","[CV] n_neighbors=5, weights=uniform ..................................\n","[CV] ..... n_neighbors=5, weights=uniform, score=-0.154, total= 0.1s\n","[CV] n_neighbors=5, weights=uniform ..................................\n","[CV] ..... n_neighbors=5, weights=uniform, score=-0.203, total= 0.1s\n","[CV] n_neighbors=5, weights=uniform ..................................\n","[CV] ..... n_neighbors=5, weights=uniform, score=-0.231, total= 0.1s\n","[CV] n_neighbors=5, weights=uniform ..................................\n","[CV] ..... n_neighbors=5, weights=uniform, score=-0.216, total= 0.1s\n","[CV] n_neighbors=5, weights=uniform ..................................\n","[CV] ..... n_neighbors=5, weights=uniform, score=-0.224, total= 0.1s\n","[CV] n_neighbors=5, weights=uniform ..................................\n","[CV] ..... n_neighbors=5, weights=uniform, score=-0.283, total= 0.1s\n","[CV] n_neighbors=5, weights=uniform ..................................\n","[CV] ..... n_neighbors=5, weights=uniform, score=-0.146, total= 0.1s\n","[CV] n_neighbors=5, weights=distance .................................\n","[CV] .... n_neighbors=5, weights=distance, score=-0.223, total= 0.1s\n","[CV] n_neighbors=5, weights=distance .................................\n","[CV] .... n_neighbors=5, weights=distance, score=-0.132, total= 0.1s\n","[CV] n_neighbors=5, weights=distance .................................\n","[CV] .... n_neighbors=5, weights=distance, score=-0.279, total= 0.1s\n","[CV] n_neighbors=5, weights=distance .................................\n","[CV] .... n_neighbors=5, weights=distance, score=-0.173, total= 0.1s\n","[CV] n_neighbors=5, weights=distance .................................\n","[CV] .... n_neighbors=5, weights=distance, score=-0.204, total= 0.1s\n","[CV] n_neighbors=5, weights=distance .................................\n","[CV] .... n_neighbors=5, weights=distance, score=-0.141, total= 0.1s\n","[CV] n_neighbors=5, weights=distance .................................\n","[CV] .... n_neighbors=5, weights=distance, score=-0.328, total= 0.1s\n","[CV] n_neighbors=5, weights=distance .................................\n","[CV] .... n_neighbors=5, weights=distance, score=-0.190, total= 0.1s\n","[CV] n_neighbors=5, weights=distance .................................\n","[CV] .... n_neighbors=5, weights=distance, score=-0.154, total= 0.1s\n","[CV] n_neighbors=5, weights=distance .................................\n","[CV] .... n_neighbors=5, weights=distance, score=-0.149, total= 0.1s\n","[CV] n_neighbors=5, weights=distance .................................\n","[CV] .... n_neighbors=5, weights=distance, score=-0.144, total= 0.1s\n","[CV] n_neighbors=5, weights=distance .................................\n","[CV] .... n_neighbors=5, weights=distance, score=-0.278, total= 0.1s\n","[CV] n_neighbors=5, weights=distance .................................\n","[CV] .... n_neighbors=5, weights=distance, score=-0.167, total= 0.1s\n","[CV] n_neighbors=5, weights=distance .................................\n","[CV] .... n_neighbors=5, weights=distance, score=-0.269, total= 0.1s\n","[CV] n_neighbors=5, weights=distance .................................\n","[CV] .... n_neighbors=5, weights=distance, score=-0.147, total= 0.1s\n","[CV] n_neighbors=5, weights=distance .................................\n","[CV] .... n_neighbors=5, weights=distance, score=-0.203, total= 0.1s\n","[CV] n_neighbors=5, weights=distance .................................\n","[CV] .... n_neighbors=5, weights=distance, score=-0.138, total= 0.1s\n","[CV] n_neighbors=5, weights=distance .................................\n","[CV] .... n_neighbors=5, weights=distance, score=-0.240, total= 0.1s\n","[CV] n_neighbors=5, weights=distance .................................\n","[CV] .... n_neighbors=5, weights=distance, score=-0.220, total= 0.1s\n","[CV] n_neighbors=5, weights=distance .................................\n","[CV] .... n_neighbors=5, weights=distance, score=-0.173, total= 0.1s\n","[CV] n_neighbors=5, weights=distance .................................\n","[CV] .... n_neighbors=5, weights=distance, score=-0.167, total= 0.1s\n","[CV] n_neighbors=5, weights=distance .................................\n","[CV] .... n_neighbors=5, weights=distance, score=-0.302, total= 0.1s\n","[CV] n_neighbors=5, weights=distance .................................\n","[CV] .... n_neighbors=5, weights=distance, score=-0.105, total= 0.1s\n","[CV] n_neighbors=5, weights=distance .................................\n","[CV] .... n_neighbors=5, weights=distance, score=-0.164, total= 0.1s\n","[CV] n_neighbors=5, weights=distance .................................\n","[CV] .... n_neighbors=5, weights=distance, score=-0.199, total= 0.1s\n","[CV] n_neighbors=5, weights=distance .................................\n","[CV] .... n_neighbors=5, weights=distance, score=-0.233, total= 0.1s\n","[CV] n_neighbors=5, weights=distance .................................\n","[CV] .... n_neighbors=5, weights=distance, score=-0.210, total= 0.1s\n","[CV] n_neighbors=5, weights=distance .................................\n","[CV] .... n_neighbors=5, weights=distance, score=-0.237, total= 0.1s\n","[CV] n_neighbors=5, weights=distance .................................\n","[CV] .... n_neighbors=5, weights=distance, score=-0.292, total= 0.1s\n","[CV] n_neighbors=5, weights=distance .................................\n","[CV] .... n_neighbors=5, weights=distance, score=-0.144, total= 0.1s\n","[CV] n_neighbors=6, weights=uniform ..................................\n","[CV] ..... n_neighbors=6, weights=uniform, score=-0.204, total= 0.1s\n","[CV] n_neighbors=6, weights=uniform ..................................\n","[CV] ..... n_neighbors=6, weights=uniform, score=-0.165, total= 0.1s\n","[CV] n_neighbors=6, weights=uniform ..................................\n","[CV] ..... n_neighbors=6, weights=uniform, score=-0.271, total= 0.1s\n","[CV] n_neighbors=6, weights=uniform ..................................\n","[CV] ..... n_neighbors=6, weights=uniform, score=-0.169, total= 0.1s\n","[CV] n_neighbors=6, weights=uniform ..................................\n","[CV] ..... n_neighbors=6, weights=uniform, score=-0.164, total= 0.1s\n","[CV] n_neighbors=6, weights=uniform ..................................\n","[CV] ..... n_neighbors=6, weights=uniform, score=-0.096, total= 0.1s\n","[CV] n_neighbors=6, weights=uniform ..................................\n","[CV] ..... n_neighbors=6, weights=uniform, score=-0.298, total= 0.1s\n","[CV] n_neighbors=6, weights=uniform ..................................\n","[CV] ..... n_neighbors=6, weights=uniform, score=-0.208, total= 0.1s\n","[CV] n_neighbors=6, weights=uniform ..................................\n","[CV] ..... n_neighbors=6, weights=uniform, score=-0.159, total= 0.1s\n","[CV] n_neighbors=6, weights=uniform ..................................\n","[CV] ..... n_neighbors=6, weights=uniform, score=-0.097, total= 0.1s\n","[CV] n_neighbors=6, weights=uniform ..................................\n","[CV] ..... n_neighbors=6, weights=uniform, score=-0.081, total= 0.1s\n","[CV] n_neighbors=6, weights=uniform ..................................\n","[CV] ..... n_neighbors=6, weights=uniform, score=-0.247, total= 0.1s\n","[CV] n_neighbors=6, weights=uniform ..................................\n","[CV] ..... n_neighbors=6, weights=uniform, score=-0.159, total= 0.1s\n","[CV] n_neighbors=6, weights=uniform ..................................\n","[CV] ..... n_neighbors=6, weights=uniform, score=-0.222, total= 0.1s\n","[CV] n_neighbors=6, weights=uniform ..................................\n","[CV] ..... n_neighbors=6, weights=uniform, score=-0.108, total= 0.1s\n","[CV] n_neighbors=6, weights=uniform ..................................\n","[CV] ..... n_neighbors=6, weights=uniform, score=-0.193, total= 0.1s\n","[CV] n_neighbors=6, weights=uniform ..................................\n","[CV] ..... n_neighbors=6, weights=uniform, score=-0.162, total= 0.1s\n","[CV] n_neighbors=6, weights=uniform ..................................\n","[CV] ..... n_neighbors=6, weights=uniform, score=-0.197, total= 0.1s\n","[CV] n_neighbors=6, weights=uniform ..................................\n","[CV] ..... n_neighbors=6, weights=uniform, score=-0.161, total= 0.1s\n","[CV] n_neighbors=6, weights=uniform ..................................\n","[CV] ..... n_neighbors=6, weights=uniform, score=-0.145, total= 0.1s\n","[CV] n_neighbors=6, weights=uniform ..................................\n","[CV] ..... n_neighbors=6, weights=uniform, score=-0.125, total= 0.1s\n","[CV] n_neighbors=6, weights=uniform ..................................\n","[CV] ..... n_neighbors=6, weights=uniform, score=-0.226, total= 0.1s\n","[CV] n_neighbors=6, weights=uniform ..................................\n","[CV] ..... n_neighbors=6, weights=uniform, score=-0.134, total= 0.1s\n","[CV] n_neighbors=6, weights=uniform ..................................\n","[CV] ..... n_neighbors=6, weights=uniform, score=-0.120, total= 0.1s\n","[CV] n_neighbors=6, weights=uniform ..................................\n","[CV] ..... n_neighbors=6, weights=uniform, score=-0.169, total= 0.1s\n","[CV] n_neighbors=6, weights=uniform ..................................\n","[CV] ..... n_neighbors=6, weights=uniform, score=-0.233, total= 0.1s\n","[CV] n_neighbors=6, weights=uniform ..................................\n","[CV] ..... n_neighbors=6, weights=uniform, score=-0.161, total= 0.1s\n","[CV] n_neighbors=6, weights=uniform ..................................\n","[CV] ..... n_neighbors=6, weights=uniform, score=-0.198, total= 0.1s\n","[CV] n_neighbors=6, weights=uniform ..................................\n","[CV] ..... n_neighbors=6, weights=uniform, score=-0.235, total= 0.1s\n","[CV] n_neighbors=6, weights=uniform ..................................\n","[CV] ..... n_neighbors=6, weights=uniform, score=-0.120, total= 0.1s\n","[CV] n_neighbors=6, weights=distance .................................\n","[CV] .... n_neighbors=6, weights=distance, score=-0.201, total= 0.1s\n","[CV] n_neighbors=6, weights=distance .................................\n","[CV] .... n_neighbors=6, weights=distance, score=-0.163, total= 0.1s\n","[CV] n_neighbors=6, weights=distance .................................\n","[CV] .... n_neighbors=6, weights=distance, score=-0.272, total= 0.1s\n","[CV] n_neighbors=6, weights=distance .................................\n","[CV] .... n_neighbors=6, weights=distance, score=-0.170, total= 0.1s\n","[CV] n_neighbors=6, weights=distance .................................\n","[CV] .... n_neighbors=6, weights=distance, score=-0.159, total= 0.1s\n","[CV] n_neighbors=6, weights=distance .................................\n","[CV] .... n_neighbors=6, weights=distance, score=-0.106, total= 0.1s\n","[CV] n_neighbors=6, weights=distance .................................\n","[CV] .... n_neighbors=6, weights=distance, score=-0.299, total= 0.1s\n","[CV] n_neighbors=6, weights=distance .................................\n","[CV] .... n_neighbors=6, weights=distance, score=-0.196, total= 0.1s\n","[CV] n_neighbors=6, weights=distance .................................\n","[CV] .... n_neighbors=6, weights=distance, score=-0.145, total= 0.1s\n","[CV] n_neighbors=6, weights=distance .................................\n","[CV] .... n_neighbors=6, weights=distance, score=-0.104, total= 0.1s\n","[CV] n_neighbors=6, weights=distance .................................\n","[CV] .... n_neighbors=6, weights=distance, score=-0.088, total= 0.1s\n","[CV] n_neighbors=6, weights=distance .................................\n","[CV] .... n_neighbors=6, weights=distance, score=-0.248, total= 0.1s\n","[CV] n_neighbors=6, weights=distance .................................\n","[CV] .... n_neighbors=6, weights=distance, score=-0.165, total= 0.1s\n","[CV] n_neighbors=6, weights=distance .................................\n","[CV] .... n_neighbors=6, weights=distance, score=-0.233, total= 0.1s\n","[CV] n_neighbors=6, weights=distance .................................\n","[CV] .... n_neighbors=6, weights=distance, score=-0.104, total= 0.1s\n","[CV] n_neighbors=6, weights=distance .................................\n","[CV] .... n_neighbors=6, weights=distance, score=-0.185, total= 0.1s\n","[CV] n_neighbors=6, weights=distance .................................\n","[CV] .... n_neighbors=6, weights=distance, score=-0.150, total= 0.1s\n","[CV] n_neighbors=6, weights=distance .................................\n","[CV] .... n_neighbors=6, weights=distance, score=-0.208, total= 0.1s\n","[CV] n_neighbors=6, weights=distance .................................\n","[CV] .... n_neighbors=6, weights=distance, score=-0.165, total= 0.1s\n","[CV] n_neighbors=6, weights=distance .................................\n","[CV] .... n_neighbors=6, weights=distance, score=-0.151, total= 0.1s\n","[CV] n_neighbors=6, weights=distance .................................\n","[CV] .... n_neighbors=6, weights=distance, score=-0.138, total= 0.1s\n","[CV] n_neighbors=6, weights=distance .................................\n","[CV] .... n_neighbors=6, weights=distance, score=-0.237, total= 0.1s\n","[CV] n_neighbors=6, weights=distance .................................\n","[CV] .... n_neighbors=6, weights=distance, score=-0.126, total= 0.1s\n","[CV] n_neighbors=6, weights=distance .................................\n","[CV] .... n_neighbors=6, weights=distance, score=-0.128, total= 0.1s\n","[CV] n_neighbors=6, weights=distance .................................\n","[CV] .... n_neighbors=6, weights=distance, score=-0.168, total= 0.1s\n","[CV] n_neighbors=6, weights=distance .................................\n","[CV] .... n_neighbors=6, weights=distance, score=-0.235, total= 0.1s\n","[CV] n_neighbors=6, weights=distance .................................\n","[CV] .... n_neighbors=6, weights=distance, score=-0.160, total= 0.1s\n","[CV] n_neighbors=6, weights=distance .................................\n","[CV] .... n_neighbors=6, weights=distance, score=-0.205, total= 0.1s\n","[CV] n_neighbors=6, weights=distance .................................\n","[CV] .... n_neighbors=6, weights=distance, score=-0.244, total= 0.1s\n","[CV] n_neighbors=6, weights=distance .................................\n","[CV] .... n_neighbors=6, weights=distance, score=-0.114, total= 0.1s\n","[Parallel(n_jobs=1)]: Done 300 out of 300 | elapsed: 16.2s finished\n","KNeighborsRegressor(algorithm='auto', leaf_size=30, metric='minkowski',\n"," metric_params=None, n_jobs=None, n_neighbors=6, p=2,\n"," weights='uniform')\n","window: 1, stide: 1, rmse: 3.01506249551346\n","[1, 4, 9, 12, 2, 0, 3, 13, 11, 10, 6, 5, 7, 8]\n","[1, 4, 9, 12, 2, 0, 3]\n","(3000, 105)\n","Number of Feature-Columns: 105\n","\n","Features Ready for undergoing selection tests done ...\n","\n","Fitting 30 folds for each of 10 candidates, totalling 300 fits\n","[Parallel(n_jobs=1)]: Using backend SequentialBackend with 1 concurrent workers.\n","[CV] n_neighbors=2, weights=uniform ..................................\n","[CV] ..... n_neighbors=2, weights=uniform, score=-0.520, total= 0.1s\n","[Parallel(n_jobs=1)]: Done 1 out of 1 | elapsed: 0.1s remaining: 0.0s\n","[CV] n_neighbors=2, weights=uniform ..................................\n","[CV] ..... n_neighbors=2, weights=uniform, score=-0.595, total= 0.1s\n","[Parallel(n_jobs=1)]: Done 2 out of 2 | elapsed: 0.1s remaining: 0.0s\n","[CV] n_neighbors=2, weights=uniform ..................................\n","[CV] ..... n_neighbors=2, weights=uniform, score=-0.688, total= 0.1s\n","[Parallel(n_jobs=1)]: Done 3 out of 3 | elapsed: 0.2s remaining: 0.0s\n","[CV] n_neighbors=2, weights=uniform ..................................\n","[CV] ..... n_neighbors=2, weights=uniform, score=-0.579, total= 0.1s\n","[Parallel(n_jobs=1)]: Done 4 out of 4 | elapsed: 0.3s remaining: 0.0s\n","[CV] n_neighbors=2, weights=uniform ..................................\n","[CV] ..... n_neighbors=2, weights=uniform, score=-0.519, total= 0.1s\n","[CV] n_neighbors=2, weights=uniform ..................................\n","[CV] ..... n_neighbors=2, weights=uniform, score=-0.405, total= 0.1s\n","[CV] n_neighbors=2, weights=uniform ..................................\n","[CV] ..... n_neighbors=2, weights=uniform, score=-0.666, total= 0.1s\n","[CV] n_neighbors=2, weights=uniform ..................................\n","[CV] ..... n_neighbors=2, weights=uniform, score=-0.515, total= 0.1s\n","[CV] n_neighbors=2, weights=uniform ..................................\n","[CV] ..... n_neighbors=2, weights=uniform, score=-0.462, total= 0.1s\n","[CV] n_neighbors=2, weights=uniform ..................................\n","[CV] ..... n_neighbors=2, weights=uniform, score=-0.530, total= 0.1s\n","[CV] n_neighbors=2, weights=uniform ..................................\n","[CV] ..... n_neighbors=2, weights=uniform, score=-0.492, total= 0.1s\n","[CV] n_neighbors=2, weights=uniform ..................................\n","[CV] ..... n_neighbors=2, weights=uniform, score=-0.493, total= 0.1s\n","[CV] n_neighbors=2, weights=uniform ..................................\n","[CV] ..... n_neighbors=2, weights=uniform, score=-0.530, total= 0.1s\n","[CV] n_neighbors=2, weights=uniform ..................................\n","[CV] ..... n_neighbors=2, weights=uniform, score=-0.529, total= 0.1s\n","[CV] n_neighbors=2, weights=uniform ..................................\n","[CV] ..... n_neighbors=2, weights=uniform, score=-0.483, total= 0.1s\n","[CV] n_neighbors=2, weights=uniform ..................................\n","[CV] ..... n_neighbors=2, weights=uniform, score=-0.511, total= 0.1s\n","[CV] n_neighbors=2, weights=uniform ..................................\n","[CV] ..... n_neighbors=2, weights=uniform, score=-0.395, total= 0.1s\n","[CV] n_neighbors=2, weights=uniform ..................................\n","[CV] ..... n_neighbors=2, weights=uniform, score=-0.694, total= 0.1s\n","[CV] n_neighbors=2, weights=uniform ..................................\n","[CV] ..... n_neighbors=2, weights=uniform, score=-0.786, total= 0.1s\n","[CV] n_neighbors=2, weights=uniform ..................................\n","[CV] ..... n_neighbors=2, weights=uniform, score=-0.301, total= 0.1s\n","[CV] n_neighbors=2, weights=uniform ..................................\n","[CV] ..... n_neighbors=2, weights=uniform, score=-0.554, total= 0.1s\n","[CV] n_neighbors=2, weights=uniform ..................................\n","[CV] ..... n_neighbors=2, weights=uniform, score=-0.527, total= 0.1s\n","[CV] n_neighbors=2, weights=uniform ..................................\n","[CV] ..... n_neighbors=2, weights=uniform, score=-0.432, total= 0.1s\n","[CV] n_neighbors=2, weights=uniform ..................................\n","[CV] ..... n_neighbors=2, weights=uniform, score=-0.544, total= 0.1s\n","[CV] n_neighbors=2, weights=uniform ..................................\n","[CV] ..... n_neighbors=2, weights=uniform, score=-0.526, total= 0.1s\n","[CV] n_neighbors=2, weights=uniform ..................................\n","[CV] ..... n_neighbors=2, weights=uniform, score=-0.591, total= 0.1s\n","[CV] n_neighbors=2, weights=uniform ..................................\n","[CV] ..... n_neighbors=2, weights=uniform, score=-0.568, total= 0.1s\n","[CV] n_neighbors=2, weights=uniform ..................................\n","[CV] ..... n_neighbors=2, weights=uniform, score=-0.497, total= 0.1s\n","[CV] n_neighbors=2, weights=uniform ..................................\n","[CV] ..... n_neighbors=2, weights=uniform, score=-0.709, total= 0.1s\n","[CV] n_neighbors=2, weights=uniform ..................................\n","[CV] ..... n_neighbors=2, weights=uniform, score=-0.412, total= 0.1s\n","[CV] n_neighbors=2, weights=distance .................................\n","[CV] .... n_neighbors=2, weights=distance, score=-0.522, total= 0.1s\n","[CV] n_neighbors=2, weights=distance .................................\n","[CV] .... n_neighbors=2, weights=distance, score=-0.582, total= 0.1s\n","[CV] n_neighbors=2, weights=distance .................................\n","[CV] .... n_neighbors=2, weights=distance, score=-0.686, total= 0.1s\n","[CV] n_neighbors=2, weights=distance .................................\n","[CV] .... n_neighbors=2, weights=distance, score=-0.582, total= 0.1s\n","[CV] n_neighbors=2, weights=distance .................................\n","[CV] .... n_neighbors=2, weights=distance, score=-0.518, total= 0.1s\n","[CV] n_neighbors=2, weights=distance .................................\n","[CV] .... n_neighbors=2, weights=distance, score=-0.410, total= 0.1s\n","[CV] n_neighbors=2, weights=distance .................................\n","[CV] .... n_neighbors=2, weights=distance, score=-0.659, total= 0.1s\n","[CV] n_neighbors=2, weights=distance .................................\n","[CV] .... n_neighbors=2, weights=distance, score=-0.516, total= 0.1s\n","[CV] n_neighbors=2, weights=distance .................................\n","[CV] .... n_neighbors=2, weights=distance, score=-0.471, total= 0.1s\n","[CV] n_neighbors=2, weights=distance .................................\n","[CV] .... n_neighbors=2, weights=distance, score=-0.540, total= 0.1s\n","[CV] n_neighbors=2, weights=distance .................................\n","[CV] .... n_neighbors=2, weights=distance, score=-0.492, total= 0.1s\n","[CV] n_neighbors=2, weights=distance .................................\n","[CV] .... n_neighbors=2, weights=distance, score=-0.492, total= 0.1s\n","[CV] n_neighbors=2, weights=distance .................................\n","[CV] .... n_neighbors=2, weights=distance, score=-0.512, total= 0.1s\n","[CV] n_neighbors=2, weights=distance .................................\n","[CV] .... n_neighbors=2, weights=distance, score=-0.526, total= 0.1s\n","[CV] n_neighbors=2, weights=distance .................................\n","[CV] .... n_neighbors=2, weights=distance, score=-0.492, total= 0.1s\n","[CV] n_neighbors=2, weights=distance .................................\n","[CV] .... n_neighbors=2, weights=distance, score=-0.496, total= 0.1s\n","[CV] n_neighbors=2, weights=distance .................................\n","[CV] .... n_neighbors=2, weights=distance, score=-0.388, total= 0.1s\n","[CV] n_neighbors=2, weights=distance .................................\n","[CV] .... n_neighbors=2, weights=distance, score=-0.708, total= 0.1s\n","[CV] n_neighbors=2, weights=distance .................................\n","[CV] .... n_neighbors=2, weights=distance, score=-0.788, total= 0.1s\n","[CV] n_neighbors=2, weights=distance .................................\n","[CV] .... n_neighbors=2, weights=distance, score=-0.302, total= 0.1s\n","[CV] n_neighbors=2, weights=distance .................................\n","[CV] .... n_neighbors=2, weights=distance, score=-0.552, total= 0.1s\n","[CV] n_neighbors=2, weights=distance .................................\n","[CV] .... n_neighbors=2, weights=distance, score=-0.530, total= 0.1s\n","[CV] n_neighbors=2, weights=distance .................................\n","[CV] .... n_neighbors=2, weights=distance, score=-0.429, total= 0.1s\n","[CV] n_neighbors=2, weights=distance .................................\n","[CV] .... n_neighbors=2, weights=distance, score=-0.536, total= 0.1s\n","[CV] n_neighbors=2, weights=distance .................................\n","[CV] .... n_neighbors=2, weights=distance, score=-0.530, total= 0.1s\n","[CV] n_neighbors=2, weights=distance .................................\n","[CV] .... n_neighbors=2, weights=distance, score=-0.590, total= 0.1s\n","[CV] n_neighbors=2, weights=distance .................................\n","[CV] .... n_neighbors=2, weights=distance, score=-0.568, total= 0.1s\n","[CV] n_neighbors=2, weights=distance .................................\n","[CV] .... n_neighbors=2, weights=distance, score=-0.513, total= 0.1s\n","[CV] n_neighbors=2, weights=distance .................................\n","[CV] .... n_neighbors=2, weights=distance, score=-0.709, total= 0.1s\n","[CV] n_neighbors=2, weights=distance .................................\n","[CV] .... n_neighbors=2, weights=distance, score=-0.416, total= 0.1s\n","[CV] n_neighbors=3, weights=uniform ..................................\n","[CV] ..... n_neighbors=3, weights=uniform, score=-0.315, total= 0.1s\n","[CV] n_neighbors=3, weights=uniform ..................................\n","[CV] ..... n_neighbors=3, weights=uniform, score=-0.335, total= 0.1s\n","[CV] n_neighbors=3, weights=uniform ..................................\n","[CV] ..... n_neighbors=3, weights=uniform, score=-0.460, total= 0.1s\n","[CV] n_neighbors=3, weights=uniform ..................................\n","[CV] ..... n_neighbors=3, weights=uniform, score=-0.405, total= 0.1s\n","[CV] n_neighbors=3, weights=uniform ..................................\n","[CV] ..... n_neighbors=3, weights=uniform, score=-0.390, total= 0.1s\n","[CV] n_neighbors=3, weights=uniform ..................................\n","[CV] ..... n_neighbors=3, weights=uniform, score=-0.218, total= 0.1s\n","[CV] n_neighbors=3, weights=uniform ..................................\n","[CV] ..... n_neighbors=3, weights=uniform, score=-0.407, total= 0.1s\n","[CV] n_neighbors=3, weights=uniform ..................................\n","[CV] ..... n_neighbors=3, weights=uniform, score=-0.322, total= 0.1s\n","[CV] n_neighbors=3, weights=uniform ..................................\n","[CV] ..... n_neighbors=3, weights=uniform, score=-0.324, total= 0.1s\n","[CV] n_neighbors=3, weights=uniform ..................................\n","[CV] ..... n_neighbors=3, weights=uniform, score=-0.320, total= 0.1s\n","[CV] n_neighbors=3, weights=uniform ..................................\n","[CV] ..... n_neighbors=3, weights=uniform, score=-0.273, total= 0.1s\n","[CV] n_neighbors=3, weights=uniform ..................................\n","[CV] ..... n_neighbors=3, weights=uniform, score=-0.324, total= 0.1s\n","[CV] n_neighbors=3, weights=uniform ..................................\n","[CV] ..... n_neighbors=3, weights=uniform, score=-0.365, total= 0.1s\n","[CV] n_neighbors=3, weights=uniform ..................................\n","[CV] ..... n_neighbors=3, weights=uniform, score=-0.402, total= 0.1s\n","[CV] n_neighbors=3, weights=uniform ..................................\n","[CV] ..... n_neighbors=3, weights=uniform, score=-0.313, total= 0.1s\n","[CV] n_neighbors=3, weights=uniform ..................................\n","[CV] ..... n_neighbors=3, weights=uniform, score=-0.259, total= 0.1s\n","[CV] n_neighbors=3, weights=uniform ..................................\n","[CV] ..... n_neighbors=3, weights=uniform, score=-0.303, total= 0.1s\n","[CV] n_neighbors=3, weights=uniform ..................................\n","[CV] ..... n_neighbors=3, weights=uniform, score=-0.414, total= 0.1s\n","[CV] n_neighbors=3, weights=uniform ..................................\n","[CV] ..... n_neighbors=3, weights=uniform, score=-0.529, total= 0.1s\n","[CV] n_neighbors=3, weights=uniform ..................................\n","[CV] ..... n_neighbors=3, weights=uniform, score=-0.226, total= 0.1s\n","[CV] n_neighbors=3, weights=uniform ..................................\n","[CV] ..... n_neighbors=3, weights=uniform, score=-0.376, total= 0.1s\n","[CV] n_neighbors=3, weights=uniform ..................................\n","[CV] ..... n_neighbors=3, weights=uniform, score=-0.347, total= 0.1s\n","[CV] n_neighbors=3, weights=uniform ..................................\n","[CV] ..... n_neighbors=3, weights=uniform, score=-0.340, total= 0.1s\n","[CV] n_neighbors=3, weights=uniform ..................................\n","[CV] ..... n_neighbors=3, weights=uniform, score=-0.241, total= 0.1s\n","[CV] n_neighbors=3, weights=uniform ..................................\n","[CV] ..... n_neighbors=3, weights=uniform, score=-0.368, total= 0.1s\n","[CV] n_neighbors=3, weights=uniform ..................................\n","[CV] ..... n_neighbors=3, weights=uniform, score=-0.373, total= 0.1s\n","[CV] n_neighbors=3, weights=uniform ..................................\n","[CV] ..... n_neighbors=3, weights=uniform, score=-0.417, total= 0.1s\n","[CV] n_neighbors=3, weights=uniform ..................................\n","[CV] ..... n_neighbors=3, weights=uniform, score=-0.267, total= 0.1s\n","[CV] n_neighbors=3, weights=uniform ..................................\n","[CV] ..... n_neighbors=3, weights=uniform, score=-0.501, total= 0.1s\n","[CV] n_neighbors=3, weights=uniform ..................................\n","[CV] ..... n_neighbors=3, weights=uniform, score=-0.198, total= 0.1s\n","[CV] n_neighbors=3, weights=distance .................................\n","[CV] .... n_neighbors=3, weights=distance, score=-0.320, total= 0.1s\n","[CV] n_neighbors=3, weights=distance .................................\n","[CV] .... n_neighbors=3, weights=distance, score=-0.333, total= 0.1s\n","[CV] n_neighbors=3, weights=distance .................................\n","[CV] .... n_neighbors=3, weights=distance, score=-0.464, total= 0.1s\n","[CV] n_neighbors=3, weights=distance .................................\n","[CV] .... n_neighbors=3, weights=distance, score=-0.405, total= 0.1s\n","[CV] n_neighbors=3, weights=distance .................................\n","[CV] .... n_neighbors=3, weights=distance, score=-0.394, total= 0.1s\n","[CV] n_neighbors=3, weights=distance .................................\n","[CV] .... n_neighbors=3, weights=distance, score=-0.220, total= 0.1s\n","[CV] n_neighbors=3, weights=distance .................................\n","[CV] .... n_neighbors=3, weights=distance, score=-0.412, total= 0.1s\n","[CV] n_neighbors=3, weights=distance .................................\n","[CV] .... n_neighbors=3, weights=distance, score=-0.325, total= 0.1s\n","[CV] n_neighbors=3, weights=distance .................................\n","[CV] .... n_neighbors=3, weights=distance, score=-0.329, total= 0.1s\n","[CV] n_neighbors=3, weights=distance .................................\n","[CV] .... n_neighbors=3, weights=distance, score=-0.325, total= 0.1s\n","[CV] n_neighbors=3, weights=distance .................................\n","[CV] .... n_neighbors=3, weights=distance, score=-0.280, total= 0.1s\n","[CV] n_neighbors=3, weights=distance .................................\n","[CV] .... n_neighbors=3, weights=distance, score=-0.334, total= 0.1s\n","[CV] n_neighbors=3, weights=distance .................................\n","[CV] .... n_neighbors=3, weights=distance, score=-0.356, total= 0.1s\n","[CV] n_neighbors=3, weights=distance .................................\n","[CV] .... n_neighbors=3, weights=distance, score=-0.401, total= 0.1s\n","[CV] n_neighbors=3, weights=distance .................................\n","[CV] .... n_neighbors=3, weights=distance, score=-0.319, total= 0.1s\n","[CV] n_neighbors=3, weights=distance .................................\n","[CV] .... n_neighbors=3, weights=distance, score=-0.255, total= 0.1s\n","[CV] n_neighbors=3, weights=distance .................................\n","[CV] .... n_neighbors=3, weights=distance, score=-0.289, total= 0.1s\n","[CV] n_neighbors=3, weights=distance .................................\n","[CV] .... n_neighbors=3, weights=distance, score=-0.437, total= 0.1s\n","[CV] n_neighbors=3, weights=distance .................................\n","[CV] .... n_neighbors=3, weights=distance, score=-0.543, total= 0.1s\n","[CV] n_neighbors=3, weights=distance .................................\n","[CV] .... n_neighbors=3, weights=distance, score=-0.214, total= 0.1s\n","[CV] n_neighbors=3, weights=distance .................................\n","[CV] .... n_neighbors=3, weights=distance, score=-0.376, total= 0.1s\n","[CV] n_neighbors=3, weights=distance .................................\n","[CV] .... n_neighbors=3, weights=distance, score=-0.361, total= 0.1s\n","[CV] n_neighbors=3, weights=distance .................................\n","[CV] .... n_neighbors=3, weights=distance, score=-0.331, total= 0.1s\n","[CV] n_neighbors=3, weights=distance .................................\n","[CV] .... n_neighbors=3, weights=distance, score=-0.253, total= 0.1s\n","[CV] n_neighbors=3, weights=distance .................................\n","[CV] .... n_neighbors=3, weights=distance, score=-0.365, total= 0.1s\n","[CV] n_neighbors=3, weights=distance .................................\n","[CV] .... n_neighbors=3, weights=distance, score=-0.373, total= 0.1s\n","[CV] n_neighbors=3, weights=distance .................................\n","[CV] .... n_neighbors=3, weights=distance, score=-0.419, total= 0.1s\n","[CV] n_neighbors=3, weights=distance .................................\n","[CV] .... n_neighbors=3, weights=distance, score=-0.285, total= 0.1s\n","[CV] n_neighbors=3, weights=distance .................................\n","[CV] .... n_neighbors=3, weights=distance, score=-0.512, total= 0.1s\n","[CV] n_neighbors=3, weights=distance .................................\n","[CV] .... n_neighbors=3, weights=distance, score=-0.210, total= 0.1s\n","[CV] n_neighbors=4, weights=uniform ..................................\n","[CV] ..... n_neighbors=4, weights=uniform, score=-0.269, total= 0.1s\n","[CV] n_neighbors=4, weights=uniform ..................................\n","[CV] ..... n_neighbors=4, weights=uniform, score=-0.305, total= 0.1s\n","[CV] n_neighbors=4, weights=uniform ..................................\n","[CV] ..... n_neighbors=4, weights=uniform, score=-0.350, total= 0.1s\n","[CV] n_neighbors=4, weights=uniform ..................................\n","[CV] ..... n_neighbors=4, weights=uniform, score=-0.328, total= 0.1s\n","[CV] n_neighbors=4, weights=uniform ..................................\n","[CV] ..... n_neighbors=4, weights=uniform, score=-0.307, total= 0.1s\n","[CV] n_neighbors=4, weights=uniform ..................................\n","[CV] ..... n_neighbors=4, weights=uniform, score=-0.096, total= 0.1s\n","[CV] n_neighbors=4, weights=uniform ..................................\n","[CV] ..... n_neighbors=4, weights=uniform, score=-0.252, total= 0.1s\n","[CV] n_neighbors=4, weights=uniform ..................................\n","[CV] ..... n_neighbors=4, weights=uniform, score=-0.226, total= 0.1s\n","[CV] n_neighbors=4, weights=uniform ..................................\n","[CV] ..... n_neighbors=4, weights=uniform, score=-0.281, total= 0.1s\n","[CV] n_neighbors=4, weights=uniform ..................................\n","[CV] ..... n_neighbors=4, weights=uniform, score=-0.225, total= 0.1s\n","[CV] n_neighbors=4, weights=uniform ..................................\n","[CV] ..... n_neighbors=4, weights=uniform, score=-0.169, total= 0.1s\n","[CV] n_neighbors=4, weights=uniform ..................................\n","[CV] ..... n_neighbors=4, weights=uniform, score=-0.333, total= 0.1s\n","[CV] n_neighbors=4, weights=uniform ..................................\n","[CV] ..... n_neighbors=4, weights=uniform, score=-0.246, total= 0.1s\n","[CV] n_neighbors=4, weights=uniform ..................................\n","[CV] ..... n_neighbors=4, weights=uniform, score=-0.277, total= 0.1s\n","[CV] n_neighbors=4, weights=uniform ..................................\n","[CV] ..... n_neighbors=4, weights=uniform, score=-0.232, total= 0.1s\n","[CV] n_neighbors=4, weights=uniform ..................................\n","[CV] ..... n_neighbors=4, weights=uniform, score=-0.147, total= 0.1s\n","[CV] n_neighbors=4, weights=uniform ..................................\n","[CV] ..... n_neighbors=4, weights=uniform, score=-0.211, total= 0.1s\n","[CV] n_neighbors=4, weights=uniform ..................................\n","[CV] ..... n_neighbors=4, weights=uniform, score=-0.306, total= 0.1s\n","[CV] n_neighbors=4, weights=uniform ..................................\n","[CV] ..... n_neighbors=4, weights=uniform, score=-0.314, total= 0.1s\n","[CV] n_neighbors=4, weights=uniform ..................................\n","[CV] ..... n_neighbors=4, weights=uniform, score=-0.188, total= 0.1s\n","[CV] n_neighbors=4, weights=uniform ..................................\n","[CV] ..... n_neighbors=4, weights=uniform, score=-0.226, total= 0.1s\n","[CV] n_neighbors=4, weights=uniform ..................................\n","[CV] ..... n_neighbors=4, weights=uniform, score=-0.293, total= 0.1s\n","[CV] n_neighbors=4, weights=uniform ..................................\n","[CV] ..... n_neighbors=4, weights=uniform, score=-0.299, total= 0.1s\n","[CV] n_neighbors=4, weights=uniform ..................................\n","[CV] ..... n_neighbors=4, weights=uniform, score=-0.151, total= 0.1s\n","[CV] n_neighbors=4, weights=uniform ..................................\n","[CV] ..... n_neighbors=4, weights=uniform, score=-0.287, total= 0.1s\n","[CV] n_neighbors=4, weights=uniform ..................................\n","[CV] ..... n_neighbors=4, weights=uniform, score=-0.269, total= 0.1s\n","[CV] n_neighbors=4, weights=uniform ..................................\n","[CV] ..... n_neighbors=4, weights=uniform, score=-0.313, total= 0.1s\n","[CV] n_neighbors=4, weights=uniform ..................................\n","[CV] ..... n_neighbors=4, weights=uniform, score=-0.119, total= 0.1s\n","[CV] n_neighbors=4, weights=uniform ..................................\n","[CV] ..... n_neighbors=4, weights=uniform, score=-0.370, total= 0.1s\n","[CV] n_neighbors=4, weights=uniform ..................................\n","[CV] ..... n_neighbors=4, weights=uniform, score=-0.142, total= 0.1s\n","[CV] n_neighbors=4, weights=distance .................................\n","[CV] .... n_neighbors=4, weights=distance, score=-0.267, total= 0.1s\n","[CV] n_neighbors=4, weights=distance .................................\n","[CV] .... n_neighbors=4, weights=distance, score=-0.302, total= 0.1s\n","[CV] n_neighbors=4, weights=distance .................................\n","[CV] .... n_neighbors=4, weights=distance, score=-0.355, total= 0.1s\n","[CV] n_neighbors=4, weights=distance .................................\n","[CV] .... n_neighbors=4, weights=distance, score=-0.330, total= 0.1s\n","[CV] n_neighbors=4, weights=distance .................................\n","[CV] .... n_neighbors=4, weights=distance, score=-0.309, total= 0.1s\n","[CV] n_neighbors=4, weights=distance .................................\n","[CV] .... n_neighbors=4, weights=distance, score=-0.101, total= 0.1s\n","[CV] n_neighbors=4, weights=distance .................................\n","[CV] .... n_neighbors=4, weights=distance, score=-0.263, total= 0.1s\n","[CV] n_neighbors=4, weights=distance .................................\n","[CV] .... n_neighbors=4, weights=distance, score=-0.229, total= 0.1s\n","[CV] n_neighbors=4, weights=distance .................................\n","[CV] .... n_neighbors=4, weights=distance, score=-0.283, total= 0.1s\n","[CV] n_neighbors=4, weights=distance .................................\n","[CV] .... n_neighbors=4, weights=distance, score=-0.229, total= 0.1s\n","[CV] n_neighbors=4, weights=distance .................................\n","[CV] .... n_neighbors=4, weights=distance, score=-0.176, total= 0.1s\n","[CV] n_neighbors=4, weights=distance .................................\n","[CV] .... n_neighbors=4, weights=distance, score=-0.334, total= 0.1s\n","[CV] n_neighbors=4, weights=distance .................................\n","[CV] .... n_neighbors=4, weights=distance, score=-0.243, total= 0.1s\n","[CV] n_neighbors=4, weights=distance .................................\n","[CV] .... n_neighbors=4, weights=distance, score=-0.277, total= 0.1s\n","[CV] n_neighbors=4, weights=distance .................................\n","[CV] .... n_neighbors=4, weights=distance, score=-0.232, total= 0.1s\n","[CV] n_neighbors=4, weights=distance .................................\n","[CV] .... n_neighbors=4, weights=distance, score=-0.149, total= 0.1s\n","[CV] n_neighbors=4, weights=distance .................................\n","[CV] .... n_neighbors=4, weights=distance, score=-0.196, total= 0.1s\n","[CV] n_neighbors=4, weights=distance .................................\n","[CV] .... n_neighbors=4, weights=distance, score=-0.327, total= 0.1s\n","[CV] n_neighbors=4, weights=distance .................................\n","[CV] .... n_neighbors=4, weights=distance, score=-0.333, total= 0.1s\n","[CV] n_neighbors=4, weights=distance .................................\n","[CV] .... n_neighbors=4, weights=distance, score=-0.179, total= 0.1s\n","[CV] n_neighbors=4, weights=distance .................................\n","[CV] .... n_neighbors=4, weights=distance, score=-0.238, total= 0.1s\n","[CV] n_neighbors=4, weights=distance .................................\n","[CV] .... n_neighbors=4, weights=distance, score=-0.304, total= 0.1s\n","[CV] n_neighbors=4, weights=distance .................................\n","[CV] .... n_neighbors=4, weights=distance, score=-0.285, total= 0.1s\n","[CV] n_neighbors=4, weights=distance .................................\n","[CV] .... n_neighbors=4, weights=distance, score=-0.163, total= 0.1s\n","[CV] n_neighbors=4, weights=distance .................................\n","[CV] .... n_neighbors=4, weights=distance, score=-0.285, total= 0.1s\n","[CV] n_neighbors=4, weights=distance .................................\n","[CV] .... n_neighbors=4, weights=distance, score=-0.267, total= 0.1s\n","[CV] n_neighbors=4, weights=distance .................................\n","[CV] .... n_neighbors=4, weights=distance, score=-0.312, total= 0.1s\n","[CV] n_neighbors=4, weights=distance .................................\n","[CV] .... n_neighbors=4, weights=distance, score=-0.141, total= 0.1s\n","[CV] n_neighbors=4, weights=distance .................................\n","[CV] .... n_neighbors=4, weights=distance, score=-0.383, total= 0.1s\n","[CV] n_neighbors=4, weights=distance .................................\n","[CV] .... n_neighbors=4, weights=distance, score=-0.151, total= 0.1s\n","[CV] n_neighbors=5, weights=uniform ..................................\n","[CV] ..... n_neighbors=5, weights=uniform, score=-0.194, total= 0.1s\n","[CV] n_neighbors=5, weights=uniform ..................................\n","[CV] ..... n_neighbors=5, weights=uniform, score=-0.280, total= 0.1s\n","[CV] n_neighbors=5, weights=uniform ..................................\n","[CV] ..... n_neighbors=5, weights=uniform, score=-0.249, total= 0.1s\n","[CV] n_neighbors=5, weights=uniform ..................................\n","[CV] ..... n_neighbors=5, weights=uniform, score=-0.246, total= 0.1s\n","[CV] n_neighbors=5, weights=uniform ..................................\n","[CV] ..... n_neighbors=5, weights=uniform, score=-0.281, total= 0.1s\n","[CV] n_neighbors=5, weights=uniform ..................................\n","[CV] ..... n_neighbors=5, weights=uniform, score=-0.055, total= 0.1s\n","[CV] n_neighbors=5, weights=uniform ..................................\n","[CV] ..... n_neighbors=5, weights=uniform, score=-0.220, total= 0.1s\n","[CV] n_neighbors=5, weights=uniform ..................................\n","[CV] ..... n_neighbors=5, weights=uniform, score=-0.215, total= 0.1s\n","[CV] n_neighbors=5, weights=uniform ..................................\n","[CV] ..... n_neighbors=5, weights=uniform, score=-0.222, total= 0.1s\n","[CV] n_neighbors=5, weights=uniform ..................................\n","[CV] ..... n_neighbors=5, weights=uniform, score=-0.203, total= 0.1s\n","[CV] n_neighbors=5, weights=uniform ..................................\n","[CV] ..... n_neighbors=5, weights=uniform, score=-0.091, total= 0.1s\n","[CV] n_neighbors=5, weights=uniform ..................................\n","[CV] ..... n_neighbors=5, weights=uniform, score=-0.273, total= 0.1s\n","[CV] n_neighbors=5, weights=uniform ..................................\n","[CV] ..... n_neighbors=5, weights=uniform, score=-0.133, total= 0.1s\n","[CV] n_neighbors=5, weights=uniform ..................................\n","[CV] ..... n_neighbors=5, weights=uniform, score=-0.228, total= 0.1s\n","[CV] n_neighbors=5, weights=uniform ..................................\n","[CV] ..... n_neighbors=5, weights=uniform, score=-0.175, total= 0.1s\n","[CV] n_neighbors=5, weights=uniform ..................................\n","[CV] ..... n_neighbors=5, weights=uniform, score=-0.152, total= 0.1s\n","[CV] n_neighbors=5, weights=uniform ..................................\n","[CV] ..... n_neighbors=5, weights=uniform, score=-0.166, total= 0.1s\n","[CV] n_neighbors=5, weights=uniform ..................................\n","[CV] ..... n_neighbors=5, weights=uniform, score=-0.225, total= 0.1s\n","[CV] n_neighbors=5, weights=uniform ..................................\n","[CV] ..... n_neighbors=5, weights=uniform, score=-0.299, total= 0.1s\n","[CV] n_neighbors=5, weights=uniform ..................................\n","[CV] ..... n_neighbors=5, weights=uniform, score=-0.123, total= 0.1s\n","[CV] n_neighbors=5, weights=uniform ..................................\n","[CV] ..... n_neighbors=5, weights=uniform, score=-0.215, total= 0.1s\n","[CV] n_neighbors=5, weights=uniform ..................................\n","[CV] ..... n_neighbors=5, weights=uniform, score=-0.228, total= 0.1s\n","[CV] n_neighbors=5, weights=uniform ..................................\n","[CV] ..... n_neighbors=5, weights=uniform, score=-0.258, total= 0.1s\n","[CV] n_neighbors=5, weights=uniform ..................................\n","[CV] ..... n_neighbors=5, weights=uniform, score=-0.146, total= 0.1s\n","[CV] n_neighbors=5, weights=uniform ..................................\n","[CV] ..... n_neighbors=5, weights=uniform, score=-0.203, total= 0.1s\n","[CV] n_neighbors=5, weights=uniform ..................................\n","[CV] ..... n_neighbors=5, weights=uniform, score=-0.179, total= 0.1s\n","[CV] n_neighbors=5, weights=uniform ..................................\n","[CV] ..... n_neighbors=5, weights=uniform, score=-0.204, total= 0.1s\n","[CV] n_neighbors=5, weights=uniform ..................................\n","[CV] ..... n_neighbors=5, weights=uniform, score=-0.066, total= 0.1s\n","[CV] n_neighbors=5, weights=uniform ..................................\n","[CV] ..... n_neighbors=5, weights=uniform, score=-0.303, total= 0.1s\n","[CV] n_neighbors=5, weights=uniform ..................................\n","[CV] ..... n_neighbors=5, weights=uniform, score=-0.080, total= 0.1s\n","[CV] n_neighbors=5, weights=distance .................................\n","[CV] .... n_neighbors=5, weights=distance, score=-0.200, total= 0.1s\n","[CV] n_neighbors=5, weights=distance .................................\n","[CV] .... n_neighbors=5, weights=distance, score=-0.271, total= 0.1s\n","[CV] n_neighbors=5, weights=distance .................................\n","[CV] .... n_neighbors=5, weights=distance, score=-0.263, total= 0.1s\n","[CV] n_neighbors=5, weights=distance .................................\n","[CV] .... n_neighbors=5, weights=distance, score=-0.250, total= 0.1s\n","[CV] n_neighbors=5, weights=distance .................................\n","[CV] .... n_neighbors=5, weights=distance, score=-0.280, total= 0.1s\n","[CV] n_neighbors=5, weights=distance .................................\n","[CV] .... n_neighbors=5, weights=distance, score=-0.059, total= 0.1s\n","[CV] n_neighbors=5, weights=distance .................................\n","[CV] .... n_neighbors=5, weights=distance, score=-0.225, total= 0.1s\n","[CV] n_neighbors=5, weights=distance .................................\n","[CV] .... n_neighbors=5, weights=distance, score=-0.210, total= 0.1s\n","[CV] n_neighbors=5, weights=distance .................................\n","[CV] .... n_neighbors=5, weights=distance, score=-0.224, total= 0.1s\n","[CV] n_neighbors=5, weights=distance .................................\n","[CV] .... n_neighbors=5, weights=distance, score=-0.202, total= 0.1s\n","[CV] n_neighbors=5, weights=distance .................................\n","[CV] .... n_neighbors=5, weights=distance, score=-0.098, total= 0.1s\n","[CV] n_neighbors=5, weights=distance .................................\n","[CV] .... n_neighbors=5, weights=distance, score=-0.277, total= 0.1s\n","[CV] n_neighbors=5, weights=distance .................................\n","[CV] .... n_neighbors=5, weights=distance, score=-0.132, total= 0.1s\n","[CV] n_neighbors=5, weights=distance .................................\n","[CV] .... n_neighbors=5, weights=distance, score=-0.234, total= 0.1s\n","[CV] n_neighbors=5, weights=distance .................................\n","[CV] .... n_neighbors=5, weights=distance, score=-0.170, total= 0.1s\n","[CV] n_neighbors=5, weights=distance .................................\n","[CV] .... n_neighbors=5, weights=distance, score=-0.146, total= 0.1s\n","[CV] n_neighbors=5, weights=distance .................................\n","[CV] .... n_neighbors=5, weights=distance, score=-0.154, total= 0.1s\n","[CV] n_neighbors=5, weights=distance .................................\n","[CV] .... n_neighbors=5, weights=distance, score=-0.248, total= 0.1s\n","[CV] n_neighbors=5, weights=distance .................................\n","[CV] .... n_neighbors=5, weights=distance, score=-0.310, total= 0.1s\n","[CV] n_neighbors=5, weights=distance .................................\n","[CV] .... n_neighbors=5, weights=distance, score=-0.122, total= 0.1s\n","[CV] n_neighbors=5, weights=distance .................................\n","[CV] .... n_neighbors=5, weights=distance, score=-0.222, total= 0.1s\n","[CV] n_neighbors=5, weights=distance .................................\n","[CV] .... n_neighbors=5, weights=distance, score=-0.236, total= 0.1s\n","[CV] n_neighbors=5, weights=distance .................................\n","[CV] .... n_neighbors=5, weights=distance, score=-0.249, total= 0.1s\n","[CV] n_neighbors=5, weights=distance .................................\n","[CV] .... n_neighbors=5, weights=distance, score=-0.151, total= 0.1s\n","[CV] n_neighbors=5, weights=distance .................................\n","[CV] .... n_neighbors=5, weights=distance, score=-0.204, total= 0.1s\n","[CV] n_neighbors=5, weights=distance .................................\n","[CV] .... n_neighbors=5, weights=distance, score=-0.182, total= 0.1s\n","[CV] n_neighbors=5, weights=distance .................................\n","[CV] .... n_neighbors=5, weights=distance, score=-0.207, total= 0.1s\n","[CV] n_neighbors=5, weights=distance .................................\n","[CV] .... n_neighbors=5, weights=distance, score=-0.085, total= 0.1s\n","[CV] n_neighbors=5, weights=distance .................................\n","[CV] .... n_neighbors=5, weights=distance, score=-0.312, total= 0.1s\n","[CV] n_neighbors=5, weights=distance .................................\n","[CV] .... n_neighbors=5, weights=distance, score=-0.086, total= 0.1s\n","[CV] n_neighbors=6, weights=uniform ..................................\n","[CV] ..... n_neighbors=6, weights=uniform, score=-0.145, total= 0.1s\n","[CV] n_neighbors=6, weights=uniform ..................................\n","[CV] ..... n_neighbors=6, weights=uniform, score=-0.209, total= 0.1s\n","[CV] n_neighbors=6, weights=uniform ..................................\n","[CV] ..... n_neighbors=6, weights=uniform, score=-0.222, total= 0.1s\n","[CV] n_neighbors=6, weights=uniform ..................................\n","[CV] ..... n_neighbors=6, weights=uniform, score=-0.211, total= 0.1s\n","[CV] n_neighbors=6, weights=uniform ..................................\n","[CV] ..... n_neighbors=6, weights=uniform, score=-0.224, total= 0.1s\n","[CV] n_neighbors=6, weights=uniform ..................................\n","[CV] ..... n_neighbors=6, weights=uniform, score=-0.026, total= 0.1s\n","[CV] n_neighbors=6, weights=uniform ..................................\n","[CV] ..... n_neighbors=6, weights=uniform, score=-0.146, total= 0.1s\n","[CV] n_neighbors=6, weights=uniform ..................................\n","[CV] ..... n_neighbors=6, weights=uniform, score=-0.172, total= 0.1s\n","[CV] n_neighbors=6, weights=uniform ..................................\n","[CV] ..... n_neighbors=6, weights=uniform, score=-0.172, total= 0.1s\n","[CV] n_neighbors=6, weights=uniform ..................................\n","[CV] ..... n_neighbors=6, weights=uniform, score=-0.136, total= 0.1s\n","[CV] n_neighbors=6, weights=uniform ..................................\n","[CV] ..... n_neighbors=6, weights=uniform, score=-0.066, total= 0.1s\n","[CV] n_neighbors=6, weights=uniform ..................................\n","[CV] ..... n_neighbors=6, weights=uniform, score=-0.216, total= 0.1s\n","[CV] n_neighbors=6, weights=uniform ..................................\n","[CV] ..... n_neighbors=6, weights=uniform, score=-0.096, total= 0.1s\n","[CV] n_neighbors=6, weights=uniform ..................................\n","[CV] ..... n_neighbors=6, weights=uniform, score=-0.157, total= 0.1s\n","[CV] n_neighbors=6, weights=uniform ..................................\n","[CV] ..... n_neighbors=6, weights=uniform, score=-0.122, total= 0.1s\n","[CV] n_neighbors=6, weights=uniform ..................................\n","[CV] ..... n_neighbors=6, weights=uniform, score=-0.172, total= 0.1s\n","[CV] n_neighbors=6, weights=uniform ..................................\n","[CV] ..... n_neighbors=6, weights=uniform, score=-0.107, total= 0.1s\n","[CV] n_neighbors=6, weights=uniform ..................................\n","[CV] ..... n_neighbors=6, weights=uniform, score=-0.221, total= 0.1s\n","[CV] n_neighbors=6, weights=uniform ..................................\n","[CV] ..... n_neighbors=6, weights=uniform, score=-0.272, total= 0.1s\n","[CV] n_neighbors=6, weights=uniform ..................................\n","[CV] ..... n_neighbors=6, weights=uniform, score=-0.105, total= 0.1s\n","[CV] n_neighbors=6, weights=uniform ..................................\n","[CV] ..... n_neighbors=6, weights=uniform, score=-0.175, total= 0.1s\n","[CV] n_neighbors=6, weights=uniform ..................................\n","[CV] ..... n_neighbors=6, weights=uniform, score=-0.223, total= 0.1s\n","[CV] n_neighbors=6, weights=uniform ..................................\n","[CV] ..... n_neighbors=6, weights=uniform, score=-0.173, total= 0.1s\n","[CV] n_neighbors=6, weights=uniform ..................................\n","[CV] ..... n_neighbors=6, weights=uniform, score=-0.100, total= 0.1s\n","[CV] n_neighbors=6, weights=uniform ..................................\n","[CV] ..... n_neighbors=6, weights=uniform, score=-0.158, total= 0.1s\n","[CV] n_neighbors=6, weights=uniform ..................................\n","[CV] ..... n_neighbors=6, weights=uniform, score=-0.181, total= 0.1s\n","[CV] n_neighbors=6, weights=uniform ..................................\n","[CV] ..... n_neighbors=6, weights=uniform, score=-0.190, total= 0.1s\n","[CV] n_neighbors=6, weights=uniform ..................................\n","[CV] ..... n_neighbors=6, weights=uniform, score=-0.090, total= 0.1s\n","[CV] n_neighbors=6, weights=uniform ..................................\n","[CV] ..... n_neighbors=6, weights=uniform, score=-0.270, total= 0.1s\n","[CV] n_neighbors=6, weights=uniform ..................................\n","[CV] ..... n_neighbors=6, weights=uniform, score=-0.076, total= 0.1s\n","[CV] n_neighbors=6, weights=distance .................................\n","[CV] .... n_neighbors=6, weights=distance, score=-0.152, total= 0.1s\n","[CV] n_neighbors=6, weights=distance .................................\n","[CV] .... n_neighbors=6, weights=distance, score=-0.205, total= 0.1s\n","[CV] n_neighbors=6, weights=distance .................................\n","[CV] .... n_neighbors=6, weights=distance, score=-0.234, total= 0.1s\n","[CV] n_neighbors=6, weights=distance .................................\n","[CV] .... n_neighbors=6, weights=distance, score=-0.213, total= 0.1s\n","[CV] n_neighbors=6, weights=distance .................................\n","[CV] .... n_neighbors=6, weights=distance, score=-0.224, total= 0.1s\n","[CV] n_neighbors=6, weights=distance .................................\n","[CV] .... n_neighbors=6, weights=distance, score=-0.027, total= 0.1s\n","[CV] n_neighbors=6, weights=distance .................................\n","[CV] .... n_neighbors=6, weights=distance, score=-0.155, total= 0.1s\n","[CV] n_neighbors=6, weights=distance .................................\n","[CV] .... n_neighbors=6, weights=distance, score=-0.169, total= 0.1s\n","[CV] n_neighbors=6, weights=distance .................................\n","[CV] .... n_neighbors=6, weights=distance, score=-0.175, total= 0.1s\n","[CV] n_neighbors=6, weights=distance .................................\n","[CV] .... n_neighbors=6, weights=distance, score=-0.141, total= 0.1s\n","[CV] n_neighbors=6, weights=distance .................................\n","[CV] .... n_neighbors=6, weights=distance, score=-0.074, total= 0.1s\n","[CV] n_neighbors=6, weights=distance .................................\n","[CV] .... n_neighbors=6, weights=distance, score=-0.224, total= 0.1s\n","[CV] n_neighbors=6, weights=distance .................................\n","[CV] .... n_neighbors=6, weights=distance, score=-0.099, total= 0.1s\n","[CV] n_neighbors=6, weights=distance .................................\n","[CV] .... n_neighbors=6, weights=distance, score=-0.169, total= 0.1s\n","[CV] n_neighbors=6, weights=distance .................................\n","[CV] .... n_neighbors=6, weights=distance, score=-0.120, total= 0.1s\n","[CV] n_neighbors=6, weights=distance .................................\n","[CV] .... n_neighbors=6, weights=distance, score=-0.165, total= 0.1s\n","[CV] n_neighbors=6, weights=distance .................................\n","[CV] .... n_neighbors=6, weights=distance, score=-0.100, total= 0.1s\n","[CV] n_neighbors=6, weights=distance .................................\n","[CV] .... n_neighbors=6, weights=distance, score=-0.237, total= 0.1s\n","[CV] n_neighbors=6, weights=distance .................................\n","[CV] .... n_neighbors=6, weights=distance, score=-0.281, total= 0.1s\n","[CV] n_neighbors=6, weights=distance .................................\n","[CV] .... n_neighbors=6, weights=distance, score=-0.102, total= 0.1s\n","[CV] n_neighbors=6, weights=distance .................................\n","[CV] .... n_neighbors=6, weights=distance, score=-0.181, total= 0.1s\n","[CV] n_neighbors=6, weights=distance .................................\n","[CV] .... n_neighbors=6, weights=distance, score=-0.228, total= 0.1s\n","[CV] n_neighbors=6, weights=distance .................................\n","[CV] .... n_neighbors=6, weights=distance, score=-0.173, total= 0.1s\n","[CV] n_neighbors=6, weights=distance .................................\n","[CV] .... n_neighbors=6, weights=distance, score=-0.107, total= 0.1s\n","[CV] n_neighbors=6, weights=distance .................................\n","[CV] .... n_neighbors=6, weights=distance, score=-0.162, total= 0.1s\n","[CV] n_neighbors=6, weights=distance .................................\n","[CV] .... n_neighbors=6, weights=distance, score=-0.176, total= 0.1s\n","[CV] n_neighbors=6, weights=distance .................................\n","[CV] .... n_neighbors=6, weights=distance, score=-0.188, total= 0.1s\n","[CV] n_neighbors=6, weights=distance .................................\n","[CV] .... n_neighbors=6, weights=distance, score=-0.101, total= 0.1s\n","[CV] n_neighbors=6, weights=distance .................................\n","[CV] .... n_neighbors=6, weights=distance, score=-0.278, total= 0.1s\n","[CV] n_neighbors=6, weights=distance .................................\n","[CV] .... n_neighbors=6, weights=distance, score=-0.077, total= 0.1s\n","[Parallel(n_jobs=1)]: Done 300 out of 300 | elapsed: 23.0s finished\n","KNeighborsRegressor(algorithm='auto', leaf_size=30, metric='minkowski',\n"," metric_params=None, n_jobs=None, n_neighbors=6, p=2,\n"," weights='uniform')\n","window: 1, stide: 1, rmse: 2.9831471077817575\n","[1, 4, 9, 12, 2, 0, 3, 13, 11, 10, 6, 5, 7, 8]\n","[1, 4, 9, 12, 2, 0, 3, 13]\n","(3000, 120)\n","Number of Feature-Columns: 120\n","\n","Features Ready for undergoing selection tests done ...\n","\n","Fitting 30 folds for each of 10 candidates, totalling 300 fits\n","[Parallel(n_jobs=1)]: Using backend SequentialBackend with 1 concurrent workers.\n","[CV] n_neighbors=2, weights=uniform ..................................\n","[CV] ..... n_neighbors=2, weights=uniform, score=-0.500, total= 0.1s\n","[Parallel(n_jobs=1)]: Done 1 out of 1 | elapsed: 0.1s remaining: 0.0s\n","[CV] n_neighbors=2, weights=uniform ..................................\n","[CV] ..... n_neighbors=2, weights=uniform, score=-0.585, total= 0.1s\n","[Parallel(n_jobs=1)]: Done 2 out of 2 | elapsed: 0.2s remaining: 0.0s\n","[CV] n_neighbors=2, weights=uniform ..................................\n","[CV] ..... n_neighbors=2, weights=uniform, score=-0.500, total= 0.1s\n","[Parallel(n_jobs=1)]: Done 3 out of 3 | elapsed: 0.3s remaining: 0.0s\n","[CV] n_neighbors=2, weights=uniform ..................................\n","[CV] ..... n_neighbors=2, weights=uniform, score=-0.743, total= 0.1s\n","[Parallel(n_jobs=1)]: Done 4 out of 4 | elapsed: 0.4s remaining: 0.0s\n","[CV] n_neighbors=2, weights=uniform ..................................\n","[CV] ..... n_neighbors=2, weights=uniform, score=-0.528, total= 0.1s\n","[CV] n_neighbors=2, weights=uniform ..................................\n","[CV] ..... n_neighbors=2, weights=uniform, score=-0.280, total= 0.1s\n","[CV] n_neighbors=2, weights=uniform ..................................\n","[CV] ..... n_neighbors=2, weights=uniform, score=-0.560, total= 0.1s\n","[CV] n_neighbors=2, weights=uniform ..................................\n","[CV] ..... n_neighbors=2, weights=uniform, score=-0.628, total= 0.1s\n","[CV] n_neighbors=2, weights=uniform ..................................\n","[CV] ..... n_neighbors=2, weights=uniform, score=-0.519, total= 0.1s\n","[CV] n_neighbors=2, weights=uniform ..................................\n","[CV] ..... n_neighbors=2, weights=uniform, score=-0.467, total= 0.1s\n","[CV] n_neighbors=2, weights=uniform ..................................\n","[CV] ..... n_neighbors=2, weights=uniform, score=-0.519, total= 0.1s\n","[CV] n_neighbors=2, weights=uniform ..................................\n","[CV] ..... n_neighbors=2, weights=uniform, score=-0.587, total= 0.1s\n","[CV] n_neighbors=2, weights=uniform ..................................\n","[CV] ..... n_neighbors=2, weights=uniform, score=-0.468, total= 0.1s\n","[CV] n_neighbors=2, weights=uniform ..................................\n","[CV] ..... n_neighbors=2, weights=uniform, score=-0.462, total= 0.1s\n","[CV] n_neighbors=2, weights=uniform ..................................\n","[CV] ..... n_neighbors=2, weights=uniform, score=-0.485, total= 0.1s\n","[CV] n_neighbors=2, weights=uniform ..................................\n","[CV] ..... n_neighbors=2, weights=uniform, score=-0.384, total= 0.1s\n","[CV] n_neighbors=2, weights=uniform ..................................\n","[CV] ..... n_neighbors=2, weights=uniform, score=-0.481, total= 0.1s\n","[CV] n_neighbors=2, weights=uniform ..................................\n","[CV] ..... n_neighbors=2, weights=uniform, score=-0.384, total= 0.1s\n","[CV] n_neighbors=2, weights=uniform ..................................\n","[CV] ..... n_neighbors=2, weights=uniform, score=-0.712, total= 0.1s\n","[CV] n_neighbors=2, weights=uniform ..................................\n","[CV] ..... n_neighbors=2, weights=uniform, score=-0.690, total= 0.1s\n","[CV] n_neighbors=2, weights=uniform ..................................\n","[CV] ..... n_neighbors=2, weights=uniform, score=-0.429, total= 0.1s\n","[CV] n_neighbors=2, weights=uniform ..................................\n","[CV] ..... n_neighbors=2, weights=uniform, score=-0.546, total= 0.1s\n","[CV] n_neighbors=2, weights=uniform ..................................\n","[CV] ..... n_neighbors=2, weights=uniform, score=-0.467, total= 0.1s\n","[CV] n_neighbors=2, weights=uniform ..................................\n","[CV] ..... n_neighbors=2, weights=uniform, score=-0.547, total= 0.1s\n","[CV] n_neighbors=2, weights=uniform ..................................\n","[CV] ..... n_neighbors=2, weights=uniform, score=-0.455, total= 0.1s\n","[CV] n_neighbors=2, weights=uniform ..................................\n","[CV] ..... n_neighbors=2, weights=uniform, score=-0.506, total= 0.1s\n","[CV] n_neighbors=2, weights=uniform ..................................\n","[CV] ..... n_neighbors=2, weights=uniform, score=-0.686, total= 0.1s\n","[CV] n_neighbors=2, weights=uniform ..................................\n","[CV] ..... n_neighbors=2, weights=uniform, score=-0.395, total= 0.1s\n","[CV] n_neighbors=2, weights=uniform ..................................\n","[CV] ..... n_neighbors=2, weights=uniform, score=-0.551, total= 0.1s\n","[CV] n_neighbors=2, weights=uniform ..................................\n","[CV] ..... n_neighbors=2, weights=uniform, score=-0.358, total= 0.1s\n","[CV] n_neighbors=2, weights=distance .................................\n","[CV] .... n_neighbors=2, weights=distance, score=-0.516, total= 0.1s\n","[CV] n_neighbors=2, weights=distance .................................\n","[CV] .... n_neighbors=2, weights=distance, score=-0.561, total= 0.1s\n","[CV] n_neighbors=2, weights=distance .................................\n","[CV] .... n_neighbors=2, weights=distance, score=-0.503, total= 0.1s\n","[CV] n_neighbors=2, weights=distance .................................\n","[CV] .... n_neighbors=2, weights=distance, score=-0.741, total= 0.1s\n","[CV] n_neighbors=2, weights=distance .................................\n","[CV] .... n_neighbors=2, weights=distance, score=-0.537, total= 0.1s\n","[CV] n_neighbors=2, weights=distance .................................\n","[CV] .... n_neighbors=2, weights=distance, score=-0.286, total= 0.1s\n","[CV] n_neighbors=2, weights=distance .................................\n","[CV] .... n_neighbors=2, weights=distance, score=-0.573, total= 0.1s\n","[CV] n_neighbors=2, weights=distance .................................\n","[CV] .... n_neighbors=2, weights=distance, score=-0.619, total= 0.1s\n","[CV] n_neighbors=2, weights=distance .................................\n","[CV] .... n_neighbors=2, weights=distance, score=-0.528, total= 0.1s\n","[CV] n_neighbors=2, weights=distance .................................\n","[CV] .... n_neighbors=2, weights=distance, score=-0.466, total= 0.1s\n","[CV] n_neighbors=2, weights=distance .................................\n","[CV] .... n_neighbors=2, weights=distance, score=-0.533, total= 0.1s\n","[CV] n_neighbors=2, weights=distance .................................\n","[CV] .... n_neighbors=2, weights=distance, score=-0.575, total= 0.1s\n","[CV] n_neighbors=2, weights=distance .................................\n","[CV] .... n_neighbors=2, weights=distance, score=-0.465, total= 0.1s\n","[CV] n_neighbors=2, weights=distance .................................\n","[CV] .... n_neighbors=2, weights=distance, score=-0.463, total= 0.1s\n","[CV] n_neighbors=2, weights=distance .................................\n","[CV] .... n_neighbors=2, weights=distance, score=-0.488, total= 0.1s\n","[CV] n_neighbors=2, weights=distance .................................\n","[CV] .... n_neighbors=2, weights=distance, score=-0.378, total= 0.1s\n","[CV] n_neighbors=2, weights=distance .................................\n","[CV] .... n_neighbors=2, weights=distance, score=-0.473, total= 0.1s\n","[CV] n_neighbors=2, weights=distance .................................\n","[CV] .... n_neighbors=2, weights=distance, score=-0.397, total= 0.1s\n","[CV] n_neighbors=2, weights=distance .................................\n","[CV] .... n_neighbors=2, weights=distance, score=-0.717, total= 0.1s\n","[CV] n_neighbors=2, weights=distance .................................\n","[CV] .... n_neighbors=2, weights=distance, score=-0.679, total= 0.1s\n","[CV] n_neighbors=2, weights=distance .................................\n","[CV] .... n_neighbors=2, weights=distance, score=-0.436, total= 0.1s\n","[CV] n_neighbors=2, weights=distance .................................\n","[CV] .... n_neighbors=2, weights=distance, score=-0.554, total= 0.1s\n","[CV] n_neighbors=2, weights=distance .................................\n","[CV] .... n_neighbors=2, weights=distance, score=-0.453, total= 0.1s\n","[CV] n_neighbors=2, weights=distance .................................\n","[CV] .... n_neighbors=2, weights=distance, score=-0.573, total= 0.1s\n","[CV] n_neighbors=2, weights=distance .................................\n","[CV] .... n_neighbors=2, weights=distance, score=-0.461, total= 0.1s\n","[CV] n_neighbors=2, weights=distance .................................\n","[CV] .... n_neighbors=2, weights=distance, score=-0.505, total= 0.1s\n","[CV] n_neighbors=2, weights=distance .................................\n","[CV] .... n_neighbors=2, weights=distance, score=-0.680, total= 0.1s\n","[CV] n_neighbors=2, weights=distance .................................\n","[CV] .... n_neighbors=2, weights=distance, score=-0.390, total= 0.1s\n","[CV] n_neighbors=2, weights=distance .................................\n","[CV] .... n_neighbors=2, weights=distance, score=-0.554, total= 0.1s\n","[CV] n_neighbors=2, weights=distance .................................\n","[CV] .... n_neighbors=2, weights=distance, score=-0.360, total= 0.1s\n","[CV] n_neighbors=3, weights=uniform ..................................\n","[CV] ..... n_neighbors=3, weights=uniform, score=-0.336, total= 0.1s\n","[CV] n_neighbors=3, weights=uniform ..................................\n","[CV] ..... n_neighbors=3, weights=uniform, score=-0.466, total= 0.1s\n","[CV] n_neighbors=3, weights=uniform ..................................\n","[CV] ..... n_neighbors=3, weights=uniform, score=-0.261, total= 0.1s\n","[CV] n_neighbors=3, weights=uniform ..................................\n","[CV] ..... n_neighbors=3, weights=uniform, score=-0.478, total= 0.1s\n","[CV] n_neighbors=3, weights=uniform ..................................\n","[CV] ..... n_neighbors=3, weights=uniform, score=-0.359, total= 0.1s\n","[CV] n_neighbors=3, weights=uniform ..................................\n","[CV] ..... n_neighbors=3, weights=uniform, score=-0.127, total= 0.1s\n","[CV] n_neighbors=3, weights=uniform ..................................\n","[CV] ..... n_neighbors=3, weights=uniform, score=-0.418, total= 0.1s\n","[CV] n_neighbors=3, weights=uniform ..................................\n","[CV] ..... n_neighbors=3, weights=uniform, score=-0.514, total= 0.1s\n","[CV] n_neighbors=3, weights=uniform ..................................\n","[CV] ..... n_neighbors=3, weights=uniform, score=-0.359, total= 0.1s\n","[CV] n_neighbors=3, weights=uniform ..................................\n","[CV] ..... n_neighbors=3, weights=uniform, score=-0.268, total= 0.1s\n","[CV] n_neighbors=3, weights=uniform ..................................\n","[CV] ..... n_neighbors=3, weights=uniform, score=-0.249, total= 0.1s\n","[CV] n_neighbors=3, weights=uniform ..................................\n","[CV] ..... n_neighbors=3, weights=uniform, score=-0.446, total= 0.1s\n","[CV] n_neighbors=3, weights=uniform ..................................\n","[CV] ..... n_neighbors=3, weights=uniform, score=-0.286, total= 0.1s\n","[CV] n_neighbors=3, weights=uniform ..................................\n","[CV] ..... n_neighbors=3, weights=uniform, score=-0.301, total= 0.1s\n","[CV] n_neighbors=3, weights=uniform ..................................\n","[CV] ..... n_neighbors=3, weights=uniform, score=-0.257, total= 0.1s\n","[CV] n_neighbors=3, weights=uniform ..................................\n","[CV] ..... n_neighbors=3, weights=uniform, score=-0.234, total= 0.1s\n","[CV] n_neighbors=3, weights=uniform ..................................\n","[CV] ..... n_neighbors=3, weights=uniform, score=-0.332, total= 0.1s\n","[CV] n_neighbors=3, weights=uniform ..................................\n","[CV] ..... n_neighbors=3, weights=uniform, score=-0.244, total= 0.1s\n","[CV] n_neighbors=3, weights=uniform ..................................\n","[CV] ..... n_neighbors=3, weights=uniform, score=-0.500, total= 0.1s\n","[CV] n_neighbors=3, weights=uniform ..................................\n","[CV] ..... n_neighbors=3, weights=uniform, score=-0.325, total= 0.1s\n","[CV] n_neighbors=3, weights=uniform ..................................\n","[CV] ..... n_neighbors=3, weights=uniform, score=-0.292, total= 0.1s\n","[CV] n_neighbors=3, weights=uniform ..................................\n","[CV] ..... n_neighbors=3, weights=uniform, score=-0.382, total= 0.1s\n","[CV] n_neighbors=3, weights=uniform ..................................\n","[CV] ..... n_neighbors=3, weights=uniform, score=-0.316, total= 0.1s\n","[CV] n_neighbors=3, weights=uniform ..................................\n","[CV] ..... n_neighbors=3, weights=uniform, score=-0.337, total= 0.1s\n","[CV] n_neighbors=3, weights=uniform ..................................\n","[CV] ..... n_neighbors=3, weights=uniform, score=-0.328, total= 0.1s\n","[CV] n_neighbors=3, weights=uniform ..................................\n","[CV] ..... n_neighbors=3, weights=uniform, score=-0.403, total= 0.1s\n","[CV] n_neighbors=3, weights=uniform ..................................\n","[CV] ..... n_neighbors=3, weights=uniform, score=-0.334, total= 0.1s\n","[CV] n_neighbors=3, weights=uniform ..................................\n","[CV] ..... n_neighbors=3, weights=uniform, score=-0.227, total= 0.1s\n","[CV] n_neighbors=3, weights=uniform ..................................\n","[CV] ..... n_neighbors=3, weights=uniform, score=-0.425, total= 0.1s\n","[CV] n_neighbors=3, weights=uniform ..................................\n","[CV] ..... n_neighbors=3, weights=uniform, score=-0.251, total= 0.1s\n","[CV] n_neighbors=3, weights=distance .................................\n","[CV] .... n_neighbors=3, weights=distance, score=-0.344, total= 0.1s\n","[CV] n_neighbors=3, weights=distance .................................\n","[CV] .... n_neighbors=3, weights=distance, score=-0.447, total= 0.1s\n","[CV] n_neighbors=3, weights=distance .................................\n","[CV] .... n_neighbors=3, weights=distance, score=-0.273, total= 0.1s\n","[CV] n_neighbors=3, weights=distance .................................\n","[CV] .... n_neighbors=3, weights=distance, score=-0.482, total= 0.1s\n","[CV] n_neighbors=3, weights=distance .................................\n","[CV] .... n_neighbors=3, weights=distance, score=-0.365, total= 0.1s\n","[CV] n_neighbors=3, weights=distance .................................\n","[CV] .... n_neighbors=3, weights=distance, score=-0.135, total= 0.1s\n","[CV] n_neighbors=3, weights=distance .................................\n","[CV] .... n_neighbors=3, weights=distance, score=-0.425, total= 0.1s\n","[CV] n_neighbors=3, weights=distance .................................\n","[CV] .... n_neighbors=3, weights=distance, score=-0.502, total= 0.1s\n","[CV] n_neighbors=3, weights=distance .................................\n","[CV] .... n_neighbors=3, weights=distance, score=-0.358, total= 0.1s\n","[CV] n_neighbors=3, weights=distance .................................\n","[CV] .... n_neighbors=3, weights=distance, score=-0.267, total= 0.1s\n","[CV] n_neighbors=3, weights=distance .................................\n","[CV] .... n_neighbors=3, weights=distance, score=-0.271, total= 0.1s\n","[CV] n_neighbors=3, weights=distance .................................\n","[CV] .... n_neighbors=3, weights=distance, score=-0.437, total= 0.1s\n","[CV] n_neighbors=3, weights=distance .................................\n","[CV] .... n_neighbors=3, weights=distance, score=-0.294, total= 0.1s\n","[CV] n_neighbors=3, weights=distance .................................\n","[CV] .... n_neighbors=3, weights=distance, score=-0.299, total= 0.1s\n","[CV] n_neighbors=3, weights=distance .................................\n","[CV] .... n_neighbors=3, weights=distance, score=-0.258, total= 0.1s\n","[CV] n_neighbors=3, weights=distance .................................\n","[CV] .... n_neighbors=3, weights=distance, score=-0.232, total= 0.1s\n","[CV] n_neighbors=3, weights=distance .................................\n","[CV] .... n_neighbors=3, weights=distance, score=-0.320, total= 0.1s\n","[CV] n_neighbors=3, weights=distance .................................\n","[CV] .... n_neighbors=3, weights=distance, score=-0.258, total= 0.1s\n","[CV] n_neighbors=3, weights=distance .................................\n","[CV] .... n_neighbors=3, weights=distance, score=-0.508, total= 0.1s\n","[CV] n_neighbors=3, weights=distance .................................\n","[CV] .... n_neighbors=3, weights=distance, score=-0.329, total= 0.1s\n","[CV] n_neighbors=3, weights=distance .................................\n","[CV] .... n_neighbors=3, weights=distance, score=-0.299, total= 0.1s\n","[CV] n_neighbors=3, weights=distance .................................\n","[CV] .... n_neighbors=3, weights=distance, score=-0.392, total= 0.1s\n","[CV] n_neighbors=3, weights=distance .................................\n","[CV] .... n_neighbors=3, weights=distance, score=-0.309, total= 0.1s\n","[CV] n_neighbors=3, weights=distance .................................\n","[CV] .... n_neighbors=3, weights=distance, score=-0.360, total= 0.1s\n","[CV] n_neighbors=3, weights=distance .................................\n","[CV] .... n_neighbors=3, weights=distance, score=-0.331, total= 0.1s\n","[CV] n_neighbors=3, weights=distance .................................\n","[CV] .... n_neighbors=3, weights=distance, score=-0.393, total= 0.1s\n","[CV] n_neighbors=3, weights=distance .................................\n","[CV] .... n_neighbors=3, weights=distance, score=-0.345, total= 0.1s\n","[CV] n_neighbors=3, weights=distance .................................\n","[CV] .... n_neighbors=3, weights=distance, score=-0.222, total= 0.1s\n","[CV] n_neighbors=3, weights=distance .................................\n","[CV] .... n_neighbors=3, weights=distance, score=-0.427, total= 0.1s\n","[CV] n_neighbors=3, weights=distance .................................\n","[CV] .... n_neighbors=3, weights=distance, score=-0.250, total= 0.1s\n","[CV] n_neighbors=4, weights=uniform ..................................\n","[CV] ..... n_neighbors=4, weights=uniform, score=-0.279, total= 0.1s\n","[CV] n_neighbors=4, weights=uniform ..................................\n","[CV] ..... n_neighbors=4, weights=uniform, score=-0.285, total= 0.1s\n","[CV] n_neighbors=4, weights=uniform ..................................\n","[CV] ..... n_neighbors=4, weights=uniform, score=-0.173, total= 0.1s\n","[CV] n_neighbors=4, weights=uniform ..................................\n","[CV] ..... n_neighbors=4, weights=uniform, score=-0.393, total= 0.1s\n","[CV] n_neighbors=4, weights=uniform ..................................\n","[CV] ..... n_neighbors=4, weights=uniform, score=-0.246, total= 0.1s\n","[CV] n_neighbors=4, weights=uniform ..................................\n","[CV] ..... n_neighbors=4, weights=uniform, score=-0.154, total= 0.1s\n","[CV] n_neighbors=4, weights=uniform ..................................\n","[CV] ..... n_neighbors=4, weights=uniform, score=-0.262, total= 0.1s\n","[CV] n_neighbors=4, weights=uniform ..................................\n","[CV] ..... n_neighbors=4, weights=uniform, score=-0.345, total= 0.1s\n","[CV] n_neighbors=4, weights=uniform ..................................\n","[CV] ..... n_neighbors=4, weights=uniform, score=-0.241, total= 0.1s\n","[CV] n_neighbors=4, weights=uniform ..................................\n","[CV] ..... n_neighbors=4, weights=uniform, score=-0.216, total= 0.1s\n","[CV] n_neighbors=4, weights=uniform ..................................\n","[CV] ..... n_neighbors=4, weights=uniform, score=-0.247, total= 0.1s\n","[CV] n_neighbors=4, weights=uniform ..................................\n","[CV] ..... n_neighbors=4, weights=uniform, score=-0.358, total= 0.1s\n","[CV] n_neighbors=4, weights=uniform ..................................\n","[CV] ..... n_neighbors=4, weights=uniform, score=-0.235, total= 0.1s\n","[CV] n_neighbors=4, weights=uniform ..................................\n","[CV] ..... n_neighbors=4, weights=uniform, score=-0.214, total= 0.1s\n","[CV] n_neighbors=4, weights=uniform ..................................\n","[CV] ..... n_neighbors=4, weights=uniform, score=-0.158, total= 0.1s\n","[CV] n_neighbors=4, weights=uniform ..................................\n","[CV] ..... n_neighbors=4, weights=uniform, score=-0.173, total= 0.1s\n","[CV] n_neighbors=4, weights=uniform ..................................\n","[CV] ..... n_neighbors=4, weights=uniform, score=-0.201, total= 0.1s\n","[CV] n_neighbors=4, weights=uniform ..................................\n","[CV] ..... n_neighbors=4, weights=uniform, score=-0.211, total= 0.1s\n","[CV] n_neighbors=4, weights=uniform ..................................\n","[CV] ..... n_neighbors=4, weights=uniform, score=-0.444, total= 0.1s\n","[CV] n_neighbors=4, weights=uniform ..................................\n","[CV] ..... n_neighbors=4, weights=uniform, score=-0.263, total= 0.1s\n","[CV] n_neighbors=4, weights=uniform ..................................\n","[CV] ..... n_neighbors=4, weights=uniform, score=-0.202, total= 0.1s\n","[CV] n_neighbors=4, weights=uniform ..................................\n","[CV] ..... n_neighbors=4, weights=uniform, score=-0.277, total= 0.1s\n","[CV] n_neighbors=4, weights=uniform ..................................\n","[CV] ..... n_neighbors=4, weights=uniform, score=-0.155, total= 0.1s\n","[CV] n_neighbors=4, weights=uniform ..................................\n","[CV] ..... n_neighbors=4, weights=uniform, score=-0.258, total= 0.1s\n","[CV] n_neighbors=4, weights=uniform ..................................\n","[CV] ..... n_neighbors=4, weights=uniform, score=-0.323, total= 0.1s\n","[CV] n_neighbors=4, weights=uniform ..................................\n","[CV] ..... n_neighbors=4, weights=uniform, score=-0.283, total= 0.1s\n","[CV] n_neighbors=4, weights=uniform ..................................\n","[CV] ..... n_neighbors=4, weights=uniform, score=-0.240, total= 0.1s\n","[CV] n_neighbors=4, weights=uniform ..................................\n","[CV] ..... n_neighbors=4, weights=uniform, score=-0.177, total= 0.1s\n","[CV] n_neighbors=4, weights=uniform ..................................\n","[CV] ..... n_neighbors=4, weights=uniform, score=-0.381, total= 0.1s\n","[CV] n_neighbors=4, weights=uniform ..................................\n","[CV] ..... n_neighbors=4, weights=uniform, score=-0.178, total= 0.1s\n","[CV] n_neighbors=4, weights=distance .................................\n","[CV] .... n_neighbors=4, weights=distance, score=-0.280, total= 0.1s\n","[CV] n_neighbors=4, weights=distance .................................\n","[CV] .... n_neighbors=4, weights=distance, score=-0.279, total= 0.1s\n","[CV] n_neighbors=4, weights=distance .................................\n","[CV] .... n_neighbors=4, weights=distance, score=-0.184, total= 0.1s\n","[CV] n_neighbors=4, weights=distance .................................\n","[CV] .... n_neighbors=4, weights=distance, score=-0.395, total= 0.1s\n","[CV] n_neighbors=4, weights=distance .................................\n","[CV] .... n_neighbors=4, weights=distance, score=-0.256, total= 0.1s\n","[CV] n_neighbors=4, weights=distance .................................\n","[CV] .... n_neighbors=4, weights=distance, score=-0.152, total= 0.1s\n","[CV] n_neighbors=4, weights=distance .................................\n","[CV] .... n_neighbors=4, weights=distance, score=-0.277, total= 0.1s\n","[CV] n_neighbors=4, weights=distance .................................\n","[CV] .... n_neighbors=4, weights=distance, score=-0.343, total= 0.1s\n","[CV] n_neighbors=4, weights=distance .................................\n","[CV] .... n_neighbors=4, weights=distance, score=-0.244, total= 0.1s\n","[CV] n_neighbors=4, weights=distance .................................\n","[CV] .... n_neighbors=4, weights=distance, score=-0.215, total= 0.1s\n","[CV] n_neighbors=4, weights=distance .................................\n","[CV] .... n_neighbors=4, weights=distance, score=-0.256, total= 0.1s\n","[CV] n_neighbors=4, weights=distance .................................\n","[CV] .... n_neighbors=4, weights=distance, score=-0.353, total= 0.1s\n","[CV] n_neighbors=4, weights=distance .................................\n","[CV] .... n_neighbors=4, weights=distance, score=-0.234, total= 0.1s\n","[CV] n_neighbors=4, weights=distance .................................\n","[CV] .... n_neighbors=4, weights=distance, score=-0.214, total= 0.1s\n","[CV] n_neighbors=4, weights=distance .................................\n","[CV] .... n_neighbors=4, weights=distance, score=-0.158, total= 0.1s\n","[CV] n_neighbors=4, weights=distance .................................\n","[CV] .... n_neighbors=4, weights=distance, score=-0.170, total= 0.1s\n","[CV] n_neighbors=4, weights=distance .................................\n","[CV] .... n_neighbors=4, weights=distance, score=-0.194, total= 0.1s\n","[CV] n_neighbors=4, weights=distance .................................\n","[CV] .... n_neighbors=4, weights=distance, score=-0.219, total= 0.1s\n","[CV] n_neighbors=4, weights=distance .................................\n","[CV] .... n_neighbors=4, weights=distance, score=-0.456, total= 0.1s\n","[CV] n_neighbors=4, weights=distance .................................\n","[CV] .... n_neighbors=4, weights=distance, score=-0.263, total= 0.1s\n","[CV] n_neighbors=4, weights=distance .................................\n","[CV] .... n_neighbors=4, weights=distance, score=-0.206, total= 0.1s\n","[CV] n_neighbors=4, weights=distance .................................\n","[CV] .... n_neighbors=4, weights=distance, score=-0.288, total= 0.1s\n","[CV] n_neighbors=4, weights=distance .................................\n","[CV] .... n_neighbors=4, weights=distance, score=-0.156, total= 0.1s\n","[CV] n_neighbors=4, weights=distance .................................\n","[CV] .... n_neighbors=4, weights=distance, score=-0.278, total= 0.1s\n","[CV] n_neighbors=4, weights=distance .................................\n","[CV] .... n_neighbors=4, weights=distance, score=-0.323, total= 0.1s\n","[CV] n_neighbors=4, weights=distance .................................\n","[CV] .... n_neighbors=4, weights=distance, score=-0.280, total= 0.1s\n","[CV] n_neighbors=4, weights=distance .................................\n","[CV] .... n_neighbors=4, weights=distance, score=-0.252, total= 0.1s\n","[CV] n_neighbors=4, weights=distance .................................\n","[CV] .... n_neighbors=4, weights=distance, score=-0.171, total= 0.1s\n","[CV] n_neighbors=4, weights=distance .................................\n","[CV] .... n_neighbors=4, weights=distance, score=-0.382, total= 0.1s\n","[CV] n_neighbors=4, weights=distance .................................\n","[CV] .... n_neighbors=4, weights=distance, score=-0.174, total= 0.1s\n","[CV] n_neighbors=5, weights=uniform ..................................\n","[CV] ..... n_neighbors=5, weights=uniform, score=-0.229, total= 0.1s\n","[CV] n_neighbors=5, weights=uniform ..................................\n","[CV] ..... n_neighbors=5, weights=uniform, score=-0.213, total= 0.1s\n","[CV] n_neighbors=5, weights=uniform ..................................\n","[CV] ..... n_neighbors=5, weights=uniform, score=-0.141, total= 0.1s\n","[CV] n_neighbors=5, weights=uniform ..................................\n","[CV] ..... n_neighbors=5, weights=uniform, score=-0.324, total= 0.1s\n","[CV] n_neighbors=5, weights=uniform ..................................\n","[CV] ..... n_neighbors=5, weights=uniform, score=-0.242, total= 0.1s\n","[CV] n_neighbors=5, weights=uniform ..................................\n","[CV] ..... n_neighbors=5, weights=uniform, score=-0.135, total= 0.1s\n","[CV] n_neighbors=5, weights=uniform ..................................\n","[CV] ..... n_neighbors=5, weights=uniform, score=-0.225, total= 0.1s\n","[CV] n_neighbors=5, weights=uniform ..................................\n","[CV] ..... n_neighbors=5, weights=uniform, score=-0.250, total= 0.1s\n","[CV] n_neighbors=5, weights=uniform ..................................\n","[CV] ..... n_neighbors=5, weights=uniform, score=-0.210, total= 0.1s\n","[CV] n_neighbors=5, weights=uniform ..................................\n","[CV] ..... n_neighbors=5, weights=uniform, score=-0.140, total= 0.1s\n","[CV] n_neighbors=5, weights=uniform ..................................\n","[CV] ..... n_neighbors=5, weights=uniform, score=-0.200, total= 0.1s\n","[CV] n_neighbors=5, weights=uniform ..................................\n","[CV] ..... n_neighbors=5, weights=uniform, score=-0.309, total= 0.1s\n","[CV] n_neighbors=5, weights=uniform ..................................\n","[CV] ..... n_neighbors=5, weights=uniform, score=-0.205, total= 0.1s\n","[CV] n_neighbors=5, weights=uniform ..................................\n","[CV] ..... n_neighbors=5, weights=uniform, score=-0.161, total= 0.1s\n","[CV] n_neighbors=5, weights=uniform ..................................\n","[CV] ..... n_neighbors=5, weights=uniform, score=-0.139, total= 0.1s\n","[CV] n_neighbors=5, weights=uniform ..................................\n","[CV] ..... n_neighbors=5, weights=uniform, score=-0.115, total= 0.1s\n","[CV] n_neighbors=5, weights=uniform ..................................\n","[CV] ..... n_neighbors=5, weights=uniform, score=-0.157, total= 0.1s\n","[CV] n_neighbors=5, weights=uniform ..................................\n","[CV] ..... n_neighbors=5, weights=uniform, score=-0.231, total= 0.1s\n","[CV] n_neighbors=5, weights=uniform ..................................\n","[CV] ..... n_neighbors=5, weights=uniform, score=-0.331, total= 0.1s\n","[CV] n_neighbors=5, weights=uniform ..................................\n","[CV] ..... n_neighbors=5, weights=uniform, score=-0.184, total= 0.1s\n","[CV] n_neighbors=5, weights=uniform ..................................\n","[CV] ..... n_neighbors=5, weights=uniform, score=-0.152, total= 0.1s\n","[CV] n_neighbors=5, weights=uniform ..................................\n","[CV] ..... n_neighbors=5, weights=uniform, score=-0.259, total= 0.1s\n","[CV] n_neighbors=5, weights=uniform ..................................\n","[CV] ..... n_neighbors=5, weights=uniform, score=-0.140, total= 0.1s\n","[CV] n_neighbors=5, weights=uniform ..................................\n","[CV] ..... n_neighbors=5, weights=uniform, score=-0.215, total= 0.1s\n","[CV] n_neighbors=5, weights=uniform ..................................\n","[CV] ..... n_neighbors=5, weights=uniform, score=-0.293, total= 0.1s\n","[CV] n_neighbors=5, weights=uniform ..................................\n","[CV] ..... n_neighbors=5, weights=uniform, score=-0.222, total= 0.1s\n","[CV] n_neighbors=5, weights=uniform ..................................\n","[CV] ..... n_neighbors=5, weights=uniform, score=-0.226, total= 0.1s\n","[CV] n_neighbors=5, weights=uniform ..................................\n","[CV] ..... n_neighbors=5, weights=uniform, score=-0.168, total= 0.1s\n","[CV] n_neighbors=5, weights=uniform ..................................\n","[CV] ..... n_neighbors=5, weights=uniform, score=-0.339, total= 0.1s\n","[CV] n_neighbors=5, weights=uniform ..................................\n","[CV] ..... n_neighbors=5, weights=uniform, score=-0.073, total= 0.1s\n","[CV] n_neighbors=5, weights=distance .................................\n","[CV] .... n_neighbors=5, weights=distance, score=-0.227, total= 0.1s\n","[CV] n_neighbors=5, weights=distance .................................\n","[CV] .... n_neighbors=5, weights=distance, score=-0.214, total= 0.1s\n","[CV] n_neighbors=5, weights=distance .................................\n","[CV] .... n_neighbors=5, weights=distance, score=-0.148, total= 0.1s\n","[CV] n_neighbors=5, weights=distance .................................\n","[CV] .... n_neighbors=5, weights=distance, score=-0.324, total= 0.1s\n","[CV] n_neighbors=5, weights=distance .................................\n","[CV] .... n_neighbors=5, weights=distance, score=-0.246, total= 0.1s\n","[CV] n_neighbors=5, weights=distance .................................\n","[CV] .... n_neighbors=5, weights=distance, score=-0.127, total= 0.1s\n","[CV] n_neighbors=5, weights=distance .................................\n","[CV] .... n_neighbors=5, weights=distance, score=-0.233, total= 0.1s\n","[CV] n_neighbors=5, weights=distance .................................\n","[CV] .... n_neighbors=5, weights=distance, score=-0.254, total= 0.1s\n","[CV] n_neighbors=5, weights=distance .................................\n","[CV] .... n_neighbors=5, weights=distance, score=-0.212, total= 0.1s\n","[CV] n_neighbors=5, weights=distance .................................\n","[CV] .... n_neighbors=5, weights=distance, score=-0.144, total= 0.1s\n","[CV] n_neighbors=5, weights=distance .................................\n","[CV] .... n_neighbors=5, weights=distance, score=-0.208, total= 0.1s\n","[CV] n_neighbors=5, weights=distance .................................\n","[CV] .... n_neighbors=5, weights=distance, score=-0.305, total= 0.1s\n","[CV] n_neighbors=5, weights=distance .................................\n","[CV] .... n_neighbors=5, weights=distance, score=-0.201, total= 0.1s\n","[CV] n_neighbors=5, weights=distance .................................\n","[CV] .... n_neighbors=5, weights=distance, score=-0.166, total= 0.1s\n","[CV] n_neighbors=5, weights=distance .................................\n","[CV] .... n_neighbors=5, weights=distance, score=-0.135, total= 0.1s\n","[CV] n_neighbors=5, weights=distance .................................\n","[CV] .... n_neighbors=5, weights=distance, score=-0.114, total= 0.1s\n","[CV] n_neighbors=5, weights=distance .................................\n","[CV] .... n_neighbors=5, weights=distance, score=-0.151, total= 0.1s\n","[CV] n_neighbors=5, weights=distance .................................\n","[CV] .... n_neighbors=5, weights=distance, score=-0.231, total= 0.1s\n","[CV] n_neighbors=5, weights=distance .................................\n","[CV] .... n_neighbors=5, weights=distance, score=-0.348, total= 0.1s\n","[CV] n_neighbors=5, weights=distance .................................\n","[CV] .... n_neighbors=5, weights=distance, score=-0.186, total= 0.1s\n","[CV] n_neighbors=5, weights=distance .................................\n","[CV] .... n_neighbors=5, weights=distance, score=-0.157, total= 0.1s\n","[CV] n_neighbors=5, weights=distance .................................\n","[CV] .... n_neighbors=5, weights=distance, score=-0.264, total= 0.1s\n","[CV] n_neighbors=5, weights=distance .................................\n","[CV] .... n_neighbors=5, weights=distance, score=-0.136, total= 0.1s\n","[CV] n_neighbors=5, weights=distance .................................\n","[CV] .... n_neighbors=5, weights=distance, score=-0.229, total= 0.1s\n","[CV] n_neighbors=5, weights=distance .................................\n","[CV] .... n_neighbors=5, weights=distance, score=-0.292, total= 0.1s\n","[CV] n_neighbors=5, weights=distance .................................\n","[CV] .... n_neighbors=5, weights=distance, score=-0.219, total= 0.1s\n","[CV] n_neighbors=5, weights=distance .................................\n","[CV] .... n_neighbors=5, weights=distance, score=-0.233, total= 0.1s\n","[CV] n_neighbors=5, weights=distance .................................\n","[CV] .... n_neighbors=5, weights=distance, score=-0.157, total= 0.1s\n","[CV] n_neighbors=5, weights=distance .................................\n","[CV] .... n_neighbors=5, weights=distance, score=-0.339, total= 0.1s\n","[CV] n_neighbors=5, weights=distance .................................\n","[CV] .... n_neighbors=5, weights=distance, score=-0.076, total= 0.1s\n","[CV] n_neighbors=6, weights=uniform ..................................\n","[CV] ..... n_neighbors=6, weights=uniform, score=-0.162, total= 0.1s\n","[CV] n_neighbors=6, weights=uniform ..................................\n","[CV] ..... n_neighbors=6, weights=uniform, score=-0.168, total= 0.1s\n","[CV] n_neighbors=6, weights=uniform ..................................\n","[CV] ..... n_neighbors=6, weights=uniform, score=-0.141, total= 0.1s\n","[CV] n_neighbors=6, weights=uniform ..................................\n","[CV] ..... n_neighbors=6, weights=uniform, score=-0.258, total= 0.1s\n","[CV] n_neighbors=6, weights=uniform ..................................\n","[CV] ..... n_neighbors=6, weights=uniform, score=-0.253, total= 0.1s\n","[CV] n_neighbors=6, weights=uniform ..................................\n","[CV] ..... n_neighbors=6, weights=uniform, score=-0.133, total= 0.1s\n","[CV] n_neighbors=6, weights=uniform ..................................\n","[CV] ..... n_neighbors=6, weights=uniform, score=-0.180, total= 0.1s\n","[CV] n_neighbors=6, weights=uniform ..................................\n","[CV] ..... n_neighbors=6, weights=uniform, score=-0.239, total= 0.1s\n","[CV] n_neighbors=6, weights=uniform ..................................\n","[CV] ..... n_neighbors=6, weights=uniform, score=-0.168, total= 0.1s\n","[CV] n_neighbors=6, weights=uniform ..................................\n","[CV] ..... n_neighbors=6, weights=uniform, score=-0.120, total= 0.1s\n","[CV] n_neighbors=6, weights=uniform ..................................\n","[CV] ..... n_neighbors=6, weights=uniform, score=-0.188, total= 0.1s\n","[CV] n_neighbors=6, weights=uniform ..................................\n","[CV] ..... n_neighbors=6, weights=uniform, score=-0.269, total= 0.1s\n","[CV] n_neighbors=6, weights=uniform ..................................\n","[CV] ..... n_neighbors=6, weights=uniform, score=-0.158, total= 0.1s\n","[CV] n_neighbors=6, weights=uniform ..................................\n","[CV] ..... n_neighbors=6, weights=uniform, score=-0.112, total= 0.1s\n","[CV] n_neighbors=6, weights=uniform ..................................\n","[CV] ..... n_neighbors=6, weights=uniform, score=-0.090, total= 0.1s\n","[CV] n_neighbors=6, weights=uniform ..................................\n","[CV] ..... n_neighbors=6, weights=uniform, score=-0.122, total= 0.1s\n","[CV] n_neighbors=6, weights=uniform ..................................\n","[CV] ..... n_neighbors=6, weights=uniform, score=-0.132, total= 0.1s\n","[CV] n_neighbors=6, weights=uniform ..................................\n","[CV] ..... n_neighbors=6, weights=uniform, score=-0.204, total= 0.1s\n","[CV] n_neighbors=6, weights=uniform ..................................\n","[CV] ..... n_neighbors=6, weights=uniform, score=-0.272, total= 0.1s\n","[CV] n_neighbors=6, weights=uniform ..................................\n","[CV] ..... n_neighbors=6, weights=uniform, score=-0.149, total= 0.1s\n","[CV] n_neighbors=6, weights=uniform ..................................\n","[CV] ..... n_neighbors=6, weights=uniform, score=-0.110, total= 0.1s\n","[CV] n_neighbors=6, weights=uniform ..................................\n","[CV] ..... n_neighbors=6, weights=uniform, score=-0.226, total= 0.1s\n","[CV] n_neighbors=6, weights=uniform ..................................\n","[CV] ..... n_neighbors=6, weights=uniform, score=-0.099, total= 0.1s\n","[CV] n_neighbors=6, weights=uniform ..................................\n","[CV] ..... n_neighbors=6, weights=uniform, score=-0.149, total= 0.1s\n","[CV] n_neighbors=6, weights=uniform ..................................\n","[CV] ..... n_neighbors=6, weights=uniform, score=-0.254, total= 0.1s\n","[CV] n_neighbors=6, weights=uniform ..................................\n","[CV] ..... n_neighbors=6, weights=uniform, score=-0.192, total= 0.1s\n","[CV] n_neighbors=6, weights=uniform ..................................\n","[CV] ..... n_neighbors=6, weights=uniform, score=-0.182, total= 0.1s\n","[CV] n_neighbors=6, weights=uniform ..................................\n","[CV] ..... n_neighbors=6, weights=uniform, score=-0.146, total= 0.1s\n","[CV] n_neighbors=6, weights=uniform ..................................\n","[CV] ..... n_neighbors=6, weights=uniform, score=-0.285, total= 0.1s\n","[CV] n_neighbors=6, weights=uniform ..................................\n","[CV] ..... n_neighbors=6, weights=uniform, score=-0.063, total= 0.1s\n","[CV] n_neighbors=6, weights=distance .................................\n","[CV] .... n_neighbors=6, weights=distance, score=-0.163, total= 0.1s\n","[CV] n_neighbors=6, weights=distance .................................\n","[CV] .... n_neighbors=6, weights=distance, score=-0.169, total= 0.1s\n","[CV] n_neighbors=6, weights=distance .................................\n","[CV] .... n_neighbors=6, weights=distance, score=-0.144, total= 0.1s\n","[CV] n_neighbors=6, weights=distance .................................\n","[CV] .... n_neighbors=6, weights=distance, score=-0.259, total= 0.1s\n","[CV] n_neighbors=6, weights=distance .................................\n","[CV] .... n_neighbors=6, weights=distance, score=-0.249, total= 0.1s\n","[CV] n_neighbors=6, weights=distance .................................\n","[CV] .... n_neighbors=6, weights=distance, score=-0.123, total= 0.1s\n","[CV] n_neighbors=6, weights=distance .................................\n","[CV] .... n_neighbors=6, weights=distance, score=-0.186, total= 0.1s\n","[CV] n_neighbors=6, weights=distance .................................\n","[CV] .... n_neighbors=6, weights=distance, score=-0.240, total= 0.1s\n","[CV] n_neighbors=6, weights=distance .................................\n","[CV] .... n_neighbors=6, weights=distance, score=-0.170, total= 0.1s\n","[CV] n_neighbors=6, weights=distance .................................\n","[CV] .... n_neighbors=6, weights=distance, score=-0.118, total= 0.1s\n","[CV] n_neighbors=6, weights=distance .................................\n","[CV] .... n_neighbors=6, weights=distance, score=-0.191, total= 0.1s\n","[CV] n_neighbors=6, weights=distance .................................\n","[CV] .... n_neighbors=6, weights=distance, score=-0.266, total= 0.1s\n","[CV] n_neighbors=6, weights=distance .................................\n","[CV] .... n_neighbors=6, weights=distance, score=-0.152, total= 0.1s\n","[CV] n_neighbors=6, weights=distance .................................\n","[CV] .... n_neighbors=6, weights=distance, score=-0.117, total= 0.1s\n","[CV] n_neighbors=6, weights=distance .................................\n","[CV] .... n_neighbors=6, weights=distance, score=-0.087, total= 0.1s\n","[CV] n_neighbors=6, weights=distance .................................\n","[CV] .... n_neighbors=6, weights=distance, score=-0.118, total= 0.1s\n","[CV] n_neighbors=6, weights=distance .................................\n","[CV] .... n_neighbors=6, weights=distance, score=-0.125, total= 0.1s\n","[CV] n_neighbors=6, weights=distance .................................\n","[CV] .... n_neighbors=6, weights=distance, score=-0.204, total= 0.1s\n","[CV] n_neighbors=6, weights=distance .................................\n","[CV] .... n_neighbors=6, weights=distance, score=-0.288, total= 0.1s\n","[CV] n_neighbors=6, weights=distance .................................\n","[CV] .... n_neighbors=6, weights=distance, score=-0.149, total= 0.1s\n","[CV] n_neighbors=6, weights=distance .................................\n","[CV] .... n_neighbors=6, weights=distance, score=-0.116, total= 0.1s\n","[CV] n_neighbors=6, weights=distance .................................\n","[CV] .... n_neighbors=6, weights=distance, score=-0.229, total= 0.1s\n","[CV] n_neighbors=6, weights=distance .................................\n","[CV] .... n_neighbors=6, weights=distance, score=-0.095, total= 0.1s\n","[CV] n_neighbors=6, weights=distance .................................\n","[CV] .... n_neighbors=6, weights=distance, score=-0.164, total= 0.1s\n","[CV] n_neighbors=6, weights=distance .................................\n","[CV] .... n_neighbors=6, weights=distance, score=-0.255, total= 0.1s\n","[CV] n_neighbors=6, weights=distance .................................\n","[CV] .... n_neighbors=6, weights=distance, score=-0.189, total= 0.1s\n","[CV] n_neighbors=6, weights=distance .................................\n","[CV] .... n_neighbors=6, weights=distance, score=-0.190, total= 0.1s\n","[CV] n_neighbors=6, weights=distance .................................\n","[CV] .... n_neighbors=6, weights=distance, score=-0.135, total= 0.1s\n","[CV] n_neighbors=6, weights=distance .................................\n","[CV] .... n_neighbors=6, weights=distance, score=-0.287, total= 0.1s\n","[CV] n_neighbors=6, weights=distance .................................\n","[CV] .... n_neighbors=6, weights=distance, score=-0.064, total= 0.1s\n","[Parallel(n_jobs=1)]: Done 300 out of 300 | elapsed: 31.1s finished\n","KNeighborsRegressor(algorithm='auto', leaf_size=30, metric='minkowski',\n"," metric_params=None, n_jobs=None, n_neighbors=6, p=2,\n"," weights='uniform')\n","window: 1, stide: 1, rmse: 3.0304320679597025\n","[1, 4, 9, 12, 2, 0, 3, 13, 11, 10, 6, 5, 7, 8]\n","[1, 4, 9, 12, 2, 0, 3, 13, 11]\n","(3000, 135)\n","Number of Feature-Columns: 135\n","\n","Features Ready for undergoing selection tests done ...\n","\n","Fitting 30 folds for each of 10 candidates, totalling 300 fits\n","[Parallel(n_jobs=1)]: Using backend SequentialBackend with 1 concurrent workers.\n","[CV] n_neighbors=2, weights=uniform ..................................\n","[CV] ..... n_neighbors=2, weights=uniform, score=-0.534, total= 0.1s\n","[Parallel(n_jobs=1)]: Done 1 out of 1 | elapsed: 0.1s remaining: 0.0s\n","[CV] n_neighbors=2, weights=uniform ..................................\n","[CV] ..... n_neighbors=2, weights=uniform, score=-0.533, total= 0.1s\n","[Parallel(n_jobs=1)]: Done 2 out of 2 | elapsed: 0.2s remaining: 0.0s\n","[CV] n_neighbors=2, weights=uniform ..................................\n","[CV] ..... n_neighbors=2, weights=uniform, score=-0.491, total= 0.1s\n","[Parallel(n_jobs=1)]: Done 3 out of 3 | elapsed: 0.4s remaining: 0.0s\n","[CV] n_neighbors=2, weights=uniform ..................................\n","[CV] ..... n_neighbors=2, weights=uniform, score=-0.605, total= 0.1s\n","[Parallel(n_jobs=1)]: Done 4 out of 4 | elapsed: 0.5s remaining: 0.0s\n","[CV] n_neighbors=2, weights=uniform ..................................\n","[CV] ..... n_neighbors=2, weights=uniform, score=-0.634, total= 0.1s\n","[CV] n_neighbors=2, weights=uniform ..................................\n","[CV] ..... n_neighbors=2, weights=uniform, score=-0.471, total= 0.1s\n","[CV] n_neighbors=2, weights=uniform ..................................\n","[CV] ..... n_neighbors=2, weights=uniform, score=-0.523, total= 0.1s\n","[CV] n_neighbors=2, weights=uniform ..................................\n","[CV] ..... n_neighbors=2, weights=uniform, score=-0.513, total= 0.1s\n","[CV] n_neighbors=2, weights=uniform ..................................\n","[CV] ..... n_neighbors=2, weights=uniform, score=-0.415, total= 0.1s\n","[CV] n_neighbors=2, weights=uniform ..................................\n","[CV] ..... n_neighbors=2, weights=uniform, score=-0.683, total= 0.1s\n","[CV] n_neighbors=2, weights=uniform ..................................\n","[CV] ..... n_neighbors=2, weights=uniform, score=-0.610, total= 0.1s\n","[CV] n_neighbors=2, weights=uniform ..................................\n","[CV] ..... n_neighbors=2, weights=uniform, score=-0.573, total= 0.1s\n","[CV] n_neighbors=2, weights=uniform ..................................\n","[CV] ..... n_neighbors=2, weights=uniform, score=-0.400, total= 0.1s\n","[CV] n_neighbors=2, weights=uniform ..................................\n","[CV] ..... n_neighbors=2, weights=uniform, score=-0.459, total= 0.1s\n","[CV] n_neighbors=2, weights=uniform ..................................\n","[CV] ..... n_neighbors=2, weights=uniform, score=-0.526, total= 0.1s\n","[CV] n_neighbors=2, weights=uniform ..................................\n","[CV] ..... n_neighbors=2, weights=uniform, score=-0.372, total= 0.1s\n","[CV] n_neighbors=2, weights=uniform ..................................\n","[CV] ..... n_neighbors=2, weights=uniform, score=-0.507, total= 0.1s\n","[CV] n_neighbors=2, weights=uniform ..................................\n","[CV] ..... n_neighbors=2, weights=uniform, score=-0.398, total= 0.1s\n","[CV] n_neighbors=2, weights=uniform ..................................\n","[CV] ..... n_neighbors=2, weights=uniform, score=-0.589, total= 0.1s\n","[CV] n_neighbors=2, weights=uniform ..................................\n","[CV] ..... n_neighbors=2, weights=uniform, score=-0.597, total= 0.1s\n","[CV] n_neighbors=2, weights=uniform ..................................\n","[CV] ..... n_neighbors=2, weights=uniform, score=-0.404, total= 0.1s\n","[CV] n_neighbors=2, weights=uniform ..................................\n","[CV] ..... n_neighbors=2, weights=uniform, score=-0.568, total= 0.1s\n","[CV] n_neighbors=2, weights=uniform ..................................\n","[CV] ..... n_neighbors=2, weights=uniform, score=-0.333, total= 0.1s\n","[CV] n_neighbors=2, weights=uniform ..................................\n","[CV] ..... n_neighbors=2, weights=uniform, score=-0.457, total= 0.1s\n","[CV] n_neighbors=2, weights=uniform ..................................\n","[CV] ..... n_neighbors=2, weights=uniform, score=-0.579, total= 0.1s\n","[CV] n_neighbors=2, weights=uniform ..................................\n","[CV] ..... n_neighbors=2, weights=uniform, score=-0.586, total= 0.1s\n","[CV] n_neighbors=2, weights=uniform ..................................\n","[CV] ..... n_neighbors=2, weights=uniform, score=-0.606, total= 0.1s\n","[CV] n_neighbors=2, weights=uniform ..................................\n","[CV] ..... n_neighbors=2, weights=uniform, score=-0.290, total= 0.1s\n","[CV] n_neighbors=2, weights=uniform ..................................\n","[CV] ..... n_neighbors=2, weights=uniform, score=-0.594, total= 0.1s\n","[CV] n_neighbors=2, weights=uniform ..................................\n","[CV] ..... n_neighbors=2, weights=uniform, score=-0.540, total= 0.1s\n","[CV] n_neighbors=2, weights=distance .................................\n","[CV] .... n_neighbors=2, weights=distance, score=-0.539, total= 0.1s\n","[CV] n_neighbors=2, weights=distance .................................\n","[CV] .... n_neighbors=2, weights=distance, score=-0.531, total= 0.1s\n","[CV] n_neighbors=2, weights=distance .................................\n","[CV] .... n_neighbors=2, weights=distance, score=-0.503, total= 0.1s\n","[CV] n_neighbors=2, weights=distance .................................\n","[CV] .... n_neighbors=2, weights=distance, score=-0.597, total= 0.1s\n","[CV] n_neighbors=2, weights=distance .................................\n","[CV] .... n_neighbors=2, weights=distance, score=-0.642, total= 0.1s\n","[CV] n_neighbors=2, weights=distance .................................\n","[CV] .... n_neighbors=2, weights=distance, score=-0.472, total= 0.1s\n","[CV] n_neighbors=2, weights=distance .................................\n","[CV] .... n_neighbors=2, weights=distance, score=-0.535, total= 0.1s\n","[CV] n_neighbors=2, weights=distance .................................\n","[CV] .... n_neighbors=2, weights=distance, score=-0.518, total= 0.1s\n","[CV] n_neighbors=2, weights=distance .................................\n","[CV] .... n_neighbors=2, weights=distance, score=-0.428, total= 0.1s\n","[CV] n_neighbors=2, weights=distance .................................\n","[CV] .... n_neighbors=2, weights=distance, score=-0.676, total= 0.1s\n","[CV] n_neighbors=2, weights=distance .................................\n","[CV] .... n_neighbors=2, weights=distance, score=-0.613, total= 0.1s\n","[CV] n_neighbors=2, weights=distance .................................\n","[CV] .... n_neighbors=2, weights=distance, score=-0.568, total= 0.1s\n","[CV] n_neighbors=2, weights=distance .................................\n","[CV] .... n_neighbors=2, weights=distance, score=-0.401, total= 0.1s\n","[CV] n_neighbors=2, weights=distance .................................\n","[CV] .... n_neighbors=2, weights=distance, score=-0.453, total= 0.1s\n","[CV] n_neighbors=2, weights=distance .................................\n","[CV] .... n_neighbors=2, weights=distance, score=-0.532, total= 0.1s\n","[CV] n_neighbors=2, weights=distance .................................\n","[CV] .... n_neighbors=2, weights=distance, score=-0.378, total= 0.1s\n","[CV] n_neighbors=2, weights=distance .................................\n","[CV] .... n_neighbors=2, weights=distance, score=-0.501, total= 0.1s\n","[CV] n_neighbors=2, weights=distance .................................\n","[CV] .... n_neighbors=2, weights=distance, score=-0.402, total= 0.1s\n","[CV] n_neighbors=2, weights=distance .................................\n","[CV] .... n_neighbors=2, weights=distance, score=-0.596, total= 0.1s\n","[CV] n_neighbors=2, weights=distance .................................\n","[CV] .... n_neighbors=2, weights=distance, score=-0.609, total= 0.1s\n","[CV] n_neighbors=2, weights=distance .................................\n","[CV] .... n_neighbors=2, weights=distance, score=-0.405, total= 0.1s\n","[CV] n_neighbors=2, weights=distance .................................\n","[CV] .... n_neighbors=2, weights=distance, score=-0.592, total= 0.1s\n","[CV] n_neighbors=2, weights=distance .................................\n","[CV] .... n_neighbors=2, weights=distance, score=-0.338, total= 0.1s\n","[CV] n_neighbors=2, weights=distance .................................\n","[CV] .... n_neighbors=2, weights=distance, score=-0.459, total= 0.1s\n","[CV] n_neighbors=2, weights=distance .................................\n","[CV] .... n_neighbors=2, weights=distance, score=-0.569, total= 0.1s\n","[CV] n_neighbors=2, weights=distance .................................\n","[CV] .... n_neighbors=2, weights=distance, score=-0.579, total= 0.1s\n","[CV] n_neighbors=2, weights=distance .................................\n","[CV] .... n_neighbors=2, weights=distance, score=-0.618, total= 0.1s\n","[CV] n_neighbors=2, weights=distance .................................\n","[CV] .... n_neighbors=2, weights=distance, score=-0.291, total= 0.1s\n","[CV] n_neighbors=2, weights=distance .................................\n","[CV] .... n_neighbors=2, weights=distance, score=-0.590, total= 0.1s\n","[CV] n_neighbors=2, weights=distance .................................\n","[CV] .... n_neighbors=2, weights=distance, score=-0.538, total= 0.1s\n","[CV] n_neighbors=3, weights=uniform ..................................\n","[CV] ..... n_neighbors=3, weights=uniform, score=-0.319, total= 0.1s\n","[CV] n_neighbors=3, weights=uniform ..................................\n","[CV] ..... n_neighbors=3, weights=uniform, score=-0.314, total= 0.1s\n","[CV] n_neighbors=3, weights=uniform ..................................\n","[CV] ..... n_neighbors=3, weights=uniform, score=-0.375, total= 0.1s\n","[CV] n_neighbors=3, weights=uniform ..................................\n","[CV] ..... n_neighbors=3, weights=uniform, score=-0.403, total= 0.1s\n","[CV] n_neighbors=3, weights=uniform ..................................\n","[CV] ..... n_neighbors=3, weights=uniform, score=-0.431, total= 0.1s\n","[CV] n_neighbors=3, weights=uniform ..................................\n","[CV] ..... n_neighbors=3, weights=uniform, score=-0.314, total= 0.1s\n","[CV] n_neighbors=3, weights=uniform ..................................\n","[CV] ..... n_neighbors=3, weights=uniform, score=-0.291, total= 0.1s\n","[CV] n_neighbors=3, weights=uniform ..................................\n","[CV] ..... n_neighbors=3, weights=uniform, score=-0.384, total= 0.1s\n","[CV] n_neighbors=3, weights=uniform ..................................\n","[CV] ..... n_neighbors=3, weights=uniform, score=-0.362, total= 0.1s\n","[CV] n_neighbors=3, weights=uniform ..................................\n","[CV] ..... n_neighbors=3, weights=uniform, score=-0.440, total= 0.1s\n","[CV] n_neighbors=3, weights=uniform ..................................\n","[CV] ..... n_neighbors=3, weights=uniform, score=-0.382, total= 0.1s\n","[CV] n_neighbors=3, weights=uniform ..................................\n","[CV] ..... n_neighbors=3, weights=uniform, score=-0.357, total= 0.1s\n","[CV] n_neighbors=3, weights=uniform ..................................\n","[CV] ..... n_neighbors=3, weights=uniform, score=-0.299, total= 0.1s\n","[CV] n_neighbors=3, weights=uniform ..................................\n","[CV] ..... n_neighbors=3, weights=uniform, score=-0.315, total= 0.1s\n","[CV] n_neighbors=3, weights=uniform ..................................\n","[CV] ..... n_neighbors=3, weights=uniform, score=-0.322, total= 0.1s\n","[CV] n_neighbors=3, weights=uniform ..................................\n","[CV] ..... n_neighbors=3, weights=uniform, score=-0.236, total= 0.1s\n","[CV] n_neighbors=3, weights=uniform ..................................\n","[CV] ..... n_neighbors=3, weights=uniform, score=-0.368, total= 0.1s\n","[CV] n_neighbors=3, weights=uniform ..................................\n","[CV] ..... n_neighbors=3, weights=uniform, score=-0.381, total= 0.1s\n","[CV] n_neighbors=3, weights=uniform ..................................\n","[CV] ..... n_neighbors=3, weights=uniform, score=-0.387, total= 0.1s\n","[CV] n_neighbors=3, weights=uniform ..................................\n","[CV] ..... n_neighbors=3, weights=uniform, score=-0.421, total= 0.1s\n","[CV] n_neighbors=3, weights=uniform ..................................\n","[CV] ..... n_neighbors=3, weights=uniform, score=-0.358, total= 0.1s\n","[CV] n_neighbors=3, weights=uniform ..................................\n","[CV] ..... n_neighbors=3, weights=uniform, score=-0.344, total= 0.1s\n","[CV] n_neighbors=3, weights=uniform ..................................\n","[CV] ..... n_neighbors=3, weights=uniform, score=-0.192, total= 0.1s\n","[CV] n_neighbors=3, weights=uniform ..................................\n","[CV] ..... n_neighbors=3, weights=uniform, score=-0.287, total= 0.1s\n","[CV] n_neighbors=3, weights=uniform ..................................\n","[CV] ..... n_neighbors=3, weights=uniform, score=-0.359, total= 0.1s\n","[CV] n_neighbors=3, weights=uniform ..................................\n","[CV] ..... n_neighbors=3, weights=uniform, score=-0.354, total= 0.1s\n","[CV] n_neighbors=3, weights=uniform ..................................\n","[CV] ..... n_neighbors=3, weights=uniform, score=-0.485, total= 0.1s\n","[CV] n_neighbors=3, weights=uniform ..................................\n","[CV] ..... n_neighbors=3, weights=uniform, score=-0.207, total= 0.1s\n","[CV] n_neighbors=3, weights=uniform ..................................\n","[CV] ..... n_neighbors=3, weights=uniform, score=-0.459, total= 0.1s\n","[CV] n_neighbors=3, weights=uniform ..................................\n","[CV] ..... n_neighbors=3, weights=uniform, score=-0.323, total= 0.1s\n","[CV] n_neighbors=3, weights=distance .................................\n","[CV] .... n_neighbors=3, weights=distance, score=-0.328, total= 0.1s\n","[CV] n_neighbors=3, weights=distance .................................\n","[CV] .... n_neighbors=3, weights=distance, score=-0.314, total= 0.1s\n","[CV] n_neighbors=3, weights=distance .................................\n","[CV] .... n_neighbors=3, weights=distance, score=-0.373, total= 0.1s\n","[CV] n_neighbors=3, weights=distance .................................\n","[CV] .... n_neighbors=3, weights=distance, score=-0.397, total= 0.1s\n","[CV] n_neighbors=3, weights=distance .................................\n","[CV] .... n_neighbors=3, weights=distance, score=-0.436, total= 0.1s\n","[CV] n_neighbors=3, weights=distance .................................\n","[CV] .... n_neighbors=3, weights=distance, score=-0.317, total= 0.1s\n","[CV] n_neighbors=3, weights=distance .................................\n","[CV] .... n_neighbors=3, weights=distance, score=-0.302, total= 0.1s\n","[CV] n_neighbors=3, weights=distance .................................\n","[CV] .... n_neighbors=3, weights=distance, score=-0.384, total= 0.1s\n","[CV] n_neighbors=3, weights=distance .................................\n","[CV] .... n_neighbors=3, weights=distance, score=-0.366, total= 0.1s\n","[CV] n_neighbors=3, weights=distance .................................\n","[CV] .... n_neighbors=3, weights=distance, score=-0.441, total= 0.1s\n","[CV] n_neighbors=3, weights=distance .................................\n","[CV] .... n_neighbors=3, weights=distance, score=-0.393, total= 0.1s\n","[CV] n_neighbors=3, weights=distance .................................\n","[CV] .... n_neighbors=3, weights=distance, score=-0.357, total= 0.1s\n","[CV] n_neighbors=3, weights=distance .................................\n","[CV] .... n_neighbors=3, weights=distance, score=-0.291, total= 0.1s\n","[CV] n_neighbors=3, weights=distance .................................\n","[CV] .... n_neighbors=3, weights=distance, score=-0.309, total= 0.1s\n","[CV] n_neighbors=3, weights=distance .................................\n","[CV] .... n_neighbors=3, weights=distance, score=-0.327, total= 0.1s\n","[CV] n_neighbors=3, weights=distance .................................\n","[CV] .... n_neighbors=3, weights=distance, score=-0.242, total= 0.1s\n","[CV] n_neighbors=3, weights=distance .................................\n","[CV] .... n_neighbors=3, weights=distance, score=-0.360, total= 0.1s\n","[CV] n_neighbors=3, weights=distance .................................\n","[CV] .... n_neighbors=3, weights=distance, score=-0.377, total= 0.1s\n","[CV] n_neighbors=3, weights=distance .................................\n","[CV] .... n_neighbors=3, weights=distance, score=-0.397, total= 0.1s\n","[CV] n_neighbors=3, weights=distance .................................\n","[CV] .... n_neighbors=3, weights=distance, score=-0.421, total= 0.1s\n","[CV] n_neighbors=3, weights=distance .................................\n","[CV] .... n_neighbors=3, weights=distance, score=-0.351, total= 0.1s\n","[CV] n_neighbors=3, weights=distance .................................\n","[CV] .... n_neighbors=3, weights=distance, score=-0.363, total= 0.1s\n","[CV] n_neighbors=3, weights=distance .................................\n","[CV] .... n_neighbors=3, weights=distance, score=-0.191, total= 0.1s\n","[CV] n_neighbors=3, weights=distance .................................\n","[CV] .... n_neighbors=3, weights=distance, score=-0.282, total= 0.1s\n","[CV] n_neighbors=3, weights=distance .................................\n","[CV] .... n_neighbors=3, weights=distance, score=-0.356, total= 0.1s\n","[CV] n_neighbors=3, weights=distance .................................\n","[CV] .... n_neighbors=3, weights=distance, score=-0.349, total= 0.1s\n","[CV] n_neighbors=3, weights=distance .................................\n","[CV] .... n_neighbors=3, weights=distance, score=-0.496, total= 0.1s\n","[CV] n_neighbors=3, weights=distance .................................\n","[CV] .... n_neighbors=3, weights=distance, score=-0.202, total= 0.1s\n","[CV] n_neighbors=3, weights=distance .................................\n","[CV] .... n_neighbors=3, weights=distance, score=-0.460, total= 0.1s\n","[CV] n_neighbors=3, weights=distance .................................\n","[CV] .... n_neighbors=3, weights=distance, score=-0.332, total= 0.1s\n","[CV] n_neighbors=4, weights=uniform ..................................\n","[CV] ..... n_neighbors=4, weights=uniform, score=-0.221, total= 0.1s\n","[CV] n_neighbors=4, weights=uniform ..................................\n","[CV] ..... n_neighbors=4, weights=uniform, score=-0.242, total= 0.1s\n","[CV] n_neighbors=4, weights=uniform ..................................\n","[CV] ..... n_neighbors=4, weights=uniform, score=-0.300, total= 0.1s\n","[CV] n_neighbors=4, weights=uniform ..................................\n","[CV] ..... n_neighbors=4, weights=uniform, score=-0.329, total= 0.1s\n","[CV] n_neighbors=4, weights=uniform ..................................\n","[CV] ..... n_neighbors=4, weights=uniform, score=-0.341, total= 0.1s\n","[CV] n_neighbors=4, weights=uniform ..................................\n","[CV] ..... n_neighbors=4, weights=uniform, score=-0.258, total= 0.1s\n","[CV] n_neighbors=4, weights=uniform ..................................\n","[CV] ..... n_neighbors=4, weights=uniform, score=-0.218, total= 0.1s\n","[CV] n_neighbors=4, weights=uniform ..................................\n","[CV] ..... n_neighbors=4, weights=uniform, score=-0.266, total= 0.1s\n","[CV] n_neighbors=4, weights=uniform ..................................\n","[CV] ..... n_neighbors=4, weights=uniform, score=-0.235, total= 0.1s\n","[CV] n_neighbors=4, weights=uniform ..................................\n","[CV] ..... n_neighbors=4, weights=uniform, score=-0.267, total= 0.1s\n","[CV] n_neighbors=4, weights=uniform ..................................\n","[CV] ..... n_neighbors=4, weights=uniform, score=-0.293, total= 0.1s\n","[CV] n_neighbors=4, weights=uniform ..................................\n","[CV] ..... n_neighbors=4, weights=uniform, score=-0.270, total= 0.1s\n","[CV] n_neighbors=4, weights=uniform ..................................\n","[CV] ..... n_neighbors=4, weights=uniform, score=-0.269, total= 0.1s\n","[CV] n_neighbors=4, weights=uniform ..................................\n","[CV] ..... n_neighbors=4, weights=uniform, score=-0.220, total= 0.1s\n","[CV] n_neighbors=4, weights=uniform ..................................\n","[CV] ..... n_neighbors=4, weights=uniform, score=-0.260, total= 0.1s\n","[CV] n_neighbors=4, weights=uniform ..................................\n","[CV] ..... n_neighbors=4, weights=uniform, score=-0.166, total= 0.1s\n","[CV] n_neighbors=4, weights=uniform ..................................\n","[CV] ..... n_neighbors=4, weights=uniform, score=-0.268, total= 0.1s\n","[CV] n_neighbors=4, weights=uniform ..................................\n","[CV] ..... n_neighbors=4, weights=uniform, score=-0.319, total= 0.1s\n","[CV] n_neighbors=4, weights=uniform ..................................\n","[CV] ..... n_neighbors=4, weights=uniform, score=-0.230, total= 0.1s\n","[CV] n_neighbors=4, weights=uniform ..................................\n","[CV] ..... n_neighbors=4, weights=uniform, score=-0.317, total= 0.1s\n","[CV] n_neighbors=4, weights=uniform ..................................\n","[CV] ..... n_neighbors=4, weights=uniform, score=-0.284, total= 0.1s\n","[CV] n_neighbors=4, weights=uniform ..................................\n","[CV] ..... n_neighbors=4, weights=uniform, score=-0.263, total= 0.1s\n","[CV] n_neighbors=4, weights=uniform ..................................\n","[CV] ..... n_neighbors=4, weights=uniform, score=-0.203, total= 0.1s\n","[CV] n_neighbors=4, weights=uniform ..................................\n","[CV] ..... n_neighbors=4, weights=uniform, score=-0.248, total= 0.1s\n","[CV] n_neighbors=4, weights=uniform ..................................\n","[CV] ..... n_neighbors=4, weights=uniform, score=-0.304, total= 0.1s\n","[CV] n_neighbors=4, weights=uniform ..................................\n","[CV] ..... n_neighbors=4, weights=uniform, score=-0.258, total= 0.1s\n","[CV] n_neighbors=4, weights=uniform ..................................\n","[CV] ..... n_neighbors=4, weights=uniform, score=-0.315, total= 0.1s\n","[CV] n_neighbors=4, weights=uniform ..................................\n","[CV] ..... n_neighbors=4, weights=uniform, score=-0.151, total= 0.1s\n","[CV] n_neighbors=4, weights=uniform ..................................\n","[CV] ..... n_neighbors=4, weights=uniform, score=-0.281, total= 0.1s\n","[CV] n_neighbors=4, weights=uniform ..................................\n","[CV] ..... n_neighbors=4, weights=uniform, score=-0.249, total= 0.1s\n","[CV] n_neighbors=4, weights=distance .................................\n","[CV] .... n_neighbors=4, weights=distance, score=-0.228, total= 0.1s\n","[CV] n_neighbors=4, weights=distance .................................\n","[CV] .... n_neighbors=4, weights=distance, score=-0.242, total= 0.1s\n","[CV] n_neighbors=4, weights=distance .................................\n","[CV] .... n_neighbors=4, weights=distance, score=-0.300, total= 0.1s\n","[CV] n_neighbors=4, weights=distance .................................\n","[CV] .... n_neighbors=4, weights=distance, score=-0.323, total= 0.1s\n","[CV] n_neighbors=4, weights=distance .................................\n","[CV] .... n_neighbors=4, weights=distance, score=-0.348, total= 0.1s\n","[CV] n_neighbors=4, weights=distance .................................\n","[CV] .... n_neighbors=4, weights=distance, score=-0.259, total= 0.1s\n","[CV] n_neighbors=4, weights=distance .................................\n","[CV] .... n_neighbors=4, weights=distance, score=-0.230, total= 0.1s\n","[CV] n_neighbors=4, weights=distance .................................\n","[CV] .... n_neighbors=4, weights=distance, score=-0.267, total= 0.1s\n","[CV] n_neighbors=4, weights=distance .................................\n","[CV] .... n_neighbors=4, weights=distance, score=-0.246, total= 0.1s\n","[CV] n_neighbors=4, weights=distance .................................\n","[CV] .... n_neighbors=4, weights=distance, score=-0.278, total= 0.1s\n","[CV] n_neighbors=4, weights=distance .................................\n","[CV] .... n_neighbors=4, weights=distance, score=-0.304, total= 0.1s\n","[CV] n_neighbors=4, weights=distance .................................\n","[CV] .... n_neighbors=4, weights=distance, score=-0.271, total= 0.1s\n","[CV] n_neighbors=4, weights=distance .................................\n","[CV] .... n_neighbors=4, weights=distance, score=-0.263, total= 0.1s\n","[CV] n_neighbors=4, weights=distance .................................\n","[CV] .... n_neighbors=4, weights=distance, score=-0.215, total= 0.1s\n","[CV] n_neighbors=4, weights=distance .................................\n","[CV] .... n_neighbors=4, weights=distance, score=-0.260, total= 0.1s\n","[CV] n_neighbors=4, weights=distance .................................\n","[CV] .... n_neighbors=4, weights=distance, score=-0.168, total= 0.1s\n","[CV] n_neighbors=4, weights=distance .................................\n","[CV] .... n_neighbors=4, weights=distance, score=-0.261, total= 0.1s\n","[CV] n_neighbors=4, weights=distance .................................\n","[CV] .... n_neighbors=4, weights=distance, score=-0.313, total= 0.1s\n","[CV] n_neighbors=4, weights=distance .................................\n","[CV] .... n_neighbors=4, weights=distance, score=-0.248, total= 0.1s\n","[CV] n_neighbors=4, weights=distance .................................\n","[CV] .... n_neighbors=4, weights=distance, score=-0.321, total= 0.1s\n","[CV] n_neighbors=4, weights=distance .................................\n","[CV] .... n_neighbors=4, weights=distance, score=-0.278, total= 0.1s\n","[CV] n_neighbors=4, weights=distance .................................\n","[CV] .... n_neighbors=4, weights=distance, score=-0.278, total= 0.1s\n","[CV] n_neighbors=4, weights=distance .................................\n","[CV] .... n_neighbors=4, weights=distance, score=-0.195, total= 0.1s\n","[CV] n_neighbors=4, weights=distance .................................\n","[CV] .... n_neighbors=4, weights=distance, score=-0.241, total= 0.1s\n","[CV] n_neighbors=4, weights=distance .................................\n","[CV] .... n_neighbors=4, weights=distance, score=-0.305, total= 0.1s\n","[CV] n_neighbors=4, weights=distance .................................\n","[CV] .... n_neighbors=4, weights=distance, score=-0.251, total= 0.1s\n","[CV] n_neighbors=4, weights=distance .................................\n","[CV] .... n_neighbors=4, weights=distance, score=-0.334, total= 0.1s\n","[CV] n_neighbors=4, weights=distance .................................\n","[CV] .... n_neighbors=4, weights=distance, score=-0.145, total= 0.1s\n","[CV] n_neighbors=4, weights=distance .................................\n","[CV] .... n_neighbors=4, weights=distance, score=-0.296, total= 0.1s\n","[CV] n_neighbors=4, weights=distance .................................\n","[CV] .... n_neighbors=4, weights=distance, score=-0.256, total= 0.1s\n","[CV] n_neighbors=5, weights=uniform ..................................\n","[CV] ..... n_neighbors=5, weights=uniform, score=-0.189, total= 0.1s\n","[CV] n_neighbors=5, weights=uniform ..................................\n","[CV] ..... n_neighbors=5, weights=uniform, score=-0.188, total= 0.1s\n","[CV] n_neighbors=5, weights=uniform ..................................\n","[CV] ..... n_neighbors=5, weights=uniform, score=-0.264, total= 0.1s\n","[CV] n_neighbors=5, weights=uniform ..................................\n","[CV] ..... n_neighbors=5, weights=uniform, score=-0.277, total= 0.1s\n","[CV] n_neighbors=5, weights=uniform ..................................\n","[CV] ..... n_neighbors=5, weights=uniform, score=-0.308, total= 0.1s\n","[CV] n_neighbors=5, weights=uniform ..................................\n","[CV] ..... n_neighbors=5, weights=uniform, score=-0.190, total= 0.1s\n","[CV] n_neighbors=5, weights=uniform ..................................\n","[CV] ..... n_neighbors=5, weights=uniform, score=-0.195, total= 0.1s\n","[CV] n_neighbors=5, weights=uniform ..................................\n","[CV] ..... n_neighbors=5, weights=uniform, score=-0.288, total= 0.1s\n","[CV] n_neighbors=5, weights=uniform ..................................\n","[CV] ..... n_neighbors=5, weights=uniform, score=-0.146, total= 0.1s\n","[CV] n_neighbors=5, weights=uniform ..................................\n","[CV] ..... n_neighbors=5, weights=uniform, score=-0.204, total= 0.1s\n","[CV] n_neighbors=5, weights=uniform ..................................\n","[CV] ..... n_neighbors=5, weights=uniform, score=-0.261, total= 0.1s\n","[CV] n_neighbors=5, weights=uniform ..................................\n","[CV] ..... n_neighbors=5, weights=uniform, score=-0.202, total= 0.1s\n","[CV] n_neighbors=5, weights=uniform ..................................\n","[CV] ..... n_neighbors=5, weights=uniform, score=-0.236, total= 0.1s\n","[CV] n_neighbors=5, weights=uniform ..................................\n","[CV] ..... n_neighbors=5, weights=uniform, score=-0.196, total= 0.1s\n","[CV] n_neighbors=5, weights=uniform ..................................\n","[CV] ..... n_neighbors=5, weights=uniform, score=-0.159, total= 0.1s\n","[CV] n_neighbors=5, weights=uniform ..................................\n","[CV] ..... n_neighbors=5, weights=uniform, score=-0.156, total= 0.1s\n","[CV] n_neighbors=5, weights=uniform ..................................\n","[CV] ..... n_neighbors=5, weights=uniform, score=-0.181, total= 0.1s\n","[CV] n_neighbors=5, weights=uniform ..................................\n","[CV] ..... n_neighbors=5, weights=uniform, score=-0.283, total= 0.1s\n","[CV] n_neighbors=5, weights=uniform ..................................\n","[CV] ..... n_neighbors=5, weights=uniform, score=-0.219, total= 0.1s\n","[CV] n_neighbors=5, weights=uniform ..................................\n","[CV] ..... n_neighbors=5, weights=uniform, score=-0.224, total= 0.1s\n","[CV] n_neighbors=5, weights=uniform ..................................\n","[CV] ..... n_neighbors=5, weights=uniform, score=-0.234, total= 0.1s\n","[CV] n_neighbors=5, weights=uniform ..................................\n","[CV] ..... n_neighbors=5, weights=uniform, score=-0.155, total= 0.1s\n","[CV] n_neighbors=5, weights=uniform ..................................\n","[CV] ..... n_neighbors=5, weights=uniform, score=-0.202, total= 0.1s\n","[CV] n_neighbors=5, weights=uniform ..................................\n","[CV] ..... n_neighbors=5, weights=uniform, score=-0.193, total= 0.1s\n","[CV] n_neighbors=5, weights=uniform ..................................\n","[CV] ..... n_neighbors=5, weights=uniform, score=-0.273, total= 0.1s\n","[CV] n_neighbors=5, weights=uniform ..................................\n","[CV] ..... n_neighbors=5, weights=uniform, score=-0.240, total= 0.1s\n","[CV] n_neighbors=5, weights=uniform ..................................\n","[CV] ..... n_neighbors=5, weights=uniform, score=-0.199, total= 0.1s\n","[CV] n_neighbors=5, weights=uniform ..................................\n","[CV] ..... n_neighbors=5, weights=uniform, score=-0.149, total= 0.1s\n","[CV] n_neighbors=5, weights=uniform ..................................\n","[CV] ..... n_neighbors=5, weights=uniform, score=-0.223, total= 0.1s\n","[CV] n_neighbors=5, weights=uniform ..................................\n","[CV] ..... n_neighbors=5, weights=uniform, score=-0.204, total= 0.1s\n","[CV] n_neighbors=5, weights=distance .................................\n","[CV] .... n_neighbors=5, weights=distance, score=-0.193, total= 0.1s\n","[CV] n_neighbors=5, weights=distance .................................\n","[CV] .... n_neighbors=5, weights=distance, score=-0.190, total= 0.1s\n","[CV] n_neighbors=5, weights=distance .................................\n","[CV] .... n_neighbors=5, weights=distance, score=-0.265, total= 0.1s\n","[CV] n_neighbors=5, weights=distance .................................\n","[CV] .... n_neighbors=5, weights=distance, score=-0.276, total= 0.1s\n","[CV] n_neighbors=5, weights=distance .................................\n","[CV] .... n_neighbors=5, weights=distance, score=-0.311, total= 0.1s\n","[CV] n_neighbors=5, weights=distance .................................\n","[CV] .... n_neighbors=5, weights=distance, score=-0.192, total= 0.1s\n","[CV] n_neighbors=5, weights=distance .................................\n","[CV] .... n_neighbors=5, weights=distance, score=-0.203, total= 0.1s\n","[CV] n_neighbors=5, weights=distance .................................\n","[CV] .... n_neighbors=5, weights=distance, score=-0.281, total= 0.1s\n","[CV] n_neighbors=5, weights=distance .................................\n","[CV] .... n_neighbors=5, weights=distance, score=-0.159, total= 0.1s\n","[CV] n_neighbors=5, weights=distance .................................\n","[CV] .... n_neighbors=5, weights=distance, score=-0.213, total= 0.1s\n","[CV] n_neighbors=5, weights=distance .................................\n","[CV] .... n_neighbors=5, weights=distance, score=-0.269, total= 0.1s\n","[CV] n_neighbors=5, weights=distance .................................\n","[CV] .... n_neighbors=5, weights=distance, score=-0.207, total= 0.1s\n","[CV] n_neighbors=5, weights=distance .................................\n","[CV] .... n_neighbors=5, weights=distance, score=-0.230, total= 0.1s\n","[CV] n_neighbors=5, weights=distance .................................\n","[CV] .... n_neighbors=5, weights=distance, score=-0.189, total= 0.1s\n","[CV] n_neighbors=5, weights=distance .................................\n","[CV] .... n_neighbors=5, weights=distance, score=-0.163, total= 0.1s\n","[CV] n_neighbors=5, weights=distance .................................\n","[CV] .... n_neighbors=5, weights=distance, score=-0.153, total= 0.1s\n","[CV] n_neighbors=5, weights=distance .................................\n","[CV] .... n_neighbors=5, weights=distance, score=-0.181, total= 0.1s\n","[CV] n_neighbors=5, weights=distance .................................\n","[CV] .... n_neighbors=5, weights=distance, score=-0.276, total= 0.1s\n","[CV] n_neighbors=5, weights=distance .................................\n","[CV] .... n_neighbors=5, weights=distance, score=-0.230, total= 0.1s\n","[CV] n_neighbors=5, weights=distance .................................\n","[CV] .... n_neighbors=5, weights=distance, score=-0.231, total= 0.1s\n","[CV] n_neighbors=5, weights=distance .................................\n","[CV] .... n_neighbors=5, weights=distance, score=-0.231, total= 0.1s\n","[CV] n_neighbors=5, weights=distance .................................\n","[CV] .... n_neighbors=5, weights=distance, score=-0.172, total= 0.1s\n","[CV] n_neighbors=5, weights=distance .................................\n","[CV] .... n_neighbors=5, weights=distance, score=-0.191, total= 0.1s\n","[CV] n_neighbors=5, weights=distance .................................\n","[CV] .... n_neighbors=5, weights=distance, score=-0.186, total= 0.1s\n","[CV] n_neighbors=5, weights=distance .................................\n","[CV] .... n_neighbors=5, weights=distance, score=-0.272, total= 0.1s\n","[CV] n_neighbors=5, weights=distance .................................\n","[CV] .... n_neighbors=5, weights=distance, score=-0.230, total= 0.1s\n","[CV] n_neighbors=5, weights=distance .................................\n","[CV] .... n_neighbors=5, weights=distance, score=-0.221, total= 0.1s\n","[CV] n_neighbors=5, weights=distance .................................\n","[CV] .... n_neighbors=5, weights=distance, score=-0.141, total= 0.1s\n","[CV] n_neighbors=5, weights=distance .................................\n","[CV] .... n_neighbors=5, weights=distance, score=-0.235, total= 0.1s\n","[CV] n_neighbors=5, weights=distance .................................\n","[CV] .... n_neighbors=5, weights=distance, score=-0.209, total= 0.1s\n","[CV] n_neighbors=6, weights=uniform ..................................\n","[CV] ..... n_neighbors=6, weights=uniform, score=-0.200, total= 0.1s\n","[CV] n_neighbors=6, weights=uniform ..................................\n","[CV] ..... n_neighbors=6, weights=uniform, score=-0.166, total= 0.1s\n","[CV] n_neighbors=6, weights=uniform ..................................\n","[CV] ..... n_neighbors=6, weights=uniform, score=-0.184, total= 0.1s\n","[CV] n_neighbors=6, weights=uniform ..................................\n","[CV] ..... n_neighbors=6, weights=uniform, score=-0.253, total= 0.1s\n","[CV] n_neighbors=6, weights=uniform ..................................\n","[CV] ..... n_neighbors=6, weights=uniform, score=-0.282, total= 0.1s\n","[CV] n_neighbors=6, weights=uniform ..................................\n","[CV] ..... n_neighbors=6, weights=uniform, score=-0.186, total= 0.1s\n","[CV] n_neighbors=6, weights=uniform ..................................\n","[CV] ..... n_neighbors=6, weights=uniform, score=-0.180, total= 0.1s\n","[CV] n_neighbors=6, weights=uniform ..................................\n","[CV] ..... n_neighbors=6, weights=uniform, score=-0.236, total= 0.1s\n","[CV] n_neighbors=6, weights=uniform ..................................\n","[CV] ..... n_neighbors=6, weights=uniform, score=-0.155, total= 0.1s\n","[CV] n_neighbors=6, weights=uniform ..................................\n","[CV] ..... n_neighbors=6, weights=uniform, score=-0.176, total= 0.1s\n","[CV] n_neighbors=6, weights=uniform ..................................\n","[CV] ..... n_neighbors=6, weights=uniform, score=-0.205, total= 0.1s\n","[CV] n_neighbors=6, weights=uniform ..................................\n","[CV] ..... n_neighbors=6, weights=uniform, score=-0.181, total= 0.1s\n","[CV] n_neighbors=6, weights=uniform ..................................\n","[CV] ..... n_neighbors=6, weights=uniform, score=-0.181, total= 0.1s\n","[CV] n_neighbors=6, weights=uniform ..................................\n","[CV] ..... n_neighbors=6, weights=uniform, score=-0.201, total= 0.1s\n","[CV] n_neighbors=6, weights=uniform ..................................\n","[CV] ..... n_neighbors=6, weights=uniform, score=-0.135, total= 0.1s\n","[CV] n_neighbors=6, weights=uniform ..................................\n","[CV] ..... n_neighbors=6, weights=uniform, score=-0.159, total= 0.1s\n","[CV] n_neighbors=6, weights=uniform ..................................\n","[CV] ..... n_neighbors=6, weights=uniform, score=-0.164, total= 0.1s\n","[CV] n_neighbors=6, weights=uniform ..................................\n","[CV] ..... n_neighbors=6, weights=uniform, score=-0.288, total= 0.1s\n","[CV] n_neighbors=6, weights=uniform ..................................\n","[CV] ..... n_neighbors=6, weights=uniform, score=-0.210, total= 0.1s\n","[CV] n_neighbors=6, weights=uniform ..................................\n","[CV] ..... n_neighbors=6, weights=uniform, score=-0.216, total= 0.1s\n","[CV] n_neighbors=6, weights=uniform ..................................\n","[CV] ..... n_neighbors=6, weights=uniform, score=-0.183, total= 0.1s\n","[CV] n_neighbors=6, weights=uniform ..................................\n","[CV] ..... n_neighbors=6, weights=uniform, score=-0.169, total= 0.1s\n","[CV] n_neighbors=6, weights=uniform ..................................\n","[CV] ..... n_neighbors=6, weights=uniform, score=-0.191, total= 0.1s\n","[CV] n_neighbors=6, weights=uniform ..................................\n","[CV] ..... n_neighbors=6, weights=uniform, score=-0.179, total= 0.1s\n","[CV] n_neighbors=6, weights=uniform ..................................\n","[CV] ..... n_neighbors=6, weights=uniform, score=-0.229, total= 0.1s\n","[CV] n_neighbors=6, weights=uniform ..................................\n","[CV] ..... n_neighbors=6, weights=uniform, score=-0.202, total= 0.1s\n","[CV] n_neighbors=6, weights=uniform ..................................\n","[CV] ..... n_neighbors=6, weights=uniform, score=-0.165, total= 0.1s\n","[CV] n_neighbors=6, weights=uniform ..................................\n","[CV] ..... n_neighbors=6, weights=uniform, score=-0.155, total= 0.1s\n","[CV] n_neighbors=6, weights=uniform ..................................\n","[CV] ..... n_neighbors=6, weights=uniform, score=-0.185, total= 0.1s\n","[CV] n_neighbors=6, weights=uniform ..................................\n","[CV] ..... n_neighbors=6, weights=uniform, score=-0.185, total= 0.1s\n","[CV] n_neighbors=6, weights=distance .................................\n","[CV] .... n_neighbors=6, weights=distance, score=-0.199, total= 0.1s\n","[CV] n_neighbors=6, weights=distance .................................\n","[CV] .... n_neighbors=6, weights=distance, score=-0.166, total= 0.1s\n","[CV] n_neighbors=6, weights=distance .................................\n","[CV] .... n_neighbors=6, weights=distance, score=-0.189, total= 0.1s\n","[CV] n_neighbors=6, weights=distance .................................\n","[CV] .... n_neighbors=6, weights=distance, score=-0.250, total= 0.1s\n","[CV] n_neighbors=6, weights=distance .................................\n","[CV] .... n_neighbors=6, weights=distance, score=-0.284, total= 0.1s\n","[CV] n_neighbors=6, weights=distance .................................\n","[CV] .... n_neighbors=6, weights=distance, score=-0.187, total= 0.1s\n","[CV] n_neighbors=6, weights=distance .................................\n","[CV] .... n_neighbors=6, weights=distance, score=-0.185, total= 0.1s\n","[CV] n_neighbors=6, weights=distance .................................\n","[CV] .... n_neighbors=6, weights=distance, score=-0.233, total= 0.1s\n","[CV] n_neighbors=6, weights=distance .................................\n","[CV] .... n_neighbors=6, weights=distance, score=-0.160, total= 0.1s\n","[CV] n_neighbors=6, weights=distance .................................\n","[CV] .... n_neighbors=6, weights=distance, score=-0.182, total= 0.1s\n","[CV] n_neighbors=6, weights=distance .................................\n","[CV] .... n_neighbors=6, weights=distance, score=-0.217, total= 0.1s\n","[CV] n_neighbors=6, weights=distance .................................\n","[CV] .... n_neighbors=6, weights=distance, score=-0.186, total= 0.1s\n","[CV] n_neighbors=6, weights=distance .................................\n","[CV] .... n_neighbors=6, weights=distance, score=-0.180, total= 0.1s\n","[CV] n_neighbors=6, weights=distance .................................\n","[CV] .... n_neighbors=6, weights=distance, score=-0.191, total= 0.1s\n","[CV] n_neighbors=6, weights=distance .................................\n","[CV] .... n_neighbors=6, weights=distance, score=-0.135, total= 0.1s\n","[CV] n_neighbors=6, weights=distance .................................\n","[CV] .... n_neighbors=6, weights=distance, score=-0.151, total= 0.1s\n","[CV] n_neighbors=6, weights=distance .................................\n","[CV] .... n_neighbors=6, weights=distance, score=-0.159, total= 0.1s\n","[CV] n_neighbors=6, weights=distance .................................\n","[CV] .... n_neighbors=6, weights=distance, score=-0.279, total= 0.1s\n","[CV] n_neighbors=6, weights=distance .................................\n","[CV] .... n_neighbors=6, weights=distance, score=-0.215, total= 0.1s\n","[CV] n_neighbors=6, weights=distance .................................\n","[CV] .... n_neighbors=6, weights=distance, score=-0.217, total= 0.1s\n","[CV] n_neighbors=6, weights=distance .................................\n","[CV] .... n_neighbors=6, weights=distance, score=-0.181, total= 0.1s\n","[CV] n_neighbors=6, weights=distance .................................\n","[CV] .... n_neighbors=6, weights=distance, score=-0.178, total= 0.1s\n","[CV] n_neighbors=6, weights=distance .................................\n","[CV] .... n_neighbors=6, weights=distance, score=-0.178, total= 0.1s\n","[CV] n_neighbors=6, weights=distance .................................\n","[CV] .... n_neighbors=6, weights=distance, score=-0.172, total= 0.1s\n","[CV] n_neighbors=6, weights=distance .................................\n","[CV] .... n_neighbors=6, weights=distance, score=-0.231, total= 0.1s\n","[CV] n_neighbors=6, weights=distance .................................\n","[CV] .... n_neighbors=6, weights=distance, score=-0.194, total= 0.1s\n","[CV] n_neighbors=6, weights=distance .................................\n","[CV] .... n_neighbors=6, weights=distance, score=-0.184, total= 0.1s\n","[CV] n_neighbors=6, weights=distance .................................\n","[CV] .... n_neighbors=6, weights=distance, score=-0.143, total= 0.1s\n","[CV] n_neighbors=6, weights=distance .................................\n","[CV] .... n_neighbors=6, weights=distance, score=-0.196, total= 0.1s\n","[CV] n_neighbors=6, weights=distance .................................\n","[CV] .... n_neighbors=6, weights=distance, score=-0.191, total= 0.1s\n","[Parallel(n_jobs=1)]: Done 300 out of 300 | elapsed: 40.4s finished\n","KNeighborsRegressor(algorithm='auto', leaf_size=30, metric='minkowski',\n"," metric_params=None, n_jobs=None, n_neighbors=6, p=2,\n"," weights='uniform')\n","window: 1, stide: 1, rmse: 2.9937666724193606\n","[1, 4, 9, 12, 2, 0, 3, 13, 11, 10, 6, 5, 7, 8]\n","[1, 4, 9, 12, 2, 0, 3, 13, 11, 10]\n","(3000, 150)\n","Number of Feature-Columns: 150\n","\n","Features Ready for undergoing selection tests done ...\n","\n","Fitting 30 folds for each of 10 candidates, totalling 300 fits\n","[Parallel(n_jobs=1)]: Using backend SequentialBackend with 1 concurrent workers.\n","[CV] n_neighbors=2, weights=uniform ..................................\n","[CV] ..... n_neighbors=2, weights=uniform, score=-0.435, total= 0.2s\n","[Parallel(n_jobs=1)]: Done 1 out of 1 | elapsed: 0.2s remaining: 0.0s\n","[CV] n_neighbors=2, weights=uniform ..................................\n","[CV] ..... n_neighbors=2, weights=uniform, score=-0.654, total= 0.2s\n","[Parallel(n_jobs=1)]: Done 2 out of 2 | elapsed: 0.3s remaining: 0.0s\n","[CV] n_neighbors=2, weights=uniform ..................................\n","[CV] ..... n_neighbors=2, weights=uniform, score=-0.481, total= 0.2s\n","[Parallel(n_jobs=1)]: Done 3 out of 3 | elapsed: 0.5s remaining: 0.0s\n","[CV] n_neighbors=2, weights=uniform ..................................\n","[CV] ..... n_neighbors=2, weights=uniform, score=-0.574, total= 0.2s\n","[Parallel(n_jobs=1)]: Done 4 out of 4 | elapsed: 0.6s remaining: 0.0s\n","[CV] n_neighbors=2, weights=uniform ..................................\n","[CV] ..... n_neighbors=2, weights=uniform, score=-0.497, total= 0.2s\n","[CV] n_neighbors=2, weights=uniform ..................................\n","[CV] ..... n_neighbors=2, weights=uniform, score=-0.455, total= 0.2s\n","[CV] n_neighbors=2, weights=uniform ..................................\n","[CV] ..... n_neighbors=2, weights=uniform, score=-0.450, total= 0.2s\n","[CV] n_neighbors=2, weights=uniform ..................................\n","[CV] ..... n_neighbors=2, weights=uniform, score=-0.582, total= 0.2s\n","[CV] n_neighbors=2, weights=uniform ..................................\n","[CV] ..... n_neighbors=2, weights=uniform, score=-0.376, total= 0.2s\n","[CV] n_neighbors=2, weights=uniform ..................................\n","[CV] ..... n_neighbors=2, weights=uniform, score=-0.602, total= 0.2s\n","[CV] n_neighbors=2, weights=uniform ..................................\n","[CV] ..... n_neighbors=2, weights=uniform, score=-0.403, total= 0.2s\n","[CV] n_neighbors=2, weights=uniform ..................................\n","[CV] ..... n_neighbors=2, weights=uniform, score=-0.359, total= 0.2s\n","[CV] n_neighbors=2, weights=uniform ..................................\n","[CV] ..... n_neighbors=2, weights=uniform, score=-0.354, total= 0.2s\n","[CV] n_neighbors=2, weights=uniform ..................................\n","[CV] ..... n_neighbors=2, weights=uniform, score=-0.510, total= 0.2s\n","[CV] n_neighbors=2, weights=uniform ..................................\n","[CV] ..... n_neighbors=2, weights=uniform, score=-0.519, total= 0.2s\n","[CV] n_neighbors=2, weights=uniform ..................................\n","[CV] ..... n_neighbors=2, weights=uniform, score=-0.517, total= 0.2s\n","[CV] n_neighbors=2, weights=uniform ..................................\n","[CV] ..... n_neighbors=2, weights=uniform, score=-0.495, total= 0.2s\n","[CV] n_neighbors=2, weights=uniform ..................................\n","[CV] ..... n_neighbors=2, weights=uniform, score=-0.586, total= 0.2s\n","[CV] n_neighbors=2, weights=uniform ..................................\n","[CV] ..... n_neighbors=2, weights=uniform, score=-0.390, total= 0.2s\n","[CV] n_neighbors=2, weights=uniform ..................................\n","[CV] ..... n_neighbors=2, weights=uniform, score=-0.610, total= 0.2s\n","[CV] n_neighbors=2, weights=uniform ..................................\n","[CV] ..... n_neighbors=2, weights=uniform, score=-0.478, total= 0.2s\n","[CV] n_neighbors=2, weights=uniform ..................................\n","[CV] ..... n_neighbors=2, weights=uniform, score=-0.614, total= 0.2s\n","[CV] n_neighbors=2, weights=uniform ..................................\n","[CV] ..... n_neighbors=2, weights=uniform, score=-0.449, total= 0.2s\n","[CV] n_neighbors=2, weights=uniform ..................................\n","[CV] ..... n_neighbors=2, weights=uniform, score=-0.478, total= 0.2s\n","[CV] n_neighbors=2, weights=uniform ..................................\n","[CV] ..... n_neighbors=2, weights=uniform, score=-0.506, total= 0.2s\n","[CV] n_neighbors=2, weights=uniform ..................................\n","[CV] ..... n_neighbors=2, weights=uniform, score=-0.318, total= 0.2s\n","[CV] n_neighbors=2, weights=uniform ..................................\n","[CV] ..... n_neighbors=2, weights=uniform, score=-0.534, total= 0.2s\n","[CV] n_neighbors=2, weights=uniform ..................................\n","[CV] ..... n_neighbors=2, weights=uniform, score=-0.343, total= 0.1s\n","[CV] n_neighbors=2, weights=uniform ..................................\n","[CV] ..... n_neighbors=2, weights=uniform, score=-0.498, total= 0.1s\n","[CV] n_neighbors=2, weights=uniform ..................................\n","[CV] ..... n_neighbors=2, weights=uniform, score=-0.388, total= 0.2s\n","[CV] n_neighbors=2, weights=distance .................................\n","[CV] .... n_neighbors=2, weights=distance, score=-0.438, total= 0.2s\n","[CV] n_neighbors=2, weights=distance .................................\n","[CV] .... n_neighbors=2, weights=distance, score=-0.656, total= 0.2s\n","[CV] n_neighbors=2, weights=distance .................................\n","[CV] .... n_neighbors=2, weights=distance, score=-0.491, total= 0.2s\n","[CV] n_neighbors=2, weights=distance .................................\n","[CV] .... n_neighbors=2, weights=distance, score=-0.581, total= 0.2s\n","[CV] n_neighbors=2, weights=distance .................................\n","[CV] .... n_neighbors=2, weights=distance, score=-0.502, total= 0.1s\n","[CV] n_neighbors=2, weights=distance .................................\n","[CV] .... n_neighbors=2, weights=distance, score=-0.459, total= 0.2s\n","[CV] n_neighbors=2, weights=distance .................................\n","[CV] .... n_neighbors=2, weights=distance, score=-0.462, total= 0.2s\n","[CV] n_neighbors=2, weights=distance .................................\n","[CV] .... n_neighbors=2, weights=distance, score=-0.584, total= 0.2s\n","[CV] n_neighbors=2, weights=distance .................................\n","[CV] .... n_neighbors=2, weights=distance, score=-0.385, total= 0.2s\n","[CV] n_neighbors=2, weights=distance .................................\n","[CV] .... n_neighbors=2, weights=distance, score=-0.590, total= 0.2s\n","[CV] n_neighbors=2, weights=distance .................................\n","[CV] .... n_neighbors=2, weights=distance, score=-0.417, total= 0.2s\n","[CV] n_neighbors=2, weights=distance .................................\n","[CV] .... n_neighbors=2, weights=distance, score=-0.353, total= 0.2s\n","[CV] n_neighbors=2, weights=distance .................................\n","[CV] .... n_neighbors=2, weights=distance, score=-0.353, total= 0.2s\n","[CV] n_neighbors=2, weights=distance .................................\n","[CV] .... n_neighbors=2, weights=distance, score=-0.508, total= 0.1s\n","[CV] n_neighbors=2, weights=distance .................................\n","[CV] .... n_neighbors=2, weights=distance, score=-0.512, total= 0.2s\n","[CV] n_neighbors=2, weights=distance .................................\n","[CV] .... n_neighbors=2, weights=distance, score=-0.524, total= 0.2s\n","[CV] n_neighbors=2, weights=distance .................................\n","[CV] .... n_neighbors=2, weights=distance, score=-0.490, total= 0.2s\n","[CV] n_neighbors=2, weights=distance .................................\n","[CV] .... n_neighbors=2, weights=distance, score=-0.601, total= 0.2s\n","[CV] n_neighbors=2, weights=distance .................................\n","[CV] .... n_neighbors=2, weights=distance, score=-0.400, total= 0.2s\n","[CV] n_neighbors=2, weights=distance .................................\n","[CV] .... n_neighbors=2, weights=distance, score=-0.604, total= 0.2s\n","[CV] n_neighbors=2, weights=distance .................................\n","[CV] .... n_neighbors=2, weights=distance, score=-0.469, total= 0.2s\n","[CV] n_neighbors=2, weights=distance .................................\n","[CV] .... n_neighbors=2, weights=distance, score=-0.630, total= 0.2s\n","[CV] n_neighbors=2, weights=distance .................................\n","[CV] .... n_neighbors=2, weights=distance, score=-0.450, total= 0.1s\n","[CV] n_neighbors=2, weights=distance .................................\n","[CV] .... n_neighbors=2, weights=distance, score=-0.468, total= 0.2s\n","[CV] n_neighbors=2, weights=distance .................................\n","[CV] .... n_neighbors=2, weights=distance, score=-0.513, total= 0.2s\n","[CV] n_neighbors=2, weights=distance .................................\n","[CV] .... n_neighbors=2, weights=distance, score=-0.317, total= 0.2s\n","[CV] n_neighbors=2, weights=distance .................................\n","[CV] .... n_neighbors=2, weights=distance, score=-0.540, total= 0.2s\n","[CV] n_neighbors=2, weights=distance .................................\n","[CV] .... n_neighbors=2, weights=distance, score=-0.343, total= 0.2s\n","[CV] n_neighbors=2, weights=distance .................................\n","[CV] .... n_neighbors=2, weights=distance, score=-0.504, total= 0.1s\n","[CV] n_neighbors=2, weights=distance .................................\n","[CV] .... n_neighbors=2, weights=distance, score=-0.392, total= 0.2s\n","[CV] n_neighbors=3, weights=uniform ..................................\n","[CV] ..... n_neighbors=3, weights=uniform, score=-0.259, total= 0.2s\n","[CV] n_neighbors=3, weights=uniform ..................................\n","[CV] ..... n_neighbors=3, weights=uniform, score=-0.379, total= 0.2s\n","[CV] n_neighbors=3, weights=uniform ..................................\n","[CV] ..... n_neighbors=3, weights=uniform, score=-0.370, total= 0.2s\n","[CV] n_neighbors=3, weights=uniform ..................................\n","[CV] ..... n_neighbors=3, weights=uniform, score=-0.440, total= 0.2s\n","[CV] n_neighbors=3, weights=uniform ..................................\n","[CV] ..... n_neighbors=3, weights=uniform, score=-0.360, total= 0.2s\n","[CV] n_neighbors=3, weights=uniform ..................................\n","[CV] ..... n_neighbors=3, weights=uniform, score=-0.379, total= 0.2s\n","[CV] n_neighbors=3, weights=uniform ..................................\n","[CV] ..... n_neighbors=3, weights=uniform, score=-0.387, total= 0.2s\n","[CV] n_neighbors=3, weights=uniform ..................................\n","[CV] ..... n_neighbors=3, weights=uniform, score=-0.355, total= 0.2s\n","[CV] n_neighbors=3, weights=uniform ..................................\n","[CV] ..... n_neighbors=3, weights=uniform, score=-0.315, total= 0.2s\n","[CV] n_neighbors=3, weights=uniform ..................................\n","[CV] ..... n_neighbors=3, weights=uniform, score=-0.483, total= 0.2s\n","[CV] n_neighbors=3, weights=uniform ..................................\n","[CV] ..... n_neighbors=3, weights=uniform, score=-0.325, total= 0.2s\n","[CV] n_neighbors=3, weights=uniform ..................................\n","[CV] ..... n_neighbors=3, weights=uniform, score=-0.286, total= 0.2s\n","[CV] n_neighbors=3, weights=uniform ..................................\n","[CV] ..... n_neighbors=3, weights=uniform, score=-0.304, total= 0.2s\n","[CV] n_neighbors=3, weights=uniform ..................................\n","[CV] ..... n_neighbors=3, weights=uniform, score=-0.342, total= 0.2s\n","[CV] n_neighbors=3, weights=uniform ..................................\n","[CV] ..... n_neighbors=3, weights=uniform, score=-0.359, total= 0.2s\n","[CV] n_neighbors=3, weights=uniform ..................................\n","[CV] ..... n_neighbors=3, weights=uniform, score=-0.413, total= 0.2s\n","[CV] n_neighbors=3, weights=uniform ..................................\n","[CV] ..... n_neighbors=3, weights=uniform, score=-0.401, total= 0.2s\n","[CV] n_neighbors=3, weights=uniform ..................................\n","[CV] ..... n_neighbors=3, weights=uniform, score=-0.409, total= 0.2s\n","[CV] n_neighbors=3, weights=uniform ..................................\n","[CV] ..... n_neighbors=3, weights=uniform, score=-0.331, total= 0.2s\n","[CV] n_neighbors=3, weights=uniform ..................................\n","[CV] ..... n_neighbors=3, weights=uniform, score=-0.395, total= 0.2s\n","[CV] n_neighbors=3, weights=uniform ..................................\n","[CV] ..... n_neighbors=3, weights=uniform, score=-0.334, total= 0.2s\n","[CV] n_neighbors=3, weights=uniform ..................................\n","[CV] ..... n_neighbors=3, weights=uniform, score=-0.310, total= 0.2s\n","[CV] n_neighbors=3, weights=uniform ..................................\n","[CV] ..... n_neighbors=3, weights=uniform, score=-0.287, total= 0.2s\n","[CV] n_neighbors=3, weights=uniform ..................................\n","[CV] ..... n_neighbors=3, weights=uniform, score=-0.357, total= 0.2s\n","[CV] n_neighbors=3, weights=uniform ..................................\n","[CV] ..... n_neighbors=3, weights=uniform, score=-0.438, total= 0.2s\n","[CV] n_neighbors=3, weights=uniform ..................................\n","[CV] ..... n_neighbors=3, weights=uniform, score=-0.290, total= 0.2s\n","[CV] n_neighbors=3, weights=uniform ..................................\n","[CV] ..... n_neighbors=3, weights=uniform, score=-0.359, total= 0.2s\n","[CV] n_neighbors=3, weights=uniform ..................................\n","[CV] ..... n_neighbors=3, weights=uniform, score=-0.334, total= 0.2s\n","[CV] n_neighbors=3, weights=uniform ..................................\n","[CV] ..... n_neighbors=3, weights=uniform, score=-0.336, total= 0.2s\n","[CV] n_neighbors=3, weights=uniform ..................................\n","[CV] ..... n_neighbors=3, weights=uniform, score=-0.349, total= 0.2s\n","[CV] n_neighbors=3, weights=distance .................................\n","[CV] .... n_neighbors=3, weights=distance, score=-0.259, total= 0.2s\n","[CV] n_neighbors=3, weights=distance .................................\n","[CV] .... n_neighbors=3, weights=distance, score=-0.391, total= 0.2s\n","[CV] n_neighbors=3, weights=distance .................................\n","[CV] .... n_neighbors=3, weights=distance, score=-0.372, total= 0.2s\n","[CV] n_neighbors=3, weights=distance .................................\n","[CV] .... n_neighbors=3, weights=distance, score=-0.434, total= 0.2s\n","[CV] n_neighbors=3, weights=distance .................................\n","[CV] .... n_neighbors=3, weights=distance, score=-0.363, total= 0.2s\n","[CV] n_neighbors=3, weights=distance .................................\n","[CV] .... n_neighbors=3, weights=distance, score=-0.383, total= 0.2s\n","[CV] n_neighbors=3, weights=distance .................................\n","[CV] .... n_neighbors=3, weights=distance, score=-0.388, total= 0.2s\n","[CV] n_neighbors=3, weights=distance .................................\n","[CV] .... n_neighbors=3, weights=distance, score=-0.364, total= 0.2s\n","[CV] n_neighbors=3, weights=distance .................................\n","[CV] .... n_neighbors=3, weights=distance, score=-0.311, total= 0.2s\n","[CV] n_neighbors=3, weights=distance .................................\n","[CV] .... n_neighbors=3, weights=distance, score=-0.467, total= 0.2s\n","[CV] n_neighbors=3, weights=distance .................................\n","[CV] .... n_neighbors=3, weights=distance, score=-0.329, total= 0.2s\n","[CV] n_neighbors=3, weights=distance .................................\n","[CV] .... n_neighbors=3, weights=distance, score=-0.273, total= 0.2s\n","[CV] n_neighbors=3, weights=distance .................................\n","[CV] .... n_neighbors=3, weights=distance, score=-0.290, total= 0.2s\n","[CV] n_neighbors=3, weights=distance .................................\n","[CV] .... n_neighbors=3, weights=distance, score=-0.337, total= 0.2s\n","[CV] n_neighbors=3, weights=distance .................................\n","[CV] .... n_neighbors=3, weights=distance, score=-0.357, total= 0.2s\n","[CV] n_neighbors=3, weights=distance .................................\n","[CV] .... n_neighbors=3, weights=distance, score=-0.414, total= 0.2s\n","[CV] n_neighbors=3, weights=distance .................................\n","[CV] .... n_neighbors=3, weights=distance, score=-0.398, total= 0.2s\n","[CV] n_neighbors=3, weights=distance .................................\n","[CV] .... n_neighbors=3, weights=distance, score=-0.419, total= 0.2s\n","[CV] n_neighbors=3, weights=distance .................................\n","[CV] .... n_neighbors=3, weights=distance, score=-0.329, total= 0.2s\n","[CV] n_neighbors=3, weights=distance .................................\n","[CV] .... n_neighbors=3, weights=distance, score=-0.395, total= 0.2s\n","[CV] n_neighbors=3, weights=distance .................................\n","[CV] .... n_neighbors=3, weights=distance, score=-0.327, total= 0.2s\n","[CV] n_neighbors=3, weights=distance .................................\n","[CV] .... n_neighbors=3, weights=distance, score=-0.326, total= 0.2s\n","[CV] n_neighbors=3, weights=distance .................................\n","[CV] .... n_neighbors=3, weights=distance, score=-0.289, total= 0.2s\n","[CV] n_neighbors=3, weights=distance .................................\n","[CV] .... n_neighbors=3, weights=distance, score=-0.353, total= 0.2s\n","[CV] n_neighbors=3, weights=distance .................................\n","[CV] .... n_neighbors=3, weights=distance, score=-0.439, total= 0.2s\n","[CV] n_neighbors=3, weights=distance .................................\n","[CV] .... n_neighbors=3, weights=distance, score=-0.277, total= 0.2s\n","[CV] n_neighbors=3, weights=distance .................................\n","[CV] .... n_neighbors=3, weights=distance, score=-0.372, total= 0.2s\n","[CV] n_neighbors=3, weights=distance .................................\n","[CV] .... n_neighbors=3, weights=distance, score=-0.324, total= 0.2s\n","[CV] n_neighbors=3, weights=distance .................................\n","[CV] .... n_neighbors=3, weights=distance, score=-0.338, total= 0.2s\n","[CV] n_neighbors=3, weights=distance .................................\n","[CV] .... n_neighbors=3, weights=distance, score=-0.344, total= 0.2s\n","[CV] n_neighbors=4, weights=uniform ..................................\n","[CV] ..... n_neighbors=4, weights=uniform, score=-0.205, total= 0.2s\n","[CV] n_neighbors=4, weights=uniform ..................................\n","[CV] ..... n_neighbors=4, weights=uniform, score=-0.284, total= 0.2s\n","[CV] n_neighbors=4, weights=uniform ..................................\n","[CV] ..... n_neighbors=4, weights=uniform, score=-0.321, total= 0.2s\n","[CV] n_neighbors=4, weights=uniform ..................................\n","[CV] ..... n_neighbors=4, weights=uniform, score=-0.342, total= 0.2s\n","[CV] n_neighbors=4, weights=uniform ..................................\n","[CV] ..... n_neighbors=4, weights=uniform, score=-0.291, total= 0.2s\n","[CV] n_neighbors=4, weights=uniform ..................................\n","[CV] ..... n_neighbors=4, weights=uniform, score=-0.359, total= 0.2s\n","[CV] n_neighbors=4, weights=uniform ..................................\n","[CV] ..... n_neighbors=4, weights=uniform, score=-0.253, total= 0.2s\n","[CV] n_neighbors=4, weights=uniform ..................................\n","[CV] ..... n_neighbors=4, weights=uniform, score=-0.296, total= 0.2s\n","[CV] n_neighbors=4, weights=uniform ..................................\n","[CV] ..... n_neighbors=4, weights=uniform, score=-0.251, total= 0.2s\n","[CV] n_neighbors=4, weights=uniform ..................................\n","[CV] ..... n_neighbors=4, weights=uniform, score=-0.314, total= 0.2s\n","[CV] n_neighbors=4, weights=uniform ..................................\n","[CV] ..... n_neighbors=4, weights=uniform, score=-0.302, total= 0.2s\n","[CV] n_neighbors=4, weights=uniform ..................................\n","[CV] ..... n_neighbors=4, weights=uniform, score=-0.186, total= 0.2s\n","[CV] n_neighbors=4, weights=uniform ..................................\n","[CV] ..... n_neighbors=4, weights=uniform, score=-0.288, total= 0.2s\n","[CV] n_neighbors=4, weights=uniform ..................................\n","[CV] ..... n_neighbors=4, weights=uniform, score=-0.324, total= 0.2s\n","[CV] n_neighbors=4, weights=uniform ..................................\n","[CV] ..... n_neighbors=4, weights=uniform, score=-0.314, total= 0.2s\n","[CV] n_neighbors=4, weights=uniform ..................................\n","[CV] ..... n_neighbors=4, weights=uniform, score=-0.270, total= 0.2s\n","[CV] n_neighbors=4, weights=uniform ..................................\n","[CV] ..... n_neighbors=4, weights=uniform, score=-0.227, total= 0.2s\n","[CV] n_neighbors=4, weights=uniform ..................................\n","[CV] ..... n_neighbors=4, weights=uniform, score=-0.279, total= 0.2s\n","[CV] n_neighbors=4, weights=uniform ..................................\n","[CV] ..... n_neighbors=4, weights=uniform, score=-0.243, total= 0.2s\n","[CV] n_neighbors=4, weights=uniform ..................................\n","[CV] ..... n_neighbors=4, weights=uniform, score=-0.387, total= 0.2s\n","[CV] n_neighbors=4, weights=uniform ..................................\n","[CV] ..... n_neighbors=4, weights=uniform, score=-0.288, total= 0.2s\n","[CV] n_neighbors=4, weights=uniform ..................................\n","[CV] ..... n_neighbors=4, weights=uniform, score=-0.300, total= 0.2s\n","[CV] n_neighbors=4, weights=uniform ..................................\n","[CV] ..... n_neighbors=4, weights=uniform, score=-0.234, total= 0.2s\n","[CV] n_neighbors=4, weights=uniform ..................................\n","[CV] ..... n_neighbors=4, weights=uniform, score=-0.285, total= 0.2s\n","[CV] n_neighbors=4, weights=uniform ..................................\n","[CV] ..... n_neighbors=4, weights=uniform, score=-0.328, total= 0.2s\n","[CV] n_neighbors=4, weights=uniform ..................................\n","[CV] ..... n_neighbors=4, weights=uniform, score=-0.242, total= 0.2s\n","[CV] n_neighbors=4, weights=uniform ..................................\n","[CV] ..... n_neighbors=4, weights=uniform, score=-0.253, total= 0.2s\n","[CV] n_neighbors=4, weights=uniform ..................................\n","[CV] ..... n_neighbors=4, weights=uniform, score=-0.227, total= 0.2s\n","[CV] n_neighbors=4, weights=uniform ..................................\n","[CV] ..... n_neighbors=4, weights=uniform, score=-0.293, total= 0.2s\n","[CV] n_neighbors=4, weights=uniform ..................................\n","[CV] ..... n_neighbors=4, weights=uniform, score=-0.282, total= 0.2s\n","[CV] n_neighbors=4, weights=distance .................................\n","[CV] .... n_neighbors=4, weights=distance, score=-0.205, total= 0.2s\n","[CV] n_neighbors=4, weights=distance .................................\n","[CV] .... n_neighbors=4, weights=distance, score=-0.290, total= 0.2s\n","[CV] n_neighbors=4, weights=distance .................................\n","[CV] .... n_neighbors=4, weights=distance, score=-0.316, total= 0.2s\n","[CV] n_neighbors=4, weights=distance .................................\n","[CV] .... n_neighbors=4, weights=distance, score=-0.340, total= 0.2s\n","[CV] n_neighbors=4, weights=distance .................................\n","[CV] .... n_neighbors=4, weights=distance, score=-0.293, total= 0.2s\n","[CV] n_neighbors=4, weights=distance .................................\n","[CV] .... n_neighbors=4, weights=distance, score=-0.361, total= 0.2s\n","[CV] n_neighbors=4, weights=distance .................................\n","[CV] .... n_neighbors=4, weights=distance, score=-0.258, total= 0.2s\n","[CV] n_neighbors=4, weights=distance .................................\n","[CV] .... n_neighbors=4, weights=distance, score=-0.301, total= 0.2s\n","[CV] n_neighbors=4, weights=distance .................................\n","[CV] .... n_neighbors=4, weights=distance, score=-0.249, total= 0.2s\n","[CV] n_neighbors=4, weights=distance .................................\n","[CV] .... n_neighbors=4, weights=distance, score=-0.313, total= 0.2s\n","[CV] n_neighbors=4, weights=distance .................................\n","[CV] .... n_neighbors=4, weights=distance, score=-0.304, total= 0.2s\n","[CV] n_neighbors=4, weights=distance .................................\n","[CV] .... n_neighbors=4, weights=distance, score=-0.180, total= 0.2s\n","[CV] n_neighbors=4, weights=distance .................................\n","[CV] .... n_neighbors=4, weights=distance, score=-0.272, total= 0.2s\n","[CV] n_neighbors=4, weights=distance .................................\n","[CV] .... n_neighbors=4, weights=distance, score=-0.316, total= 0.2s\n","[CV] n_neighbors=4, weights=distance .................................\n","[CV] .... n_neighbors=4, weights=distance, score=-0.306, total= 0.2s\n","[CV] n_neighbors=4, weights=distance .................................\n","[CV] .... n_neighbors=4, weights=distance, score=-0.275, total= 0.2s\n","[CV] n_neighbors=4, weights=distance .................................\n","[CV] .... n_neighbors=4, weights=distance, score=-0.232, total= 0.2s\n","[CV] n_neighbors=4, weights=distance .................................\n","[CV] .... n_neighbors=4, weights=distance, score=-0.288, total= 0.2s\n","[CV] n_neighbors=4, weights=distance .................................\n","[CV] .... n_neighbors=4, weights=distance, score=-0.245, total= 0.2s\n","[CV] n_neighbors=4, weights=distance .................................\n","[CV] .... n_neighbors=4, weights=distance, score=-0.381, total= 0.2s\n","[CV] n_neighbors=4, weights=distance .................................\n","[CV] .... n_neighbors=4, weights=distance, score=-0.276, total= 0.2s\n","[CV] n_neighbors=4, weights=distance .................................\n","[CV] .... n_neighbors=4, weights=distance, score=-0.309, total= 0.2s\n","[CV] n_neighbors=4, weights=distance .................................\n","[CV] .... n_neighbors=4, weights=distance, score=-0.232, total= 0.2s\n","[CV] n_neighbors=4, weights=distance .................................\n","[CV] .... n_neighbors=4, weights=distance, score=-0.280, total= 0.2s\n","[CV] n_neighbors=4, weights=distance .................................\n","[CV] .... n_neighbors=4, weights=distance, score=-0.332, total= 0.2s\n","[CV] n_neighbors=4, weights=distance .................................\n","[CV] .... n_neighbors=4, weights=distance, score=-0.227, total= 0.2s\n","[CV] n_neighbors=4, weights=distance .................................\n","[CV] .... n_neighbors=4, weights=distance, score=-0.264, total= 0.2s\n","[CV] n_neighbors=4, weights=distance .................................\n","[CV] .... n_neighbors=4, weights=distance, score=-0.222, total= 0.2s\n","[CV] n_neighbors=4, weights=distance .................................\n","[CV] .... n_neighbors=4, weights=distance, score=-0.291, total= 0.2s\n","[CV] n_neighbors=4, weights=distance .................................\n","[CV] .... n_neighbors=4, weights=distance, score=-0.279, total= 0.2s\n","[CV] n_neighbors=5, weights=uniform ..................................\n","[CV] ..... n_neighbors=5, weights=uniform, score=-0.166, total= 0.2s\n","[CV] n_neighbors=5, weights=uniform ..................................\n","[CV] ..... n_neighbors=5, weights=uniform, score=-0.169, total= 0.2s\n","[CV] n_neighbors=5, weights=uniform ..................................\n","[CV] ..... n_neighbors=5, weights=uniform, score=-0.262, total= 0.2s\n","[CV] n_neighbors=5, weights=uniform ..................................\n","[CV] ..... n_neighbors=5, weights=uniform, score=-0.310, total= 0.2s\n","[CV] n_neighbors=5, weights=uniform ..................................\n","[CV] ..... n_neighbors=5, weights=uniform, score=-0.262, total= 0.2s\n","[CV] n_neighbors=5, weights=uniform ..................................\n","[CV] ..... n_neighbors=5, weights=uniform, score=-0.305, total= 0.2s\n","[CV] n_neighbors=5, weights=uniform ..................................\n","[CV] ..... n_neighbors=5, weights=uniform, score=-0.221, total= 0.2s\n","[CV] n_neighbors=5, weights=uniform ..................................\n","[CV] ..... n_neighbors=5, weights=uniform, score=-0.247, total= 0.2s\n","[CV] n_neighbors=5, weights=uniform ..................................\n","[CV] ..... n_neighbors=5, weights=uniform, score=-0.162, total= 0.2s\n","[CV] n_neighbors=5, weights=uniform ..................................\n","[CV] ..... n_neighbors=5, weights=uniform, score=-0.266, total= 0.2s\n","[CV] n_neighbors=5, weights=uniform ..................................\n","[CV] ..... n_neighbors=5, weights=uniform, score=-0.274, total= 0.2s\n","[CV] n_neighbors=5, weights=uniform ..................................\n","[CV] ..... n_neighbors=5, weights=uniform, score=-0.186, total= 0.2s\n","[CV] n_neighbors=5, weights=uniform ..................................\n","[CV] ..... n_neighbors=5, weights=uniform, score=-0.224, total= 0.2s\n","[CV] n_neighbors=5, weights=uniform ..................................\n","[CV] ..... n_neighbors=5, weights=uniform, score=-0.235, total= 0.2s\n","[CV] n_neighbors=5, weights=uniform ..................................\n","[CV] ..... n_neighbors=5, weights=uniform, score=-0.202, total= 0.2s\n","[CV] n_neighbors=5, weights=uniform ..................................\n","[CV] ..... n_neighbors=5, weights=uniform, score=-0.243, total= 0.2s\n","[CV] n_neighbors=5, weights=uniform ..................................\n","[CV] ..... n_neighbors=5, weights=uniform, score=-0.155, total= 0.2s\n","[CV] n_neighbors=5, weights=uniform ..................................\n","[CV] ..... n_neighbors=5, weights=uniform, score=-0.261, total= 0.2s\n","[CV] n_neighbors=5, weights=uniform ..................................\n","[CV] ..... n_neighbors=5, weights=uniform, score=-0.224, total= 0.2s\n","[CV] n_neighbors=5, weights=uniform ..................................\n","[CV] ..... n_neighbors=5, weights=uniform, score=-0.329, total= 0.2s\n","[CV] n_neighbors=5, weights=uniform ..................................\n","[CV] ..... n_neighbors=5, weights=uniform, score=-0.176, total= 0.2s\n","[CV] n_neighbors=5, weights=uniform ..................................\n","[CV] ..... n_neighbors=5, weights=uniform, score=-0.268, total= 0.2s\n","[CV] n_neighbors=5, weights=uniform ..................................\n","[CV] ..... n_neighbors=5, weights=uniform, score=-0.192, total= 0.2s\n","[CV] n_neighbors=5, weights=uniform ..................................\n","[CV] ..... n_neighbors=5, weights=uniform, score=-0.202, total= 0.2s\n","[CV] n_neighbors=5, weights=uniform ..................................\n","[CV] ..... n_neighbors=5, weights=uniform, score=-0.301, total= 0.2s\n","[CV] n_neighbors=5, weights=uniform ..................................\n","[CV] ..... n_neighbors=5, weights=uniform, score=-0.215, total= 0.2s\n","[CV] n_neighbors=5, weights=uniform ..................................\n","[CV] ..... n_neighbors=5, weights=uniform, score=-0.239, total= 0.2s\n","[CV] n_neighbors=5, weights=uniform ..................................\n","[CV] ..... n_neighbors=5, weights=uniform, score=-0.179, total= 0.2s\n","[CV] n_neighbors=5, weights=uniform ..................................\n","[CV] ..... n_neighbors=5, weights=uniform, score=-0.259, total= 0.2s\n","[CV] n_neighbors=5, weights=uniform ..................................\n","[CV] ..... n_neighbors=5, weights=uniform, score=-0.213, total= 0.2s\n","[CV] n_neighbors=5, weights=distance .................................\n","[CV] .... n_neighbors=5, weights=distance, score=-0.166, total= 0.2s\n","[CV] n_neighbors=5, weights=distance .................................\n","[CV] .... n_neighbors=5, weights=distance, score=-0.178, total= 0.2s\n","[CV] n_neighbors=5, weights=distance .................................\n","[CV] .... n_neighbors=5, weights=distance, score=-0.260, total= 0.2s\n","[CV] n_neighbors=5, weights=distance .................................\n","[CV] .... n_neighbors=5, weights=distance, score=-0.307, total= 0.2s\n","[CV] n_neighbors=5, weights=distance .................................\n","[CV] .... n_neighbors=5, weights=distance, score=-0.260, total= 0.2s\n","[CV] n_neighbors=5, weights=distance .................................\n","[CV] .... n_neighbors=5, weights=distance, score=-0.306, total= 0.2s\n","[CV] n_neighbors=5, weights=distance .................................\n","[CV] .... n_neighbors=5, weights=distance, score=-0.224, total= 0.2s\n","[CV] n_neighbors=5, weights=distance .................................\n","[CV] .... n_neighbors=5, weights=distance, score=-0.250, total= 0.2s\n","[CV] n_neighbors=5, weights=distance .................................\n","[CV] .... n_neighbors=5, weights=distance, score=-0.161, total= 0.2s\n","[CV] n_neighbors=5, weights=distance .................................\n","[CV] .... n_neighbors=5, weights=distance, score=-0.266, total= 0.2s\n","[CV] n_neighbors=5, weights=distance .................................\n","[CV] .... n_neighbors=5, weights=distance, score=-0.274, total= 0.2s\n","[CV] n_neighbors=5, weights=distance .................................\n","[CV] .... n_neighbors=5, weights=distance, score=-0.177, total= 0.2s\n","[CV] n_neighbors=5, weights=distance .................................\n","[CV] .... n_neighbors=5, weights=distance, score=-0.211, total= 0.2s\n","[CV] n_neighbors=5, weights=distance .................................\n","[CV] .... n_neighbors=5, weights=distance, score=-0.232, total= 0.2s\n","[CV] n_neighbors=5, weights=distance .................................\n","[CV] .... n_neighbors=5, weights=distance, score=-0.200, total= 0.2s\n","[CV] n_neighbors=5, weights=distance .................................\n","[CV] .... n_neighbors=5, weights=distance, score=-0.246, total= 0.2s\n","[CV] n_neighbors=5, weights=distance .................................\n","[CV] .... n_neighbors=5, weights=distance, score=-0.159, total= 0.2s\n","[CV] n_neighbors=5, weights=distance .................................\n","[CV] .... n_neighbors=5, weights=distance, score=-0.266, total= 0.2s\n","[CV] n_neighbors=5, weights=distance .................................\n","[CV] .... n_neighbors=5, weights=distance, score=-0.225, total= 0.2s\n","[CV] n_neighbors=5, weights=distance .................................\n","[CV] .... n_neighbors=5, weights=distance, score=-0.325, total= 0.2s\n","[CV] n_neighbors=5, weights=distance .................................\n","[CV] .... n_neighbors=5, weights=distance, score=-0.172, total= 0.2s\n","[CV] n_neighbors=5, weights=distance .................................\n","[CV] .... n_neighbors=5, weights=distance, score=-0.277, total= 0.2s\n","[CV] n_neighbors=5, weights=distance .................................\n","[CV] .... n_neighbors=5, weights=distance, score=-0.189, total= 0.2s\n","[CV] n_neighbors=5, weights=distance .................................\n","[CV] .... n_neighbors=5, weights=distance, score=-0.203, total= 0.2s\n","[CV] n_neighbors=5, weights=distance .................................\n","[CV] .... n_neighbors=5, weights=distance, score=-0.303, total= 0.2s\n","[CV] n_neighbors=5, weights=distance .................................\n","[CV] .... n_neighbors=5, weights=distance, score=-0.201, total= 0.2s\n","[CV] n_neighbors=5, weights=distance .................................\n","[CV] .... n_neighbors=5, weights=distance, score=-0.244, total= 0.2s\n","[CV] n_neighbors=5, weights=distance .................................\n","[CV] .... n_neighbors=5, weights=distance, score=-0.176, total= 0.2s\n","[CV] n_neighbors=5, weights=distance .................................\n","[CV] .... n_neighbors=5, weights=distance, score=-0.255, total= 0.2s\n","[CV] n_neighbors=5, weights=distance .................................\n","[CV] .... n_neighbors=5, weights=distance, score=-0.213, total= 0.2s\n","[CV] n_neighbors=6, weights=uniform ..................................\n","[CV] ..... n_neighbors=6, weights=uniform, score=-0.149, total= 0.2s\n","[CV] n_neighbors=6, weights=uniform ..................................\n","[CV] ..... n_neighbors=6, weights=uniform, score=-0.153, total= 0.2s\n","[CV] n_neighbors=6, weights=uniform ..................................\n","[CV] ..... n_neighbors=6, weights=uniform, score=-0.239, total= 0.2s\n","[CV] n_neighbors=6, weights=uniform ..................................\n","[CV] ..... n_neighbors=6, weights=uniform, score=-0.285, total= 0.2s\n","[CV] n_neighbors=6, weights=uniform ..................................\n","[CV] ..... n_neighbors=6, weights=uniform, score=-0.240, total= 0.2s\n","[CV] n_neighbors=6, weights=uniform ..................................\n","[CV] ..... n_neighbors=6, weights=uniform, score=-0.253, total= 0.2s\n","[CV] n_neighbors=6, weights=uniform ..................................\n","[CV] ..... n_neighbors=6, weights=uniform, score=-0.172, total= 0.2s\n","[CV] n_neighbors=6, weights=uniform ..................................\n","[CV] ..... n_neighbors=6, weights=uniform, score=-0.231, total= 0.2s\n","[CV] n_neighbors=6, weights=uniform ..................................\n","[CV] ..... n_neighbors=6, weights=uniform, score=-0.137, total= 0.2s\n","[CV] n_neighbors=6, weights=uniform ..................................\n","[CV] ..... n_neighbors=6, weights=uniform, score=-0.230, total= 0.2s\n","[CV] n_neighbors=6, weights=uniform ..................................\n","[CV] ..... n_neighbors=6, weights=uniform, score=-0.191, total= 0.2s\n","[CV] n_neighbors=6, weights=uniform ..................................\n","[CV] ..... n_neighbors=6, weights=uniform, score=-0.151, total= 0.2s\n","[CV] n_neighbors=6, weights=uniform ..................................\n","[CV] ..... n_neighbors=6, weights=uniform, score=-0.201, total= 0.2s\n","[CV] n_neighbors=6, weights=uniform ..................................\n","[CV] ..... n_neighbors=6, weights=uniform, score=-0.198, total= 0.2s\n","[CV] n_neighbors=6, weights=uniform ..................................\n","[CV] ..... n_neighbors=6, weights=uniform, score=-0.174, total= 0.2s\n","[CV] n_neighbors=6, weights=uniform ..................................\n","[CV] ..... n_neighbors=6, weights=uniform, score=-0.215, total= 0.2s\n","[CV] n_neighbors=6, weights=uniform ..................................\n","[CV] ..... n_neighbors=6, weights=uniform, score=-0.147, total= 0.2s\n","[CV] n_neighbors=6, weights=uniform ..................................\n","[CV] ..... n_neighbors=6, weights=uniform, score=-0.208, total= 0.2s\n","[CV] n_neighbors=6, weights=uniform ..................................\n","[CV] ..... n_neighbors=6, weights=uniform, score=-0.153, total= 0.2s\n","[CV] n_neighbors=6, weights=uniform ..................................\n","[CV] ..... n_neighbors=6, weights=uniform, score=-0.254, total= 0.2s\n","[CV] n_neighbors=6, weights=uniform ..................................\n","[CV] ..... n_neighbors=6, weights=uniform, score=-0.131, total= 0.2s\n","[CV] n_neighbors=6, weights=uniform ..................................\n","[CV] ..... n_neighbors=6, weights=uniform, score=-0.189, total= 0.2s\n","[CV] n_neighbors=6, weights=uniform ..................................\n","[CV] ..... n_neighbors=6, weights=uniform, score=-0.169, total= 0.2s\n","[CV] n_neighbors=6, weights=uniform ..................................\n","[CV] ..... n_neighbors=6, weights=uniform, score=-0.189, total= 0.2s\n","[CV] n_neighbors=6, weights=uniform ..................................\n","[CV] ..... n_neighbors=6, weights=uniform, score=-0.243, total= 0.2s\n","[CV] n_neighbors=6, weights=uniform ..................................\n","[CV] ..... n_neighbors=6, weights=uniform, score=-0.158, total= 0.2s\n","[CV] n_neighbors=6, weights=uniform ..................................\n","[CV] ..... n_neighbors=6, weights=uniform, score=-0.252, total= 0.2s\n","[CV] n_neighbors=6, weights=uniform ..................................\n","[CV] ..... n_neighbors=6, weights=uniform, score=-0.186, total= 0.2s\n","[CV] n_neighbors=6, weights=uniform ..................................\n","[CV] ..... n_neighbors=6, weights=uniform, score=-0.235, total= 0.2s\n","[CV] n_neighbors=6, weights=uniform ..................................\n","[CV] ..... n_neighbors=6, weights=uniform, score=-0.176, total= 0.2s\n","[CV] n_neighbors=6, weights=distance .................................\n","[CV] .... n_neighbors=6, weights=distance, score=-0.148, total= 0.2s\n","[CV] n_neighbors=6, weights=distance .................................\n","[CV] .... n_neighbors=6, weights=distance, score=-0.158, total= 0.2s\n","[CV] n_neighbors=6, weights=distance .................................\n","[CV] .... n_neighbors=6, weights=distance, score=-0.236, total= 0.2s\n","[CV] n_neighbors=6, weights=distance .................................\n","[CV] .... n_neighbors=6, weights=distance, score=-0.282, total= 0.2s\n","[CV] n_neighbors=6, weights=distance .................................\n","[CV] .... n_neighbors=6, weights=distance, score=-0.238, total= 0.2s\n","[CV] n_neighbors=6, weights=distance .................................\n","[CV] .... n_neighbors=6, weights=distance, score=-0.257, total= 0.2s\n","[CV] n_neighbors=6, weights=distance .................................\n","[CV] .... n_neighbors=6, weights=distance, score=-0.175, total= 0.2s\n","[CV] n_neighbors=6, weights=distance .................................\n","[CV] .... n_neighbors=6, weights=distance, score=-0.234, total= 0.2s\n","[CV] n_neighbors=6, weights=distance .................................\n","[CV] .... n_neighbors=6, weights=distance, score=-0.137, total= 0.2s\n","[CV] n_neighbors=6, weights=distance .................................\n","[CV] .... n_neighbors=6, weights=distance, score=-0.232, total= 0.2s\n","[CV] n_neighbors=6, weights=distance .................................\n","[CV] .... n_neighbors=6, weights=distance, score=-0.194, total= 0.2s\n","[CV] n_neighbors=6, weights=distance .................................\n","[CV] .... n_neighbors=6, weights=distance, score=-0.145, total= 0.2s\n","[CV] n_neighbors=6, weights=distance .................................\n","[CV] .... n_neighbors=6, weights=distance, score=-0.189, total= 0.2s\n","[CV] n_neighbors=6, weights=distance .................................\n","[CV] .... n_neighbors=6, weights=distance, score=-0.194, total= 0.2s\n","[CV] n_neighbors=6, weights=distance .................................\n","[CV] .... n_neighbors=6, weights=distance, score=-0.171, total= 0.2s\n","[CV] n_neighbors=6, weights=distance .................................\n","[CV] .... n_neighbors=6, weights=distance, score=-0.217, total= 0.2s\n","[CV] n_neighbors=6, weights=distance .................................\n","[CV] .... n_neighbors=6, weights=distance, score=-0.147, total= 0.2s\n","[CV] n_neighbors=6, weights=distance .................................\n","[CV] .... n_neighbors=6, weights=distance, score=-0.214, total= 0.2s\n","[CV] n_neighbors=6, weights=distance .................................\n","[CV] .... n_neighbors=6, weights=distance, score=-0.158, total= 0.2s\n","[CV] n_neighbors=6, weights=distance .................................\n","[CV] .... n_neighbors=6, weights=distance, score=-0.255, total= 0.2s\n","[CV] n_neighbors=6, weights=distance .................................\n","[CV] .... n_neighbors=6, weights=distance, score=-0.131, total= 0.2s\n","[CV] n_neighbors=6, weights=distance .................................\n","[CV] .... n_neighbors=6, weights=distance, score=-0.200, total= 0.2s\n","[CV] n_neighbors=6, weights=distance .................................\n","[CV] .... n_neighbors=6, weights=distance, score=-0.165, total= 0.2s\n","[CV] n_neighbors=6, weights=distance .................................\n","[CV] .... n_neighbors=6, weights=distance, score=-0.188, total= 0.2s\n","[CV] n_neighbors=6, weights=distance .................................\n","[CV] .... n_neighbors=6, weights=distance, score=-0.248, total= 0.2s\n","[CV] n_neighbors=6, weights=distance .................................\n","[CV] .... n_neighbors=6, weights=distance, score=-0.148, total= 0.2s\n","[CV] n_neighbors=6, weights=distance .................................\n","[CV] .... n_neighbors=6, weights=distance, score=-0.252, total= 0.2s\n","[CV] n_neighbors=6, weights=distance .................................\n","[CV] .... n_neighbors=6, weights=distance, score=-0.180, total= 0.2s\n","[CV] n_neighbors=6, weights=distance .................................\n","[CV] .... n_neighbors=6, weights=distance, score=-0.231, total= 0.2s\n","[CV] n_neighbors=6, weights=distance .................................\n","[CV] .... n_neighbors=6, weights=distance, score=-0.176, total= 0.2s\n","[Parallel(n_jobs=1)]: Done 300 out of 300 | elapsed: 49.5s finished\n","KNeighborsRegressor(algorithm='auto', leaf_size=30, metric='minkowski',\n"," metric_params=None, n_jobs=None, n_neighbors=6, p=2,\n"," weights='distance')\n","window: 1, stide: 1, rmse: 2.980451435225651\n","[1, 4, 9, 12, 2, 0, 3, 13, 11, 10, 6, 5, 7, 8]\n","[1, 4, 9, 12, 2, 0, 3, 13, 11, 10, 6]\n","(3000, 165)\n","Number of Feature-Columns: 165\n","\n","Features Ready for undergoing selection tests done ...\n","\n","Fitting 30 folds for each of 10 candidates, totalling 300 fits\n","[Parallel(n_jobs=1)]: Using backend SequentialBackend with 1 concurrent workers.\n","[CV] n_neighbors=2, weights=uniform ..................................\n","[CV] ..... n_neighbors=2, weights=uniform, score=-0.500, total= 0.2s\n","[Parallel(n_jobs=1)]: Done 1 out of 1 | elapsed: 0.2s remaining: 0.0s\n","[CV] n_neighbors=2, weights=uniform ..................................\n","[CV] ..... n_neighbors=2, weights=uniform, score=-0.571, total= 0.2s\n","[Parallel(n_jobs=1)]: Done 2 out of 2 | elapsed: 0.4s remaining: 0.0s\n","[CV] n_neighbors=2, weights=uniform ..................................\n","[CV] ..... n_neighbors=2, weights=uniform, score=-0.573, total= 0.2s\n","[Parallel(n_jobs=1)]: Done 3 out of 3 | elapsed: 0.6s remaining: 0.0s\n","[CV] n_neighbors=2, weights=uniform ..................................\n","[CV] ..... n_neighbors=2, weights=uniform, score=-0.561, total= 0.2s\n","[Parallel(n_jobs=1)]: Done 4 out of 4 | elapsed: 0.8s remaining: 0.0s\n","[CV] n_neighbors=2, weights=uniform ..................................\n","[CV] ..... n_neighbors=2, weights=uniform, score=-0.556, total= 0.2s\n","[CV] n_neighbors=2, weights=uniform ..................................\n","[CV] ..... n_neighbors=2, weights=uniform, score=-0.642, total= 0.2s\n","[CV] n_neighbors=2, weights=uniform ..................................\n","[CV] ..... n_neighbors=2, weights=uniform, score=-0.570, total= 0.2s\n","[CV] n_neighbors=2, weights=uniform ..................................\n","[CV] ..... n_neighbors=2, weights=uniform, score=-0.545, total= 0.2s\n","[CV] n_neighbors=2, weights=uniform ..................................\n","[CV] ..... n_neighbors=2, weights=uniform, score=-0.505, total= 0.2s\n","[CV] n_neighbors=2, weights=uniform ..................................\n","[CV] ..... n_neighbors=2, weights=uniform, score=-0.660, total= 0.2s\n","[CV] n_neighbors=2, weights=uniform ..................................\n","[CV] ..... n_neighbors=2, weights=uniform, score=-0.593, total= 0.2s\n","[CV] n_neighbors=2, weights=uniform ..................................\n","[CV] ..... n_neighbors=2, weights=uniform, score=-0.386, total= 0.2s\n","[CV] n_neighbors=2, weights=uniform ..................................\n","[CV] ..... n_neighbors=2, weights=uniform, score=-0.571, total= 0.2s\n","[CV] n_neighbors=2, weights=uniform ..................................\n","[CV] ..... n_neighbors=2, weights=uniform, score=-0.537, total= 0.2s\n","[CV] n_neighbors=2, weights=uniform ..................................\n","[CV] ..... n_neighbors=2, weights=uniform, score=-0.456, total= 0.2s\n","[CV] n_neighbors=2, weights=uniform ..................................\n","[CV] ..... n_neighbors=2, weights=uniform, score=-0.412, total= 0.2s\n","[CV] n_neighbors=2, weights=uniform ..................................\n","[CV] ..... n_neighbors=2, weights=uniform, score=-0.540, total= 0.2s\n","[CV] n_neighbors=2, weights=uniform ..................................\n","[CV] ..... n_neighbors=2, weights=uniform, score=-0.613, total= 0.2s\n","[CV] n_neighbors=2, weights=uniform ..................................\n","[CV] ..... n_neighbors=2, weights=uniform, score=-0.583, total= 0.2s\n","[CV] n_neighbors=2, weights=uniform ..................................\n","[CV] ..... n_neighbors=2, weights=uniform, score=-0.609, total= 0.2s\n","[CV] n_neighbors=2, weights=uniform ..................................\n","[CV] ..... n_neighbors=2, weights=uniform, score=-0.650, total= 0.2s\n","[CV] n_neighbors=2, weights=uniform ..................................\n","[CV] ..... n_neighbors=2, weights=uniform, score=-0.611, total= 0.2s\n","[CV] n_neighbors=2, weights=uniform ..................................\n","[CV] ..... n_neighbors=2, weights=uniform, score=-0.472, total= 0.2s\n","[CV] n_neighbors=2, weights=uniform ..................................\n","[CV] ..... n_neighbors=2, weights=uniform, score=-0.554, total= 0.2s\n","[CV] n_neighbors=2, weights=uniform ..................................\n","[CV] ..... n_neighbors=2, weights=uniform, score=-0.550, total= 0.2s\n","[CV] n_neighbors=2, weights=uniform ..................................\n","[CV] ..... n_neighbors=2, weights=uniform, score=-0.350, total= 0.2s\n","[CV] n_neighbors=2, weights=uniform ..................................\n","[CV] ..... n_neighbors=2, weights=uniform, score=-0.633, total= 0.2s\n","[CV] n_neighbors=2, weights=uniform ..................................\n","[CV] ..... n_neighbors=2, weights=uniform, score=-0.518, total= 0.2s\n","[CV] n_neighbors=2, weights=uniform ..................................\n","[CV] ..... n_neighbors=2, weights=uniform, score=-0.516, total= 0.2s\n","[CV] n_neighbors=2, weights=uniform ..................................\n","[CV] ..... n_neighbors=2, weights=uniform, score=-0.403, total= 0.2s\n","[CV] n_neighbors=2, weights=distance .................................\n","[CV] .... n_neighbors=2, weights=distance, score=-0.506, total= 0.2s\n","[CV] n_neighbors=2, weights=distance .................................\n","[CV] .... n_neighbors=2, weights=distance, score=-0.567, total= 0.2s\n","[CV] n_neighbors=2, weights=distance .................................\n","[CV] .... n_neighbors=2, weights=distance, score=-0.572, total= 0.2s\n","[CV] n_neighbors=2, weights=distance .................................\n","[CV] .... n_neighbors=2, weights=distance, score=-0.557, total= 0.2s\n","[CV] n_neighbors=2, weights=distance .................................\n","[CV] .... n_neighbors=2, weights=distance, score=-0.546, total= 0.2s\n","[CV] n_neighbors=2, weights=distance .................................\n","[CV] .... n_neighbors=2, weights=distance, score=-0.646, total= 0.2s\n","[CV] n_neighbors=2, weights=distance .................................\n","[CV] .... n_neighbors=2, weights=distance, score=-0.577, total= 0.2s\n","[CV] n_neighbors=2, weights=distance .................................\n","[CV] .... n_neighbors=2, weights=distance, score=-0.539, total= 0.2s\n","[CV] n_neighbors=2, weights=distance .................................\n","[CV] .... n_neighbors=2, weights=distance, score=-0.507, total= 0.2s\n","[CV] n_neighbors=2, weights=distance .................................\n","[CV] .... n_neighbors=2, weights=distance, score=-0.649, total= 0.2s\n","[CV] n_neighbors=2, weights=distance .................................\n","[CV] .... n_neighbors=2, weights=distance, score=-0.586, total= 0.2s\n","[CV] n_neighbors=2, weights=distance .................................\n","[CV] .... n_neighbors=2, weights=distance, score=-0.380, total= 0.2s\n","[CV] n_neighbors=2, weights=distance .................................\n","[CV] .... n_neighbors=2, weights=distance, score=-0.560, total= 0.2s\n","[CV] n_neighbors=2, weights=distance .................................\n","[CV] .... n_neighbors=2, weights=distance, score=-0.531, total= 0.2s\n","[CV] n_neighbors=2, weights=distance .................................\n","[CV] .... n_neighbors=2, weights=distance, score=-0.458, total= 0.2s\n","[CV] n_neighbors=2, weights=distance .................................\n","[CV] .... n_neighbors=2, weights=distance, score=-0.405, total= 0.2s\n","[CV] n_neighbors=2, weights=distance .................................\n","[CV] .... n_neighbors=2, weights=distance, score=-0.546, total= 0.2s\n","[CV] n_neighbors=2, weights=distance .................................\n","[CV] .... n_neighbors=2, weights=distance, score=-0.619, total= 0.2s\n","[CV] n_neighbors=2, weights=distance .................................\n","[CV] .... n_neighbors=2, weights=distance, score=-0.585, total= 0.2s\n","[CV] n_neighbors=2, weights=distance .................................\n","[CV] .... n_neighbors=2, weights=distance, score=-0.606, total= 0.2s\n","[CV] n_neighbors=2, weights=distance .................................\n","[CV] .... n_neighbors=2, weights=distance, score=-0.636, total= 0.2s\n","[CV] n_neighbors=2, weights=distance .................................\n","[CV] .... n_neighbors=2, weights=distance, score=-0.621, total= 0.2s\n","[CV] n_neighbors=2, weights=distance .................................\n","[CV] .... n_neighbors=2, weights=distance, score=-0.469, total= 0.2s\n","[CV] n_neighbors=2, weights=distance .................................\n","[CV] .... n_neighbors=2, weights=distance, score=-0.552, total= 0.2s\n","[CV] n_neighbors=2, weights=distance .................................\n","[CV] .... n_neighbors=2, weights=distance, score=-0.547, total= 0.2s\n","[CV] n_neighbors=2, weights=distance .................................\n","[CV] .... n_neighbors=2, weights=distance, score=-0.355, total= 0.2s\n","[CV] n_neighbors=2, weights=distance .................................\n","[CV] .... n_neighbors=2, weights=distance, score=-0.636, total= 0.2s\n","[CV] n_neighbors=2, weights=distance .................................\n","[CV] .... n_neighbors=2, weights=distance, score=-0.510, total= 0.2s\n","[CV] n_neighbors=2, weights=distance .................................\n","[CV] .... n_neighbors=2, weights=distance, score=-0.512, total= 0.2s\n","[CV] n_neighbors=2, weights=distance .................................\n","[CV] .... n_neighbors=2, weights=distance, score=-0.404, total= 0.2s\n","[CV] n_neighbors=3, weights=uniform ..................................\n","[CV] ..... n_neighbors=3, weights=uniform, score=-0.295, total= 0.2s\n","[CV] n_neighbors=3, weights=uniform ..................................\n","[CV] ..... n_neighbors=3, weights=uniform, score=-0.296, total= 0.2s\n","[CV] n_neighbors=3, weights=uniform ..................................\n","[CV] ..... n_neighbors=3, weights=uniform, score=-0.355, total= 0.2s\n","[CV] n_neighbors=3, weights=uniform ..................................\n","[CV] ..... n_neighbors=3, weights=uniform, score=-0.572, total= 0.2s\n","[CV] n_neighbors=3, weights=uniform ..................................\n","[CV] ..... n_neighbors=3, weights=uniform, score=-0.392, total= 0.2s\n","[CV] n_neighbors=3, weights=uniform ..................................\n","[CV] ..... n_neighbors=3, weights=uniform, score=-0.393, total= 0.2s\n","[CV] n_neighbors=3, weights=uniform ..................................\n","[CV] ..... n_neighbors=3, weights=uniform, score=-0.369, total= 0.2s\n","[CV] n_neighbors=3, weights=uniform ..................................\n","[CV] ..... n_neighbors=3, weights=uniform, score=-0.415, total= 0.2s\n","[CV] n_neighbors=3, weights=uniform ..................................\n","[CV] ..... n_neighbors=3, weights=uniform, score=-0.344, total= 0.2s\n","[CV] n_neighbors=3, weights=uniform ..................................\n","[CV] ..... n_neighbors=3, weights=uniform, score=-0.496, total= 0.2s\n","[CV] n_neighbors=3, weights=uniform ..................................\n","[CV] ..... n_neighbors=3, weights=uniform, score=-0.381, total= 0.2s\n","[CV] n_neighbors=3, weights=uniform ..................................\n","[CV] ..... n_neighbors=3, weights=uniform, score=-0.278, total= 0.2s\n","[CV] n_neighbors=3, weights=uniform ..................................\n","[CV] ..... n_neighbors=3, weights=uniform, score=-0.408, total= 0.2s\n","[CV] n_neighbors=3, weights=uniform ..................................\n","[CV] ..... n_neighbors=3, weights=uniform, score=-0.466, total= 0.2s\n","[CV] n_neighbors=3, weights=uniform ..................................\n","[CV] ..... n_neighbors=3, weights=uniform, score=-0.395, total= 0.2s\n","[CV] n_neighbors=3, weights=uniform ..................................\n","[CV] ..... n_neighbors=3, weights=uniform, score=-0.283, total= 0.2s\n","[CV] n_neighbors=3, weights=uniform ..................................\n","[CV] ..... n_neighbors=3, weights=uniform, score=-0.399, total= 0.2s\n","[CV] n_neighbors=3, weights=uniform ..................................\n","[CV] ..... n_neighbors=3, weights=uniform, score=-0.351, total= 0.2s\n","[CV] n_neighbors=3, weights=uniform ..................................\n","[CV] ..... n_neighbors=3, weights=uniform, score=-0.348, total= 0.2s\n","[CV] n_neighbors=3, weights=uniform ..................................\n","[CV] ..... n_neighbors=3, weights=uniform, score=-0.490, total= 0.2s\n","[CV] n_neighbors=3, weights=uniform ..................................\n","[CV] ..... n_neighbors=3, weights=uniform, score=-0.360, total= 0.2s\n","[CV] n_neighbors=3, weights=uniform ..................................\n","[CV] ..... n_neighbors=3, weights=uniform, score=-0.441, total= 0.2s\n","[CV] n_neighbors=3, weights=uniform ..................................\n","[CV] ..... n_neighbors=3, weights=uniform, score=-0.314, total= 0.2s\n","[CV] n_neighbors=3, weights=uniform ..................................\n","[CV] ..... n_neighbors=3, weights=uniform, score=-0.304, total= 0.2s\n","[CV] n_neighbors=3, weights=uniform ..................................\n","[CV] ..... n_neighbors=3, weights=uniform, score=-0.410, total= 0.2s\n","[CV] n_neighbors=3, weights=uniform ..................................\n","[CV] ..... n_neighbors=3, weights=uniform, score=-0.261, total= 0.2s\n","[CV] n_neighbors=3, weights=uniform ..................................\n","[CV] ..... n_neighbors=3, weights=uniform, score=-0.458, total= 0.2s\n","[CV] n_neighbors=3, weights=uniform ..................................\n","[CV] ..... n_neighbors=3, weights=uniform, score=-0.361, total= 0.2s\n","[CV] n_neighbors=3, weights=uniform ..................................\n","[CV] ..... n_neighbors=3, weights=uniform, score=-0.323, total= 0.2s\n","[CV] n_neighbors=3, weights=uniform ..................................\n","[CV] ..... n_neighbors=3, weights=uniform, score=-0.292, total= 0.2s\n","[CV] n_neighbors=3, weights=distance .................................\n","[CV] .... n_neighbors=3, weights=distance, score=-0.299, total= 0.2s\n","[CV] n_neighbors=3, weights=distance .................................\n","[CV] .... n_neighbors=3, weights=distance, score=-0.301, total= 0.2s\n","[CV] n_neighbors=3, weights=distance .................................\n","[CV] .... n_neighbors=3, weights=distance, score=-0.358, total= 0.2s\n","[CV] n_neighbors=3, weights=distance .................................\n","[CV] .... n_neighbors=3, weights=distance, score=-0.556, total= 0.2s\n","[CV] n_neighbors=3, weights=distance .................................\n","[CV] .... n_neighbors=3, weights=distance, score=-0.391, total= 0.2s\n","[CV] n_neighbors=3, weights=distance .................................\n","[CV] .... n_neighbors=3, weights=distance, score=-0.400, total= 0.2s\n","[CV] n_neighbors=3, weights=distance .................................\n","[CV] .... n_neighbors=3, weights=distance, score=-0.379, total= 0.2s\n","[CV] n_neighbors=3, weights=distance .................................\n","[CV] .... n_neighbors=3, weights=distance, score=-0.406, total= 0.2s\n","[CV] n_neighbors=3, weights=distance .................................\n","[CV] .... n_neighbors=3, weights=distance, score=-0.344, total= 0.2s\n","[CV] n_neighbors=3, weights=distance .................................\n","[CV] .... n_neighbors=3, weights=distance, score=-0.490, total= 0.2s\n","[CV] n_neighbors=3, weights=distance .................................\n","Traceback (most recent call last):\n"," File \"feature_select_main.py\", line 54, in \n"," topElectrodeFSRegressionRanking(dataset, window, stride, sfreq, clf, label, scale=False, pca=False, mutual_info = False, method=fs_method, ht = True)\n"," File \"/gdrive/MyDrive/IEEE_EEG_Project/TopNByFSMethods.py\", line 332, in topElectrodeFSRegressionRanking\n"," grid_search.fit(X_train,y_train)\n"," File \"/usr/local/lib/python3.7/dist-packages/sklearn/model_selection/_search.py\", line 710, in fit\n"," self._run_search(evaluate_candidates)\n"," File \"/usr/local/lib/python3.7/dist-packages/sklearn/model_selection/_search.py\", line 1151, in _run_search\n"," evaluate_candidates(ParameterGrid(self.param_grid))\n"," File \"/usr/local/lib/python3.7/dist-packages/sklearn/model_selection/_search.py\", line 689, in evaluate_candidates\n"," cv.split(X, y, groups)))\n"," File \"/usr/local/lib/python3.7/dist-packages/joblib/parallel.py\", line 1044, in __call__\n"," while self.dispatch_one_batch(iterator):\n"," File \"/usr/local/lib/python3.7/dist-packages/joblib/parallel.py\", line 859, in dispatch_one_batch\n"," self._dispatch(tasks)\n"," File \"/usr/local/lib/python3.7/dist-packages/joblib/parallel.py\", line 777, in _dispatch\n"," job = self._backend.apply_async(batch, callback=cb)\n"," File \"/usr/local/lib/python3.7/dist-packages/joblib/_parallel_backends.py\", line 208, in apply_async\n"," result = ImmediateResult(func)\n"," File \"/usr/local/lib/python3.7/dist-packages/joblib/_parallel_backends.py\", line 572, in __init__\n"," self.results = batch()\n"," File \"/usr/local/lib/python3.7/dist-packages/joblib/parallel.py\", line 263, in __call__\n"," for func, args, kwargs in self.items]\n"," File \"/usr/local/lib/python3.7/dist-packages/joblib/parallel.py\", line 263, in \n"," for func, args, kwargs in self.items]\n"," File \"/usr/local/lib/python3.7/dist-packages/sklearn/model_selection/_validation.py\", line 544, in _fit_and_score\n"," test_scores = _score(estimator, X_test, y_test, scorer)\n"," File \"/usr/local/lib/python3.7/dist-packages/sklearn/model_selection/_validation.py\", line 591, in _score\n"," scores = scorer(estimator, X_test, y_test)\n"," File \"/usr/local/lib/python3.7/dist-packages/sklearn/metrics/_scorer.py\", line 89, in __call__\n"," score = scorer(estimator, *args, **kwargs)\n"," File \"/usr/local/lib/python3.7/dist-packages/sklearn/metrics/_scorer.py\", line 371, in _passthrough_scorer\n"," return estimator.score(*args, **kwargs)\n"," File \"/usr/local/lib/python3.7/dist-packages/sklearn/base.py\", line 422, in score\n"," y_pred = self.predict(X)\n"," File \"/usr/local/lib/python3.7/dist-packages/sklearn/neighbors/_regression.py\", line 174, in predict\n"," neigh_dist, neigh_ind = self.kneighbors(X)\n"," File \"/usr/local/lib/python3.7/dist-packages/sklearn/neighbors/_base.py\", line 664, in kneighbors\n"," for s in gen_even_slices(X.shape[0], n_jobs)\n"," File \"/usr/local/lib/python3.7/dist-packages/joblib/parallel.py\", line 1041, in __call__\n"," if self.dispatch_one_batch(iterator):\n"," File \"/usr/local/lib/python3.7/dist-packages/joblib/parallel.py\", line 859, in dispatch_one_batch\n"," self._dispatch(tasks)\n"," File \"/usr/local/lib/python3.7/dist-packages/joblib/parallel.py\", line 777, in _dispatch\n"," job = self._backend.apply_async(batch, callback=cb)\n"," File \"/usr/local/lib/python3.7/dist-packages/joblib/_parallel_backends.py\", line 208, in apply_async\n"," result = ImmediateResult(func)\n"," File \"/usr/local/lib/python3.7/dist-packages/joblib/_parallel_backends.py\", line 572, in __init__\n"," self.results = batch()\n"," File \"/usr/local/lib/python3.7/dist-packages/joblib/parallel.py\", line 263, in __call__\n"," for func, args, kwargs in self.items]\n"," File \"/usr/local/lib/python3.7/dist-packages/joblib/parallel.py\", line 263, in \n"," for func, args, kwargs in self.items]\n"," File \"/usr/local/lib/python3.7/dist-packages/sklearn/neighbors/_base.py\", line 491, in _tree_query_parallel_helper\n"," return tree.query(*args, **kwargs)\n","KeyboardInterrupt\n"]}],"source":["!python3 feature_select_main.py --dataset OASIS --window 1 --stride 1 --sfreq 128 --model knn --label 0 --approach byfs --ml_algo regression --top e --fs_method SelectKBest"]},{"cell_type":"code","execution_count":null,"metadata":{"colab":{"background_save":true,"base_uri":"https://localhost:8080/"},"id":"LPRB7Li0rn28","outputId":"a6875a08-7aeb-45c2-dc9a-473e7a947e5b"},"outputs":[{"name":"stdout","output_type":"stream","text":["\u001b[1;30;43mStreaming output truncated to the last 5000 lines.\u001b[0m\n","[CV] ..... n_neighbors=6, weights=distance, score=0.003, total= 0.2s\n","[CV] n_neighbors=6, weights=distance .................................\n","[CV] .... n_neighbors=6, weights=distance, score=-0.017, total= 0.2s\n","[CV] n_neighbors=6, weights=distance .................................\n","[CV] .... n_neighbors=6, weights=distance, score=-0.071, total= 0.2s\n","[CV] n_neighbors=6, weights=distance .................................\n","[CV] .... n_neighbors=6, weights=distance, score=-0.074, total= 0.2s\n","[CV] n_neighbors=6, weights=distance .................................\n","[CV] .... n_neighbors=6, weights=distance, score=-0.072, total= 0.2s\n","[CV] n_neighbors=6, weights=distance .................................\n","[CV] ..... n_neighbors=6, weights=distance, score=0.040, total= 0.2s\n","[CV] n_neighbors=6, weights=distance .................................\n","[CV] .... n_neighbors=6, weights=distance, score=-0.168, total= 0.2s\n","[CV] n_neighbors=6, weights=distance .................................\n","[CV] ..... n_neighbors=6, weights=distance, score=0.063, total= 0.2s\n","[CV] n_neighbors=6, weights=distance .................................\n","[CV] ..... n_neighbors=6, weights=distance, score=0.044, total= 0.2s\n","[CV] n_neighbors=6, weights=distance .................................\n","[CV] ..... n_neighbors=6, weights=distance, score=0.005, total= 0.2s\n","[CV] n_neighbors=6, weights=distance .................................\n","[CV] ..... n_neighbors=6, weights=distance, score=0.039, total= 0.2s\n","[CV] n_neighbors=6, weights=distance .................................\n","[CV] .... n_neighbors=6, weights=distance, score=-0.010, total= 0.2s\n","[CV] n_neighbors=6, weights=distance .................................\n","[CV] .... n_neighbors=6, weights=distance, score=-0.058, total= 0.2s\n","[CV] n_neighbors=6, weights=distance .................................\n","[CV] ..... n_neighbors=6, weights=distance, score=0.016, total= 0.2s\n","[CV] n_neighbors=6, weights=distance .................................\n","[CV] .... n_neighbors=6, weights=distance, score=-0.004, total= 0.2s\n","[CV] n_neighbors=6, weights=distance .................................\n","[CV] .... n_neighbors=6, weights=distance, score=-0.028, total= 0.2s\n","[CV] n_neighbors=6, weights=distance .................................\n","[CV] ..... n_neighbors=6, weights=distance, score=0.006, total= 0.2s\n","[CV] n_neighbors=6, weights=distance .................................\n","[CV] ..... n_neighbors=6, weights=distance, score=0.049, total= 0.2s\n","[CV] n_neighbors=6, weights=distance .................................\n","[CV] .... n_neighbors=6, weights=distance, score=-0.019, total= 0.2s\n","[CV] n_neighbors=6, weights=distance .................................\n","[CV] .... n_neighbors=6, weights=distance, score=-0.053, total= 0.2s\n","[CV] n_neighbors=6, weights=distance .................................\n","[CV] .... n_neighbors=6, weights=distance, score=-0.059, total= 0.2s\n","[Parallel(n_jobs=1)]: Done 300 out of 300 | elapsed: 54.1s finished\n","KNeighborsRegressor(algorithm='auto', leaf_size=30, metric='minkowski',\n"," metric_params=None, n_jobs=None, n_neighbors=6, p=2,\n"," weights='distance')\n","window: 1, stide: 1, rmse: 2.7267311140079844\n","(3000, 203)\n","Number of Feature-Columns: 203\n","\n","Features Ready for undergoing selection tests done ...\n","\n","Fitting 30 folds for each of 10 candidates, totalling 300 fits\n","[Parallel(n_jobs=1)]: Using backend SequentialBackend with 1 concurrent workers.\n","[CV] n_neighbors=2, weights=uniform ..................................\n","[CV] ..... n_neighbors=2, weights=uniform, score=-0.136, total= 0.2s\n","[Parallel(n_jobs=1)]: Done 1 out of 1 | elapsed: 0.2s remaining: 0.0s\n","[CV] n_neighbors=2, weights=uniform ..................................\n","[CV] ..... n_neighbors=2, weights=uniform, score=-0.393, total= 0.2s\n","[Parallel(n_jobs=1)]: Done 2 out of 2 | elapsed: 0.4s remaining: 0.0s\n","[CV] n_neighbors=2, weights=uniform ..................................\n","[CV] ..... n_neighbors=2, weights=uniform, score=-0.271, total= 0.2s\n","[Parallel(n_jobs=1)]: Done 3 out of 3 | elapsed: 0.6s remaining: 0.0s\n","[CV] n_neighbors=2, weights=uniform ..................................\n","[CV] ..... n_neighbors=2, weights=uniform, score=-0.316, total= 0.2s\n","[Parallel(n_jobs=1)]: Done 4 out of 4 | elapsed: 0.8s remaining: 0.0s\n","[CV] n_neighbors=2, weights=uniform ..................................\n","[CV] ..... n_neighbors=2, weights=uniform, score=-0.407, total= 0.2s\n","[CV] n_neighbors=2, weights=uniform ..................................\n","[CV] ..... n_neighbors=2, weights=uniform, score=-0.094, total= 0.2s\n","[CV] n_neighbors=2, weights=uniform ..................................\n","[CV] ..... n_neighbors=2, weights=uniform, score=-0.211, total= 0.2s\n","[CV] n_neighbors=2, weights=uniform ..................................\n","[CV] ..... n_neighbors=2, weights=uniform, score=-0.261, total= 0.2s\n","[CV] n_neighbors=2, weights=uniform ..................................\n","[CV] ..... n_neighbors=2, weights=uniform, score=-0.282, total= 0.2s\n","[CV] n_neighbors=2, weights=uniform ..................................\n","[CV] ..... n_neighbors=2, weights=uniform, score=-0.222, total= 0.2s\n","[CV] n_neighbors=2, weights=uniform ..................................\n","[CV] ..... n_neighbors=2, weights=uniform, score=-0.172, total= 0.2s\n","[CV] n_neighbors=2, weights=uniform ..................................\n","[CV] ..... n_neighbors=2, weights=uniform, score=-0.379, total= 0.2s\n","[CV] n_neighbors=2, weights=uniform ..................................\n","[CV] ..... n_neighbors=2, weights=uniform, score=-0.349, total= 0.2s\n","[CV] n_neighbors=2, weights=uniform ..................................\n","[CV] ..... n_neighbors=2, weights=uniform, score=-0.384, total= 0.2s\n","[CV] n_neighbors=2, weights=uniform ..................................\n","[CV] ..... n_neighbors=2, weights=uniform, score=-0.160, total= 0.2s\n","[CV] n_neighbors=2, weights=uniform ..................................\n","[CV] ..... n_neighbors=2, weights=uniform, score=-0.560, total= 0.2s\n","[CV] n_neighbors=2, weights=uniform ..................................\n","[CV] ..... n_neighbors=2, weights=uniform, score=-0.038, total= 0.2s\n","[CV] n_neighbors=2, weights=uniform ..................................\n","[CV] ..... n_neighbors=2, weights=uniform, score=-0.078, total= 0.1s\n","[CV] n_neighbors=2, weights=uniform ..................................\n","[CV] ..... n_neighbors=2, weights=uniform, score=-0.279, total= 0.2s\n","[CV] n_neighbors=2, weights=uniform ..................................\n","[CV] ..... n_neighbors=2, weights=uniform, score=-0.279, total= 0.2s\n","[CV] n_neighbors=2, weights=uniform ..................................\n","[CV] ..... n_neighbors=2, weights=uniform, score=-0.304, total= 0.2s\n","[CV] n_neighbors=2, weights=uniform ..................................\n","[CV] ..... n_neighbors=2, weights=uniform, score=-0.245, total= 0.2s\n","[CV] n_neighbors=2, weights=uniform ..................................\n","[CV] ..... n_neighbors=2, weights=uniform, score=-0.257, total= 0.2s\n","[CV] n_neighbors=2, weights=uniform ..................................\n","[CV] ..... n_neighbors=2, weights=uniform, score=-0.181, total= 0.2s\n","[CV] n_neighbors=2, weights=uniform ..................................\n","[CV] ..... n_neighbors=2, weights=uniform, score=-0.249, total= 0.2s\n","[CV] n_neighbors=2, weights=uniform ..................................\n","[CV] ..... n_neighbors=2, weights=uniform, score=-0.400, total= 0.2s\n","[CV] n_neighbors=2, weights=uniform ..................................\n","[CV] ..... n_neighbors=2, weights=uniform, score=-0.049, total= 0.2s\n","[CV] n_neighbors=2, weights=uniform ..................................\n","[CV] ..... n_neighbors=2, weights=uniform, score=-0.169, total= 0.1s\n","[CV] n_neighbors=2, weights=uniform ..................................\n","[CV] ..... n_neighbors=2, weights=uniform, score=-0.351, total= 0.2s\n","[CV] n_neighbors=2, weights=uniform ..................................\n","[CV] ..... n_neighbors=2, weights=uniform, score=-0.366, total= 0.2s\n","[CV] n_neighbors=2, weights=distance .................................\n","[CV] .... n_neighbors=2, weights=distance, score=-0.126, total= 0.2s\n","[CV] n_neighbors=2, weights=distance .................................\n","[CV] .... n_neighbors=2, weights=distance, score=-0.394, total= 0.2s\n","[CV] n_neighbors=2, weights=distance .................................\n","[CV] .... n_neighbors=2, weights=distance, score=-0.275, total= 0.2s\n","[CV] n_neighbors=2, weights=distance .................................\n","[CV] .... n_neighbors=2, weights=distance, score=-0.309, total= 0.2s\n","[CV] n_neighbors=2, weights=distance .................................\n","[CV] .... n_neighbors=2, weights=distance, score=-0.397, total= 0.1s\n","[CV] n_neighbors=2, weights=distance .................................\n","[CV] .... n_neighbors=2, weights=distance, score=-0.100, total= 0.2s\n","[CV] n_neighbors=2, weights=distance .................................\n","[CV] .... n_neighbors=2, weights=distance, score=-0.220, total= 0.2s\n","[CV] n_neighbors=2, weights=distance .................................\n","[CV] .... n_neighbors=2, weights=distance, score=-0.261, total= 0.2s\n","[CV] n_neighbors=2, weights=distance .................................\n","[CV] .... n_neighbors=2, weights=distance, score=-0.272, total= 0.2s\n","[CV] n_neighbors=2, weights=distance .................................\n","[CV] .... n_neighbors=2, weights=distance, score=-0.217, total= 0.2s\n","[CV] n_neighbors=2, weights=distance .................................\n","[CV] .... n_neighbors=2, weights=distance, score=-0.173, total= 0.2s\n","[CV] n_neighbors=2, weights=distance .................................\n","[CV] .... n_neighbors=2, weights=distance, score=-0.373, total= 0.2s\n","[CV] n_neighbors=2, weights=distance .................................\n","[CV] .... n_neighbors=2, weights=distance, score=-0.339, total= 0.2s\n","[CV] n_neighbors=2, weights=distance .................................\n","[CV] .... n_neighbors=2, weights=distance, score=-0.386, total= 0.2s\n","[CV] n_neighbors=2, weights=distance .................................\n","[CV] .... n_neighbors=2, weights=distance, score=-0.160, total= 0.2s\n","[CV] n_neighbors=2, weights=distance .................................\n","[CV] .... n_neighbors=2, weights=distance, score=-0.562, total= 0.2s\n","[CV] n_neighbors=2, weights=distance .................................\n","[CV] .... n_neighbors=2, weights=distance, score=-0.031, total= 0.2s\n","[CV] n_neighbors=2, weights=distance .................................\n","[CV] .... n_neighbors=2, weights=distance, score=-0.067, total= 0.2s\n","[CV] n_neighbors=2, weights=distance .................................\n","[CV] .... n_neighbors=2, weights=distance, score=-0.283, total= 0.2s\n","[CV] n_neighbors=2, weights=distance .................................\n","[CV] .... n_neighbors=2, weights=distance, score=-0.264, total= 0.2s\n","[CV] n_neighbors=2, weights=distance .................................\n","[CV] .... n_neighbors=2, weights=distance, score=-0.301, total= 0.2s\n","[CV] n_neighbors=2, weights=distance .................................\n","[CV] .... n_neighbors=2, weights=distance, score=-0.246, total= 0.2s\n","[CV] n_neighbors=2, weights=distance .................................\n","[CV] .... n_neighbors=2, weights=distance, score=-0.256, total= 0.2s\n","[CV] n_neighbors=2, weights=distance .................................\n","[CV] .... n_neighbors=2, weights=distance, score=-0.185, total= 0.2s\n","[CV] n_neighbors=2, weights=distance .................................\n","[CV] .... n_neighbors=2, weights=distance, score=-0.243, total= 0.2s\n","[CV] n_neighbors=2, weights=distance .................................\n","[CV] .... n_neighbors=2, weights=distance, score=-0.403, total= 0.2s\n","[CV] n_neighbors=2, weights=distance .................................\n","[CV] .... n_neighbors=2, weights=distance, score=-0.046, total= 0.2s\n","[CV] n_neighbors=2, weights=distance .................................\n","[CV] .... n_neighbors=2, weights=distance, score=-0.165, total= 0.2s\n","[CV] n_neighbors=2, weights=distance .................................\n","[CV] .... n_neighbors=2, weights=distance, score=-0.350, total= 0.2s\n","[CV] n_neighbors=2, weights=distance .................................\n","[CV] .... n_neighbors=2, weights=distance, score=-0.349, total= 0.2s\n","[CV] n_neighbors=3, weights=uniform ..................................\n","[CV] ..... n_neighbors=3, weights=uniform, score=-0.091, total= 0.2s\n","[CV] n_neighbors=3, weights=uniform ..................................\n","[CV] ..... n_neighbors=3, weights=uniform, score=-0.114, total= 0.2s\n","[CV] n_neighbors=3, weights=uniform ..................................\n","[CV] ..... n_neighbors=3, weights=uniform, score=-0.128, total= 0.2s\n","[CV] n_neighbors=3, weights=uniform ..................................\n","[CV] ..... n_neighbors=3, weights=uniform, score=-0.126, total= 0.2s\n","[CV] n_neighbors=3, weights=uniform ..................................\n","[CV] ..... n_neighbors=3, weights=uniform, score=-0.267, total= 0.2s\n","[CV] n_neighbors=3, weights=uniform ..................................\n","[CV] ..... n_neighbors=3, weights=uniform, score=-0.025, total= 0.2s\n","[CV] n_neighbors=3, weights=uniform ..................................\n","[CV] ..... n_neighbors=3, weights=uniform, score=-0.137, total= 0.2s\n","[CV] n_neighbors=3, weights=uniform ..................................\n","[CV] ..... n_neighbors=3, weights=uniform, score=-0.186, total= 0.2s\n","[CV] n_neighbors=3, weights=uniform ..................................\n","[CV] ..... n_neighbors=3, weights=uniform, score=-0.198, total= 0.2s\n","[CV] n_neighbors=3, weights=uniform ..................................\n","[CV] ..... n_neighbors=3, weights=uniform, score=-0.068, total= 0.2s\n","[CV] n_neighbors=3, weights=uniform ..................................\n","[CV] ..... n_neighbors=3, weights=uniform, score=-0.070, total= 0.2s\n","[CV] n_neighbors=3, weights=uniform ..................................\n","[CV] ..... n_neighbors=3, weights=uniform, score=-0.168, total= 0.2s\n","[CV] n_neighbors=3, weights=uniform ..................................\n","[CV] ..... n_neighbors=3, weights=uniform, score=-0.244, total= 0.2s\n","[CV] n_neighbors=3, weights=uniform ..................................\n","[CV] ..... n_neighbors=3, weights=uniform, score=-0.328, total= 0.2s\n","[CV] n_neighbors=3, weights=uniform ..................................\n","[CV] ..... n_neighbors=3, weights=uniform, score=-0.053, total= 0.2s\n","[CV] n_neighbors=3, weights=uniform ..................................\n","[CV] ..... n_neighbors=3, weights=uniform, score=-0.287, total= 0.2s\n","[CV] n_neighbors=3, weights=uniform ..................................\n","[CV] ...... n_neighbors=3, weights=uniform, score=0.042, total= 0.2s\n","[CV] n_neighbors=3, weights=uniform ..................................\n","[CV] ..... n_neighbors=3, weights=uniform, score=-0.025, total= 0.2s\n","[CV] n_neighbors=3, weights=uniform ..................................\n","[CV] ..... n_neighbors=3, weights=uniform, score=-0.150, total= 0.2s\n","[CV] n_neighbors=3, weights=uniform ..................................\n","[CV] ..... n_neighbors=3, weights=uniform, score=-0.114, total= 0.2s\n","[CV] n_neighbors=3, weights=uniform ..................................\n","[CV] ..... n_neighbors=3, weights=uniform, score=-0.138, total= 0.2s\n","[CV] n_neighbors=3, weights=uniform ..................................\n","[CV] ..... n_neighbors=3, weights=uniform, score=-0.127, total= 0.2s\n","[CV] n_neighbors=3, weights=uniform ..................................\n","[CV] ..... n_neighbors=3, weights=uniform, score=-0.106, total= 0.2s\n","[CV] n_neighbors=3, weights=uniform ..................................\n","[CV] ..... n_neighbors=3, weights=uniform, score=-0.115, total= 0.2s\n","[CV] n_neighbors=3, weights=uniform ..................................\n","[CV] ..... n_neighbors=3, weights=uniform, score=-0.182, total= 0.2s\n","[CV] n_neighbors=3, weights=uniform ..................................\n","[CV] ..... n_neighbors=3, weights=uniform, score=-0.182, total= 0.2s\n","[CV] n_neighbors=3, weights=uniform ..................................\n","[CV] ..... n_neighbors=3, weights=uniform, score=-0.006, total= 0.2s\n","[CV] n_neighbors=3, weights=uniform ..................................\n","[CV] ..... n_neighbors=3, weights=uniform, score=-0.079, total= 0.2s\n","[CV] n_neighbors=3, weights=uniform ..................................\n","[CV] ..... n_neighbors=3, weights=uniform, score=-0.244, total= 0.2s\n","[CV] n_neighbors=3, weights=uniform ..................................\n","[CV] ..... n_neighbors=3, weights=uniform, score=-0.190, total= 0.2s\n","[CV] n_neighbors=3, weights=distance .................................\n","[CV] .... n_neighbors=3, weights=distance, score=-0.073, total= 0.2s\n","[CV] n_neighbors=3, weights=distance .................................\n","[CV] .... n_neighbors=3, weights=distance, score=-0.128, total= 0.2s\n","[CV] n_neighbors=3, weights=distance .................................\n","[CV] .... n_neighbors=3, weights=distance, score=-0.128, total= 0.2s\n","[CV] n_neighbors=3, weights=distance .................................\n","[CV] .... n_neighbors=3, weights=distance, score=-0.120, total= 0.2s\n","[CV] n_neighbors=3, weights=distance .................................\n","[CV] .... n_neighbors=3, weights=distance, score=-0.260, total= 0.2s\n","[CV] n_neighbors=3, weights=distance .................................\n","[CV] .... n_neighbors=3, weights=distance, score=-0.027, total= 0.2s\n","[CV] n_neighbors=3, weights=distance .................................\n","[CV] .... n_neighbors=3, weights=distance, score=-0.132, total= 0.2s\n","[CV] n_neighbors=3, weights=distance .................................\n","[CV] .... n_neighbors=3, weights=distance, score=-0.183, total= 0.2s\n","[CV] n_neighbors=3, weights=distance .................................\n","[CV] .... n_neighbors=3, weights=distance, score=-0.177, total= 0.2s\n","[CV] n_neighbors=3, weights=distance .................................\n","[CV] .... n_neighbors=3, weights=distance, score=-0.072, total= 0.2s\n","[CV] n_neighbors=3, weights=distance .................................\n","[CV] .... n_neighbors=3, weights=distance, score=-0.068, total= 0.2s\n","[CV] n_neighbors=3, weights=distance .................................\n","[CV] .... n_neighbors=3, weights=distance, score=-0.167, total= 0.2s\n","[CV] n_neighbors=3, weights=distance .................................\n","[CV] .... n_neighbors=3, weights=distance, score=-0.232, total= 0.2s\n","[CV] n_neighbors=3, weights=distance .................................\n","[CV] .... n_neighbors=3, weights=distance, score=-0.318, total= 0.2s\n","[CV] n_neighbors=3, weights=distance .................................\n","[CV] .... n_neighbors=3, weights=distance, score=-0.045, total= 0.2s\n","[CV] n_neighbors=3, weights=distance .................................\n","[CV] .... n_neighbors=3, weights=distance, score=-0.291, total= 0.2s\n","[CV] n_neighbors=3, weights=distance .................................\n","[CV] ..... n_neighbors=3, weights=distance, score=0.053, total= 0.2s\n","[CV] n_neighbors=3, weights=distance .................................\n","[CV] .... n_neighbors=3, weights=distance, score=-0.007, total= 0.2s\n","[CV] n_neighbors=3, weights=distance .................................\n","[CV] .... n_neighbors=3, weights=distance, score=-0.150, total= 0.2s\n","[CV] n_neighbors=3, weights=distance .................................\n","[CV] .... n_neighbors=3, weights=distance, score=-0.106, total= 0.2s\n","[CV] n_neighbors=3, weights=distance .................................\n","[CV] .... n_neighbors=3, weights=distance, score=-0.139, total= 0.2s\n","[CV] n_neighbors=3, weights=distance .................................\n","[CV] .... n_neighbors=3, weights=distance, score=-0.122, total= 0.2s\n","[CV] n_neighbors=3, weights=distance .................................\n","[CV] .... n_neighbors=3, weights=distance, score=-0.104, total= 0.2s\n","[CV] n_neighbors=3, weights=distance .................................\n","[CV] .... n_neighbors=3, weights=distance, score=-0.106, total= 0.2s\n","[CV] n_neighbors=3, weights=distance .................................\n","[CV] .... n_neighbors=3, weights=distance, score=-0.163, total= 0.2s\n","[CV] n_neighbors=3, weights=distance .................................\n","[CV] .... n_neighbors=3, weights=distance, score=-0.190, total= 0.2s\n","[CV] n_neighbors=3, weights=distance .................................\n","[CV] .... n_neighbors=3, weights=distance, score=-0.000, total= 0.2s\n","[CV] n_neighbors=3, weights=distance .................................\n","[CV] .... n_neighbors=3, weights=distance, score=-0.078, total= 0.2s\n","[CV] n_neighbors=3, weights=distance .................................\n","[CV] .... n_neighbors=3, weights=distance, score=-0.237, total= 0.2s\n","[CV] n_neighbors=3, weights=distance .................................\n","[CV] .... n_neighbors=3, weights=distance, score=-0.180, total= 0.2s\n","[CV] n_neighbors=4, weights=uniform ..................................\n","[CV] ..... n_neighbors=4, weights=uniform, score=-0.056, total= 0.2s\n","[CV] n_neighbors=4, weights=uniform ..................................\n","[CV] ..... n_neighbors=4, weights=uniform, score=-0.125, total= 0.2s\n","[CV] n_neighbors=4, weights=uniform ..................................\n","[CV] ..... n_neighbors=4, weights=uniform, score=-0.093, total= 0.2s\n","[CV] n_neighbors=4, weights=uniform ..................................\n","[CV] ..... n_neighbors=4, weights=uniform, score=-0.107, total= 0.2s\n","[CV] n_neighbors=4, weights=uniform ..................................\n","[CV] ..... n_neighbors=4, weights=uniform, score=-0.208, total= 0.2s\n","[CV] n_neighbors=4, weights=uniform ..................................\n","[CV] ...... n_neighbors=4, weights=uniform, score=0.013, total= 0.2s\n","[CV] n_neighbors=4, weights=uniform ..................................\n","[CV] ..... n_neighbors=4, weights=uniform, score=-0.024, total= 0.2s\n","[CV] n_neighbors=4, weights=uniform ..................................\n","[CV] ..... n_neighbors=4, weights=uniform, score=-0.093, total= 0.2s\n","[CV] n_neighbors=4, weights=uniform ..................................\n","[CV] ..... n_neighbors=4, weights=uniform, score=-0.130, total= 0.2s\n","[CV] n_neighbors=4, weights=uniform ..................................\n","[CV] ...... n_neighbors=4, weights=uniform, score=0.004, total= 0.2s\n","[CV] n_neighbors=4, weights=uniform ..................................\n","[CV] ..... n_neighbors=4, weights=uniform, score=-0.086, total= 0.2s\n","[CV] n_neighbors=4, weights=uniform ..................................\n","[CV] ..... n_neighbors=4, weights=uniform, score=-0.154, total= 0.2s\n","[CV] n_neighbors=4, weights=uniform ..................................\n","[CV] ..... n_neighbors=4, weights=uniform, score=-0.186, total= 0.2s\n","[CV] n_neighbors=4, weights=uniform ..................................\n","[CV] ..... n_neighbors=4, weights=uniform, score=-0.186, total= 0.2s\n","[CV] n_neighbors=4, weights=uniform ..................................\n","[CV] ...... n_neighbors=4, weights=uniform, score=0.002, total= 0.2s\n","[CV] n_neighbors=4, weights=uniform ..................................\n","[CV] ..... n_neighbors=4, weights=uniform, score=-0.177, total= 0.2s\n","[CV] n_neighbors=4, weights=uniform ..................................\n","[CV] ...... n_neighbors=4, weights=uniform, score=0.023, total= 0.2s\n","[CV] n_neighbors=4, weights=uniform ..................................\n","[CV] ..... n_neighbors=4, weights=uniform, score=-0.018, total= 0.2s\n","[CV] n_neighbors=4, weights=uniform ..................................\n","[CV] ..... n_neighbors=4, weights=uniform, score=-0.019, total= 0.2s\n","[CV] n_neighbors=4, weights=uniform ..................................\n","[CV] ..... n_neighbors=4, weights=uniform, score=-0.023, total= 0.2s\n","[CV] n_neighbors=4, weights=uniform ..................................\n","[CV] ..... n_neighbors=4, weights=uniform, score=-0.092, total= 0.2s\n","[CV] n_neighbors=4, weights=uniform ..................................\n","[CV] ..... n_neighbors=4, weights=uniform, score=-0.074, total= 0.2s\n","[CV] n_neighbors=4, weights=uniform ..................................\n","[CV] ..... n_neighbors=4, weights=uniform, score=-0.061, total= 0.2s\n","[CV] n_neighbors=4, weights=uniform ..................................\n","[CV] ..... n_neighbors=4, weights=uniform, score=-0.072, total= 0.2s\n","[CV] n_neighbors=4, weights=uniform ..................................\n","[CV] ..... n_neighbors=4, weights=uniform, score=-0.116, total= 0.2s\n","[CV] n_neighbors=4, weights=uniform ..................................\n","[CV] ..... n_neighbors=4, weights=uniform, score=-0.133, total= 0.2s\n","[CV] n_neighbors=4, weights=uniform ..................................\n","[CV] ...... n_neighbors=4, weights=uniform, score=0.011, total= 0.2s\n","[CV] n_neighbors=4, weights=uniform ..................................\n","[CV] ..... n_neighbors=4, weights=uniform, score=-0.050, total= 0.2s\n","[CV] n_neighbors=4, weights=uniform ..................................\n","[CV] ..... n_neighbors=4, weights=uniform, score=-0.168, total= 0.2s\n","[CV] n_neighbors=4, weights=uniform ..................................\n","[CV] ..... n_neighbors=4, weights=uniform, score=-0.052, total= 0.2s\n","[CV] n_neighbors=4, weights=distance .................................\n","[CV] .... n_neighbors=4, weights=distance, score=-0.036, total= 0.2s\n","[CV] n_neighbors=4, weights=distance .................................\n","[CV] .... n_neighbors=4, weights=distance, score=-0.126, total= 0.2s\n","[CV] n_neighbors=4, weights=distance .................................\n","[CV] .... n_neighbors=4, weights=distance, score=-0.093, total= 0.2s\n","[CV] n_neighbors=4, weights=distance .................................\n","[CV] .... n_neighbors=4, weights=distance, score=-0.092, total= 0.2s\n","[CV] n_neighbors=4, weights=distance .................................\n","[CV] .... n_neighbors=4, weights=distance, score=-0.205, total= 0.2s\n","[CV] n_neighbors=4, weights=distance .................................\n","[CV] ..... n_neighbors=4, weights=distance, score=0.010, total= 0.2s\n","[CV] n_neighbors=4, weights=distance .................................\n","[CV] .... n_neighbors=4, weights=distance, score=-0.024, total= 0.2s\n","[CV] n_neighbors=4, weights=distance .................................\n","[CV] .... n_neighbors=4, weights=distance, score=-0.096, total= 0.2s\n","[CV] n_neighbors=4, weights=distance .................................\n","[CV] .... n_neighbors=4, weights=distance, score=-0.112, total= 0.2s\n","[CV] n_neighbors=4, weights=distance .................................\n","[CV] ..... n_neighbors=4, weights=distance, score=0.001, total= 0.2s\n","[CV] n_neighbors=4, weights=distance .................................\n","[CV] .... n_neighbors=4, weights=distance, score=-0.076, total= 0.2s\n","[CV] n_neighbors=4, weights=distance .................................\n","[CV] .... n_neighbors=4, weights=distance, score=-0.150, total= 0.2s\n","[CV] n_neighbors=4, weights=distance .................................\n","[CV] .... n_neighbors=4, weights=distance, score=-0.171, total= 0.2s\n","[CV] n_neighbors=4, weights=distance .................................\n","[CV] .... n_neighbors=4, weights=distance, score=-0.184, total= 0.2s\n","[CV] n_neighbors=4, weights=distance .................................\n","[CV] ..... n_neighbors=4, weights=distance, score=0.013, total= 0.2s\n","[CV] n_neighbors=4, weights=distance .................................\n","[CV] .... n_neighbors=4, weights=distance, score=-0.182, total= 0.2s\n","[CV] n_neighbors=4, weights=distance .................................\n","[CV] ..... n_neighbors=4, weights=distance, score=0.037, total= 0.2s\n","[CV] n_neighbors=4, weights=distance .................................\n","[CV] .... n_neighbors=4, weights=distance, score=-0.005, total= 0.2s\n","[CV] n_neighbors=4, weights=distance .................................\n","[CV] .... n_neighbors=4, weights=distance, score=-0.020, total= 0.2s\n","[CV] n_neighbors=4, weights=distance .................................\n","[CV] .... n_neighbors=4, weights=distance, score=-0.018, total= 0.2s\n","[CV] n_neighbors=4, weights=distance .................................\n","[CV] .... n_neighbors=4, weights=distance, score=-0.092, total= 0.2s\n","[CV] n_neighbors=4, weights=distance .................................\n","[CV] .... n_neighbors=4, weights=distance, score=-0.068, total= 0.2s\n","[CV] n_neighbors=4, weights=distance .................................\n","[CV] .... n_neighbors=4, weights=distance, score=-0.055, total= 0.2s\n","[CV] n_neighbors=4, weights=distance .................................\n","[CV] .... n_neighbors=4, weights=distance, score=-0.064, total= 0.2s\n","[CV] n_neighbors=4, weights=distance .................................\n","[CV] .... n_neighbors=4, weights=distance, score=-0.105, total= 0.2s\n","[CV] n_neighbors=4, weights=distance .................................\n","[CV] .... n_neighbors=4, weights=distance, score=-0.135, total= 0.2s\n","[CV] n_neighbors=4, weights=distance .................................\n","[CV] ..... n_neighbors=4, weights=distance, score=0.020, total= 0.2s\n","[CV] n_neighbors=4, weights=distance .................................\n","[CV] .... n_neighbors=4, weights=distance, score=-0.050, total= 0.2s\n","[CV] n_neighbors=4, weights=distance .................................\n","[CV] .... n_neighbors=4, weights=distance, score=-0.163, total= 0.2s\n","[CV] n_neighbors=4, weights=distance .................................\n","[CV] .... n_neighbors=4, weights=distance, score=-0.047, total= 0.2s\n","[CV] n_neighbors=5, weights=uniform ..................................\n","[CV] ...... n_neighbors=5, weights=uniform, score=0.014, total= 0.2s\n","[CV] n_neighbors=5, weights=uniform ..................................\n","[CV] ..... n_neighbors=5, weights=uniform, score=-0.077, total= 0.2s\n","[CV] n_neighbors=5, weights=uniform ..................................\n","[CV] ..... n_neighbors=5, weights=uniform, score=-0.052, total= 0.2s\n","[CV] n_neighbors=5, weights=uniform ..................................\n","[CV] ..... n_neighbors=5, weights=uniform, score=-0.067, total= 0.2s\n","[CV] n_neighbors=5, weights=uniform ..................................\n","[CV] ..... n_neighbors=5, weights=uniform, score=-0.176, total= 0.2s\n","[CV] n_neighbors=5, weights=uniform ..................................\n","[CV] ...... n_neighbors=5, weights=uniform, score=0.047, total= 0.2s\n","[CV] n_neighbors=5, weights=uniform ..................................\n","[CV] ..... n_neighbors=5, weights=uniform, score=-0.009, total= 0.2s\n","[CV] n_neighbors=5, weights=uniform ..................................\n","[CV] ..... n_neighbors=5, weights=uniform, score=-0.038, total= 0.2s\n","[CV] n_neighbors=5, weights=uniform ..................................\n","[CV] ..... n_neighbors=5, weights=uniform, score=-0.137, total= 0.2s\n","[CV] n_neighbors=5, weights=uniform ..................................\n","[CV] ..... n_neighbors=5, weights=uniform, score=-0.007, total= 0.2s\n","[CV] n_neighbors=5, weights=uniform ..................................\n","[CV] ..... n_neighbors=5, weights=uniform, score=-0.053, total= 0.2s\n","[CV] n_neighbors=5, weights=uniform ..................................\n","[CV] ..... n_neighbors=5, weights=uniform, score=-0.146, total= 0.2s\n","[CV] n_neighbors=5, weights=uniform ..................................\n","[CV] ..... n_neighbors=5, weights=uniform, score=-0.127, total= 0.2s\n","[CV] n_neighbors=5, weights=uniform ..................................\n","[CV] ..... n_neighbors=5, weights=uniform, score=-0.126, total= 0.2s\n","[CV] n_neighbors=5, weights=uniform ..................................\n","[CV] ...... n_neighbors=5, weights=uniform, score=0.033, total= 0.2s\n","[CV] n_neighbors=5, weights=uniform ..................................\n","[CV] ..... n_neighbors=5, weights=uniform, score=-0.181, total= 0.2s\n","[CV] n_neighbors=5, weights=uniform ..................................\n","[CV] ...... n_neighbors=5, weights=uniform, score=0.048, total= 0.2s\n","[CV] n_neighbors=5, weights=uniform ..................................\n","[CV] ...... n_neighbors=5, weights=uniform, score=0.019, total= 0.2s\n","[CV] n_neighbors=5, weights=uniform ..................................\n","[CV] ..... n_neighbors=5, weights=uniform, score=-0.033, total= 0.2s\n","[CV] n_neighbors=5, weights=uniform ..................................\n","[CV] ...... n_neighbors=5, weights=uniform, score=0.019, total= 0.2s\n","[CV] n_neighbors=5, weights=uniform ..................................\n","[CV] ..... n_neighbors=5, weights=uniform, score=-0.069, total= 0.2s\n","[CV] n_neighbors=5, weights=uniform ..................................\n","[CV] ..... n_neighbors=5, weights=uniform, score=-0.064, total= 0.2s\n","[CV] n_neighbors=5, weights=uniform ..................................\n","[CV] ...... n_neighbors=5, weights=uniform, score=0.008, total= 0.2s\n","[CV] n_neighbors=5, weights=uniform ..................................\n","[CV] ..... n_neighbors=5, weights=uniform, score=-0.056, total= 0.2s\n","[CV] n_neighbors=5, weights=uniform ..................................\n","[CV] ..... n_neighbors=5, weights=uniform, score=-0.057, total= 0.2s\n","[CV] n_neighbors=5, weights=uniform ..................................\n","[CV] ..... n_neighbors=5, weights=uniform, score=-0.042, total= 0.2s\n","[CV] n_neighbors=5, weights=uniform ..................................\n","[CV] ...... n_neighbors=5, weights=uniform, score=0.020, total= 0.2s\n","[CV] n_neighbors=5, weights=uniform ..................................\n","[CV] ..... n_neighbors=5, weights=uniform, score=-0.039, total= 0.2s\n","[CV] n_neighbors=5, weights=uniform ..................................\n","[CV] ..... n_neighbors=5, weights=uniform, score=-0.115, total= 0.2s\n","[CV] n_neighbors=5, weights=uniform ..................................\n","[CV] ..... n_neighbors=5, weights=uniform, score=-0.050, total= 0.2s\n","[CV] n_neighbors=5, weights=distance .................................\n","[CV] ..... n_neighbors=5, weights=distance, score=0.028, total= 0.2s\n","[CV] n_neighbors=5, weights=distance .................................\n","[CV] .... n_neighbors=5, weights=distance, score=-0.076, total= 0.2s\n","[CV] n_neighbors=5, weights=distance .................................\n","[CV] .... n_neighbors=5, weights=distance, score=-0.051, total= 0.2s\n","[CV] n_neighbors=5, weights=distance .................................\n","[CV] .... n_neighbors=5, weights=distance, score=-0.053, total= 0.2s\n","[CV] n_neighbors=5, weights=distance .................................\n","[CV] .... n_neighbors=5, weights=distance, score=-0.174, total= 0.2s\n","[CV] n_neighbors=5, weights=distance .................................\n","[CV] ..... n_neighbors=5, weights=distance, score=0.043, total= 0.2s\n","[CV] n_neighbors=5, weights=distance .................................\n","[CV] .... n_neighbors=5, weights=distance, score=-0.003, total= 0.2s\n","[CV] n_neighbors=5, weights=distance .................................\n","[CV] .... n_neighbors=5, weights=distance, score=-0.042, total= 0.2s\n","[CV] n_neighbors=5, weights=distance .................................\n","[CV] .... n_neighbors=5, weights=distance, score=-0.116, total= 0.2s\n","[CV] n_neighbors=5, weights=distance .................................\n","[CV] .... n_neighbors=5, weights=distance, score=-0.007, total= 0.2s\n","[CV] n_neighbors=5, weights=distance .................................\n","[CV] .... n_neighbors=5, weights=distance, score=-0.045, total= 0.2s\n","[CV] n_neighbors=5, weights=distance .................................\n","[CV] .... n_neighbors=5, weights=distance, score=-0.140, total= 0.2s\n","[CV] n_neighbors=5, weights=distance .................................\n","[CV] .... n_neighbors=5, weights=distance, score=-0.116, total= 0.2s\n","[CV] n_neighbors=5, weights=distance .................................\n","[CV] .... n_neighbors=5, weights=distance, score=-0.129, total= 0.2s\n","[CV] n_neighbors=5, weights=distance .................................\n","[CV] ..... n_neighbors=5, weights=distance, score=0.045, total= 0.2s\n","[CV] n_neighbors=5, weights=distance .................................\n","[CV] .... n_neighbors=5, weights=distance, score=-0.179, total= 0.2s\n","[CV] n_neighbors=5, weights=distance .................................\n","[CV] ..... n_neighbors=5, weights=distance, score=0.059, total= 0.2s\n","[CV] n_neighbors=5, weights=distance .................................\n","[CV] ..... n_neighbors=5, weights=distance, score=0.033, total= 0.2s\n","[CV] n_neighbors=5, weights=distance .................................\n","[CV] .... n_neighbors=5, weights=distance, score=-0.026, total= 0.2s\n","[CV] n_neighbors=5, weights=distance .................................\n","[CV] ..... n_neighbors=5, weights=distance, score=0.023, total= 0.2s\n","[CV] n_neighbors=5, weights=distance .................................\n","[CV] .... n_neighbors=5, weights=distance, score=-0.066, total= 0.2s\n","[CV] n_neighbors=5, weights=distance .................................\n","[CV] .... n_neighbors=5, weights=distance, score=-0.056, total= 0.2s\n","[CV] n_neighbors=5, weights=distance .................................\n","[CV] ..... n_neighbors=5, weights=distance, score=0.008, total= 0.2s\n","[CV] n_neighbors=5, weights=distance .................................\n","[CV] .... n_neighbors=5, weights=distance, score=-0.051, total= 0.2s\n","[CV] n_neighbors=5, weights=distance .................................\n","[CV] .... n_neighbors=5, weights=distance, score=-0.050, total= 0.2s\n","[CV] n_neighbors=5, weights=distance .................................\n","[CV] .... n_neighbors=5, weights=distance, score=-0.051, total= 0.2s\n","[CV] n_neighbors=5, weights=distance .................................\n","[CV] ..... n_neighbors=5, weights=distance, score=0.028, total= 0.2s\n","[CV] n_neighbors=5, weights=distance .................................\n","[CV] .... n_neighbors=5, weights=distance, score=-0.037, total= 0.2s\n","[CV] n_neighbors=5, weights=distance .................................\n","[CV] .... n_neighbors=5, weights=distance, score=-0.106, total= 0.2s\n","[CV] n_neighbors=5, weights=distance .................................\n","[CV] .... n_neighbors=5, weights=distance, score=-0.044, total= 0.2s\n","[CV] n_neighbors=6, weights=uniform ..................................\n","[CV] ...... n_neighbors=6, weights=uniform, score=0.007, total= 0.2s\n","[CV] n_neighbors=6, weights=uniform ..................................\n","[CV] ..... n_neighbors=6, weights=uniform, score=-0.047, total= 0.2s\n","[CV] n_neighbors=6, weights=uniform ..................................\n","[CV] ...... n_neighbors=6, weights=uniform, score=0.011, total= 0.2s\n","[CV] n_neighbors=6, weights=uniform ..................................\n","[CV] ..... n_neighbors=6, weights=uniform, score=-0.061, total= 0.2s\n","[CV] n_neighbors=6, weights=uniform ..................................\n","[CV] ..... n_neighbors=6, weights=uniform, score=-0.144, total= 0.2s\n","[CV] n_neighbors=6, weights=uniform ..................................\n","[CV] ...... n_neighbors=6, weights=uniform, score=0.086, total= 0.2s\n","[CV] n_neighbors=6, weights=uniform ..................................\n","[CV] ..... n_neighbors=6, weights=uniform, score=-0.030, total= 0.2s\n","[CV] n_neighbors=6, weights=uniform ..................................\n","[CV] ..... n_neighbors=6, weights=uniform, score=-0.008, total= 0.2s\n","[CV] n_neighbors=6, weights=uniform ..................................\n","[CV] ..... n_neighbors=6, weights=uniform, score=-0.084, total= 0.2s\n","[CV] n_neighbors=6, weights=uniform ..................................\n","[CV] ...... n_neighbors=6, weights=uniform, score=0.002, total= 0.2s\n","[CV] n_neighbors=6, weights=uniform ..................................\n","[CV] ..... n_neighbors=6, weights=uniform, score=-0.024, total= 0.2s\n","[CV] n_neighbors=6, weights=uniform ..................................\n","[CV] ..... n_neighbors=6, weights=uniform, score=-0.070, total= 0.2s\n","[CV] n_neighbors=6, weights=uniform ..................................\n","[CV] ..... n_neighbors=6, weights=uniform, score=-0.080, total= 0.2s\n","[CV] n_neighbors=6, weights=uniform ..................................\n","[CV] ..... n_neighbors=6, weights=uniform, score=-0.064, total= 0.2s\n","[CV] n_neighbors=6, weights=uniform ..................................\n","[CV] ...... n_neighbors=6, weights=uniform, score=0.025, total= 0.2s\n","[CV] n_neighbors=6, weights=uniform ..................................\n","[CV] ..... n_neighbors=6, weights=uniform, score=-0.173, total= 0.2s\n","[CV] n_neighbors=6, weights=uniform ..................................\n","[CV] ...... n_neighbors=6, weights=uniform, score=0.050, total= 0.2s\n","[CV] n_neighbors=6, weights=uniform ..................................\n","[CV] ...... n_neighbors=6, weights=uniform, score=0.031, total= 0.2s\n","[CV] n_neighbors=6, weights=uniform ..................................\n","[CV] ...... n_neighbors=6, weights=uniform, score=0.001, total= 0.2s\n","[CV] n_neighbors=6, weights=uniform ..................................\n","[CV] ...... n_neighbors=6, weights=uniform, score=0.033, total= 0.2s\n","[CV] n_neighbors=6, weights=uniform ..................................\n","[CV] ..... n_neighbors=6, weights=uniform, score=-0.007, total= 0.2s\n","[CV] n_neighbors=6, weights=uniform ..................................\n","[CV] ..... n_neighbors=6, weights=uniform, score=-0.069, total= 0.2s\n","[CV] n_neighbors=6, weights=uniform ..................................\n","[CV] ...... n_neighbors=6, weights=uniform, score=0.015, total= 0.2s\n","[CV] n_neighbors=6, weights=uniform ..................................\n","[CV] ..... n_neighbors=6, weights=uniform, score=-0.006, total= 0.2s\n","[CV] n_neighbors=6, weights=uniform ..................................\n","[CV] ..... n_neighbors=6, weights=uniform, score=-0.036, total= 0.2s\n","[CV] n_neighbors=6, weights=uniform ..................................\n","[CV] ...... n_neighbors=6, weights=uniform, score=0.017, total= 0.2s\n","[CV] n_neighbors=6, weights=uniform ..................................\n","[CV] ...... n_neighbors=6, weights=uniform, score=0.045, total= 0.2s\n","[CV] n_neighbors=6, weights=uniform ..................................\n","[CV] ..... n_neighbors=6, weights=uniform, score=-0.024, total= 0.2s\n","[CV] n_neighbors=6, weights=uniform ..................................\n","[CV] ..... n_neighbors=6, weights=uniform, score=-0.063, total= 0.2s\n","[CV] n_neighbors=6, weights=uniform ..................................\n","[CV] ..... n_neighbors=6, weights=uniform, score=-0.070, total= 0.2s\n","[CV] n_neighbors=6, weights=distance .................................\n","[CV] ..... n_neighbors=6, weights=distance, score=0.024, total= 0.2s\n","[CV] n_neighbors=6, weights=distance .................................\n","[CV] .... n_neighbors=6, weights=distance, score=-0.044, total= 0.2s\n","[CV] n_neighbors=6, weights=distance .................................\n","[CV] ..... n_neighbors=6, weights=distance, score=0.010, total= 0.2s\n","[CV] n_neighbors=6, weights=distance .................................\n","[CV] .... n_neighbors=6, weights=distance, score=-0.043, total= 0.2s\n","[CV] n_neighbors=6, weights=distance .................................\n","[CV] .... n_neighbors=6, weights=distance, score=-0.142, total= 0.2s\n","[CV] n_neighbors=6, weights=distance .................................\n","[CV] ..... n_neighbors=6, weights=distance, score=0.084, total= 0.2s\n","[CV] n_neighbors=6, weights=distance .................................\n","[CV] .... n_neighbors=6, weights=distance, score=-0.023, total= 0.2s\n","[CV] n_neighbors=6, weights=distance .................................\n","[CV] .... n_neighbors=6, weights=distance, score=-0.011, total= 0.2s\n","[CV] n_neighbors=6, weights=distance .................................\n","[CV] .... n_neighbors=6, weights=distance, score=-0.066, total= 0.2s\n","[CV] n_neighbors=6, weights=distance .................................\n","[CV] ..... n_neighbors=6, weights=distance, score=0.003, total= 0.2s\n","[CV] n_neighbors=6, weights=distance .................................\n","[CV] .... n_neighbors=6, weights=distance, score=-0.017, total= 0.2s\n","[CV] n_neighbors=6, weights=distance .................................\n","[CV] .... n_neighbors=6, weights=distance, score=-0.071, total= 0.2s\n","[CV] n_neighbors=6, weights=distance .................................\n","[CV] .... n_neighbors=6, weights=distance, score=-0.074, total= 0.2s\n","[CV] n_neighbors=6, weights=distance .................................\n","[CV] .... n_neighbors=6, weights=distance, score=-0.072, total= 0.2s\n","[CV] n_neighbors=6, weights=distance .................................\n","[CV] ..... n_neighbors=6, weights=distance, score=0.040, total= 0.2s\n","[CV] n_neighbors=6, weights=distance .................................\n","[CV] .... n_neighbors=6, weights=distance, score=-0.168, total= 0.2s\n","[CV] n_neighbors=6, weights=distance .................................\n","[CV] ..... n_neighbors=6, weights=distance, score=0.063, total= 0.2s\n","[CV] n_neighbors=6, weights=distance .................................\n","[CV] ..... n_neighbors=6, weights=distance, score=0.044, total= 0.2s\n","[CV] n_neighbors=6, weights=distance .................................\n","[CV] ..... n_neighbors=6, weights=distance, score=0.005, total= 0.2s\n","[CV] n_neighbors=6, weights=distance .................................\n","[CV] ..... n_neighbors=6, weights=distance, score=0.039, total= 0.2s\n","[CV] n_neighbors=6, weights=distance .................................\n","[CV] .... n_neighbors=6, weights=distance, score=-0.010, total= 0.2s\n","[CV] n_neighbors=6, weights=distance .................................\n","[CV] .... n_neighbors=6, weights=distance, score=-0.058, total= 0.2s\n","[CV] n_neighbors=6, weights=distance .................................\n","[CV] ..... n_neighbors=6, weights=distance, score=0.016, total= 0.2s\n","[CV] n_neighbors=6, weights=distance .................................\n","[CV] .... n_neighbors=6, weights=distance, score=-0.004, total= 0.2s\n","[CV] n_neighbors=6, weights=distance .................................\n","[CV] .... n_neighbors=6, weights=distance, score=-0.028, total= 0.2s\n","[CV] n_neighbors=6, weights=distance .................................\n","[CV] ..... n_neighbors=6, weights=distance, score=0.006, total= 0.2s\n","[CV] n_neighbors=6, weights=distance .................................\n","[CV] ..... n_neighbors=6, weights=distance, score=0.049, total= 0.2s\n","[CV] n_neighbors=6, weights=distance .................................\n","[CV] .... n_neighbors=6, weights=distance, score=-0.019, total= 0.2s\n","[CV] n_neighbors=6, weights=distance .................................\n","[CV] .... n_neighbors=6, weights=distance, score=-0.053, total= 0.2s\n","[CV] n_neighbors=6, weights=distance .................................\n","[CV] .... n_neighbors=6, weights=distance, score=-0.059, total= 0.2s\n","[Parallel(n_jobs=1)]: Done 300 out of 300 | elapsed: 56.8s finished\n","KNeighborsRegressor(algorithm='auto', leaf_size=30, metric='minkowski',\n"," metric_params=None, n_jobs=None, n_neighbors=6, p=2,\n"," weights='distance')\n","window: 1, stide: 1, rmse: 2.7267311139260477\n","(3000, 210)\n","Number of Feature-Columns: 210\n","\n","Features Ready for undergoing selection tests done ...\n","\n","Fitting 30 folds for each of 10 candidates, totalling 300 fits\n","[Parallel(n_jobs=1)]: Using backend SequentialBackend with 1 concurrent workers.\n","[CV] n_neighbors=2, weights=uniform ..................................\n","[CV] ..... n_neighbors=2, weights=uniform, score=-0.136, total= 0.2s\n","[Parallel(n_jobs=1)]: Done 1 out of 1 | elapsed: 0.2s remaining: 0.0s\n","[CV] n_neighbors=2, weights=uniform ..................................\n","[CV] ..... n_neighbors=2, weights=uniform, score=-0.393, total= 0.2s\n","[Parallel(n_jobs=1)]: Done 2 out of 2 | elapsed: 0.4s remaining: 0.0s\n","[CV] n_neighbors=2, weights=uniform ..................................\n","[CV] ..... n_neighbors=2, weights=uniform, score=-0.271, total= 0.2s\n","[Parallel(n_jobs=1)]: Done 3 out of 3 | elapsed: 0.6s remaining: 0.0s\n","[CV] n_neighbors=2, weights=uniform ..................................\n","[CV] ..... n_neighbors=2, weights=uniform, score=-0.316, total= 0.2s\n","[Parallel(n_jobs=1)]: Done 4 out of 4 | elapsed: 0.7s remaining: 0.0s\n","[CV] n_neighbors=2, weights=uniform ..................................\n","[CV] ..... n_neighbors=2, weights=uniform, score=-0.407, total= 0.1s\n","[CV] n_neighbors=2, weights=uniform ..................................\n","[CV] ..... n_neighbors=2, weights=uniform, score=-0.094, total= 0.2s\n","[CV] n_neighbors=2, weights=uniform ..................................\n","[CV] ..... n_neighbors=2, weights=uniform, score=-0.211, total= 0.2s\n","[CV] n_neighbors=2, weights=uniform ..................................\n","[CV] ..... n_neighbors=2, weights=uniform, score=-0.261, total= 0.2s\n","[CV] n_neighbors=2, weights=uniform ..................................\n","[CV] ..... n_neighbors=2, weights=uniform, score=-0.282, total= 0.2s\n","[CV] n_neighbors=2, weights=uniform ..................................\n","[CV] ..... n_neighbors=2, weights=uniform, score=-0.222, total= 0.2s\n","[CV] n_neighbors=2, weights=uniform ..................................\n","[CV] ..... n_neighbors=2, weights=uniform, score=-0.172, total= 0.2s\n","[CV] n_neighbors=2, weights=uniform ..................................\n","[CV] ..... n_neighbors=2, weights=uniform, score=-0.379, total= 0.2s\n","[CV] n_neighbors=2, weights=uniform ..................................\n","[CV] ..... n_neighbors=2, weights=uniform, score=-0.349, total= 0.2s\n","[CV] n_neighbors=2, weights=uniform ..................................\n","[CV] ..... n_neighbors=2, weights=uniform, score=-0.384, total= 0.2s\n","[CV] n_neighbors=2, weights=uniform ..................................\n","[CV] ..... n_neighbors=2, weights=uniform, score=-0.160, total= 0.2s\n","[CV] n_neighbors=2, weights=uniform ..................................\n","[CV] ..... n_neighbors=2, weights=uniform, score=-0.560, total= 0.2s\n","[CV] n_neighbors=2, weights=uniform ..................................\n","[CV] ..... n_neighbors=2, weights=uniform, score=-0.038, total= 0.2s\n","[CV] n_neighbors=2, weights=uniform ..................................\n","[CV] ..... n_neighbors=2, weights=uniform, score=-0.078, total= 0.2s\n","[CV] n_neighbors=2, weights=uniform ..................................\n","[CV] ..... n_neighbors=2, weights=uniform, score=-0.279, total= 0.2s\n","[CV] n_neighbors=2, weights=uniform ..................................\n","[CV] ..... n_neighbors=2, weights=uniform, score=-0.279, total= 0.2s\n","[CV] n_neighbors=2, weights=uniform ..................................\n","[CV] ..... n_neighbors=2, weights=uniform, score=-0.304, total= 0.2s\n","[CV] n_neighbors=2, weights=uniform ..................................\n","[CV] ..... n_neighbors=2, weights=uniform, score=-0.245, total= 0.2s\n","[CV] n_neighbors=2, weights=uniform ..................................\n","[CV] ..... n_neighbors=2, weights=uniform, score=-0.257, total= 0.2s\n","[CV] n_neighbors=2, weights=uniform ..................................\n","[CV] ..... n_neighbors=2, weights=uniform, score=-0.181, total= 0.2s\n","[CV] n_neighbors=2, weights=uniform ..................................\n","[CV] ..... n_neighbors=2, weights=uniform, score=-0.249, total= 0.2s\n","[CV] n_neighbors=2, weights=uniform ..................................\n","[CV] ..... n_neighbors=2, weights=uniform, score=-0.400, total= 0.2s\n","[CV] n_neighbors=2, weights=uniform ..................................\n","[CV] ..... n_neighbors=2, weights=uniform, score=-0.049, total= 0.2s\n","[CV] n_neighbors=2, weights=uniform ..................................\n","[CV] ..... n_neighbors=2, weights=uniform, score=-0.169, total= 0.2s\n","[CV] n_neighbors=2, weights=uniform ..................................\n","[CV] ..... n_neighbors=2, weights=uniform, score=-0.351, total= 0.2s\n","[CV] n_neighbors=2, weights=uniform ..................................\n","[CV] ..... n_neighbors=2, weights=uniform, score=-0.366, total= 0.2s\n","[CV] n_neighbors=2, weights=distance .................................\n","[CV] .... n_neighbors=2, weights=distance, score=-0.126, total= 0.2s\n","[CV] n_neighbors=2, weights=distance .................................\n","[CV] .... n_neighbors=2, weights=distance, score=-0.394, total= 0.2s\n","[CV] n_neighbors=2, weights=distance .................................\n","[CV] .... n_neighbors=2, weights=distance, score=-0.275, total= 0.2s\n","[CV] n_neighbors=2, weights=distance .................................\n","[CV] .... n_neighbors=2, weights=distance, score=-0.309, total= 0.2s\n","[CV] n_neighbors=2, weights=distance .................................\n","[CV] .... n_neighbors=2, weights=distance, score=-0.397, total= 0.2s\n","[CV] n_neighbors=2, weights=distance .................................\n","[CV] .... n_neighbors=2, weights=distance, score=-0.100, total= 0.2s\n","[CV] n_neighbors=2, weights=distance .................................\n","[CV] .... n_neighbors=2, weights=distance, score=-0.220, total= 0.2s\n","[CV] n_neighbors=2, weights=distance .................................\n","[CV] .... n_neighbors=2, weights=distance, score=-0.261, total= 0.2s\n","[CV] n_neighbors=2, weights=distance .................................\n","[CV] .... n_neighbors=2, weights=distance, score=-0.272, total= 0.2s\n","[CV] n_neighbors=2, weights=distance .................................\n","[CV] .... n_neighbors=2, weights=distance, score=-0.217, total= 0.2s\n","[CV] n_neighbors=2, weights=distance .................................\n","[CV] .... n_neighbors=2, weights=distance, score=-0.173, total= 0.2s\n","[CV] n_neighbors=2, weights=distance .................................\n","[CV] .... n_neighbors=2, weights=distance, score=-0.373, total= 0.2s\n","[CV] n_neighbors=2, weights=distance .................................\n","[CV] .... n_neighbors=2, weights=distance, score=-0.339, total= 0.2s\n","[CV] n_neighbors=2, weights=distance .................................\n","[CV] .... n_neighbors=2, weights=distance, score=-0.386, total= 0.2s\n","[CV] n_neighbors=2, weights=distance .................................\n","[CV] .... n_neighbors=2, weights=distance, score=-0.160, total= 0.2s\n","[CV] n_neighbors=2, weights=distance .................................\n","[CV] .... n_neighbors=2, weights=distance, score=-0.562, total= 0.2s\n","[CV] n_neighbors=2, weights=distance .................................\n","[CV] .... n_neighbors=2, weights=distance, score=-0.031, total= 0.2s\n","[CV] n_neighbors=2, weights=distance .................................\n","[CV] .... n_neighbors=2, weights=distance, score=-0.067, total= 0.2s\n","[CV] n_neighbors=2, weights=distance .................................\n","[CV] .... n_neighbors=2, weights=distance, score=-0.283, total= 0.2s\n","[CV] n_neighbors=2, weights=distance .................................\n","[CV] .... n_neighbors=2, weights=distance, score=-0.264, total= 0.2s\n","[CV] n_neighbors=2, weights=distance .................................\n","[CV] .... n_neighbors=2, weights=distance, score=-0.301, total= 0.2s\n","[CV] n_neighbors=2, weights=distance .................................\n","[CV] .... n_neighbors=2, weights=distance, score=-0.246, total= 0.2s\n","[CV] n_neighbors=2, weights=distance .................................\n","[CV] .... n_neighbors=2, weights=distance, score=-0.256, total= 0.2s\n","[CV] n_neighbors=2, weights=distance .................................\n","[CV] .... n_neighbors=2, weights=distance, score=-0.185, total= 0.2s\n","[CV] n_neighbors=2, weights=distance .................................\n","[CV] .... n_neighbors=2, weights=distance, score=-0.243, total= 0.2s\n","[CV] n_neighbors=2, weights=distance .................................\n","[CV] .... n_neighbors=2, weights=distance, score=-0.403, total= 0.2s\n","[CV] n_neighbors=2, weights=distance .................................\n","[CV] .... n_neighbors=2, weights=distance, score=-0.046, total= 0.2s\n","[CV] n_neighbors=2, weights=distance .................................\n","[CV] .... n_neighbors=2, weights=distance, score=-0.165, total= 0.2s\n","[CV] n_neighbors=2, weights=distance .................................\n","[CV] .... n_neighbors=2, weights=distance, score=-0.350, total= 0.2s\n","[CV] n_neighbors=2, weights=distance .................................\n","[CV] .... n_neighbors=2, weights=distance, score=-0.349, total= 0.2s\n","[CV] n_neighbors=3, weights=uniform ..................................\n","[CV] ..... n_neighbors=3, weights=uniform, score=-0.091, total= 0.2s\n","[CV] n_neighbors=3, weights=uniform ..................................\n","[CV] ..... n_neighbors=3, weights=uniform, score=-0.114, total= 0.2s\n","[CV] n_neighbors=3, weights=uniform ..................................\n","[CV] ..... n_neighbors=3, weights=uniform, score=-0.128, total= 0.2s\n","[CV] n_neighbors=3, weights=uniform ..................................\n","[CV] ..... n_neighbors=3, weights=uniform, score=-0.126, total= 0.2s\n","[CV] n_neighbors=3, weights=uniform ..................................\n","[CV] ..... n_neighbors=3, weights=uniform, score=-0.267, total= 0.2s\n","[CV] n_neighbors=3, weights=uniform ..................................\n","[CV] ..... n_neighbors=3, weights=uniform, score=-0.025, total= 0.2s\n","[CV] n_neighbors=3, weights=uniform ..................................\n","[CV] ..... n_neighbors=3, weights=uniform, score=-0.137, total= 0.2s\n","[CV] n_neighbors=3, weights=uniform ..................................\n","[CV] ..... n_neighbors=3, weights=uniform, score=-0.186, total= 0.2s\n","[CV] n_neighbors=3, weights=uniform ..................................\n","[CV] ..... n_neighbors=3, weights=uniform, score=-0.198, total= 0.2s\n","[CV] n_neighbors=3, weights=uniform ..................................\n","[CV] ..... n_neighbors=3, weights=uniform, score=-0.068, total= 0.2s\n","[CV] n_neighbors=3, weights=uniform ..................................\n","[CV] ..... n_neighbors=3, weights=uniform, score=-0.070, total= 0.2s\n","[CV] n_neighbors=3, weights=uniform ..................................\n","[CV] ..... n_neighbors=3, weights=uniform, score=-0.168, total= 0.2s\n","[CV] n_neighbors=3, weights=uniform ..................................\n","[CV] ..... n_neighbors=3, weights=uniform, score=-0.244, total= 0.2s\n","[CV] n_neighbors=3, weights=uniform ..................................\n","[CV] ..... n_neighbors=3, weights=uniform, score=-0.328, total= 0.2s\n","[CV] n_neighbors=3, weights=uniform ..................................\n","[CV] ..... n_neighbors=3, weights=uniform, score=-0.053, total= 0.2s\n","[CV] n_neighbors=3, weights=uniform ..................................\n","[CV] ..... n_neighbors=3, weights=uniform, score=-0.287, total= 0.2s\n","[CV] n_neighbors=3, weights=uniform ..................................\n","[CV] ...... n_neighbors=3, weights=uniform, score=0.042, total= 0.2s\n","[CV] n_neighbors=3, weights=uniform ..................................\n","[CV] ..... n_neighbors=3, weights=uniform, score=-0.025, total= 0.2s\n","[CV] n_neighbors=3, weights=uniform ..................................\n","[CV] ..... n_neighbors=3, weights=uniform, score=-0.150, total= 0.2s\n","[CV] n_neighbors=3, weights=uniform ..................................\n","[CV] ..... n_neighbors=3, weights=uniform, score=-0.114, total= 0.2s\n","[CV] n_neighbors=3, weights=uniform ..................................\n","[CV] ..... n_neighbors=3, weights=uniform, score=-0.138, total= 0.2s\n","[CV] n_neighbors=3, weights=uniform ..................................\n","[CV] ..... n_neighbors=3, weights=uniform, score=-0.127, total= 0.2s\n","[CV] n_neighbors=3, weights=uniform ..................................\n","[CV] ..... n_neighbors=3, weights=uniform, score=-0.106, total= 0.2s\n","[CV] n_neighbors=3, weights=uniform ..................................\n","[CV] ..... n_neighbors=3, weights=uniform, score=-0.115, total= 0.2s\n","[CV] n_neighbors=3, weights=uniform ..................................\n","[CV] ..... n_neighbors=3, weights=uniform, score=-0.182, total= 0.2s\n","[CV] n_neighbors=3, weights=uniform ..................................\n","[CV] ..... n_neighbors=3, weights=uniform, score=-0.182, total= 0.2s\n","[CV] n_neighbors=3, weights=uniform ..................................\n","[CV] ..... n_neighbors=3, weights=uniform, score=-0.006, total= 0.2s\n","[CV] n_neighbors=3, weights=uniform ..................................\n","[CV] ..... n_neighbors=3, weights=uniform, score=-0.079, total= 0.2s\n","[CV] n_neighbors=3, weights=uniform ..................................\n","[CV] ..... n_neighbors=3, weights=uniform, score=-0.244, total= 0.2s\n","[CV] n_neighbors=3, weights=uniform ..................................\n","[CV] ..... n_neighbors=3, weights=uniform, score=-0.190, total= 0.2s\n","[CV] n_neighbors=3, weights=distance .................................\n","[CV] .... n_neighbors=3, weights=distance, score=-0.073, total= 0.2s\n","[CV] n_neighbors=3, weights=distance .................................\n","[CV] .... n_neighbors=3, weights=distance, score=-0.128, total= 0.2s\n","[CV] n_neighbors=3, weights=distance .................................\n","[CV] .... n_neighbors=3, weights=distance, score=-0.128, total= 0.2s\n","[CV] n_neighbors=3, weights=distance .................................\n","[CV] .... n_neighbors=3, weights=distance, score=-0.120, total= 0.2s\n","[CV] n_neighbors=3, weights=distance .................................\n","[CV] .... n_neighbors=3, weights=distance, score=-0.260, total= 0.2s\n","[CV] n_neighbors=3, weights=distance .................................\n","[CV] .... n_neighbors=3, weights=distance, score=-0.027, total= 0.2s\n","[CV] n_neighbors=3, weights=distance .................................\n","[CV] .... n_neighbors=3, weights=distance, score=-0.132, total= 0.2s\n","[CV] n_neighbors=3, weights=distance .................................\n","[CV] .... n_neighbors=3, weights=distance, score=-0.183, total= 0.2s\n","[CV] n_neighbors=3, weights=distance .................................\n","[CV] .... n_neighbors=3, weights=distance, score=-0.177, total= 0.2s\n","[CV] n_neighbors=3, weights=distance .................................\n","[CV] .... n_neighbors=3, weights=distance, score=-0.072, total= 0.2s\n","[CV] n_neighbors=3, weights=distance .................................\n","[CV] .... n_neighbors=3, weights=distance, score=-0.068, total= 0.2s\n","[CV] n_neighbors=3, weights=distance .................................\n","[CV] .... n_neighbors=3, weights=distance, score=-0.167, total= 0.2s\n","[CV] n_neighbors=3, weights=distance .................................\n","[CV] .... n_neighbors=3, weights=distance, score=-0.232, total= 0.2s\n","[CV] n_neighbors=3, weights=distance .................................\n","[CV] .... n_neighbors=3, weights=distance, score=-0.318, total= 0.2s\n","[CV] n_neighbors=3, weights=distance .................................\n","[CV] .... n_neighbors=3, weights=distance, score=-0.045, total= 0.2s\n","[CV] n_neighbors=3, weights=distance .................................\n","[CV] .... n_neighbors=3, weights=distance, score=-0.291, total= 0.2s\n","[CV] n_neighbors=3, weights=distance .................................\n","[CV] ..... n_neighbors=3, weights=distance, score=0.053, total= 0.2s\n","[CV] n_neighbors=3, weights=distance .................................\n","[CV] .... n_neighbors=3, weights=distance, score=-0.007, total= 0.2s\n","[CV] n_neighbors=3, weights=distance .................................\n","[CV] .... n_neighbors=3, weights=distance, score=-0.150, total= 0.2s\n","[CV] n_neighbors=3, weights=distance .................................\n","[CV] .... n_neighbors=3, weights=distance, score=-0.106, total= 0.2s\n","[CV] n_neighbors=3, weights=distance .................................\n","[CV] .... n_neighbors=3, weights=distance, score=-0.139, total= 0.2s\n","[CV] n_neighbors=3, weights=distance .................................\n","[CV] .... n_neighbors=3, weights=distance, score=-0.122, total= 0.2s\n","[CV] n_neighbors=3, weights=distance .................................\n","[CV] .... n_neighbors=3, weights=distance, score=-0.104, total= 0.2s\n","[CV] n_neighbors=3, weights=distance .................................\n","[CV] .... n_neighbors=3, weights=distance, score=-0.106, total= 0.2s\n","[CV] n_neighbors=3, weights=distance .................................\n","[CV] .... n_neighbors=3, weights=distance, score=-0.163, total= 0.2s\n","[CV] n_neighbors=3, weights=distance .................................\n","[CV] .... n_neighbors=3, weights=distance, score=-0.190, total= 0.2s\n","[CV] n_neighbors=3, weights=distance .................................\n","[CV] .... n_neighbors=3, weights=distance, score=-0.000, total= 0.2s\n","[CV] n_neighbors=3, weights=distance .................................\n","[CV] .... n_neighbors=3, weights=distance, score=-0.078, total= 0.2s\n","[CV] n_neighbors=3, weights=distance .................................\n","[CV] .... n_neighbors=3, weights=distance, score=-0.237, total= 0.2s\n","[CV] n_neighbors=3, weights=distance .................................\n","[CV] .... n_neighbors=3, weights=distance, score=-0.180, total= 0.2s\n","[CV] n_neighbors=4, weights=uniform ..................................\n","[CV] ..... n_neighbors=4, weights=uniform, score=-0.056, total= 0.2s\n","[CV] n_neighbors=4, weights=uniform ..................................\n","[CV] ..... n_neighbors=4, weights=uniform, score=-0.125, total= 0.2s\n","[CV] n_neighbors=4, weights=uniform ..................................\n","[CV] ..... n_neighbors=4, weights=uniform, score=-0.093, total= 0.2s\n","[CV] n_neighbors=4, weights=uniform ..................................\n","[CV] ..... n_neighbors=4, weights=uniform, score=-0.107, total= 0.2s\n","[CV] n_neighbors=4, weights=uniform ..................................\n","[CV] ..... n_neighbors=4, weights=uniform, score=-0.208, total= 0.2s\n","[CV] n_neighbors=4, weights=uniform ..................................\n","[CV] ...... n_neighbors=4, weights=uniform, score=0.013, total= 0.2s\n","[CV] n_neighbors=4, weights=uniform ..................................\n","[CV] ..... n_neighbors=4, weights=uniform, score=-0.024, total= 0.2s\n","[CV] n_neighbors=4, weights=uniform ..................................\n","[CV] ..... n_neighbors=4, weights=uniform, score=-0.093, total= 0.2s\n","[CV] n_neighbors=4, weights=uniform ..................................\n","[CV] ..... n_neighbors=4, weights=uniform, score=-0.130, total= 0.2s\n","[CV] n_neighbors=4, weights=uniform ..................................\n","[CV] ...... n_neighbors=4, weights=uniform, score=0.004, total= 0.2s\n","[CV] n_neighbors=4, weights=uniform ..................................\n","[CV] ..... n_neighbors=4, weights=uniform, score=-0.086, total= 0.2s\n","[CV] n_neighbors=4, weights=uniform ..................................\n","[CV] ..... n_neighbors=4, weights=uniform, score=-0.154, total= 0.2s\n","[CV] n_neighbors=4, weights=uniform ..................................\n","[CV] ..... n_neighbors=4, weights=uniform, score=-0.186, total= 0.2s\n","[CV] n_neighbors=4, weights=uniform ..................................\n","[CV] ..... n_neighbors=4, weights=uniform, score=-0.186, total= 0.2s\n","[CV] n_neighbors=4, weights=uniform ..................................\n","[CV] ...... n_neighbors=4, weights=uniform, score=0.002, total= 0.2s\n","[CV] n_neighbors=4, weights=uniform ..................................\n","[CV] ..... n_neighbors=4, weights=uniform, score=-0.177, total= 0.2s\n","[CV] n_neighbors=4, weights=uniform ..................................\n","[CV] ...... n_neighbors=4, weights=uniform, score=0.023, total= 0.2s\n","[CV] n_neighbors=4, weights=uniform ..................................\n","[CV] ..... n_neighbors=4, weights=uniform, score=-0.018, total= 0.2s\n","[CV] n_neighbors=4, weights=uniform ..................................\n","[CV] ..... n_neighbors=4, weights=uniform, score=-0.019, total= 0.2s\n","[CV] n_neighbors=4, weights=uniform ..................................\n","[CV] ..... n_neighbors=4, weights=uniform, score=-0.023, total= 0.2s\n","[CV] n_neighbors=4, weights=uniform ..................................\n","[CV] ..... n_neighbors=4, weights=uniform, score=-0.092, total= 0.2s\n","[CV] n_neighbors=4, weights=uniform ..................................\n","[CV] ..... n_neighbors=4, weights=uniform, score=-0.074, total= 0.2s\n","[CV] n_neighbors=4, weights=uniform ..................................\n","[CV] ..... n_neighbors=4, weights=uniform, score=-0.061, total= 0.2s\n","[CV] n_neighbors=4, weights=uniform ..................................\n","[CV] ..... n_neighbors=4, weights=uniform, score=-0.072, total= 0.2s\n","[CV] n_neighbors=4, weights=uniform ..................................\n","[CV] ..... n_neighbors=4, weights=uniform, score=-0.116, total= 0.2s\n","[CV] n_neighbors=4, weights=uniform ..................................\n","[CV] ..... n_neighbors=4, weights=uniform, score=-0.133, total= 0.2s\n","[CV] n_neighbors=4, weights=uniform ..................................\n","[CV] ...... n_neighbors=4, weights=uniform, score=0.011, total= 0.2s\n","[CV] n_neighbors=4, weights=uniform ..................................\n","[CV] ..... n_neighbors=4, weights=uniform, score=-0.050, total= 0.2s\n","[CV] n_neighbors=4, weights=uniform ..................................\n","[CV] ..... n_neighbors=4, weights=uniform, score=-0.168, total= 0.2s\n","[CV] n_neighbors=4, weights=uniform ..................................\n","[CV] ..... n_neighbors=4, weights=uniform, score=-0.052, total= 0.2s\n","[CV] n_neighbors=4, weights=distance .................................\n","[CV] .... n_neighbors=4, weights=distance, score=-0.036, total= 0.2s\n","[CV] n_neighbors=4, weights=distance .................................\n","[CV] .... n_neighbors=4, weights=distance, score=-0.126, total= 0.2s\n","[CV] n_neighbors=4, weights=distance .................................\n","[CV] .... n_neighbors=4, weights=distance, score=-0.093, total= 0.2s\n","[CV] n_neighbors=4, weights=distance .................................\n","[CV] .... n_neighbors=4, weights=distance, score=-0.092, total= 0.2s\n","[CV] n_neighbors=4, weights=distance .................................\n","[CV] .... n_neighbors=4, weights=distance, score=-0.205, total= 0.2s\n","[CV] n_neighbors=4, weights=distance .................................\n","[CV] ..... n_neighbors=4, weights=distance, score=0.010, total= 0.2s\n","[CV] n_neighbors=4, weights=distance .................................\n","[CV] .... n_neighbors=4, weights=distance, score=-0.024, total= 0.2s\n","[CV] n_neighbors=4, weights=distance .................................\n","[CV] .... n_neighbors=4, weights=distance, score=-0.096, total= 0.2s\n","[CV] n_neighbors=4, weights=distance .................................\n","[CV] .... n_neighbors=4, weights=distance, score=-0.112, total= 0.2s\n","[CV] n_neighbors=4, weights=distance .................................\n","[CV] ..... n_neighbors=4, weights=distance, score=0.001, total= 0.2s\n","[CV] n_neighbors=4, weights=distance .................................\n","[CV] .... n_neighbors=4, weights=distance, score=-0.076, total= 0.2s\n","[CV] n_neighbors=4, weights=distance .................................\n","[CV] .... n_neighbors=4, weights=distance, score=-0.150, total= 0.2s\n","[CV] n_neighbors=4, weights=distance .................................\n","[CV] .... n_neighbors=4, weights=distance, score=-0.171, total= 0.2s\n","[CV] n_neighbors=4, weights=distance .................................\n","[CV] .... n_neighbors=4, weights=distance, score=-0.184, total= 0.2s\n","[CV] n_neighbors=4, weights=distance .................................\n","[CV] ..... n_neighbors=4, weights=distance, score=0.013, total= 0.2s\n","[CV] n_neighbors=4, weights=distance .................................\n","[CV] .... n_neighbors=4, weights=distance, score=-0.182, total= 0.2s\n","[CV] n_neighbors=4, weights=distance .................................\n","[CV] ..... n_neighbors=4, weights=distance, score=0.037, total= 0.2s\n","[CV] n_neighbors=4, weights=distance .................................\n","[CV] .... n_neighbors=4, weights=distance, score=-0.005, total= 0.2s\n","[CV] n_neighbors=4, weights=distance .................................\n","[CV] .... n_neighbors=4, weights=distance, score=-0.020, total= 0.2s\n","[CV] n_neighbors=4, weights=distance .................................\n","[CV] .... n_neighbors=4, weights=distance, score=-0.018, total= 0.2s\n","[CV] n_neighbors=4, weights=distance .................................\n","[CV] .... n_neighbors=4, weights=distance, score=-0.092, total= 0.2s\n","[CV] n_neighbors=4, weights=distance .................................\n","[CV] .... n_neighbors=4, weights=distance, score=-0.068, total= 0.2s\n","[CV] n_neighbors=4, weights=distance .................................\n","[CV] .... n_neighbors=4, weights=distance, score=-0.055, total= 0.2s\n","[CV] n_neighbors=4, weights=distance .................................\n","[CV] .... n_neighbors=4, weights=distance, score=-0.064, total= 0.2s\n","[CV] n_neighbors=4, weights=distance .................................\n","[CV] .... n_neighbors=4, weights=distance, score=-0.105, total= 0.2s\n","[CV] n_neighbors=4, weights=distance .................................\n","[CV] .... n_neighbors=4, weights=distance, score=-0.135, total= 0.2s\n","[CV] n_neighbors=4, weights=distance .................................\n","[CV] ..... n_neighbors=4, weights=distance, score=0.020, total= 0.2s\n","[CV] n_neighbors=4, weights=distance .................................\n","[CV] .... n_neighbors=4, weights=distance, score=-0.050, total= 0.2s\n","[CV] n_neighbors=4, weights=distance .................................\n","[CV] .... n_neighbors=4, weights=distance, score=-0.163, total= 0.2s\n","[CV] n_neighbors=4, weights=distance .................................\n","[CV] .... n_neighbors=4, weights=distance, score=-0.047, total= 0.2s\n","[CV] n_neighbors=5, weights=uniform ..................................\n","[CV] ...... n_neighbors=5, weights=uniform, score=0.014, total= 0.2s\n","[CV] n_neighbors=5, weights=uniform ..................................\n","[CV] ..... n_neighbors=5, weights=uniform, score=-0.077, total= 0.2s\n","[CV] n_neighbors=5, weights=uniform ..................................\n","[CV] ..... n_neighbors=5, weights=uniform, score=-0.052, total= 0.2s\n","[CV] n_neighbors=5, weights=uniform ..................................\n","[CV] ..... n_neighbors=5, weights=uniform, score=-0.067, total= 0.2s\n","[CV] n_neighbors=5, weights=uniform ..................................\n","[CV] ..... n_neighbors=5, weights=uniform, score=-0.176, total= 0.2s\n","[CV] n_neighbors=5, weights=uniform ..................................\n","[CV] ...... n_neighbors=5, weights=uniform, score=0.047, total= 0.2s\n","[CV] n_neighbors=5, weights=uniform ..................................\n","[CV] ..... n_neighbors=5, weights=uniform, score=-0.009, total= 0.2s\n","[CV] n_neighbors=5, weights=uniform ..................................\n","[CV] ..... n_neighbors=5, weights=uniform, score=-0.038, total= 0.2s\n","[CV] n_neighbors=5, weights=uniform ..................................\n","[CV] ..... n_neighbors=5, weights=uniform, score=-0.137, total= 0.2s\n","[CV] n_neighbors=5, weights=uniform ..................................\n","[CV] ..... n_neighbors=5, weights=uniform, score=-0.007, total= 0.2s\n","[CV] n_neighbors=5, weights=uniform ..................................\n","[CV] ..... n_neighbors=5, weights=uniform, score=-0.053, total= 0.2s\n","[CV] n_neighbors=5, weights=uniform ..................................\n","[CV] ..... n_neighbors=5, weights=uniform, score=-0.146, total= 0.2s\n","[CV] n_neighbors=5, weights=uniform ..................................\n","[CV] ..... n_neighbors=5, weights=uniform, score=-0.127, total= 0.2s\n","[CV] n_neighbors=5, weights=uniform ..................................\n","[CV] ..... n_neighbors=5, weights=uniform, score=-0.126, total= 0.2s\n","[CV] n_neighbors=5, weights=uniform ..................................\n","[CV] ...... n_neighbors=5, weights=uniform, score=0.033, total= 0.2s\n","[CV] n_neighbors=5, weights=uniform ..................................\n","[CV] ..... n_neighbors=5, weights=uniform, score=-0.181, total= 0.2s\n","[CV] n_neighbors=5, weights=uniform ..................................\n","[CV] ...... n_neighbors=5, weights=uniform, score=0.048, total= 0.2s\n","[CV] n_neighbors=5, weights=uniform ..................................\n","[CV] ...... n_neighbors=5, weights=uniform, score=0.019, total= 0.2s\n","[CV] n_neighbors=5, weights=uniform ..................................\n","[CV] ..... n_neighbors=5, weights=uniform, score=-0.033, total= 0.2s\n","[CV] n_neighbors=5, weights=uniform ..................................\n","[CV] ...... n_neighbors=5, weights=uniform, score=0.019, total= 0.2s\n","[CV] n_neighbors=5, weights=uniform ..................................\n","[CV] ..... n_neighbors=5, weights=uniform, score=-0.069, total= 0.2s\n","[CV] n_neighbors=5, weights=uniform ..................................\n","[CV] ..... n_neighbors=5, weights=uniform, score=-0.064, total= 0.2s\n","[CV] n_neighbors=5, weights=uniform ..................................\n","[CV] ...... n_neighbors=5, weights=uniform, score=0.008, total= 0.2s\n","[CV] n_neighbors=5, weights=uniform ..................................\n","[CV] ..... n_neighbors=5, weights=uniform, score=-0.056, total= 0.2s\n","[CV] n_neighbors=5, weights=uniform ..................................\n","[CV] ..... n_neighbors=5, weights=uniform, score=-0.057, total= 0.2s\n","[CV] n_neighbors=5, weights=uniform ..................................\n","[CV] ..... n_neighbors=5, weights=uniform, score=-0.042, total= 0.2s\n","[CV] n_neighbors=5, weights=uniform ..................................\n","[CV] ...... n_neighbors=5, weights=uniform, score=0.020, total= 0.2s\n","[CV] n_neighbors=5, weights=uniform ..................................\n","[CV] ..... n_neighbors=5, weights=uniform, score=-0.039, total= 0.2s\n","[CV] n_neighbors=5, weights=uniform ..................................\n","[CV] ..... n_neighbors=5, weights=uniform, score=-0.115, total= 0.2s\n","[CV] n_neighbors=5, weights=uniform ..................................\n","[CV] ..... n_neighbors=5, weights=uniform, score=-0.050, total= 0.2s\n","[CV] n_neighbors=5, weights=distance .................................\n","[CV] ..... n_neighbors=5, weights=distance, score=0.028, total= 0.2s\n","[CV] n_neighbors=5, weights=distance .................................\n","[CV] .... n_neighbors=5, weights=distance, score=-0.076, total= 0.2s\n","[CV] n_neighbors=5, weights=distance .................................\n","[CV] .... n_neighbors=5, weights=distance, score=-0.051, total= 0.2s\n","[CV] n_neighbors=5, weights=distance .................................\n","[CV] .... n_neighbors=5, weights=distance, score=-0.053, total= 0.2s\n","[CV] n_neighbors=5, weights=distance .................................\n","[CV] .... n_neighbors=5, weights=distance, score=-0.174, total= 0.2s\n","[CV] n_neighbors=5, weights=distance .................................\n","[CV] ..... n_neighbors=5, weights=distance, score=0.043, total= 0.2s\n","[CV] n_neighbors=5, weights=distance .................................\n","[CV] .... n_neighbors=5, weights=distance, score=-0.003, total= 0.2s\n","[CV] n_neighbors=5, weights=distance .................................\n","[CV] .... n_neighbors=5, weights=distance, score=-0.042, total= 0.2s\n","[CV] n_neighbors=5, weights=distance .................................\n","[CV] .... n_neighbors=5, weights=distance, score=-0.116, total= 0.2s\n","[CV] n_neighbors=5, weights=distance .................................\n","[CV] .... n_neighbors=5, weights=distance, score=-0.007, total= 0.2s\n","[CV] n_neighbors=5, weights=distance .................................\n","[CV] .... n_neighbors=5, weights=distance, score=-0.045, total= 0.2s\n","[CV] n_neighbors=5, weights=distance .................................\n","[CV] .... n_neighbors=5, weights=distance, score=-0.140, total= 0.2s\n","[CV] n_neighbors=5, weights=distance .................................\n","[CV] .... n_neighbors=5, weights=distance, score=-0.116, total= 0.2s\n","[CV] n_neighbors=5, weights=distance .................................\n","[CV] .... n_neighbors=5, weights=distance, score=-0.129, total= 0.2s\n","[CV] n_neighbors=5, weights=distance .................................\n","[CV] ..... n_neighbors=5, weights=distance, score=0.045, total= 0.2s\n","[CV] n_neighbors=5, weights=distance .................................\n","[CV] .... n_neighbors=5, weights=distance, score=-0.179, total= 0.2s\n","[CV] n_neighbors=5, weights=distance .................................\n","[CV] ..... n_neighbors=5, weights=distance, score=0.059, total= 0.2s\n","[CV] n_neighbors=5, weights=distance .................................\n","[CV] ..... n_neighbors=5, weights=distance, score=0.033, total= 0.2s\n","[CV] n_neighbors=5, weights=distance .................................\n","[CV] .... n_neighbors=5, weights=distance, score=-0.026, total= 0.2s\n","[CV] n_neighbors=5, weights=distance .................................\n","[CV] ..... n_neighbors=5, weights=distance, score=0.023, total= 0.2s\n","[CV] n_neighbors=5, weights=distance .................................\n","[CV] .... n_neighbors=5, weights=distance, score=-0.066, total= 0.2s\n","[CV] n_neighbors=5, weights=distance .................................\n","[CV] .... n_neighbors=5, weights=distance, score=-0.056, total= 0.2s\n","[CV] n_neighbors=5, weights=distance .................................\n","[CV] ..... n_neighbors=5, weights=distance, score=0.008, total= 0.2s\n","[CV] n_neighbors=5, weights=distance .................................\n","[CV] .... n_neighbors=5, weights=distance, score=-0.051, total= 0.2s\n","[CV] n_neighbors=5, weights=distance .................................\n","[CV] .... n_neighbors=5, weights=distance, score=-0.050, total= 0.2s\n","[CV] n_neighbors=5, weights=distance .................................\n","[CV] .... n_neighbors=5, weights=distance, score=-0.051, total= 0.2s\n","[CV] n_neighbors=5, weights=distance .................................\n","[CV] ..... n_neighbors=5, weights=distance, score=0.028, total= 0.2s\n","[CV] n_neighbors=5, weights=distance .................................\n","[CV] .... n_neighbors=5, weights=distance, score=-0.037, total= 0.2s\n","[CV] n_neighbors=5, weights=distance .................................\n","[CV] .... n_neighbors=5, weights=distance, score=-0.106, total= 0.2s\n","[CV] n_neighbors=5, weights=distance .................................\n","[CV] .... n_neighbors=5, weights=distance, score=-0.044, total= 0.2s\n","[CV] n_neighbors=6, weights=uniform ..................................\n","[CV] ...... n_neighbors=6, weights=uniform, score=0.007, total= 0.2s\n","[CV] n_neighbors=6, weights=uniform ..................................\n","[CV] ..... n_neighbors=6, weights=uniform, score=-0.047, total= 0.2s\n","[CV] n_neighbors=6, weights=uniform ..................................\n","[CV] ...... n_neighbors=6, weights=uniform, score=0.011, total= 0.2s\n","[CV] n_neighbors=6, weights=uniform ..................................\n","[CV] ..... n_neighbors=6, weights=uniform, score=-0.061, total= 0.2s\n","[CV] n_neighbors=6, weights=uniform ..................................\n","[CV] ..... n_neighbors=6, weights=uniform, score=-0.144, total= 0.2s\n","[CV] n_neighbors=6, weights=uniform ..................................\n","[CV] ...... n_neighbors=6, weights=uniform, score=0.086, total= 0.2s\n","[CV] n_neighbors=6, weights=uniform ..................................\n","[CV] ..... n_neighbors=6, weights=uniform, score=-0.030, total= 0.2s\n","[CV] n_neighbors=6, weights=uniform ..................................\n","[CV] ..... n_neighbors=6, weights=uniform, score=-0.008, total= 0.2s\n","[CV] n_neighbors=6, weights=uniform ..................................\n","[CV] ..... n_neighbors=6, weights=uniform, score=-0.084, total= 0.2s\n","[CV] n_neighbors=6, weights=uniform ..................................\n","[CV] ...... n_neighbors=6, weights=uniform, score=0.002, total= 0.2s\n","[CV] n_neighbors=6, weights=uniform ..................................\n","[CV] ..... n_neighbors=6, weights=uniform, score=-0.024, total= 0.2s\n","[CV] n_neighbors=6, weights=uniform ..................................\n","[CV] ..... n_neighbors=6, weights=uniform, score=-0.070, total= 0.2s\n","[CV] n_neighbors=6, weights=uniform ..................................\n","[CV] ..... n_neighbors=6, weights=uniform, score=-0.080, total= 0.2s\n","[CV] n_neighbors=6, weights=uniform ..................................\n","[CV] ..... n_neighbors=6, weights=uniform, score=-0.064, total= 0.2s\n","[CV] n_neighbors=6, weights=uniform ..................................\n","[CV] ...... n_neighbors=6, weights=uniform, score=0.025, total= 0.2s\n","[CV] n_neighbors=6, weights=uniform ..................................\n","[CV] ..... n_neighbors=6, weights=uniform, score=-0.173, total= 0.2s\n","[CV] n_neighbors=6, weights=uniform ..................................\n","[CV] ...... n_neighbors=6, weights=uniform, score=0.050, total= 0.2s\n","[CV] n_neighbors=6, weights=uniform ..................................\n","[CV] ...... n_neighbors=6, weights=uniform, score=0.031, total= 0.2s\n","[CV] n_neighbors=6, weights=uniform ..................................\n","[CV] ...... n_neighbors=6, weights=uniform, score=0.001, total= 0.2s\n","[CV] n_neighbors=6, weights=uniform ..................................\n","[CV] ...... n_neighbors=6, weights=uniform, score=0.033, total= 0.2s\n","[CV] n_neighbors=6, weights=uniform ..................................\n","[CV] ..... n_neighbors=6, weights=uniform, score=-0.007, total= 0.2s\n","[CV] n_neighbors=6, weights=uniform ..................................\n","[CV] ..... n_neighbors=6, weights=uniform, score=-0.069, total= 0.2s\n","[CV] n_neighbors=6, weights=uniform ..................................\n","[CV] ...... n_neighbors=6, weights=uniform, score=0.015, total= 0.2s\n","[CV] n_neighbors=6, weights=uniform ..................................\n","[CV] ..... n_neighbors=6, weights=uniform, score=-0.006, total= 0.2s\n","[CV] n_neighbors=6, weights=uniform ..................................\n","[CV] ..... n_neighbors=6, weights=uniform, score=-0.036, total= 0.2s\n","[CV] n_neighbors=6, weights=uniform ..................................\n","[CV] ...... n_neighbors=6, weights=uniform, score=0.017, total= 0.2s\n","[CV] n_neighbors=6, weights=uniform ..................................\n","[CV] ...... n_neighbors=6, weights=uniform, score=0.045, total= 0.2s\n","[CV] n_neighbors=6, weights=uniform ..................................\n","[CV] ..... n_neighbors=6, weights=uniform, score=-0.024, total= 0.2s\n","[CV] n_neighbors=6, weights=uniform ..................................\n","[CV] ..... n_neighbors=6, weights=uniform, score=-0.063, total= 0.2s\n","[CV] n_neighbors=6, weights=uniform ..................................\n","[CV] ..... n_neighbors=6, weights=uniform, score=-0.070, total= 0.2s\n","[CV] n_neighbors=6, weights=distance .................................\n","[CV] ..... n_neighbors=6, weights=distance, score=0.024, total= 0.2s\n","[CV] n_neighbors=6, weights=distance .................................\n","[CV] .... n_neighbors=6, weights=distance, score=-0.044, total= 0.2s\n","[CV] n_neighbors=6, weights=distance .................................\n","[CV] ..... n_neighbors=6, weights=distance, score=0.010, total= 0.2s\n","[CV] n_neighbors=6, weights=distance .................................\n","[CV] .... n_neighbors=6, weights=distance, score=-0.043, total= 0.2s\n","[CV] n_neighbors=6, weights=distance .................................\n","[CV] .... n_neighbors=6, weights=distance, score=-0.142, total= 0.2s\n","[CV] n_neighbors=6, weights=distance .................................\n","[CV] ..... n_neighbors=6, weights=distance, score=0.084, total= 0.2s\n","[CV] n_neighbors=6, weights=distance .................................\n","[CV] .... n_neighbors=6, weights=distance, score=-0.023, total= 0.2s\n","[CV] n_neighbors=6, weights=distance .................................\n","[CV] .... n_neighbors=6, weights=distance, score=-0.011, total= 0.2s\n","[CV] n_neighbors=6, weights=distance .................................\n","[CV] .... n_neighbors=6, weights=distance, score=-0.066, total= 0.2s\n","[CV] n_neighbors=6, weights=distance .................................\n","[CV] ..... n_neighbors=6, weights=distance, score=0.003, total= 0.2s\n","[CV] n_neighbors=6, weights=distance .................................\n","[CV] .... n_neighbors=6, weights=distance, score=-0.017, total= 0.2s\n","[CV] n_neighbors=6, weights=distance .................................\n","[CV] .... n_neighbors=6, weights=distance, score=-0.071, total= 0.2s\n","[CV] n_neighbors=6, weights=distance .................................\n","[CV] .... n_neighbors=6, weights=distance, score=-0.074, total= 0.2s\n","[CV] n_neighbors=6, weights=distance .................................\n","[CV] .... n_neighbors=6, weights=distance, score=-0.072, total= 0.2s\n","[CV] n_neighbors=6, weights=distance .................................\n","[CV] ..... n_neighbors=6, weights=distance, score=0.040, total= 0.2s\n","[CV] n_neighbors=6, weights=distance .................................\n","[CV] .... n_neighbors=6, weights=distance, score=-0.168, total= 0.2s\n","[CV] n_neighbors=6, weights=distance .................................\n","[CV] ..... n_neighbors=6, weights=distance, score=0.063, total= 0.2s\n","[CV] n_neighbors=6, weights=distance .................................\n","[CV] ..... n_neighbors=6, weights=distance, score=0.044, total= 0.2s\n","[CV] n_neighbors=6, weights=distance .................................\n","[CV] ..... n_neighbors=6, weights=distance, score=0.005, total= 0.2s\n","[CV] n_neighbors=6, weights=distance .................................\n","[CV] ..... n_neighbors=6, weights=distance, score=0.039, total= 0.2s\n","[CV] n_neighbors=6, weights=distance .................................\n","[CV] .... n_neighbors=6, weights=distance, score=-0.010, total= 0.2s\n","[CV] n_neighbors=6, weights=distance .................................\n","[CV] .... n_neighbors=6, weights=distance, score=-0.058, total= 0.2s\n","[CV] n_neighbors=6, weights=distance .................................\n","[CV] ..... n_neighbors=6, weights=distance, score=0.016, total= 0.2s\n","[CV] n_neighbors=6, weights=distance .................................\n","[CV] .... n_neighbors=6, weights=distance, score=-0.004, total= 0.2s\n","[CV] n_neighbors=6, weights=distance .................................\n","[CV] .... n_neighbors=6, weights=distance, score=-0.028, total= 0.2s\n","[CV] n_neighbors=6, weights=distance .................................\n","[CV] ..... n_neighbors=6, weights=distance, score=0.006, total= 0.2s\n","[CV] n_neighbors=6, weights=distance .................................\n","[CV] ..... n_neighbors=6, weights=distance, score=0.049, total= 0.2s\n","[CV] n_neighbors=6, weights=distance .................................\n","[CV] .... n_neighbors=6, weights=distance, score=-0.019, total= 0.2s\n","[CV] n_neighbors=6, weights=distance .................................\n","[CV] .... n_neighbors=6, weights=distance, score=-0.053, total= 0.2s\n","[CV] n_neighbors=6, weights=distance .................................\n","[CV] .... n_neighbors=6, weights=distance, score=-0.059, total= 0.2s\n","[Parallel(n_jobs=1)]: Done 300 out of 300 | elapsed: 58.0s finished\n","KNeighborsRegressor(algorithm='auto', leaf_size=30, metric='minkowski',\n"," metric_params=None, n_jobs=None, n_neighbors=6, p=2,\n"," weights='distance')\n","window: 1, stide: 1, rmse: 2.7267311139249917\n","(3000, 224)\n","Number of Feature-Columns: 224\n","\n","Features Ready for undergoing selection tests done ...\n","\n","Fitting 30 folds for each of 10 candidates, totalling 300 fits\n","[Parallel(n_jobs=1)]: Using backend SequentialBackend with 1 concurrent workers.\n","[CV] n_neighbors=2, weights=uniform ..................................\n","[CV] ..... n_neighbors=2, weights=uniform, score=-0.137, total= 0.2s\n","[Parallel(n_jobs=1)]: Done 1 out of 1 | elapsed: 0.2s remaining: 0.0s\n","[CV] n_neighbors=2, weights=uniform ..................................\n","[CV] ..... n_neighbors=2, weights=uniform, score=-0.393, total= 0.2s\n","[Parallel(n_jobs=1)]: Done 2 out of 2 | elapsed: 0.4s remaining: 0.0s\n","[CV] n_neighbors=2, weights=uniform ..................................\n","[CV] ..... n_neighbors=2, weights=uniform, score=-0.270, total= 0.2s\n","[Parallel(n_jobs=1)]: Done 3 out of 3 | elapsed: 0.6s remaining: 0.0s\n","[CV] n_neighbors=2, weights=uniform ..................................\n","[CV] ..... n_neighbors=2, weights=uniform, score=-0.324, total= 0.2s\n","[Parallel(n_jobs=1)]: Done 4 out of 4 | elapsed: 0.8s remaining: 0.0s\n","[CV] n_neighbors=2, weights=uniform ..................................\n","[CV] ..... n_neighbors=2, weights=uniform, score=-0.407, total= 0.2s\n","[CV] n_neighbors=2, weights=uniform ..................................\n","[CV] ..... n_neighbors=2, weights=uniform, score=-0.126, total= 0.2s\n","[CV] n_neighbors=2, weights=uniform ..................................\n","[CV] ..... n_neighbors=2, weights=uniform, score=-0.212, total= 0.2s\n","[CV] n_neighbors=2, weights=uniform ..................................\n","[CV] ..... n_neighbors=2, weights=uniform, score=-0.270, total= 0.2s\n","[CV] n_neighbors=2, weights=uniform ..................................\n","[CV] ..... n_neighbors=2, weights=uniform, score=-0.283, total= 0.2s\n","[CV] n_neighbors=2, weights=uniform ..................................\n","[CV] ..... n_neighbors=2, weights=uniform, score=-0.225, total= 0.2s\n","[CV] n_neighbors=2, weights=uniform ..................................\n","[CV] ..... n_neighbors=2, weights=uniform, score=-0.180, total= 0.2s\n","[CV] n_neighbors=2, weights=uniform ..................................\n","[CV] ..... n_neighbors=2, weights=uniform, score=-0.382, total= 0.2s\n","[CV] n_neighbors=2, weights=uniform ..................................\n","[CV] ..... n_neighbors=2, weights=uniform, score=-0.353, total= 0.2s\n","[CV] n_neighbors=2, weights=uniform ..................................\n","[CV] ..... n_neighbors=2, weights=uniform, score=-0.391, total= 0.2s\n","[CV] n_neighbors=2, weights=uniform ..................................\n","[CV] ..... n_neighbors=2, weights=uniform, score=-0.161, total= 0.2s\n","[CV] n_neighbors=2, weights=uniform ..................................\n","[CV] ..... n_neighbors=2, weights=uniform, score=-0.559, total= 0.2s\n","[CV] n_neighbors=2, weights=uniform ..................................\n","[CV] ..... n_neighbors=2, weights=uniform, score=-0.038, total= 0.2s\n","[CV] n_neighbors=2, weights=uniform ..................................\n","[CV] ..... n_neighbors=2, weights=uniform, score=-0.079, total= 0.2s\n","[CV] n_neighbors=2, weights=uniform ..................................\n","[CV] ..... n_neighbors=2, weights=uniform, score=-0.279, total= 0.2s\n","[CV] n_neighbors=2, weights=uniform ..................................\n","[CV] ..... n_neighbors=2, weights=uniform, score=-0.323, total= 0.2s\n","[CV] n_neighbors=2, weights=uniform ..................................\n","[CV] ..... n_neighbors=2, weights=uniform, score=-0.305, total= 0.2s\n","[CV] n_neighbors=2, weights=uniform ..................................\n","[CV] ..... n_neighbors=2, weights=uniform, score=-0.233, total= 0.2s\n","[CV] n_neighbors=2, weights=uniform ..................................\n","[CV] ..... n_neighbors=2, weights=uniform, score=-0.257, total= 0.2s\n","[CV] n_neighbors=2, weights=uniform ..................................\n","[CV] ..... n_neighbors=2, weights=uniform, score=-0.220, total= 0.2s\n","[CV] n_neighbors=2, weights=uniform ..................................\n","[CV] ..... n_neighbors=2, weights=uniform, score=-0.249, total= 0.2s\n","[CV] n_neighbors=2, weights=uniform ..................................\n","[CV] ..... n_neighbors=2, weights=uniform, score=-0.401, total= 0.2s\n","[CV] n_neighbors=2, weights=uniform ..................................\n","[CV] ..... n_neighbors=2, weights=uniform, score=-0.044, total= 0.2s\n","[CV] n_neighbors=2, weights=uniform ..................................\n","[CV] ..... n_neighbors=2, weights=uniform, score=-0.170, total= 0.2s\n","[CV] n_neighbors=2, weights=uniform ..................................\n","[CV] ..... n_neighbors=2, weights=uniform, score=-0.351, total= 0.2s\n","[CV] n_neighbors=2, weights=uniform ..................................\n","[CV] ..... n_neighbors=2, weights=uniform, score=-0.366, total= 0.2s\n","[CV] n_neighbors=2, weights=distance .................................\n","[CV] .... n_neighbors=2, weights=distance, score=-0.127, total= 0.2s\n","[CV] n_neighbors=2, weights=distance .................................\n","[CV] .... n_neighbors=2, weights=distance, score=-0.394, total= 0.2s\n","[CV] n_neighbors=2, weights=distance .................................\n","[CV] .... n_neighbors=2, weights=distance, score=-0.274, total= 0.2s\n","[CV] n_neighbors=2, weights=distance .................................\n","[CV] .... n_neighbors=2, weights=distance, score=-0.316, total= 0.2s\n","[CV] n_neighbors=2, weights=distance .................................\n","[CV] .... n_neighbors=2, weights=distance, score=-0.397, total= 0.2s\n","[CV] n_neighbors=2, weights=distance .................................\n","[CV] .... n_neighbors=2, weights=distance, score=-0.131, total= 0.2s\n","[CV] n_neighbors=2, weights=distance .................................\n","[CV] .... n_neighbors=2, weights=distance, score=-0.221, total= 0.2s\n","[CV] n_neighbors=2, weights=distance .................................\n","[CV] .... n_neighbors=2, weights=distance, score=-0.270, total= 0.2s\n","[CV] n_neighbors=2, weights=distance .................................\n","[CV] .... n_neighbors=2, weights=distance, score=-0.273, total= 0.2s\n","[CV] n_neighbors=2, weights=distance .................................\n","[CV] .... n_neighbors=2, weights=distance, score=-0.220, total= 0.2s\n","[CV] n_neighbors=2, weights=distance .................................\n","[CV] .... n_neighbors=2, weights=distance, score=-0.181, total= 0.2s\n","[CV] n_neighbors=2, weights=distance .................................\n","[CV] .... n_neighbors=2, weights=distance, score=-0.376, total= 0.2s\n","[CV] n_neighbors=2, weights=distance .................................\n","[CV] .... n_neighbors=2, weights=distance, score=-0.341, total= 0.2s\n","[CV] n_neighbors=2, weights=distance .................................\n","[CV] .... n_neighbors=2, weights=distance, score=-0.393, total= 0.2s\n","[CV] n_neighbors=2, weights=distance .................................\n","[CV] .... n_neighbors=2, weights=distance, score=-0.161, total= 0.2s\n","[CV] n_neighbors=2, weights=distance .................................\n","[CV] .... n_neighbors=2, weights=distance, score=-0.561, total= 0.2s\n","[CV] n_neighbors=2, weights=distance .................................\n","[CV] .... n_neighbors=2, weights=distance, score=-0.032, total= 0.2s\n","[CV] n_neighbors=2, weights=distance .................................\n","[CV] .... n_neighbors=2, weights=distance, score=-0.069, total= 0.2s\n","[CV] n_neighbors=2, weights=distance .................................\n","[CV] .... n_neighbors=2, weights=distance, score=-0.283, total= 0.2s\n","[CV] n_neighbors=2, weights=distance .................................\n","[CV] .... n_neighbors=2, weights=distance, score=-0.306, total= 0.2s\n","[CV] n_neighbors=2, weights=distance .................................\n","[CV] .... n_neighbors=2, weights=distance, score=-0.301, total= 0.2s\n","[CV] n_neighbors=2, weights=distance .................................\n","[CV] .... n_neighbors=2, weights=distance, score=-0.235, total= 0.2s\n","[CV] n_neighbors=2, weights=distance .................................\n","[CV] .... n_neighbors=2, weights=distance, score=-0.256, total= 0.2s\n","[CV] n_neighbors=2, weights=distance .................................\n","[CV] .... n_neighbors=2, weights=distance, score=-0.221, total= 0.2s\n","[CV] n_neighbors=2, weights=distance .................................\n","[CV] .... n_neighbors=2, weights=distance, score=-0.243, total= 0.2s\n","[CV] n_neighbors=2, weights=distance .................................\n","[CV] .... n_neighbors=2, weights=distance, score=-0.404, total= 0.2s\n","[CV] n_neighbors=2, weights=distance .................................\n","[CV] .... n_neighbors=2, weights=distance, score=-0.040, total= 0.2s\n","[CV] n_neighbors=2, weights=distance .................................\n","[CV] .... n_neighbors=2, weights=distance, score=-0.166, total= 0.2s\n","[CV] n_neighbors=2, weights=distance .................................\n","[CV] .... n_neighbors=2, weights=distance, score=-0.350, total= 0.2s\n","[CV] n_neighbors=2, weights=distance .................................\n","[CV] .... n_neighbors=2, weights=distance, score=-0.349, total= 0.2s\n","[CV] n_neighbors=3, weights=uniform ..................................\n","[CV] ..... n_neighbors=3, weights=uniform, score=-0.091, total= 0.2s\n","[CV] n_neighbors=3, weights=uniform ..................................\n","[CV] ..... n_neighbors=3, weights=uniform, score=-0.122, total= 0.2s\n","[CV] n_neighbors=3, weights=uniform ..................................\n","[CV] ..... n_neighbors=3, weights=uniform, score=-0.130, total= 0.2s\n","[CV] n_neighbors=3, weights=uniform ..................................\n","[CV] ..... n_neighbors=3, weights=uniform, score=-0.120, total= 0.2s\n","[CV] n_neighbors=3, weights=uniform ..................................\n","[CV] ..... n_neighbors=3, weights=uniform, score=-0.267, total= 0.2s\n","[CV] n_neighbors=3, weights=uniform ..................................\n","[CV] ..... n_neighbors=3, weights=uniform, score=-0.026, total= 0.2s\n","[CV] n_neighbors=3, weights=uniform ..................................\n","[CV] ..... n_neighbors=3, weights=uniform, score=-0.135, total= 0.2s\n","[CV] n_neighbors=3, weights=uniform ..................................\n","[CV] ..... n_neighbors=3, weights=uniform, score=-0.178, total= 0.2s\n","[CV] n_neighbors=3, weights=uniform ..................................\n","[CV] ..... n_neighbors=3, weights=uniform, score=-0.194, total= 0.2s\n","[CV] n_neighbors=3, weights=uniform ..................................\n","[CV] ..... n_neighbors=3, weights=uniform, score=-0.066, total= 0.2s\n","[CV] n_neighbors=3, weights=uniform ..................................\n","[CV] ..... n_neighbors=3, weights=uniform, score=-0.072, total= 0.2s\n","[CV] n_neighbors=3, weights=uniform ..................................\n","[CV] ..... n_neighbors=3, weights=uniform, score=-0.177, total= 0.2s\n","[CV] n_neighbors=3, weights=uniform ..................................\n","[CV] ..... n_neighbors=3, weights=uniform, score=-0.248, total= 0.2s\n","[CV] n_neighbors=3, weights=uniform ..................................\n","[CV] ..... n_neighbors=3, weights=uniform, score=-0.324, total= 0.2s\n","[CV] n_neighbors=3, weights=uniform ..................................\n","[CV] ..... n_neighbors=3, weights=uniform, score=-0.041, total= 0.2s\n","[CV] n_neighbors=3, weights=uniform ..................................\n","[CV] ..... n_neighbors=3, weights=uniform, score=-0.288, total= 0.2s\n","[CV] n_neighbors=3, weights=uniform ..................................\n","[CV] ...... n_neighbors=3, weights=uniform, score=0.040, total= 0.2s\n","[CV] n_neighbors=3, weights=uniform ..................................\n","[CV] ..... n_neighbors=3, weights=uniform, score=-0.017, total= 0.2s\n","[CV] n_neighbors=3, weights=uniform ..................................\n","[CV] ..... n_neighbors=3, weights=uniform, score=-0.148, total= 0.2s\n","[CV] n_neighbors=3, weights=uniform ..................................\n","[CV] ..... n_neighbors=3, weights=uniform, score=-0.117, total= 0.2s\n","[CV] n_neighbors=3, weights=uniform ..................................\n","[CV] ..... n_neighbors=3, weights=uniform, score=-0.145, total= 0.2s\n","[CV] n_neighbors=3, weights=uniform ..................................\n","[CV] ..... n_neighbors=3, weights=uniform, score=-0.130, total= 0.2s\n","[CV] n_neighbors=3, weights=uniform ..................................\n","[CV] ..... n_neighbors=3, weights=uniform, score=-0.099, total= 0.2s\n","[CV] n_neighbors=3, weights=uniform ..................................\n","[CV] ..... n_neighbors=3, weights=uniform, score=-0.115, total= 0.2s\n","[CV] n_neighbors=3, weights=uniform ..................................\n","[CV] ..... n_neighbors=3, weights=uniform, score=-0.180, total= 0.2s\n","[CV] n_neighbors=3, weights=uniform ..................................\n","[CV] ..... n_neighbors=3, weights=uniform, score=-0.181, total= 0.2s\n","[CV] n_neighbors=3, weights=uniform ..................................\n","[CV] ..... n_neighbors=3, weights=uniform, score=-0.006, total= 0.2s\n","[CV] n_neighbors=3, weights=uniform ..................................\n","[CV] ..... n_neighbors=3, weights=uniform, score=-0.078, total= 0.2s\n","[CV] n_neighbors=3, weights=uniform ..................................\n","[CV] ..... n_neighbors=3, weights=uniform, score=-0.250, total= 0.2s\n","[CV] n_neighbors=3, weights=uniform ..................................\n","[CV] ..... n_neighbors=3, weights=uniform, score=-0.184, total= 0.2s\n","[CV] n_neighbors=3, weights=distance .................................\n","[CV] .... n_neighbors=3, weights=distance, score=-0.073, total= 0.2s\n","[CV] n_neighbors=3, weights=distance .................................\n","[CV] .... n_neighbors=3, weights=distance, score=-0.136, total= 0.2s\n","[CV] n_neighbors=3, weights=distance .................................\n","[CV] .... n_neighbors=3, weights=distance, score=-0.130, total= 0.2s\n","[CV] n_neighbors=3, weights=distance .................................\n","[CV] .... n_neighbors=3, weights=distance, score=-0.114, total= 0.2s\n","[CV] n_neighbors=3, weights=distance .................................\n","[CV] .... n_neighbors=3, weights=distance, score=-0.260, total= 0.2s\n","[CV] n_neighbors=3, weights=distance .................................\n","[CV] .... n_neighbors=3, weights=distance, score=-0.028, total= 0.2s\n","[CV] n_neighbors=3, weights=distance .................................\n","[CV] .... n_neighbors=3, weights=distance, score=-0.130, total= 0.2s\n","[CV] n_neighbors=3, weights=distance .................................\n","[CV] .... n_neighbors=3, weights=distance, score=-0.175, total= 0.2s\n","[CV] n_neighbors=3, weights=distance .................................\n","[CV] .... n_neighbors=3, weights=distance, score=-0.174, total= 0.2s\n","[CV] n_neighbors=3, weights=distance .................................\n","[CV] .... n_neighbors=3, weights=distance, score=-0.070, total= 0.2s\n","[CV] n_neighbors=3, weights=distance .................................\n","[CV] .... n_neighbors=3, weights=distance, score=-0.070, total= 0.2s\n","[CV] n_neighbors=3, weights=distance .................................\n","[CV] .... n_neighbors=3, weights=distance, score=-0.176, total= 0.2s\n","[CV] n_neighbors=3, weights=distance .................................\n","[CV] .... n_neighbors=3, weights=distance, score=-0.236, total= 0.2s\n","[CV] n_neighbors=3, weights=distance .................................\n","[CV] .... n_neighbors=3, weights=distance, score=-0.315, total= 0.2s\n","[CV] n_neighbors=3, weights=distance .................................\n","[CV] .... n_neighbors=3, weights=distance, score=-0.034, total= 0.2s\n","[CV] n_neighbors=3, weights=distance .................................\n","[CV] .... n_neighbors=3, weights=distance, score=-0.292, total= 0.2s\n","[CV] n_neighbors=3, weights=distance .................................\n","[CV] ..... n_neighbors=3, weights=distance, score=0.052, total= 0.2s\n","[CV] n_neighbors=3, weights=distance .................................\n","[CV] ..... n_neighbors=3, weights=distance, score=0.000, total= 0.2s\n","[CV] n_neighbors=3, weights=distance .................................\n","[CV] .... n_neighbors=3, weights=distance, score=-0.147, total= 0.2s\n","[CV] n_neighbors=3, weights=distance .................................\n","[CV] .... n_neighbors=3, weights=distance, score=-0.108, total= 0.2s\n","[CV] n_neighbors=3, weights=distance .................................\n","[CV] .... n_neighbors=3, weights=distance, score=-0.145, total= 0.2s\n","[CV] n_neighbors=3, weights=distance .................................\n","[CV] .... n_neighbors=3, weights=distance, score=-0.125, total= 0.2s\n","[CV] n_neighbors=3, weights=distance .................................\n","[CV] .... n_neighbors=3, weights=distance, score=-0.098, total= 0.2s\n","[CV] n_neighbors=3, weights=distance .................................\n","[CV] .... n_neighbors=3, weights=distance, score=-0.107, total= 0.2s\n","[CV] n_neighbors=3, weights=distance .................................\n","[CV] .... n_neighbors=3, weights=distance, score=-0.162, total= 0.2s\n","[CV] n_neighbors=3, weights=distance .................................\n","[CV] .... n_neighbors=3, weights=distance, score=-0.189, total= 0.2s\n","[CV] n_neighbors=3, weights=distance .................................\n","[CV] .... n_neighbors=3, weights=distance, score=-0.000, total= 0.2s\n","[CV] n_neighbors=3, weights=distance .................................\n","[CV] .... n_neighbors=3, weights=distance, score=-0.078, total= 0.2s\n","[CV] n_neighbors=3, weights=distance .................................\n","[CV] .... n_neighbors=3, weights=distance, score=-0.243, total= 0.2s\n","[CV] n_neighbors=3, weights=distance .................................\n","[CV] .... n_neighbors=3, weights=distance, score=-0.175, total= 0.2s\n","[CV] n_neighbors=4, weights=uniform ..................................\n","[CV] ..... n_neighbors=4, weights=uniform, score=-0.056, total= 0.2s\n","[CV] n_neighbors=4, weights=uniform ..................................\n","[CV] ..... n_neighbors=4, weights=uniform, score=-0.123, total= 0.2s\n","[CV] n_neighbors=4, weights=uniform ..................................\n","[CV] ..... n_neighbors=4, weights=uniform, score=-0.099, total= 0.2s\n","[CV] n_neighbors=4, weights=uniform ..................................\n","[CV] ..... n_neighbors=4, weights=uniform, score=-0.098, total= 0.2s\n","[CV] n_neighbors=4, weights=uniform ..................................\n","[CV] ..... n_neighbors=4, weights=uniform, score=-0.208, total= 0.2s\n","[CV] n_neighbors=4, weights=uniform ..................................\n","[CV] ...... n_neighbors=4, weights=uniform, score=0.009, total= 0.2s\n","[CV] n_neighbors=4, weights=uniform ..................................\n","[CV] ..... n_neighbors=4, weights=uniform, score=-0.031, total= 0.2s\n","[CV] n_neighbors=4, weights=uniform ..................................\n","[CV] ..... n_neighbors=4, weights=uniform, score=-0.109, total= 0.2s\n","[CV] n_neighbors=4, weights=uniform ..................................\n","[CV] ..... n_neighbors=4, weights=uniform, score=-0.130, total= 0.2s\n","[CV] n_neighbors=4, weights=uniform ..................................\n","[CV] ...... n_neighbors=4, weights=uniform, score=0.002, total= 0.2s\n","[CV] n_neighbors=4, weights=uniform ..................................\n","[CV] ..... n_neighbors=4, weights=uniform, score=-0.092, total= 0.2s\n","[CV] n_neighbors=4, weights=uniform ..................................\n","[CV] ..... n_neighbors=4, weights=uniform, score=-0.155, total= 0.2s\n","[CV] n_neighbors=4, weights=uniform ..................................\n","[CV] ..... n_neighbors=4, weights=uniform, score=-0.186, total= 0.2s\n","[CV] n_neighbors=4, weights=uniform ..................................\n","[CV] ..... n_neighbors=4, weights=uniform, score=-0.190, total= 0.2s\n","[CV] n_neighbors=4, weights=uniform ..................................\n","[CV] ...... n_neighbors=4, weights=uniform, score=0.010, total= 0.2s\n","[CV] n_neighbors=4, weights=uniform ..................................\n","[CV] ..... n_neighbors=4, weights=uniform, score=-0.175, total= 0.2s\n","[CV] n_neighbors=4, weights=uniform ..................................\n","[CV] ...... n_neighbors=4, weights=uniform, score=0.010, total= 0.2s\n","[CV] n_neighbors=4, weights=uniform ..................................\n","[CV] ..... n_neighbors=4, weights=uniform, score=-0.016, total= 0.2s\n","[CV] n_neighbors=4, weights=uniform ..................................\n","[CV] ..... n_neighbors=4, weights=uniform, score=-0.020, total= 0.2s\n","[CV] n_neighbors=4, weights=uniform ..................................\n","[CV] ..... n_neighbors=4, weights=uniform, score=-0.028, total= 0.2s\n","[CV] n_neighbors=4, weights=uniform ..................................\n","[CV] ..... n_neighbors=4, weights=uniform, score=-0.089, total= 0.2s\n","[CV] n_neighbors=4, weights=uniform ..................................\n","[CV] ..... n_neighbors=4, weights=uniform, score=-0.073, total= 0.2s\n","[CV] n_neighbors=4, weights=uniform ..................................\n","[CV] ..... n_neighbors=4, weights=uniform, score=-0.070, total= 0.2s\n","[CV] n_neighbors=4, weights=uniform ..................................\n","[CV] ..... n_neighbors=4, weights=uniform, score=-0.080, total= 0.2s\n","[CV] n_neighbors=4, weights=uniform ..................................\n","[CV] ..... n_neighbors=4, weights=uniform, score=-0.112, total= 0.2s\n","[CV] n_neighbors=4, weights=uniform ..................................\n","[CV] ..... n_neighbors=4, weights=uniform, score=-0.133, total= 0.2s\n","[CV] n_neighbors=4, weights=uniform ..................................\n","[CV] ...... n_neighbors=4, weights=uniform, score=0.016, total= 0.2s\n","[CV] n_neighbors=4, weights=uniform ..................................\n","[CV] ..... n_neighbors=4, weights=uniform, score=-0.053, total= 0.2s\n","[CV] n_neighbors=4, weights=uniform ..................................\n","[CV] ..... n_neighbors=4, weights=uniform, score=-0.172, total= 0.2s\n","[CV] n_neighbors=4, weights=uniform ..................................\n","[CV] ..... n_neighbors=4, weights=uniform, score=-0.051, total= 0.2s\n","[CV] n_neighbors=4, weights=distance .................................\n","[CV] .... n_neighbors=4, weights=distance, score=-0.036, total= 0.2s\n","[CV] n_neighbors=4, weights=distance .................................\n","[CV] .... n_neighbors=4, weights=distance, score=-0.124, total= 0.2s\n","[CV] n_neighbors=4, weights=distance .................................\n","[CV] .... n_neighbors=4, weights=distance, score=-0.098, total= 0.2s\n","[CV] n_neighbors=4, weights=distance .................................\n","[CV] .... n_neighbors=4, weights=distance, score=-0.083, total= 0.2s\n","[CV] n_neighbors=4, weights=distance .................................\n","[CV] .... n_neighbors=4, weights=distance, score=-0.205, total= 0.2s\n","[CV] n_neighbors=4, weights=distance .................................\n","[CV] ..... n_neighbors=4, weights=distance, score=0.006, total= 0.2s\n","[CV] n_neighbors=4, weights=distance .................................\n","[CV] .... n_neighbors=4, weights=distance, score=-0.030, total= 0.2s\n","[CV] n_neighbors=4, weights=distance .................................\n","[CV] .... n_neighbors=4, weights=distance, score=-0.107, total= 0.2s\n","[CV] n_neighbors=4, weights=distance .................................\n","[CV] .... n_neighbors=4, weights=distance, score=-0.113, total= 0.2s\n","[CV] n_neighbors=4, weights=distance .................................\n","[CV] .... n_neighbors=4, weights=distance, score=-0.001, total= 0.2s\n","[CV] n_neighbors=4, weights=distance .................................\n","[CV] .... n_neighbors=4, weights=distance, score=-0.081, total= 0.2s\n","[CV] n_neighbors=4, weights=distance .................................\n","[CV] .... n_neighbors=4, weights=distance, score=-0.151, total= 0.2s\n","[CV] n_neighbors=4, weights=distance .................................\n","[CV] .... n_neighbors=4, weights=distance, score=-0.171, total= 0.2s\n","[CV] n_neighbors=4, weights=distance .................................\n","[CV] .... n_neighbors=4, weights=distance, score=-0.185, total= 0.2s\n","[CV] n_neighbors=4, weights=distance .................................\n","[CV] ..... n_neighbors=4, weights=distance, score=0.021, total= 0.2s\n","[CV] n_neighbors=4, weights=distance .................................\n","[CV] .... n_neighbors=4, weights=distance, score=-0.180, total= 0.2s\n","[CV] n_neighbors=4, weights=distance .................................\n","[CV] ..... n_neighbors=4, weights=distance, score=0.025, total= 0.2s\n","[CV] n_neighbors=4, weights=distance .................................\n","[CV] .... n_neighbors=4, weights=distance, score=-0.004, total= 0.2s\n","[CV] n_neighbors=4, weights=distance .................................\n","[CV] .... n_neighbors=4, weights=distance, score=-0.021, total= 0.2s\n","[CV] n_neighbors=4, weights=distance .................................\n","[CV] .... n_neighbors=4, weights=distance, score=-0.022, total= 0.2s\n","[CV] n_neighbors=4, weights=distance .................................\n","[CV] .... n_neighbors=4, weights=distance, score=-0.089, total= 0.2s\n","[CV] n_neighbors=4, weights=distance .................................\n","[CV] .... n_neighbors=4, weights=distance, score=-0.067, total= 0.2s\n","[CV] n_neighbors=4, weights=distance .................................\n","[CV] .... n_neighbors=4, weights=distance, score=-0.063, total= 0.2s\n","[CV] n_neighbors=4, weights=distance .................................\n","[CV] .... n_neighbors=4, weights=distance, score=-0.071, total= 0.2s\n","[CV] n_neighbors=4, weights=distance .................................\n","[CV] .... n_neighbors=4, weights=distance, score=-0.102, total= 0.2s\n","[CV] n_neighbors=4, weights=distance .................................\n","[CV] .... n_neighbors=4, weights=distance, score=-0.136, total= 0.2s\n","[CV] n_neighbors=4, weights=distance .................................\n","[CV] ..... n_neighbors=4, weights=distance, score=0.025, total= 0.2s\n","[CV] n_neighbors=4, weights=distance .................................\n","[CV] .... n_neighbors=4, weights=distance, score=-0.052, total= 0.2s\n","[CV] n_neighbors=4, weights=distance .................................\n","[CV] .... n_neighbors=4, weights=distance, score=-0.167, total= 0.2s\n","[CV] n_neighbors=4, weights=distance .................................\n","[CV] .... n_neighbors=4, weights=distance, score=-0.047, total= 0.2s\n","[CV] n_neighbors=5, weights=uniform ..................................\n","[CV] ...... n_neighbors=5, weights=uniform, score=0.016, total= 0.2s\n","[CV] n_neighbors=5, weights=uniform ..................................\n","[CV] ..... n_neighbors=5, weights=uniform, score=-0.080, total= 0.2s\n","[CV] n_neighbors=5, weights=uniform ..................................\n","[CV] ..... n_neighbors=5, weights=uniform, score=-0.058, total= 0.2s\n","[CV] n_neighbors=5, weights=uniform ..................................\n","[CV] ..... n_neighbors=5, weights=uniform, score=-0.062, total= 0.2s\n","[CV] n_neighbors=5, weights=uniform ..................................\n","[CV] ..... n_neighbors=5, weights=uniform, score=-0.174, total= 0.2s\n","[CV] n_neighbors=5, weights=uniform ..................................\n","[CV] ...... n_neighbors=5, weights=uniform, score=0.051, total= 0.2s\n","[CV] n_neighbors=5, weights=uniform ..................................\n","[CV] ..... n_neighbors=5, weights=uniform, score=-0.022, total= 0.2s\n","[CV] n_neighbors=5, weights=uniform ..................................\n","[CV] ..... n_neighbors=5, weights=uniform, score=-0.037, total= 0.2s\n","[CV] n_neighbors=5, weights=uniform ..................................\n","[CV] ..... n_neighbors=5, weights=uniform, score=-0.131, total= 0.2s\n","[CV] n_neighbors=5, weights=uniform ..................................\n","[CV] ..... n_neighbors=5, weights=uniform, score=-0.007, total= 0.2s\n","[CV] n_neighbors=5, weights=uniform ..................................\n","[CV] ..... n_neighbors=5, weights=uniform, score=-0.045, total= 0.2s\n","[CV] n_neighbors=5, weights=uniform ..................................\n","[CV] ..... n_neighbors=5, weights=uniform, score=-0.153, total= 0.2s\n","[CV] n_neighbors=5, weights=uniform ..................................\n","[CV] ..... n_neighbors=5, weights=uniform, score=-0.127, total= 0.2s\n","[CV] n_neighbors=5, weights=uniform ..................................\n","[CV] ..... n_neighbors=5, weights=uniform, score=-0.125, total= 0.2s\n","[CV] n_neighbors=5, weights=uniform ..................................\n","[CV] ...... n_neighbors=5, weights=uniform, score=0.037, total= 0.2s\n","[CV] n_neighbors=5, weights=uniform ..................................\n","[CV] ..... n_neighbors=5, weights=uniform, score=-0.182, total= 0.2s\n","[CV] n_neighbors=5, weights=uniform ..................................\n","[CV] ...... n_neighbors=5, weights=uniform, score=0.050, total= 0.2s\n","[CV] n_neighbors=5, weights=uniform ..................................\n","[CV] ...... n_neighbors=5, weights=uniform, score=0.021, total= 0.2s\n","[CV] n_neighbors=5, weights=uniform ..................................\n","[CV] ..... n_neighbors=5, weights=uniform, score=-0.039, total= 0.2s\n","[CV] n_neighbors=5, weights=uniform ..................................\n","[CV] ...... n_neighbors=5, weights=uniform, score=0.019, total= 0.2s\n","[CV] n_neighbors=5, weights=uniform ..................................\n","[CV] ..... n_neighbors=5, weights=uniform, score=-0.067, total= 0.2s\n","[CV] n_neighbors=5, weights=uniform ..................................\n","[CV] ..... n_neighbors=5, weights=uniform, score=-0.066, total= 0.2s\n","[CV] n_neighbors=5, weights=uniform ..................................\n","[CV] ...... n_neighbors=5, weights=uniform, score=0.008, total= 0.2s\n","[CV] n_neighbors=5, weights=uniform ..................................\n","[CV] ..... n_neighbors=5, weights=uniform, score=-0.050, total= 0.2s\n","[CV] n_neighbors=5, weights=uniform ..................................\n","[CV] ..... n_neighbors=5, weights=uniform, score=-0.048, total= 0.2s\n","[CV] n_neighbors=5, weights=uniform ..................................\n","[CV] ..... n_neighbors=5, weights=uniform, score=-0.045, total= 0.2s\n","[CV] n_neighbors=5, weights=uniform ..................................\n","[CV] ...... n_neighbors=5, weights=uniform, score=0.025, total= 0.2s\n","[CV] n_neighbors=5, weights=uniform ..................................\n","[CV] ..... n_neighbors=5, weights=uniform, score=-0.039, total= 0.2s\n","[CV] n_neighbors=5, weights=uniform ..................................\n","[CV] ..... n_neighbors=5, weights=uniform, score=-0.110, total= 0.2s\n","[CV] n_neighbors=5, weights=uniform ..................................\n","[CV] ..... n_neighbors=5, weights=uniform, score=-0.049, total= 0.2s\n","[CV] n_neighbors=5, weights=distance .................................\n","[CV] ..... n_neighbors=5, weights=distance, score=0.030, total= 0.2s\n","[CV] n_neighbors=5, weights=distance .................................\n","[CV] .... n_neighbors=5, weights=distance, score=-0.079, total= 0.2s\n","[CV] n_neighbors=5, weights=distance .................................\n","[CV] .... n_neighbors=5, weights=distance, score=-0.056, total= 0.2s\n","[CV] n_neighbors=5, weights=distance .................................\n","[CV] .... n_neighbors=5, weights=distance, score=-0.048, total= 0.2s\n","[CV] n_neighbors=5, weights=distance .................................\n","[CV] .... n_neighbors=5, weights=distance, score=-0.173, total= 0.2s\n","[CV] n_neighbors=5, weights=distance .................................\n","[CV] ..... n_neighbors=5, weights=distance, score=0.046, total= 0.2s\n","[CV] n_neighbors=5, weights=distance .................................\n","[CV] .... n_neighbors=5, weights=distance, score=-0.014, total= 0.2s\n","[CV] n_neighbors=5, weights=distance .................................\n","[CV] .... n_neighbors=5, weights=distance, score=-0.041, total= 0.2s\n","[CV] n_neighbors=5, weights=distance .................................\n","[CV] .... n_neighbors=5, weights=distance, score=-0.111, total= 0.2s\n","[CV] n_neighbors=5, weights=distance .................................\n","[CV] .... n_neighbors=5, weights=distance, score=-0.007, total= 0.2s\n","[CV] n_neighbors=5, weights=distance .................................\n","[CV] .... n_neighbors=5, weights=distance, score=-0.038, total= 0.2s\n","[CV] n_neighbors=5, weights=distance .................................\n","[CV] .... n_neighbors=5, weights=distance, score=-0.146, total= 0.2s\n","[CV] n_neighbors=5, weights=distance .................................\n","[CV] .... n_neighbors=5, weights=distance, score=-0.116, total= 0.2s\n","[CV] n_neighbors=5, weights=distance .................................\n","[CV] .... n_neighbors=5, weights=distance, score=-0.128, total= 0.2s\n","[CV] n_neighbors=5, weights=distance .................................\n","[CV] ..... n_neighbors=5, weights=distance, score=0.049, total= 0.2s\n","[CV] n_neighbors=5, weights=distance .................................\n","[CV] .... n_neighbors=5, weights=distance, score=-0.181, total= 0.2s\n","[CV] n_neighbors=5, weights=distance .................................\n","[CV] ..... n_neighbors=5, weights=distance, score=0.060, total= 0.2s\n","[CV] n_neighbors=5, weights=distance .................................\n","[CV] ..... n_neighbors=5, weights=distance, score=0.035, total= 0.2s\n","[CV] n_neighbors=5, weights=distance .................................\n","[CV] .... n_neighbors=5, weights=distance, score=-0.033, total= 0.2s\n","[CV] n_neighbors=5, weights=distance .................................\n","[CV] ..... n_neighbors=5, weights=distance, score=0.023, total= 0.2s\n","[CV] n_neighbors=5, weights=distance .................................\n","[CV] .... n_neighbors=5, weights=distance, score=-0.064, total= 0.2s\n","[CV] n_neighbors=5, weights=distance .................................\n","[CV] .... n_neighbors=5, weights=distance, score=-0.057, total= 0.2s\n","[CV] n_neighbors=5, weights=distance .................................\n","[CV] ..... n_neighbors=5, weights=distance, score=0.008, total= 0.2s\n","[CV] n_neighbors=5, weights=distance .................................\n","[CV] .... n_neighbors=5, weights=distance, score=-0.046, total= 0.2s\n","[CV] n_neighbors=5, weights=distance .................................\n","[CV] .... n_neighbors=5, weights=distance, score=-0.041, total= 0.2s\n","[CV] n_neighbors=5, weights=distance .................................\n","[CV] .... n_neighbors=5, weights=distance, score=-0.054, total= 0.2s\n","[CV] n_neighbors=5, weights=distance .................................\n","[CV] ..... n_neighbors=5, weights=distance, score=0.033, total= 0.2s\n","[CV] n_neighbors=5, weights=distance .................................\n","[CV] .... n_neighbors=5, weights=distance, score=-0.037, total= 0.2s\n","[CV] n_neighbors=5, weights=distance .................................\n","[CV] .... n_neighbors=5, weights=distance, score=-0.102, total= 0.2s\n","[CV] n_neighbors=5, weights=distance .................................\n","[CV] .... n_neighbors=5, weights=distance, score=-0.043, total= 0.2s\n","[CV] n_neighbors=6, weights=uniform ..................................\n","[CV] ...... n_neighbors=6, weights=uniform, score=0.008, total= 0.2s\n","[CV] n_neighbors=6, weights=uniform ..................................\n","[CV] ..... n_neighbors=6, weights=uniform, score=-0.049, total= 0.2s\n","[CV] n_neighbors=6, weights=uniform ..................................\n","[CV] ...... n_neighbors=6, weights=uniform, score=0.018, total= 0.2s\n","[CV] n_neighbors=6, weights=uniform ..................................\n","[CV] ..... n_neighbors=6, weights=uniform, score=-0.063, total= 0.2s\n","[CV] n_neighbors=6, weights=uniform ..................................\n","[CV] ..... n_neighbors=6, weights=uniform, score=-0.154, total= 0.2s\n","[CV] n_neighbors=6, weights=uniform ..................................\n","[CV] ...... n_neighbors=6, weights=uniform, score=0.092, total= 0.2s\n","[CV] n_neighbors=6, weights=uniform ..................................\n","[CV] ..... n_neighbors=6, weights=uniform, score=-0.040, total= 0.2s\n","[CV] n_neighbors=6, weights=uniform ..................................\n","[CV] ..... n_neighbors=6, weights=uniform, score=-0.009, total= 0.2s\n","[CV] n_neighbors=6, weights=uniform ..................................\n","[CV] ..... n_neighbors=6, weights=uniform, score=-0.078, total= 0.2s\n","[CV] n_neighbors=6, weights=uniform ..................................\n","[CV] ...... n_neighbors=6, weights=uniform, score=0.005, total= 0.2s\n","[CV] n_neighbors=6, weights=uniform ..................................\n","[CV] ..... n_neighbors=6, weights=uniform, score=-0.022, total= 0.2s\n","[CV] n_neighbors=6, weights=uniform ..................................\n","[CV] ..... n_neighbors=6, weights=uniform, score=-0.070, total= 0.2s\n","[CV] n_neighbors=6, weights=uniform ..................................\n","[CV] ..... n_neighbors=6, weights=uniform, score=-0.079, total= 0.2s\n","[CV] n_neighbors=6, weights=uniform ..................................\n","[CV] ..... n_neighbors=6, weights=uniform, score=-0.062, total= 0.2s\n","[CV] n_neighbors=6, weights=uniform ..................................\n","[CV] ...... n_neighbors=6, weights=uniform, score=0.026, total= 0.2s\n","[CV] n_neighbors=6, weights=uniform ..................................\n","[CV] ..... n_neighbors=6, weights=uniform, score=-0.172, total= 0.2s\n","[CV] n_neighbors=6, weights=uniform ..................................\n","[CV] ...... n_neighbors=6, weights=uniform, score=0.050, total= 0.2s\n","[CV] n_neighbors=6, weights=uniform ..................................\n","[CV] ...... n_neighbors=6, weights=uniform, score=0.025, total= 0.2s\n","[CV] n_neighbors=6, weights=uniform ..................................\n","[CV] ...... n_neighbors=6, weights=uniform, score=0.000, total= 0.2s\n","[CV] n_neighbors=6, weights=uniform ..................................\n","[CV] ...... n_neighbors=6, weights=uniform, score=0.039, total= 0.2s\n","[CV] n_neighbors=6, weights=uniform ..................................\n","[CV] ..... n_neighbors=6, weights=uniform, score=-0.007, total= 0.2s\n","[CV] n_neighbors=6, weights=uniform ..................................\n","[CV] ..... n_neighbors=6, weights=uniform, score=-0.073, total= 0.2s\n","[CV] n_neighbors=6, weights=uniform ..................................\n","[CV] ...... n_neighbors=6, weights=uniform, score=0.017, total= 0.2s\n","[CV] n_neighbors=6, weights=uniform ..................................\n","[CV] ..... n_neighbors=6, weights=uniform, score=-0.000, total= 0.2s\n","[CV] n_neighbors=6, weights=uniform ..................................\n","[CV] ..... n_neighbors=6, weights=uniform, score=-0.037, total= 0.2s\n","[CV] n_neighbors=6, weights=uniform ..................................\n","[CV] ...... n_neighbors=6, weights=uniform, score=0.018, total= 0.2s\n","[CV] n_neighbors=6, weights=uniform ..................................\n","[CV] ...... n_neighbors=6, weights=uniform, score=0.051, total= 0.2s\n","[CV] n_neighbors=6, weights=uniform ..................................\n","[CV] ..... n_neighbors=6, weights=uniform, score=-0.022, total= 0.2s\n","[CV] n_neighbors=6, weights=uniform ..................................\n","[CV] ..... n_neighbors=6, weights=uniform, score=-0.069, total= 0.2s\n","[CV] n_neighbors=6, weights=uniform ..................................\n","[CV] ..... n_neighbors=6, weights=uniform, score=-0.072, total= 0.2s\n","[CV] n_neighbors=6, weights=distance .................................\n","[CV] ..... n_neighbors=6, weights=distance, score=0.025, total= 0.2s\n","[CV] n_neighbors=6, weights=distance .................................\n","[CV] .... n_neighbors=6, weights=distance, score=-0.045, total= 0.2s\n","[CV] n_neighbors=6, weights=distance .................................\n","[CV] ..... n_neighbors=6, weights=distance, score=0.016, total= 0.2s\n","[CV] n_neighbors=6, weights=distance .................................\n","[CV] .... n_neighbors=6, weights=distance, score=-0.046, total= 0.2s\n","[CV] n_neighbors=6, weights=distance .................................\n","[CV] .... n_neighbors=6, weights=distance, score=-0.152, total= 0.2s\n","[CV] n_neighbors=6, weights=distance .................................\n","[CV] ..... n_neighbors=6, weights=distance, score=0.089, total= 0.2s\n","[CV] n_neighbors=6, weights=distance .................................\n","[CV] .... n_neighbors=6, weights=distance, score=-0.034, total= 0.2s\n","[CV] n_neighbors=6, weights=distance .................................\n","[CV] .... n_neighbors=6, weights=distance, score=-0.012, total= 0.2s\n","[CV] n_neighbors=6, weights=distance .................................\n","[CV] .... n_neighbors=6, weights=distance, score=-0.061, total= 0.2s\n","[CV] n_neighbors=6, weights=distance .................................\n","[CV] ..... n_neighbors=6, weights=distance, score=0.005, total= 0.2s\n","[CV] n_neighbors=6, weights=distance .................................\n","[CV] .... n_neighbors=6, weights=distance, score=-0.015, total= 0.2s\n","[CV] n_neighbors=6, weights=distance .................................\n","[CV] .... n_neighbors=6, weights=distance, score=-0.071, total= 0.2s\n","[CV] n_neighbors=6, weights=distance .................................\n","[CV] .... n_neighbors=6, weights=distance, score=-0.073, total= 0.2s\n","[CV] n_neighbors=6, weights=distance .................................\n","[CV] .... n_neighbors=6, weights=distance, score=-0.071, total= 0.2s\n","[CV] n_neighbors=6, weights=distance .................................\n","[CV] ..... n_neighbors=6, weights=distance, score=0.041, total= 0.2s\n","[CV] n_neighbors=6, weights=distance .................................\n","[CV] .... n_neighbors=6, weights=distance, score=-0.168, total= 0.2s\n","[CV] n_neighbors=6, weights=distance .................................\n","[CV] ..... n_neighbors=6, weights=distance, score=0.064, total= 0.2s\n","[CV] n_neighbors=6, weights=distance .................................\n","[CV] ..... n_neighbors=6, weights=distance, score=0.038, total= 0.2s\n","[CV] n_neighbors=6, weights=distance .................................\n","[CV] ..... n_neighbors=6, weights=distance, score=0.005, total= 0.2s\n","[CV] n_neighbors=6, weights=distance .................................\n","[CV] ..... n_neighbors=6, weights=distance, score=0.045, total= 0.2s\n","[CV] n_neighbors=6, weights=distance .................................\n","[CV] .... n_neighbors=6, weights=distance, score=-0.010, total= 0.2s\n","[CV] n_neighbors=6, weights=distance .................................\n","[CV] .... n_neighbors=6, weights=distance, score=-0.062, total= 0.2s\n","[CV] n_neighbors=6, weights=distance .................................\n","[CV] ..... n_neighbors=6, weights=distance, score=0.018, total= 0.2s\n","[CV] n_neighbors=6, weights=distance .................................\n","[CV] ..... n_neighbors=6, weights=distance, score=0.001, total= 0.2s\n","[CV] n_neighbors=6, weights=distance .................................\n","[CV] .... n_neighbors=6, weights=distance, score=-0.029, total= 0.2s\n","[CV] n_neighbors=6, weights=distance .................................\n","[CV] ..... n_neighbors=6, weights=distance, score=0.006, total= 0.2s\n","[CV] n_neighbors=6, weights=distance .................................\n","[CV] ..... n_neighbors=6, weights=distance, score=0.054, total= 0.2s\n","[CV] n_neighbors=6, weights=distance .................................\n","[CV] .... n_neighbors=6, weights=distance, score=-0.018, total= 0.2s\n","[CV] n_neighbors=6, weights=distance .................................\n","[CV] .... n_neighbors=6, weights=distance, score=-0.060, total= 0.2s\n","[CV] n_neighbors=6, weights=distance .................................\n","[CV] .... n_neighbors=6, weights=distance, score=-0.061, total= 0.2s\n","[Parallel(n_jobs=1)]: Done 300 out of 300 | elapsed: 1.0min finished\n","KNeighborsRegressor(algorithm='auto', leaf_size=30, metric='minkowski',\n"," metric_params=None, n_jobs=None, n_neighbors=6, p=2,\n"," weights='distance')\n","window: 1, stide: 1, rmse: 2.72715499362577\n","(3000, 231)\n","Number of Feature-Columns: 231\n","\n","Features Ready for undergoing selection tests done ...\n","\n","Fitting 30 folds for each of 10 candidates, totalling 300 fits\n","[Parallel(n_jobs=1)]: Using backend SequentialBackend with 1 concurrent workers.\n","[CV] n_neighbors=2, weights=uniform ..................................\n","[CV] ..... n_neighbors=2, weights=uniform, score=-0.137, total= 0.2s\n","[Parallel(n_jobs=1)]: Done 1 out of 1 | elapsed: 0.2s remaining: 0.0s\n","[CV] n_neighbors=2, weights=uniform ..................................\n","[CV] ..... n_neighbors=2, weights=uniform, score=-0.393, total= 0.2s\n","[Parallel(n_jobs=1)]: Done 2 out of 2 | elapsed: 0.4s remaining: 0.0s\n","[CV] n_neighbors=2, weights=uniform ..................................\n","[CV] ..... n_neighbors=2, weights=uniform, score=-0.270, total= 0.2s\n","[Parallel(n_jobs=1)]: Done 3 out of 3 | elapsed: 0.6s remaining: 0.0s\n","[CV] n_neighbors=2, weights=uniform ..................................\n","[CV] ..... n_neighbors=2, weights=uniform, score=-0.324, total= 0.2s\n","[Parallel(n_jobs=1)]: Done 4 out of 4 | elapsed: 0.8s remaining: 0.0s\n","[CV] n_neighbors=2, weights=uniform ..................................\n","[CV] ..... n_neighbors=2, weights=uniform, score=-0.407, total= 0.2s\n","[CV] n_neighbors=2, weights=uniform ..................................\n","[CV] ..... n_neighbors=2, weights=uniform, score=-0.126, total= 0.2s\n","[CV] n_neighbors=2, weights=uniform ..................................\n","[CV] ..... n_neighbors=2, weights=uniform, score=-0.212, total= 0.2s\n","[CV] n_neighbors=2, weights=uniform ..................................\n","[CV] ..... n_neighbors=2, weights=uniform, score=-0.270, total= 0.2s\n","[CV] n_neighbors=2, weights=uniform ..................................\n","[CV] ..... n_neighbors=2, weights=uniform, score=-0.283, total= 0.2s\n","[CV] n_neighbors=2, weights=uniform ..................................\n","[CV] ..... n_neighbors=2, weights=uniform, score=-0.225, total= 0.2s\n","[CV] n_neighbors=2, weights=uniform ..................................\n","[CV] ..... n_neighbors=2, weights=uniform, score=-0.180, total= 0.2s\n","[CV] n_neighbors=2, weights=uniform ..................................\n","[CV] ..... n_neighbors=2, weights=uniform, score=-0.382, total= 0.2s\n","[CV] n_neighbors=2, weights=uniform ..................................\n","[CV] ..... n_neighbors=2, weights=uniform, score=-0.353, total= 0.2s\n","[CV] n_neighbors=2, weights=uniform ..................................\n","[CV] ..... n_neighbors=2, weights=uniform, score=-0.391, total= 0.2s\n","[CV] n_neighbors=2, weights=uniform ..................................\n","[CV] ..... n_neighbors=2, weights=uniform, score=-0.161, total= 0.2s\n","[CV] n_neighbors=2, weights=uniform ..................................\n","[CV] ..... n_neighbors=2, weights=uniform, score=-0.559, total= 0.2s\n","[CV] n_neighbors=2, weights=uniform ..................................\n","[CV] ..... n_neighbors=2, weights=uniform, score=-0.038, total= 0.2s\n","[CV] n_neighbors=2, weights=uniform ..................................\n","[CV] ..... n_neighbors=2, weights=uniform, score=-0.079, total= 0.2s\n","[CV] n_neighbors=2, weights=uniform ..................................\n","[CV] ..... n_neighbors=2, weights=uniform, score=-0.279, total= 0.2s\n","[CV] n_neighbors=2, weights=uniform ..................................\n","[CV] ..... n_neighbors=2, weights=uniform, score=-0.323, total= 0.2s\n","[CV] n_neighbors=2, weights=uniform ..................................\n","[CV] ..... n_neighbors=2, weights=uniform, score=-0.305, total= 0.2s\n","[CV] n_neighbors=2, weights=uniform ..................................\n","[CV] ..... n_neighbors=2, weights=uniform, score=-0.233, total= 0.2s\n","[CV] n_neighbors=2, weights=uniform ..................................\n","[CV] ..... n_neighbors=2, weights=uniform, score=-0.257, total= 0.2s\n","[CV] n_neighbors=2, weights=uniform ..................................\n","[CV] ..... n_neighbors=2, weights=uniform, score=-0.220, total= 0.2s\n","[CV] n_neighbors=2, weights=uniform ..................................\n","[CV] ..... n_neighbors=2, weights=uniform, score=-0.249, total= 0.2s\n","[CV] n_neighbors=2, weights=uniform ..................................\n","[CV] ..... n_neighbors=2, weights=uniform, score=-0.401, total= 0.2s\n","[CV] n_neighbors=2, weights=uniform ..................................\n","[CV] ..... n_neighbors=2, weights=uniform, score=-0.044, total= 0.2s\n","[CV] n_neighbors=2, weights=uniform ..................................\n","[CV] ..... n_neighbors=2, weights=uniform, score=-0.170, total= 0.2s\n","[CV] n_neighbors=2, weights=uniform ..................................\n","[CV] ..... n_neighbors=2, weights=uniform, score=-0.351, total= 0.2s\n","[CV] n_neighbors=2, weights=uniform ..................................\n","[CV] ..... n_neighbors=2, weights=uniform, score=-0.366, total= 0.2s\n","[CV] n_neighbors=2, weights=distance .................................\n","[CV] .... n_neighbors=2, weights=distance, score=-0.127, total= 0.2s\n","[CV] n_neighbors=2, weights=distance .................................\n","[CV] .... n_neighbors=2, weights=distance, score=-0.394, total= 0.2s\n","[CV] n_neighbors=2, weights=distance .................................\n","[CV] .... n_neighbors=2, weights=distance, score=-0.274, total= 0.2s\n","[CV] n_neighbors=2, weights=distance .................................\n","[CV] .... n_neighbors=2, weights=distance, score=-0.316, total= 0.2s\n","[CV] n_neighbors=2, weights=distance .................................\n","[CV] .... n_neighbors=2, weights=distance, score=-0.397, total= 0.2s\n","[CV] n_neighbors=2, weights=distance .................................\n","[CV] .... n_neighbors=2, weights=distance, score=-0.131, total= 0.2s\n","[CV] n_neighbors=2, weights=distance .................................\n","[CV] .... n_neighbors=2, weights=distance, score=-0.221, total= 0.2s\n","[CV] n_neighbors=2, weights=distance .................................\n","[CV] .... n_neighbors=2, weights=distance, score=-0.270, total= 0.2s\n","[CV] n_neighbors=2, weights=distance .................................\n","[CV] .... n_neighbors=2, weights=distance, score=-0.273, total= 0.2s\n","[CV] n_neighbors=2, weights=distance .................................\n","[CV] .... n_neighbors=2, weights=distance, score=-0.220, total= 0.2s\n","[CV] n_neighbors=2, weights=distance .................................\n","[CV] .... n_neighbors=2, weights=distance, score=-0.181, total= 0.2s\n","[CV] n_neighbors=2, weights=distance .................................\n","[CV] .... n_neighbors=2, weights=distance, score=-0.376, total= 0.2s\n","[CV] n_neighbors=2, weights=distance .................................\n","[CV] .... n_neighbors=2, weights=distance, score=-0.341, total= 0.2s\n","[CV] n_neighbors=2, weights=distance .................................\n","[CV] .... n_neighbors=2, weights=distance, score=-0.393, total= 0.2s\n","[CV] n_neighbors=2, weights=distance .................................\n","[CV] .... n_neighbors=2, weights=distance, score=-0.161, total= 0.2s\n","[CV] n_neighbors=2, weights=distance .................................\n","[CV] .... n_neighbors=2, weights=distance, score=-0.561, total= 0.2s\n","[CV] n_neighbors=2, weights=distance .................................\n","[CV] .... n_neighbors=2, weights=distance, score=-0.032, total= 0.2s\n","[CV] n_neighbors=2, weights=distance .................................\n","[CV] .... n_neighbors=2, weights=distance, score=-0.069, total= 0.2s\n","[CV] n_neighbors=2, weights=distance .................................\n","[CV] .... n_neighbors=2, weights=distance, score=-0.283, total= 0.2s\n","[CV] n_neighbors=2, weights=distance .................................\n","[CV] .... n_neighbors=2, weights=distance, score=-0.306, total= 0.2s\n","[CV] n_neighbors=2, weights=distance .................................\n","[CV] .... n_neighbors=2, weights=distance, score=-0.301, total= 0.2s\n","[CV] n_neighbors=2, weights=distance .................................\n","[CV] .... n_neighbors=2, weights=distance, score=-0.235, total= 0.2s\n","[CV] n_neighbors=2, weights=distance .................................\n","[CV] .... n_neighbors=2, weights=distance, score=-0.256, total= 0.2s\n","[CV] n_neighbors=2, weights=distance .................................\n","[CV] .... n_neighbors=2, weights=distance, score=-0.221, total= 0.2s\n","[CV] n_neighbors=2, weights=distance .................................\n","[CV] .... n_neighbors=2, weights=distance, score=-0.243, total= 0.2s\n","[CV] n_neighbors=2, weights=distance .................................\n","[CV] .... n_neighbors=2, weights=distance, score=-0.404, total= 0.2s\n","[CV] n_neighbors=2, weights=distance .................................\n","[CV] .... n_neighbors=2, weights=distance, score=-0.040, total= 0.2s\n","[CV] n_neighbors=2, weights=distance .................................\n","[CV] .... n_neighbors=2, weights=distance, score=-0.166, total= 0.2s\n","[CV] n_neighbors=2, weights=distance .................................\n","[CV] .... n_neighbors=2, weights=distance, score=-0.350, total= 0.2s\n","[CV] n_neighbors=2, weights=distance .................................\n","[CV] .... n_neighbors=2, weights=distance, score=-0.349, total= 0.2s\n","[CV] n_neighbors=3, weights=uniform ..................................\n","[CV] ..... n_neighbors=3, weights=uniform, score=-0.091, total= 0.2s\n","[CV] n_neighbors=3, weights=uniform ..................................\n","[CV] ..... n_neighbors=3, weights=uniform, score=-0.122, total= 0.2s\n","[CV] n_neighbors=3, weights=uniform ..................................\n","[CV] ..... n_neighbors=3, weights=uniform, score=-0.130, total= 0.2s\n","[CV] n_neighbors=3, weights=uniform ..................................\n","[CV] ..... n_neighbors=3, weights=uniform, score=-0.120, total= 0.2s\n","[CV] n_neighbors=3, weights=uniform ..................................\n","[CV] ..... n_neighbors=3, weights=uniform, score=-0.267, total= 0.2s\n","[CV] n_neighbors=3, weights=uniform ..................................\n","[CV] ..... n_neighbors=3, weights=uniform, score=-0.026, total= 0.2s\n","[CV] n_neighbors=3, weights=uniform ..................................\n","[CV] ..... n_neighbors=3, weights=uniform, score=-0.135, total= 0.2s\n","[CV] n_neighbors=3, weights=uniform ..................................\n","[CV] ..... n_neighbors=3, weights=uniform, score=-0.178, total= 0.2s\n","[CV] n_neighbors=3, weights=uniform ..................................\n","[CV] ..... n_neighbors=3, weights=uniform, score=-0.194, total= 0.2s\n","[CV] n_neighbors=3, weights=uniform ..................................\n","[CV] ..... n_neighbors=3, weights=uniform, score=-0.066, total= 0.2s\n","[CV] n_neighbors=3, weights=uniform ..................................\n","[CV] ..... n_neighbors=3, weights=uniform, score=-0.072, total= 0.2s\n","[CV] n_neighbors=3, weights=uniform ..................................\n","[CV] ..... n_neighbors=3, weights=uniform, score=-0.177, total= 0.2s\n","[CV] n_neighbors=3, weights=uniform ..................................\n","[CV] ..... n_neighbors=3, weights=uniform, score=-0.248, total= 0.2s\n","[CV] n_neighbors=3, weights=uniform ..................................\n","[CV] ..... n_neighbors=3, weights=uniform, score=-0.324, total= 0.2s\n","[CV] n_neighbors=3, weights=uniform ..................................\n","[CV] ..... n_neighbors=3, weights=uniform, score=-0.041, total= 0.2s\n","[CV] n_neighbors=3, weights=uniform ..................................\n","[CV] ..... n_neighbors=3, weights=uniform, score=-0.288, total= 0.2s\n","[CV] n_neighbors=3, weights=uniform ..................................\n","[CV] ...... n_neighbors=3, weights=uniform, score=0.040, total= 0.2s\n","[CV] n_neighbors=3, weights=uniform ..................................\n","[CV] ..... n_neighbors=3, weights=uniform, score=-0.017, total= 0.2s\n","[CV] n_neighbors=3, weights=uniform ..................................\n","[CV] ..... n_neighbors=3, weights=uniform, score=-0.148, total= 0.2s\n","[CV] n_neighbors=3, weights=uniform ..................................\n","[CV] ..... n_neighbors=3, weights=uniform, score=-0.117, total= 0.2s\n","[CV] n_neighbors=3, weights=uniform ..................................\n","[CV] ..... n_neighbors=3, weights=uniform, score=-0.145, total= 0.2s\n","[CV] n_neighbors=3, weights=uniform ..................................\n","[CV] ..... n_neighbors=3, weights=uniform, score=-0.130, total= 0.2s\n","[CV] n_neighbors=3, weights=uniform ..................................\n","[CV] ..... n_neighbors=3, weights=uniform, score=-0.099, total= 0.2s\n","[CV] n_neighbors=3, weights=uniform ..................................\n","[CV] ..... n_neighbors=3, weights=uniform, score=-0.115, total= 0.2s\n","[CV] n_neighbors=3, weights=uniform ..................................\n","[CV] ..... n_neighbors=3, weights=uniform, score=-0.180, total= 0.2s\n","[CV] n_neighbors=3, weights=uniform ..................................\n","[CV] ..... n_neighbors=3, weights=uniform, score=-0.181, total= 0.2s\n","[CV] n_neighbors=3, weights=uniform ..................................\n","[CV] ..... n_neighbors=3, weights=uniform, score=-0.006, total= 0.2s\n","[CV] n_neighbors=3, weights=uniform ..................................\n","[CV] ..... n_neighbors=3, weights=uniform, score=-0.078, total= 0.2s\n","[CV] n_neighbors=3, weights=uniform ..................................\n","[CV] ..... n_neighbors=3, weights=uniform, score=-0.250, total= 0.2s\n","[CV] n_neighbors=3, weights=uniform ..................................\n","[CV] ..... n_neighbors=3, weights=uniform, score=-0.184, total= 0.2s\n","[CV] n_neighbors=3, weights=distance .................................\n","[CV] .... n_neighbors=3, weights=distance, score=-0.073, total= 0.2s\n","[CV] n_neighbors=3, weights=distance .................................\n","[CV] .... n_neighbors=3, weights=distance, score=-0.136, total= 0.2s\n","[CV] n_neighbors=3, weights=distance .................................\n","[CV] .... n_neighbors=3, weights=distance, score=-0.130, total= 0.2s\n","[CV] n_neighbors=3, weights=distance .................................\n","[CV] .... n_neighbors=3, weights=distance, score=-0.114, total= 0.2s\n","[CV] n_neighbors=3, weights=distance .................................\n","[CV] .... n_neighbors=3, weights=distance, score=-0.260, total= 0.2s\n","[CV] n_neighbors=3, weights=distance .................................\n","[CV] .... n_neighbors=3, weights=distance, score=-0.028, total= 0.2s\n","[CV] n_neighbors=3, weights=distance .................................\n","[CV] .... n_neighbors=3, weights=distance, score=-0.130, total= 0.2s\n","[CV] n_neighbors=3, weights=distance .................................\n","[CV] .... n_neighbors=3, weights=distance, score=-0.175, total= 0.2s\n","[CV] n_neighbors=3, weights=distance .................................\n","[CV] .... n_neighbors=3, weights=distance, score=-0.174, total= 0.2s\n","[CV] n_neighbors=3, weights=distance .................................\n","[CV] .... n_neighbors=3, weights=distance, score=-0.070, total= 0.2s\n","[CV] n_neighbors=3, weights=distance .................................\n","[CV] .... n_neighbors=3, weights=distance, score=-0.070, total= 0.2s\n","[CV] n_neighbors=3, weights=distance .................................\n","[CV] .... n_neighbors=3, weights=distance, score=-0.176, total= 0.2s\n","[CV] n_neighbors=3, weights=distance .................................\n","[CV] .... n_neighbors=3, weights=distance, score=-0.236, total= 0.2s\n","[CV] n_neighbors=3, weights=distance .................................\n","[CV] .... n_neighbors=3, weights=distance, score=-0.315, total= 0.2s\n","[CV] n_neighbors=3, weights=distance .................................\n","[CV] .... n_neighbors=3, weights=distance, score=-0.034, total= 0.2s\n","[CV] n_neighbors=3, weights=distance .................................\n","[CV] .... n_neighbors=3, weights=distance, score=-0.292, total= 0.2s\n","[CV] n_neighbors=3, weights=distance .................................\n","[CV] ..... n_neighbors=3, weights=distance, score=0.052, total= 0.2s\n","[CV] n_neighbors=3, weights=distance .................................\n","[CV] ..... n_neighbors=3, weights=distance, score=0.000, total= 0.2s\n","[CV] n_neighbors=3, weights=distance .................................\n","[CV] .... n_neighbors=3, weights=distance, score=-0.147, total= 0.2s\n","[CV] n_neighbors=3, weights=distance .................................\n","[CV] .... n_neighbors=3, weights=distance, score=-0.108, total= 0.2s\n","[CV] n_neighbors=3, weights=distance .................................\n","[CV] .... n_neighbors=3, weights=distance, score=-0.145, total= 0.2s\n","[CV] n_neighbors=3, weights=distance .................................\n","[CV] .... n_neighbors=3, weights=distance, score=-0.125, total= 0.2s\n","[CV] n_neighbors=3, weights=distance .................................\n","[CV] .... n_neighbors=3, weights=distance, score=-0.098, total= 0.2s\n","[CV] n_neighbors=3, weights=distance .................................\n","[CV] .... n_neighbors=3, weights=distance, score=-0.107, total= 0.2s\n","[CV] n_neighbors=3, weights=distance .................................\n","[CV] .... n_neighbors=3, weights=distance, score=-0.162, total= 0.2s\n","[CV] n_neighbors=3, weights=distance .................................\n","[CV] .... n_neighbors=3, weights=distance, score=-0.189, total= 0.2s\n","[CV] n_neighbors=3, weights=distance .................................\n","[CV] .... n_neighbors=3, weights=distance, score=-0.000, total= 0.2s\n","[CV] n_neighbors=3, weights=distance .................................\n","[CV] .... n_neighbors=3, weights=distance, score=-0.078, total= 0.2s\n","[CV] n_neighbors=3, weights=distance .................................\n","[CV] .... n_neighbors=3, weights=distance, score=-0.243, total= 0.2s\n","[CV] n_neighbors=3, weights=distance .................................\n","[CV] .... n_neighbors=3, weights=distance, score=-0.175, total= 0.2s\n","[CV] n_neighbors=4, weights=uniform ..................................\n","[CV] ..... n_neighbors=4, weights=uniform, score=-0.056, total= 0.2s\n","[CV] n_neighbors=4, weights=uniform ..................................\n","[CV] ..... n_neighbors=4, weights=uniform, score=-0.123, total= 0.2s\n","[CV] n_neighbors=4, weights=uniform ..................................\n","[CV] ..... n_neighbors=4, weights=uniform, score=-0.099, total= 0.2s\n","[CV] n_neighbors=4, weights=uniform ..................................\n","[CV] ..... n_neighbors=4, weights=uniform, score=-0.098, total= 0.2s\n","[CV] n_neighbors=4, weights=uniform ..................................\n","[CV] ..... n_neighbors=4, weights=uniform, score=-0.208, total= 0.2s\n","[CV] n_neighbors=4, weights=uniform ..................................\n","[CV] ...... n_neighbors=4, weights=uniform, score=0.009, total= 0.2s\n","[CV] n_neighbors=4, weights=uniform ..................................\n","[CV] ..... n_neighbors=4, weights=uniform, score=-0.031, total= 0.2s\n","[CV] n_neighbors=4, weights=uniform ..................................\n","[CV] ..... n_neighbors=4, weights=uniform, score=-0.109, total= 0.2s\n","[CV] n_neighbors=4, weights=uniform ..................................\n","[CV] ..... n_neighbors=4, weights=uniform, score=-0.130, total= 0.2s\n","[CV] n_neighbors=4, weights=uniform ..................................\n","[CV] ...... n_neighbors=4, weights=uniform, score=0.002, total= 0.2s\n","[CV] n_neighbors=4, weights=uniform ..................................\n","[CV] ..... n_neighbors=4, weights=uniform, score=-0.092, total= 0.2s\n","[CV] n_neighbors=4, weights=uniform ..................................\n","[CV] ..... n_neighbors=4, weights=uniform, score=-0.155, total= 0.2s\n","[CV] n_neighbors=4, weights=uniform ..................................\n","[CV] ..... n_neighbors=4, weights=uniform, score=-0.186, total= 0.2s\n","[CV] n_neighbors=4, weights=uniform ..................................\n","[CV] ..... n_neighbors=4, weights=uniform, score=-0.190, total= 0.2s\n","[CV] n_neighbors=4, weights=uniform ..................................\n","[CV] ...... n_neighbors=4, weights=uniform, score=0.010, total= 0.2s\n","[CV] n_neighbors=4, weights=uniform ..................................\n","[CV] ..... n_neighbors=4, weights=uniform, score=-0.175, total= 0.2s\n","[CV] n_neighbors=4, weights=uniform ..................................\n","[CV] ...... n_neighbors=4, weights=uniform, score=0.010, total= 0.2s\n","[CV] n_neighbors=4, weights=uniform ..................................\n","[CV] ..... n_neighbors=4, weights=uniform, score=-0.016, total= 0.2s\n","[CV] n_neighbors=4, weights=uniform ..................................\n","[CV] ..... n_neighbors=4, weights=uniform, score=-0.020, total= 0.2s\n","[CV] n_neighbors=4, weights=uniform ..................................\n","[CV] ..... n_neighbors=4, weights=uniform, score=-0.028, total= 0.2s\n","[CV] n_neighbors=4, weights=uniform ..................................\n","[CV] ..... n_neighbors=4, weights=uniform, score=-0.089, total= 0.2s\n","[CV] n_neighbors=4, weights=uniform ..................................\n","[CV] ..... n_neighbors=4, weights=uniform, score=-0.073, total= 0.2s\n","[CV] n_neighbors=4, weights=uniform ..................................\n","[CV] ..... n_neighbors=4, weights=uniform, score=-0.070, total= 0.2s\n","[CV] n_neighbors=4, weights=uniform ..................................\n","[CV] ..... n_neighbors=4, weights=uniform, score=-0.080, total= 0.2s\n","[CV] n_neighbors=4, weights=uniform ..................................\n","[CV] ..... n_neighbors=4, weights=uniform, score=-0.112, total= 0.2s\n","[CV] n_neighbors=4, weights=uniform ..................................\n","[CV] ..... n_neighbors=4, weights=uniform, score=-0.133, total= 0.2s\n","[CV] n_neighbors=4, weights=uniform ..................................\n","[CV] ...... n_neighbors=4, weights=uniform, score=0.016, total= 0.2s\n","[CV] n_neighbors=4, weights=uniform ..................................\n","[CV] ..... n_neighbors=4, weights=uniform, score=-0.053, total= 0.2s\n","[CV] n_neighbors=4, weights=uniform ..................................\n","[CV] ..... n_neighbors=4, weights=uniform, score=-0.172, total= 0.2s\n","[CV] n_neighbors=4, weights=uniform ..................................\n","[CV] ..... n_neighbors=4, weights=uniform, score=-0.051, total= 0.2s\n","[CV] n_neighbors=4, weights=distance .................................\n","[CV] .... n_neighbors=4, weights=distance, score=-0.036, total= 0.2s\n","[CV] n_neighbors=4, weights=distance .................................\n","[CV] .... n_neighbors=4, weights=distance, score=-0.124, total= 0.2s\n","[CV] n_neighbors=4, weights=distance .................................\n","[CV] .... n_neighbors=4, weights=distance, score=-0.098, total= 0.2s\n","[CV] n_neighbors=4, weights=distance .................................\n","[CV] .... n_neighbors=4, weights=distance, score=-0.083, total= 0.2s\n","[CV] n_neighbors=4, weights=distance .................................\n","[CV] .... n_neighbors=4, weights=distance, score=-0.205, total= 0.2s\n","[CV] n_neighbors=4, weights=distance .................................\n","[CV] ..... n_neighbors=4, weights=distance, score=0.006, total= 0.2s\n","[CV] n_neighbors=4, weights=distance .................................\n","[CV] .... n_neighbors=4, weights=distance, score=-0.030, total= 0.2s\n","[CV] n_neighbors=4, weights=distance .................................\n","[CV] .... n_neighbors=4, weights=distance, score=-0.107, total= 0.2s\n","[CV] n_neighbors=4, weights=distance .................................\n","[CV] .... n_neighbors=4, weights=distance, score=-0.113, total= 0.2s\n","[CV] n_neighbors=4, weights=distance .................................\n","[CV] .... n_neighbors=4, weights=distance, score=-0.001, total= 0.2s\n","[CV] n_neighbors=4, weights=distance .................................\n","[CV] .... n_neighbors=4, weights=distance, score=-0.081, total= 0.2s\n","[CV] n_neighbors=4, weights=distance .................................\n","[CV] .... n_neighbors=4, weights=distance, score=-0.151, total= 0.2s\n","[CV] n_neighbors=4, weights=distance .................................\n","[CV] .... n_neighbors=4, weights=distance, score=-0.171, total= 0.2s\n","[CV] n_neighbors=4, weights=distance .................................\n","[CV] .... n_neighbors=4, weights=distance, score=-0.185, total= 0.2s\n","[CV] n_neighbors=4, weights=distance .................................\n","[CV] ..... n_neighbors=4, weights=distance, score=0.021, total= 0.2s\n","[CV] n_neighbors=4, weights=distance .................................\n","[CV] .... n_neighbors=4, weights=distance, score=-0.180, total= 0.2s\n","[CV] n_neighbors=4, weights=distance .................................\n","[CV] ..... n_neighbors=4, weights=distance, score=0.025, total= 0.2s\n","[CV] n_neighbors=4, weights=distance .................................\n","[CV] .... n_neighbors=4, weights=distance, score=-0.004, total= 0.2s\n","[CV] n_neighbors=4, weights=distance .................................\n","[CV] .... n_neighbors=4, weights=distance, score=-0.021, total= 0.2s\n","[CV] n_neighbors=4, weights=distance .................................\n","[CV] .... n_neighbors=4, weights=distance, score=-0.022, total= 0.2s\n","[CV] n_neighbors=4, weights=distance .................................\n","[CV] .... n_neighbors=4, weights=distance, score=-0.089, total= 0.2s\n","[CV] n_neighbors=4, weights=distance .................................\n","[CV] .... n_neighbors=4, weights=distance, score=-0.067, total= 0.2s\n","[CV] n_neighbors=4, weights=distance .................................\n","[CV] .... n_neighbors=4, weights=distance, score=-0.063, total= 0.2s\n","[CV] n_neighbors=4, weights=distance .................................\n","[CV] .... n_neighbors=4, weights=distance, score=-0.071, total= 0.2s\n","[CV] n_neighbors=4, weights=distance .................................\n","[CV] .... n_neighbors=4, weights=distance, score=-0.102, total= 0.2s\n","[CV] n_neighbors=4, weights=distance .................................\n","[CV] .... n_neighbors=4, weights=distance, score=-0.136, total= 0.2s\n","[CV] n_neighbors=4, weights=distance .................................\n","[CV] ..... n_neighbors=4, weights=distance, score=0.025, total= 0.2s\n","[CV] n_neighbors=4, weights=distance .................................\n","[CV] .... n_neighbors=4, weights=distance, score=-0.052, total= 0.2s\n","[CV] n_neighbors=4, weights=distance .................................\n","[CV] .... n_neighbors=4, weights=distance, score=-0.167, total= 0.2s\n","[CV] n_neighbors=4, weights=distance .................................\n","[CV] .... n_neighbors=4, weights=distance, score=-0.047, total= 0.2s\n","[CV] n_neighbors=5, weights=uniform ..................................\n","[CV] ...... n_neighbors=5, weights=uniform, score=0.016, total= 0.2s\n","[CV] n_neighbors=5, weights=uniform ..................................\n","[CV] ..... n_neighbors=5, weights=uniform, score=-0.080, total= 0.2s\n","[CV] n_neighbors=5, weights=uniform ..................................\n","[CV] ..... n_neighbors=5, weights=uniform, score=-0.058, total= 0.2s\n","[CV] n_neighbors=5, weights=uniform ..................................\n","[CV] ..... n_neighbors=5, weights=uniform, score=-0.062, total= 0.2s\n","[CV] n_neighbors=5, weights=uniform ..................................\n","[CV] ..... n_neighbors=5, weights=uniform, score=-0.174, total= 0.2s\n","[CV] n_neighbors=5, weights=uniform ..................................\n","[CV] ...... n_neighbors=5, weights=uniform, score=0.051, total= 0.2s\n","[CV] n_neighbors=5, weights=uniform ..................................\n","[CV] ..... n_neighbors=5, weights=uniform, score=-0.022, total= 0.2s\n","[CV] n_neighbors=5, weights=uniform ..................................\n","[CV] ..... n_neighbors=5, weights=uniform, score=-0.037, total= 0.2s\n","[CV] n_neighbors=5, weights=uniform ..................................\n","[CV] ..... n_neighbors=5, weights=uniform, score=-0.131, total= 0.2s\n","[CV] n_neighbors=5, weights=uniform ..................................\n","[CV] ..... n_neighbors=5, weights=uniform, score=-0.007, total= 0.2s\n","[CV] n_neighbors=5, weights=uniform ..................................\n","[CV] ..... n_neighbors=5, weights=uniform, score=-0.045, total= 0.2s\n","[CV] n_neighbors=5, weights=uniform ..................................\n","[CV] ..... n_neighbors=5, weights=uniform, score=-0.153, total= 0.2s\n","[CV] n_neighbors=5, weights=uniform ..................................\n","[CV] ..... n_neighbors=5, weights=uniform, score=-0.127, total= 0.2s\n","[CV] n_neighbors=5, weights=uniform ..................................\n","[CV] ..... n_neighbors=5, weights=uniform, score=-0.125, total= 0.2s\n","[CV] n_neighbors=5, weights=uniform ..................................\n","[CV] ...... n_neighbors=5, weights=uniform, score=0.037, total= 0.2s\n","[CV] n_neighbors=5, weights=uniform ..................................\n","[CV] ..... n_neighbors=5, weights=uniform, score=-0.182, total= 0.2s\n","[CV] n_neighbors=5, weights=uniform ..................................\n","[CV] ...... n_neighbors=5, weights=uniform, score=0.050, total= 0.2s\n","[CV] n_neighbors=5, weights=uniform ..................................\n","[CV] ...... n_neighbors=5, weights=uniform, score=0.021, total= 0.2s\n","[CV] n_neighbors=5, weights=uniform ..................................\n","[CV] ..... n_neighbors=5, weights=uniform, score=-0.039, total= 0.2s\n","[CV] n_neighbors=5, weights=uniform ..................................\n","[CV] ...... n_neighbors=5, weights=uniform, score=0.019, total= 0.2s\n","[CV] n_neighbors=5, weights=uniform ..................................\n","[CV] ..... n_neighbors=5, weights=uniform, score=-0.067, total= 0.2s\n","[CV] n_neighbors=5, weights=uniform ..................................\n","[CV] ..... n_neighbors=5, weights=uniform, score=-0.066, total= 0.2s\n","[CV] n_neighbors=5, weights=uniform ..................................\n","[CV] ...... n_neighbors=5, weights=uniform, score=0.008, total= 0.2s\n","[CV] n_neighbors=5, weights=uniform ..................................\n","[CV] ..... n_neighbors=5, weights=uniform, score=-0.050, total= 0.2s\n","[CV] n_neighbors=5, weights=uniform ..................................\n","[CV] ..... n_neighbors=5, weights=uniform, score=-0.048, total= 0.2s\n","[CV] n_neighbors=5, weights=uniform ..................................\n","[CV] ..... n_neighbors=5, weights=uniform, score=-0.045, total= 0.2s\n","[CV] n_neighbors=5, weights=uniform ..................................\n","[CV] ...... n_neighbors=5, weights=uniform, score=0.025, total= 0.2s\n","[CV] n_neighbors=5, weights=uniform ..................................\n","[CV] ..... n_neighbors=5, weights=uniform, score=-0.039, total= 0.2s\n","[CV] n_neighbors=5, weights=uniform ..................................\n","[CV] ..... n_neighbors=5, weights=uniform, score=-0.110, total= 0.2s\n","[CV] n_neighbors=5, weights=uniform ..................................\n","[CV] ..... n_neighbors=5, weights=uniform, score=-0.049, total= 0.2s\n","[CV] n_neighbors=5, weights=distance .................................\n","[CV] ..... n_neighbors=5, weights=distance, score=0.030, total= 0.2s\n","[CV] n_neighbors=5, weights=distance .................................\n","[CV] .... n_neighbors=5, weights=distance, score=-0.079, total= 0.2s\n","[CV] n_neighbors=5, weights=distance .................................\n","[CV] .... n_neighbors=5, weights=distance, score=-0.056, total= 0.2s\n","[CV] n_neighbors=5, weights=distance .................................\n","[CV] .... n_neighbors=5, weights=distance, score=-0.048, total= 0.2s\n","[CV] n_neighbors=5, weights=distance .................................\n","[CV] .... n_neighbors=5, weights=distance, score=-0.173, total= 0.2s\n","[CV] n_neighbors=5, weights=distance .................................\n","[CV] ..... n_neighbors=5, weights=distance, score=0.046, total= 0.2s\n","[CV] n_neighbors=5, weights=distance .................................\n","[CV] .... n_neighbors=5, weights=distance, score=-0.014, total= 0.2s\n","[CV] n_neighbors=5, weights=distance .................................\n","[CV] .... n_neighbors=5, weights=distance, score=-0.041, total= 0.2s\n","[CV] n_neighbors=5, weights=distance .................................\n","[CV] .... n_neighbors=5, weights=distance, score=-0.111, total= 0.2s\n","[CV] n_neighbors=5, weights=distance .................................\n","[CV] .... n_neighbors=5, weights=distance, score=-0.007, total= 0.2s\n","[CV] n_neighbors=5, weights=distance .................................\n","[CV] .... n_neighbors=5, weights=distance, score=-0.038, total= 0.2s\n","[CV] n_neighbors=5, weights=distance .................................\n","[CV] .... n_neighbors=5, weights=distance, score=-0.146, total= 0.2s\n","[CV] n_neighbors=5, weights=distance .................................\n","[CV] .... n_neighbors=5, weights=distance, score=-0.116, total= 0.2s\n","[CV] n_neighbors=5, weights=distance .................................\n","[CV] .... n_neighbors=5, weights=distance, score=-0.128, total= 0.2s\n","[CV] n_neighbors=5, weights=distance .................................\n","[CV] ..... n_neighbors=5, weights=distance, score=0.049, total= 0.2s\n","[CV] n_neighbors=5, weights=distance .................................\n","[CV] .... n_neighbors=5, weights=distance, score=-0.181, total= 0.2s\n","[CV] n_neighbors=5, weights=distance .................................\n","[CV] ..... n_neighbors=5, weights=distance, score=0.060, total= 0.2s\n","[CV] n_neighbors=5, weights=distance .................................\n","[CV] ..... n_neighbors=5, weights=distance, score=0.035, total= 0.2s\n","[CV] n_neighbors=5, weights=distance .................................\n","[CV] .... n_neighbors=5, weights=distance, score=-0.033, total= 0.2s\n","[CV] n_neighbors=5, weights=distance .................................\n","[CV] ..... n_neighbors=5, weights=distance, score=0.023, total= 0.2s\n","[CV] n_neighbors=5, weights=distance .................................\n","[CV] .... n_neighbors=5, weights=distance, score=-0.064, total= 0.2s\n","[CV] n_neighbors=5, weights=distance .................................\n","[CV] .... n_neighbors=5, weights=distance, score=-0.057, total= 0.2s\n","[CV] n_neighbors=5, weights=distance .................................\n","[CV] ..... n_neighbors=5, weights=distance, score=0.008, total= 0.2s\n","[CV] n_neighbors=5, weights=distance .................................\n","[CV] .... n_neighbors=5, weights=distance, score=-0.046, total= 0.2s\n","[CV] n_neighbors=5, weights=distance .................................\n","[CV] .... n_neighbors=5, weights=distance, score=-0.041, total= 0.2s\n","[CV] n_neighbors=5, weights=distance .................................\n","[CV] .... n_neighbors=5, weights=distance, score=-0.054, total= 0.2s\n","[CV] n_neighbors=5, weights=distance .................................\n","[CV] ..... n_neighbors=5, weights=distance, score=0.033, total= 0.2s\n","[CV] n_neighbors=5, weights=distance .................................\n","[CV] .... n_neighbors=5, weights=distance, score=-0.037, total= 0.2s\n","[CV] n_neighbors=5, weights=distance .................................\n","[CV] .... n_neighbors=5, weights=distance, score=-0.102, total= 0.2s\n","[CV] n_neighbors=5, weights=distance .................................\n","[CV] .... n_neighbors=5, weights=distance, score=-0.043, total= 0.2s\n","[CV] n_neighbors=6, weights=uniform ..................................\n","[CV] ...... n_neighbors=6, weights=uniform, score=0.008, total= 0.2s\n","[CV] n_neighbors=6, weights=uniform ..................................\n","[CV] ..... n_neighbors=6, weights=uniform, score=-0.049, total= 0.2s\n","[CV] n_neighbors=6, weights=uniform ..................................\n","[CV] ...... n_neighbors=6, weights=uniform, score=0.018, total= 0.2s\n","[CV] n_neighbors=6, weights=uniform ..................................\n","[CV] ..... n_neighbors=6, weights=uniform, score=-0.063, total= 0.2s\n","[CV] n_neighbors=6, weights=uniform ..................................\n","[CV] ..... n_neighbors=6, weights=uniform, score=-0.154, total= 0.2s\n","[CV] n_neighbors=6, weights=uniform ..................................\n","[CV] ...... n_neighbors=6, weights=uniform, score=0.092, total= 0.2s\n","[CV] n_neighbors=6, weights=uniform ..................................\n","[CV] ..... n_neighbors=6, weights=uniform, score=-0.040, total= 0.2s\n","[CV] n_neighbors=6, weights=uniform ..................................\n","[CV] ..... n_neighbors=6, weights=uniform, score=-0.009, total= 0.2s\n","[CV] n_neighbors=6, weights=uniform ..................................\n","[CV] ..... n_neighbors=6, weights=uniform, score=-0.078, total= 0.2s\n","[CV] n_neighbors=6, weights=uniform ..................................\n","[CV] ...... n_neighbors=6, weights=uniform, score=0.005, total= 0.2s\n","[CV] n_neighbors=6, weights=uniform ..................................\n","[CV] ..... n_neighbors=6, weights=uniform, score=-0.022, total= 0.2s\n","[CV] n_neighbors=6, weights=uniform ..................................\n","[CV] ..... n_neighbors=6, weights=uniform, score=-0.070, total= 0.2s\n","[CV] n_neighbors=6, weights=uniform ..................................\n","[CV] ..... n_neighbors=6, weights=uniform, score=-0.079, total= 0.2s\n","[CV] n_neighbors=6, weights=uniform ..................................\n","[CV] ..... n_neighbors=6, weights=uniform, score=-0.062, total= 0.2s\n","[CV] n_neighbors=6, weights=uniform ..................................\n","[CV] ...... n_neighbors=6, weights=uniform, score=0.026, total= 0.2s\n","[CV] n_neighbors=6, weights=uniform ..................................\n","[CV] ..... n_neighbors=6, weights=uniform, score=-0.172, total= 0.2s\n","[CV] n_neighbors=6, weights=uniform ..................................\n","[CV] ...... n_neighbors=6, weights=uniform, score=0.050, total= 0.2s\n","[CV] n_neighbors=6, weights=uniform ..................................\n","[CV] ...... n_neighbors=6, weights=uniform, score=0.025, total= 0.2s\n","[CV] n_neighbors=6, weights=uniform ..................................\n","[CV] ...... n_neighbors=6, weights=uniform, score=0.000, total= 0.2s\n","[CV] n_neighbors=6, weights=uniform ..................................\n","[CV] ...... n_neighbors=6, weights=uniform, score=0.039, total= 0.2s\n","[CV] n_neighbors=6, weights=uniform ..................................\n","[CV] ..... n_neighbors=6, weights=uniform, score=-0.007, total= 0.2s\n","[CV] n_neighbors=6, weights=uniform ..................................\n","[CV] ..... n_neighbors=6, weights=uniform, score=-0.073, total= 0.2s\n","[CV] n_neighbors=6, weights=uniform ..................................\n","[CV] ...... n_neighbors=6, weights=uniform, score=0.017, total= 0.2s\n","[CV] n_neighbors=6, weights=uniform ..................................\n","[CV] ..... n_neighbors=6, weights=uniform, score=-0.000, total= 0.2s\n","[CV] n_neighbors=6, weights=uniform ..................................\n","[CV] ..... n_neighbors=6, weights=uniform, score=-0.037, total= 0.2s\n","[CV] n_neighbors=6, weights=uniform ..................................\n","[CV] ...... n_neighbors=6, weights=uniform, score=0.018, total= 0.2s\n","[CV] n_neighbors=6, weights=uniform ..................................\n","[CV] ...... n_neighbors=6, weights=uniform, score=0.051, total= 0.2s\n","[CV] n_neighbors=6, weights=uniform ..................................\n","[CV] ..... n_neighbors=6, weights=uniform, score=-0.022, total= 0.2s\n","[CV] n_neighbors=6, weights=uniform ..................................\n","[CV] ..... n_neighbors=6, weights=uniform, score=-0.069, total= 0.2s\n","[CV] n_neighbors=6, weights=uniform ..................................\n","[CV] ..... n_neighbors=6, weights=uniform, score=-0.072, total= 0.2s\n","[CV] n_neighbors=6, weights=distance .................................\n","[CV] ..... n_neighbors=6, weights=distance, score=0.025, total= 0.2s\n","[CV] n_neighbors=6, weights=distance .................................\n","[CV] .... n_neighbors=6, weights=distance, score=-0.045, total= 0.2s\n","[CV] n_neighbors=6, weights=distance .................................\n","[CV] ..... n_neighbors=6, weights=distance, score=0.016, total= 0.2s\n","[CV] n_neighbors=6, weights=distance .................................\n","[CV] .... n_neighbors=6, weights=distance, score=-0.046, total= 0.2s\n","[CV] n_neighbors=6, weights=distance .................................\n","[CV] .... n_neighbors=6, weights=distance, score=-0.152, total= 0.2s\n","[CV] n_neighbors=6, weights=distance .................................\n","[CV] ..... n_neighbors=6, weights=distance, score=0.089, total= 0.2s\n","[CV] n_neighbors=6, weights=distance .................................\n","[CV] .... n_neighbors=6, weights=distance, score=-0.034, total= 0.2s\n","[CV] n_neighbors=6, weights=distance .................................\n","[CV] .... n_neighbors=6, weights=distance, score=-0.012, total= 0.2s\n","[CV] n_neighbors=6, weights=distance .................................\n","[CV] .... n_neighbors=6, weights=distance, score=-0.061, total= 0.2s\n","[CV] n_neighbors=6, weights=distance .................................\n","[CV] ..... n_neighbors=6, weights=distance, score=0.005, total= 0.2s\n","[CV] n_neighbors=6, weights=distance .................................\n","[CV] .... n_neighbors=6, weights=distance, score=-0.015, total= 0.2s\n","[CV] n_neighbors=6, weights=distance .................................\n","[CV] .... n_neighbors=6, weights=distance, score=-0.071, total= 0.2s\n","[CV] n_neighbors=6, weights=distance .................................\n","[CV] .... n_neighbors=6, weights=distance, score=-0.073, total= 0.2s\n","[CV] n_neighbors=6, weights=distance .................................\n","[CV] .... n_neighbors=6, weights=distance, score=-0.071, total= 0.2s\n","[CV] n_neighbors=6, weights=distance .................................\n","[CV] ..... n_neighbors=6, weights=distance, score=0.041, total= 0.2s\n","[CV] n_neighbors=6, weights=distance .................................\n","[CV] .... n_neighbors=6, weights=distance, score=-0.168, total= 0.2s\n","[CV] n_neighbors=6, weights=distance .................................\n","[CV] ..... n_neighbors=6, weights=distance, score=0.064, total= 0.2s\n","[CV] n_neighbors=6, weights=distance .................................\n","[CV] ..... n_neighbors=6, weights=distance, score=0.038, total= 0.2s\n","[CV] n_neighbors=6, weights=distance .................................\n","[CV] ..... n_neighbors=6, weights=distance, score=0.005, total= 0.2s\n","[CV] n_neighbors=6, weights=distance .................................\n","[CV] ..... n_neighbors=6, weights=distance, score=0.045, total= 0.2s\n","[CV] n_neighbors=6, weights=distance .................................\n","[CV] .... n_neighbors=6, weights=distance, score=-0.010, total= 0.2s\n","[CV] n_neighbors=6, weights=distance .................................\n","[CV] .... n_neighbors=6, weights=distance, score=-0.062, total= 0.2s\n","[CV] n_neighbors=6, weights=distance .................................\n","[CV] ..... n_neighbors=6, weights=distance, score=0.018, total= 0.2s\n","[CV] n_neighbors=6, weights=distance .................................\n","[CV] ..... n_neighbors=6, weights=distance, score=0.001, total= 0.2s\n","[CV] n_neighbors=6, weights=distance .................................\n","[CV] .... n_neighbors=6, weights=distance, score=-0.029, total= 0.2s\n","[CV] n_neighbors=6, weights=distance .................................\n","[CV] ..... n_neighbors=6, weights=distance, score=0.006, total= 0.2s\n","[CV] n_neighbors=6, weights=distance .................................\n","[CV] ..... n_neighbors=6, weights=distance, score=0.054, total= 0.2s\n","[CV] n_neighbors=6, weights=distance .................................\n","[CV] .... n_neighbors=6, weights=distance, score=-0.018, total= 0.2s\n","[CV] n_neighbors=6, weights=distance .................................\n","[CV] .... n_neighbors=6, weights=distance, score=-0.060, total= 0.2s\n","[CV] n_neighbors=6, weights=distance .................................\n","[CV] .... n_neighbors=6, weights=distance, score=-0.061, total= 0.2s\n","[Parallel(n_jobs=1)]: Done 300 out of 300 | elapsed: 1.1min finished\n","KNeighborsRegressor(algorithm='auto', leaf_size=30, metric='minkowski',\n"," metric_params=None, n_jobs=None, n_neighbors=6, p=2,\n"," weights='distance')\n","window: 1, stide: 1, rmse: 2.7271550361703247\n","(3000, 238)\n","Number of Feature-Columns: 238\n","\n","Features Ready for undergoing selection tests done ...\n","\n","Fitting 30 folds for each of 10 candidates, totalling 300 fits\n","[Parallel(n_jobs=1)]: Using backend SequentialBackend with 1 concurrent workers.\n","[CV] n_neighbors=2, weights=uniform ..................................\n","[CV] ..... n_neighbors=2, weights=uniform, score=-0.137, total= 0.2s\n","[Parallel(n_jobs=1)]: Done 1 out of 1 | elapsed: 0.2s remaining: 0.0s\n","[CV] n_neighbors=2, weights=uniform ..................................\n","[CV] ..... n_neighbors=2, weights=uniform, score=-0.393, total= 0.2s\n","[Parallel(n_jobs=1)]: Done 2 out of 2 | elapsed: 0.4s remaining: 0.0s\n","[CV] n_neighbors=2, weights=uniform ..................................\n","[CV] ..... n_neighbors=2, weights=uniform, score=-0.270, total= 0.2s\n","[Parallel(n_jobs=1)]: Done 3 out of 3 | elapsed: 0.6s remaining: 0.0s\n","[CV] n_neighbors=2, weights=uniform ..................................\n","[CV] ..... n_neighbors=2, weights=uniform, score=-0.324, total= 0.2s\n","[Parallel(n_jobs=1)]: Done 4 out of 4 | elapsed: 0.8s remaining: 0.0s\n","[CV] n_neighbors=2, weights=uniform ..................................\n","[CV] ..... n_neighbors=2, weights=uniform, score=-0.407, total= 0.2s\n","[CV] n_neighbors=2, weights=uniform ..................................\n","[CV] ..... n_neighbors=2, weights=uniform, score=-0.126, total= 0.2s\n","[CV] n_neighbors=2, weights=uniform ..................................\n","[CV] ..... n_neighbors=2, weights=uniform, score=-0.212, total= 0.2s\n","[CV] n_neighbors=2, weights=uniform ..................................\n","[CV] ..... n_neighbors=2, weights=uniform, score=-0.270, total= 0.2s\n","[CV] n_neighbors=2, weights=uniform ..................................\n","[CV] ..... n_neighbors=2, weights=uniform, score=-0.283, total= 0.2s\n","[CV] n_neighbors=2, weights=uniform ..................................\n","[CV] ..... n_neighbors=2, weights=uniform, score=-0.225, total= 0.2s\n","[CV] n_neighbors=2, weights=uniform ..................................\n","[CV] ..... n_neighbors=2, weights=uniform, score=-0.180, total= 0.2s\n","[CV] n_neighbors=2, weights=uniform ..................................\n","[CV] ..... n_neighbors=2, weights=uniform, score=-0.382, total= 0.2s\n","[CV] n_neighbors=2, weights=uniform ..................................\n","[CV] ..... n_neighbors=2, weights=uniform, score=-0.353, total= 0.2s\n","[CV] n_neighbors=2, weights=uniform ..................................\n","[CV] ..... n_neighbors=2, weights=uniform, score=-0.391, total= 0.2s\n","[CV] n_neighbors=2, weights=uniform ..................................\n","[CV] ..... n_neighbors=2, weights=uniform, score=-0.161, total= 0.2s\n","[CV] n_neighbors=2, weights=uniform ..................................\n","[CV] ..... n_neighbors=2, weights=uniform, score=-0.559, total= 0.2s\n","[CV] n_neighbors=2, weights=uniform ..................................\n","[CV] ..... n_neighbors=2, weights=uniform, score=-0.038, total= 0.2s\n","[CV] n_neighbors=2, weights=uniform ..................................\n","[CV] ..... n_neighbors=2, weights=uniform, score=-0.079, total= 0.2s\n","[CV] n_neighbors=2, weights=uniform ..................................\n","[CV] ..... n_neighbors=2, weights=uniform, score=-0.279, total= 0.2s\n","[CV] n_neighbors=2, weights=uniform ..................................\n","[CV] ..... n_neighbors=2, weights=uniform, score=-0.323, total= 0.2s\n","[CV] n_neighbors=2, weights=uniform ..................................\n","[CV] ..... n_neighbors=2, weights=uniform, score=-0.305, total= 0.2s\n","[CV] n_neighbors=2, weights=uniform ..................................\n","[CV] ..... n_neighbors=2, weights=uniform, score=-0.233, total= 0.2s\n","[CV] n_neighbors=2, weights=uniform ..................................\n","[CV] ..... n_neighbors=2, weights=uniform, score=-0.257, total= 0.2s\n","[CV] n_neighbors=2, weights=uniform ..................................\n","[CV] ..... n_neighbors=2, weights=uniform, score=-0.220, total= 0.2s\n","[CV] n_neighbors=2, weights=uniform ..................................\n","[CV] ..... n_neighbors=2, weights=uniform, score=-0.249, total= 0.2s\n","[CV] n_neighbors=2, weights=uniform ..................................\n","[CV] ..... n_neighbors=2, weights=uniform, score=-0.401, total= 0.2s\n","[CV] n_neighbors=2, weights=uniform ..................................\n","[CV] ..... n_neighbors=2, weights=uniform, score=-0.044, total= 0.2s\n","[CV] n_neighbors=2, weights=uniform ..................................\n","[CV] ..... n_neighbors=2, weights=uniform, score=-0.170, total= 0.2s\n","[CV] n_neighbors=2, weights=uniform ..................................\n","[CV] ..... n_neighbors=2, weights=uniform, score=-0.351, total= 0.2s\n","[CV] n_neighbors=2, weights=uniform ..................................\n","[CV] ..... n_neighbors=2, weights=uniform, score=-0.366, total= 0.2s\n","[CV] n_neighbors=2, weights=distance .................................\n","[CV] .... n_neighbors=2, weights=distance, score=-0.127, total= 0.2s\n","[CV] n_neighbors=2, weights=distance .................................\n","[CV] .... n_neighbors=2, weights=distance, score=-0.394, total= 0.2s\n","[CV] n_neighbors=2, weights=distance .................................\n","[CV] .... n_neighbors=2, weights=distance, score=-0.274, total= 0.2s\n","[CV] n_neighbors=2, weights=distance .................................\n","[CV] .... n_neighbors=2, weights=distance, score=-0.316, total= 0.2s\n","[CV] n_neighbors=2, weights=distance .................................\n","[CV] .... n_neighbors=2, weights=distance, score=-0.397, total= 0.2s\n","[CV] n_neighbors=2, weights=distance .................................\n","[CV] .... n_neighbors=2, weights=distance, score=-0.131, total= 0.2s\n","[CV] n_neighbors=2, weights=distance .................................\n","[CV] .... n_neighbors=2, weights=distance, score=-0.221, total= 0.2s\n","[CV] n_neighbors=2, weights=distance .................................\n","[CV] .... n_neighbors=2, weights=distance, score=-0.270, total= 0.2s\n","[CV] n_neighbors=2, weights=distance .................................\n","[CV] .... n_neighbors=2, weights=distance, score=-0.273, total= 0.2s\n","[CV] n_neighbors=2, weights=distance .................................\n","[CV] .... n_neighbors=2, weights=distance, score=-0.220, total= 0.2s\n","[CV] n_neighbors=2, weights=distance .................................\n","[CV] .... n_neighbors=2, weights=distance, score=-0.181, total= 0.2s\n","[CV] n_neighbors=2, weights=distance .................................\n","[CV] .... n_neighbors=2, weights=distance, score=-0.376, total= 0.2s\n","[CV] n_neighbors=2, weights=distance .................................\n","[CV] .... n_neighbors=2, weights=distance, score=-0.341, total= 0.2s\n","[CV] n_neighbors=2, weights=distance .................................\n","[CV] .... n_neighbors=2, weights=distance, score=-0.393, total= 0.2s\n","[CV] n_neighbors=2, weights=distance .................................\n","[CV] .... n_neighbors=2, weights=distance, score=-0.161, total= 0.2s\n","[CV] n_neighbors=2, weights=distance .................................\n","[CV] .... n_neighbors=2, weights=distance, score=-0.561, total= 0.2s\n","[CV] n_neighbors=2, weights=distance .................................\n","[CV] .... n_neighbors=2, weights=distance, score=-0.032, total= 0.2s\n","[CV] n_neighbors=2, weights=distance .................................\n","[CV] .... n_neighbors=2, weights=distance, score=-0.069, total= 0.2s\n","[CV] n_neighbors=2, weights=distance .................................\n","[CV] .... n_neighbors=2, weights=distance, score=-0.283, total= 0.2s\n","[CV] n_neighbors=2, weights=distance .................................\n","[CV] .... n_neighbors=2, weights=distance, score=-0.306, total= 0.2s\n","[CV] n_neighbors=2, weights=distance .................................\n","[CV] .... n_neighbors=2, weights=distance, score=-0.301, total= 0.2s\n","[CV] n_neighbors=2, weights=distance .................................\n","[CV] .... n_neighbors=2, weights=distance, score=-0.235, total= 0.2s\n","[CV] n_neighbors=2, weights=distance .................................\n","[CV] .... n_neighbors=2, weights=distance, score=-0.256, total= 0.2s\n","[CV] n_neighbors=2, weights=distance .................................\n","[CV] .... n_neighbors=2, weights=distance, score=-0.221, total= 0.2s\n","[CV] n_neighbors=2, weights=distance .................................\n","[CV] .... n_neighbors=2, weights=distance, score=-0.243, total= 0.2s\n","[CV] n_neighbors=2, weights=distance .................................\n","[CV] .... n_neighbors=2, weights=distance, score=-0.404, total= 0.2s\n","[CV] n_neighbors=2, weights=distance .................................\n","[CV] .... n_neighbors=2, weights=distance, score=-0.040, total= 0.2s\n","[CV] n_neighbors=2, weights=distance .................................\n","[CV] .... n_neighbors=2, weights=distance, score=-0.166, total= 0.2s\n","[CV] n_neighbors=2, weights=distance .................................\n","[CV] .... n_neighbors=2, weights=distance, score=-0.350, total= 0.2s\n","[CV] n_neighbors=2, weights=distance .................................\n","[CV] .... n_neighbors=2, weights=distance, score=-0.349, total= 0.2s\n","[CV] n_neighbors=3, weights=uniform ..................................\n","[CV] ..... n_neighbors=3, weights=uniform, score=-0.091, total= 0.2s\n","[CV] n_neighbors=3, weights=uniform ..................................\n","[CV] ..... n_neighbors=3, weights=uniform, score=-0.122, total= 0.2s\n","[CV] n_neighbors=3, weights=uniform ..................................\n","[CV] ..... n_neighbors=3, weights=uniform, score=-0.130, total= 0.2s\n","[CV] n_neighbors=3, weights=uniform ..................................\n","[CV] ..... n_neighbors=3, weights=uniform, score=-0.120, total= 0.2s\n","[CV] n_neighbors=3, weights=uniform ..................................\n","[CV] ..... n_neighbors=3, weights=uniform, score=-0.267, total= 0.2s\n","[CV] n_neighbors=3, weights=uniform ..................................\n","[CV] ..... n_neighbors=3, weights=uniform, score=-0.026, total= 0.2s\n","[CV] n_neighbors=3, weights=uniform ..................................\n","[CV] ..... n_neighbors=3, weights=uniform, score=-0.135, total= 0.2s\n","[CV] n_neighbors=3, weights=uniform ..................................\n","[CV] ..... n_neighbors=3, weights=uniform, score=-0.178, total= 0.2s\n","[CV] n_neighbors=3, weights=uniform ..................................\n","[CV] ..... n_neighbors=3, weights=uniform, score=-0.194, total= 0.2s\n","[CV] n_neighbors=3, weights=uniform ..................................\n","[CV] ..... n_neighbors=3, weights=uniform, score=-0.066, total= 0.2s\n","[CV] n_neighbors=3, weights=uniform ..................................\n","[CV] ..... n_neighbors=3, weights=uniform, score=-0.072, total= 0.2s\n","[CV] n_neighbors=3, weights=uniform ..................................\n","[CV] ..... n_neighbors=3, weights=uniform, score=-0.177, total= 0.2s\n","[CV] n_neighbors=3, weights=uniform ..................................\n","[CV] ..... n_neighbors=3, weights=uniform, score=-0.248, total= 0.2s\n","[CV] n_neighbors=3, weights=uniform ..................................\n","[CV] ..... n_neighbors=3, weights=uniform, score=-0.324, total= 0.2s\n","[CV] n_neighbors=3, weights=uniform ..................................\n","[CV] ..... n_neighbors=3, weights=uniform, score=-0.041, total= 0.2s\n","[CV] n_neighbors=3, weights=uniform ..................................\n","[CV] ..... n_neighbors=3, weights=uniform, score=-0.288, total= 0.2s\n","[CV] n_neighbors=3, weights=uniform ..................................\n","[CV] ...... n_neighbors=3, weights=uniform, score=0.040, total= 0.2s\n","[CV] n_neighbors=3, weights=uniform ..................................\n","[CV] ..... n_neighbors=3, weights=uniform, score=-0.017, total= 0.2s\n","[CV] n_neighbors=3, weights=uniform ..................................\n","[CV] ..... n_neighbors=3, weights=uniform, score=-0.148, total= 0.2s\n","[CV] n_neighbors=3, weights=uniform ..................................\n","[CV] ..... n_neighbors=3, weights=uniform, score=-0.117, total= 0.2s\n","[CV] n_neighbors=3, weights=uniform ..................................\n","[CV] ..... n_neighbors=3, weights=uniform, score=-0.145, total= 0.2s\n","[CV] n_neighbors=3, weights=uniform ..................................\n","[CV] ..... n_neighbors=3, weights=uniform, score=-0.130, total= 0.2s\n","[CV] n_neighbors=3, weights=uniform ..................................\n","[CV] ..... n_neighbors=3, weights=uniform, score=-0.099, total= 0.2s\n","[CV] n_neighbors=3, weights=uniform ..................................\n","[CV] ..... n_neighbors=3, weights=uniform, score=-0.115, total= 0.2s\n","[CV] n_neighbors=3, weights=uniform ..................................\n","[CV] ..... n_neighbors=3, weights=uniform, score=-0.180, total= 0.2s\n","[CV] n_neighbors=3, weights=uniform ..................................\n","[CV] ..... n_neighbors=3, weights=uniform, score=-0.181, total= 0.2s\n","[CV] n_neighbors=3, weights=uniform ..................................\n","[CV] ..... n_neighbors=3, weights=uniform, score=-0.006, total= 0.2s\n","[CV] n_neighbors=3, weights=uniform ..................................\n","[CV] ..... n_neighbors=3, weights=uniform, score=-0.078, total= 0.2s\n","[CV] n_neighbors=3, weights=uniform ..................................\n","[CV] ..... n_neighbors=3, weights=uniform, score=-0.250, total= 0.2s\n","[CV] n_neighbors=3, weights=uniform ..................................\n","[CV] ..... n_neighbors=3, weights=uniform, score=-0.184, total= 0.2s\n","[CV] n_neighbors=3, weights=distance .................................\n","[CV] .... n_neighbors=3, weights=distance, score=-0.073, total= 0.2s\n","[CV] n_neighbors=3, weights=distance .................................\n","[CV] .... n_neighbors=3, weights=distance, score=-0.136, total= 0.2s\n","[CV] n_neighbors=3, weights=distance .................................\n","[CV] .... n_neighbors=3, weights=distance, score=-0.130, total= 0.2s\n","[CV] n_neighbors=3, weights=distance .................................\n","[CV] .... n_neighbors=3, weights=distance, score=-0.114, total= 0.2s\n","[CV] n_neighbors=3, weights=distance .................................\n","[CV] .... n_neighbors=3, weights=distance, score=-0.260, total= 0.2s\n","[CV] n_neighbors=3, weights=distance .................................\n","[CV] .... n_neighbors=3, weights=distance, score=-0.028, total= 0.2s\n","[CV] n_neighbors=3, weights=distance .................................\n","[CV] .... n_neighbors=3, weights=distance, score=-0.130, total= 0.2s\n","[CV] n_neighbors=3, weights=distance .................................\n","[CV] .... n_neighbors=3, weights=distance, score=-0.175, total= 0.2s\n","[CV] n_neighbors=3, weights=distance .................................\n","[CV] .... n_neighbors=3, weights=distance, score=-0.174, total= 0.2s\n","[CV] n_neighbors=3, weights=distance .................................\n","[CV] .... n_neighbors=3, weights=distance, score=-0.070, total= 0.2s\n","[CV] n_neighbors=3, weights=distance .................................\n","[CV] .... n_neighbors=3, weights=distance, score=-0.070, total= 0.2s\n","[CV] n_neighbors=3, weights=distance .................................\n","[CV] .... n_neighbors=3, weights=distance, score=-0.176, total= 0.2s\n","[CV] n_neighbors=3, weights=distance .................................\n","[CV] .... n_neighbors=3, weights=distance, score=-0.236, total= 0.2s\n","[CV] n_neighbors=3, weights=distance .................................\n","[CV] .... n_neighbors=3, weights=distance, score=-0.315, total= 0.2s\n","[CV] n_neighbors=3, weights=distance .................................\n","[CV] .... n_neighbors=3, weights=distance, score=-0.034, total= 0.2s\n","[CV] n_neighbors=3, weights=distance .................................\n","[CV] .... n_neighbors=3, weights=distance, score=-0.292, total= 0.2s\n","[CV] n_neighbors=3, weights=distance .................................\n","[CV] ..... n_neighbors=3, weights=distance, score=0.052, total= 0.2s\n","[CV] n_neighbors=3, weights=distance .................................\n","[CV] ..... n_neighbors=3, weights=distance, score=0.000, total= 0.2s\n","[CV] n_neighbors=3, weights=distance .................................\n","[CV] .... n_neighbors=3, weights=distance, score=-0.147, total= 0.2s\n","[CV] n_neighbors=3, weights=distance .................................\n","[CV] .... n_neighbors=3, weights=distance, score=-0.108, total= 0.2s\n","[CV] n_neighbors=3, weights=distance .................................\n","[CV] .... n_neighbors=3, weights=distance, score=-0.145, total= 0.2s\n","[CV] n_neighbors=3, weights=distance .................................\n","[CV] .... n_neighbors=3, weights=distance, score=-0.125, total= 0.2s\n","[CV] n_neighbors=3, weights=distance .................................\n","[CV] .... n_neighbors=3, weights=distance, score=-0.098, total= 0.2s\n","[CV] n_neighbors=3, weights=distance .................................\n","[CV] .... n_neighbors=3, weights=distance, score=-0.107, total= 0.2s\n","[CV] n_neighbors=3, weights=distance .................................\n","[CV] .... n_neighbors=3, weights=distance, score=-0.162, total= 0.2s\n","[CV] n_neighbors=3, weights=distance .................................\n","[CV] .... n_neighbors=3, weights=distance, score=-0.189, total= 0.2s\n","[CV] n_neighbors=3, weights=distance .................................\n","[CV] .... n_neighbors=3, weights=distance, score=-0.000, total= 0.2s\n","[CV] n_neighbors=3, weights=distance .................................\n","[CV] .... n_neighbors=3, weights=distance, score=-0.078, total= 0.2s\n","[CV] n_neighbors=3, weights=distance .................................\n","[CV] .... n_neighbors=3, weights=distance, score=-0.243, total= 0.2s\n","[CV] n_neighbors=3, weights=distance .................................\n","[CV] .... n_neighbors=3, weights=distance, score=-0.175, total= 0.2s\n","[CV] n_neighbors=4, weights=uniform ..................................\n","[CV] ..... n_neighbors=4, weights=uniform, score=-0.056, total= 0.2s\n","[CV] n_neighbors=4, weights=uniform ..................................\n","[CV] ..... n_neighbors=4, weights=uniform, score=-0.123, total= 0.2s\n","[CV] n_neighbors=4, weights=uniform ..................................\n","[CV] ..... n_neighbors=4, weights=uniform, score=-0.099, total= 0.2s\n","[CV] n_neighbors=4, weights=uniform ..................................\n","[CV] ..... n_neighbors=4, weights=uniform, score=-0.098, total= 0.2s\n","[CV] n_neighbors=4, weights=uniform ..................................\n","[CV] ..... n_neighbors=4, weights=uniform, score=-0.208, total= 0.2s\n","[CV] n_neighbors=4, weights=uniform ..................................\n","[CV] ...... n_neighbors=4, weights=uniform, score=0.009, total= 0.2s\n","[CV] n_neighbors=4, weights=uniform ..................................\n","[CV] ..... n_neighbors=4, weights=uniform, score=-0.031, total= 0.2s\n","[CV] n_neighbors=4, weights=uniform ..................................\n","[CV] ..... n_neighbors=4, weights=uniform, score=-0.109, total= 0.2s\n","[CV] n_neighbors=4, weights=uniform ..................................\n","[CV] ..... n_neighbors=4, weights=uniform, score=-0.130, total= 0.2s\n","[CV] n_neighbors=4, weights=uniform ..................................\n","[CV] ...... n_neighbors=4, weights=uniform, score=0.002, total= 0.2s\n","[CV] n_neighbors=4, weights=uniform ..................................\n","[CV] ..... n_neighbors=4, weights=uniform, score=-0.092, total= 0.2s\n","[CV] n_neighbors=4, weights=uniform ..................................\n","[CV] ..... n_neighbors=4, weights=uniform, score=-0.155, total= 0.2s\n","[CV] n_neighbors=4, weights=uniform ..................................\n","[CV] ..... n_neighbors=4, weights=uniform, score=-0.186, total= 0.2s\n","[CV] n_neighbors=4, weights=uniform ..................................\n","[CV] ..... n_neighbors=4, weights=uniform, score=-0.190, total= 0.2s\n","[CV] n_neighbors=4, weights=uniform ..................................\n","[CV] ...... n_neighbors=4, weights=uniform, score=0.010, total= 0.2s\n","[CV] n_neighbors=4, weights=uniform ..................................\n","[CV] ..... n_neighbors=4, weights=uniform, score=-0.175, total= 0.2s\n","[CV] n_neighbors=4, weights=uniform ..................................\n","[CV] ...... n_neighbors=4, weights=uniform, score=0.010, total= 0.2s\n","[CV] n_neighbors=4, weights=uniform ..................................\n","[CV] ..... n_neighbors=4, weights=uniform, score=-0.016, total= 0.2s\n","[CV] n_neighbors=4, weights=uniform ..................................\n","[CV] ..... n_neighbors=4, weights=uniform, score=-0.020, total= 0.2s\n","[CV] n_neighbors=4, weights=uniform ..................................\n","[CV] ..... n_neighbors=4, weights=uniform, score=-0.028, total= 0.2s\n","[CV] n_neighbors=4, weights=uniform ..................................\n","[CV] ..... n_neighbors=4, weights=uniform, score=-0.089, total= 0.2s\n","[CV] n_neighbors=4, weights=uniform ..................................\n","[CV] ..... n_neighbors=4, weights=uniform, score=-0.073, total= 0.2s\n","[CV] n_neighbors=4, weights=uniform ..................................\n","[CV] ..... n_neighbors=4, weights=uniform, score=-0.070, total= 0.2s\n","[CV] n_neighbors=4, weights=uniform ..................................\n","[CV] ..... n_neighbors=4, weights=uniform, score=-0.080, total= 0.2s\n","[CV] n_neighbors=4, weights=uniform ..................................\n","[CV] ..... n_neighbors=4, weights=uniform, score=-0.112, total= 0.2s\n","[CV] n_neighbors=4, weights=uniform ..................................\n","[CV] ..... n_neighbors=4, weights=uniform, score=-0.133, total= 0.2s\n","[CV] n_neighbors=4, weights=uniform ..................................\n","[CV] ...... n_neighbors=4, weights=uniform, score=0.016, total= 0.2s\n","[CV] n_neighbors=4, weights=uniform ..................................\n","[CV] ..... n_neighbors=4, weights=uniform, score=-0.053, total= 0.2s\n","[CV] n_neighbors=4, weights=uniform ..................................\n","[CV] ..... n_neighbors=4, weights=uniform, score=-0.172, total= 0.2s\n","[CV] n_neighbors=4, weights=uniform ..................................\n","[CV] ..... n_neighbors=4, weights=uniform, score=-0.051, total= 0.2s\n","[CV] n_neighbors=4, weights=distance .................................\n","[CV] .... n_neighbors=4, weights=distance, score=-0.036, total= 0.2s\n","[CV] n_neighbors=4, weights=distance .................................\n","[CV] .... n_neighbors=4, weights=distance, score=-0.124, total= 0.2s\n","[CV] n_neighbors=4, weights=distance .................................\n","[CV] .... n_neighbors=4, weights=distance, score=-0.098, total= 0.2s\n","[CV] n_neighbors=4, weights=distance .................................\n","[CV] .... n_neighbors=4, weights=distance, score=-0.083, total= 0.2s\n","[CV] n_neighbors=4, weights=distance .................................\n","[CV] .... n_neighbors=4, weights=distance, score=-0.205, total= 0.2s\n","[CV] n_neighbors=4, weights=distance .................................\n","[CV] ..... n_neighbors=4, weights=distance, score=0.006, total= 0.2s\n","[CV] n_neighbors=4, weights=distance .................................\n","[CV] .... n_neighbors=4, weights=distance, score=-0.030, total= 0.2s\n","[CV] n_neighbors=4, weights=distance .................................\n","[CV] .... n_neighbors=4, weights=distance, score=-0.107, total= 0.2s\n","[CV] n_neighbors=4, weights=distance .................................\n","[CV] .... n_neighbors=4, weights=distance, score=-0.113, total= 0.2s\n","[CV] n_neighbors=4, weights=distance .................................\n","[CV] .... n_neighbors=4, weights=distance, score=-0.001, total= 0.2s\n","[CV] n_neighbors=4, weights=distance .................................\n","[CV] .... n_neighbors=4, weights=distance, score=-0.081, total= 0.2s\n","[CV] n_neighbors=4, weights=distance .................................\n","[CV] .... n_neighbors=4, weights=distance, score=-0.151, total= 0.2s\n","[CV] n_neighbors=4, weights=distance .................................\n","[CV] .... n_neighbors=4, weights=distance, score=-0.171, total= 0.2s\n","[CV] n_neighbors=4, weights=distance .................................\n","[CV] .... n_neighbors=4, weights=distance, score=-0.185, total= 0.2s\n","[CV] n_neighbors=4, weights=distance .................................\n","[CV] ..... n_neighbors=4, weights=distance, score=0.021, total= 0.2s\n","[CV] n_neighbors=4, weights=distance .................................\n","[CV] .... n_neighbors=4, weights=distance, score=-0.180, total= 0.2s\n","[CV] n_neighbors=4, weights=distance .................................\n","[CV] ..... n_neighbors=4, weights=distance, score=0.025, total= 0.2s\n","[CV] n_neighbors=4, weights=distance .................................\n","[CV] .... n_neighbors=4, weights=distance, score=-0.004, total= 0.2s\n","[CV] n_neighbors=4, weights=distance .................................\n","[CV] .... n_neighbors=4, weights=distance, score=-0.021, total= 0.2s\n","[CV] n_neighbors=4, weights=distance .................................\n","[CV] .... n_neighbors=4, weights=distance, score=-0.022, total= 0.2s\n","[CV] n_neighbors=4, weights=distance .................................\n","[CV] .... n_neighbors=4, weights=distance, score=-0.089, total= 0.2s\n","[CV] n_neighbors=4, weights=distance .................................\n","[CV] .... n_neighbors=4, weights=distance, score=-0.067, total= 0.2s\n","[CV] n_neighbors=4, weights=distance .................................\n","[CV] .... n_neighbors=4, weights=distance, score=-0.063, total= 0.2s\n","[CV] n_neighbors=4, weights=distance .................................\n","[CV] .... n_neighbors=4, weights=distance, score=-0.071, total= 0.2s\n","[CV] n_neighbors=4, weights=distance .................................\n","[CV] .... n_neighbors=4, weights=distance, score=-0.102, total= 0.2s\n","[CV] n_neighbors=4, weights=distance .................................\n","[CV] .... n_neighbors=4, weights=distance, score=-0.136, total= 0.2s\n","[CV] n_neighbors=4, weights=distance .................................\n","[CV] ..... n_neighbors=4, weights=distance, score=0.025, total= 0.2s\n","[CV] n_neighbors=4, weights=distance .................................\n","[CV] .... n_neighbors=4, weights=distance, score=-0.052, total= 0.2s\n","[CV] n_neighbors=4, weights=distance .................................\n","[CV] .... n_neighbors=4, weights=distance, score=-0.167, total= 0.2s\n","[CV] n_neighbors=4, weights=distance .................................\n","[CV] .... n_neighbors=4, weights=distance, score=-0.047, total= 0.2s\n","[CV] n_neighbors=5, weights=uniform ..................................\n","[CV] ...... n_neighbors=5, weights=uniform, score=0.016, total= 0.2s\n","[CV] n_neighbors=5, weights=uniform ..................................\n","[CV] ..... n_neighbors=5, weights=uniform, score=-0.080, total= 0.2s\n","[CV] n_neighbors=5, weights=uniform ..................................\n","[CV] ..... n_neighbors=5, weights=uniform, score=-0.058, total= 0.2s\n","[CV] n_neighbors=5, weights=uniform ..................................\n","[CV] ..... n_neighbors=5, weights=uniform, score=-0.062, total= 0.2s\n","[CV] n_neighbors=5, weights=uniform ..................................\n","[CV] ..... n_neighbors=5, weights=uniform, score=-0.174, total= 0.2s\n","[CV] n_neighbors=5, weights=uniform ..................................\n","[CV] ...... n_neighbors=5, weights=uniform, score=0.051, total= 0.2s\n","[CV] n_neighbors=5, weights=uniform ..................................\n","[CV] ..... n_neighbors=5, weights=uniform, score=-0.022, total= 0.2s\n","[CV] n_neighbors=5, weights=uniform ..................................\n","[CV] ..... n_neighbors=5, weights=uniform, score=-0.037, total= 0.2s\n","[CV] n_neighbors=5, weights=uniform ..................................\n","[CV] ..... n_neighbors=5, weights=uniform, score=-0.131, total= 0.2s\n","[CV] n_neighbors=5, weights=uniform ..................................\n","[CV] ..... n_neighbors=5, weights=uniform, score=-0.007, total= 0.2s\n","[CV] n_neighbors=5, weights=uniform ..................................\n","[CV] ..... n_neighbors=5, weights=uniform, score=-0.045, total= 0.2s\n","[CV] n_neighbors=5, weights=uniform ..................................\n","[CV] ..... n_neighbors=5, weights=uniform, score=-0.153, total= 0.2s\n","[CV] n_neighbors=5, weights=uniform ..................................\n","[CV] ..... n_neighbors=5, weights=uniform, score=-0.127, total= 0.2s\n","[CV] n_neighbors=5, weights=uniform ..................................\n","[CV] ..... n_neighbors=5, weights=uniform, score=-0.125, total= 0.2s\n","[CV] n_neighbors=5, weights=uniform ..................................\n","[CV] ...... n_neighbors=5, weights=uniform, score=0.037, total= 0.2s\n","[CV] n_neighbors=5, weights=uniform ..................................\n","[CV] ..... n_neighbors=5, weights=uniform, score=-0.182, total= 0.2s\n","[CV] n_neighbors=5, weights=uniform ..................................\n","[CV] ...... n_neighbors=5, weights=uniform, score=0.050, total= 0.2s\n","[CV] n_neighbors=5, weights=uniform ..................................\n","[CV] ...... n_neighbors=5, weights=uniform, score=0.021, total= 0.2s\n","[CV] n_neighbors=5, weights=uniform ..................................\n","[CV] ..... n_neighbors=5, weights=uniform, score=-0.039, total= 0.2s\n","[CV] n_neighbors=5, weights=uniform ..................................\n","[CV] ...... n_neighbors=5, weights=uniform, score=0.019, total= 0.2s\n","[CV] n_neighbors=5, weights=uniform ..................................\n","[CV] ..... n_neighbors=5, weights=uniform, score=-0.067, total= 0.2s\n","[CV] n_neighbors=5, weights=uniform ..................................\n","[CV] ..... n_neighbors=5, weights=uniform, score=-0.066, total= 0.2s\n","[CV] n_neighbors=5, weights=uniform ..................................\n","[CV] ...... n_neighbors=5, weights=uniform, score=0.008, total= 0.2s\n","[CV] n_neighbors=5, weights=uniform ..................................\n","[CV] ..... n_neighbors=5, weights=uniform, score=-0.050, total= 0.2s\n","[CV] n_neighbors=5, weights=uniform ..................................\n","[CV] ..... n_neighbors=5, weights=uniform, score=-0.048, total= 0.2s\n","[CV] n_neighbors=5, weights=uniform ..................................\n","[CV] ..... n_neighbors=5, weights=uniform, score=-0.045, total= 0.2s\n","[CV] n_neighbors=5, weights=uniform ..................................\n","[CV] ...... n_neighbors=5, weights=uniform, score=0.025, total= 0.2s\n","[CV] n_neighbors=5, weights=uniform ..................................\n","[CV] ..... n_neighbors=5, weights=uniform, score=-0.039, total= 0.2s\n","[CV] n_neighbors=5, weights=uniform ..................................\n","[CV] ..... n_neighbors=5, weights=uniform, score=-0.110, total= 0.2s\n","[CV] n_neighbors=5, weights=uniform ..................................\n","[CV] ..... n_neighbors=5, weights=uniform, score=-0.049, total= 0.2s\n","[CV] n_neighbors=5, weights=distance .................................\n","[CV] ..... n_neighbors=5, weights=distance, score=0.030, total= 0.2s\n","[CV] n_neighbors=5, weights=distance .................................\n","[CV] .... n_neighbors=5, weights=distance, score=-0.079, total= 0.2s\n","[CV] n_neighbors=5, weights=distance .................................\n","[CV] .... n_neighbors=5, weights=distance, score=-0.056, total= 0.2s\n","[CV] n_neighbors=5, weights=distance .................................\n","[CV] .... n_neighbors=5, weights=distance, score=-0.048, total= 0.2s\n","[CV] n_neighbors=5, weights=distance .................................\n","[CV] .... n_neighbors=5, weights=distance, score=-0.173, total= 0.2s\n","[CV] n_neighbors=5, weights=distance .................................\n","[CV] ..... n_neighbors=5, weights=distance, score=0.046, total= 0.2s\n","[CV] n_neighbors=5, weights=distance .................................\n","[CV] .... n_neighbors=5, weights=distance, score=-0.014, total= 0.2s\n","[CV] n_neighbors=5, weights=distance .................................\n","[CV] .... n_neighbors=5, weights=distance, score=-0.041, total= 0.2s\n","[CV] n_neighbors=5, weights=distance .................................\n","[CV] .... n_neighbors=5, weights=distance, score=-0.111, total= 0.2s\n","[CV] n_neighbors=5, weights=distance .................................\n","[CV] .... n_neighbors=5, weights=distance, score=-0.007, total= 0.2s\n","[CV] n_neighbors=5, weights=distance .................................\n","[CV] .... n_neighbors=5, weights=distance, score=-0.038, total= 0.2s\n","[CV] n_neighbors=5, weights=distance .................................\n","[CV] .... n_neighbors=5, weights=distance, score=-0.146, total= 0.2s\n","[CV] n_neighbors=5, weights=distance .................................\n","[CV] .... n_neighbors=5, weights=distance, score=-0.116, total= 0.2s\n","[CV] n_neighbors=5, weights=distance .................................\n","[CV] .... n_neighbors=5, weights=distance, score=-0.128, total= 0.2s\n","[CV] n_neighbors=5, weights=distance .................................\n","[CV] ..... n_neighbors=5, weights=distance, score=0.049, total= 0.2s\n","[CV] n_neighbors=5, weights=distance .................................\n","[CV] .... n_neighbors=5, weights=distance, score=-0.181, total= 0.2s\n","[CV] n_neighbors=5, weights=distance .................................\n","[CV] ..... n_neighbors=5, weights=distance, score=0.060, total= 0.2s\n","[CV] n_neighbors=5, weights=distance .................................\n","[CV] ..... n_neighbors=5, weights=distance, score=0.035, total= 0.2s\n","[CV] n_neighbors=5, weights=distance .................................\n","[CV] .... n_neighbors=5, weights=distance, score=-0.033, total= 0.2s\n","[CV] n_neighbors=5, weights=distance .................................\n","[CV] ..... n_neighbors=5, weights=distance, score=0.023, total= 0.2s\n","[CV] n_neighbors=5, weights=distance .................................\n","[CV] .... n_neighbors=5, weights=distance, score=-0.064, total= 0.2s\n","[CV] n_neighbors=5, weights=distance .................................\n","[CV] .... n_neighbors=5, weights=distance, score=-0.057, total= 0.2s\n","[CV] n_neighbors=5, weights=distance .................................\n","[CV] ..... n_neighbors=5, weights=distance, score=0.008, total= 0.2s\n","[CV] n_neighbors=5, weights=distance .................................\n","[CV] .... n_neighbors=5, weights=distance, score=-0.046, total= 0.2s\n","[CV] n_neighbors=5, weights=distance .................................\n","[CV] .... n_neighbors=5, weights=distance, score=-0.041, total= 0.2s\n","[CV] n_neighbors=5, weights=distance .................................\n","[CV] .... n_neighbors=5, weights=distance, score=-0.054, total= 0.2s\n","[CV] n_neighbors=5, weights=distance .................................\n","[CV] ..... n_neighbors=5, weights=distance, score=0.033, total= 0.2s\n","[CV] n_neighbors=5, weights=distance .................................\n","[CV] .... n_neighbors=5, weights=distance, score=-0.037, total= 0.2s\n","[CV] n_neighbors=5, weights=distance .................................\n","[CV] .... n_neighbors=5, weights=distance, score=-0.102, total= 0.2s\n","[CV] n_neighbors=5, weights=distance .................................\n","[CV] .... n_neighbors=5, weights=distance, score=-0.043, total= 0.2s\n","[CV] n_neighbors=6, weights=uniform ..................................\n","[CV] ...... n_neighbors=6, weights=uniform, score=0.008, total= 0.2s\n","[CV] n_neighbors=6, weights=uniform ..................................\n","[CV] ..... n_neighbors=6, weights=uniform, score=-0.049, total= 0.2s\n","[CV] n_neighbors=6, weights=uniform ..................................\n","[CV] ...... n_neighbors=6, weights=uniform, score=0.018, total= 0.2s\n","[CV] n_neighbors=6, weights=uniform ..................................\n","[CV] ..... n_neighbors=6, weights=uniform, score=-0.063, total= 0.2s\n","[CV] n_neighbors=6, weights=uniform ..................................\n","[CV] ..... n_neighbors=6, weights=uniform, score=-0.154, total= 0.2s\n","[CV] n_neighbors=6, weights=uniform ..................................\n","[CV] ...... n_neighbors=6, weights=uniform, score=0.092, total= 0.2s\n","[CV] n_neighbors=6, weights=uniform ..................................\n","[CV] ..... n_neighbors=6, weights=uniform, score=-0.040, total= 0.2s\n","[CV] n_neighbors=6, weights=uniform ..................................\n","[CV] ..... n_neighbors=6, weights=uniform, score=-0.009, total= 0.2s\n","[CV] n_neighbors=6, weights=uniform ..................................\n","[CV] ..... n_neighbors=6, weights=uniform, score=-0.078, total= 0.2s\n","[CV] n_neighbors=6, weights=uniform ..................................\n","[CV] ...... n_neighbors=6, weights=uniform, score=0.005, total= 0.2s\n","[CV] n_neighbors=6, weights=uniform ..................................\n","[CV] ..... n_neighbors=6, weights=uniform, score=-0.022, total= 0.2s\n","[CV] n_neighbors=6, weights=uniform ..................................\n","[CV] ..... n_neighbors=6, weights=uniform, score=-0.070, total= 0.2s\n","[CV] n_neighbors=6, weights=uniform ..................................\n","[CV] ..... n_neighbors=6, weights=uniform, score=-0.079, total= 0.2s\n","[CV] n_neighbors=6, weights=uniform ..................................\n","[CV] ..... n_neighbors=6, weights=uniform, score=-0.062, total= 0.2s\n","[CV] n_neighbors=6, weights=uniform ..................................\n","[CV] ...... n_neighbors=6, weights=uniform, score=0.026, total= 0.2s\n","[CV] n_neighbors=6, weights=uniform ..................................\n","[CV] ..... n_neighbors=6, weights=uniform, score=-0.172, total= 0.2s\n","[CV] n_neighbors=6, weights=uniform ..................................\n","[CV] ...... n_neighbors=6, weights=uniform, score=0.050, total= 0.2s\n","[CV] n_neighbors=6, weights=uniform ..................................\n","[CV] ...... n_neighbors=6, weights=uniform, score=0.025, total= 0.2s\n","[CV] n_neighbors=6, weights=uniform ..................................\n","[CV] ...... n_neighbors=6, weights=uniform, score=0.000, total= 0.2s\n","[CV] n_neighbors=6, weights=uniform ..................................\n","[CV] ...... n_neighbors=6, weights=uniform, score=0.039, total= 0.2s\n","[CV] n_neighbors=6, weights=uniform ..................................\n","[CV] ..... n_neighbors=6, weights=uniform, score=-0.007, total= 0.2s\n","[CV] n_neighbors=6, weights=uniform ..................................\n","[CV] ..... n_neighbors=6, weights=uniform, score=-0.073, total= 0.2s\n","[CV] n_neighbors=6, weights=uniform ..................................\n","[CV] ...... n_neighbors=6, weights=uniform, score=0.017, total= 0.2s\n","[CV] n_neighbors=6, weights=uniform ..................................\n","[CV] ..... n_neighbors=6, weights=uniform, score=-0.000, total= 0.2s\n","[CV] n_neighbors=6, weights=uniform ..................................\n","[CV] ..... n_neighbors=6, weights=uniform, score=-0.037, total= 0.2s\n","[CV] n_neighbors=6, weights=uniform ..................................\n","[CV] ...... n_neighbors=6, weights=uniform, score=0.018, total= 0.2s\n","[CV] n_neighbors=6, weights=uniform ..................................\n","[CV] ...... n_neighbors=6, weights=uniform, score=0.051, total= 0.2s\n","[CV] n_neighbors=6, weights=uniform ..................................\n","[CV] ..... n_neighbors=6, weights=uniform, score=-0.022, total= 0.2s\n","[CV] n_neighbors=6, weights=uniform ..................................\n","[CV] ..... n_neighbors=6, weights=uniform, score=-0.069, total= 0.2s\n","[CV] n_neighbors=6, weights=uniform ..................................\n","[CV] ..... n_neighbors=6, weights=uniform, score=-0.072, total= 0.2s\n","[CV] n_neighbors=6, weights=distance .................................\n","[CV] ..... n_neighbors=6, weights=distance, score=0.025, total= 0.2s\n","[CV] n_neighbors=6, weights=distance .................................\n","[CV] .... n_neighbors=6, weights=distance, score=-0.045, total= 0.2s\n","[CV] n_neighbors=6, weights=distance .................................\n","[CV] ..... n_neighbors=6, weights=distance, score=0.016, total= 0.2s\n","[CV] n_neighbors=6, weights=distance .................................\n","[CV] .... n_neighbors=6, weights=distance, score=-0.046, total= 0.2s\n","[CV] n_neighbors=6, weights=distance .................................\n","[CV] .... n_neighbors=6, weights=distance, score=-0.152, total= 0.2s\n","[CV] n_neighbors=6, weights=distance .................................\n","[CV] ..... n_neighbors=6, weights=distance, score=0.089, total= 0.2s\n","[CV] n_neighbors=6, weights=distance .................................\n","[CV] .... n_neighbors=6, weights=distance, score=-0.034, total= 0.2s\n","[CV] n_neighbors=6, weights=distance .................................\n","[CV] .... n_neighbors=6, weights=distance, score=-0.012, total= 0.2s\n","[CV] n_neighbors=6, weights=distance .................................\n","[CV] .... n_neighbors=6, weights=distance, score=-0.061, total= 0.2s\n","[CV] n_neighbors=6, weights=distance .................................\n","[CV] ..... n_neighbors=6, weights=distance, score=0.005, total= 0.2s\n","[CV] n_neighbors=6, weights=distance .................................\n","[CV] .... n_neighbors=6, weights=distance, score=-0.015, total= 0.2s\n","[CV] n_neighbors=6, weights=distance .................................\n","[CV] .... n_neighbors=6, weights=distance, score=-0.071, total= 0.2s\n","[CV] n_neighbors=6, weights=distance .................................\n","[CV] .... n_neighbors=6, weights=distance, score=-0.073, total= 0.2s\n","[CV] n_neighbors=6, weights=distance .................................\n","[CV] .... n_neighbors=6, weights=distance, score=-0.071, total= 0.2s\n","[CV] n_neighbors=6, weights=distance .................................\n","[CV] ..... n_neighbors=6, weights=distance, score=0.041, total= 0.2s\n","[CV] n_neighbors=6, weights=distance .................................\n","[CV] .... n_neighbors=6, weights=distance, score=-0.168, total= 0.2s\n","[CV] n_neighbors=6, weights=distance .................................\n","[CV] ..... n_neighbors=6, weights=distance, score=0.064, total= 0.2s\n","[CV] n_neighbors=6, weights=distance .................................\n","[CV] ..... n_neighbors=6, weights=distance, score=0.038, total= 0.2s\n","[CV] n_neighbors=6, weights=distance .................................\n","[CV] ..... n_neighbors=6, weights=distance, score=0.005, total= 0.2s\n","[CV] n_neighbors=6, weights=distance .................................\n","[CV] ..... n_neighbors=6, weights=distance, score=0.045, total= 0.2s\n","[CV] n_neighbors=6, weights=distance .................................\n","[CV] .... n_neighbors=6, weights=distance, score=-0.010, total= 0.2s\n","[CV] n_neighbors=6, weights=distance .................................\n","[CV] .... n_neighbors=6, weights=distance, score=-0.062, total= 0.2s\n","[CV] n_neighbors=6, weights=distance .................................\n","[CV] ..... n_neighbors=6, weights=distance, score=0.018, total= 0.2s\n","[CV] n_neighbors=6, weights=distance .................................\n","[CV] ..... n_neighbors=6, weights=distance, score=0.001, total= 0.2s\n","[CV] n_neighbors=6, weights=distance .................................\n","[CV] .... n_neighbors=6, weights=distance, score=-0.029, total= 0.2s\n","[CV] n_neighbors=6, weights=distance .................................\n","[CV] ..... n_neighbors=6, weights=distance, score=0.006, total= 0.2s\n","[CV] n_neighbors=6, weights=distance .................................\n","[CV] ..... n_neighbors=6, weights=distance, score=0.054, total= 0.2s\n","[CV] n_neighbors=6, weights=distance .................................\n","[CV] .... n_neighbors=6, weights=distance, score=-0.018, total= 0.2s\n","[CV] n_neighbors=6, weights=distance .................................\n","[CV] .... n_neighbors=6, weights=distance, score=-0.060, total= 0.2s\n","[CV] n_neighbors=6, weights=distance .................................\n","[CV] .... n_neighbors=6, weights=distance, score=-0.061, total= 0.2s\n","[Parallel(n_jobs=1)]: Done 300 out of 300 | elapsed: 1.1min finished\n","KNeighborsRegressor(algorithm='auto', leaf_size=30, metric='minkowski',\n"," metric_params=None, n_jobs=None, n_neighbors=6, p=2,\n"," weights='distance')\n","window: 1, stide: 1, rmse: 2.72715503641267\n","(3000, 252)\n","Number of Feature-Columns: 252\n","\n","Features Ready for undergoing selection tests done ...\n","\n","Fitting 30 folds for each of 10 candidates, totalling 300 fits\n","[Parallel(n_jobs=1)]: Using backend SequentialBackend with 1 concurrent workers.\n","[CV] n_neighbors=2, weights=uniform ..................................\n","[CV] ..... n_neighbors=2, weights=uniform, score=-0.724, total= 0.3s\n","[Parallel(n_jobs=1)]: Done 1 out of 1 | elapsed: 0.3s remaining: 0.0s\n","[CV] n_neighbors=2, weights=uniform ..................................\n","[CV] ..... n_neighbors=2, weights=uniform, score=-0.629, total= 0.3s\n","[Parallel(n_jobs=1)]: Done 2 out of 2 | elapsed: 0.7s remaining: 0.0s\n","[CV] n_neighbors=2, weights=uniform ..................................\n","[CV] ..... n_neighbors=2, weights=uniform, score=-0.490, total= 0.3s\n","[Parallel(n_jobs=1)]: Done 3 out of 3 | elapsed: 1.0s remaining: 0.0s\n","[CV] n_neighbors=2, weights=uniform ..................................\n","[CV] ..... n_neighbors=2, weights=uniform, score=-0.693, total= 0.3s\n","[Parallel(n_jobs=1)]: Done 4 out of 4 | elapsed: 1.3s remaining: 0.0s\n","[CV] n_neighbors=2, weights=uniform ..................................\n","[CV] ..... n_neighbors=2, weights=uniform, score=-0.458, total= 0.3s\n","[CV] n_neighbors=2, weights=uniform ..................................\n","[CV] ..... n_neighbors=2, weights=uniform, score=-0.564, total= 0.3s\n","[CV] n_neighbors=2, weights=uniform ..................................\n","[CV] ..... n_neighbors=2, weights=uniform, score=-0.578, total= 0.3s\n","[CV] n_neighbors=2, weights=uniform ..................................\n","[CV] ..... n_neighbors=2, weights=uniform, score=-0.680, total= 0.3s\n","[CV] n_neighbors=2, weights=uniform ..................................\n","[CV] ..... n_neighbors=2, weights=uniform, score=-0.459, total= 0.3s\n","[CV] n_neighbors=2, weights=uniform ..................................\n","[CV] ..... n_neighbors=2, weights=uniform, score=-0.657, total= 0.3s\n","[CV] n_neighbors=2, weights=uniform ..................................\n","[CV] ..... n_neighbors=2, weights=uniform, score=-0.566, total= 0.3s\n","[CV] n_neighbors=2, weights=uniform ..................................\n","[CV] ..... n_neighbors=2, weights=uniform, score=-0.552, total= 0.3s\n","[CV] n_neighbors=2, weights=uniform ..................................\n","[CV] ..... n_neighbors=2, weights=uniform, score=-0.540, total= 0.3s\n","[CV] n_neighbors=2, weights=uniform ..................................\n","[CV] ..... n_neighbors=2, weights=uniform, score=-0.602, total= 0.3s\n","[CV] n_neighbors=2, weights=uniform ..................................\n","[CV] ..... n_neighbors=2, weights=uniform, score=-0.576, total= 0.3s\n","[CV] n_neighbors=2, weights=uniform ..................................\n","[CV] ..... n_neighbors=2, weights=uniform, score=-0.835, total= 0.3s\n","[CV] n_neighbors=2, weights=uniform ..................................\n","[CV] ..... n_neighbors=2, weights=uniform, score=-0.507, total= 0.3s\n","[CV] n_neighbors=2, weights=uniform ..................................\n","[CV] ..... n_neighbors=2, weights=uniform, score=-0.652, total= 0.3s\n","[CV] n_neighbors=2, weights=uniform ..................................\n","[CV] ..... n_neighbors=2, weights=uniform, score=-0.367, total= 0.3s\n","[CV] n_neighbors=2, weights=uniform ..................................\n","[CV] ..... n_neighbors=2, weights=uniform, score=-0.472, total= 0.3s\n","[CV] n_neighbors=2, weights=uniform ..................................\n","[CV] ..... n_neighbors=2, weights=uniform, score=-0.530, total= 0.3s\n","[CV] n_neighbors=2, weights=uniform ..................................\n","[CV] ..... n_neighbors=2, weights=uniform, score=-0.731, total= 0.3s\n","[CV] n_neighbors=2, weights=uniform ..................................\n","[CV] ..... n_neighbors=2, weights=uniform, score=-0.600, total= 0.3s\n","[CV] n_neighbors=2, weights=uniform ..................................\n","[CV] ..... n_neighbors=2, weights=uniform, score=-0.486, total= 0.3s\n","[CV] n_neighbors=2, weights=uniform ..................................\n","[CV] ..... n_neighbors=2, weights=uniform, score=-0.605, total= 0.3s\n","[CV] n_neighbors=2, weights=uniform ..................................\n","[CV] ..... n_neighbors=2, weights=uniform, score=-0.464, total= 0.3s\n","[CV] n_neighbors=2, weights=uniform ..................................\n","[CV] ..... n_neighbors=2, weights=uniform, score=-0.683, total= 0.3s\n","[CV] n_neighbors=2, weights=uniform ..................................\n","[CV] ..... n_neighbors=2, weights=uniform, score=-0.583, total= 0.3s\n","[CV] n_neighbors=2, weights=uniform ..................................\n","[CV] ..... n_neighbors=2, weights=uniform, score=-0.704, total= 0.3s\n","[CV] n_neighbors=2, weights=uniform ..................................\n","[CV] ..... n_neighbors=2, weights=uniform, score=-0.430, total= 0.3s\n","[CV] n_neighbors=2, weights=distance .................................\n","[CV] .... n_neighbors=2, weights=distance, score=-0.721, total= 0.3s\n","[CV] n_neighbors=2, weights=distance .................................\n","[CV] .... n_neighbors=2, weights=distance, score=-0.630, total= 0.3s\n","[CV] n_neighbors=2, weights=distance .................................\n","[CV] .... n_neighbors=2, weights=distance, score=-0.489, total= 0.3s\n","[CV] n_neighbors=2, weights=distance .................................\n","[CV] .... n_neighbors=2, weights=distance, score=-0.686, total= 0.3s\n","[CV] n_neighbors=2, weights=distance .................................\n","[CV] .... n_neighbors=2, weights=distance, score=-0.470, total= 0.3s\n","[CV] n_neighbors=2, weights=distance .................................\n","[CV] .... n_neighbors=2, weights=distance, score=-0.577, total= 0.3s\n","[CV] n_neighbors=2, weights=distance .................................\n","[CV] .... n_neighbors=2, weights=distance, score=-0.573, total= 0.3s\n","[CV] n_neighbors=2, weights=distance .................................\n","[CV] .... n_neighbors=2, weights=distance, score=-0.675, total= 0.3s\n","[CV] n_neighbors=2, weights=distance .................................\n","[CV] .... n_neighbors=2, weights=distance, score=-0.456, total= 0.3s\n","[CV] n_neighbors=2, weights=distance .................................\n","[CV] .... n_neighbors=2, weights=distance, score=-0.651, total= 0.3s\n","[CV] n_neighbors=2, weights=distance .................................\n","[CV] .... n_neighbors=2, weights=distance, score=-0.575, total= 0.3s\n","[CV] n_neighbors=2, weights=distance .................................\n","[CV] .... n_neighbors=2, weights=distance, score=-0.551, total= 0.3s\n","[CV] n_neighbors=2, weights=distance .................................\n","[CV] .... n_neighbors=2, weights=distance, score=-0.536, total= 0.3s\n","[CV] n_neighbors=2, weights=distance .................................\n","[CV] .... n_neighbors=2, weights=distance, score=-0.602, total= 0.3s\n","[CV] n_neighbors=2, weights=distance .................................\n","[CV] .... n_neighbors=2, weights=distance, score=-0.578, total= 0.3s\n","[CV] n_neighbors=2, weights=distance .................................\n","[CV] .... n_neighbors=2, weights=distance, score=-0.831, total= 0.3s\n","[CV] n_neighbors=2, weights=distance .................................\n","[CV] .... n_neighbors=2, weights=distance, score=-0.504, total= 0.3s\n","[CV] n_neighbors=2, weights=distance .................................\n","[CV] .... n_neighbors=2, weights=distance, score=-0.662, total= 0.3s\n","[CV] n_neighbors=2, weights=distance .................................\n","[CV] .... n_neighbors=2, weights=distance, score=-0.366, total= 0.3s\n","[CV] n_neighbors=2, weights=distance .................................\n","[CV] .... n_neighbors=2, weights=distance, score=-0.468, total= 0.3s\n","[CV] n_neighbors=2, weights=distance .................................\n","[CV] .... n_neighbors=2, weights=distance, score=-0.527, total= 0.3s\n","[CV] n_neighbors=2, weights=distance .................................\n","[CV] .... n_neighbors=2, weights=distance, score=-0.728, total= 0.3s\n","[CV] n_neighbors=2, weights=distance .................................\n","[CV] .... n_neighbors=2, weights=distance, score=-0.600, total= 0.3s\n","[CV] n_neighbors=2, weights=distance .................................\n","[CV] .... n_neighbors=2, weights=distance, score=-0.489, total= 0.3s\n","[CV] n_neighbors=2, weights=distance .................................\n","[CV] .... n_neighbors=2, weights=distance, score=-0.601, total= 0.3s\n","[CV] n_neighbors=2, weights=distance .................................\n","[CV] .... n_neighbors=2, weights=distance, score=-0.470, total= 0.3s\n","[CV] n_neighbors=2, weights=distance .................................\n","[CV] .... n_neighbors=2, weights=distance, score=-0.678, total= 0.3s\n","[CV] n_neighbors=2, weights=distance .................................\n","[CV] .... n_neighbors=2, weights=distance, score=-0.567, total= 0.3s\n","[CV] n_neighbors=2, weights=distance .................................\n","[CV] .... n_neighbors=2, weights=distance, score=-0.716, total= 0.3s\n","[CV] n_neighbors=2, weights=distance .................................\n","[CV] .... n_neighbors=2, weights=distance, score=-0.429, total= 0.3s\n","[CV] n_neighbors=3, weights=uniform ..................................\n","[CV] ..... n_neighbors=3, weights=uniform, score=-0.476, total= 0.3s\n","[CV] n_neighbors=3, weights=uniform ..................................\n","[CV] ..... n_neighbors=3, weights=uniform, score=-0.446, total= 0.3s\n","[CV] n_neighbors=3, weights=uniform ..................................\n","[CV] ..... n_neighbors=3, weights=uniform, score=-0.314, total= 0.3s\n","[CV] n_neighbors=3, weights=uniform ..................................\n","[CV] ..... n_neighbors=3, weights=uniform, score=-0.353, total= 0.3s\n","[CV] n_neighbors=3, weights=uniform ..................................\n","[CV] ..... n_neighbors=3, weights=uniform, score=-0.322, total= 0.3s\n","[CV] n_neighbors=3, weights=uniform ..................................\n","[CV] ..... n_neighbors=3, weights=uniform, score=-0.373, total= 0.3s\n","[CV] n_neighbors=3, weights=uniform ..................................\n","[CV] ..... n_neighbors=3, weights=uniform, score=-0.371, total= 0.3s\n","[CV] n_neighbors=3, weights=uniform ..................................\n","[CV] ..... n_neighbors=3, weights=uniform, score=-0.398, total= 0.3s\n","[CV] n_neighbors=3, weights=uniform ..................................\n","[CV] ..... n_neighbors=3, weights=uniform, score=-0.292, total= 0.3s\n","[CV] n_neighbors=3, weights=uniform ..................................\n","[CV] ..... n_neighbors=3, weights=uniform, score=-0.373, total= 0.3s\n","[CV] n_neighbors=3, weights=uniform ..................................\n","[CV] ..... n_neighbors=3, weights=uniform, score=-0.413, total= 0.3s\n","[CV] n_neighbors=3, weights=uniform ..................................\n","[CV] ..... n_neighbors=3, weights=uniform, score=-0.369, total= 0.3s\n","[CV] n_neighbors=3, weights=uniform ..................................\n","[CV] ..... n_neighbors=3, weights=uniform, score=-0.280, total= 0.3s\n","[CV] n_neighbors=3, weights=uniform ..................................\n","[CV] ..... n_neighbors=3, weights=uniform, score=-0.454, total= 0.3s\n","[CV] n_neighbors=3, weights=uniform ..................................\n","[CV] ..... n_neighbors=3, weights=uniform, score=-0.375, total= 0.3s\n","[CV] n_neighbors=3, weights=uniform ..................................\n","[CV] ..... n_neighbors=3, weights=uniform, score=-0.456, total= 0.3s\n","[CV] n_neighbors=3, weights=uniform ..................................\n","[CV] ..... n_neighbors=3, weights=uniform, score=-0.414, total= 0.3s\n","[CV] n_neighbors=3, weights=uniform ..................................\n","[CV] ..... n_neighbors=3, weights=uniform, score=-0.433, total= 0.3s\n","[CV] n_neighbors=3, weights=uniform ..................................\n","[CV] ..... n_neighbors=3, weights=uniform, score=-0.281, total= 0.3s\n","[CV] n_neighbors=3, weights=uniform ..................................\n","[CV] ..... n_neighbors=3, weights=uniform, score=-0.346, total= 0.3s\n","[CV] n_neighbors=3, weights=uniform ..................................\n","[CV] ..... n_neighbors=3, weights=uniform, score=-0.325, total= 0.3s\n","[CV] n_neighbors=3, weights=uniform ..................................\n","[CV] ..... n_neighbors=3, weights=uniform, score=-0.498, total= 0.3s\n","[CV] n_neighbors=3, weights=uniform ..................................\n","[CV] ..... n_neighbors=3, weights=uniform, score=-0.349, total= 0.3s\n","[CV] n_neighbors=3, weights=uniform ..................................\n","[CV] ..... n_neighbors=3, weights=uniform, score=-0.329, total= 0.3s\n","[CV] n_neighbors=3, weights=uniform ..................................\n","[CV] ..... n_neighbors=3, weights=uniform, score=-0.409, total= 0.3s\n","[CV] n_neighbors=3, weights=uniform ..................................\n","[CV] ..... n_neighbors=3, weights=uniform, score=-0.228, total= 0.3s\n","[CV] n_neighbors=3, weights=uniform ..................................\n","[CV] ..... n_neighbors=3, weights=uniform, score=-0.482, total= 0.3s\n","[CV] n_neighbors=3, weights=uniform ..................................\n","[CV] ..... n_neighbors=3, weights=uniform, score=-0.440, total= 0.3s\n","[CV] n_neighbors=3, weights=uniform ..................................\n","[CV] ..... n_neighbors=3, weights=uniform, score=-0.458, total= 0.3s\n","[CV] n_neighbors=3, weights=uniform ..................................\n","[CV] ..... n_neighbors=3, weights=uniform, score=-0.284, total= 0.3s\n","[CV] n_neighbors=3, weights=distance .................................\n","[CV] .... n_neighbors=3, weights=distance, score=-0.476, total= 0.3s\n","[CV] n_neighbors=3, weights=distance .................................\n","[CV] .... n_neighbors=3, weights=distance, score=-0.444, total= 0.3s\n","[CV] n_neighbors=3, weights=distance .................................\n","[CV] .... n_neighbors=3, weights=distance, score=-0.317, total= 0.3s\n","[CV] n_neighbors=3, weights=distance .................................\n","[CV] .... n_neighbors=3, weights=distance, score=-0.358, total= 0.3s\n","[CV] n_neighbors=3, weights=distance .................................\n","[CV] .... n_neighbors=3, weights=distance, score=-0.332, total= 0.3s\n","[CV] n_neighbors=3, weights=distance .................................\n","[CV] .... n_neighbors=3, weights=distance, score=-0.385, total= 0.3s\n","[CV] n_neighbors=3, weights=distance .................................\n","[CV] .... n_neighbors=3, weights=distance, score=-0.371, total= 0.3s\n","[CV] n_neighbors=3, weights=distance .................................\n","[CV] .... n_neighbors=3, weights=distance, score=-0.398, total= 0.3s\n","[CV] n_neighbors=3, weights=distance .................................\n","[CV] .... n_neighbors=3, weights=distance, score=-0.290, total= 0.3s\n","[CV] n_neighbors=3, weights=distance .................................\n","[CV] .... n_neighbors=3, weights=distance, score=-0.378, total= 0.3s\n","[CV] n_neighbors=3, weights=distance .................................\n","[CV] .... n_neighbors=3, weights=distance, score=-0.419, total= 0.3s\n","[CV] n_neighbors=3, weights=distance .................................\n","[CV] .... n_neighbors=3, weights=distance, score=-0.368, total= 0.3s\n","[CV] n_neighbors=3, weights=distance .................................\n","[CV] .... n_neighbors=3, weights=distance, score=-0.280, total= 0.3s\n","[CV] n_neighbors=3, weights=distance .................................\n","[CV] .... n_neighbors=3, weights=distance, score=-0.449, total= 0.3s\n","[CV] n_neighbors=3, weights=distance .................................\n","[CV] .... n_neighbors=3, weights=distance, score=-0.379, total= 0.3s\n","[CV] n_neighbors=3, weights=distance .................................\n","[CV] .... n_neighbors=3, weights=distance, score=-0.461, total= 0.3s\n","[CV] n_neighbors=3, weights=distance .................................\n","[CV] .... n_neighbors=3, weights=distance, score=-0.406, total= 0.3s\n","[CV] n_neighbors=3, weights=distance .................................\n","[CV] .... n_neighbors=3, weights=distance, score=-0.441, total= 0.3s\n","[CV] n_neighbors=3, weights=distance .................................\n","[CV] .... n_neighbors=3, weights=distance, score=-0.276, total= 0.3s\n","[CV] n_neighbors=3, weights=distance .................................\n","[CV] .... n_neighbors=3, weights=distance, score=-0.343, total= 0.3s\n","[CV] n_neighbors=3, weights=distance .................................\n","[CV] .... n_neighbors=3, weights=distance, score=-0.326, total= 0.3s\n","[CV] n_neighbors=3, weights=distance .................................\n","[CV] .... n_neighbors=3, weights=distance, score=-0.501, total= 0.3s\n","[CV] n_neighbors=3, weights=distance .................................\n","[CV] .... n_neighbors=3, weights=distance, score=-0.355, total= 0.3s\n","[CV] n_neighbors=3, weights=distance .................................\n","[CV] .... n_neighbors=3, weights=distance, score=-0.332, total= 0.3s\n","[CV] n_neighbors=3, weights=distance .................................\n","[CV] .... n_neighbors=3, weights=distance, score=-0.406, total= 0.3s\n","[CV] n_neighbors=3, weights=distance .................................\n","[CV] .... n_neighbors=3, weights=distance, score=-0.234, total= 0.3s\n","[CV] n_neighbors=3, weights=distance .................................\n","[CV] .... n_neighbors=3, weights=distance, score=-0.477, total= 0.3s\n","[CV] n_neighbors=3, weights=distance .................................\n","[CV] .... n_neighbors=3, weights=distance, score=-0.428, total= 0.3s\n","[CV] n_neighbors=3, weights=distance .................................\n","[CV] .... n_neighbors=3, weights=distance, score=-0.467, total= 0.3s\n","[CV] n_neighbors=3, weights=distance .................................\n","[CV] .... n_neighbors=3, weights=distance, score=-0.283, total= 0.3s\n","[CV] n_neighbors=4, weights=uniform ..................................\n","[CV] ..... n_neighbors=4, weights=uniform, score=-0.309, total= 0.3s\n","[CV] n_neighbors=4, weights=uniform ..................................\n","[CV] ..... n_neighbors=4, weights=uniform, score=-0.308, total= 0.4s\n","[CV] n_neighbors=4, weights=uniform ..................................\n","[CV] ..... n_neighbors=4, weights=uniform, score=-0.253, total= 0.3s\n","[CV] n_neighbors=4, weights=uniform ..................................\n","[CV] ..... n_neighbors=4, weights=uniform, score=-0.242, total= 0.3s\n","[CV] n_neighbors=4, weights=uniform ..................................\n","[CV] ..... n_neighbors=4, weights=uniform, score=-0.245, total= 0.3s\n","[CV] n_neighbors=4, weights=uniform ..................................\n","[CV] ..... n_neighbors=4, weights=uniform, score=-0.288, total= 0.3s\n","[CV] n_neighbors=4, weights=uniform ..................................\n","[CV] ..... n_neighbors=4, weights=uniform, score=-0.221, total= 0.3s\n","[CV] n_neighbors=4, weights=uniform ..................................\n","[CV] ..... n_neighbors=4, weights=uniform, score=-0.275, total= 0.3s\n","[CV] n_neighbors=4, weights=uniform ..................................\n","[CV] ..... n_neighbors=4, weights=uniform, score=-0.245, total= 0.3s\n","[CV] n_neighbors=4, weights=uniform ..................................\n","[CV] ..... n_neighbors=4, weights=uniform, score=-0.248, total= 0.3s\n","[CV] n_neighbors=4, weights=uniform ..................................\n","[CV] ..... n_neighbors=4, weights=uniform, score=-0.363, total= 0.3s\n","[CV] n_neighbors=4, weights=uniform ..................................\n","[CV] ..... n_neighbors=4, weights=uniform, score=-0.275, total= 0.3s\n","[CV] n_neighbors=4, weights=uniform ..................................\n","[CV] ..... n_neighbors=4, weights=uniform, score=-0.194, total= 0.3s\n","[CV] n_neighbors=4, weights=uniform ..................................\n","[CV] ..... n_neighbors=4, weights=uniform, score=-0.329, total= 0.3s\n","[CV] n_neighbors=4, weights=uniform ..................................\n","[CV] ..... n_neighbors=4, weights=uniform, score=-0.299, total= 0.3s\n","[CV] n_neighbors=4, weights=uniform ..................................\n","[CV] ..... n_neighbors=4, weights=uniform, score=-0.346, total= 0.3s\n","[CV] n_neighbors=4, weights=uniform ..................................\n","[CV] ..... n_neighbors=4, weights=uniform, score=-0.340, total= 0.3s\n","[CV] n_neighbors=4, weights=uniform ..................................\n","[CV] ..... n_neighbors=4, weights=uniform, score=-0.272, total= 0.3s\n","[CV] n_neighbors=4, weights=uniform ..................................\n","[CV] ..... n_neighbors=4, weights=uniform, score=-0.230, total= 0.3s\n","[CV] n_neighbors=4, weights=uniform ..................................\n","[CV] ..... n_neighbors=4, weights=uniform, score=-0.204, total= 0.3s\n","[CV] n_neighbors=4, weights=uniform ..................................\n","[CV] ..... n_neighbors=4, weights=uniform, score=-0.247, total= 0.3s\n","[CV] n_neighbors=4, weights=uniform ..................................\n","[CV] ..... n_neighbors=4, weights=uniform, score=-0.339, total= 0.3s\n","[CV] n_neighbors=4, weights=uniform ..................................\n","[CV] ..... n_neighbors=4, weights=uniform, score=-0.255, total= 0.3s\n","[CV] n_neighbors=4, weights=uniform ..................................\n","[CV] ..... n_neighbors=4, weights=uniform, score=-0.182, total= 0.3s\n","[CV] n_neighbors=4, weights=uniform ..................................\n","[CV] ..... n_neighbors=4, weights=uniform, score=-0.282, total= 0.3s\n","[CV] n_neighbors=4, weights=uniform ..................................\n","[CV] ..... n_neighbors=4, weights=uniform, score=-0.127, total= 0.3s\n","[CV] n_neighbors=4, weights=uniform ..................................\n","[CV] ..... n_neighbors=4, weights=uniform, score=-0.397, total= 0.3s\n","[CV] n_neighbors=4, weights=uniform ..................................\n","[CV] ..... n_neighbors=4, weights=uniform, score=-0.287, total= 0.3s\n","[CV] n_neighbors=4, weights=uniform ..................................\n","[CV] ..... n_neighbors=4, weights=uniform, score=-0.368, total= 0.3s\n","[CV] n_neighbors=4, weights=uniform ..................................\n","[CV] ..... n_neighbors=4, weights=uniform, score=-0.159, total= 0.3s\n","[CV] n_neighbors=4, weights=distance .................................\n","[CV] .... n_neighbors=4, weights=distance, score=-0.314, total= 0.3s\n","[CV] n_neighbors=4, weights=distance .................................\n","[CV] .... n_neighbors=4, weights=distance, score=-0.312, total= 0.3s\n","[CV] n_neighbors=4, weights=distance .................................\n","[CV] .... n_neighbors=4, weights=distance, score=-0.254, total= 0.3s\n","[CV] n_neighbors=4, weights=distance .................................\n","[CV] .... n_neighbors=4, weights=distance, score=-0.246, total= 0.3s\n","[CV] n_neighbors=4, weights=distance .................................\n","[CV] .... n_neighbors=4, weights=distance, score=-0.253, total= 0.3s\n","[CV] n_neighbors=4, weights=distance .................................\n","[CV] .... n_neighbors=4, weights=distance, score=-0.296, total= 0.3s\n","[CV] n_neighbors=4, weights=distance .................................\n","[CV] .... n_neighbors=4, weights=distance, score=-0.228, total= 0.3s\n","[CV] n_neighbors=4, weights=distance .................................\n","[CV] .... n_neighbors=4, weights=distance, score=-0.277, total= 0.3s\n","[CV] n_neighbors=4, weights=distance .................................\n","[CV] .... n_neighbors=4, weights=distance, score=-0.242, total= 0.3s\n","[CV] n_neighbors=4, weights=distance .................................\n","[CV] .... n_neighbors=4, weights=distance, score=-0.256, total= 0.3s\n","[CV] n_neighbors=4, weights=distance .................................\n","[CV] .... n_neighbors=4, weights=distance, score=-0.368, total= 0.3s\n","[CV] n_neighbors=4, weights=distance .................................\n","[CV] .... n_neighbors=4, weights=distance, score=-0.276, total= 0.3s\n","[CV] n_neighbors=4, weights=distance .................................\n","[CV] .... n_neighbors=4, weights=distance, score=-0.194, total= 0.3s\n","[CV] n_neighbors=4, weights=distance .................................\n","[CV] .... n_neighbors=4, weights=distance, score=-0.329, total= 0.3s\n","[CV] n_neighbors=4, weights=distance .................................\n","[CV] .... n_neighbors=4, weights=distance, score=-0.302, total= 0.3s\n","[CV] n_neighbors=4, weights=distance .................................\n","[CV] .... n_neighbors=4, weights=distance, score=-0.349, total= 0.3s\n","[CV] n_neighbors=4, weights=distance .................................\n","[CV] .... n_neighbors=4, weights=distance, score=-0.337, total= 0.3s\n","[CV] n_neighbors=4, weights=distance .................................\n","[CV] .... n_neighbors=4, weights=distance, score=-0.284, total= 0.3s\n","[CV] n_neighbors=4, weights=distance .................................\n","[CV] .... n_neighbors=4, weights=distance, score=-0.223, total= 0.3s\n","[CV] n_neighbors=4, weights=distance .................................\n","[CV] .... n_neighbors=4, weights=distance, score=-0.206, total= 0.3s\n","[CV] n_neighbors=4, weights=distance .................................\n","[CV] .... n_neighbors=4, weights=distance, score=-0.246, total= 0.3s\n","[CV] n_neighbors=4, weights=distance .................................\n","[CV] .... n_neighbors=4, weights=distance, score=-0.347, total= 0.3s\n","[CV] n_neighbors=4, weights=distance .................................\n","[CV] .... n_neighbors=4, weights=distance, score=-0.261, total= 0.3s\n","[CV] n_neighbors=4, weights=distance .................................\n","[CV] .... n_neighbors=4, weights=distance, score=-0.185, total= 0.3s\n","[CV] n_neighbors=4, weights=distance .................................\n","[CV] .... n_neighbors=4, weights=distance, score=-0.278, total= 0.3s\n","[CV] n_neighbors=4, weights=distance .................................\n","[CV] .... n_neighbors=4, weights=distance, score=-0.133, total= 0.3s\n","[CV] n_neighbors=4, weights=distance .................................\n","[CV] .... n_neighbors=4, weights=distance, score=-0.392, total= 0.3s\n","[CV] n_neighbors=4, weights=distance .................................\n","[CV] .... n_neighbors=4, weights=distance, score=-0.283, total= 0.3s\n","[CV] n_neighbors=4, weights=distance .................................\n","[CV] .... n_neighbors=4, weights=distance, score=-0.378, total= 0.3s\n","[CV] n_neighbors=4, weights=distance .................................\n","[CV] .... n_neighbors=4, weights=distance, score=-0.163, total= 0.3s\n","[CV] n_neighbors=5, weights=uniform ..................................\n","[CV] ..... n_neighbors=5, weights=uniform, score=-0.226, total= 0.3s\n","[CV] n_neighbors=5, weights=uniform ..................................\n","[CV] ..... n_neighbors=5, weights=uniform, score=-0.213, total= 0.3s\n","[CV] n_neighbors=5, weights=uniform ..................................\n","[CV] ..... n_neighbors=5, weights=uniform, score=-0.200, total= 0.3s\n","[CV] n_neighbors=5, weights=uniform ..................................\n","[CV] ..... n_neighbors=5, weights=uniform, score=-0.236, total= 0.3s\n","[CV] n_neighbors=5, weights=uniform ..................................\n","[CV] ..... n_neighbors=5, weights=uniform, score=-0.225, total= 0.3s\n","[CV] n_neighbors=5, weights=uniform ..................................\n","[CV] ..... n_neighbors=5, weights=uniform, score=-0.261, total= 0.3s\n","[CV] n_neighbors=5, weights=uniform ..................................\n","[CV] ..... n_neighbors=5, weights=uniform, score=-0.183, total= 0.3s\n","[CV] n_neighbors=5, weights=uniform ..................................\n","[CV] ..... n_neighbors=5, weights=uniform, score=-0.222, total= 0.3s\n","[CV] n_neighbors=5, weights=uniform ..................................\n","[CV] ..... n_neighbors=5, weights=uniform, score=-0.220, total= 0.3s\n","[CV] n_neighbors=5, weights=uniform ..................................\n","[CV] ..... n_neighbors=5, weights=uniform, score=-0.146, total= 0.3s\n","[CV] n_neighbors=5, weights=uniform ..................................\n","[CV] ..... n_neighbors=5, weights=uniform, score=-0.258, total= 0.3s\n","[CV] n_neighbors=5, weights=uniform ..................................\n","[CV] ..... n_neighbors=5, weights=uniform, score=-0.226, total= 0.3s\n","[CV] n_neighbors=5, weights=uniform ..................................\n","[CV] ..... n_neighbors=5, weights=uniform, score=-0.220, total= 0.3s\n","[CV] n_neighbors=5, weights=uniform ..................................\n","[CV] ..... n_neighbors=5, weights=uniform, score=-0.233, total= 0.3s\n","[CV] n_neighbors=5, weights=uniform ..................................\n","[CV] ..... n_neighbors=5, weights=uniform, score=-0.259, total= 0.3s\n","[CV] n_neighbors=5, weights=uniform ..................................\n","[CV] ..... n_neighbors=5, weights=uniform, score=-0.254, total= 0.3s\n","[CV] n_neighbors=5, weights=uniform ..................................\n","[CV] ..... n_neighbors=5, weights=uniform, score=-0.241, total= 0.3s\n","[CV] n_neighbors=5, weights=uniform ..................................\n","[CV] ..... n_neighbors=5, weights=uniform, score=-0.190, total= 0.3s\n","[CV] n_neighbors=5, weights=uniform ..................................\n","[CV] ..... n_neighbors=5, weights=uniform, score=-0.161, total= 0.3s\n","[CV] n_neighbors=5, weights=uniform ..................................\n","[CV] ..... n_neighbors=5, weights=uniform, score=-0.146, total= 0.3s\n","[CV] n_neighbors=5, weights=uniform ..................................\n","[CV] ..... n_neighbors=5, weights=uniform, score=-0.186, total= 0.3s\n","[CV] n_neighbors=5, weights=uniform ..................................\n","[CV] ..... n_neighbors=5, weights=uniform, score=-0.297, total= 0.3s\n","[CV] n_neighbors=5, weights=uniform ..................................\n","[CV] ..... n_neighbors=5, weights=uniform, score=-0.223, total= 0.3s\n","[CV] n_neighbors=5, weights=uniform ..................................\n","[CV] ..... n_neighbors=5, weights=uniform, score=-0.114, total= 0.3s\n","[CV] n_neighbors=5, weights=uniform ..................................\n","[CV] ..... n_neighbors=5, weights=uniform, score=-0.249, total= 0.3s\n","[CV] n_neighbors=5, weights=uniform ..................................\n","[CV] ..... n_neighbors=5, weights=uniform, score=-0.076, total= 0.3s\n","[CV] n_neighbors=5, weights=uniform ..................................\n","[CV] ..... n_neighbors=5, weights=uniform, score=-0.353, total= 0.3s\n","[CV] n_neighbors=5, weights=uniform ..................................\n","[CV] ..... n_neighbors=5, weights=uniform, score=-0.200, total= 0.3s\n","[CV] n_neighbors=5, weights=uniform ..................................\n","[CV] ..... n_neighbors=5, weights=uniform, score=-0.299, total= 0.3s\n","[CV] n_neighbors=5, weights=uniform ..................................\n","[CV] ..... n_neighbors=5, weights=uniform, score=-0.135, total= 0.3s\n","[CV] n_neighbors=5, weights=distance .................................\n","[CV] .... n_neighbors=5, weights=distance, score=-0.231, total= 0.3s\n","[CV] n_neighbors=5, weights=distance .................................\n","[CV] .... n_neighbors=5, weights=distance, score=-0.219, total= 0.3s\n","[CV] n_neighbors=5, weights=distance .................................\n","[CV] .... n_neighbors=5, weights=distance, score=-0.200, total= 0.3s\n","[CV] n_neighbors=5, weights=distance .................................\n","[CV] .... n_neighbors=5, weights=distance, score=-0.234, total= 0.3s\n","[CV] n_neighbors=5, weights=distance .................................\n","[CV] .... n_neighbors=5, weights=distance, score=-0.232, total= 0.3s\n","[CV] n_neighbors=5, weights=distance .................................\n","[CV] .... n_neighbors=5, weights=distance, score=-0.266, total= 0.3s\n","[CV] n_neighbors=5, weights=distance .................................\n","[CV] .... n_neighbors=5, weights=distance, score=-0.186, total= 0.3s\n","[CV] n_neighbors=5, weights=distance .................................\n","[CV] .... n_neighbors=5, weights=distance, score=-0.222, total= 0.3s\n","[CV] n_neighbors=5, weights=distance .................................\n","[CV] .... n_neighbors=5, weights=distance, score=-0.216, total= 0.3s\n","[CV] n_neighbors=5, weights=distance .................................\n","[CV] .... n_neighbors=5, weights=distance, score=-0.154, total= 0.3s\n","[CV] n_neighbors=5, weights=distance .................................\n","[CV] .... n_neighbors=5, weights=distance, score=-0.266, total= 0.3s\n","[CV] n_neighbors=5, weights=distance .................................\n","[CV] .... n_neighbors=5, weights=distance, score=-0.225, total= 0.3s\n","[CV] n_neighbors=5, weights=distance .................................\n","[CV] .... n_neighbors=5, weights=distance, score=-0.215, total= 0.3s\n","[CV] n_neighbors=5, weights=distance .................................\n","[CV] .... n_neighbors=5, weights=distance, score=-0.237, total= 0.3s\n","[CV] n_neighbors=5, weights=distance .................................\n","[CV] .... n_neighbors=5, weights=distance, score=-0.259, total= 0.3s\n","[CV] n_neighbors=5, weights=distance .................................\n","[CV] .... n_neighbors=5, weights=distance, score=-0.259, total= 0.3s\n","[CV] n_neighbors=5, weights=distance .................................\n","[CV] .... n_neighbors=5, weights=distance, score=-0.241, total= 0.3s\n","[CV] n_neighbors=5, weights=distance .................................\n","[CV] .... n_neighbors=5, weights=distance, score=-0.199, total= 0.3s\n","[CV] n_neighbors=5, weights=distance .................................\n","[CV] .... n_neighbors=5, weights=distance, score=-0.158, total= 0.3s\n","[CV] n_neighbors=5, weights=distance .................................\n","[CV] .... n_neighbors=5, weights=distance, score=-0.147, total= 0.3s\n","[CV] n_neighbors=5, weights=distance .................................\n","[CV] .... n_neighbors=5, weights=distance, score=-0.187, total= 0.3s\n","[CV] n_neighbors=5, weights=distance .................................\n","[CV] .... n_neighbors=5, weights=distance, score=-0.304, total= 0.3s\n","[CV] n_neighbors=5, weights=distance .................................\n","[CV] .... n_neighbors=5, weights=distance, score=-0.225, total= 0.3s\n","[CV] n_neighbors=5, weights=distance .................................\n","[CV] .... n_neighbors=5, weights=distance, score=-0.117, total= 0.3s\n","[CV] n_neighbors=5, weights=distance .................................\n","[CV] .... n_neighbors=5, weights=distance, score=-0.246, total= 0.3s\n","[CV] n_neighbors=5, weights=distance .................................\n","[CV] .... n_neighbors=5, weights=distance, score=-0.082, total= 0.3s\n","[CV] n_neighbors=5, weights=distance .................................\n","[CV] .... n_neighbors=5, weights=distance, score=-0.350, total= 0.3s\n","[CV] n_neighbors=5, weights=distance .................................\n","[CV] .... n_neighbors=5, weights=distance, score=-0.201, total= 0.3s\n","[CV] n_neighbors=5, weights=distance .................................\n","[CV] .... n_neighbors=5, weights=distance, score=-0.304, total= 0.3s\n","[CV] n_neighbors=5, weights=distance .................................\n","[CV] .... n_neighbors=5, weights=distance, score=-0.138, total= 0.3s\n","[CV] n_neighbors=6, weights=uniform ..................................\n","[CV] ..... n_neighbors=6, weights=uniform, score=-0.214, total= 0.3s\n","[CV] n_neighbors=6, weights=uniform ..................................\n","[CV] ..... n_neighbors=6, weights=uniform, score=-0.162, total= 0.3s\n","[CV] n_neighbors=6, weights=uniform ..................................\n","[CV] ..... n_neighbors=6, weights=uniform, score=-0.212, total= 0.3s\n","[CV] n_neighbors=6, weights=uniform ..................................\n","[CV] ..... n_neighbors=6, weights=uniform, score=-0.231, total= 0.3s\n","[CV] n_neighbors=6, weights=uniform ..................................\n","[CV] ..... n_neighbors=6, weights=uniform, score=-0.177, total= 0.3s\n","[CV] n_neighbors=6, weights=uniform ..................................\n","[CV] ..... n_neighbors=6, weights=uniform, score=-0.219, total= 0.3s\n","[CV] n_neighbors=6, weights=uniform ..................................\n","[CV] ..... n_neighbors=6, weights=uniform, score=-0.154, total= 0.3s\n","[CV] n_neighbors=6, weights=uniform ..................................\n","[CV] ..... n_neighbors=6, weights=uniform, score=-0.224, total= 0.3s\n","[CV] n_neighbors=6, weights=uniform ..................................\n","[CV] ..... n_neighbors=6, weights=uniform, score=-0.208, total= 0.3s\n","[CV] n_neighbors=6, weights=uniform ..................................\n","[CV] ..... n_neighbors=6, weights=uniform, score=-0.100, total= 0.3s\n","[CV] n_neighbors=6, weights=uniform ..................................\n","[CV] ..... n_neighbors=6, weights=uniform, score=-0.225, total= 0.3s\n","[CV] n_neighbors=6, weights=uniform ..................................\n","[CV] ..... n_neighbors=6, weights=uniform, score=-0.236, total= 0.3s\n","[CV] n_neighbors=6, weights=uniform ..................................\n","[CV] ..... n_neighbors=6, weights=uniform, score=-0.149, total= 0.3s\n","[CV] n_neighbors=6, weights=uniform ..................................\n","[CV] ..... n_neighbors=6, weights=uniform, score=-0.234, total= 0.3s\n","[CV] n_neighbors=6, weights=uniform ..................................\n","[CV] ..... n_neighbors=6, weights=uniform, score=-0.203, total= 0.3s\n","[CV] n_neighbors=6, weights=uniform ..................................\n","[CV] ..... n_neighbors=6, weights=uniform, score=-0.232, total= 0.3s\n","[CV] n_neighbors=6, weights=uniform ..................................\n","[CV] ..... n_neighbors=6, weights=uniform, score=-0.183, total= 0.3s\n","[CV] n_neighbors=6, weights=uniform ..................................\n","[CV] ..... n_neighbors=6, weights=uniform, score=-0.158, total= 0.3s\n","[CV] n_neighbors=6, weights=uniform ..................................\n","[CV] ..... n_neighbors=6, weights=uniform, score=-0.137, total= 0.3s\n","[CV] n_neighbors=6, weights=uniform ..................................\n","[CV] ..... n_neighbors=6, weights=uniform, score=-0.161, total= 0.3s\n","[CV] n_neighbors=6, weights=uniform ..................................\n","[CV] ..... n_neighbors=6, weights=uniform, score=-0.175, total= 0.3s\n","[CV] n_neighbors=6, weights=uniform ..................................\n","[CV] ..... n_neighbors=6, weights=uniform, score=-0.258, total= 0.3s\n","[CV] n_neighbors=6, weights=uniform ..................................\n","[CV] ..... n_neighbors=6, weights=uniform, score=-0.207, total= 0.3s\n","[CV] n_neighbors=6, weights=uniform ..................................\n","[CV] ..... n_neighbors=6, weights=uniform, score=-0.106, total= 0.3s\n","[CV] n_neighbors=6, weights=uniform ..................................\n","[CV] ..... n_neighbors=6, weights=uniform, score=-0.217, total= 0.3s\n","[CV] n_neighbors=6, weights=uniform ..................................\n","[CV] ..... n_neighbors=6, weights=uniform, score=-0.081, total= 0.3s\n","[CV] n_neighbors=6, weights=uniform ..................................\n","[CV] ..... n_neighbors=6, weights=uniform, score=-0.294, total= 0.4s\n","[CV] n_neighbors=6, weights=uniform ..................................\n","[CV] ..... n_neighbors=6, weights=uniform, score=-0.160, total= 0.3s\n","[CV] n_neighbors=6, weights=uniform ..................................\n","[CV] ..... n_neighbors=6, weights=uniform, score=-0.289, total= 0.3s\n","[CV] n_neighbors=6, weights=uniform ..................................\n","[CV] ..... n_neighbors=6, weights=uniform, score=-0.118, total= 0.3s\n","[CV] n_neighbors=6, weights=distance .................................\n","[CV] .... n_neighbors=6, weights=distance, score=-0.213, total= 0.4s\n","[CV] n_neighbors=6, weights=distance .................................\n","[CV] .... n_neighbors=6, weights=distance, score=-0.169, total= 0.3s\n","[CV] n_neighbors=6, weights=distance .................................\n","[CV] .... n_neighbors=6, weights=distance, score=-0.207, total= 0.3s\n","[CV] n_neighbors=6, weights=distance .................................\n","[CV] .... n_neighbors=6, weights=distance, score=-0.227, total= 0.3s\n","[CV] n_neighbors=6, weights=distance .................................\n","[CV] .... n_neighbors=6, weights=distance, score=-0.183, total= 0.3s\n","[CV] n_neighbors=6, weights=distance .................................\n","[CV] .... n_neighbors=6, weights=distance, score=-0.225, total= 0.3s\n","[CV] n_neighbors=6, weights=distance .................................\n","[CV] .... n_neighbors=6, weights=distance, score=-0.158, total= 0.3s\n","[CV] n_neighbors=6, weights=distance .................................\n","[CV] .... n_neighbors=6, weights=distance, score=-0.222, total= 0.3s\n","[CV] n_neighbors=6, weights=distance .................................\n","[CV] .... n_neighbors=6, weights=distance, score=-0.203, total= 0.3s\n","[CV] n_neighbors=6, weights=distance .................................\n","[CV] .... n_neighbors=6, weights=distance, score=-0.107, total= 0.3s\n","[CV] n_neighbors=6, weights=distance .................................\n","[CV] .... n_neighbors=6, weights=distance, score=-0.233, total= 0.3s\n","[CV] n_neighbors=6, weights=distance .................................\n","[CV] .... n_neighbors=6, weights=distance, score=-0.232, total= 0.3s\n","[CV] n_neighbors=6, weights=distance .................................\n","[CV] .... n_neighbors=6, weights=distance, score=-0.149, total= 0.3s\n","[CV] n_neighbors=6, weights=distance .................................\n","[CV] .... n_neighbors=6, weights=distance, score=-0.236, total= 0.3s\n","[CV] n_neighbors=6, weights=distance .................................\n","[CV] .... n_neighbors=6, weights=distance, score=-0.204, total= 0.3s\n","[CV] n_neighbors=6, weights=distance .................................\n","[CV] .... n_neighbors=6, weights=distance, score=-0.236, total= 0.3s\n","[CV] n_neighbors=6, weights=distance .................................\n","[CV] .... n_neighbors=6, weights=distance, score=-0.183, total= 0.3s\n","[CV] n_neighbors=6, weights=distance .................................\n","[CV] .... n_neighbors=6, weights=distance, score=-0.168, total= 0.3s\n","[CV] n_neighbors=6, weights=distance .................................\n","[CV] .... n_neighbors=6, weights=distance, score=-0.134, total= 0.3s\n","[CV] n_neighbors=6, weights=distance .................................\n","[CV] .... n_neighbors=6, weights=distance, score=-0.159, total= 0.3s\n","[CV] n_neighbors=6, weights=distance .................................\n","[CV] .... n_neighbors=6, weights=distance, score=-0.174, total= 0.3s\n","[CV] n_neighbors=6, weights=distance .................................\n","[CV] .... n_neighbors=6, weights=distance, score=-0.264, total= 0.3s\n","[CV] n_neighbors=6, weights=distance .................................\n","[CV] .... n_neighbors=6, weights=distance, score=-0.205, total= 0.3s\n","[CV] n_neighbors=6, weights=distance .................................\n","[CV] .... n_neighbors=6, weights=distance, score=-0.107, total= 0.3s\n","[CV] n_neighbors=6, weights=distance .................................\n","[CV] .... n_neighbors=6, weights=distance, score=-0.214, total= 0.3s\n","[CV] n_neighbors=6, weights=distance .................................\n","[CV] .... n_neighbors=6, weights=distance, score=-0.081, total= 0.3s\n","[CV] n_neighbors=6, weights=distance .................................\n","[CV] .... n_neighbors=6, weights=distance, score=-0.295, total= 0.4s\n","[CV] n_neighbors=6, weights=distance .................................\n","[CV] .... n_neighbors=6, weights=distance, score=-0.160, total= 0.3s\n","[CV] n_neighbors=6, weights=distance .................................\n","[CV] .... n_neighbors=6, weights=distance, score=-0.291, total= 0.3s\n","[CV] n_neighbors=6, weights=distance .................................\n","[CV] .... n_neighbors=6, weights=distance, score=-0.120, total= 0.3s\n","[Parallel(n_jobs=1)]: Done 300 out of 300 | elapsed: 1.7min finished\n","KNeighborsRegressor(algorithm='auto', leaf_size=30, metric='minkowski',\n"," metric_params=None, n_jobs=None, n_neighbors=6, p=2,\n"," weights='uniform')\n","window: 1, stide: 1, rmse: 2.9457989546948857\n","(3000, 266)\n","Number of Feature-Columns: 266\n","\n","Features Ready for undergoing selection tests done ...\n","\n","Fitting 30 folds for each of 10 candidates, totalling 300 fits\n","[Parallel(n_jobs=1)]: Using backend SequentialBackend with 1 concurrent workers.\n","[CV] n_neighbors=2, weights=uniform ..................................\n","[CV] ..... n_neighbors=2, weights=uniform, score=-0.724, total= 0.4s\n","[Parallel(n_jobs=1)]: Done 1 out of 1 | elapsed: 0.4s remaining: 0.0s\n","[CV] n_neighbors=2, weights=uniform ..................................\n","[CV] ..... n_neighbors=2, weights=uniform, score=-0.629, total= 0.4s\n","[Parallel(n_jobs=1)]: Done 2 out of 2 | elapsed: 0.7s remaining: 0.0s\n","[CV] n_neighbors=2, weights=uniform ..................................\n","[CV] ..... n_neighbors=2, weights=uniform, score=-0.490, total= 0.3s\n","[Parallel(n_jobs=1)]: Done 3 out of 3 | elapsed: 1.1s remaining: 0.0s\n","[CV] n_neighbors=2, weights=uniform ..................................\n","[CV] ..... n_neighbors=2, weights=uniform, score=-0.693, total= 0.3s\n","[Parallel(n_jobs=1)]: Done 4 out of 4 | elapsed: 1.4s remaining: 0.0s\n","[CV] n_neighbors=2, weights=uniform ..................................\n","[CV] ..... n_neighbors=2, weights=uniform, score=-0.458, total= 0.3s\n","[CV] n_neighbors=2, weights=uniform ..................................\n","[CV] ..... n_neighbors=2, weights=uniform, score=-0.564, total= 0.4s\n","[CV] n_neighbors=2, weights=uniform ..................................\n","[CV] ..... n_neighbors=2, weights=uniform, score=-0.578, total= 0.4s\n","[CV] n_neighbors=2, weights=uniform ..................................\n","[CV] ..... n_neighbors=2, weights=uniform, score=-0.680, total= 0.4s\n","[CV] n_neighbors=2, weights=uniform ..................................\n","[CV] ..... n_neighbors=2, weights=uniform, score=-0.459, total= 0.3s\n","[CV] n_neighbors=2, weights=uniform ..................................\n","[CV] ..... n_neighbors=2, weights=uniform, score=-0.657, total= 0.3s\n","[CV] n_neighbors=2, weights=uniform ..................................\n","[CV] ..... n_neighbors=2, weights=uniform, score=-0.566, total= 0.3s\n","[CV] n_neighbors=2, weights=uniform ..................................\n","[CV] ..... n_neighbors=2, weights=uniform, score=-0.552, total= 0.4s\n","[CV] n_neighbors=2, weights=uniform ..................................\n","[CV] ..... n_neighbors=2, weights=uniform, score=-0.540, total= 0.4s\n","[CV] n_neighbors=2, weights=uniform ..................................\n","[CV] ..... n_neighbors=2, weights=uniform, score=-0.602, total= 0.4s\n","[CV] n_neighbors=2, weights=uniform ..................................\n","[CV] ..... n_neighbors=2, weights=uniform, score=-0.576, total= 0.3s\n","[CV] n_neighbors=2, weights=uniform ..................................\n","[CV] ..... n_neighbors=2, weights=uniform, score=-0.835, total= 0.4s\n","[CV] n_neighbors=2, weights=uniform ..................................\n","[CV] ..... n_neighbors=2, weights=uniform, score=-0.507, total= 0.4s\n","[CV] n_neighbors=2, weights=uniform ..................................\n","[CV] ..... n_neighbors=2, weights=uniform, score=-0.652, total= 0.3s\n","[CV] n_neighbors=2, weights=uniform ..................................\n","[CV] ..... n_neighbors=2, weights=uniform, score=-0.367, total= 0.4s\n","[CV] n_neighbors=2, weights=uniform ..................................\n","[CV] ..... n_neighbors=2, weights=uniform, score=-0.472, total= 0.4s\n","[CV] n_neighbors=2, weights=uniform ..................................\n","[CV] ..... n_neighbors=2, weights=uniform, score=-0.530, total= 0.4s\n","[CV] n_neighbors=2, weights=uniform ..................................\n","[CV] ..... n_neighbors=2, weights=uniform, score=-0.731, total= 0.4s\n","[CV] n_neighbors=2, weights=uniform ..................................\n","[CV] ..... n_neighbors=2, weights=uniform, score=-0.600, total= 0.4s\n","[CV] n_neighbors=2, weights=uniform ..................................\n","[CV] ..... n_neighbors=2, weights=uniform, score=-0.486, total= 0.4s\n","[CV] n_neighbors=2, weights=uniform ..................................\n","[CV] ..... n_neighbors=2, weights=uniform, score=-0.605, total= 0.4s\n","[CV] n_neighbors=2, weights=uniform ..................................\n","[CV] ..... n_neighbors=2, weights=uniform, score=-0.464, total= 0.4s\n","[CV] n_neighbors=2, weights=uniform ..................................\n","[CV] ..... n_neighbors=2, weights=uniform, score=-0.683, total= 0.4s\n","[CV] n_neighbors=2, weights=uniform ..................................\n","[CV] ..... n_neighbors=2, weights=uniform, score=-0.583, total= 0.4s\n","[CV] n_neighbors=2, weights=uniform ..................................\n","[CV] ..... n_neighbors=2, weights=uniform, score=-0.704, total= 0.4s\n","[CV] n_neighbors=2, weights=uniform ..................................\n","[CV] ..... n_neighbors=2, weights=uniform, score=-0.430, total= 0.4s\n","[CV] n_neighbors=2, weights=distance .................................\n","[CV] .... n_neighbors=2, weights=distance, score=-0.721, total= 0.4s\n","[CV] n_neighbors=2, weights=distance .................................\n","[CV] .... n_neighbors=2, weights=distance, score=-0.630, total= 0.4s\n","[CV] n_neighbors=2, weights=distance .................................\n","[CV] .... n_neighbors=2, weights=distance, score=-0.489, total= 0.3s\n","[CV] n_neighbors=2, weights=distance .................................\n","[CV] .... n_neighbors=2, weights=distance, score=-0.686, total= 0.4s\n","[CV] n_neighbors=2, weights=distance .................................\n","[CV] .... n_neighbors=2, weights=distance, score=-0.470, total= 0.3s\n","[CV] n_neighbors=2, weights=distance .................................\n","[CV] .... n_neighbors=2, weights=distance, score=-0.577, total= 0.4s\n","[CV] n_neighbors=2, weights=distance .................................\n","[CV] .... n_neighbors=2, weights=distance, score=-0.573, total= 0.4s\n","[CV] n_neighbors=2, weights=distance .................................\n","[CV] .... n_neighbors=2, weights=distance, score=-0.675, total= 0.3s\n","[CV] n_neighbors=2, weights=distance .................................\n","[CV] .... n_neighbors=2, weights=distance, score=-0.456, total= 0.4s\n","[CV] n_neighbors=2, weights=distance .................................\n","[CV] .... n_neighbors=2, weights=distance, score=-0.651, total= 0.4s\n","[CV] n_neighbors=2, weights=distance .................................\n","[CV] .... n_neighbors=2, weights=distance, score=-0.575, total= 0.3s\n","[CV] n_neighbors=2, weights=distance .................................\n","[CV] .... n_neighbors=2, weights=distance, score=-0.551, total= 0.3s\n","[CV] n_neighbors=2, weights=distance .................................\n","[CV] .... n_neighbors=2, weights=distance, score=-0.536, total= 0.4s\n","[CV] n_neighbors=2, weights=distance .................................\n","[CV] .... n_neighbors=2, weights=distance, score=-0.602, total= 0.3s\n","[CV] n_neighbors=2, weights=distance .................................\n","[CV] .... n_neighbors=2, weights=distance, score=-0.578, total= 0.4s\n","[CV] n_neighbors=2, weights=distance .................................\n","[CV] .... n_neighbors=2, weights=distance, score=-0.831, total= 0.4s\n","[CV] n_neighbors=2, weights=distance .................................\n","[CV] .... n_neighbors=2, weights=distance, score=-0.504, total= 0.4s\n","[CV] n_neighbors=2, weights=distance .................................\n","[CV] .... n_neighbors=2, weights=distance, score=-0.662, total= 0.3s\n","[CV] n_neighbors=2, weights=distance .................................\n","[CV] .... n_neighbors=2, weights=distance, score=-0.366, total= 0.3s\n","[CV] n_neighbors=2, weights=distance .................................\n","[CV] .... n_neighbors=2, weights=distance, score=-0.468, total= 0.3s\n","[CV] n_neighbors=2, weights=distance .................................\n","[CV] .... n_neighbors=2, weights=distance, score=-0.527, total= 0.3s\n","[CV] n_neighbors=2, weights=distance .................................\n","[CV] .... n_neighbors=2, weights=distance, score=-0.728, total= 0.4s\n","[CV] n_neighbors=2, weights=distance .................................\n","[CV] .... n_neighbors=2, weights=distance, score=-0.600, total= 0.3s\n","[CV] n_neighbors=2, weights=distance .................................\n","[CV] .... n_neighbors=2, weights=distance, score=-0.489, total= 0.3s\n","[CV] n_neighbors=2, weights=distance .................................\n","[CV] .... n_neighbors=2, weights=distance, score=-0.601, total= 0.4s\n","[CV] n_neighbors=2, weights=distance .................................\n","[CV] .... n_neighbors=2, weights=distance, score=-0.470, total= 0.4s\n","[CV] n_neighbors=2, weights=distance .................................\n","[CV] .... n_neighbors=2, weights=distance, score=-0.678, total= 0.4s\n","[CV] n_neighbors=2, weights=distance .................................\n","[CV] .... n_neighbors=2, weights=distance, score=-0.567, total= 0.3s\n","[CV] n_neighbors=2, weights=distance .................................\n","[CV] .... n_neighbors=2, weights=distance, score=-0.716, total= 0.4s\n","[CV] n_neighbors=2, weights=distance .................................\n","[CV] .... n_neighbors=2, weights=distance, score=-0.429, total= 0.3s\n","[CV] n_neighbors=3, weights=uniform ..................................\n","[CV] ..... n_neighbors=3, weights=uniform, score=-0.476, total= 0.4s\n","[CV] n_neighbors=3, weights=uniform ..................................\n","[CV] ..... n_neighbors=3, weights=uniform, score=-0.446, total= 0.4s\n","[CV] n_neighbors=3, weights=uniform ..................................\n","[CV] ..... n_neighbors=3, weights=uniform, score=-0.314, total= 0.4s\n","[CV] n_neighbors=3, weights=uniform ..................................\n","[CV] ..... n_neighbors=3, weights=uniform, score=-0.353, total= 0.4s\n","[CV] n_neighbors=3, weights=uniform ..................................\n","[CV] ..... n_neighbors=3, weights=uniform, score=-0.322, total= 0.4s\n","[CV] n_neighbors=3, weights=uniform ..................................\n","[CV] ..... n_neighbors=3, weights=uniform, score=-0.373, total= 0.4s\n","[CV] n_neighbors=3, weights=uniform ..................................\n","[CV] ..... n_neighbors=3, weights=uniform, score=-0.371, total= 0.4s\n","[CV] n_neighbors=3, weights=uniform ..................................\n","[CV] ..... n_neighbors=3, weights=uniform, score=-0.398, total= 0.4s\n","[CV] n_neighbors=3, weights=uniform ..................................\n","[CV] ..... n_neighbors=3, weights=uniform, score=-0.292, total= 0.4s\n","[CV] n_neighbors=3, weights=uniform ..................................\n","[CV] ..... n_neighbors=3, weights=uniform, score=-0.373, total= 0.4s\n","[CV] n_neighbors=3, weights=uniform ..................................\n","[CV] ..... n_neighbors=3, weights=uniform, score=-0.413, total= 0.4s\n","[CV] n_neighbors=3, weights=uniform ..................................\n","[CV] ..... n_neighbors=3, weights=uniform, score=-0.369, total= 0.4s\n","[CV] n_neighbors=3, weights=uniform ..................................\n","[CV] ..... n_neighbors=3, weights=uniform, score=-0.280, total= 0.4s\n","[CV] n_neighbors=3, weights=uniform ..................................\n","[CV] ..... n_neighbors=3, weights=uniform, score=-0.454, total= 0.4s\n","[CV] n_neighbors=3, weights=uniform ..................................\n","[CV] ..... n_neighbors=3, weights=uniform, score=-0.375, total= 0.4s\n","[CV] n_neighbors=3, weights=uniform ..................................\n","[CV] ..... n_neighbors=3, weights=uniform, score=-0.456, total= 0.4s\n","[CV] n_neighbors=3, weights=uniform ..................................\n","[CV] ..... n_neighbors=3, weights=uniform, score=-0.414, total= 0.4s\n","[CV] n_neighbors=3, weights=uniform ..................................\n","[CV] ..... n_neighbors=3, weights=uniform, score=-0.433, total= 0.4s\n","[CV] n_neighbors=3, weights=uniform ..................................\n","[CV] ..... n_neighbors=3, weights=uniform, score=-0.281, total= 0.4s\n","[CV] n_neighbors=3, weights=uniform ..................................\n","[CV] ..... n_neighbors=3, weights=uniform, score=-0.346, total= 0.4s\n","[CV] n_neighbors=3, weights=uniform ..................................\n","[CV] ..... n_neighbors=3, weights=uniform, score=-0.325, total= 0.4s\n","[CV] n_neighbors=3, weights=uniform ..................................\n","[CV] ..... n_neighbors=3, weights=uniform, score=-0.498, total= 0.4s\n","[CV] n_neighbors=3, weights=uniform ..................................\n","[CV] ..... n_neighbors=3, weights=uniform, score=-0.349, total= 0.4s\n","[CV] n_neighbors=3, weights=uniform ..................................\n","[CV] ..... n_neighbors=3, weights=uniform, score=-0.329, total= 0.4s\n","[CV] n_neighbors=3, weights=uniform ..................................\n","[CV] ..... n_neighbors=3, weights=uniform, score=-0.409, total= 0.4s\n","[CV] n_neighbors=3, weights=uniform ..................................\n","[CV] ..... n_neighbors=3, weights=uniform, score=-0.228, total= 0.4s\n","[CV] n_neighbors=3, weights=uniform ..................................\n","[CV] ..... n_neighbors=3, weights=uniform, score=-0.482, total= 0.4s\n","[CV] n_neighbors=3, weights=uniform ..................................\n","[CV] ..... n_neighbors=3, weights=uniform, score=-0.440, total= 0.4s\n","[CV] n_neighbors=3, weights=uniform ..................................\n","[CV] ..... n_neighbors=3, weights=uniform, score=-0.458, total= 0.4s\n","[CV] n_neighbors=3, weights=uniform ..................................\n","[CV] ..... n_neighbors=3, weights=uniform, score=-0.284, total= 0.4s\n","[CV] n_neighbors=3, weights=distance .................................\n","[CV] .... n_neighbors=3, weights=distance, score=-0.476, total= 0.4s\n","[CV] n_neighbors=3, weights=distance .................................\n","[CV] .... n_neighbors=3, weights=distance, score=-0.444, total= 0.4s\n","[CV] n_neighbors=3, weights=distance .................................\n","[CV] .... n_neighbors=3, weights=distance, score=-0.317, total= 0.4s\n","[CV] n_neighbors=3, weights=distance .................................\n","[CV] .... n_neighbors=3, weights=distance, score=-0.358, total= 0.4s\n","[CV] n_neighbors=3, weights=distance .................................\n","[CV] .... n_neighbors=3, weights=distance, score=-0.332, total= 0.4s\n","[CV] n_neighbors=3, weights=distance .................................\n","[CV] .... n_neighbors=3, weights=distance, score=-0.385, total= 0.4s\n","[CV] n_neighbors=3, weights=distance .................................\n","[CV] .... n_neighbors=3, weights=distance, score=-0.371, total= 0.4s\n","[CV] n_neighbors=3, weights=distance .................................\n","[CV] .... n_neighbors=3, weights=distance, score=-0.398, total= 0.4s\n","[CV] n_neighbors=3, weights=distance .................................\n","[CV] .... n_neighbors=3, weights=distance, score=-0.290, total= 0.4s\n","[CV] n_neighbors=3, weights=distance .................................\n","[CV] .... n_neighbors=3, weights=distance, score=-0.378, total= 0.4s\n","[CV] n_neighbors=3, weights=distance .................................\n","[CV] .... n_neighbors=3, weights=distance, score=-0.419, total= 0.4s\n","[CV] n_neighbors=3, weights=distance .................................\n","[CV] .... n_neighbors=3, weights=distance, score=-0.368, total= 0.4s\n","[CV] n_neighbors=3, weights=distance .................................\n","[CV] .... n_neighbors=3, weights=distance, score=-0.280, total= 0.4s\n","[CV] n_neighbors=3, weights=distance .................................\n","[CV] .... n_neighbors=3, weights=distance, score=-0.449, total= 0.4s\n","[CV] n_neighbors=3, weights=distance .................................\n","[CV] .... n_neighbors=3, weights=distance, score=-0.379, total= 0.4s\n","[CV] n_neighbors=3, weights=distance .................................\n","[CV] .... n_neighbors=3, weights=distance, score=-0.461, total= 0.4s\n","[CV] n_neighbors=3, weights=distance .................................\n","[CV] .... n_neighbors=3, weights=distance, score=-0.406, total= 0.4s\n","[CV] n_neighbors=3, weights=distance .................................\n","[CV] .... n_neighbors=3, weights=distance, score=-0.441, total= 0.4s\n","[CV] n_neighbors=3, weights=distance .................................\n","[CV] .... n_neighbors=3, weights=distance, score=-0.276, total= 0.4s\n","[CV] n_neighbors=3, weights=distance .................................\n","[CV] .... n_neighbors=3, weights=distance, score=-0.343, total= 0.4s\n","[CV] n_neighbors=3, weights=distance .................................\n","[CV] .... n_neighbors=3, weights=distance, score=-0.326, total= 0.4s\n","[CV] n_neighbors=3, weights=distance .................................\n","[CV] .... n_neighbors=3, weights=distance, score=-0.501, total= 0.4s\n","[CV] n_neighbors=3, weights=distance .................................\n","[CV] .... n_neighbors=3, weights=distance, score=-0.355, total= 0.4s\n","[CV] n_neighbors=3, weights=distance .................................\n","[CV] .... n_neighbors=3, weights=distance, score=-0.332, total= 0.4s\n","[CV] n_neighbors=3, weights=distance .................................\n","[CV] .... n_neighbors=3, weights=distance, score=-0.406, total= 0.4s\n","[CV] n_neighbors=3, weights=distance .................................\n","[CV] .... n_neighbors=3, weights=distance, score=-0.234, total= 0.4s\n","[CV] n_neighbors=3, weights=distance .................................\n","[CV] .... n_neighbors=3, weights=distance, score=-0.477, total= 0.4s\n","[CV] n_neighbors=3, weights=distance .................................\n","[CV] .... n_neighbors=3, weights=distance, score=-0.428, total= 0.4s\n","[CV] n_neighbors=3, weights=distance .................................\n","[CV] .... n_neighbors=3, weights=distance, score=-0.467, total= 0.4s\n","[CV] n_neighbors=3, weights=distance .................................\n","[CV] .... n_neighbors=3, weights=distance, score=-0.283, total= 0.4s\n","[CV] n_neighbors=4, weights=uniform ..................................\n","[CV] ..... n_neighbors=4, weights=uniform, score=-0.309, total= 0.4s\n","[CV] n_neighbors=4, weights=uniform ..................................\n","[CV] ..... n_neighbors=4, weights=uniform, score=-0.308, total= 0.4s\n","[CV] n_neighbors=4, weights=uniform ..................................\n","[CV] ..... n_neighbors=4, weights=uniform, score=-0.253, total= 0.4s\n","[CV] n_neighbors=4, weights=uniform ..................................\n","[CV] ..... n_neighbors=4, weights=uniform, score=-0.242, total= 0.4s\n","[CV] n_neighbors=4, weights=uniform ..................................\n","[CV] ..... n_neighbors=4, weights=uniform, score=-0.245, total= 0.4s\n","[CV] n_neighbors=4, weights=uniform ..................................\n","[CV] ..... n_neighbors=4, weights=uniform, score=-0.288, total= 0.4s\n","[CV] n_neighbors=4, weights=uniform ..................................\n","[CV] ..... n_neighbors=4, weights=uniform, score=-0.221, total= 0.4s\n","[CV] n_neighbors=4, weights=uniform ..................................\n","[CV] ..... n_neighbors=4, weights=uniform, score=-0.275, total= 0.4s\n","[CV] n_neighbors=4, weights=uniform ..................................\n","[CV] ..... n_neighbors=4, weights=uniform, score=-0.245, total= 0.4s\n","[CV] n_neighbors=4, weights=uniform ..................................\n","[CV] ..... n_neighbors=4, weights=uniform, score=-0.248, total= 0.4s\n","[CV] n_neighbors=4, weights=uniform ..................................\n","[CV] ..... n_neighbors=4, weights=uniform, score=-0.363, total= 0.4s\n","[CV] n_neighbors=4, weights=uniform ..................................\n","[CV] ..... n_neighbors=4, weights=uniform, score=-0.275, total= 0.4s\n","[CV] n_neighbors=4, weights=uniform ..................................\n","[CV] ..... n_neighbors=4, weights=uniform, score=-0.194, total= 0.4s\n","[CV] n_neighbors=4, weights=uniform ..................................\n","[CV] ..... n_neighbors=4, weights=uniform, score=-0.329, total= 0.4s\n","[CV] n_neighbors=4, weights=uniform ..................................\n","[CV] ..... n_neighbors=4, weights=uniform, score=-0.299, total= 0.4s\n","[CV] n_neighbors=4, weights=uniform ..................................\n","[CV] ..... n_neighbors=4, weights=uniform, score=-0.346, total= 0.4s\n","[CV] n_neighbors=4, weights=uniform ..................................\n","[CV] ..... n_neighbors=4, weights=uniform, score=-0.340, total= 0.4s\n","[CV] n_neighbors=4, weights=uniform ..................................\n","[CV] ..... n_neighbors=4, weights=uniform, score=-0.272, total= 0.4s\n","[CV] n_neighbors=4, weights=uniform ..................................\n","[CV] ..... n_neighbors=4, weights=uniform, score=-0.230, total= 0.4s\n","[CV] n_neighbors=4, weights=uniform ..................................\n","[CV] ..... n_neighbors=4, weights=uniform, score=-0.204, total= 0.4s\n","[CV] n_neighbors=4, weights=uniform ..................................\n","[CV] ..... n_neighbors=4, weights=uniform, score=-0.247, total= 0.4s\n","[CV] n_neighbors=4, weights=uniform ..................................\n","[CV] ..... n_neighbors=4, weights=uniform, score=-0.339, total= 0.4s\n","[CV] n_neighbors=4, weights=uniform ..................................\n","[CV] ..... n_neighbors=4, weights=uniform, score=-0.255, total= 0.4s\n","[CV] n_neighbors=4, weights=uniform ..................................\n","[CV] ..... n_neighbors=4, weights=uniform, score=-0.182, total= 0.4s\n","[CV] n_neighbors=4, weights=uniform ..................................\n","[CV] ..... n_neighbors=4, weights=uniform, score=-0.282, total= 0.4s\n","[CV] n_neighbors=4, weights=uniform ..................................\n","[CV] ..... n_neighbors=4, weights=uniform, score=-0.127, total= 0.4s\n","[CV] n_neighbors=4, weights=uniform ..................................\n","[CV] ..... n_neighbors=4, weights=uniform, score=-0.397, total= 0.4s\n","[CV] n_neighbors=4, weights=uniform ..................................\n","[CV] ..... n_neighbors=4, weights=uniform, score=-0.287, total= 0.4s\n","[CV] n_neighbors=4, weights=uniform ..................................\n","[CV] ..... n_neighbors=4, weights=uniform, score=-0.368, total= 0.4s\n","[CV] n_neighbors=4, weights=uniform ..................................\n","[CV] ..... n_neighbors=4, weights=uniform, score=-0.159, total= 0.4s\n","[CV] n_neighbors=4, weights=distance .................................\n","[CV] .... n_neighbors=4, weights=distance, score=-0.314, total= 0.4s\n","[CV] n_neighbors=4, weights=distance .................................\n","[CV] .... n_neighbors=4, weights=distance, score=-0.312, total= 0.4s\n","[CV] n_neighbors=4, weights=distance .................................\n","[CV] .... n_neighbors=4, weights=distance, score=-0.254, total= 0.4s\n","[CV] n_neighbors=4, weights=distance .................................\n","[CV] .... n_neighbors=4, weights=distance, score=-0.246, total= 0.4s\n","[CV] n_neighbors=4, weights=distance .................................\n","[CV] .... n_neighbors=4, weights=distance, score=-0.253, total= 0.4s\n","[CV] n_neighbors=4, weights=distance .................................\n","[CV] .... n_neighbors=4, weights=distance, score=-0.296, total= 0.4s\n","[CV] n_neighbors=4, weights=distance .................................\n","[CV] .... n_neighbors=4, weights=distance, score=-0.228, total= 0.4s\n","[CV] n_neighbors=4, weights=distance .................................\n","[CV] .... n_neighbors=4, weights=distance, score=-0.277, total= 0.4s\n","[CV] n_neighbors=4, weights=distance .................................\n","[CV] .... n_neighbors=4, weights=distance, score=-0.242, total= 0.4s\n","[CV] n_neighbors=4, weights=distance .................................\n","[CV] .... n_neighbors=4, weights=distance, score=-0.256, total= 0.4s\n","[CV] n_neighbors=4, weights=distance .................................\n","[CV] .... n_neighbors=4, weights=distance, score=-0.368, total= 0.4s\n","[CV] n_neighbors=4, weights=distance .................................\n","[CV] .... n_neighbors=4, weights=distance, score=-0.276, total= 0.4s\n","[CV] n_neighbors=4, weights=distance .................................\n","[CV] .... n_neighbors=4, weights=distance, score=-0.194, total= 0.4s\n","[CV] n_neighbors=4, weights=distance .................................\n","[CV] .... n_neighbors=4, weights=distance, score=-0.329, total= 0.4s\n","[CV] n_neighbors=4, weights=distance .................................\n","[CV] .... n_neighbors=4, weights=distance, score=-0.302, total= 0.4s\n","[CV] n_neighbors=4, weights=distance .................................\n","[CV] .... n_neighbors=4, weights=distance, score=-0.349, total= 0.4s\n","[CV] n_neighbors=4, weights=distance .................................\n","[CV] .... n_neighbors=4, weights=distance, score=-0.337, total= 0.4s\n","[CV] n_neighbors=4, weights=distance .................................\n","[CV] .... n_neighbors=4, weights=distance, score=-0.284, total= 0.4s\n","[CV] n_neighbors=4, weights=distance .................................\n","[CV] .... n_neighbors=4, weights=distance, score=-0.223, total= 0.4s\n","[CV] n_neighbors=4, weights=distance .................................\n","[CV] .... n_neighbors=4, weights=distance, score=-0.206, total= 0.4s\n","[CV] n_neighbors=4, weights=distance .................................\n","[CV] .... n_neighbors=4, weights=distance, score=-0.246, total= 0.4s\n","[CV] n_neighbors=4, weights=distance .................................\n","[CV] .... n_neighbors=4, weights=distance, score=-0.347, total= 0.4s\n","[CV] n_neighbors=4, weights=distance .................................\n","[CV] .... n_neighbors=4, weights=distance, score=-0.261, total= 0.4s\n","[CV] n_neighbors=4, weights=distance .................................\n","[CV] .... n_neighbors=4, weights=distance, score=-0.185, total= 0.4s\n","[CV] n_neighbors=4, weights=distance .................................\n","[CV] .... n_neighbors=4, weights=distance, score=-0.278, total= 0.4s\n","[CV] n_neighbors=4, weights=distance .................................\n","[CV] .... n_neighbors=4, weights=distance, score=-0.133, total= 0.4s\n","[CV] n_neighbors=4, weights=distance .................................\n","[CV] .... n_neighbors=4, weights=distance, score=-0.392, total= 0.4s\n","[CV] n_neighbors=4, weights=distance .................................\n","[CV] .... n_neighbors=4, weights=distance, score=-0.283, total= 0.4s\n","[CV] n_neighbors=4, weights=distance .................................\n","[CV] .... n_neighbors=4, weights=distance, score=-0.378, total= 0.4s\n","[CV] n_neighbors=4, weights=distance .................................\n","[CV] .... n_neighbors=4, weights=distance, score=-0.163, total= 0.4s\n","[CV] n_neighbors=5, weights=uniform ..................................\n","[CV] ..... n_neighbors=5, weights=uniform, score=-0.226, total= 0.4s\n","[CV] n_neighbors=5, weights=uniform ..................................\n","[CV] ..... n_neighbors=5, weights=uniform, score=-0.213, total= 0.4s\n","[CV] n_neighbors=5, weights=uniform ..................................\n","[CV] ..... n_neighbors=5, weights=uniform, score=-0.200, total= 0.4s\n","[CV] n_neighbors=5, weights=uniform ..................................\n","[CV] ..... n_neighbors=5, weights=uniform, score=-0.236, total= 0.4s\n","[CV] n_neighbors=5, weights=uniform ..................................\n","[CV] ..... n_neighbors=5, weights=uniform, score=-0.225, total= 0.4s\n","[CV] n_neighbors=5, weights=uniform ..................................\n","[CV] ..... n_neighbors=5, weights=uniform, score=-0.261, total= 0.4s\n","[CV] n_neighbors=5, weights=uniform ..................................\n","[CV] ..... n_neighbors=5, weights=uniform, score=-0.183, total= 0.4s\n","[CV] n_neighbors=5, weights=uniform ..................................\n","[CV] ..... n_neighbors=5, weights=uniform, score=-0.222, total= 0.4s\n","[CV] n_neighbors=5, weights=uniform ..................................\n","[CV] ..... n_neighbors=5, weights=uniform, score=-0.220, total= 0.4s\n","[CV] n_neighbors=5, weights=uniform ..................................\n","[CV] ..... n_neighbors=5, weights=uniform, score=-0.146, total= 0.4s\n","[CV] n_neighbors=5, weights=uniform ..................................\n","[CV] ..... n_neighbors=5, weights=uniform, score=-0.258, total= 0.4s\n","[CV] n_neighbors=5, weights=uniform ..................................\n","[CV] ..... n_neighbors=5, weights=uniform, score=-0.226, total= 0.4s\n","[CV] n_neighbors=5, weights=uniform ..................................\n","[CV] ..... n_neighbors=5, weights=uniform, score=-0.220, total= 0.4s\n","[CV] n_neighbors=5, weights=uniform ..................................\n","[CV] ..... n_neighbors=5, weights=uniform, score=-0.233, total= 0.4s\n","[CV] n_neighbors=5, weights=uniform ..................................\n","[CV] ..... n_neighbors=5, weights=uniform, score=-0.259, total= 0.4s\n","[CV] n_neighbors=5, weights=uniform ..................................\n","[CV] ..... n_neighbors=5, weights=uniform, score=-0.254, total= 0.4s\n","[CV] n_neighbors=5, weights=uniform ..................................\n","[CV] ..... n_neighbors=5, weights=uniform, score=-0.241, total= 0.4s\n","[CV] n_neighbors=5, weights=uniform ..................................\n","[CV] ..... n_neighbors=5, weights=uniform, score=-0.190, total= 0.4s\n","[CV] n_neighbors=5, weights=uniform ..................................\n","[CV] ..... n_neighbors=5, weights=uniform, score=-0.161, total= 0.4s\n","[CV] n_neighbors=5, weights=uniform ..................................\n","[CV] ..... n_neighbors=5, weights=uniform, score=-0.146, total= 0.4s\n","[CV] n_neighbors=5, weights=uniform ..................................\n","[CV] ..... n_neighbors=5, weights=uniform, score=-0.186, total= 0.4s\n","[CV] n_neighbors=5, weights=uniform ..................................\n","[CV] ..... n_neighbors=5, weights=uniform, score=-0.297, total= 0.4s\n","[CV] n_neighbors=5, weights=uniform ..................................\n","[CV] ..... n_neighbors=5, weights=uniform, score=-0.223, total= 0.4s\n","[CV] n_neighbors=5, weights=uniform ..................................\n","[CV] ..... n_neighbors=5, weights=uniform, score=-0.114, total= 0.4s\n","[CV] n_neighbors=5, weights=uniform ..................................\n","[CV] ..... n_neighbors=5, weights=uniform, score=-0.249, total= 0.4s\n","[CV] n_neighbors=5, weights=uniform ..................................\n","[CV] ..... n_neighbors=5, weights=uniform, score=-0.076, total= 0.4s\n","[CV] n_neighbors=5, weights=uniform ..................................\n","[CV] ..... n_neighbors=5, weights=uniform, score=-0.353, total= 0.4s\n","[CV] n_neighbors=5, weights=uniform ..................................\n","[CV] ..... n_neighbors=5, weights=uniform, score=-0.200, total= 0.4s\n","[CV] n_neighbors=5, weights=uniform ..................................\n","[CV] ..... n_neighbors=5, weights=uniform, score=-0.299, total= 0.4s\n","[CV] n_neighbors=5, weights=uniform ..................................\n","[CV] ..... n_neighbors=5, weights=uniform, score=-0.135, total= 0.4s\n","[CV] n_neighbors=5, weights=distance .................................\n","[CV] .... n_neighbors=5, weights=distance, score=-0.231, total= 0.4s\n","[CV] n_neighbors=5, weights=distance .................................\n","[CV] .... n_neighbors=5, weights=distance, score=-0.219, total= 0.4s\n","[CV] n_neighbors=5, weights=distance .................................\n","[CV] .... n_neighbors=5, weights=distance, score=-0.200, total= 0.4s\n","[CV] n_neighbors=5, weights=distance .................................\n","[CV] .... n_neighbors=5, weights=distance, score=-0.234, total= 0.4s\n","[CV] n_neighbors=5, weights=distance .................................\n","[CV] .... n_neighbors=5, weights=distance, score=-0.232, total= 0.4s\n","[CV] n_neighbors=5, weights=distance .................................\n","[CV] .... n_neighbors=5, weights=distance, score=-0.266, total= 0.4s\n","[CV] n_neighbors=5, weights=distance .................................\n","[CV] .... n_neighbors=5, weights=distance, score=-0.186, total= 0.4s\n","[CV] n_neighbors=5, weights=distance .................................\n","[CV] .... n_neighbors=5, weights=distance, score=-0.222, total= 0.4s\n","[CV] n_neighbors=5, weights=distance .................................\n","[CV] .... n_neighbors=5, weights=distance, score=-0.216, total= 0.4s\n","[CV] n_neighbors=5, weights=distance .................................\n","[CV] .... n_neighbors=5, weights=distance, score=-0.154, total= 0.4s\n","[CV] n_neighbors=5, weights=distance .................................\n","[CV] .... n_neighbors=5, weights=distance, score=-0.266, total= 0.4s\n","[CV] n_neighbors=5, weights=distance .................................\n","[CV] .... n_neighbors=5, weights=distance, score=-0.225, total= 0.4s\n","[CV] n_neighbors=5, weights=distance .................................\n","[CV] .... n_neighbors=5, weights=distance, score=-0.215, total= 0.4s\n","[CV] n_neighbors=5, weights=distance .................................\n","[CV] .... n_neighbors=5, weights=distance, score=-0.237, total= 0.4s\n","[CV] n_neighbors=5, weights=distance .................................\n","[CV] .... n_neighbors=5, weights=distance, score=-0.259, total= 0.4s\n","[CV] n_neighbors=5, weights=distance .................................\n","[CV] .... n_neighbors=5, weights=distance, score=-0.259, total= 0.4s\n","[CV] n_neighbors=5, weights=distance .................................\n","[CV] .... n_neighbors=5, weights=distance, score=-0.241, total= 0.4s\n","[CV] n_neighbors=5, weights=distance .................................\n","[CV] .... n_neighbors=5, weights=distance, score=-0.199, total= 0.4s\n","[CV] n_neighbors=5, weights=distance .................................\n","[CV] .... n_neighbors=5, weights=distance, score=-0.158, total= 0.4s\n","[CV] n_neighbors=5, weights=distance .................................\n","[CV] .... n_neighbors=5, weights=distance, score=-0.147, total= 0.4s\n","[CV] n_neighbors=5, weights=distance .................................\n","[CV] .... n_neighbors=5, weights=distance, score=-0.187, total= 0.4s\n","[CV] n_neighbors=5, weights=distance .................................\n","[CV] .... n_neighbors=5, weights=distance, score=-0.304, total= 0.4s\n","[CV] n_neighbors=5, weights=distance .................................\n","[CV] .... n_neighbors=5, weights=distance, score=-0.225, total= 0.4s\n","[CV] n_neighbors=5, weights=distance .................................\n","[CV] .... n_neighbors=5, weights=distance, score=-0.117, total= 0.4s\n","[CV] n_neighbors=5, weights=distance .................................\n","[CV] .... n_neighbors=5, weights=distance, score=-0.246, total= 0.4s\n","[CV] n_neighbors=5, weights=distance .................................\n","[CV] .... n_neighbors=5, weights=distance, score=-0.082, total= 0.4s\n","[CV] n_neighbors=5, weights=distance .................................\n","[CV] .... n_neighbors=5, weights=distance, score=-0.350, total= 0.4s\n","[CV] n_neighbors=5, weights=distance .................................\n","[CV] .... n_neighbors=5, weights=distance, score=-0.201, total= 0.4s\n","[CV] n_neighbors=5, weights=distance .................................\n","[CV] .... n_neighbors=5, weights=distance, score=-0.304, total= 0.4s\n","[CV] n_neighbors=5, weights=distance .................................\n","[CV] .... n_neighbors=5, weights=distance, score=-0.138, total= 0.4s\n","[CV] n_neighbors=6, weights=uniform ..................................\n","[CV] ..... n_neighbors=6, weights=uniform, score=-0.214, total= 0.4s\n","[CV] n_neighbors=6, weights=uniform ..................................\n","[CV] ..... n_neighbors=6, weights=uniform, score=-0.162, total= 0.4s\n","[CV] n_neighbors=6, weights=uniform ..................................\n","[CV] ..... n_neighbors=6, weights=uniform, score=-0.212, total= 0.4s\n","[CV] n_neighbors=6, weights=uniform ..................................\n","[CV] ..... n_neighbors=6, weights=uniform, score=-0.231, total= 0.4s\n","[CV] n_neighbors=6, weights=uniform ..................................\n","[CV] ..... n_neighbors=6, weights=uniform, score=-0.177, total= 0.4s\n","[CV] n_neighbors=6, weights=uniform ..................................\n","[CV] ..... n_neighbors=6, weights=uniform, score=-0.219, total= 0.4s\n","[CV] n_neighbors=6, weights=uniform ..................................\n","[CV] ..... n_neighbors=6, weights=uniform, score=-0.154, total= 0.4s\n","[CV] n_neighbors=6, weights=uniform ..................................\n","[CV] ..... n_neighbors=6, weights=uniform, score=-0.224, total= 0.4s\n","[CV] n_neighbors=6, weights=uniform ..................................\n","[CV] ..... n_neighbors=6, weights=uniform, score=-0.208, total= 0.4s\n","[CV] n_neighbors=6, weights=uniform ..................................\n","[CV] ..... n_neighbors=6, weights=uniform, score=-0.100, total= 0.4s\n","[CV] n_neighbors=6, weights=uniform ..................................\n","[CV] ..... n_neighbors=6, weights=uniform, score=-0.225, total= 0.4s\n","[CV] n_neighbors=6, weights=uniform ..................................\n","[CV] ..... n_neighbors=6, weights=uniform, score=-0.236, total= 0.4s\n","[CV] n_neighbors=6, weights=uniform ..................................\n","[CV] ..... n_neighbors=6, weights=uniform, score=-0.149, total= 0.4s\n","[CV] n_neighbors=6, weights=uniform ..................................\n","[CV] ..... n_neighbors=6, weights=uniform, score=-0.234, total= 0.4s\n","[CV] n_neighbors=6, weights=uniform ..................................\n","[CV] ..... n_neighbors=6, weights=uniform, score=-0.203, total= 0.4s\n","[CV] n_neighbors=6, weights=uniform ..................................\n","[CV] ..... n_neighbors=6, weights=uniform, score=-0.232, total= 0.4s\n","[CV] n_neighbors=6, weights=uniform ..................................\n","[CV] ..... n_neighbors=6, weights=uniform, score=-0.183, total= 0.4s\n","[CV] n_neighbors=6, weights=uniform ..................................\n","[CV] ..... n_neighbors=6, weights=uniform, score=-0.158, total= 0.4s\n","[CV] n_neighbors=6, weights=uniform ..................................\n","[CV] ..... n_neighbors=6, weights=uniform, score=-0.137, total= 0.4s\n","[CV] n_neighbors=6, weights=uniform ..................................\n","[CV] ..... n_neighbors=6, weights=uniform, score=-0.161, total= 0.4s\n","[CV] n_neighbors=6, weights=uniform ..................................\n","[CV] ..... n_neighbors=6, weights=uniform, score=-0.175, total= 0.4s\n","[CV] n_neighbors=6, weights=uniform ..................................\n","[CV] ..... n_neighbors=6, weights=uniform, score=-0.258, total= 0.4s\n","[CV] n_neighbors=6, weights=uniform ..................................\n","[CV] ..... n_neighbors=6, weights=uniform, score=-0.207, total= 0.4s\n","[CV] n_neighbors=6, weights=uniform ..................................\n","[CV] ..... n_neighbors=6, weights=uniform, score=-0.106, total= 0.4s\n","[CV] n_neighbors=6, weights=uniform ..................................\n","[CV] ..... n_neighbors=6, weights=uniform, score=-0.217, total= 0.4s\n","[CV] n_neighbors=6, weights=uniform ..................................\n","[CV] ..... n_neighbors=6, weights=uniform, score=-0.081, total= 0.4s\n","[CV] n_neighbors=6, weights=uniform ..................................\n","[CV] ..... n_neighbors=6, weights=uniform, score=-0.294, total= 0.4s\n","[CV] n_neighbors=6, weights=uniform ..................................\n","[CV] ..... n_neighbors=6, weights=uniform, score=-0.160, total= 0.4s\n","[CV] n_neighbors=6, weights=uniform ..................................\n","[CV] ..... n_neighbors=6, weights=uniform, score=-0.289, total= 0.4s\n","[CV] n_neighbors=6, weights=uniform ..................................\n","[CV] ..... n_neighbors=6, weights=uniform, score=-0.118, total= 0.4s\n","[CV] n_neighbors=6, weights=distance .................................\n","[CV] .... n_neighbors=6, weights=distance, score=-0.213, total= 0.4s\n","[CV] n_neighbors=6, weights=distance .................................\n","[CV] .... n_neighbors=6, weights=distance, score=-0.169, total= 0.4s\n","[CV] n_neighbors=6, weights=distance .................................\n","[CV] .... n_neighbors=6, weights=distance, score=-0.207, total= 0.4s\n","[CV] n_neighbors=6, weights=distance .................................\n","[CV] .... n_neighbors=6, weights=distance, score=-0.227, total= 0.4s\n","[CV] n_neighbors=6, weights=distance .................................\n","[CV] .... n_neighbors=6, weights=distance, score=-0.183, total= 0.4s\n","[CV] n_neighbors=6, weights=distance .................................\n","[CV] .... n_neighbors=6, weights=distance, score=-0.225, total= 0.4s\n","[CV] n_neighbors=6, weights=distance .................................\n","[CV] .... n_neighbors=6, weights=distance, score=-0.158, total= 0.4s\n","[CV] n_neighbors=6, weights=distance .................................\n","[CV] .... n_neighbors=6, weights=distance, score=-0.222, total= 0.4s\n","[CV] n_neighbors=6, weights=distance .................................\n","[CV] .... n_neighbors=6, weights=distance, score=-0.203, total= 0.4s\n","[CV] n_neighbors=6, weights=distance .................................\n","[CV] .... n_neighbors=6, weights=distance, score=-0.107, total= 0.4s\n","[CV] n_neighbors=6, weights=distance .................................\n","[CV] .... n_neighbors=6, weights=distance, score=-0.233, total= 0.4s\n","[CV] n_neighbors=6, weights=distance .................................\n","[CV] .... n_neighbors=6, weights=distance, score=-0.232, total= 0.4s\n","[CV] n_neighbors=6, weights=distance .................................\n","[CV] .... n_neighbors=6, weights=distance, score=-0.149, total= 0.4s\n","[CV] n_neighbors=6, weights=distance .................................\n","[CV] .... n_neighbors=6, weights=distance, score=-0.236, total= 0.4s\n","[CV] n_neighbors=6, weights=distance .................................\n","[CV] .... n_neighbors=6, weights=distance, score=-0.204, total= 0.4s\n","[CV] n_neighbors=6, weights=distance .................................\n","[CV] .... n_neighbors=6, weights=distance, score=-0.236, total= 0.4s\n","[CV] n_neighbors=6, weights=distance .................................\n","[CV] .... n_neighbors=6, weights=distance, score=-0.183, total= 0.4s\n","[CV] n_neighbors=6, weights=distance .................................\n","[CV] .... n_neighbors=6, weights=distance, score=-0.168, total= 0.4s\n","[CV] n_neighbors=6, weights=distance .................................\n","[CV] .... n_neighbors=6, weights=distance, score=-0.134, total= 0.4s\n","[CV] n_neighbors=6, weights=distance .................................\n","[CV] .... n_neighbors=6, weights=distance, score=-0.159, total= 0.4s\n","[CV] n_neighbors=6, weights=distance .................................\n","[CV] .... n_neighbors=6, weights=distance, score=-0.174, total= 0.4s\n","[CV] n_neighbors=6, weights=distance .................................\n","[CV] .... n_neighbors=6, weights=distance, score=-0.264, total= 0.4s\n","[CV] n_neighbors=6, weights=distance .................................\n","[CV] .... n_neighbors=6, weights=distance, score=-0.205, total= 0.4s\n","[CV] n_neighbors=6, weights=distance .................................\n","[CV] .... n_neighbors=6, weights=distance, score=-0.107, total= 0.4s\n","[CV] n_neighbors=6, weights=distance .................................\n","[CV] .... n_neighbors=6, weights=distance, score=-0.214, total= 0.4s\n","[CV] n_neighbors=6, weights=distance .................................\n","[CV] .... n_neighbors=6, weights=distance, score=-0.081, total= 0.4s\n","[CV] n_neighbors=6, weights=distance .................................\n","[CV] .... n_neighbors=6, weights=distance, score=-0.295, total= 0.4s\n","[CV] n_neighbors=6, weights=distance .................................\n","[CV] .... n_neighbors=6, weights=distance, score=-0.160, total= 0.4s\n","[CV] n_neighbors=6, weights=distance .................................\n","[CV] .... n_neighbors=6, weights=distance, score=-0.291, total= 0.4s\n","[CV] n_neighbors=6, weights=distance .................................\n","[CV] .... n_neighbors=6, weights=distance, score=-0.120, total= 0.4s\n","[Parallel(n_jobs=1)]: Done 300 out of 300 | elapsed: 1.8min finished\n","KNeighborsRegressor(algorithm='auto', leaf_size=30, metric='minkowski',\n"," metric_params=None, n_jobs=None, n_neighbors=6, p=2,\n"," weights='uniform')\n","window: 1, stide: 1, rmse: 2.9457989546948857\n","(3000, 280)\n","Number of Feature-Columns: 280\n","\n","Features Ready for undergoing selection tests done ...\n","\n","Fitting 30 folds for each of 10 candidates, totalling 300 fits\n","[Parallel(n_jobs=1)]: Using backend SequentialBackend with 1 concurrent workers.\n","[CV] n_neighbors=2, weights=uniform ..................................\n","[CV] ..... n_neighbors=2, weights=uniform, score=-0.724, total= 0.4s\n","[Parallel(n_jobs=1)]: Done 1 out of 1 | elapsed: 0.4s remaining: 0.0s\n","[CV] n_neighbors=2, weights=uniform ..................................\n","[CV] ..... n_neighbors=2, weights=uniform, score=-0.629, total= 0.4s\n","[Parallel(n_jobs=1)]: Done 2 out of 2 | elapsed: 0.7s remaining: 0.0s\n","[CV] n_neighbors=2, weights=uniform ..................................\n","[CV] ..... n_neighbors=2, weights=uniform, score=-0.490, total= 0.4s\n","[Parallel(n_jobs=1)]: Done 3 out of 3 | elapsed: 1.1s remaining: 0.0s\n","[CV] n_neighbors=2, weights=uniform ..................................\n","[CV] ..... n_neighbors=2, weights=uniform, score=-0.693, total= 0.4s\n","[Parallel(n_jobs=1)]: Done 4 out of 4 | elapsed: 1.5s remaining: 0.0s\n","[CV] n_neighbors=2, weights=uniform ..................................\n","[CV] ..... n_neighbors=2, weights=uniform, score=-0.458, total= 0.4s\n","[CV] n_neighbors=2, weights=uniform ..................................\n","[CV] ..... n_neighbors=2, weights=uniform, score=-0.564, total= 0.4s\n","[CV] n_neighbors=2, weights=uniform ..................................\n","[CV] ..... n_neighbors=2, weights=uniform, score=-0.578, total= 0.4s\n","[CV] n_neighbors=2, weights=uniform ..................................\n","[CV] ..... n_neighbors=2, weights=uniform, score=-0.680, total= 0.4s\n","[CV] n_neighbors=2, weights=uniform ..................................\n","[CV] ..... n_neighbors=2, weights=uniform, score=-0.459, total= 0.4s\n","[CV] n_neighbors=2, weights=uniform ..................................\n","[CV] ..... n_neighbors=2, weights=uniform, score=-0.657, total= 0.4s\n","[CV] n_neighbors=2, weights=uniform ..................................\n","[CV] ..... n_neighbors=2, weights=uniform, score=-0.566, total= 0.4s\n","[CV] n_neighbors=2, weights=uniform ..................................\n","[CV] ..... n_neighbors=2, weights=uniform, score=-0.552, total= 0.4s\n","[CV] n_neighbors=2, weights=uniform ..................................\n","[CV] ..... n_neighbors=2, weights=uniform, score=-0.540, total= 0.4s\n","[CV] n_neighbors=2, weights=uniform ..................................\n","[CV] ..... n_neighbors=2, weights=uniform, score=-0.602, total= 0.4s\n","[CV] n_neighbors=2, weights=uniform ..................................\n","[CV] ..... n_neighbors=2, weights=uniform, score=-0.576, total= 0.4s\n","[CV] n_neighbors=2, weights=uniform ..................................\n","[CV] ..... n_neighbors=2, weights=uniform, score=-0.835, total= 0.4s\n","[CV] n_neighbors=2, weights=uniform ..................................\n","[CV] ..... n_neighbors=2, weights=uniform, score=-0.507, total= 0.4s\n","[CV] n_neighbors=2, weights=uniform ..................................\n","[CV] ..... n_neighbors=2, weights=uniform, score=-0.652, total= 0.4s\n","[CV] n_neighbors=2, weights=uniform ..................................\n","[CV] ..... n_neighbors=2, weights=uniform, score=-0.367, total= 0.4s\n","[CV] n_neighbors=2, weights=uniform ..................................\n","[CV] ..... n_neighbors=2, weights=uniform, score=-0.472, total= 0.4s\n","[CV] n_neighbors=2, weights=uniform ..................................\n","[CV] ..... n_neighbors=2, weights=uniform, score=-0.530, total= 0.4s\n","[CV] n_neighbors=2, weights=uniform ..................................\n","[CV] ..... n_neighbors=2, weights=uniform, score=-0.731, total= 0.4s\n","[CV] n_neighbors=2, weights=uniform ..................................\n","[CV] ..... n_neighbors=2, weights=uniform, score=-0.600, total= 0.4s\n","[CV] n_neighbors=2, weights=uniform ..................................\n","[CV] ..... n_neighbors=2, weights=uniform, score=-0.486, total= 0.4s\n","[CV] n_neighbors=2, weights=uniform ..................................\n","[CV] ..... n_neighbors=2, weights=uniform, score=-0.605, total= 0.4s\n","[CV] n_neighbors=2, weights=uniform ..................................\n","[CV] ..... n_neighbors=2, weights=uniform, score=-0.464, total= 0.4s\n","[CV] n_neighbors=2, weights=uniform ..................................\n","[CV] ..... n_neighbors=2, weights=uniform, score=-0.683, total= 0.4s\n","[CV] n_neighbors=2, weights=uniform ..................................\n","[CV] ..... n_neighbors=2, weights=uniform, score=-0.583, total= 0.4s\n","[CV] n_neighbors=2, weights=uniform ..................................\n","[CV] ..... n_neighbors=2, weights=uniform, score=-0.704, total= 0.4s\n","[CV] n_neighbors=2, weights=uniform ..................................\n","[CV] ..... n_neighbors=2, weights=uniform, score=-0.430, total= 0.4s\n","[CV] n_neighbors=2, weights=distance .................................\n","[CV] .... n_neighbors=2, weights=distance, score=-0.721, total= 0.4s\n","[CV] n_neighbors=2, weights=distance .................................\n","[CV] .... n_neighbors=2, weights=distance, score=-0.630, total= 0.4s\n","[CV] n_neighbors=2, weights=distance .................................\n","[CV] .... n_neighbors=2, weights=distance, score=-0.489, total= 0.4s\n","[CV] n_neighbors=2, weights=distance .................................\n","[CV] .... n_neighbors=2, weights=distance, score=-0.686, total= 0.4s\n","[CV] n_neighbors=2, weights=distance .................................\n","[CV] .... n_neighbors=2, weights=distance, score=-0.470, total= 0.4s\n","[CV] n_neighbors=2, weights=distance .................................\n","[CV] .... n_neighbors=2, weights=distance, score=-0.577, total= 0.4s\n","[CV] n_neighbors=2, weights=distance .................................\n","[CV] .... n_neighbors=2, weights=distance, score=-0.573, total= 0.4s\n","[CV] n_neighbors=2, weights=distance .................................\n","[CV] .... n_neighbors=2, weights=distance, score=-0.675, total= 0.4s\n","[CV] n_neighbors=2, weights=distance .................................\n","[CV] .... n_neighbors=2, weights=distance, score=-0.456, total= 0.4s\n","[CV] n_neighbors=2, weights=distance .................................\n","[CV] .... n_neighbors=2, weights=distance, score=-0.651, total= 0.4s\n","[CV] n_neighbors=2, weights=distance .................................\n","[CV] .... n_neighbors=2, weights=distance, score=-0.575, total= 0.4s\n","[CV] n_neighbors=2, weights=distance .................................\n","[CV] .... n_neighbors=2, weights=distance, score=-0.551, total= 0.4s\n","[CV] n_neighbors=2, weights=distance .................................\n","[CV] .... n_neighbors=2, weights=distance, score=-0.536, total= 0.4s\n","[CV] n_neighbors=2, weights=distance .................................\n","[CV] .... n_neighbors=2, weights=distance, score=-0.602, total= 0.4s\n","[CV] n_neighbors=2, weights=distance .................................\n","[CV] .... n_neighbors=2, weights=distance, score=-0.578, total= 0.4s\n","[CV] n_neighbors=2, weights=distance .................................\n","[CV] .... n_neighbors=2, weights=distance, score=-0.831, total= 0.4s\n","[CV] n_neighbors=2, weights=distance .................................\n","[CV] .... n_neighbors=2, weights=distance, score=-0.504, total= 0.4s\n","[CV] n_neighbors=2, weights=distance .................................\n","[CV] .... n_neighbors=2, weights=distance, score=-0.662, total= 0.4s\n","[CV] n_neighbors=2, weights=distance .................................\n","[CV] .... n_neighbors=2, weights=distance, score=-0.366, total= 0.4s\n","[CV] n_neighbors=2, weights=distance .................................\n","[CV] .... n_neighbors=2, weights=distance, score=-0.468, total= 0.4s\n","[CV] n_neighbors=2, weights=distance .................................\n","[CV] .... n_neighbors=2, weights=distance, score=-0.527, total= 0.4s\n","[CV] n_neighbors=2, weights=distance .................................\n","[CV] .... n_neighbors=2, weights=distance, score=-0.728, total= 0.4s\n","[CV] n_neighbors=2, weights=distance .................................\n","[CV] .... n_neighbors=2, weights=distance, score=-0.600, total= 0.4s\n","[CV] n_neighbors=2, weights=distance .................................\n","[CV] .... n_neighbors=2, weights=distance, score=-0.489, total= 0.4s\n","[CV] n_neighbors=2, weights=distance .................................\n","[CV] .... n_neighbors=2, weights=distance, score=-0.601, total= 0.4s\n","[CV] n_neighbors=2, weights=distance .................................\n","[CV] .... n_neighbors=2, weights=distance, score=-0.470, total= 0.4s\n","[CV] n_neighbors=2, weights=distance .................................\n","[CV] .... n_neighbors=2, weights=distance, score=-0.678, total= 0.4s\n","[CV] n_neighbors=2, weights=distance .................................\n","[CV] .... n_neighbors=2, weights=distance, score=-0.567, total= 0.4s\n","[CV] n_neighbors=2, weights=distance .................................\n","[CV] .... n_neighbors=2, weights=distance, score=-0.716, total= 0.4s\n","[CV] n_neighbors=2, weights=distance .................................\n","[CV] .... n_neighbors=2, weights=distance, score=-0.429, total= 0.4s\n","[CV] n_neighbors=3, weights=uniform ..................................\n","[CV] ..... n_neighbors=3, weights=uniform, score=-0.476, total= 0.4s\n","[CV] n_neighbors=3, weights=uniform ..................................\n","[CV] ..... n_neighbors=3, weights=uniform, score=-0.446, total= 0.4s\n","[CV] n_neighbors=3, weights=uniform ..................................\n","[CV] ..... n_neighbors=3, weights=uniform, score=-0.314, total= 0.4s\n","[CV] n_neighbors=3, weights=uniform ..................................\n","[CV] ..... n_neighbors=3, weights=uniform, score=-0.353, total= 0.4s\n","[CV] n_neighbors=3, weights=uniform ..................................\n","[CV] ..... n_neighbors=3, weights=uniform, score=-0.322, total= 0.4s\n","[CV] n_neighbors=3, weights=uniform ..................................\n","[CV] ..... n_neighbors=3, weights=uniform, score=-0.373, total= 0.4s\n","[CV] n_neighbors=3, weights=uniform ..................................\n","[CV] ..... n_neighbors=3, weights=uniform, score=-0.371, total= 0.4s\n","[CV] n_neighbors=3, weights=uniform ..................................\n","[CV] ..... n_neighbors=3, weights=uniform, score=-0.398, total= 0.4s\n","[CV] n_neighbors=3, weights=uniform ..................................\n","[CV] ..... n_neighbors=3, weights=uniform, score=-0.292, total= 0.4s\n","[CV] n_neighbors=3, weights=uniform ..................................\n","[CV] ..... n_neighbors=3, weights=uniform, score=-0.373, total= 0.4s\n","[CV] n_neighbors=3, weights=uniform ..................................\n","[CV] ..... n_neighbors=3, weights=uniform, score=-0.413, total= 0.4s\n","[CV] n_neighbors=3, weights=uniform ..................................\n","[CV] ..... n_neighbors=3, weights=uniform, score=-0.369, total= 0.4s\n","[CV] n_neighbors=3, weights=uniform ..................................\n","[CV] ..... n_neighbors=3, weights=uniform, score=-0.280, total= 0.4s\n","[CV] n_neighbors=3, weights=uniform ..................................\n","[CV] ..... n_neighbors=3, weights=uniform, score=-0.454, total= 0.4s\n","[CV] n_neighbors=3, weights=uniform ..................................\n","[CV] ..... n_neighbors=3, weights=uniform, score=-0.375, total= 0.4s\n","[CV] n_neighbors=3, weights=uniform ..................................\n","[CV] ..... n_neighbors=3, weights=uniform, score=-0.456, total= 0.4s\n","[CV] n_neighbors=3, weights=uniform ..................................\n","[CV] ..... n_neighbors=3, weights=uniform, score=-0.414, total= 0.4s\n","[CV] n_neighbors=3, weights=uniform ..................................\n","[CV] ..... n_neighbors=3, weights=uniform, score=-0.433, total= 0.4s\n","[CV] n_neighbors=3, weights=uniform ..................................\n","[CV] ..... n_neighbors=3, weights=uniform, score=-0.281, total= 0.4s\n","[CV] n_neighbors=3, weights=uniform ..................................\n","[CV] ..... n_neighbors=3, weights=uniform, score=-0.346, total= 0.4s\n","[CV] n_neighbors=3, weights=uniform ..................................\n","[CV] ..... n_neighbors=3, weights=uniform, score=-0.325, total= 0.4s\n","[CV] n_neighbors=3, weights=uniform ..................................\n","[CV] ..... n_neighbors=3, weights=uniform, score=-0.498, total= 0.4s\n","[CV] n_neighbors=3, weights=uniform ..................................\n","[CV] ..... n_neighbors=3, weights=uniform, score=-0.349, total= 0.4s\n","[CV] n_neighbors=3, weights=uniform ..................................\n","[CV] ..... n_neighbors=3, weights=uniform, score=-0.329, total= 0.4s\n","[CV] n_neighbors=3, weights=uniform ..................................\n","[CV] ..... n_neighbors=3, weights=uniform, score=-0.409, total= 0.4s\n","[CV] n_neighbors=3, weights=uniform ..................................\n","[CV] ..... n_neighbors=3, weights=uniform, score=-0.228, total= 0.4s\n","[CV] n_neighbors=3, weights=uniform ..................................\n","[CV] ..... n_neighbors=3, weights=uniform, score=-0.482, total= 0.4s\n","[CV] n_neighbors=3, weights=uniform ..................................\n","[CV] ..... n_neighbors=3, weights=uniform, score=-0.440, total= 0.4s\n","[CV] n_neighbors=3, weights=uniform ..................................\n","[CV] ..... n_neighbors=3, weights=uniform, score=-0.458, total= 0.4s\n","[CV] n_neighbors=3, weights=uniform ..................................\n","[CV] ..... n_neighbors=3, weights=uniform, score=-0.284, total= 0.4s\n","[CV] n_neighbors=3, weights=distance .................................\n","[CV] .... n_neighbors=3, weights=distance, score=-0.476, total= 0.4s\n","[CV] n_neighbors=3, weights=distance .................................\n","[CV] .... n_neighbors=3, weights=distance, score=-0.444, total= 0.4s\n","[CV] n_neighbors=3, weights=distance .................................\n","[CV] .... n_neighbors=3, weights=distance, score=-0.317, total= 0.4s\n","[CV] n_neighbors=3, weights=distance .................................\n","[CV] .... n_neighbors=3, weights=distance, score=-0.358, total= 0.4s\n","[CV] n_neighbors=3, weights=distance .................................\n","[CV] .... n_neighbors=3, weights=distance, score=-0.332, total= 0.4s\n","[CV] n_neighbors=3, weights=distance .................................\n","[CV] .... n_neighbors=3, weights=distance, score=-0.385, total= 0.4s\n","[CV] n_neighbors=3, weights=distance .................................\n","[CV] .... n_neighbors=3, weights=distance, score=-0.371, total= 0.4s\n","[CV] n_neighbors=3, weights=distance .................................\n","[CV] .... n_neighbors=3, weights=distance, score=-0.398, total= 0.4s\n","[CV] n_neighbors=3, weights=distance .................................\n","[CV] .... n_neighbors=3, weights=distance, score=-0.290, total= 0.4s\n","[CV] n_neighbors=3, weights=distance .................................\n","[CV] .... n_neighbors=3, weights=distance, score=-0.378, total= 0.4s\n","[CV] n_neighbors=3, weights=distance .................................\n","[CV] .... n_neighbors=3, weights=distance, score=-0.419, total= 0.4s\n","[CV] n_neighbors=3, weights=distance .................................\n","[CV] .... n_neighbors=3, weights=distance, score=-0.368, total= 0.4s\n","[CV] n_neighbors=3, weights=distance .................................\n","[CV] .... n_neighbors=3, weights=distance, score=-0.280, total= 0.4s\n","[CV] n_neighbors=3, weights=distance .................................\n","[CV] .... n_neighbors=3, weights=distance, score=-0.449, total= 0.4s\n","[CV] n_neighbors=3, weights=distance .................................\n","[CV] .... n_neighbors=3, weights=distance, score=-0.379, total= 0.4s\n","[CV] n_neighbors=3, weights=distance .................................\n","[CV] .... n_neighbors=3, weights=distance, score=-0.461, total= 0.4s\n","[CV] n_neighbors=3, weights=distance .................................\n","[CV] .... n_neighbors=3, weights=distance, score=-0.406, total= 0.4s\n","[CV] n_neighbors=3, weights=distance .................................\n","[CV] .... n_neighbors=3, weights=distance, score=-0.441, total= 0.4s\n","[CV] n_neighbors=3, weights=distance .................................\n","[CV] .... n_neighbors=3, weights=distance, score=-0.276, total= 0.4s\n","[CV] n_neighbors=3, weights=distance .................................\n","[CV] .... n_neighbors=3, weights=distance, score=-0.343, total= 0.4s\n","[CV] n_neighbors=3, weights=distance .................................\n","[CV] .... n_neighbors=3, weights=distance, score=-0.326, total= 0.4s\n","[CV] n_neighbors=3, weights=distance .................................\n","[CV] .... n_neighbors=3, weights=distance, score=-0.501, total= 0.4s\n","[CV] n_neighbors=3, weights=distance .................................\n","[CV] .... n_neighbors=3, weights=distance, score=-0.355, total= 0.4s\n","[CV] n_neighbors=3, weights=distance .................................\n","[CV] .... n_neighbors=3, weights=distance, score=-0.332, total= 0.4s\n","[CV] n_neighbors=3, weights=distance .................................\n","[CV] .... n_neighbors=3, weights=distance, score=-0.406, total= 0.4s\n","[CV] n_neighbors=3, weights=distance .................................\n","[CV] .... n_neighbors=3, weights=distance, score=-0.234, total= 0.4s\n","[CV] n_neighbors=3, weights=distance .................................\n","[CV] .... n_neighbors=3, weights=distance, score=-0.477, total= 0.4s\n","[CV] n_neighbors=3, weights=distance .................................\n","[CV] .... n_neighbors=3, weights=distance, score=-0.428, total= 0.4s\n","[CV] n_neighbors=3, weights=distance .................................\n","[CV] .... n_neighbors=3, weights=distance, score=-0.467, total= 0.4s\n","[CV] n_neighbors=3, weights=distance .................................\n","[CV] .... n_neighbors=3, weights=distance, score=-0.283, total= 0.4s\n","[CV] n_neighbors=4, weights=uniform ..................................\n","[CV] ..... n_neighbors=4, weights=uniform, score=-0.309, total= 0.4s\n","[CV] n_neighbors=4, weights=uniform ..................................\n","[CV] ..... n_neighbors=4, weights=uniform, score=-0.308, total= 0.4s\n","[CV] n_neighbors=4, weights=uniform ..................................\n","[CV] ..... n_neighbors=4, weights=uniform, score=-0.253, total= 0.4s\n","[CV] n_neighbors=4, weights=uniform ..................................\n","[CV] ..... n_neighbors=4, weights=uniform, score=-0.242, total= 0.4s\n","[CV] n_neighbors=4, weights=uniform ..................................\n","[CV] ..... n_neighbors=4, weights=uniform, score=-0.245, total= 0.4s\n","[CV] n_neighbors=4, weights=uniform ..................................\n","[CV] ..... n_neighbors=4, weights=uniform, score=-0.288, total= 0.4s\n","[CV] n_neighbors=4, weights=uniform ..................................\n","[CV] ..... n_neighbors=4, weights=uniform, score=-0.221, total= 0.4s\n","[CV] n_neighbors=4, weights=uniform ..................................\n","[CV] ..... n_neighbors=4, weights=uniform, score=-0.275, total= 0.4s\n","[CV] n_neighbors=4, weights=uniform ..................................\n","[CV] ..... n_neighbors=4, weights=uniform, score=-0.245, total= 0.4s\n","[CV] n_neighbors=4, weights=uniform ..................................\n","[CV] ..... n_neighbors=4, weights=uniform, score=-0.248, total= 0.4s\n","[CV] n_neighbors=4, weights=uniform ..................................\n","[CV] ..... n_neighbors=4, weights=uniform, score=-0.363, total= 0.4s\n","[CV] n_neighbors=4, weights=uniform ..................................\n","[CV] ..... n_neighbors=4, weights=uniform, score=-0.275, total= 0.4s\n","[CV] n_neighbors=4, weights=uniform ..................................\n","[CV] ..... n_neighbors=4, weights=uniform, score=-0.194, total= 0.4s\n","[CV] n_neighbors=4, weights=uniform ..................................\n","[CV] ..... n_neighbors=4, weights=uniform, score=-0.329, total= 0.4s\n","[CV] n_neighbors=4, weights=uniform ..................................\n","[CV] ..... n_neighbors=4, weights=uniform, score=-0.299, total= 0.4s\n","[CV] n_neighbors=4, weights=uniform ..................................\n","[CV] ..... n_neighbors=4, weights=uniform, score=-0.346, total= 0.4s\n","[CV] n_neighbors=4, weights=uniform ..................................\n","[CV] ..... n_neighbors=4, weights=uniform, score=-0.340, total= 0.4s\n","[CV] n_neighbors=4, weights=uniform ..................................\n","[CV] ..... n_neighbors=4, weights=uniform, score=-0.272, total= 0.4s\n","[CV] n_neighbors=4, weights=uniform ..................................\n","[CV] ..... n_neighbors=4, weights=uniform, score=-0.230, total= 0.4s\n","[CV] n_neighbors=4, weights=uniform ..................................\n","[CV] ..... n_neighbors=4, weights=uniform, score=-0.204, total= 0.4s\n","[CV] n_neighbors=4, weights=uniform ..................................\n","[CV] ..... n_neighbors=4, weights=uniform, score=-0.247, total= 0.4s\n","[CV] n_neighbors=4, weights=uniform ..................................\n","[CV] ..... n_neighbors=4, weights=uniform, score=-0.339, total= 0.4s\n","[CV] n_neighbors=4, weights=uniform ..................................\n","[CV] ..... n_neighbors=4, weights=uniform, score=-0.255, total= 0.4s\n","[CV] n_neighbors=4, weights=uniform ..................................\n","[CV] ..... n_neighbors=4, weights=uniform, score=-0.182, total= 0.4s\n","[CV] n_neighbors=4, weights=uniform ..................................\n","[CV] ..... n_neighbors=4, weights=uniform, score=-0.282, total= 0.4s\n","[CV] n_neighbors=4, weights=uniform ..................................\n","[CV] ..... n_neighbors=4, weights=uniform, score=-0.127, total= 0.4s\n","[CV] n_neighbors=4, weights=uniform ..................................\n","[CV] ..... n_neighbors=4, weights=uniform, score=-0.397, total= 0.4s\n","[CV] n_neighbors=4, weights=uniform ..................................\n","[CV] ..... n_neighbors=4, weights=uniform, score=-0.287, total= 0.4s\n","[CV] n_neighbors=4, weights=uniform ..................................\n","[CV] ..... n_neighbors=4, weights=uniform, score=-0.368, total= 0.4s\n","[CV] n_neighbors=4, weights=uniform ..................................\n","[CV] ..... n_neighbors=4, weights=uniform, score=-0.159, total= 0.4s\n","[CV] n_neighbors=4, weights=distance .................................\n","[CV] .... n_neighbors=4, weights=distance, score=-0.314, total= 0.4s\n","[CV] n_neighbors=4, weights=distance .................................\n","[CV] .... n_neighbors=4, weights=distance, score=-0.312, total= 0.4s\n","[CV] n_neighbors=4, weights=distance .................................\n","[CV] .... n_neighbors=4, weights=distance, score=-0.254, total= 0.4s\n","[CV] n_neighbors=4, weights=distance .................................\n","[CV] .... n_neighbors=4, weights=distance, score=-0.246, total= 0.4s\n","[CV] n_neighbors=4, weights=distance .................................\n","[CV] .... n_neighbors=4, weights=distance, score=-0.253, total= 0.4s\n","[CV] n_neighbors=4, weights=distance .................................\n","[CV] .... n_neighbors=4, weights=distance, score=-0.296, total= 0.4s\n","[CV] n_neighbors=4, weights=distance .................................\n","[CV] .... n_neighbors=4, weights=distance, score=-0.228, total= 0.4s\n","[CV] n_neighbors=4, weights=distance .................................\n","[CV] .... n_neighbors=4, weights=distance, score=-0.277, total= 0.4s\n","[CV] n_neighbors=4, weights=distance .................................\n","[CV] .... n_neighbors=4, weights=distance, score=-0.242, total= 0.4s\n","[CV] n_neighbors=4, weights=distance .................................\n","[CV] .... n_neighbors=4, weights=distance, score=-0.256, total= 0.4s\n","[CV] n_neighbors=4, weights=distance .................................\n","[CV] .... n_neighbors=4, weights=distance, score=-0.368, total= 0.4s\n","[CV] n_neighbors=4, weights=distance .................................\n","[CV] .... n_neighbors=4, weights=distance, score=-0.276, total= 0.4s\n","[CV] n_neighbors=4, weights=distance .................................\n","[CV] .... n_neighbors=4, weights=distance, score=-0.194, total= 0.4s\n","[CV] n_neighbors=4, weights=distance .................................\n","[CV] .... n_neighbors=4, weights=distance, score=-0.329, total= 0.4s\n","[CV] n_neighbors=4, weights=distance .................................\n","[CV] .... n_neighbors=4, weights=distance, score=-0.302, total= 0.4s\n","[CV] n_neighbors=4, weights=distance .................................\n","[CV] .... n_neighbors=4, weights=distance, score=-0.349, total= 0.4s\n","[CV] n_neighbors=4, weights=distance .................................\n","[CV] .... n_neighbors=4, weights=distance, score=-0.337, total= 0.4s\n","[CV] n_neighbors=4, weights=distance .................................\n","[CV] .... n_neighbors=4, weights=distance, score=-0.284, total= 0.4s\n","[CV] n_neighbors=4, weights=distance .................................\n","[CV] .... n_neighbors=4, weights=distance, score=-0.223, total= 0.4s\n","[CV] n_neighbors=4, weights=distance .................................\n","[CV] .... n_neighbors=4, weights=distance, score=-0.206, total= 0.4s\n","[CV] n_neighbors=4, weights=distance .................................\n","[CV] .... n_neighbors=4, weights=distance, score=-0.246, total= 0.4s\n","[CV] n_neighbors=4, weights=distance .................................\n","[CV] .... n_neighbors=4, weights=distance, score=-0.347, total= 0.4s\n","[CV] n_neighbors=4, weights=distance .................................\n","[CV] .... n_neighbors=4, weights=distance, score=-0.261, total= 0.4s\n","[CV] n_neighbors=4, weights=distance .................................\n","[CV] .... n_neighbors=4, weights=distance, score=-0.185, total= 0.4s\n","[CV] n_neighbors=4, weights=distance .................................\n","[CV] .... n_neighbors=4, weights=distance, score=-0.278, total= 0.4s\n","[CV] n_neighbors=4, weights=distance .................................\n","[CV] .... n_neighbors=4, weights=distance, score=-0.133, total= 0.4s\n","[CV] n_neighbors=4, weights=distance .................................\n","[CV] .... n_neighbors=4, weights=distance, score=-0.392, total= 0.4s\n","[CV] n_neighbors=4, weights=distance .................................\n","[CV] .... n_neighbors=4, weights=distance, score=-0.283, total= 0.4s\n","[CV] n_neighbors=4, weights=distance .................................\n","[CV] .... n_neighbors=4, weights=distance, score=-0.378, total= 0.4s\n","[CV] n_neighbors=4, weights=distance .................................\n","[CV] .... n_neighbors=4, weights=distance, score=-0.163, total= 0.4s\n","[CV] n_neighbors=5, weights=uniform ..................................\n","[CV] ..... n_neighbors=5, weights=uniform, score=-0.226, total= 0.4s\n","[CV] n_neighbors=5, weights=uniform ..................................\n","[CV] ..... n_neighbors=5, weights=uniform, score=-0.213, total= 0.4s\n","[CV] n_neighbors=5, weights=uniform ..................................\n","[CV] ..... n_neighbors=5, weights=uniform, score=-0.200, total= 0.4s\n","[CV] n_neighbors=5, weights=uniform ..................................\n","[CV] ..... n_neighbors=5, weights=uniform, score=-0.236, total= 0.4s\n","[CV] n_neighbors=5, weights=uniform ..................................\n","[CV] ..... n_neighbors=5, weights=uniform, score=-0.225, total= 0.4s\n","[CV] n_neighbors=5, weights=uniform ..................................\n","[CV] ..... n_neighbors=5, weights=uniform, score=-0.261, total= 0.4s\n","[CV] n_neighbors=5, weights=uniform ..................................\n","[CV] ..... n_neighbors=5, weights=uniform, score=-0.183, total= 0.4s\n","[CV] n_neighbors=5, weights=uniform ..................................\n","[CV] ..... n_neighbors=5, weights=uniform, score=-0.222, total= 0.4s\n","[CV] n_neighbors=5, weights=uniform ..................................\n","[CV] ..... n_neighbors=5, weights=uniform, score=-0.220, total= 0.4s\n","[CV] n_neighbors=5, weights=uniform ..................................\n","[CV] ..... n_neighbors=5, weights=uniform, score=-0.146, total= 0.4s\n","[CV] n_neighbors=5, weights=uniform ..................................\n","[CV] ..... n_neighbors=5, weights=uniform, score=-0.258, total= 0.4s\n","[CV] n_neighbors=5, weights=uniform ..................................\n","[CV] ..... n_neighbors=5, weights=uniform, score=-0.226, total= 0.4s\n","[CV] n_neighbors=5, weights=uniform ..................................\n","[CV] ..... n_neighbors=5, weights=uniform, score=-0.220, total= 0.4s\n","[CV] n_neighbors=5, weights=uniform ..................................\n","[CV] ..... n_neighbors=5, weights=uniform, score=-0.233, total= 0.4s\n","[CV] n_neighbors=5, weights=uniform ..................................\n","[CV] ..... n_neighbors=5, weights=uniform, score=-0.259, total= 0.4s\n","[CV] n_neighbors=5, weights=uniform ..................................\n","[CV] ..... n_neighbors=5, weights=uniform, score=-0.254, total= 0.4s\n","[CV] n_neighbors=5, weights=uniform ..................................\n","[CV] ..... n_neighbors=5, weights=uniform, score=-0.241, total= 0.4s\n","[CV] n_neighbors=5, weights=uniform ..................................\n","[CV] ..... n_neighbors=5, weights=uniform, score=-0.190, total= 0.4s\n","[CV] n_neighbors=5, weights=uniform ..................................\n","[CV] ..... n_neighbors=5, weights=uniform, score=-0.161, total= 0.4s\n","[CV] n_neighbors=5, weights=uniform ..................................\n","[CV] ..... n_neighbors=5, weights=uniform, score=-0.146, total= 0.4s\n","[CV] n_neighbors=5, weights=uniform ..................................\n","[CV] ..... n_neighbors=5, weights=uniform, score=-0.186, total= 0.4s\n","[CV] n_neighbors=5, weights=uniform ..................................\n","[CV] ..... n_neighbors=5, weights=uniform, score=-0.297, total= 0.4s\n","[CV] n_neighbors=5, weights=uniform ..................................\n","[CV] ..... n_neighbors=5, weights=uniform, score=-0.223, total= 0.4s\n","[CV] n_neighbors=5, weights=uniform ..................................\n","[CV] ..... n_neighbors=5, weights=uniform, score=-0.114, total= 0.4s\n","[CV] n_neighbors=5, weights=uniform ..................................\n","[CV] ..... n_neighbors=5, weights=uniform, score=-0.249, total= 0.4s\n","[CV] n_neighbors=5, weights=uniform ..................................\n","[CV] ..... n_neighbors=5, weights=uniform, score=-0.076, total= 0.4s\n","[CV] n_neighbors=5, weights=uniform ..................................\n","[CV] ..... n_neighbors=5, weights=uniform, score=-0.353, total= 0.4s\n","[CV] n_neighbors=5, weights=uniform ..................................\n","[CV] ..... n_neighbors=5, weights=uniform, score=-0.200, total= 0.4s\n","[CV] n_neighbors=5, weights=uniform ..................................\n","[CV] ..... n_neighbors=5, weights=uniform, score=-0.299, total= 0.4s\n","[CV] n_neighbors=5, weights=uniform ..................................\n","[CV] ..... n_neighbors=5, weights=uniform, score=-0.135, total= 0.4s\n","[CV] n_neighbors=5, weights=distance .................................\n","[CV] .... n_neighbors=5, weights=distance, score=-0.231, total= 0.4s\n","[CV] n_neighbors=5, weights=distance .................................\n","[CV] .... n_neighbors=5, weights=distance, score=-0.219, total= 0.4s\n","[CV] n_neighbors=5, weights=distance .................................\n","[CV] .... n_neighbors=5, weights=distance, score=-0.200, total= 0.4s\n","[CV] n_neighbors=5, weights=distance .................................\n","[CV] .... n_neighbors=5, weights=distance, score=-0.234, total= 0.4s\n","[CV] n_neighbors=5, weights=distance .................................\n","[CV] .... n_neighbors=5, weights=distance, score=-0.232, total= 0.4s\n","[CV] n_neighbors=5, weights=distance .................................\n","[CV] .... n_neighbors=5, weights=distance, score=-0.266, total= 0.4s\n","[CV] n_neighbors=5, weights=distance .................................\n","[CV] .... n_neighbors=5, weights=distance, score=-0.186, total= 0.4s\n","[CV] n_neighbors=5, weights=distance .................................\n","[CV] .... n_neighbors=5, weights=distance, score=-0.222, total= 0.4s\n","[CV] n_neighbors=5, weights=distance .................................\n","[CV] .... n_neighbors=5, weights=distance, score=-0.216, total= 0.4s\n","[CV] n_neighbors=5, weights=distance .................................\n","[CV] .... n_neighbors=5, weights=distance, score=-0.154, total= 0.4s\n","[CV] n_neighbors=5, weights=distance .................................\n","[CV] .... n_neighbors=5, weights=distance, score=-0.266, total= 0.4s\n","[CV] n_neighbors=5, weights=distance .................................\n","[CV] .... n_neighbors=5, weights=distance, score=-0.225, total= 0.4s\n","[CV] n_neighbors=5, weights=distance .................................\n","[CV] .... n_neighbors=5, weights=distance, score=-0.215, total= 0.4s\n","[CV] n_neighbors=5, weights=distance .................................\n","[CV] .... n_neighbors=5, weights=distance, score=-0.237, total= 0.4s\n","[CV] n_neighbors=5, weights=distance .................................\n","[CV] .... n_neighbors=5, weights=distance, score=-0.259, total= 0.4s\n","[CV] n_neighbors=5, weights=distance .................................\n","[CV] .... n_neighbors=5, weights=distance, score=-0.259, total= 0.4s\n","[CV] n_neighbors=5, weights=distance .................................\n","[CV] .... n_neighbors=5, weights=distance, score=-0.241, total= 0.4s\n","[CV] n_neighbors=5, weights=distance .................................\n","[CV] .... n_neighbors=5, weights=distance, score=-0.199, total= 0.4s\n","[CV] n_neighbors=5, weights=distance .................................\n","[CV] .... n_neighbors=5, weights=distance, score=-0.158, total= 0.4s\n","[CV] n_neighbors=5, weights=distance .................................\n","[CV] .... n_neighbors=5, weights=distance, score=-0.147, total= 0.4s\n","[CV] n_neighbors=5, weights=distance .................................\n","[CV] .... n_neighbors=5, weights=distance, score=-0.187, total= 0.4s\n","[CV] n_neighbors=5, weights=distance .................................\n","[CV] .... n_neighbors=5, weights=distance, score=-0.304, total= 0.4s\n","[CV] n_neighbors=5, weights=distance .................................\n","[CV] .... n_neighbors=5, weights=distance, score=-0.225, total= 0.4s\n","[CV] n_neighbors=5, weights=distance .................................\n","[CV] .... n_neighbors=5, weights=distance, score=-0.117, total= 0.4s\n","[CV] n_neighbors=5, weights=distance .................................\n","[CV] .... n_neighbors=5, weights=distance, score=-0.246, total= 0.4s\n","[CV] n_neighbors=5, weights=distance .................................\n","[CV] .... n_neighbors=5, weights=distance, score=-0.082, total= 0.4s\n","[CV] n_neighbors=5, weights=distance .................................\n","[CV] .... n_neighbors=5, weights=distance, score=-0.350, total= 0.4s\n","[CV] n_neighbors=5, weights=distance .................................\n","[CV] .... n_neighbors=5, weights=distance, score=-0.201, total= 0.4s\n","[CV] n_neighbors=5, weights=distance .................................\n","[CV] .... n_neighbors=5, weights=distance, score=-0.304, total= 0.4s\n","[CV] n_neighbors=5, weights=distance .................................\n","[CV] .... n_neighbors=5, weights=distance, score=-0.138, total= 0.4s\n","[CV] n_neighbors=6, weights=uniform ..................................\n","[CV] ..... n_neighbors=6, weights=uniform, score=-0.214, total= 0.4s\n","[CV] n_neighbors=6, weights=uniform ..................................\n","[CV] ..... n_neighbors=6, weights=uniform, score=-0.162, total= 0.4s\n","[CV] n_neighbors=6, weights=uniform ..................................\n","[CV] ..... n_neighbors=6, weights=uniform, score=-0.212, total= 0.4s\n","[CV] n_neighbors=6, weights=uniform ..................................\n","[CV] ..... n_neighbors=6, weights=uniform, score=-0.231, total= 0.4s\n","[CV] n_neighbors=6, weights=uniform ..................................\n","[CV] ..... n_neighbors=6, weights=uniform, score=-0.177, total= 0.4s\n","[CV] n_neighbors=6, weights=uniform ..................................\n","[CV] ..... n_neighbors=6, weights=uniform, score=-0.219, total= 0.4s\n","[CV] n_neighbors=6, weights=uniform ..................................\n","[CV] ..... n_neighbors=6, weights=uniform, score=-0.154, total= 0.4s\n","[CV] n_neighbors=6, weights=uniform ..................................\n","[CV] ..... n_neighbors=6, weights=uniform, score=-0.224, total= 0.4s\n","[CV] n_neighbors=6, weights=uniform ..................................\n","[CV] ..... n_neighbors=6, weights=uniform, score=-0.208, total= 0.4s\n","[CV] n_neighbors=6, weights=uniform ..................................\n","[CV] ..... n_neighbors=6, weights=uniform, score=-0.100, total= 0.4s\n","[CV] n_neighbors=6, weights=uniform ..................................\n","[CV] ..... n_neighbors=6, weights=uniform, score=-0.225, total= 0.4s\n","[CV] n_neighbors=6, weights=uniform ..................................\n","[CV] ..... n_neighbors=6, weights=uniform, score=-0.236, total= 0.4s\n","[CV] n_neighbors=6, weights=uniform ..................................\n","[CV] ..... n_neighbors=6, weights=uniform, score=-0.149, total= 0.4s\n","[CV] n_neighbors=6, weights=uniform ..................................\n","[CV] ..... n_neighbors=6, weights=uniform, score=-0.234, total= 0.4s\n","[CV] n_neighbors=6, weights=uniform ..................................\n","[CV] ..... n_neighbors=6, weights=uniform, score=-0.203, total= 0.4s\n","[CV] n_neighbors=6, weights=uniform ..................................\n","[CV] ..... n_neighbors=6, weights=uniform, score=-0.232, total= 0.4s\n","[CV] n_neighbors=6, weights=uniform ..................................\n","[CV] ..... n_neighbors=6, weights=uniform, score=-0.183, total= 0.4s\n","[CV] n_neighbors=6, weights=uniform ..................................\n","[CV] ..... n_neighbors=6, weights=uniform, score=-0.158, total= 0.4s\n","[CV] n_neighbors=6, weights=uniform ..................................\n","[CV] ..... n_neighbors=6, weights=uniform, score=-0.137, total= 0.4s\n","[CV] n_neighbors=6, weights=uniform ..................................\n","[CV] ..... n_neighbors=6, weights=uniform, score=-0.161, total= 0.4s\n","[CV] n_neighbors=6, weights=uniform ..................................\n","[CV] ..... n_neighbors=6, weights=uniform, score=-0.175, total= 0.4s\n","[CV] n_neighbors=6, weights=uniform ..................................\n","[CV] ..... n_neighbors=6, weights=uniform, score=-0.258, total= 0.4s\n","[CV] n_neighbors=6, weights=uniform ..................................\n","[CV] ..... n_neighbors=6, weights=uniform, score=-0.207, total= 0.4s\n","[CV] n_neighbors=6, weights=uniform ..................................\n","[CV] ..... n_neighbors=6, weights=uniform, score=-0.106, total= 0.4s\n","[CV] n_neighbors=6, weights=uniform ..................................\n","[CV] ..... n_neighbors=6, weights=uniform, score=-0.217, total= 0.4s\n","[CV] n_neighbors=6, weights=uniform ..................................\n","[CV] ..... n_neighbors=6, weights=uniform, score=-0.081, total= 0.4s\n","[CV] n_neighbors=6, weights=uniform ..................................\n","[CV] ..... n_neighbors=6, weights=uniform, score=-0.294, total= 0.4s\n","[CV] n_neighbors=6, weights=uniform ..................................\n","[CV] ..... n_neighbors=6, weights=uniform, score=-0.160, total= 0.4s\n","[CV] n_neighbors=6, weights=uniform ..................................\n","[CV] ..... n_neighbors=6, weights=uniform, score=-0.289, total= 0.4s\n","[CV] n_neighbors=6, weights=uniform ..................................\n","[CV] ..... n_neighbors=6, weights=uniform, score=-0.118, total= 0.4s\n","[CV] n_neighbors=6, weights=distance .................................\n","[CV] .... n_neighbors=6, weights=distance, score=-0.213, total= 0.4s\n","[CV] n_neighbors=6, weights=distance .................................\n","[CV] .... n_neighbors=6, weights=distance, score=-0.169, total= 0.4s\n","[CV] n_neighbors=6, weights=distance .................................\n","[CV] .... n_neighbors=6, weights=distance, score=-0.207, total= 0.4s\n","[CV] n_neighbors=6, weights=distance .................................\n","[CV] .... n_neighbors=6, weights=distance, score=-0.227, total= 0.4s\n","[CV] n_neighbors=6, weights=distance .................................\n","[CV] .... n_neighbors=6, weights=distance, score=-0.183, total= 0.4s\n","[CV] n_neighbors=6, weights=distance .................................\n","[CV] .... n_neighbors=6, weights=distance, score=-0.225, total= 0.4s\n","[CV] n_neighbors=6, weights=distance .................................\n","[CV] .... n_neighbors=6, weights=distance, score=-0.158, total= 0.4s\n","[CV] n_neighbors=6, weights=distance .................................\n","[CV] .... n_neighbors=6, weights=distance, score=-0.222, total= 0.4s\n","[CV] n_neighbors=6, weights=distance .................................\n","[CV] .... n_neighbors=6, weights=distance, score=-0.203, total= 0.4s\n","[CV] n_neighbors=6, weights=distance .................................\n","[CV] .... n_neighbors=6, weights=distance, score=-0.107, total= 0.4s\n","[CV] n_neighbors=6, weights=distance .................................\n","[CV] .... n_neighbors=6, weights=distance, score=-0.233, total= 0.4s\n","[CV] n_neighbors=6, weights=distance .................................\n","[CV] .... n_neighbors=6, weights=distance, score=-0.232, total= 0.4s\n","[CV] n_neighbors=6, weights=distance .................................\n","[CV] .... n_neighbors=6, weights=distance, score=-0.149, total= 0.4s\n","[CV] n_neighbors=6, weights=distance .................................\n","[CV] .... n_neighbors=6, weights=distance, score=-0.236, total= 0.4s\n","[CV] n_neighbors=6, weights=distance .................................\n","[CV] .... n_neighbors=6, weights=distance, score=-0.204, total= 0.4s\n","[CV] n_neighbors=6, weights=distance .................................\n","[CV] .... n_neighbors=6, weights=distance, score=-0.236, total= 0.4s\n","[CV] n_neighbors=6, weights=distance .................................\n","[CV] .... n_neighbors=6, weights=distance, score=-0.183, total= 0.4s\n","[CV] n_neighbors=6, weights=distance .................................\n","[CV] .... n_neighbors=6, weights=distance, score=-0.168, total= 0.4s\n","[CV] n_neighbors=6, weights=distance .................................\n","[CV] .... n_neighbors=6, weights=distance, score=-0.134, total= 0.4s\n","[CV] n_neighbors=6, weights=distance .................................\n","[CV] .... n_neighbors=6, weights=distance, score=-0.159, total= 0.4s\n","[CV] n_neighbors=6, weights=distance .................................\n","[CV] .... n_neighbors=6, weights=distance, score=-0.174, total= 0.4s\n","[CV] n_neighbors=6, weights=distance .................................\n","[CV] .... n_neighbors=6, weights=distance, score=-0.264, total= 0.4s\n","[CV] n_neighbors=6, weights=distance .................................\n","[CV] .... n_neighbors=6, weights=distance, score=-0.205, total= 0.4s\n","[CV] n_neighbors=6, weights=distance .................................\n","[CV] .... n_neighbors=6, weights=distance, score=-0.107, total= 0.4s\n","[CV] n_neighbors=6, weights=distance .................................\n","[CV] .... n_neighbors=6, weights=distance, score=-0.214, total= 0.4s\n","[CV] n_neighbors=6, weights=distance .................................\n","[CV] .... n_neighbors=6, weights=distance, score=-0.081, total= 0.4s\n","[CV] n_neighbors=6, weights=distance .................................\n","[CV] .... n_neighbors=6, weights=distance, score=-0.295, total= 0.4s\n","[CV] n_neighbors=6, weights=distance .................................\n","[CV] .... n_neighbors=6, weights=distance, score=-0.160, total= 0.4s\n","[CV] n_neighbors=6, weights=distance .................................\n","[CV] .... n_neighbors=6, weights=distance, score=-0.291, total= 0.4s\n","[CV] n_neighbors=6, weights=distance .................................\n","[CV] .... n_neighbors=6, weights=distance, score=-0.120, total= 0.4s\n","[Parallel(n_jobs=1)]: Done 300 out of 300 | elapsed: 1.9min finished\n","KNeighborsRegressor(algorithm='auto', leaf_size=30, metric='minkowski',\n"," metric_params=None, n_jobs=None, n_neighbors=6, p=2,\n"," weights='uniform')\n","window: 1, stide: 1, rmse: 2.9457989546948857\n"," Feature RMSE\n","0 1 2.940267\n","1 2 2.941889\n","2 3 2.873012\n","3 4 2.746155\n","4 5 2.744214\n","5 6 2.753434\n","6 7 2.753434\n","7 8 2.753434\n","8 9 2.753434\n","9 10 2.753434\n","10 11 2.753434\n","11 12 2.753434\n","12 13 2.785559\n","13 14 2.785559\n","14 15 2.763732\n","15 16 2.763732\n","16 17 2.726731\n","17 18 2.726731\n","18 19 2.726731\n","19 20 2.727155\n","20 21 2.727155\n","21 22 2.727155\n","22 23 2.945799\n","23 24 2.945799\n","24 25 2.945799\n"]}],"source":["!python3 feature_select_main.py --dataset OASIS --window 1 --stride 1 --sfreq 128 --model knn --label 0 --approach byfs --ml_algo regression --top f --fs_method SelectKBest"]},{"cell_type":"code","execution_count":null,"metadata":{"colab":{"base_uri":"https://localhost:8080/"},"executionInfo":{"elapsed":12246,"status":"ok","timestamp":1635940821803,"user":{"displayName":"Rohit Garg","photoUrl":"https://lh3.googleusercontent.com/a-/AOh14Gj3Za6nmSY12sPvpCoWOtWQcVR5CMMSPBRGTexH_w=s64","userId":"11252576926243182044"},"user_tz":-330},"id":"TgbwXFmnHnvx","outputId":"1d003b47-cbea-4c14-bbdd-f5c3c222879c"},"outputs":[{"name":"stdout","output_type":"stream","text":["/usr/local/lib/python3.7/dist-packages/statsmodels/tools/_testing.py:19: FutureWarning: pandas.util.testing is deprecated. Use the functions in the public API at pandas.testing instead.\n"," import pandas.util.testing as tm\n","{'dataset': 'DREAMER', 'window': 1, 'stride': 1, 'sfreq': 128, 'model': 'lr', 'label': 0, 'approach': 'byclassifier', 'ml_algo': 'regression', 'top': 'f', 'fs_method': 'None'}\n","Number of segments are: 188370\n","Traceback (most recent call last):\n"," File \"feature_select_main.py\", line 58, in \n"," topFeaturesRegressionRanking(dataset, window, stride, sfreq, clf, label, scale=False, pca=False, ht = True)\n"," File \"/gdrive/My Drive/IEEE_EEG_Project/TopNByClassifier.py\", line 302, in topFeaturesRegressionRanking\n"," featuresDict = loadFeaturesDict(dataset)\n"," File \"/gdrive/My Drive/IEEE_EEG_Project/ImportUtils.py\", line 116, in loadFeaturesDict\n"," featuresDict['ShannonRes_gamma'] = np.load(featurepath + \"ShannonRes_sub_bands_gamma_1_1.npz\", allow_pickle=True)['features']\n"," File \"/usr/local/lib/python3.7/dist-packages/numpy/lib/npyio.py\", line 255, in __getitem__\n"," pickle_kwargs=self.pickle_kwargs)\n"," File \"/usr/local/lib/python3.7/dist-packages/numpy/lib/format.py\", line 763, in read_array\n"," data = _read_bytes(fp, read_size, \"array data\")\n"," File \"/usr/local/lib/python3.7/dist-packages/numpy/lib/format.py\", line 892, in _read_bytes\n"," r = fp.read(size - len(data))\n"," File \"/usr/lib/python3.7/zipfile.py\", line 930, in read\n"," data = self._read1(n)\n"," File \"/usr/lib/python3.7/zipfile.py\", line 1000, in _read1\n"," data = self._read2(n)\n"," File \"/usr/lib/python3.7/zipfile.py\", line 1030, in _read2\n"," data = self._fileobj.read(n)\n"," File \"/usr/lib/python3.7/zipfile.py\", line 754, in read\n"," data = self._file.read(n)\n","KeyboardInterrupt\n"]}],"source":["!python3 feature_select_main.py --dataset DREAMER --window 1 --stride 1 --sfreq 128 --model lr --label 0 --approach byclassifier --ml_algo regression --top f --fs_method None"]},{"cell_type":"code","execution_count":null,"metadata":{"colab":{"base_uri":"https://localhost:8080/"},"executionInfo":{"elapsed":1041775,"status":"ok","timestamp":1635397352192,"user":{"displayName":"Rohit Garg","photoUrl":"https://lh3.googleusercontent.com/a-/AOh14Gj3Za6nmSY12sPvpCoWOtWQcVR5CMMSPBRGTexH_w=s64","userId":"11252576926243182044"},"user_tz":-330},"id":"3gjtiFFM2ZU7","outputId":"34f173c2-0b43-4faa-9d32-e18a6d9997e1"},"outputs":[{"name":"stdout","output_type":"stream","text":["/usr/local/lib/python3.7/dist-packages/statsmodels/tools/_testing.py:19: FutureWarning: pandas.util.testing is deprecated. Use the functions in the public API at pandas.testing instead.\n"," import pandas.util.testing as tm\n","{'dataset': 'DEAP', 'window': 1, 'stride': 1, 'sfreq': 128, 'model': 'xgb', 'label': 0, 'approach': 'byfs', 'ml_algo': 'regression', 'top': 'e', 'fs_method': 'SelectKBest'}\n","Number of Feature-Columns: 210\n","\n","y.shape: (80640,)\n","/usr/local/lib/python3.7/dist-packages/sklearn/feature_selection/_univariate_selection.py:114: UserWarning: Features [196 199 203 207] are constant.\n"," UserWarning)\n"," Specs Score\n","183 stdDev01 119.015497\n","43 ShannonRes_beta01 109.396980\n","54 ShannonRes_beta12 96.831215\n","155 bandPwr_beta01 95.801674\n","57 ShannonRes_gamma01 95.727081\n",".. ... ...\n","198 shortSpikeNum02 1.578476\n","200 shortSpikeNum04 1.578476\n","205 shortSpikeNum09 1.578476\n","208 shortSpikeNum12 1.578476\n","209 shortSpikeNum13 1.578476\n","\n","[206 rows x 2 columns]\n","[1, 12, 5, 10, 8, 11, 6, 0, 7, 13, 2, 9, 4, 3]\n","[1]\n","(80640, 15)\n","Number of Feature-Columns: 15\n","\n","Features Ready for undergoing selection tests done ...\n","\n","window: 1, stide: 1, rmse: 2.1270114191169163\n","[1, 12, 5, 10, 8, 11, 6, 0, 7, 13, 2, 9, 4, 3]\n","[1, 12]\n","(80640, 30)\n","Number of Feature-Columns: 30\n","\n","Features Ready for undergoing selection tests done ...\n","\n","window: 1, stide: 1, rmse: 2.079625036676409\n","[1, 12, 5, 10, 8, 11, 6, 0, 7, 13, 2, 9, 4, 3]\n","[1, 12, 5]\n","(80640, 45)\n","Number of Feature-Columns: 45\n","\n","Features Ready for undergoing selection tests done ...\n","\n","window: 1, stide: 1, rmse: 2.0569363346159593\n","[1, 12, 5, 10, 8, 11, 6, 0, 7, 13, 2, 9, 4, 3]\n","[1, 12, 5, 10]\n","(80640, 60)\n","Number of Feature-Columns: 60\n","\n","Features Ready for undergoing selection tests done ...\n","\n","window: 1, stide: 1, rmse: 2.0319195355315234\n","[1, 12, 5, 10, 8, 11, 6, 0, 7, 13, 2, 9, 4, 3]\n","[1, 12, 5, 10, 8]\n","(80640, 75)\n","Number of Feature-Columns: 75\n","\n","Features Ready for undergoing selection tests done ...\n","\n","window: 1, stide: 1, rmse: 2.002694673382308\n","[1, 12, 5, 10, 8, 11, 6, 0, 7, 13, 2, 9, 4, 3]\n","[1, 12, 5, 10, 8, 11]\n","(80640, 90)\n","Number of Feature-Columns: 90\n","\n","Features Ready for undergoing selection tests done ...\n","\n","window: 1, stide: 1, rmse: 2.0011620348048096\n","[1, 12, 5, 10, 8, 11, 6, 0, 7, 13, 2, 9, 4, 3]\n","[1, 12, 5, 10, 8, 11, 6]\n","(80640, 105)\n","Number of Feature-Columns: 105\n","\n","Features Ready for undergoing selection tests done ...\n","\n","window: 1, stide: 1, rmse: 1.9930767210824532\n","[1, 12, 5, 10, 8, 11, 6, 0, 7, 13, 2, 9, 4, 3]\n","[1, 12, 5, 10, 8, 11, 6, 0]\n","(80640, 120)\n","Number of Feature-Columns: 120\n","\n","Features Ready for undergoing selection tests done ...\n","\n","window: 1, stide: 1, rmse: 1.9795562725452107\n","[1, 12, 5, 10, 8, 11, 6, 0, 7, 13, 2, 9, 4, 3]\n","[1, 12, 5, 10, 8, 11, 6, 0, 7]\n","(80640, 135)\n","Number of Feature-Columns: 135\n","\n","Features Ready for undergoing selection tests done ...\n","\n","window: 1, stide: 1, rmse: 1.9836233079619223\n","[1, 12, 5, 10, 8, 11, 6, 0, 7, 13, 2, 9, 4, 3]\n","[1, 12, 5, 10, 8, 11, 6, 0, 7, 13]\n","(80640, 150)\n","Number of Feature-Columns: 150\n","\n","Features Ready for undergoing selection tests done ...\n","\n","window: 1, stide: 1, rmse: 1.9755009984938887\n","[1, 12, 5, 10, 8, 11, 6, 0, 7, 13, 2, 9, 4, 3]\n","[1, 12, 5, 10, 8, 11, 6, 0, 7, 13, 2]\n","(80640, 165)\n","Number of Feature-Columns: 165\n","\n","Features Ready for undergoing selection tests done ...\n","\n","window: 1, stide: 1, rmse: 1.96606867749315\n","[1, 12, 5, 10, 8, 11, 6, 0, 7, 13, 2, 9, 4, 3]\n","[1, 12, 5, 10, 8, 11, 6, 0, 7, 13, 2, 9]\n","(80640, 180)\n","Number of Feature-Columns: 180\n","\n","Features Ready for undergoing selection tests done ...\n","\n","window: 1, stide: 1, rmse: 1.9625217234516252\n","[1, 12, 5, 10, 8, 11, 6, 0, 7, 13, 2, 9, 4, 3]\n","[1, 12, 5, 10, 8, 11, 6, 0, 7, 13, 2, 9, 4]\n","(80640, 195)\n","Number of Feature-Columns: 195\n","\n","Features Ready for undergoing selection tests done ...\n","\n","window: 1, stide: 1, rmse: 1.9535423428325895\n","[1, 12, 5, 10, 8, 11, 6, 0, 7, 13, 2, 9, 4, 3]\n","[1, 12, 5, 10, 8, 11, 6, 0, 7, 13, 2, 9, 4, 3]\n","(80640, 210)\n","Number of Feature-Columns: 210\n","\n","Features Ready for undergoing selection tests done ...\n","\n","window: 1, stide: 1, rmse: 1.947304441204818\n","Top N Electrodes Cumulative Ranking Electrode RMSE\n","0 1 2.127011\n","1 2 2.079625\n","2 3 2.056936\n","3 4 2.031920\n","4 5 2.002695\n","5 6 2.001162\n","6 7 1.993077\n","7 8 1.979556\n","8 9 1.983623\n","9 10 1.975501\n","10 11 1.966069\n","11 12 1.962522\n","12 13 1.953542\n","13 14 1.947304\n"]}],"source":["!python3 feature_select_main.py --dataset DEAP --window 1 --stride 1 --sfreq 128 --model xgb --label 0 --approach byfs --ml_algo regression --top e --fs_method SelectKBest"]},{"cell_type":"code","execution_count":null,"metadata":{"colab":{"base_uri":"https://localhost:8080/","height":961},"executionInfo":{"elapsed":927,"status":"error","timestamp":1631791001457,"user":{"displayName":"Rohit Garg","photoUrl":"https://lh3.googleusercontent.com/a-/AOh14Gj3Za6nmSY12sPvpCoWOtWQcVR5CMMSPBRGTexH_w=s64","userId":"11252576926243182044"},"user_tz":-330},"id":"9sTLoCT2gWiw","outputId":"d0886351-d98a-4a05-f963-190e78df9ac2"},"outputs":[{"name":"stderr","output_type":"stream","text":["ERROR:root:Internal Python error in the inspect module.\n","Below is the traceback from this internal error.\n","\n"]},{"name":"stdout","output_type":"stream","text":["Mounted at /gdrive\n","Traceback (most recent call last):\n"," File \"/usr/local/lib/python3.7/dist-packages/IPython/core/interactiveshell.py\", line 2882, in run_code\n"," exec(code_obj, self.user_global_ns, self.user_ns)\n"," File \"\", line 3, in \n"," get_ipython().magic('cd /gdrive/MyDrive/Project_DEAP/4.1.2021/')\n"," File \"/usr/local/lib/python3.7/dist-packages/IPython/core/interactiveshell.py\", line 2160, in magic\n"," return self.run_line_magic(magic_name, magic_arg_s)\n"," File \"/usr/local/lib/python3.7/dist-packages/IPython/core/interactiveshell.py\", line 2081, in run_line_magic\n"," result = fn(*args,**kwargs)\n"," File \"\", line 2, in cd\n"," File \"/usr/local/lib/python3.7/dist-packages/IPython/core/magic.py\", line 188, in \n"," call = lambda f, *a, **k: f(*a, **k)\n"," File \"/usr/local/lib/python3.7/dist-packages/IPython/core/magics/osm.py\", line 288, in cd\n"," oldcwd = py3compat.getcwd()\n","OSError: [Errno 107] Transport endpoint is not connected\n","\n","During handling of the above exception, another exception occurred:\n","\n","Traceback (most recent call last):\n"," File \"/usr/local/lib/python3.7/dist-packages/IPython/core/interactiveshell.py\", line 1823, in showtraceback\n"," stb = value._render_traceback_()\n","AttributeError: 'OSError' object has no attribute '_render_traceback_'\n","\n","During handling of the above exception, another exception occurred:\n","\n","Traceback (most recent call last):\n"," File \"/usr/local/lib/python3.7/dist-packages/IPython/core/ultratb.py\", line 1132, in get_records\n"," return _fixed_getinnerframes(etb, number_of_lines_of_context, tb_offset)\n"," File \"/usr/local/lib/python3.7/dist-packages/IPython/core/ultratb.py\", line 313, in wrapped\n"," return f(*args, **kwargs)\n"," File \"/usr/local/lib/python3.7/dist-packages/IPython/core/ultratb.py\", line 358, in _fixed_getinnerframes\n"," records = fix_frame_records_filenames(inspect.getinnerframes(etb, context))\n"," File \"/usr/lib/python3.7/inspect.py\", line 1502, in getinnerframes\n"," frameinfo = (tb.tb_frame,) + getframeinfo(tb, context)\n"," File \"/usr/lib/python3.7/inspect.py\", line 1460, in getframeinfo\n"," filename = getsourcefile(frame) or getfile(frame)\n"," File \"/usr/lib/python3.7/inspect.py\", line 696, in getsourcefile\n"," if getattr(getmodule(object, filename), '__loader__', None) is not None:\n"," File \"/usr/lib/python3.7/inspect.py\", line 725, in getmodule\n"," file = getabsfile(object, _filename)\n"," File \"/usr/lib/python3.7/inspect.py\", line 709, in getabsfile\n"," return os.path.normcase(os.path.abspath(_filename))\n"," File \"/usr/lib/python3.7/posixpath.py\", line 383, in abspath\n"," cwd = os.getcwd()\n","OSError: [Errno 107] Transport endpoint is not connected\n"]},{"ename":"OSError","evalue":"ignored","output_type":"error","traceback":["\u001b[0;31m---------------------------------------------------------------------------\u001b[0m"]}],"source":["from google.colab import drive\n","drive.mount('/gdrive',force_remount=True)\n","%cd /gdrive/MyDrive/Project_DEAP/4.1.2021/\n"]},{"cell_type":"code","execution_count":null,"metadata":{"colab":{"base_uri":"https://localhost:8080/"},"executionInfo":{"elapsed":20745,"status":"ok","timestamp":1635389468395,"user":{"displayName":"Rohit Garg","photoUrl":"https://lh3.googleusercontent.com/a-/AOh14Gj3Za6nmSY12sPvpCoWOtWQcVR5CMMSPBRGTexH_w=s64","userId":"11252576926243182044"},"user_tz":-330},"id":"P5iX-pDN2IwU","outputId":"5222e05e-6bfb-4225-c2f9-d4d0ed7bd028"},"outputs":[{"name":"stdout","output_type":"stream","text":["/usr/local/lib/python3.7/dist-packages/statsmodels/tools/_testing.py:19: FutureWarning: pandas.util.testing is deprecated. Use the functions in the public API at pandas.testing instead.\n"," import pandas.util.testing as tm\n","{'dataset': 'DREAMER', 'window': 1, 'stride': 1, 'sfreq': 128, 'model': 'lr', 'label': 0, 'approach': 'byfs', 'ml_algo': 'regression', 'top': 'e', 'fs_method': 'SelectKBest'}\n","Number of Feature-Columns: 210\n","\n","y.shape: (188370,)\n","/usr/local/lib/python3.7/dist-packages/sklearn/feature_selection/_univariate_selection.py:114: UserWarning: Features [196 197 198 204 207] are constant.\n"," UserWarning)\n"," Specs Score\n","65 ShannonRes_gamma09 1888.444463\n","61 ShannonRes_gamma05 1757.685620\n","63 ShannonRes_gamma07 1588.190675\n","66 ShannonRes_gamma10 1575.955842\n","68 ShannonRes_gamma12 1529.537735\n",".. ... ...\n","114 bandPwr_delta02 0.815222\n","117 bandPwr_delta05 0.758937\n","120 bandPwr_delta08 0.662457\n","124 bandPwr_delta12 0.210495\n","1 ShannonRes_delta01 0.169751\n","\n","[205 rows x 2 columns]\n","[9, 5, 7, 10, 12, 8, 4, 6, 3, 2, 13, 1, 0, 11]\n","[9]\n","(188370, 15)\n","Number of Feature-Columns: 15\n","\n","Features Ready for undergoing selection tests done ...\n","\n","window: 1, stide: 1, rmse: 1.3084327389853563\n","[9, 5, 7, 10, 12, 8, 4, 6, 3, 2, 13, 1, 0, 11]\n","[9, 5]\n","(188370, 30)\n","Number of Feature-Columns: 30\n","\n","Features Ready for undergoing selection tests done ...\n","\n","window: 1, stide: 1, rmse: 1.3085106631898256\n","[9, 5, 7, 10, 12, 8, 4, 6, 3, 2, 13, 1, 0, 11]\n","[9, 5, 7]\n","(188370, 45)\n","Number of Feature-Columns: 45\n","\n","Features Ready for undergoing selection tests done ...\n","\n","window: 1, stide: 1, rmse: 1.3067834900594988\n","[9, 5, 7, 10, 12, 8, 4, 6, 3, 2, 13, 1, 0, 11]\n","[9, 5, 7, 10]\n","(188370, 60)\n","Number of Feature-Columns: 60\n","\n","Features Ready for undergoing selection tests done ...\n","\n","window: 1, stide: 1, rmse: 1.3058296938690166\n","[9, 5, 7, 10, 12, 8, 4, 6, 3, 2, 13, 1, 0, 11]\n","[9, 5, 7, 10, 12]\n","(188370, 75)\n","Number of Feature-Columns: 75\n","\n","Features Ready for undergoing selection tests done ...\n","\n","window: 1, stide: 1, rmse: 1.305075027828666\n","[9, 5, 7, 10, 12, 8, 4, 6, 3, 2, 13, 1, 0, 11]\n","[9, 5, 7, 10, 12, 8]\n","(188370, 90)\n","Number of Feature-Columns: 90\n","\n","Features Ready for undergoing selection tests done ...\n","\n","window: 1, stide: 1, rmse: 1.314592098700178\n","[9, 5, 7, 10, 12, 8, 4, 6, 3, 2, 13, 1, 0, 11]\n","[9, 5, 7, 10, 12, 8, 4]\n","(188370, 105)\n","Number of Feature-Columns: 105\n","\n","Features Ready for undergoing selection tests done ...\n","\n","window: 1, stide: 1, rmse: 1.321157220657299\n","[9, 5, 7, 10, 12, 8, 4, 6, 3, 2, 13, 1, 0, 11]\n","[9, 5, 7, 10, 12, 8, 4, 6]\n","(188370, 120)\n","Number of Feature-Columns: 120\n","\n","Features Ready for undergoing selection tests done ...\n","\n","window: 1, stide: 1, rmse: 1.3103159527234378\n","[9, 5, 7, 10, 12, 8, 4, 6, 3, 2, 13, 1, 0, 11]\n","[9, 5, 7, 10, 12, 8, 4, 6, 3]\n","(188370, 135)\n","Number of Feature-Columns: 135\n","\n","Features Ready for undergoing selection tests done ...\n","\n","window: 1, stide: 1, rmse: 1.309771136941132\n","[9, 5, 7, 10, 12, 8, 4, 6, 3, 2, 13, 1, 0, 11]\n","[9, 5, 7, 10, 12, 8, 4, 6, 3, 2]\n","(188370, 150)\n","Number of Feature-Columns: 150\n","\n","Features Ready for undergoing selection tests done ...\n","\n","window: 1, stide: 1, rmse: 1.4797453938126466\n","[9, 5, 7, 10, 12, 8, 4, 6, 3, 2, 13, 1, 0, 11]\n","[9, 5, 7, 10, 12, 8, 4, 6, 3, 2, 13]\n","(188370, 165)\n","Number of Feature-Columns: 165\n","\n","Features Ready for undergoing selection tests done ...\n","\n","window: 1, stide: 1, rmse: 1.4687119345794493\n","[9, 5, 7, 10, 12, 8, 4, 6, 3, 2, 13, 1, 0, 11]\n","[9, 5, 7, 10, 12, 8, 4, 6, 3, 2, 13, 1]\n","(188370, 180)\n","Number of Feature-Columns: 180\n","\n","Features Ready for undergoing selection tests done ...\n","\n","window: 1, stide: 1, rmse: 1.299595305810554\n","[9, 5, 7, 10, 12, 8, 4, 6, 3, 2, 13, 1, 0, 11]\n","[9, 5, 7, 10, 12, 8, 4, 6, 3, 2, 13, 1, 0]\n","(188370, 195)\n","Number of Feature-Columns: 195\n","\n","Features Ready for undergoing selection tests done ...\n","\n","window: 1, stide: 1, rmse: 1.306262869727451\n","[9, 5, 7, 10, 12, 8, 4, 6, 3, 2, 13, 1, 0, 11]\n","[9, 5, 7, 10, 12, 8, 4, 6, 3, 2, 13, 1, 0, 11]\n","(188370, 210)\n","Number of Feature-Columns: 210\n","\n","Features Ready for undergoing selection tests done ...\n","\n","window: 1, stide: 1, rmse: 1.3594899259984405\n"," Electrode RMSE\n","0 1 1.308433\n","1 2 1.308511\n","2 3 1.306783\n","3 4 1.305830\n","4 5 1.305075\n","5 6 1.314592\n","6 7 1.321157\n","7 8 1.310316\n","8 9 1.309771\n","9 10 1.479745\n","10 11 1.468712\n","11 12 1.299595\n","12 13 1.306263\n","13 14 1.359490\n"]}],"source":["!python3 feature_select_main.py --dataset DREAMER --window 1 --stride 1 --sfreq 128 --model lr --label 0 --approach byfs --ml_algo regression --top e --fs_method SelectKBest"]},{"cell_type":"code","execution_count":null,"metadata":{"id":"BjcgBwmSIeRZ"},"outputs":[],"source":["import os\n","import glob\n","from scipy import io,signal\n","import numpy as np\n","import pandas as pd\n","from sklearn import preprocessing\n","import pickle\n","from sklearn.metrics import mean_squared_error\n","from sklearn.impute import SimpleImputer\n","%matplotlib inline\n","import matplotlib.pyplot as plt\n"]},{"cell_type":"code","execution_count":null,"metadata":{"colab":{"base_uri":"https://localhost:8080/"},"id":"BVBiZAHy2Bq5","outputId":"9569ebab-6f24-4e9c-86f5-c830f200eeaf"},"outputs":[{"name":"stdout","output_type":"stream","text":["Collecting dit\n","\u001b[?25l Downloading https://files.pythonhosted.org/packages/ac/29/8172e0ea15be3966196e8a1a4078564f4246803dd4f09b1871881e118467/dit-1.2.3-py2.py3-none-any.whl (397kB)\n","\r\u001b[K |▉ | 10kB 13.2MB/s eta 0:00:01\r\u001b[K |█▋ | 20kB 12.6MB/s eta 0:00:01\r\u001b[K |██▌ | 30kB 7.9MB/s eta 0:00:01\r\u001b[K 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0:00:01\r\u001b[K |████████████████████████████████| 399kB 5.2MB/s \n","\u001b[?25hCollecting debtcollector\n"," Downloading https://files.pythonhosted.org/packages/8e/50/07a7ccf4dbbe90b58e96f97b747ff98aef9d8c841d2616c48cc05b07db33/debtcollector-2.2.0-py3-none-any.whl\n","Collecting boltons\n","\u001b[?25l Downloading https://files.pythonhosted.org/packages/71/e1/e7979a4a6d4b296b5935e926549fff540f7670ddaf09bbf137e2b022c039/boltons-20.2.1-py2.py3-none-any.whl (170kB)\n","\u001b[K |████████████████████████████████| 174kB 18.4MB/s \n","\u001b[?25hRequirement already satisfied: numpy>=1.11 in /usr/local/lib/python3.7/dist-packages (from dit) (1.19.5)\n","Requirement already satisfied: six>=1.4.0 in /usr/local/lib/python3.7/dist-packages (from dit) (1.15.0)\n","Requirement already satisfied: prettytable in /usr/local/lib/python3.7/dist-packages (from dit) (2.1.0)\n","Requirement already satisfied: scipy>=0.15.0 in /usr/local/lib/python3.7/dist-packages (from dit) (1.4.1)\n","Requirement 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(4.4.2)\n","Requirement already satisfied: typing-extensions>=3.6.4; python_version < \"3.8\" in /usr/local/lib/python3.7/dist-packages (from importlib-metadata; python_version < \"3.8\"->prettytable->dit) (3.7.4.3)\n","Requirement already satisfied: zipp>=0.5 in /usr/local/lib/python3.7/dist-packages (from importlib-metadata; python_version < \"3.8\"->prettytable->dit) (3.4.1)\n","Installing collected packages: pbr, debtcollector, boltons, dit\n","Successfully installed boltons-20.2.1 debtcollector-2.2.0 dit-1.2.3 pbr-5.5.1\n","Collecting pyinform\n","\u001b[?25l Downloading https://files.pythonhosted.org/packages/6d/4d/915887ddc6e5c46294575a36c7055a1a75b57a867d2f5841ecc942ff8e42/pyinform-0.2.0-py3-none-any.whl (131kB)\n","\u001b[K |████████████████████████████████| 133kB 5.8MB/s \n","\u001b[?25hRequirement already satisfied: numpy in /usr/local/lib/python3.7/dist-packages (from pyinform) (1.19.5)\n","Installing collected packages: pyinform\n","Successfully installed pyinform-0.2.0\n"]}],"source":["!pip install dit\n","!pip install pyinform"]},{"cell_type":"code","execution_count":null,"metadata":{"colab":{"base_uri":"https://localhost:8080/"},"id":"0EberxF6wcfm","outputId":"c974420e-2d6d-4007-d501-fbb244a1e218"},"outputs":[{"name":"stdout","output_type":"stream","text":["/usr/local/lib/python3.7/dist-packages/statsmodels/tools/_testing.py:19: FutureWarning: pandas.util.testing is deprecated. Use the functions in the public API at pandas.testing instead.\n"," import pandas.util.testing as tm\n","{'dataset': 'DREAMER', 'window': 1, 'stride': 1, 'sfreq': 128, 'model': 'rfr', 'label': 0, 'approach': 'byfs', 'ml_algo': 'regression', 'top': 'e', 'fs_method': 'SelectKBest'}\n","tcmalloc: large alloc 2700476416 bytes == 0x563ea90a4000 @ 0x7f3ef70351e7 0x7f3ef429546e 0x7f3ef42e5c7b 0x7f3ef4298ce8 0x563ea4f73c25 0x563ea4f348e9 0x563ea4fa8ade 0x563ea4fa2b0e 0x563ea4f3577a 0x563ea4fa486a 0x563ea4f3772b 0x563ea4f785e9 0x563ea4f7855c 0x563ea501be59 0x563ea4fa3fad 0x563ea4fa2e0d 0x563ea4f3577a 0x563ea4fa3a45 0x563ea4fa2b0e 0x563ea4fa2813 0x563ea506c592 0x563ea506c90d 0x563ea506c7b6 0x563ea5044103 0x563ea5043dac 0x7f3ef5e1fbf7 0x563ea5043c8a\n","Shape After Loading\n","X.shape= (414, 58240, 14) Y.shape= (414, 2) Z.shape= (414, 2)\n","tcmalloc: large alloc 2700476416 bytes == 0x563f4a044000 @ 0x7f3ef70351e7 0x7f3ef429546e 0x7f3ef42e5c7b 0x7f3ef42e635f 0x7f3ef4388103 0x563ea4f34050 0x563ea4f33de0 0x563ea4fa8244 0x563ea4fa2e0d 0x563ea4f3577a 0x563ea4fa3a45 0x563ea4fa2b0e 0x563ea4fa2813 0x563ea506c592 0x563ea506c90d 0x563ea506c7b6 0x563ea5044103 0x563ea5043dac 0x7f3ef5e1fbf7 0x563ea5043c8a\n","tcmalloc: large alloc 2700476416 bytes == 0x563feafa4000 @ 0x7f3ef70351e7 0x7f3ef429546e 0x7f3ef42e5c7b 0x7f3ef42e5d97 0x7f3ef42df4a5 0x7f3ef437fe0c 0x563ea4f33fd7 0x563ea4f33de0 0x563ea4fa7ac2 0x563ea4fa2b0e 0x563ea4f3577a 0x563ea4fa7e50 0x563ea4fa2e0d 0x563ea4f3577a 0x563ea4fa3a45 0x563ea4fa2b0e 0x563ea4fa2813 0x563ea506c592 0x563ea506c90d 0x563ea506c7b6 0x563ea5044103 0x563ea5043dac 0x7f3ef5e1fbf7 0x563ea5043c8a\n","Shape after reshaping\n","X.shape= (414, 14, 58240) Y.shape= (414, 2) Z.shape= (414, 2)\n","Data Loaded...\n","\n","tcmalloc: large alloc 2700476416 bytes == 0x563ea90a4000 @ 0x7f3ef70351e7 0x7f3ef429546e 0x7f3ef42e5c7b 0x7f3ef42e5d97 0x7f3ef42df4a5 0x7f3ef437fe0c 0x563ea4f33fd7 0x563ea4f33de0 0x563ea4fa7ac2 0x563ea4fa2b0e 0x563ea4f3577a 0x563ea4fa7e50 0x563ea4fa2e0d 0x563ea4f3577a 0x563ea4fa3a45 0x563ea4fa2b0e 0x563ea4fa2813 0x563ea506c592 0x563ea506c90d 0x563ea506c7b6 0x563ea5044103 0x563ea5043dac 0x7f3ef5e1fbf7 0x563ea5043c8a\n","X_new.shape = (414, 14, 58240)\n","Channel selection done ...\n","\n","tcmalloc: large alloc 2700476416 bytes == 0x563f4a044000 @ 0x7f3ef70351e7 0x7f3ef429546e 0x7f3ef42e5c7b 0x7f3ef42e635f 0x7f3ef4388103 0x563ea4f34050 0x563ea4f33de0 0x563ea4fa8244 0x563ea4f3569a 0x563ea4fa3a45 0x563ea4fa2e0d 0x563ea4f3577a 0x563ea4fa3a45 0x563ea4fa2b0e 0x563ea4fa2813 0x563ea506c592 0x563ea506c90d 0x563ea506c7b6 0x563ea5044103 0x563ea5043dac 0x7f3ef5e1fbf7 0x563ea5043c8a\n","Epoching done ...\n","\n","(188370, 14, 128) (188370, 2) (188370, 2)\n","tcmalloc: large alloc 2700476416 bytes == 0x563ea90a4000 @ 0x7f3ef70351e7 0x7f3ef429546e 0x7f3ef42e5c7b 0x7f3ef42e635f 0x7f3ef4388103 0x563ea4f34050 0x563ea4f33de0 0x563ea4fa8244 0x563ea4fa2e0d 0x563ea4f3577a 0x563ea4fa3a45 0x563ea4fa2b0e 0x563ea4fa2813 0x563ea506c592 0x563ea506c90d 0x563ea506c7b6 0x563ea5044103 0x563ea5043dac 0x7f3ef5e1fbf7 0x563ea5043c8a\n","ans.shape = (14, 128, 188370)\n","Rotation of np.array done ...\n","\n","tcmalloc: large alloc 2700476416 bytes == 0x563f4a044000 @ 0x7f3ef70351e7 0x7f3ef429546e 0x7f3ef42e5c7b 0x7f3ef42e5d18 0x7f3ef43a1d79 0x7f3ef43a4e4c 0x7f3ef44c3e7f 0x7f3ef44c9fb5 0x7f3ef44cbe3d 0x7f3ef44cd516 0x563ea4f352c0 0x563ea4f34e99 0x7f3ef43ac0db 0x563ea501dbc2 0x563ea4fa4760 0x563ea4fa2b0e 0x563ea4f3577a 0x563ea4fa486a 0x563ea4fa2b0e 0x563ea4e74e2b 0x563ea4fa51e6 0x563ea4fa2b0e 0x563ea4e74e2b 0x7f3ef42d2ef7 0x563ea4f33fd7 0x563ea4f33de0 0x563ea4fa7ac2 0x563ea4fa2b0e 0x563ea4f3577a 0x563ea4fa486a 0x563ea4f3569a\n","Feature Extraction done ...\n","\n"]}],"source":["!python3 feature_select_main.py --dataset DREAMER --window 1 --stride 1 --sfreq 128 --model rfr --label 0 --approach byfs --ml_algo regression --top e --fs_method SelectKBest"]},{"cell_type":"code","execution_count":null,"metadata":{"colab":{"base_uri":"https://localhost:8080/"},"id":"8Nwy2LPw2VdQ","outputId":"c18d2b6c-b6d6-4666-f4cc-8237609a47af"},"outputs":[{"name":"stdout","output_type":"stream","text":["/usr/local/lib/python3.7/dist-packages/statsmodels/tools/_testing.py:19: FutureWarning: pandas.util.testing is deprecated. Use the functions in the public API at pandas.testing instead.\n"," import pandas.util.testing as tm\n","{'dataset': 'DEAP', 'window': 4, 'stride': 2, 'sfreq': 128, 'model': 'rfr', 'label': 0, 'approach': 'byfs', 'ml_algo': 'regression', 'top': 'ef', 'fs_method': 'SelectKBest'}\n","tcmalloc: large alloc 2202009600 bytes == 0x563fbcd60000 @ 0x7f9d3da6a1e7 0x7f9d3b62a46e 0x7f9d3b67ac7b 0x7f9d3b62dce8 0x563fb6d2fc25 0x563fb6cf08e9 0x563fb6d64ade 0x563fb6d5eb0e 0x563fb6cf177a 0x563fb6d6086a 0x563fb6cf372b 0x563fb6d345e9 0x563fb6d3455c 0x563fb6dd7e59 0x563fb6d5ffad 0x563fb6d5eb0e 0x563fb6cf177a 0x563fb6d6086a 0x563fb6d5eb0e 0x563fb6d5e813 0x563fb6e28592 0x563fb6e2890d 0x563fb6e287b6 0x563fb6e00103 0x563fb6dffdac 0x7f9d3c854bf7 0x563fb6dffc8a\n","Number of segments are: 38400\n","Shape of FeatureMatrix: (38400, 392)\n","\n","/usr/local/lib/python3.7/dist-packages/sklearn/feature_selection/_univariate_selection.py:114: UserWarning: Features [126 127 128 129 130 131 132 133 134 135 136 137 138 139 252 253 254 255\n"," 256 257 258 259 260 261 262 263 264 265 266 267 268 269 270 271 272 274\n"," 275 276 277 278 279 280 281 282 283 284 285 286 288 289 290 291 292 293] are constant.\n"," UserWarning)\n"," Column Score\n","198 bandPwr_beta2 81.573636\n","226 stdDev2 77.753574\n","208 bandPwr_beta12 68.405879\n","184 bandPwr_alpha2 63.314963\n","58 ShannonRes_beta2 61.275193\n",".. ... ...\n","142 medianFreq2 1.389429\n","149 medianFreq9 1.299767\n","146 medianFreq6 1.252763\n","154 bandPwr_delta0 1.173907\n","160 bandPwr_delta6 0.677763\n","\n","[338 rows x 2 columns]\n","1 2.5493991292120954\n","2 2.3051438642505433\n","3 2.1710133945547034\n","4 2.1266114363776074\n","5 2.1163213764970785\n","6 2.073437091296116\n","7 2.0635609614044097\n","8 2.0604663024640426\n","9 2.033553791107928\n","10 1.9788066074502586\n","11 1.9535866112272593\n","12 1.9215478146369915\n","13 1.9170806961370463\n","14 1.9219155332893687\n","15 1.9226781546090697\n","16 1.9225106441709292\n","17 1.908001341202004\n","18 1.8736626749629934\n","19 1.8732374163835512\n","20 1.854922476397683\n","21 1.8539772962833483\n","22 1.855905768921441\n","23 1.8493108821602446\n","24 1.8463046554353622\n","25 1.847628423601949\n","26 1.8414699511687103\n","27 1.8396419418220158\n","28 1.8210802610057417\n","29 1.8090926154061842\n","30 1.8005425775995128\n","31 1.7923803946309198\n","32 1.7926091984793162\n","33 1.7862505388353513\n","34 1.7915909124694815\n","35 1.7855557495023024\n","36 1.7846533772696591\n","37 1.784354774808393\n","38 1.7935591979756982\n","39 1.7870501390836684\n","40 1.7702850134329096\n","41 1.774179271303031\n","42 1.7761809113231393\n","43 1.775174122267218\n","44 1.7757422978126318\n","45 1.7780133735890342\n","46 1.7772055998935432\n","47 1.7713666695730217\n","48 1.765829865801638\n","49 1.7618988153871835\n","50 1.747793201700406\n","51 1.7510106685779954\n","52 1.7449639997954203\n","53 1.7440643918858818\n","54 1.742945515079705\n","55 1.7455039265265293\n","56 1.7382246196108704\n","57 1.742077195317881\n","58 1.7370053828603809\n","59 1.7386805111065757\n","60 1.7406847216385377\n","61 1.731158626295446\n","62 1.7338612564275047\n","63 1.7328761945653839\n","64 1.7290277529244038\n","65 1.724509353544056\n","66 1.7216521438620649\n","67 1.7261350985833057\n","68 1.7258394186088748\n","69 1.7248639790129405\n","70 1.7175283340366616\n","71 1.7147140365952787\n","72 1.7167027896265148\n","73 1.7165578685730802\n","74 1.7141585891176563\n","75 1.7072192868398814\n","76 1.704279525159416\n","77 1.7013849723686356\n","78 1.69664411649059\n","79 1.6990924097403413\n","80 1.6995048147161835\n","81 1.6886540808764468\n","82 1.689251923858729\n","83 1.6900542546699098\n","84 1.686472488472997\n","85 1.6904664542529162\n","86 1.681450568641599\n","87 1.6797095627322347\n","88 1.6731988369781712\n","89 1.6768905421401274\n","90 1.678001305076143\n","91 1.674163757574444\n","92 1.675446031790941\n","93 1.6755406202866192\n","94 1.6794007843596517\n","95 1.6755644582463192\n","96 1.6739973613937167\n","97 1.6617887433237415\n","98 1.665023300937276\n","99 1.6655504217715962\n","100 1.6666231766944357\n","101 1.6629974025813112\n","102 1.6613939896092182\n","103 1.6636812664967373\n","104 1.662306691523045\n","105 1.6664338451465752\n","106 1.6692722622736995\n","107 1.6698739180117732\n","108 1.675113757932375\n","109 1.6699090568925203\n"]}],"source":["!python3 feature_select_main.py --dataset DEAP --window 4 --stride 2 --sfreq 128 --model rfr --label 0 --approach byfs --ml_algo regression --top ef --fs_method SelectKBest"]},{"cell_type":"code","execution_count":null,"metadata":{"id":"cHaWcfXCGVmp"},"outputs":[],"source":["# def topFSColumnsRegressionRanking(dataset, window, stride, sfreq, clf, label, scale=False, pca=False, mutual_info = False, method='SelectKBest'):\n"," \n","# fs = sfreq\n","# pwd = os.getcwd()\n"," \n","# filepath = pwd + '/' + dataset + \"/data_extracted/epochedData/data\" + str(window) + str(stride) + \".npz\"\n","\n","# ans = np.load(filepath,allow_pickle=True)['arr_0'] \n","# Y_epoch = np.load(filepath,allow_pickle=True)['arr_1']\n","# print(\"Number of segments are: {}\".format(ans.shape[2]))\n"," \n","# #X##############################################################################################\n"," \n","# featuresDict = None\n","# with open(pwd + '/' + dataset + '/data_extracted/featureDicts/'+str(window)+str(stride)+ '.pkl', 'rb') as f:\n","# featuresDict = pickle.load(f)\n"," \n","# featuresToAvoid = ['volt05', 'volt10', 'volt20', 'burstBandPowers','hFD']\n","# featuresList = list(featuresDict.keys())\n","# for x in featuresToAvoid:\n","# featuresList.remove(x)\n","\n","# # defining column names\n","# feature_channel_index = []\n","\n","# for feature in featuresList:\n","# for i in range(14):\n","# feature_channel_index.append(feature + str(i))\n"," \n","# #defining feature matrix\n","# featureMatrix = np.empty((0,ans.shape[2])) #[14*32 + 1,80640]\n","# for key,value in featuresDict.items():\n","# if(key not in featuresToAvoid):\n","# featureMatrix = np.append(featureMatrix,value,axis=0)\n","\n"," \n","# print(\"Shape of FeatureMatrix: {}\\n\".format(featureMatrix.T.shape))\n"," \n","# #data-imputation and nan-removal\n","# featureMatrix = featureMatrix.astype('float64')\n"," \n","# if np.isnan(featureMatrix).any():\n","# featureMatrix = np.nan_to_num(featureMatrix,nan=0)\n","\n","# X = pd.DataFrame(featureMatrix.T)\n","# X = X.replace([np.inf, -np.inf], np.nan)\n","# X = X.fillna(0)\n","# X.columns = feature_channel_index\n"," \n","\n","# #Y#####################################################################\n","\n","# y = Y_epoch[:,label] #valence\n","# # y = pd.DataFrame(y)\n","\n","# ########################################################################\n","# dfscores = None\n","\n","# if(method == 'RandomForest'):\n","# '''Random Forest Feature Importances'''\n","# estimator = RandomForestClassifier()\n","# fit = estimator.fit(X,y)\n","# dfscores = pd.DataFrame(fit.feature_importances_)\n","# elif(method == 'RFE'):\n","# ''' RFE'''\n","# selector = RFE(clf, n_features_to_select=X.shape[1], step=1)\n","# selector = selector.fit(X, y)\n","# dfscores = pd.DataFrame(selector.ranking_)\n","\n","# elif(method == 'SelectKBest'):\n","# \"\"\"SelecKBest\"\"\"\n","# #apply SelectKBest class to extract top 10 best features\n","# func = None\n","# if mutual_info == False:\n","# func = f_classif\n","# else:\n","# func = mutual_info_classif\n","\n","# bestfeatures = SelectKBest(score_func=func, k=X.shape[1])\n","# fit = bestfeatures.fit(X,y)\n","\n","# dfscores = pd.DataFrame(fit.scores_)\n"," \n","\n","\n","\n","# dfcolumns = pd.DataFrame(X.columns)\n","\n","# #concat two dataframes for better visualization \n","# featureScores = pd.concat([dfcolumns,dfscores],axis=1)\n","# featureScores.columns = ['Column','Score'] #naming the dataframe columns\n","# features_result = featureScores.nlargest(X.shape[1],'Score')\n","# print(features_result)\n","\n","# N = len(feature_channel_index)\n","# topNRmseList = []\n","# topNList = [\"{}\".format(x) for x in range(1,N+1)]\n","\n","# for n in range(1, N+1):\n","# ranking_df = features_result.head(n)\n","# topncols = ranking_df['Column'].tolist()\n"," \n","# X_train, X_test, y_train, y_test = train_test_split(X[topncols], y, test_size=0.2, random_state=42)\n","\n","# # Normalise-scale data \n","# # Feature Scaling\n","# if(scale == True):\n","# sc = StandardScaler()\n","# X_train = sc.fit_transform(X_train)\n","# X_test = sc.transform(X_test)\n","\n","# # Apply classfier\n","# # clf = xgb.XGBClassifier(verbose = 5)\n","# clf.fit(X_train, y_train)\n","# y_predict = clf.predict(X_test)\n","# rmse = mean_squared_error(y_test, y_predict, squared=False)\n","# topNRmseList.append(rmse)\n","\n","\n","# topcol_df = pd.DataFrame(topNList)\n","# toprmse_df = pd.DataFrame(topNRmseList)\n","# #concat two dataframes for better visualization \n","# topcolRanking = pd.concat([topcol_df, toprmse_df],axis=1)\n","# topcolRanking.columns = ['Column','RMSE'] #naming the dataframe columns\n","# topfeatures_result = topcolRanking\n","# print(topfeatures_result)\n","\n","\n","# # Plotting\n","# plt.xlabel('Top N Columns')\n","# plt.ylabel('RMSE')\n","# plt.title(\"Top N Columns v/s RMSE Plot for Window:{} Stride:{} epoched data by varying N\".format(window,stride))\n","# plt.plot(topfeatures_result.loc[:,\"Column\"], topfeatures_result.loc[:,\"RMSE\"])\n","# plt.savefig(pwd + \"/\" + dataset + \"/plots/\" + \"topFSColumnsRegressionRanking\" + str(window) + str(stride) + \".jpg\", bbox_inches='tight')\n","# plt.show()\n","# plt.clf()\n"]},{"cell_type":"code","execution_count":null,"metadata":{"colab":{"base_uri":"https://localhost:8080/"},"id":"g6Ys6z4SLL5j","outputId":"1ff2b785-5470-4b16-e8cf-b9d0132aeea4"},"outputs":[{"name":"stdout","output_type":"stream","text":["fatal: destination path 'rapidsai-csp-utils' already exists and is not an empty directory.\n","PLEASE READ\n","********************************************************************************************************\n","Changes:\n","1. IMPORTANT SCRIPT CHANGES: Colab has updated to Python 3.7, and now runs our STABLE and NIGHTLY versions (0.18 and 0.19)! PLEASE update your older install script code as follows:\n","\t!bash rapidsai-csp-utils/colab/rapids-colab.sh 0.18\n","\n","\timport sys, os\n","\n","\tdist_package_index = sys.path.index('/usr/local/lib/python3.7/dist-packages')\n","\tsys.path = sys.path[:dist_package_index] + ['/usr/local/lib/python3.7/site-packages'] + sys.path[dist_package_index:]\n","\tsys.path\n","\texec(open('rapidsai-csp-utils/colab/update_modules.py').read(), globals())\n","\n","2. IMPORTANT NOTICE: CuGraph's Louvain requires a Volta+ GPU (T4, V100). If you get a P4 or P100 and intend to use Louvain, please FACTORY RESET your instance and try to get a compatible GPU\n","3. Default stable version is now 0.18. Nightly is now 0.19.\n","3. You can declare your RAPIDSAI version as a CLI option and skip the user prompts (ex: '0.18' or '0.19', between 0.17 to 0.19, without the quotes): \n"," \"!bash rapidsai-csp-utils/colab/rapids-colab.sh \"\n"," Examples: '!bash rapidsai-csp-utils/colab/rapids-colab.sh 0.18', or '!bash rapidsai-csp-utils/colab/rapids-colab.sh stable', or '!bash rapidsai-csp-utils/colab/rapids-colab.sh s'\n"," '!bash rapidsai-csp-utils/colab/rapids-colab.sh 0.19, or '!bash rapidsai-csp-utils/colab/rapids-colab.sh nightly', or '!bash rapidsai-csp-utils/colab/rapids-colab.sh n'\n","Enjoy using RAPIDS! If you have any issues with or suggestions for RAPIDSAI on Colab, please create a bug request on https://github.com/rapidsai/rapidsai-csp-utils/issues/new.\n","For a near instant entry into a RAPIDS Library experience, or if we haven't fixed a fatal issue yet, please use https://app.blazingsql.com/. Thanks!\n","Starting to prep Colab for install RAPIDS Version 0.18 stable\n","Checking for GPU type:\n","Traceback (most recent call last):\n"," File \"rapidsai-csp-utils/colab/env-check.py\", line 1, in \n"," import pynvml\n","ModuleNotFoundError: No module named 'pynvml'\n","\n","************************************************\n","Your Google Colab instance has RAPIDS installed!\n","************************************************\n","***********************************************************************\n","Let us check on those pyarrow and cffi versions...\n","***********************************************************************\n","\n","You're don't have pyarrow.\n","unloaded cffi 1.14.5\n","loaded cffi 1.14.5\n"]}],"source":["# Install RAPIDS\n","!git clone https://github.com/rapidsai/rapidsai-csp-utils.git\n","!bash rapidsai-csp-utils/colab/rapids-colab.sh stable\n","\n","import sys, os\n","\n","dist_package_index = sys.path.index('/usr/local/lib/python3.7/dist-packages')\n","sys.path = sys.path[:dist_package_index] + ['/usr/local/lib/python3.7/site-packages'] + sys.path[dist_package_index:]\n","sys.path\n","exec(open('rapidsai-csp-utils/colab/update_modules.py').read(), globals())"]},{"cell_type":"code","execution_count":null,"metadata":{"colab":{"base_uri":"https://localhost:8080/","height":865},"id":"I4Lau_ErLfgN","outputId":"65991974-8f77-497c-f65f-b203976b44be"},"outputs":[{"name":"stdout","output_type":"stream","text":["fatal: destination path 'rapidsai-csp-utils' already exists and is not an empty directory.\n","PLEASE READ\n","********************************************************************************************************\n","Changes:\n","1. IMPORTANT SCRIPT CHANGES: Colab has updated to Python 3.7, and now runs our STABLE and NIGHTLY versions (0.18 and 0.19)! PLEASE update your older install script code as follows:\n","\t!bash rapidsai-csp-utils/colab/rapids-colab.sh 0.18\n","\n","\timport sys, os\n","\n","\tdist_package_index = sys.path.index('/usr/local/lib/python3.7/dist-packages')\n","\tsys.path = sys.path[:dist_package_index] + ['/usr/local/lib/python3.7/site-packages'] + sys.path[dist_package_index:]\n","\tsys.path\n","\texec(open('rapidsai-csp-utils/colab/update_modules.py').read(), globals())\n","\n","2. IMPORTANT NOTICE: CuGraph's Louvain requires a Volta+ GPU (T4, V100). If you get a P4 or P100 and intend to use Louvain, please FACTORY RESET your instance and try to get a compatible GPU\n","3. Default stable version is now 0.18. Nightly is now 0.19.\n","3. You can declare your RAPIDSAI version as a CLI option and skip the user prompts (ex: '0.18' or '0.19', between 0.17 to 0.19, without the quotes): \n"," \"!bash rapidsai-csp-utils/colab/rapids-colab.sh \"\n"," Examples: '!bash rapidsai-csp-utils/colab/rapids-colab.sh 0.18', or '!bash rapidsai-csp-utils/colab/rapids-colab.sh stable', or '!bash rapidsai-csp-utils/colab/rapids-colab.sh s'\n"," '!bash rapidsai-csp-utils/colab/rapids-colab.sh 0.19, or '!bash rapidsai-csp-utils/colab/rapids-colab.sh nightly', or '!bash rapidsai-csp-utils/colab/rapids-colab.sh n'\n","Enjoy using RAPIDS! If you have any issues with or suggestions for RAPIDSAI on Colab, please create a bug request on https://github.com/rapidsai/rapidsai-csp-utils/issues/new.\n","For a near instant entry into a RAPIDS Library experience, or if we haven't fixed a fatal issue yet, please use https://app.blazingsql.com/. Thanks!\n","As you didn't specify a RAPIDS version, please enter in the box your desired RAPIDS version (ex: '0.18' or '0.19', between 0.17 to 0.19, without the quotes)\n","and hit Enter. If you need stability, use 0.18. If you want bleeding edge, use our nightly version (0.19), but understand that caveats that come with nightly versions.\n","0.19\n","RAPIDS Version to install is 0.19\n","Checking for GPU type:\n","Traceback (most recent call last):\n"," File \"rapidsai-csp-utils/colab/env-check.py\", line 1, in \n"," import pynvml\n","ModuleNotFoundError: No module named 'pynvml'\n","\n","************************************************\n","Your Google Colab instance has RAPIDS installed!\n","************************************************\n"]},{"ename":"ValueError","evalue":"ignored","output_type":"error","traceback":["\u001b[0;31m---------------------------------------------------------------------------\u001b[0m","\u001b[0;31mValueError\u001b[0m Traceback (most recent call last)","\u001b[0;32m\u001b[0m in \u001b[0;36m\u001b[0;34m()\u001b[0m\n\u001b[1;32m 5\u001b[0m \u001b[0;32mimport\u001b[0m \u001b[0msys\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mos\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 6\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m----> 7\u001b[0;31m \u001b[0mdist_package_index\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0msys\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mpath\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mindex\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m'/usr/local/lib/python3.6/dist-packages'\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m 8\u001b[0m \u001b[0msys\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mpath\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0msys\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mpath\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0mdist_package_index\u001b[0m\u001b[0;34m]\u001b[0m \u001b[0;34m+\u001b[0m \u001b[0;34m[\u001b[0m\u001b[0;34m'/usr/local/lib/python3.6/site-packages'\u001b[0m\u001b[0;34m]\u001b[0m \u001b[0;34m+\u001b[0m \u001b[0msys\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mpath\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0mdist_package_index\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 9\u001b[0m \u001b[0msys\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mpath\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n","\u001b[0;31mValueError\u001b[0m: '/usr/local/lib/python3.6/dist-packages' is not in list"]}],"source":["# Install RAPIDS\n","!git clone https://github.com/rapidsai/rapidsai-csp-utils.git\n","!bash rapidsai-csp-utils/colab/rapids-colab.sh\n","\n","import sys, os\n","\n","dist_package_index = sys.path.index('/usr/local/lib/python3.6/dist-packages')\n","sys.path = sys.path[:dist_package_index] + ['/usr/local/lib/python3.6/site-packages'] + sys.path[dist_package_index:]\n","sys.path\n","exec(open('rapidsai-csp-utils/colab/update_modules.py').read(), globals())\n"]},{"cell_type":"code","execution_count":null,"metadata":{"id":"jLzY0cXaJQB1"},"outputs":[],"source":["# intall miniconda\n","!wget -c https://repo.continuum.io/miniconda/Miniconda3-4.5.4-Linux-x86_64.sh\n","!chmod +x Miniconda3-4.5.4-Linux-x86_64.sh\n","!bash ./Miniconda3-4.5.4-Linux-x86_64.sh -b -f -p /usr/local\n","\n","# install RAPIDS packages\n","!conda install -q -y --prefix /usr/local -c conda-forge \\\n"," -c rapidsai-nightly/label/cuda10.0 -c nvidia/label/cuda10.0 \\\n"," cudf cuml\n","\n","# set environment vars\n","import sys, os, shutil\n","sys.path.append('/usr/local/lib/python3.6/site-packages/')\n","os.environ['NUMBAPRO_NVVM'] = '/usr/local/cuda/nvvm/lib64/libnvvm.so'\n","os.environ['NUMBAPRO_LIBDEVICE'] = '/usr/local/cuda/nvvm/libdevice/'\n","\n","# copy .so files to current working dir\n","for fn in ['libcudf.so', 'librmm.so']:\n"," shutil.copy('/usr/local/lib/'+fn, os.getcwd())"]},{"cell_type":"code","execution_count":null,"metadata":{"colab":{"base_uri":"https://localhost:8080/","height":341},"id":"cHZfNrlNI4AA","outputId":"aa730d9a-8183-4711-d619-7c1cd4630d8c"},"outputs":[{"ename":"ModuleNotFoundError","evalue":"ignored","output_type":"error","traceback":["\u001b[0;31m---------------------------------------------------------------------------\u001b[0m","\u001b[0;31mModuleNotFoundError\u001b[0m Traceback (most recent call last)","\u001b[0;32m\u001b[0m in \u001b[0;36m\u001b[0;34m()\u001b[0m\n\u001b[0;32m----> 1\u001b[0;31m \u001b[0;32mfrom\u001b[0m \u001b[0mcuml\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mtest\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mutils\u001b[0m \u001b[0;32mimport\u001b[0m \u001b[0mget_handle\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m 2\u001b[0m \u001b[0;32mfrom\u001b[0m \u001b[0mcuml\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mensemble\u001b[0m \u001b[0;32mimport\u001b[0m \u001b[0mRandomForestRegressor\u001b[0m \u001b[0;32mas\u001b[0m \u001b[0mcurfc\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 3\u001b[0m \u001b[0;32mfrom\u001b[0m \u001b[0mcuml\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mtest\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mutils\u001b[0m \u001b[0;32mimport\u001b[0m \u001b[0mget_handle\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n","\u001b[0;31mModuleNotFoundError\u001b[0m: No module named 'cuml'","","\u001b[0;31m---------------------------------------------------------------------------\u001b[0;32m\nNOTE: If your import is failing due to a missing package, you can\nmanually install dependencies using either !pip or !apt.\n\nTo view examples of installing some common dependencies, click the\n\"Open Examples\" button below.\n\u001b[0;31m---------------------------------------------------------------------------\u001b[0m\n"]}],"source":["\n","from cuml.test.utils import get_handle\n","from cuml.ensemble import RandomForestRegressor as curfc\n","from cuml.test.utils import get_handle"]},{"cell_type":"code","execution_count":null,"metadata":{"id":"cH7FvOWRIo7D"},"outputs":[],"source":["\"dataset = 'DEAP'\n","window=4\n","stride=2\n","sfreq=128\n","clf = \n","topFSColumnsRegressionRanking(dataset, window, stride, sfreq, clf, label, scale=False, pca=False, mutual_info = False, method='SelectKBest')"]},{"cell_type":"code","execution_count":null,"metadata":{"colab":{"base_uri":"https://localhost:8080/"},"id":"KBnw9mUoLbYK","outputId":"32eeecf7-250e-4276-cf1b-936b597b4813"},"outputs":[{"name":"stdout","output_type":"stream","text":["Requirement already satisfied: dit in /usr/local/lib/python3.7/dist-packages (1.2.3)\n","Requirement already satisfied: networkx in /usr/local/lib/python3.7/dist-packages (from dit) (2.5)\n","Requirement already satisfied: contextlib2 in /usr/local/lib/python3.7/dist-packages (from dit) (0.5.5)\n","Requirement already satisfied: debtcollector in /usr/local/lib/python3.7/dist-packages (from dit) (2.2.0)\n","Requirement already satisfied: boltons in /usr/local/lib/python3.7/dist-packages (from dit) (20.2.1)\n","Requirement already satisfied: scipy>=0.15.0 in /usr/local/lib/python3.7/dist-packages (from dit) (1.4.1)\n","Requirement already satisfied: prettytable in /usr/local/lib/python3.7/dist-packages (from dit) (2.1.0)\n","Requirement already satisfied: numpy>=1.11 in /usr/local/lib/python3.7/dist-packages (from dit) (1.19.5)\n","Requirement already satisfied: six>=1.4.0 in /usr/local/lib/python3.7/dist-packages (from dit) (1.15.0)\n","Requirement already satisfied: decorator>=4.3.0 in /usr/local/lib/python3.7/dist-packages (from networkx->dit) (4.4.2)\n","Requirement already satisfied: wrapt>=1.7.0 in /usr/local/lib/python3.7/dist-packages (from debtcollector->dit) (1.12.1)\n","Requirement already satisfied: pbr!=2.1.0,>=2.0.0 in /usr/local/lib/python3.7/dist-packages (from debtcollector->dit) (5.5.1)\n","Requirement already satisfied: wcwidth in /usr/local/lib/python3.7/dist-packages (from prettytable->dit) (0.2.5)\n","Requirement already satisfied: importlib-metadata; python_version < \"3.8\" in /usr/local/lib/python3.7/dist-packages (from prettytable->dit) (3.8.1)\n","Requirement already satisfied: zipp>=0.5 in /usr/local/lib/python3.7/dist-packages (from importlib-metadata; python_version < \"3.8\"->prettytable->dit) (3.4.1)\n","Requirement already satisfied: typing-extensions>=3.6.4; python_version < \"3.8\" in /usr/local/lib/python3.7/dist-packages (from importlib-metadata; python_version < \"3.8\"->prettytable->dit) (3.7.4.3)\n","Requirement already satisfied: pyinform in /usr/local/lib/python3.7/dist-packages (0.2.0)\n","Requirement already satisfied: numpy in /usr/local/lib/python3.7/dist-packages (from pyinform) (1.19.5)\n"]}],"source":["!pip install dit\n","!pip install pyinform"]},{"cell_type":"code","execution_count":null,"metadata":{"colab":{"base_uri":"https://localhost:8080/"},"id":"xHpS6j8i7pux","outputId":"48595c24-38fc-4f40-e14b-f95423352128"},"outputs":[{"name":"stdout","output_type":"stream","text":["/usr/local/lib/python3.7/dist-packages/statsmodels/tools/_testing.py:19: FutureWarning: pandas.util.testing is deprecated. Use the functions in the public API at pandas.testing instead.\n"," import pandas.util.testing as tm\n","{'dataset': 'DEAP', 'window': 4, 'stride': 2, 'sfreq': 128, 'model': 'svc', 'label': 0, 'approach': 'byfs', 'ml_algo': 'classification', 'top': 'e', 'fs_method': 'SelectKBest'}\n","tcmalloc: large alloc 2202009600 bytes == 0x55e0ddee8000 @ 0x7f4af27791e7 0x7f4af033946e 0x7f4af0389c7b 0x7f4af033cce8 0x55e0d9cabc25 0x55e0d9c6c8e9 0x55e0d9ce0ade 0x55e0d9cdab0e 0x55e0d9c6d77a 0x55e0d9cdc86a 0x55e0d9c6f72b 0x55e0d9cb05e9 0x55e0d9cb055c 0x55e0d9d53e59 0x55e0d9cdbfad 0x55e0d9cdab0e 0x55e0d9c6d77a 0x55e0d9cdc86a 0x55e0d9cdab0e 0x55e0d9cda813 0x55e0d9da4592 0x55e0d9da490d 0x55e0d9da47b6 0x55e0d9d7c103 0x55e0d9d7bdac 0x7f4af1563bf7 0x55e0d9d7bc8a\n","Number of segments are: 38400\n","Number of Feature-Columns: 392\n","\n","y.shape: (38400,)\n","Classes: [0 1]\n","/usr/local/lib/python3.7/dist-packages/sklearn/feature_selection/_univariate_selection.py:114: UserWarning: Features [126 127 128 129 130 131 132 133 134 135 136 137 138 139 252 253 254 255\n"," 256 257 258 259 260 261 262 263 264 265 266 267 268 269 270 271 272 274\n"," 275 276 277 278 279 280 281 282 283 284 285 286 288 289 290 291 292 293] are constant.\n"," UserWarning)\n"," Specs Score\n","218 numBursts07 157.064777\n","217 shortSpikeNum07 134.765995\n","231 HjorthComp08 126.479923\n","203 HjorthComp07 124.343655\n","100 stdDev03 92.928424\n",".. ... ...\n","368 ShannonRes_beta13 0.003647\n","112 shannonEntropy04 0.001146\n","71 bandPwr_gamma02 0.000966\n","371 HjorthComp13 0.000658\n","343 HjorthComp12 0.000070\n","\n","[338 rows x 2 columns]\n","[7, 8, 3, 4, 11, 6, 0, 2, 1, 13, 12, 5, 10, 9]\n","[7]\n","(38400, 28)\n","Number of Feature-Columns: 28\n","\n","Features Ready for undergoing selection tests done ...\n","\n","..........................\n","Warning: using -h 0 may be faster\n","*.........*\n","optimization finished, #iter = 35705\n","obj = -25846.263031, rho = -0.082983\n","nSV = 26379, nBSV = 25693\n","Total nSV = 26379\n","[LibSVM]electrode: 12 window: 4, stide: 2, Accuracy: 56.95312500000001\n","[7, 8, 3, 4, 11, 6, 0, 2, 1, 13, 12, 5, 10, 9]\n","[7, 8]\n","(38400, 56)\n","Number of Feature-Columns: 56\n","\n","Features Ready for undergoing selection tests done ...\n","\n","....................*....*\n","optimization finished, #iter = 24829\n","obj = -24968.207078, rho = -0.066837\n","nSV = 26141, nBSV = 25249\n","Total nSV = 26141\n","[LibSVM]electrode: 12 window: 4, stide: 2, Accuracy: 59.114583333333336\n","[7, 8, 3, 4, 11, 6, 0, 2, 1, 13, 12, 5, 10, 9]\n","[7, 8, 3]\n","(38400, 84)\n","Number of Feature-Columns: 84\n","\n","Features Ready for undergoing selection tests done ...\n","\n","....................*.....*\n","optimization finished, #iter = 25213\n","obj = -23998.894844, rho = -0.122217\n","nSV = 25711, nBSV = 24437\n","Total nSV = 25711\n","[LibSVM]electrode: 12 window: 4, stide: 2, Accuracy: 61.653645833333336\n","[7, 8, 3, 4, 11, 6, 0, 2, 1, 13, 12, 5, 10, 9]\n","[7, 8, 3, 4]\n","(38400, 112)\n","Number of Feature-Columns: 112\n","\n","Features Ready for undergoing selection tests done ...\n","\n",".....................*.....*\n","optimization finished, #iter = 26092\n","obj = -23155.456157, rho = 0.004536\n","nSV = 25484, nBSV = 23781\n","Total nSV = 25484\n","[LibSVM]electrode: 12 window: 4, stide: 2, Accuracy: 62.734375\n","[7, 8, 3, 4, 11, 6, 0, 2, 1, 13, 12, 5, 10, 9]\n","[7, 8, 3, 4, 11]\n","(38400, 140)\n","Number of Feature-Columns: 140\n","\n","Features Ready for undergoing selection tests done ...\n","\n",".....................*.....*\n","optimization finished, #iter = 26774\n","obj = -22820.481866, rho = 0.010221\n","nSV = 25463, nBSV = 23525\n","Total nSV = 25463\n","[LibSVM]electrode: 12 window: 4, stide: 2, Accuracy: 63.294270833333336\n","[7, 8, 3, 4, 11, 6, 0, 2, 1, 13, 12, 5, 10, 9]\n","[7, 8, 3, 4, 11, 6]\n","(38400, 168)\n","Number of Feature-Columns: 168\n","\n","Features Ready for undergoing selection tests done ...\n","\n",".....................*.....*\n","optimization finished, #iter = 26635\n","obj = -22540.518790, rho = 0.003929\n","nSV = 25397, nBSV = 23312\n","Total nSV = 25397\n","[LibSVM]electrode: 12 window: 4, stide: 2, Accuracy: 63.658854166666664\n","[7, 8, 3, 4, 11, 6, 0, 2, 1, 13, 12, 5, 10, 9]\n","[7, 8, 3, 4, 11, 6, 0]\n","(38400, 196)\n","Number of Feature-Columns: 196\n","\n","Features Ready for undergoing selection tests done ...\n","\n",".....................*......*\n","optimization finished, #iter = 27290\n","obj = -22327.103158, rho = -0.008134\n","nSV = 25400, nBSV = 23091\n","Total nSV = 25400\n","[LibSVM]electrode: 12 window: 4, stide: 2, Accuracy: 63.502604166666664\n","[7, 8, 3, 4, 11, 6, 0, 2, 1, 13, 12, 5, 10, 9]\n","[7, 8, 3, 4, 11, 6, 0, 2]\n","(38400, 224)\n","Number of Feature-Columns: 224\n","\n","Features Ready for undergoing selection tests done ...\n","\n",".....................*......*\n","optimization finished, #iter = 27212\n","obj = -22009.869832, rho = 0.016833\n","nSV = 25303, nBSV = 22835\n","Total nSV = 25303\n","[LibSVM]electrode: 12 window: 4, stide: 2, Accuracy: 64.7265625\n","[7, 8, 3, 4, 11, 6, 0, 2, 1, 13, 12, 5, 10, 9]\n","[7, 8, 3, 4, 11, 6, 0, 2, 1]\n","(38400, 252)\n","Number of Feature-Columns: 252\n","\n","Features Ready for undergoing selection tests done ...\n","\n",".....................*.....*\n","optimization finished, #iter = 26639\n","obj = -21857.152850, rho = 0.049968\n","nSV = 25279, nBSV = 22768\n","Total nSV = 25279\n","[LibSVM]electrode: 12 window: 4, stide: 2, Accuracy: 64.53125\n","[7, 8, 3, 4, 11, 6, 0, 2, 1, 13, 12, 5, 10, 9]\n","[7, 8, 3, 4, 11, 6, 0, 2, 1, 13]\n","(38400, 280)\n","Number of Feature-Columns: 280\n","\n","Features Ready for undergoing selection tests done ...\n","\n",".....................*......*\n","optimization finished, #iter = 27412\n","obj = -21698.278340, rho = 0.079473\n","nSV = 25245, nBSV = 22551\n","Total nSV = 25245\n","[LibSVM]electrode: 12 window: 4, stide: 2, Accuracy: 65.0\n","[7, 8, 3, 4, 11, 6, 0, 2, 1, 13, 12, 5, 10, 9]\n","[7, 8, 3, 4, 11, 6, 0, 2, 1, 13, 12]\n","(38400, 308)\n","Number of Feature-Columns: 308\n","\n","Features Ready for undergoing selection tests done ...\n","\n","......................*.....*\n","optimization finished, #iter = 27437\n","obj = -21516.258218, rho = 0.097014\n","nSV = 25234, nBSV = 22379\n","Total nSV = 25234\n","[LibSVM]electrode: 12 window: 4, stide: 2, Accuracy: 65.13020833333333\n","[7, 8, 3, 4, 11, 6, 0, 2, 1, 13, 12, 5, 10, 9]\n","[7, 8, 3, 4, 11, 6, 0, 2, 1, 13, 12, 5]\n","(38400, 336)\n","Number of Feature-Columns: 336\n","\n","Features Ready for undergoing selection tests done ...\n","\n",".....................*......*.*\n","optimization finished, #iter = 27947\n","obj = -21421.851881, rho = 0.091220\n","nSV = 25256, nBSV = 22285\n","Total nSV = 25256\n","[LibSVM]electrode: 12 window: 4, stide: 2, Accuracy: 65.57291666666667\n","[7, 8, 3, 4, 11, 6, 0, 2, 1, 13, 12, 5, 10, 9]\n","[7, 8, 3, 4, 11, 6, 0, 2, 1, 13, 12, 5, 10]\n","(38400, 364)\n","Number of Feature-Columns: 364\n","\n","Features Ready for undergoing selection tests done ...\n","\n","."]}],"source":["!python3 feature_select_main.py --dataset DEAP --window 4 --stride 2 --sfreq 128 --model svc --label 0 --approach byfs --ml_algo classification --top e --fs_method SelectKBest"]},{"cell_type":"markdown","metadata":{"id":"uto-eJqh4SGK"},"source":["* 5. Hjorth Mobility\n","* 6. Hjorth Complexity\n","* _. Bin Power\n","* 14. Lyapunov \n","* 18. FFT + Entropy\n","* 17. Shanon Entropy\n"]},{"cell_type":"markdown","metadata":{"id":"tfeOYa-942TV"},"source":["Features mentioned in Frontiers Paper :\n","\n","Time-frequency domain features\n","1. Peak-Peak Mean.\n","2. Mean Square Value.\n","3. Variance.\n","4. Hjorth Parameter: Activity.\n","5. Hjorth Parameter: Mobility.\n","6. Hjorth Parameter: Complexity.\n","7. Maximum Power Spectral Frequency.\n","8. Maximum Power Spectral Density.\n","9. Power Sum.\n","\n","Non-linear dynamical system features\n","10. Approximate Entropy.\n","11. C0 Complexity.\n","12. Correlation Dimension.\n","13. Kolmogorov Entropy.\n","14. Lyapunov Exponent.\n","15. Permutation Entropy.\n","16. Singular Entropy. \n","17. Shannon Entropy.\n","18. Spectral Entropy\n","\n"]},{"cell_type":"code","execution_count":null,"metadata":{"id":"Rs_VfY98J5s2"},"outputs":[],"source":["# %cd /gdrive/MyDrive/Project_DEAP/pyeeg/\n","# !python setup.py install"]},{"cell_type":"code","execution_count":null,"metadata":{"colab":{"base_uri":"https://localhost:8080/"},"id":"wQJhCGfaqaws","outputId":"1f37138e-ba70-4449-b311-bd97395f67b6"},"outputs":[{"name":"stdout","output_type":"stream","text":["/content\n"]}],"source":["!pwd"]},{"cell_type":"code","execution_count":null,"metadata":{"colab":{"base_uri":"https://localhost:8080/"},"id":"gI4tJXA7x5eG","outputId":"cb4a88d5-9c5e-47c0-e892-272b672a3951"},"outputs":[{"name":"stdout","output_type":"stream","text":["Mounted at /gdrive\n"]}],"source":["from google.colab import drive\n","drive.mount('/gdrive',force_remount=True)"]},{"cell_type":"code","execution_count":null,"metadata":{"colab":{"base_uri":"https://localhost:8080/"},"id":"ymP-LcS8ArJh","outputId":"1305387f-bd50-4efb-eb7a-2b402e00bdc8"},"outputs":[{"name":"stdout","output_type":"stream","text":["/gdrive/MyDrive/Project_DEAP\n"]}],"source":["%cd /gdrive/MyDrive/Project_DEAP/"]},{"cell_type":"code","execution_count":null,"metadata":{"colab":{"base_uri":"https://localhost:8080/"},"id":"leBHFAGfjRfz","outputId":"49d0756a-78f2-406b-b884-89c3b0041af8"},"outputs":[{"name":"stdout","output_type":"stream","text":[" args_eeg.py\t\t\t\t feature_select_main.py\n"," catboost_info\t\t\t\t fnins-12-00162.pdf\n","'Copy of dataset_loading.ipynb'\t\t ImportUtils.py\n","'Copy of HACKANONS COLAB 25GB RAM.ipynb' Miniconda3-4.5.4-Linux-x86_64.sh\n"," data_extracted\t\t\t\t OASIS\n"," DEAP\t\t\t\t\t Oasis_Colab.ipynb\n"," DEAP_Colab.ipynb\t\t\t out\n"," DEAP_data_preprocessed_python\t\t Preprocess_Deap.ipynb\n"," DEAP_feature_extraction.ipynb\t\t __pycache__\n"," ecbyc.ipynb\t\t\t\t pyeeg\n"," EEGExtract.py\t\t\t\t rapidsai-csp-utils\n"," ElectrodeClassificationRanking.py\t results.txt\n"," ElectrodeRegressionRanking.py\t\t TopElectrodeClassificationRanking.py\n"," EpochedFeatures.py\t\t\t TopElectrodeRegressionRanking.py\n"," example.ipynb\t\t\t\t TopFeaturesClassificationRanking.py\n"," fcbyc.ipynb\t\t\t\t TopFeaturesRegressionRanking.py\n"," FeatureClassificationRanking.py\t TopNByClassifier.py\n"," FeatureRegressionRanking.py\t\t TopNByFSMethods.py\n"]}],"source":["!ls"]},{"cell_type":"code","execution_count":null,"metadata":{"id":"MEqMhFdXBBMd"},"outputs":[],"source":["import os\n","import glob\n","from scipy import io,signal\n","import numpy as np\n","import pandas as pd\n","from sklearn import preprocessing"]},{"cell_type":"code","execution_count":null,"metadata":{"id":"CCXRYJhuBDJE"},"outputs":[],"source":["# '''\n","# Defining a general class for loading datasets\n","# name - Parameter to decide which dataset is loaded\n","# '''\n","# class Dataset:\n","# def __init__(self,name):\n","# '''\n","# Constructor\n","# X = (no_of_trials X no_of_samples X no_of_channels)\n","# Y = (no_of_trial X 2) - Valence and arousal score respectively\n","# Z = (no_of_trials X trial_id)\n","# data_ids = videos shown for trials, vary across datasets\n","# gyroscope = gyroscope data if present\n","# name_of_dataset = name of dataset we want to load\n","# channels = channel names we are using\n","# '''\n","# self.X=None\n","# self.Y=None\n","# self.Z=None\n","# self.data_ids = None\n","# self.gyroscope = None\n","# self.channels = None\n","# self.sfreq = None \n","# self.epoched_array = None\n","# self.name_of_dataset = name\n","\n"," \n","# def load_dataset(self):\n","# '''\n","# based on datatset chosen, corresponding data is returned\n","# '''\n","# if self.name_of_dataset == 'DREAMER':\n","# self.X,self.Y,self.Z = self.load_dreamer_dataset()\n","\n","# if self.name_of_dataset == 'ENTERFACE':\n","# self.X,self.Y,self.Z = self.load_enterface_dataset()\n","\n","# if self.name_of_dataset == 'DEAP':\n","# self.X,self.Y,self.Z = self.load_deap_dataset()\n","\n","# if self.name_of_dataset == 'OASIS':\n","# self.X,self.Y,self.Z = self.load_oasis_dataset()\n"," \n","\n","# return self.X,self.Y,self.Z,self.gyroscope\n","\n","# def load_dreamer_dataset(self):\n","# '''\n","# Dreamer dataset has 23 subjects, each subject was shown 18 videos \n","# Data was recorded using 14 EEG electrodes\n","# No of samples across was different for each video, so data was upsampled to sample points which was maximum of all, here 50432\n","# 7808 baseline points for each video\n","# Therefore:\n","# X = (23*18,7808+54032,14)\n","# Y = (23*18,2)\n","# Z = (23*18,2)\n","# '''\n","# #initializing variables\n","# self.Z = np.zeros((23*18,2))\n","# self.Y = np.zeros((23*18,2))\n","# self.X = np.empty((0, 7808+50432 ,14))\n","# self.sfreq = 128\n","\n","# #load mat file for data\n","# dreamer_file = 'DREAMER/DREAMER.mat'\n","# dreamer_data = io.loadmat(dreamer_file)\n","\n","# self.data_ids = {0:\"Searching for Bobby Fischer\",\n","# 1:\" D.O.A\",\n","# 2:\"The Hangover\",\n","# 3:\"The Ring\",\n","# 4:\"300\",\n","# 5:\"National Lampoons Vanwilder\",\n","# 6:\"Wall-E\",\n","# 7:\"Crash\",\n","# 8:\"My Girl\",\n","# 9:\"The Fly\",\n","# 10:\"Pride and Prejudice\",\n","# 11:\"Modern Times\",\n","# 12:\"Remember the Titans\",\n","# 13:\"Gentleman's Agreement\",\n","# 14:\"Pyscho\",\n","# 15:\"The Bourne Identity\",\n","# 16:\"The Shawshank Redemption\",\n","# 17:\"The Departed\"\n","# }\n","# self.channels = ['AF3','F7','F3','FC5','T7','P7','O1','O2','P8','T8','FC6','F4','F8','AF4']\n","\n","# #extract data froom .mat file to fill up X,Y,Z\n","# for participant in range(0,23):\n","# for video_number in range(0,18):\n","# self.Y[18*participant+video_number,0] = dreamer_data['DREAMER'][0,0]['Data'][0,participant]['ScoreValence'][0,0][video_number][0]\n","# self.Y[18*participant+video_number,1] = dreamer_data['DREAMER'][0,0]['Data'][0,participant]['ScoreArousal'][0,0][video_number][0]\n","# self.Z[18*participant+video_number,0] = participant\n","# self.Z[18*participant+video_number,1] = video_number\n","\n","# number_of_samples = dreamer_data['DREAMER'][0,0]['Data'][0,participant]['EEG'][0,0]['stimuli'][0,0][video_number,0].shape[0]\n","# if number_of_samples != 50432:\n","# temp = signal.resample(\n","# x = dreamer_data['DREAMER'][0,0]['Data'][0,participant]['EEG'][0,0]['stimuli'][0,0][video_number,0],\n","# num = 50432\n","# )\n","# else:\n","# temp = dreamer_data['DREAMER'][0,0]['Data'][0,participant]['EEG'][0,0]['stimuli'][0,0][video_number,0]\n","# baseline = dreamer_data['DREAMER'][0,0]['Data'][0,participant]['EEG'][0,0]['baseline'][0,0][video_number,0]\n","# row = np.concatenate((baseline,temp),axis=0)\n","# row=np.expand_dims(row,axis=0)\n"," \n","# self.X=np.append(self.X,row,axis=0)\n","\n","# return self.X,self.Y,self.Z\n","\n","# def load_enterface_dataset(self):\n","# pass\n","\n"," \n","# def load_oasis_dataset(self):\n","# '''\n","# Oasis dataset has <> subjects , each subject was shown 40 images,each image lasting for 5 sec, \n","# for each image their arousal and valence score was noted\n","# X = (40 * <>,640,14)\n","# Y = (40 * <>,2)\n","# Z = (40 * <>,2)\n","# '''\n"," \n","# #channels used\n","# self.channels = ['AF3', 'F7', 'F3', 'FC5', 'T7', 'P7', 'O1', 'O2', 'P8', 'T8', 'FC6', 'F4', 'F8', 'AF4']\n","# self.sfreq = 128\n","\n","# #data labels of images used in used\n","# self.data_ids = {0:'Animal carcass 5',\n","# 1:'Astronaut 1',\n","# 2:'Bored pose 3',\n","# 3:'Car crash 1',\n","# 4:'Dead bodies 3',\n","# 5:'Destruction 9',\n","# 6:'Dog 26',\n","# 7:'Dog 5',\n","# 8:'Fence 1',\n","# 9:'Fire 5',\n","# 10:'Fire 7',\n","# 11:'Fireworks 2',\n","# 12:'Fireworks 3',\n","# 13:'Flowers 6',\n","# 14:'Food 2',\n","# 15:'Food 3',\n","# 16:'Galaxy 8',\n","# 17:'Garbage dump 7',\n","# 18:'Grass 3',\n","# 19:'KKK rally 1',\n","# 20:'Lake 12',\n","# 21:'Nature 1',\n","# 22:'Nude couple 1',\n","# 23:'Nude woman 13',\n","# 24:'Pinecone 3',\n","# 25:'Present 1',\n","# 26:'Prison 2',\n","# 27:'Rollercoaster 1',\n","# 28:'Roofing 2',\n","# 29:'Rugby 1',\n","# 30:'Snow 4',\n","# 31:'Solar panel 1',\n","# 32:'Statue 1',\n","# 33:'Toilet 2',\n","# 34:'Toilet 4',\n","# 35:'Tornado 1',\n","# 36:'War 1',\n","# 37:'War 8',\n","# 38:'Wedding 12',\n","# 39:'Yoga 2'}\n","\n","# #getting file names\n","# filenames = glob.glob('EEG */*.mat')\n","# filenames_gyroscope = glob.glob('EEG */*.bp.csv')\n","# #initializing arrays \n","# self.X = np.empty((0,640,14))\n","# self.Y = np.empty((0,2))\n","# self.Z = np.zeros((40*len(filenames),2))#trial number vs image id used\n","# self.gyroscope = np.empty((0,640,7))\n","\n","# for (subject,file) in enumerate(filenames):\n","# data = io.loadmat(files)\n","# EEG_data = data['raw_eeg']\n","# ratings = data['ratings']\n","# #print(EEG_data.shape)\n","# self.X = np.append(self.X,EEG_data,axis=0)\n","# self.Y = np.append(self.Y,ratings,axis=0)\n","# for trial in range(40):\n","# self.Z[40*subject+trial,0] = 40*subject+trial\n","# self.Z[40*subject+trial,1] = trial\n","# for (subject,file) in enumerate(filenames_gyroscope):\n","# mycols = ['Timestamp','MOT.Q0','MOT.Q1','MOT.Q2','MOT.AccX','MOT.AccY','MOT.AccZ']\n","# data = pd.read_csv(file,header=1,usecols=mycols)\n","# data=data.dropna()\n","# gyro = signal.resample(x = data.to_numpy(),num = 25600 )\n","# self.gyroscope = np.append(self.gyroscope,gyro,axis=0)\n","\n","\n","# return self.X,self.Y,self.Z,self.gyroscope\n","\n","# def load_deap_dataset(self):\n","# '''\n","# Deap dataset has 32 subjects, each subject was shown 40 videos \n","# Data was recorded using 40 EEG electrode edf format headset\n","# 8064 timepoints for each video i.e. 63seconds @128Hz\n","# Therefore:\n","# X = (32*40,40,8064)\n","# Y = (32*40,4)\n","# Z = (32*40,2)\n","# '''\n"," \n","# #initializing variables\n","# self.Z = np.zeros((32*40,2))\n","# self.Y = np.zeros((32*40,4))\n","# self.X = np.zeros((32*40,40,8064))\n","# self.channels = ['F1', 'AF3', 'F3', 'F7', 'FC5', 'FC1', 'C3', 'T7', 'CP5', 'CP1', 'P3', 'P7', 'PO3', 'O1', 'Oz', 'Pz', 'Fp2', 'AF4', 'Fz', 'F4', 'F8', 'FC6', 'FC2', 'Cz', 'C4', 'T8', 'CP6', 'CP2', 'P4', 'P8', 'PO4', 'O2', 'hEOG','vEOG', 'zEMG','tEMG','GSR','Respiration belt','Plethysmograph','Temperature']\n","# self.sfreq = 128\n","# pwd = os.getcwd()\n","# # Load the data\n","# #pwd = os.getcwd()\n","# data_dir =pwd+'/DEAP_data_preprocessed_python/'\n","# dataFiles = os.listdir(data_dir)\n","# dataFiles.reverse() \n"," \n"," \n","# # labels = np.empty((0,4), dtype=np.float32)\n","# # dataframes = np.empty((32, 40, 40, 8064), dtype=np.float32)\n","# # labels = np.empty((32, 40, 4), dtype=np.float32)\n"," \n","# # fileData[b'labels'] shape = [40,4]\n","# # fileData[b'data'] shape = [40,40,8064]\n"," \n","# for participant in range(0,32):\n","# filePath = os.path.join(data_dir, dataFiles[participant])\n","# fileData = np.load(filePath, encoding='bytes', allow_pickle=True)\n","# for video in range(0,40): \n","# print('participant: ', participant, 'video: ',video) #debug\n","# print(int(dataFiles[participant][1:3])) #debug \n","# self.Y[40*participant + video,:] = fileData[b'labels'][video,:]\n","# self.Z[40*participant + video,0] = int(dataFiles[participant][1:3])\n","# self.Z[40*participant + video,1] = video\n","# self.X[40*participant + video,:,:] = fileData[b'data'][video,:,:]\n","# # a,b,c = self.X.shape\n","# # self.X = np.reshape(self.X,(a,c,b)) \n","# return self.X,self.Y,self.Z\n"," \n","# def get_data_ids(self):\n","# '''\n","# Get labels of stimulus used \n","# '''\n","# print(self.data_ids)\n","\n","# def get_channel_name(self):\n","# '''\n","# Get EEG channel names\n","# '''\n","# print(self.channels)\n","\n","# def get_data(self):\n","# '''\n","# Returns data matrix if loaded\n","# '''\n","# if self.X == None:\n","# print('Data not loaded yet')\n","# else:\n","# return self.X,Self.Y,self.Z,self.gyroscope\n","\n","# def get_segmented_data(self,segment_size):\n","# pwd = os.getcwd()\n","# '''\n","# Get epoched data from the loaded data matrix\n","# segment_size = time of each segment we want in seconds\n","# For eg if trial is of 3 seconds,we want epochs of 1 sec,we will give segment size as 1 sec\n","# '''\n","# data = self.X\n","# no_of_channels = X.shape[2]\n","# timepoints_per_trial = X.shape[1]\n","# sfreq = 128\n","# segment_size = 1\n","# number_of_timepoints = int(sfreq * segment_size)#no of time points per epoch\n","# number_of_epochs = timepoints_per_trial//number_of_timepoints\n","# epoched_array = np.empty((0,number_of_timepoints,no_of_channels))\n","# print('total_trials:',data.shape[1])\n","# for trial in range(data.shape[1]):\n","# print('trial no:',trial)\n","# for epoch in range(number_of_epochs-1):\n","# a = data[trial,epoch*number_of_timepoints:number_of_timepoints*(epoch+1),:]\n","# a = np.expand_dims(a,axis=0)\n","# epoched_array = np.append(epoched_array,a,axis=0)\n","# a = data[trial,(number_of_epochs-1)*number_of_timepoints:,:]\n","# a = np.expand_dims(a,axis=0)\n","# epoched_array = np.append(epoched_array,a,axis=0)\n","# self.epoched_array = epoched_array\n","# print('epoched array is of shape',epoched_array.shape)\n"," \n","# def save_arrays(self):\n","# pwd = os.getcwd()\n","# if self.name_of_dataset == 'DREAMER':\n","# np.savez('data_extracted/DREAMER',X=self.X,Y=self.Y,Z=self.Z)\n","# if self.name_of_dataset == 'OASIS':\n","# np.savez('data_extracted/OASIS',X=self.X,Y=self.Y,Z=self.Z,gyroscope=self.gyroscope)\n","# if self.name_of_dataset == 'DEAP':\n","# np.savez(pwd+'/'+'data_extracted/DEAP',X=self.X,Y=self.Y,Z=self.Z)\n"," "]},{"cell_type":"code","execution_count":null,"metadata":{"id":"welDSRJNeOUh"},"outputs":[],"source":["# def driver_code():\n","# # from google.colab import drive\n","# # drive.mount('/gdrive',force_remount=True)\n","# maps = { 0 : 'DREAMER',1:'DEAP',2:'OASIS'}\n","# print(maps)\n","# print('Enter number according to mapping id')\n","# key = int(input())\n","# dataset = Dataset(maps[key])\n","# X,Y,Z,_=dataset.load_dataset()\n","# dataset.save_arrays()\n"]},{"cell_type":"code","execution_count":null,"metadata":{"id":"7BF2BDV6ePz7"},"outputs":[],"source":["# driver_code()"]},{"cell_type":"code","execution_count":null,"metadata":{"colab":{"base_uri":"https://localhost:8080/"},"id":"iR1PVIjDBPJf","outputId":"618f27a3-3e6d-4517-dc2e-c171d0dcc7a7"},"outputs":[{"name":"stdout","output_type":"stream","text":["/gdrive/MyDrive/Project_DEAP\n"]}],"source":["!pwd"]},{"cell_type":"code","execution_count":null,"metadata":{"colab":{"base_uri":"https://localhost:8080/"},"id":"axGt_Lug-xC_","outputId":"cdecd979-ffca-4df9-97ea-3f8389440053"},"outputs":[{"name":"stdout","output_type":"stream","text":["/gdrive/MyDrive/Project_DEAP\n","dict_keys(['X', 'Y', 'Z'])\n"]}],"source":["%cd /gdrive/MyDrive/Project_DEAP/\n","X = None\n","Y = None\n","Z = None\n","with np.load('data_extracted/DEAP.npz', allow_pickle=True) as data:\n"," X = data['X']\n"," Y = data['Y']\n"," Z = data['Z']\n"," print(dict(data).keys())"]},{"cell_type":"code","execution_count":null,"metadata":{"colab":{"base_uri":"https://localhost:8080/"},"id":"uTByzpBXUokX","outputId":"85ca7313-c13e-467c-8d27-1c479f408f83"},"outputs":[{"data":{"text/plain":["(1280, 40, 8064)"]},"execution_count":11,"metadata":{"tags":[]},"output_type":"execute_result"}],"source":["X.shape"]},{"cell_type":"code","execution_count":null,"metadata":{"id":"6nv3pza7NGBp"},"outputs":[],"source":["def select_channels(data,channels):\n"," ans = np.empty((data.shape[0],len(channels),data.shape[2]))\n"," for sub in range(data.shape[0]):\n"," ans[sub,:,:] = np.array([data[sub,x,:] for x in channels])\n"," return ans"]},{"cell_type":"code","execution_count":null,"metadata":{"id":"wAzXwmUZSWHU"},"outputs":[],"source":["ch_names = ['F1', 'AF3', 'F3', 'F7', 'FC5', 'FC1', 'C3', 'T7', 'CP5', 'CP1', 'P3', 'P7', 'PO3', 'O1', 'Oz', 'Pz', 'Fp2', 'AF4', 'Fz', 'F4', 'F8', 'FC6', 'FC2', 'Cz', 'C4', 'T8', 'CP6', 'CP2', 'P4', 'P8', 'PO4', 'O2', 'hEOG','vEOG', 'zEMG','tEMG','GSR','Respiration belt','Plethysmograph','Temperature']\n","emotiv_channels = ['AF3', 'F3', 'F7', 'FC5', 'T7', 'P7', 'O1','AF4','F4','F8','FC6','T8','P8','O2']\n","index_arr = [ch_names.index(x) for x in emotiv_channels]\n","# print(index_arr)\n","X_new = select_channels(X,index_arr)"]},{"cell_type":"code","execution_count":null,"metadata":{"id":"y2JMvc0LT5Jt"},"outputs":[],"source":["X_new.shape\n","del X"]},{"cell_type":"code","execution_count":null,"metadata":{"colab":{"base_uri":"https://localhost:8080/"},"id":"7kzL1wAVJvlJ","outputId":"1ca2793b-9b1f-4467-a702-e2bdcd75e43a"},"outputs":[{"name":"stdout","output_type":"stream","text":["(1280, 14, 8064) (1280, 4) (1280, 2)\n"]}],"source":["'''\n","# X = (32*40,40,8064)\n","# Y = (32*40,4)\n","# Z = (32*40,2)\n","\n","# X : (nbSegments, nbChannel, nbTimepoints) : Data\n","# Y : (nbSegments, nbEmotions) : Valence and arousal data\n","# Z : (nbSegments, 2) : Participant number, and session number\n","'''\n","print(X_new.shape, Y.shape, Z.shape)"]},{"cell_type":"markdown","metadata":{"id":"VfzcjPEgqE5L"},"source":["Preprocessing "]},{"cell_type":"code","execution_count":null,"metadata":{"colab":{"base_uri":"https://localhost:8080/","height":35},"id":"fKxVUHmdPtZV","outputId":"901ff88c-2098-4239-f469-e49ca0fb0eda"},"outputs":[{"data":{"application/vnd.google.colaboratory.intrinsic+json":{"type":"string"},"text/plain":["'\\nPreprocessing already done since deap_preprocessed data\\npreprocessing()\\n'"]},"execution_count":16,"metadata":{"tags":[]},"output_type":"execute_result"}],"source":["'''\n","Preprocessing already done since deap_preprocessed data\n","preprocessing()\n","'''"]},{"cell_type":"markdown","metadata":{"id":"jubBXuXZXR4B"},"source":["Normalise Values along channel"]},{"cell_type":"code","execution_count":null,"metadata":{"id":"xGD9nuEDTTJg"},"outputs":[],"source":["def normalise(X):\n"," for video in range(X.shape[0]):\n"," for channel in range(X.shape[1]):\n"," min_max_scaler = preprocessing.MinMaxScaler()\n"," arr = X[video,channel,:].reshape(-1,1)\n"," X[video,channel,:] = np.reshape(min_max_scaler.fit_transform(arr), (X.shape[2],))\n"," \n"," return X"]},{"cell_type":"code","execution_count":null,"metadata":{"colab":{"base_uri":"https://localhost:8080/"},"id":"Jg1P7J3VYHi_","outputId":"d5285458-14f0-458f-a130-d3fe5ce6dfee"},"outputs":[{"data":{"text/plain":["array([[[ 1.24706590e-01, 1.39008270e+00, 1.83509881e+00, ...,\n"," -2.98702069e+00, -6.28780884e+00, -4.47429041e+00],\n"," [-2.21651099e+00, 2.29201682e+00, 2.74636923e+00, ...,\n"," -2.63707760e+00, -7.40651010e+00, -6.75590441e+00],\n"," [ 1.00573353e+00, 1.29792704e+00, 2.36768941e+00, ...,\n"," -4.58264935e+00, -4.40302396e+00, -1.37311781e+00],\n"," ...,\n"," [-1.87530218e+00, -4.46537134e+00, -4.63904055e+00, ...,\n"," 6.58638018e+00, 6.73638712e+00, 5.14613803e+00],\n"," [-3.55718623e+00, -1.26030574e+00, -3.09961982e+00, ...,\n"," 6.68885999e+00, 6.91232131e+00, 5.66901722e+00],\n"," [ 3.72270628e-01, 2.07619495e+00, 4.46521875e+00, ...,\n"," 2.23958511e+00, 3.18256605e+00, 4.70807159e+00]],\n","\n"," [[ 9.49186875e+00, 1.25897704e+01, 1.05740268e+01, ...,\n"," 6.03399490e+00, 9.06874552e+00, 8.74021419e+00],\n"," [ 7.12867480e+00, 1.22064700e+01, 9.49646701e+00, ...,\n"," 6.17971667e+00, 6.93374514e+00, 6.48086477e+00],\n"," [ 1.04775116e+01, 1.46462731e+01, 1.32421007e+01, ...,\n"," 6.48142142e+00, 6.93228111e+00, 1.27292433e+01],\n"," ...,\n"," [ 2.51166214e+00, 4.58227954e+00, 4.27800185e+00, ...,\n"," -6.85691545e+00, -3.44794537e+00, -4.59320215e+00],\n"," [-1.87240370e+00, -9.63711986e+00, -1.42706581e+01, ...,\n"," -1.99455613e+00, 1.02730199e+00, 1.63148018e+00],\n"," [-3.56203421e+00, -1.07756908e+01, -1.54682128e+01, ...,\n"," -6.23956064e-01, -5.42113073e+00, -9.12074717e+00]],\n","\n"," [[ 3.31725539e-02, -1.51860504e+00, 4.81957628e+00, ...,\n"," 3.98227619e-01, -1.06766739e+00, -5.90302866e+00],\n"," [ 7.22816236e-01, 5.12160665e-01, 8.04440282e+00, ...,\n"," 9.66963103e-02, 2.20454025e-01, -4.36041903e+00],\n"," [ 9.76375399e+00, 3.35348110e+00, 1.04513236e+01, ...,\n"," 5.86098064e-01, -1.82585594e-01, -4.29536828e+00],\n"," ...,\n"," [ 2.46216686e-01, 5.48670623e-01, -3.55688001e+00, ...,\n"," -2.26676593e+00, -3.41065872e+00, -2.69399187e+00],\n"," [-2.81080381e+00, -2.50530450e+00, -6.73732799e+00, ...,\n"," 2.35138961e+00, 8.75618636e-01, 4.95425282e+00],\n"," [ 4.75531691e+00, 8.47007492e+00, -8.84048792e-01, ...,\n"," 6.34267637e+00, 7.34487869e+00, 8.89437967e+00]],\n","\n"," ...,\n","\n"," [[-2.95597698e-01, 2.08624282e+01, 2.60877285e+01, ...,\n"," 6.89964886e+01, 1.05029737e+02, 1.27683921e+02],\n"," [-3.48383074e+00, 9.05882902e-01, 2.02910641e+00, ...,\n"," -3.96154034e-01, 2.96080445e+00, 9.07322049e+00],\n"," [-2.73657085e+00, 1.61630770e+01, 1.91202409e+01, ...,\n"," 7.40726923e+01, 1.08955733e+02, 1.34958582e+02],\n"," ...,\n"," [ 8.33895790e+00, 2.21566144e+01, 1.74150374e+01, ...,\n"," 7.66306730e+01, 1.10705961e+02, 1.32925569e+02],\n"," [ 4.04433914e+00, 1.26870165e+01, 1.58514278e+01, ...,\n"," 4.98427133e+01, 7.02301691e+01, 8.23254121e+01],\n"," [-9.49032024e-01, -9.21901230e+00, -1.03986446e+01, ...,\n"," -2.17637728e+01, -3.03359239e+01, -3.64187571e+01]],\n","\n"," [[-1.04061827e+01, -1.51137600e+01, -8.32298538e+00, ...,\n"," -1.57141917e+01, -1.33465851e+00, -4.62686962e+00],\n"," [-5.22864821e+00, -4.51723838e+00, -4.33921642e+00, ...,\n"," -1.30115134e+00, 7.83312824e-01, 1.98484754e+00],\n"," [-8.12682287e+00, -7.35532624e+00, -8.30673009e+00, ...,\n"," -1.60991788e+01, -8.01601573e+00, -5.27955253e+00],\n"," ...,\n"," [-3.76939820e+00, -4.47255161e+00, -5.29008791e+00, ...,\n"," -1.54615194e+01, -3.93925224e+00, -3.98565378e+00],\n"," [ 5.45741905e+00, 1.20933675e+00, -6.92999805e+00, ...,\n"," -3.84034973e+00, -4.47323525e+00, -1.13452052e+01],\n"," [ 6.84396587e+00, 7.10168247e+00, 4.17634118e+00, ...,\n"," 5.96169268e+00, -5.17013027e+00, -2.92741065e+00]],\n","\n"," [[-1.33526571e+00, 1.02596543e+01, 4.13355345e+00, ...,\n"," 1.16616992e+00, 4.28416586e+00, 3.96753581e+00],\n"," [ 4.64047983e+00, 3.34540854e+00, -9.51680496e-01, ...,\n"," -3.15826101e+00, -6.13620389e+00, -1.15622834e+01],\n"," [ 2.19572630e-01, 7.89785222e+00, -1.62480270e+00, ...,\n"," -9.24968458e+00, -4.00043605e+00, -1.80741559e+01],\n"," ...,\n"," [ 2.40664574e+00, -9.67300019e-02, 8.60529800e+00, ...,\n"," -2.25352636e+01, -1.26152308e+01, 8.92005537e+00],\n"," [-2.58873504e+00, 9.48929259e-02, -3.68716718e+00, ...,\n"," 4.71869145e+00, 7.85097279e+00, 9.54961737e+00],\n"," [-5.22661841e+00, 1.88709250e+00, -4.48953719e+00, ...,\n"," -3.56672365e+00, -3.64365220e+00, -1.09192368e+01]]])"]},"execution_count":18,"metadata":{"tags":[]},"output_type":"execute_result"}],"source":["# X = normalise(X)\n","X_new"]},{"cell_type":"markdown","metadata":{"id":"FpRXaA1Vqdxw"},"source":["Epoching Data"]},{"cell_type":"code","execution_count":null,"metadata":{"id":"D2AO15C-XgiF"},"outputs":[],"source":["# nth window end segment + (n-1)step\n","# 8064 = segment + (n-1)step\n","# => (8064-segment)/step + 1 = n"]},{"cell_type":"code","execution_count":null,"metadata":{"id":"ojex2jbHzHBK"},"outputs":[],"source":["def epoch_data(X, Y, Z, window, stride, sfreq):\n"," #X\n"," trials,channels,timepoints = X.shape #1280x40x8064 => 1280*40*63*128\n"," # print(X[1,:,x:x+128].shape)\n"," # return []\n"," segment = int(window*sfreq)\n"," step = int(stride*sfreq)\n"," epochPerTrial = int((timepoints-segment)/step + 1)\n","\n"," X_new = np.empty((trials*epochPerTrial,channels,segment))\n"," Y_new = np.empty((trials*epochPerTrial,4))\n"," Z_new = np.empty((trials*epochPerTrial,2))\n","\n"," count=0\n"," for trial in range(trials):\n"," for epoch in range(epochPerTrial):\n"," X_new[count,:,:] = X[trial,:,epoch*step:(epoch*step)+segment]\n"," Y_new[count,:] = Y[trial,:]\n"," Z_new[count,:] = Z[trial,:]\n"," count = count+1\n","\n"," #Y\n","\n","\n"," #Z \n"," return X_new, Y_new, Z_new"]},{"cell_type":"code","execution_count":null,"metadata":{"id":"JkrgBwPT5gwT"},"outputs":[],"source":["(X_epoch, Y_epoch, Z_epoch) = epoch_data( X_new, Y, Z,1,1,128)\n","del X_new\n","del Y\n","del Z"]},{"cell_type":"code","execution_count":null,"metadata":{"colab":{"base_uri":"https://localhost:8080/"},"id":"5FFv-mxXXcfM","outputId":"d958489a-8eaa-4024-efb5-48cfee1fb631"},"outputs":[{"name":"stdout","output_type":"stream","text":["(80640, 14, 128) (80640, 4) (80640, 2)\n"]}],"source":["print(X_epoch.shape, Y_epoch.shape, Z_epoch.shape)"]},{"cell_type":"code","execution_count":null,"metadata":{"id":"Ayyk7Up8Ej4h"},"outputs":[],"source":["# 1280*63,40,128\n","# trial, channel, segment\n","trials, channels, segment = X_epoch.shape\n","ans = np.empty((channels, segment, trials)) #[chans x ms x epochs] \n","for i in range(trials):\n"," ans[:,:,i] = X_epoch[i,:,]\n","del X_epoch"]},{"cell_type":"code","execution_count":null,"metadata":{"colab":{"base_uri":"https://localhost:8080/"},"id":"pukxi28nfzXI","outputId":"287bfaf5-3125-483e-d660-e549409da252"},"outputs":[{"data":{"text/plain":["(14, 128, 80640)"]},"execution_count":23,"metadata":{"tags":[]},"output_type":"execute_result"}],"source":["ans.shape"]},{"cell_type":"code","execution_count":null,"metadata":{"id":"ZJ_YvkwKB-9d"},"outputs":[],"source":["pwd = os.getcwd()\n","np.savez(pwd + \"/data_extracted/DEAP_epoched_data\",ans,Y_epoch, Z_epoch)"]},{"cell_type":"code","execution_count":null,"metadata":{"colab":{"base_uri":"https://localhost:8080/"},"id":"daASAbN4gdbi","outputId":"b9ac0560-5387-4069-edb3-9be4cf3ac947"},"outputs":[{"name":"stdout","output_type":"stream","text":["Requirement already satisfied: dit in /usr/local/lib/python3.7/dist-packages (1.2.3)\n","Requirement already satisfied: prettytable in /usr/local/lib/python3.7/dist-packages (from dit) (2.1.0)\n","Requirement already satisfied: boltons in /usr/local/lib/python3.7/dist-packages (from dit) (20.2.1)\n","Requirement already satisfied: networkx in /usr/local/lib/python3.7/dist-packages (from dit) (2.5)\n","Requirement already satisfied: contextlib2 in /usr/local/lib/python3.7/dist-packages (from dit) (0.5.5)\n","Requirement already satisfied: debtcollector in /usr/local/lib/python3.7/dist-packages (from dit) (2.2.0)\n","Requirement already satisfied: numpy>=1.11 in /usr/local/lib/python3.7/dist-packages (from dit) (1.19.5)\n","Requirement already satisfied: scipy>=0.15.0 in /usr/local/lib/python3.7/dist-packages (from dit) (1.4.1)\n","Requirement already satisfied: six>=1.4.0 in /usr/local/lib/python3.7/dist-packages (from dit) (1.15.0)\n","Requirement already satisfied: wcwidth in /usr/local/lib/python3.7/dist-packages (from prettytable->dit) (0.2.5)\n","Requirement already satisfied: importlib-metadata; python_version < \"3.8\" in /usr/local/lib/python3.7/dist-packages (from prettytable->dit) (3.7.2)\n","Requirement already satisfied: decorator>=4.3.0 in /usr/local/lib/python3.7/dist-packages (from networkx->dit) (4.4.2)\n","Requirement already satisfied: pbr!=2.1.0,>=2.0.0 in /usr/local/lib/python3.7/dist-packages (from debtcollector->dit) (5.5.1)\n","Requirement already satisfied: wrapt>=1.7.0 in /usr/local/lib/python3.7/dist-packages (from debtcollector->dit) (1.12.1)\n","Requirement already satisfied: typing-extensions>=3.6.4; python_version < \"3.8\" in /usr/local/lib/python3.7/dist-packages (from importlib-metadata; python_version < \"3.8\"->prettytable->dit) (3.7.4.3)\n","Requirement already satisfied: zipp>=0.5 in /usr/local/lib/python3.7/dist-packages (from importlib-metadata; python_version < \"3.8\"->prettytable->dit) (3.4.1)\n","Requirement already satisfied: pyinform in /usr/local/lib/python3.7/dist-packages (0.2.0)\n","Requirement already satisfied: numpy in /usr/local/lib/python3.7/dist-packages (from pyinform) (1.19.5)\n"]}],"source":["!pip install dit\n","!pip install pyinform"]},{"cell_type":"code","execution_count":null,"metadata":{"colab":{"base_uri":"https://localhost:8080/"},"id":"f2Z46kt9gYUn","outputId":"e42b1f9e-7ab5-4cd3-8200-ef8a7577a62c"},"outputs":[{"name":"stderr","output_type":"stream","text":["/usr/local/lib/python3.7/dist-packages/statsmodels/tools/_testing.py:19: FutureWarning: pandas.util.testing is deprecated. Use the functions in the public API at pandas.testing instead.\n"," import pandas.util.testing as tm\n"]}],"source":["import EEGExtract as eeg\n"]},{"cell_type":"code","execution_count":null,"metadata":{"colab":{"base_uri":"https://localhost:8080/","height":419},"id":"t_VYpmfujhOK","outputId":"507dd150-07fd-47d6-b6c8-aead98df20c9"},"outputs":[{"ename":"KeyboardInterrupt","evalue":"ignored","output_type":"error","traceback":["\u001b[0;31m---------------------------------------------------------------------------\u001b[0m","\u001b[0;31mKeyboardInterrupt\u001b[0m Traceback (most recent call last)","\u001b[0;32m\u001b[0m in \u001b[0;36m\u001b[0;34m()\u001b[0m\n\u001b[1;32m 1\u001b[0m \u001b[0;31m#Shannon Entropy\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m----> 2\u001b[0;31m \u001b[0mShannonRes\u001b[0m \u001b[0;34m=\u001b[0m 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\u001b[0;36mhistogram\u001b[0;34m(a, bins, range, normed, weights, density)\u001b[0m\n\u001b[1;32m 876\u001b[0m \u001b[0;32mfor\u001b[0m \u001b[0mi\u001b[0m \u001b[0;32min\u001b[0m \u001b[0m_range\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;36m0\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mlen\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0ma\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mBLOCK\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 877\u001b[0m \u001b[0msa\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mnp\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0msort\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0ma\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0mi\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0mi\u001b[0m\u001b[0;34m+\u001b[0m\u001b[0mBLOCK\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 878\u001b[0;31m \u001b[0mcum_n\u001b[0m \u001b[0;34m+=\u001b[0m \u001b[0m_search_sorted_inclusive\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0msa\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mbin_edges\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m 879\u001b[0m \u001b[0;32melse\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 880\u001b[0m \u001b[0mzero\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mnp\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mzeros\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;36m1\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mdtype\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mntype\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n","\u001b[0;31mKeyboardInterrupt\u001b[0m: "]}],"source":["#Shannon Entropy\n","ShannonRes = eeg.shannonEntropy(ans, bin_min=-200, bin_max=200, binWidth=2)\n","pwd = os.getcwd()\n","np.save(pwd + \"/data_extracted/DEAP_shannonEntropy\",ShannonRes)\n","del ShannonRes"]},{"cell_type":"code","execution_count":null,"metadata":{"id":"430d_tmiaQuM"},"outputs":[],"source":[]},{"cell_type":"code","execution_count":null,"metadata":{"id":"aEIEwAKX1Jro"},"outputs":[],"source":["# tsalisRes = eeg.tsalisEntropy(ans,bin_min=-200, bin_max=200, binWidth=2, orders=list(range(1,11)))"]},{"cell_type":"code","execution_count":null,"metadata":{"id":"qBFoTcC2moKL"},"outputs":[],"source":["# tsalisRes.shape"]},{"cell_type":"markdown","metadata":{"id":"BrytRoHo1_Nb"},"source":["Subband Information Quantity"]},{"cell_type":"code","execution_count":null,"metadata":{"id":"83gGjqd4jg4E"},"outputs":[],"source":["# delta (0.5–4 Hz)\n","fs = 128\n","eegData_delta = eeg.filt_data(ans, 0.5, 4, fs)\n","ShannonRes_delta = eeg.shannonEntropy(eegData_delta, bin_min=-200, bin_max=200, binWidth=2)\n","# theta (4–8 Hz)\n","eegData_theta = eeg.filt_data(ans, 4, 8, fs)\n","ShannonRes_theta = eeg.shannonEntropy(eegData_theta, bin_min=-200, bin_max=200, binWidth=2)\n","# alpha (8–12 Hz)\n","eegData_alpha = eeg.filt_data(ans, 8, 12, fs)\n","ShannonRes_alpha = eeg.shannonEntropy(eegData_alpha, bin_min=-200, bin_max=200, binWidth=2)\n","# beta (12–30 Hz)\n","eegData_beta = eeg.filt_data(ans, 12, 30, fs)\n","ShannonRes_beta = eeg.shannonEntropy(eegData_beta, bin_min=-200, bin_max=200, binWidth=2)\n","# gamma (30–63 Hz)\n","eegData_gamma = eeg.filt_data(ans, 30,63, fs)\n","ShannonRes_gamma = eeg.shannonEntropy(eegData_gamma, bin_min=-200, bin_max=200, binWidth=2)\n","\n","np.savez(pwd + \"/data_extracted/DEAP_subbandInformationQuantity\",ShannonRes_delta, ShannonRes_theta, ShannonRes_alpha, ShannonRes_beta, ShannonRes_gamma)\n","del ShannonRes_delta, ShannonRes_theta, ShannonRes_alpha, ShannonRes_beta, ShannonRes_gamma"]},{"cell_type":"code","execution_count":null,"metadata":{"id":"ZmfxuXfmfVLP"},"outputs":[],"source":[]},{"cell_type":"markdown","metadata":{"id":"RM_46f6I2Laa"},"source":["Cepstrum Coefficients (n=2)"]},{"cell_type":"code","execution_count":null,"metadata":{"id":"rK0eRudejgrE"},"outputs":[],"source":["# CepstrumRes = eeg.mfcc(ans, fs,order=2)"]},{"cell_type":"code","execution_count":null,"metadata":{"id":"oZ9vPymZjggW"},"outputs":[],"source":["# Lyapunov Exponent\n","LyapunovRes = eeg.lyapunov(ans)\n","np.save(pwd + \"/data_extracted/DEAP_lyapunov\",LyapunovRes)\n"]},{"cell_type":"code","execution_count":null,"metadata":{"id":"JNv4A02Rclr2"},"outputs":[],"source":["del LyapunovRes"]},{"cell_type":"code","execution_count":null,"metadata":{"colab":{"base_uri":"https://localhost:8080/"},"id":"5xwi0_QbjgTH","outputId":"579e9526-ba51-4d56-852d-9ec60e8c96f9"},"outputs":[{"name":"stderr","output_type":"stream","text":["/gdrive/My Drive/Project_DEAP/EEGExtract.py:244: FutureWarning: `rcond` parameter will change to the default of machine precision times ``max(M, N)`` where M and N are the input matrix dimensions.\n","To use the future default and silence this warning we advise to pass `rcond=None`, to keep using the old, explicitly pass `rcond=-1`.\n"," (p, r1, r2, s)=np.linalg.lstsq(x, L)\n"]}],"source":["# Fractal Embedding Dimension\n","HiguchiFD_Res = eeg.hFD(ans[0,:,0],3)\n","np.save(pwd + \"/data_extracted/DEAP_hFD\",HiguchiFD_Res)\n","del HiguchiFD_Res"]},{"cell_type":"code","execution_count":null,"metadata":{"id":"g1bmYC_AGzal"},"outputs":[],"source":["# temp = np.load(pwd + \"/data_extracted/DEAP_subbandInformationQuantity.npz\", allow_pickle=True)\n","temp = np.load(pwd + \"/data_extracted/DEAP_lyapunov.npy\",allow_pickle=True)"]},{"cell_type":"code","execution_count":null,"metadata":{"colab":{"base_uri":"https://localhost:8080/"},"id":"7hw8-PdhG1pu","outputId":"f96d7bfb-9e90-4887-ce35-02f26b3854b0"},"outputs":[{"data":{"text/plain":["array([[0.38437537, 0.32861633, 0.22317178, ..., 1.44509355, 1.09421493,\n"," 1.07976229],\n"," [0.49993956, 0.20966358, 0.34313011, ..., 0.52652336, 0.41880366,\n"," 0.53906758],\n"," [0.27858602, 0.2023764 , 0.19078896, ..., 1.93712203, 1.51815458,\n"," 1.55716851],\n"," ...,\n"," [0.45995024, 0.44734808, 0.43197213, ..., 2.01398181, 1.88646172,\n"," 2.01157029],\n"," [0.22400164, 0.25804005, 0.02537921, ..., 0.62308541, 0.71929832,\n"," 0.73444843],\n"," [0.25662848, 0.32577816, 0.39413567, ..., 0.6973367 , 0.40109724,\n"," 0.57487351]])"]},"execution_count":62,"metadata":{"tags":[]},"output_type":"execute_result"}],"source":["temp"]},{"cell_type":"code","execution_count":null,"metadata":{"id":"5ZroDzK2jgHP"},"outputs":[],"source":["# Hjorth Mobility\n","# Hjorth Complexity\n","HjorthMob, HjorthComp = eeg.hjorthParameters(ans)\n","np.save(pwd + \"/data_extracted/DEAP_HjorthMob\",HjorthMob)\n","\n","np.save(pwd + \"/data_extracted/DEAP_HjorthComp\",HjorthComp)\n","del HjorthComp\n","del HjorthMob"]},{"cell_type":"code","execution_count":null,"metadata":{"id":"XfPwp3A6jfM9"},"outputs":[],"source":["# False Nearest Neighbor\n","FalseNnRes = eeg.falseNearestNeighbor(ans)\n","np.save(pwd + \"/data_extracted/DEAP_falseNearestNeighbor\",FalseNnRes)\n","del FalseNnRes"]},{"cell_type":"code","execution_count":null,"metadata":{"colab":{"base_uri":"https://localhost:8080/","height":402},"id":"bQu2KbYi284v","outputId":"2e2114f0-8748-4681-d5f0-c8e28961c6f7"},"outputs":[{"ename":"ValueError","evalue":"ignored","output_type":"error","traceback":["\u001b[0;31m---------------------------------------------------------------------------\u001b[0m","\u001b[0;31mValueError\u001b[0m Traceback (most recent call last)","\u001b[0;32m\u001b[0m in \u001b[0;36m\u001b[0;34m()\u001b[0m\n\u001b[1;32m 1\u001b[0m \u001b[0;31m# ARMA Coefficients (n=2)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m----> 2\u001b[0;31m \u001b[0marmaRes\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0meeg\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0marma\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mans\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0morder\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;36m2\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m 3\u001b[0m \u001b[0mnp\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0msave\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mpwd\u001b[0m \u001b[0;34m+\u001b[0m \u001b[0;34m\"/data_extracted/DEAP_arma\"\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0marmaRes\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 4\u001b[0m \u001b[0;32mdel\u001b[0m \u001b[0marmaRes\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n","\u001b[0;32m/gdrive/My Drive/Project_DEAP/EEGExtract.py\u001b[0m in \u001b[0;36marma\u001b[0;34m(eegData, order)\u001b[0m\n\u001b[1;32m 342\u001b[0m \u001b[0;32mfor\u001b[0m \u001b[0mepoch\u001b[0m \u001b[0;32min\u001b[0m \u001b[0mrange\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mH\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mshape\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;36m1\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 343\u001b[0m \u001b[0marma_mod\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0msm\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mtsa\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mARMA\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0meegData\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0mchan\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0mepoch\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0morder\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0morder\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0morder\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 344\u001b[0;31m \u001b[0marma_res\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0marma_mod\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mfit\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mtrend\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;34m'nc'\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mdisp\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;34m-\u001b[0m\u001b[0;36m1\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m 345\u001b[0m \u001b[0mH\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0mchan\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mepoch\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m:\u001b[0m \u001b[0;34m]\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0marma_res\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0marparams\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 346\u001b[0m \u001b[0;32mreturn\u001b[0m \u001b[0mH\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n","\u001b[0;32m/usr/local/lib/python3.7/dist-packages/statsmodels/tsa/arima_model.py\u001b[0m in \u001b[0;36mfit\u001b[0;34m(self, start_params, trend, method, transparams, solver, maxiter, full_output, disp, callback, start_ar_lags, **kwargs)\u001b[0m\n\u001b[1;32m 936\u001b[0m \u001b[0;32melse\u001b[0m\u001b[0;34m:\u001b[0m \u001b[0;31m# estimate starting parameters\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 937\u001b[0m start_params = self._fit_start_params((k_ar, k_ma, k), method,\n\u001b[0;32m--> 938\u001b[0;31m start_ar_lags)\n\u001b[0m\u001b[1;32m 939\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 940\u001b[0m \u001b[0;32mif\u001b[0m \u001b[0mtransparams\u001b[0m\u001b[0;34m:\u001b[0m \u001b[0;31m# transform initial parameters to ensure invertibility\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n","\u001b[0;32m/usr/local/lib/python3.7/dist-packages/statsmodels/tsa/arima_model.py\u001b[0m in \u001b[0;36m_fit_start_params\u001b[0;34m(self, order, method, start_ar_lags)\u001b[0m\n\u001b[1;32m 552\u001b[0m \u001b[0mfunc\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0;32mlambda\u001b[0m \u001b[0mparams\u001b[0m\u001b[0;34m:\u001b[0m \u001b[0;34m-\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mloglike_css\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mparams\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 553\u001b[0m \u001b[0;31m#start_params = [.1]*(k_ar+k_ma+k_exog) # different one for k?\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 554\u001b[0;31m \u001b[0mstart_params\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_fit_start_params_hr\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0morder\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mstart_ar_lags\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m 555\u001b[0m \u001b[0;32mif\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mtransparams\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 556\u001b[0m \u001b[0mstart_params\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_invtransparams\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mstart_params\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n","\u001b[0;32m/usr/local/lib/python3.7/dist-packages/statsmodels/tsa/arima_model.py\u001b[0m in \u001b[0;36m_fit_start_params_hr\u001b[0;34m(self, order, start_ar_lags)\u001b[0m\n\u001b[1;32m 538\u001b[0m elif q and not np.all(np.abs(np.roots(np.r_[1, start_params[k + p:]]\n\u001b[1;32m 539\u001b[0m )) < 1):\n\u001b[0;32m--> 540\u001b[0;31m raise ValueError(\"The computed initial MA coefficients are not \"\n\u001b[0m\u001b[1;32m 541\u001b[0m \u001b[0;34m\"invertible\\nYou should induce invertibility, \"\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 542\u001b[0m \u001b[0;34m\"choose a different model order, or you can\\n\"\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n","\u001b[0;31mValueError\u001b[0m: The computed initial MA coefficients are not invertible\nYou should induce invertibility, choose a different model order, or you can\npass your own start_params."]}],"source":["# ARMA Coefficients (n=2)\n","armaRes = eeg.arma(ans,order=2)\n","np.save(pwd + \"/data_extracted/DEAP_arma\",armaRes)\n","del armaRes"]},{"cell_type":"code","execution_count":null,"metadata":{"id":"SSrQzdcu284x"},"outputs":[],"source":["# Median Frequency\n","medianFreqRes = eeg.medianFreq(ans,fs)\n","np.save(pwd + \"/data_extracted/DEAP_medianFreq\",medianFreqRes)\n","del medianFreqRes"]},{"cell_type":"code","execution_count":null,"metadata":{"id":"o5cmUNluPhMf"},"outputs":[],"source":[]},{"cell_type":"code","execution_count":null,"metadata":{"id":"yC9I0kMf284x"},"outputs":[],"source":["# δ band Power\n","bandPwr_delta = eeg.bandPower(ans, 0.5, 4, fs)\n","# θ band Power\n","bandPwr_theta = eeg.bandPower(ans, 4, 8, fs)\n","# α band Power\n","bandPwr_alpha = eeg.bandPower(ans, 8, 12, fs)\n","# β band Power\n","bandPwr_beta = eeg.bandPower(ans, 12, 30, fs)\n","# γ band Power\n","bandPwr_gamma = eeg.bandPower(ans, 30, 63, fs)\n","\n","np.savez(pwd + \"/data_extracted/DEAP_bandPwr\",bandPwr_delta, bandPwr_theta, bandPwr_alpha, bandPwr_beta, bandPwr_gamma)\n","del bandPwr_delta, bandPwr_theta, bandPwr_alpha, bandPwr_beta, bandPwr_gamma"]},{"cell_type":"code","execution_count":null,"metadata":{"id":"mO3PdaHA4BUW"},"outputs":[],"source":["# Standard Deviation\n","std_res = eeg.eegStd(ans)\n","np.save(pwd + \"/data_extracted/DEAP_std_res\",std_res)\n","del std_res"]},{"cell_type":"code","execution_count":null,"metadata":{"colab":{"base_uri":"https://localhost:8080/","height":342},"id":"t3VVIR254Bn7","outputId":"7749e881-6d01-4187-bc0a-b940eade6766"},"outputs":[{"ename":"NameError","evalue":"ignored","output_type":"error","traceback":["\u001b[0;31m---------------------------------------------------------------------------\u001b[0m","\u001b[0;31mNameError\u001b[0m Traceback (most recent call last)","\u001b[0;32m\u001b[0m in \u001b[0;36m\u001b[0;34m()\u001b[0m\n\u001b[1;32m 1\u001b[0m \u001b[0;31m# α/δ Ratio\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m----> 2\u001b[0;31m \u001b[0mratio_res\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0meeg\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0meegRatio\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mans\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0mfs\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m 3\u001b[0m \u001b[0mnp\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0msave\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mpwd\u001b[0m \u001b[0;34m+\u001b[0m \u001b[0;34m\"/data_extracted/DEAP_ratio_res\"\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0mratio_res\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 4\u001b[0m \u001b[0;32mdel\u001b[0m \u001b[0mratio_res\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n","\u001b[0;32m/gdrive/My Drive/Project_DEAP/EEGExtract.py\u001b[0m in \u001b[0;36meegRatio\u001b[0;34m(eegData, fs)\u001b[0m\n\u001b[1;32m 395\u001b[0m \u001b[0mpowers_delta\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mbandPower\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0meegData\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;36m0.5\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;36m4\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mfs\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 396\u001b[0m \u001b[0mratio_res\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mnp\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0msum\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mpowers_alpha\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0maxis\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;36m0\u001b[0m\u001b[0;34m)\u001b[0m \u001b[0;34m/\u001b[0m \u001b[0mnp\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0msum\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mpowers_delta\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0maxis\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;36m0\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 397\u001b[0;31m \u001b[0;32mreturn\u001b[0m \u001b[0mnp\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mexpand_dims\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mx\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0maxis\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;36m0\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m 398\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 399\u001b[0m \u001b[0;31m###########\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n","\u001b[0;31mNameError\u001b[0m: name 'x' is not defined"]}],"source":["# # α/δ Ratio\n","# ratio_res = eeg.eegRatio(ans,fs)\n","# np.save(pwd + \"/data_extracted/DEAP_ratio_res\",ratio_res)\n","# del ratio_res"]},{"cell_type":"code","execution_count":null,"metadata":{"id":"jYl314hW4BwN"},"outputs":[],"source":["# Regularity (burst-suppression)\n","regularity_res = eeg.eegRegularity(ans,fs)\n","np.save(pwd + \"/data_extracted/DEAP_regularity_res\",regularity_res)\n","del regularity_res"]},{"cell_type":"code","execution_count":null,"metadata":{"colab":{"base_uri":"https://localhost:8080/"},"id":"hQZ2MUmS4B2e","outputId":"6916ddc5-fdee-4bfb-a12f-553e3d805084"},"outputs":[{"name":"stderr","output_type":"stream","text":["/gdrive/My Drive/Project_DEAP/EEGExtract.py:422: RuntimeWarning: Mean of empty slice\n"," volt_res = np.nanmean(eegFilt,axis=1)\n"]}],"source":["# Voltage < 5μ\n","volt05_res = eeg.eegVoltage(ans,voltage=5)\n","# Voltage < 10μ\n","volt10_res = eeg.eegVoltage(ans,voltage=10)\n","# Voltage < 20μ\n","volt20_res = eeg.eegVoltage(ans,voltage=20)\n","\n","np.savez(pwd + \"/data_extracted/DEAP_voltage\",volt05_res, volt10_res, volt20_res)\n","del volt05_res, volt10_res, volt20_res"]},{"cell_type":"code","execution_count":null,"metadata":{"id":"6fl59Vx54B6r"},"outputs":[],"source":["# Diffuse Slowing\n","df_res = eeg.diffuseSlowing(ans)\n","np.save(pwd + \"/data_extracted/DEAP_df_res\",df_res)\n","del df_res"]},{"cell_type":"code","execution_count":null,"metadata":{"id":"DOZ_1ux-4CBn"},"outputs":[],"source":["# Spikes\n","minNumSamples = int(70*fs/1000)\n","spikeNum_res = eeg.spikeNum(ans,minNumSamples)\n","np.save(pwd + \"/data_extracted/DEAP_spikeNum_res\",spikeNum_res)\n","del spikeNum_res"]},{"cell_type":"code","execution_count":null,"metadata":{"id":"hz4quJm44CDz"},"outputs":[],"source":["# Delta burst after Spike\n","deltaBurst_res = eeg.burstAfterSpike(ans,eegData_delta,minNumSamples=7,stdAway = 3)\n","np.save(pwd + \"/data_extracted/DEAP_deltaBurst_res\",deltaBurst_res)\n","del deltaBurst_res"]},{"cell_type":"code","execution_count":null,"metadata":{"id":"L7JtC2lp4CHL"},"outputs":[],"source":["# Sharp spike\n","sharpSpike_res = eeg.shortSpikeNum(ans,minNumSamples)\n","np.save(pwd + \"/data_extracted/DEAP_sharpSpike_res\",sharpSpike_res)\n","del sharpSpike_res"]},{"cell_type":"code","execution_count":null,"metadata":{"id":"Yw1CCliq4Wxz"},"outputs":[],"source":["# Number of Bursts\n","numBursts_res = eeg.numBursts(ans,fs)\n","np.save(pwd + \"/data_extracted/DEAP_numBursts_res\",numBursts_res)\n","del numBursts_res"]},{"cell_type":"code","execution_count":null,"metadata":{"colab":{"base_uri":"https://localhost:8080/"},"id":"0WDUV0GS4W80","outputId":"b27a870d-e721-4045-f441-7a927a496cb2"},"outputs":[{"name":"stderr","output_type":"stream","text":["/usr/local/lib/python3.7/dist-packages/numpy/core/fromnumeric.py:3373: RuntimeWarning: Mean of empty slice.\n"," out=out, **kwargs)\n","/usr/local/lib/python3.7/dist-packages/numpy/core/_methods.py:234: RuntimeWarning: Degrees of freedom <= 0 for slice\n"," keepdims=keepdims)\n"]}],"source":["# Burst length μ and σ\n","burstLenMean_res,burstLenStd_res = eeg.burstLengthStats(ans,fs)\n","np.save(pwd + \"/data_extracted/DEAP_burstLenMean_res\",burstLenMean_res)\n","del burstLenMean_res\n","np.save(pwd + \"/data_extracted/DEAP_burstLenStd_res\",burstLenStd_res)\n","del burstLenStd_res"]},{"cell_type":"code","execution_count":null,"metadata":{"colab":{"base_uri":"https://localhost:8080/"},"id":"Fg17FdcB4XHT","outputId":"0f129719-f1dc-47c9-facd-4ca34264eeee"},"outputs":[{"name":"stderr","output_type":"stream","text":["/usr/local/lib/python3.7/dist-packages/numpy/core/fromnumeric.py:3373: RuntimeWarning: Mean of empty slice.\n"," out=out, **kwargs)\n"]}],"source":["# Burst Band Power for δ\n","burstBandPwrAlpha = eeg.burstBandPowers(ans, 0.5, 4, fs)\n","np.save(pwd + \"/data_extracted/DEAP_burstBandPwrAlpha\",burstBandPwrAlpha)\n","del burstBandPwrAlpha"]},{"cell_type":"code","execution_count":null,"metadata":{"id":"qQ3dkC9d4XQW"},"outputs":[],"source":["# Number of Suppressions\n","numSupps_res = eeg.numSuppressions(ans,fs)\n","np.save(pwd + \"/data_extracted/DEAP_numSupps_res\",numSupps_res)\n","del numSupps_res"]},{"cell_type":"code","execution_count":null,"metadata":{"colab":{"base_uri":"https://localhost:8080/"},"id":"iAovFNl94hHB","outputId":"8c09ac49-7e7e-4864-b99d-ce2c7b4b1398"},"outputs":[{"name":"stderr","output_type":"stream","text":["/usr/local/lib/python3.7/dist-packages/numpy/core/fromnumeric.py:3373: RuntimeWarning: Mean of empty slice.\n"," out=out, **kwargs)\n","/usr/local/lib/python3.7/dist-packages/numpy/core/_methods.py:234: RuntimeWarning: Degrees of freedom <= 0 for slice\n"," keepdims=keepdims)\n"]}],"source":["# Suppression length μ and σ\n","suppLenMean_res,suppLenStd_res = eeg.suppressionLengthStats(ans,fs)\n","np.save(pwd + \"/data_extracted/DEAP_suppLenMean_res\",suppLenMean_res)\n","del suppLenMean_res\n","np.save(pwd + \"/data_extracted/DEAP_suppLenStd_res\",suppLenStd_res)\n","del suppLenStd_res"]},{"cell_type":"code","execution_count":null,"metadata":{"id":"Ts4JpTNM2tmB"},"outputs":[],"source":["# features_dir =pwd+\"/data_extracted/\"\n","# featureFiles = os.listdir(features_dir)\n","# for f in featureFiles:\n","# if(f != 'DEAP.npz'):\n","# temp = \n","# features.append()"]},{"cell_type":"markdown","metadata":{"id":"0MLXIYNzCwfR"},"source":["## Loading Saved Features for Feature Importance Testing"]},{"cell_type":"code","execution_count":null,"metadata":{"id":"YqCgsWmAEF2D"},"outputs":[],"source":["\n","\n","import os\n","import glob\n","from scipy import io,signal\n","import numpy as np\n","import pandas as pd\n","from sklearn import preprocessing\n"]},{"cell_type":"code","execution_count":null,"metadata":{"id":"u9vE7fGDfOUG"},"outputs":[],"source":["featuresDict = {'shannonEntropy': None,\n"," 'ShannonRes_delta':None,\n"," 'ShannonRes_theta':None,\n"," 'ShannonRes_alpha':None,\n"," 'ShannonRes_beta':None,\n"," 'ShannonRes_gamma':None,\n"," 'lyapunov':None,\n"," 'hFD':None,\n"," 'HjorthComp':None,\n"," 'HjorthMob':None,\n"," 'falseNearestNeighbor':None,\n"," 'medianFreq':None,\n"," 'bandPwr_delta':None, \n"," 'bandPwr_theta':None, \n"," 'bandPwr_alpha':None, \n"," 'bandPwr_beta':None, \n"," 'bandPwr_gamma':None,\n"," 'stdDev':None,\n"," 'regularity':None,\n"," 'volt05':None, \n"," 'volt10':None, \n"," 'volt20':None,\n"," 'diffuseSlowing':None,\n"," 'spikeNum':None,\n"," 'deltaBurstAfterSpike':None,\n"," 'shortSpikeNum':None,\n"," 'numBursts':None,\n"," 'burstLenMean':None,\n"," 'burstLenStd':None,\n"," 'burstBandPowers':None,\n"," 'numSuppressions':None,\n"," 'suppLenMean':None,\n"," 'suppLenStd':None\n"," }"]},{"cell_type":"code","execution_count":null,"metadata":{"id":"YFEehFC551nb"},"outputs":[],"source":["pwd = os.getcwd()\n","\n","featuresDict['shannonEntropy'] = np.load(pwd + '/data_extracted/DEAP_shannonEntropy.npy', allow_pickle=True)\n","\n","featuresDict['ShannonRes_delta'] = np.load(pwd + \"/data_extracted/DEAP_subbandInformationQuantity.npz\", allow_pickle=True)['arr_0']\n","featuresDict['ShannonRes_theta'] = np.load(pwd + \"/data_extracted/DEAP_subbandInformationQuantity.npz\", allow_pickle=True)['arr_1']\n","featuresDict['ShannonRes_alpha'] = np.load(pwd + \"/data_extracted/DEAP_subbandInformationQuantity.npz\", allow_pickle=True)['arr_2']\n","featuresDict['ShannonRes_beta'] = np.load(pwd + \"/data_extracted/DEAP_subbandInformationQuantity.npz\", allow_pickle=True)['arr_3']\n","featuresDict['ShannonRes_gamma'] = np.load(pwd + \"/data_extracted/DEAP_subbandInformationQuantity.npz\", allow_pickle=True)['arr_4']\n","\n","featuresDict['lyapunov'] = np.load(pwd + \"/data_extracted/DEAP_lyapunov.npy\",allow_pickle=True)\n","\n","featuresDict['hFD'] = np.load(pwd + \"/data_extracted/DEAP_hFD.npy\",allow_pickle=True)\n","\n","featuresDict['HjorthComp'] = np.load(pwd + \"/data_extracted/DEAP_HjorthComp.npy\",allow_pickle=True)\n","\n","featuresDict['HjorthMob'] = np.load(pwd + \"/data_extracted/DEAP_HjorthMob.npy\",allow_pickle=True)\n","\n","\n","featuresDict['falseNearestNeighbor'] = np.load(pwd + \"/data_extracted/DEAP_falseNearestNeighbor.npy\",allow_pickle=True)\n","\n","featuresDict['medianFreq'] = np.load(pwd + \"/data_extracted/DEAP_medianFreq.npy\",allow_pickle=True)\n","\n","featuresDict['bandPwr_delta']=np.load(pwd + \"/data_extracted/DEAP_bandPwr.npz\", allow_pickle = True)['arr_0']\n","featuresDict['bandPwr_theta']=np.load(pwd + \"/data_extracted/DEAP_bandPwr.npz\", allow_pickle = True)['arr_1']\n","featuresDict['bandPwr_alpha']=np.load(pwd + \"/data_extracted/DEAP_bandPwr.npz\", allow_pickle = True)['arr_2'] \n","featuresDict['bandPwr_beta']=np.load(pwd + \"/data_extracted/DEAP_bandPwr.npz\", allow_pickle = True)['arr_3'] \n","featuresDict['bandPwr_gamma']=np.load(pwd + \"/data_extracted/DEAP_bandPwr.npz\", allow_pickle = True)['arr_4']\n","\n","\n","featuresDict['stdDev'] = np.load(pwd + \"/data_extracted/DEAP_std_res.npy\",allow_pickle=True)\n","\n","featuresDict['regularity'] = np.load(pwd + \"/data_extracted/DEAP_regularity_res.npy\",allow_pickle=True)\n","\n","featuresDict['volt05'] = np.load(pwd + \"/data_extracted/DEAP_voltage.npz\",allow_pickle=True)['arr_0'] \n","featuresDict['volt10'] = np.load(pwd + \"/data_extracted/DEAP_voltage.npz\",allow_pickle=True)['arr_1'] \n","featuresDict['volt20'] = np.load(pwd + \"/data_extracted/DEAP_voltage.npz\",allow_pickle=True)['arr_2'] \n","\n","\n","featuresDict['diffuseSlowing'] = np.load(pwd + \"/data_extracted/DEAP_df_res.npy\",allow_pickle=True)\n","\n","featuresDict['spikeNum'] = np.load(pwd + \"/data_extracted/DEAP_spikeNum_res.npy\",allow_pickle=True)\n","\n","featuresDict['deltaBurstAfterSpike'] = np.load(pwd + \"/data_extracted/DEAP_deltaBurst_res.npy\",allow_pickle=True)\n","\n","featuresDict['shortSpikeNum'] = np.load(pwd + \"/data_extracted/DEAP_sharpSpike_res.npy\",allow_pickle=True)\n","\n","featuresDict['numBursts'] = np.load(pwd + \"/data_extracted/DEAP_numBursts_res.npy\",allow_pickle=True)\n","\n","featuresDict['burstLenMean'] = np.load(pwd + \"/data_extracted/DEAP_burstLenMean_res.npy\",allow_pickle=True)\n","\n","featuresDict['burstLenStd'] = np.load(pwd + \"/data_extracted/DEAP_burstLenStd_res.npy\",allow_pickle=True)\n","\n","featuresDict['burstBandPowers'] = np.load(pwd + \"/data_extracted/DEAP_burstBandPwrAlpha.npy\",allow_pickle=True)\n","\n","featuresDict['numSuppressions'] = np.load(pwd + \"/data_extracted/DEAP_numSupps_res.npy\",allow_pickle=True)\n","\n","featuresDict['suppLenMean'] = np.load(pwd + \"/data_extracted/DEAP_suppLenMean_res.npy\",allow_pickle=True)\n","\n","featuresDict['suppLenStd'] = np.load(pwd + \"/data_extracted/DEAP_suppLenStd_res.npy\",allow_pickle=True)\n","\n"]},{"cell_type":"code","execution_count":null,"metadata":{"colab":{"base_uri":"https://localhost:8080/"},"id":"1FxhWi6WNltH","outputId":"b594481d-8776-44d6-eedc-5ed0936f727f"},"outputs":[{"name":"stdout","output_type":"stream","text":["shannonEntropy -> \n"," [[4.03893898 3.84299929 3.8615949 ... 5.72725039 4.84772098 4.79947771]\n"," [4.23332978 3.89894707 3.83272636 ... 4.82257732 4.25646359 4.41788063]\n"," [3.8623339 3.95583242 3.90809159 ... 6.45264476 5.48562442 5.63296101]\n"," ...\n"," [4.07878545 4.14702486 4.13926778 ... 6.1658254 5.64909329 5.8105532 ]\n"," [4.1420064 3.92951491 4.06160532 ... 4.78741018 4.40797933 4.33962685]\n"," [4.1426992 4.05556518 4.03230056 ... 4.36503279 4.26386851 4.57981271]]\n","ShannonRes_delta -> \n"," [[1.90754046 1.84942697 2.28417127 ... 4.0547615 4.00469407 2.84636954]\n"," [2.04066481 2.03628194 2.42012344 ... 2.8326542 3.09710959 2.00042476]\n"," [1.56510133 2.17982239 2.74816236 ... 4.66049705 4.64914844 3.71503591]\n"," ...\n"," [1.44886449 2.07695411 2.43058565 ... 4.03503143 4.25424477 2.17043371]\n"," [1.53683513 1.54356444 2.01918333 ... 3.3668638 2.59804615 1.58587329]\n"," [2.43323857 2.3945744 2.508294 ... 2.45661122 2.79477631 3.00578778]]\n","ShannonRes_theta -> \n"," [[2.42619941 1.43165643 2.65982754 ... 4.54321703 3.57447708 3.11479626]\n"," [2.84264335 2.17193506 2.43656694 ... 3.66650745 3.13847348 2.49814011]\n"," [2.67465916 2.61961616 2.69484673 ... 4.89173779 4.00906103 3.25748774]\n"," ...\n"," [2.23897409 2.55582551 2.72090884 ... 4.6543032 3.63665019 2.67256971]\n"," [3.08979189 2.26572126 2.39254065 ... 3.34275339 2.28388094 2.78365556]\n"," [2.9768434 2.26748679 2.72060236 ... 2.99420904 3.082403 3.00800958]]\n","ShannonRes_alpha -> \n"," [[2.90336182 2.663086 2.06057322 ... 4.04700932 2.72236148 2.40325908]\n"," [3.01225017 2.52067022 1.88371226 ... 2.94999763 2.80386443 1.90754046]\n"," [2.7754224 2.39254065 2.50173181 ... 4.43029387 3.00172506 2.96276755]\n"," ...\n"," [2.59496447 2.79333254 2.16447116 ... 4.22021745 3.26822321 2.97445182]\n"," [2.51264419 2.83036867 2.59070358 ... 3.61137465 2.39113847 2.15519732]\n"," [2.92961277 3.00681241 1.9758227 ... 2.63782986 2.52349483 2.3916895 ]]\n","ShannonRes_beta -> \n"," [[3.57803829 3.17858268 3.48484594 ... 4.57032481 4.04166789 4.01255397]\n"," [3.67847931 3.2624334 3.3467147 ... 3.43988051 3.27741095 3.3512254 ]\n"," [3.34427398 3.21117552 3.22570377 ... 5.25509953 4.26202242 4.39619529]\n"," ...\n"," [3.51855161 3.50570102 3.20868592 ... 5.01239632 5.19228494 5.14056117]\n"," [3.30109263 3.47841806 2.91667698 ... 3.85710283 3.64042908 3.54337883]\n"," [3.46874686 3.32751628 3.50986955 ... 3.56043832 3.49143018 3.62336348]]\n","ShannonRes_gamma -> \n"," [[2.01692071 2.10007811 2.1118484 ... 3.92722175 3.99986364 4.24774683]\n"," [2.20853823 2.0820741 2.25328776 ... 2.89682857 2.95244142 2.94704493]\n"," [1.93039138 1.99678108 2.22379529 ... 4.59541117 4.68863932 4.7424646 ]\n"," ...\n"," [2.43399392 2.25668444 2.224896 ... 5.20277115 4.9250147 5.42809524]\n"," [2.27211207 2.16249672 2.34784799 ... 3.21175058 3.26017917 3.56385895]\n"," [1.78038341 1.93039138 2.07280812 ... 2.97348399 2.80811461 3.07255246]]\n","lyapunov -> \n"," [[0.38437537 0.32861633 0.22317178 ... 1.44509355 1.09421493 1.07976229]\n"," [0.49993956 0.20966358 0.34313011 ... 0.52652336 0.41880366 0.53906758]\n"," [0.27858602 0.2023764 0.19078896 ... 1.93712203 1.51815458 1.55716851]\n"," ...\n"," [0.45995024 0.44734808 0.43197213 ... 2.01398181 1.88646172 2.01157029]\n"," [0.22400164 0.25804005 0.02537921 ... 0.62308541 0.71929832 0.73444843]\n"," [0.25662848 0.32577816 0.39413567 ... 0.6973367 0.40109724 0.57487351]]\n","hFD -> \n"," 1.1963165746304045\n","HjorthComp -> \n"," [[1.33484842 1.39723823 1.30196425 ... 2.08749819 1.36043533 1.23715528]\n"," [1.41189971 1.44751723 1.31519227 ... 2.14140417 1.67337897 1.74049277]\n"," [1.49175999 1.49095202 1.43201714 ... 2.26756533 1.46947662 1.71592143]\n"," ...\n"," [1.4568794 1.49636619 1.56549106 ... 1.63048976 1.21346165 1.1222029 ]\n"," [1.67051869 1.43866148 1.67333067 ... 1.79839044 1.48574884 1.36679093]\n"," [1.52231447 1.46938343 1.5118969 ... 1.4988667 1.64220114 1.69965911]]\n","HjorthMob -> \n"," [[0.78068933 0.83293564 0.83628509 ... 0.54300443 1.04586635 1.20973205]\n"," [0.74427438 0.78732753 0.857741 ... 0.55004545 0.7905658 0.74680329]\n"," [0.7274074 0.74951147 0.79885331 ... 0.5031975 0.99696095 0.84626909]\n"," ...\n"," [0.80875341 0.73487655 0.68979793 ... 0.87997098 1.16873626 1.35414126]\n"," [0.67956826 0.75902906 0.70610297 ... 0.67833068 0.9357976 1.04779527]\n"," [0.63283182 0.68670403 0.70872187 ... 0.81945193 0.74705305 0.74503065]]\n","falseNearestNeighbor -> \n"," [[0. 0. 0. ... 0. 0. 0.]\n"," [0. 0. 0. ... 0. 0. 0.]\n"," [0. 0. 0. ... 0. 0. 0.]\n"," ...\n"," [0. 0. 0. ... 0. 0. 0.]\n"," [0. 0. 0. ... 0. 0. 0.]\n"," [0. 0. 0. ... 0. 0. 0.]]\n","medianFreq -> \n"," [[45. 40. 32. ... 3. 20. 15.]\n"," [34. 35. 1. ... 31. 1. 16.]\n"," [34. 20. 32. ... 28. 45. 19.]\n"," ...\n"," [38. 37. 26. ... 3. 10. 5.]\n"," [39. 4. 3. ... 24. 27. 22.]\n"," [ 3. 42. 34. ... 27. 31. 37.]]\n","bandPwr_delta -> \n"," [[0.02517759 0.01007437 0.05825907 ... 0.48540152 0.61728988 0.21944335]\n"," [0.02953228 0.02248966 0.10065095 ... 0.06711867 0.11728527 0.01854863]\n"," [0.00564979 0.03026512 0.04234976 ... 1.18620564 1.31118292 0.70209577]\n"," ...\n"," [0.0043975 0.02563555 0.05759515 ... 0.38390124 0.51362867 0.04238684]\n"," [0.00492101 0.00438003 0.0272461 ... 0.11540272 0.0298191 0.00650211]\n"," [0.04835279 0.07266789 0.12300212 ... 0.06937813 0.05063258 0.08465092]]\n","bandPwr_theta -> \n"," [[0.0287918 0.00520802 0.03601754 ... 1.4567609 0.16999224 0.07610964]\n"," [0.04569349 0.01800619 0.02521168 ... 0.30545952 0.08594368 0.02983194]\n"," [0.03619106 0.03574862 0.03333659 ... 4.6661394 0.31188896 0.19391511]\n"," ...\n"," [0.0220107 0.03018114 0.03750759 ... 1.81909533 0.19265965 0.05545865]\n"," [0.07143851 0.0217649 0.01963999 ... 0.19267841 0.01762575 0.04020905]\n"," [0.06611359 0.01663094 0.03882099 ... 0.07872552 0.06684541 0.05892472]]\n","bandPwr_alpha -> \n"," [[0.05406126 0.0317642 0.0146719 ... 0.62092032 0.03639066 0.02858635]\n"," [0.06456492 0.03298791 0.00962731 ... 0.07385545 0.04043392 0.00763531]\n"," [0.04152343 0.02685715 0.02384954 ... 1.73618218 0.06234947 0.05257651]\n"," ...\n"," [0.0430691 0.05645463 0.01645257 ... 0.49953936 0.11218136 0.0646394 ]\n"," [0.03382363 0.0458034 0.02722353 ... 0.17342472 0.0254889 0.01491935]\n"," [0.05636124 0.07094555 0.00805685 ... 0.03260917 0.0253371 0.02622308]]\n","bandPwr_beta -> \n"," [[0.12639084 0.0757073 0.10941533 ... 0.5941878 0.26763624 0.2597733 ]\n"," [0.15675503 0.08902831 0.11010742 ... 0.11061798 0.07871223 0.09703065]\n"," [0.08908335 0.07704182 0.07563046 ... 1.63402656 0.35631334 0.47549769]\n"," ...\n"," [0.1223539 0.11052383 0.07421938 ... 1.28441032 1.56603512 1.43191508]\n"," [0.08829911 0.11285305 0.05180715 ... 0.19986428 0.13785352 0.12101537]\n"," [0.11362058 0.092906 0.11953256 ... 0.1335782 0.12042275 0.15198572]]\n","bandPwr_gamma -> \n"," [[0.01092795 0.01375756 0.01093865 ... 0.20569579 0.27702398 0.36724853]\n"," [0.01544385 0.01484355 0.01430608 ... 0.05089181 0.04590114 0.04946436]\n"," [0.00845832 0.01101961 0.0150865 ... 0.59519802 0.64454412 0.75232601]\n"," ...\n"," [0.02165393 0.01692276 0.01359764 ... 1.62517616 0.98645088 2.17198702]\n"," [0.01685511 0.01482118 0.01869252 ... 0.07484542 0.08436927 0.11744628]\n"," [0.00763563 0.00935855 0.01288141 ... 0.05302851 0.03865155 0.05965317]]\n","stdDev -> \n"," [[ 4.0141319 3.54280391 3.56417345 ... 18.10165193 7.87074916\n"," 7.50887475]\n"," [ 4.60385108 3.85888053 3.50802139 ... 8.34901975 4.83278074\n"," 5.45257672]\n"," [ 3.68821067 3.79554451 3.81724893 ... 33.19561972 12.25997573\n"," 15.77722816]\n"," ...\n"," [ 4.08439045 4.6756206 4.37345871 ... 22.27744514 14.58190767\n"," 16.47063462]\n"," [ 4.21860682 4.05532463 4.07891405 ... 7.81717172 5.42046457\n"," 5.19253124]\n"," [ 4.47945866 4.26544928 4.1845271 ... 5.3829261 5.16370962\n"," 6.59282636]]\n","regularity -> \n"," [[0.79105089 0.73548873 0.7813792 ... 0.65851756 0.79417271 0.8351467 ]\n"," [0.78774829 0.73821681 0.76712788 ... 0.61597862 0.82841695 0.66419547]\n"," [0.77618572 0.69410062 0.81627784 ... 0.64023315 0.8116929 0.61027492]\n"," ...\n"," [0.73760407 0.6065263 0.74063866 ... 0.7123241 0.79764325 0.75052337]\n"," [0.72945919 0.70291023 0.71878541 ... 0.66391781 0.81049466 0.7576609 ]\n"," [0.69450707 0.69952255 0.73463105 ... 0.80651139 0.79199389 0.72016995]]\n","volt05 -> \n"," [[-0.17703866 0.00129889 0.0786096 ... 0.63984274 -0.18950857\n"," 0.61248406]\n"," [ 0.03496 -0.07808636 -0.12108936 ... 0.24611672 -0.45696626\n"," 0.22711584]\n"," [ 0.08398531 -0.1377174 -0.05202711 ... 0.29792277 0.03356793\n"," 0.35797799]\n"," ...\n"," [ 0.18186418 -0.22274961 0.47508867 ... 1.27674017 0.35169478\n"," 0.50894841]\n"," [-0.05515646 0.57433038 0.09282918 ... -0.77817261 -0.07285427\n"," -0.17336755]\n"," [ 0.35223907 0.6085566 0.20135991 ... 0.01086545 -0.68128858\n"," -0.7442274 ]]\n","volt10 -> \n"," [[-1.52639890e-01 -1.55158578e-01 1.22297305e-01 ... -1.15761645e-01\n"," -9.28569412e-01 6.20062039e-01]\n"," [ 9.56266388e-02 -6.24129576e-02 1.39648949e-01 ... 3.76668457e-01\n"," -4.16161272e-02 -4.48527475e-02]\n"," [-5.15189969e-02 -2.97771758e-01 1.04835444e-01 ... 9.01469866e-01\n"," -1.67976956e+00 -4.42555331e-01]\n"," ...\n"," [-7.54111228e-02 -1.15775561e-01 1.01726222e-01 ... -8.29102675e-02\n"," 8.55842419e-01 1.97035189e-01]\n"," [-1.16308733e-03 1.46698066e-01 1.30138963e-01 ... -4.27965014e-01\n"," -1.25590386e-01 1.02088232e-01]\n"," [ 2.35403993e-01 2.76189619e-01 -1.28456270e-01 ... -2.47239346e-01\n"," -2.80792496e-01 -8.62526154e-01]]\n","volt20 -> \n"," [[-0.05056926 -0.07172805 0.12229731 ... -0.34831738 -0.34645645\n"," 0.49749485]\n"," [-0.01072562 -0.12892333 0.13964895 ... -0.01630693 -0.20969887\n"," 0.04226836]\n"," [ 0.0304519 -0.21470613 0.18425423 ... 0.91155043 -1.91246383\n"," 0.44188777]\n"," ...\n"," [ 0.01588348 -0.10627011 0.09038663 ... -0.40404336 -1.42689383\n"," 0.17387536]\n"," [ 0.01676423 -0.05658816 0.03982393 ... 0.65667026 -0.01338512\n"," 0.26878718]\n"," [ 0.03458322 0.09387305 -0.12845627 ... 0.0404474 -0.10186858\n"," -0.04279189]]\n","diffuseSlowing -> \n"," [[0. 0. 0. ... 0. 0. 0.]\n"," [0. 0. 0. ... 0. 0. 0.]\n"," [0. 0. 0. ... 0. 0. 0.]\n"," ...\n"," [0. 0. 0. ... 0. 0. 0.]\n"," [0. 0. 0. ... 0. 0. 0.]\n"," [0. 0. 0. ... 0. 0. 0.]]\n","spikeNum -> \n"," [[0. 0. 0. ... 0. 0. 0.]\n"," [0. 0. 0. ... 0. 0. 0.]\n"," [0. 0. 0. ... 0. 0. 0.]\n"," ...\n"," [0. 0. 0. ... 0. 0. 0.]\n"," [0. 0. 0. ... 0. 0. 0.]\n"," [0. 0. 0. ... 0. 0. 0.]]\n","deltaBurstAfterSpike -> \n"," [[0. 0. 0. ... 0. 0. 0.]\n"," [0. 0. 0. ... 0. 0. 0.]\n"," [0. 0. 0. ... 0. 0. 0.]\n"," ...\n"," [0. 0. 0. ... 0. 0. 0.]\n"," [0. 0. 0. ... 0. 0. 0.]\n"," [0. 0. 0. ... 0. 0. 0.]]\n","shortSpikeNum -> \n"," [[0. 0. 0. ... 0. 0. 0.]\n"," [0. 1. 0. ... 0. 0. 0.]\n"," [0. 0. 0. ... 0. 0. 0.]\n"," ...\n"," [0. 1. 0. ... 0. 0. 0.]\n"," [1. 1. 0. ... 0. 0. 0.]\n"," [0. 0. 0. ... 0. 0. 0.]]\n","numBursts -> \n"," [[0. 0. 0. ... 1. 1. 1.]\n"," [0. 0. 0. ... 1. 0. 0.]\n"," [0. 0. 0. ... 0. 0. 1.]\n"," ...\n"," [0. 0. 0. ... 0. 1. 0.]\n"," [0. 0. 0. ... 0. 0. 0.]\n"," [0. 0. 0. ... 0. 0. 0.]]\n","burstLenMean -> \n"," [[ 0. 0. 0. ... 385. 388. 388.]\n"," [ 0. 0. 0. ... 399. 0. 0.]\n"," [ 0. 0. 0. ... 0. 0. 375.]\n"," ...\n"," [ 0. 0. 0. ... 0. 376. 0.]\n"," [ 0. 0. 0. ... 0. 0. 0.]\n"," [ 0. 0. 0. ... 0. 0. 0.]]\n","burstLenStd -> \n"," [[0. 0. 0. ... 0. 0. 0.]\n"," [0. 0. 0. ... 0. 0. 0.]\n"," [0. 0. 0. ... 0. 0. 0.]\n"," ...\n"," [0. 0. 0. ... 0. 0. 0.]\n"," [0. 0. 0. ... 0. 0. 0.]\n"," [0. 0. 0. ... 0. 0. 0.]]\n","burstBandPowers -> \n"," [[ nan nan nan ... 0.13538589 0.12489846 0.0539891 ]\n"," [ nan nan nan ... 0.96528244 nan nan]\n"," [ nan nan nan ... nan nan 0.00292337]\n"," ...\n"," [ nan nan nan ... nan 0.01915207 nan]\n"," [ nan nan nan ... nan nan nan]\n"," [ nan nan nan ... nan nan nan]]\n","numSuppressions -> \n"," [[0. 0. 0. ... 0. 0. 0.]\n"," [0. 0. 0. ... 0. 0. 0.]\n"," [0. 0. 0. ... 0. 0. 0.]\n"," ...\n"," [0. 0. 0. ... 0. 0. 0.]\n"," [0. 0. 0. ... 0. 0. 0.]\n"," [0. 0. 0. ... 0. 0. 0.]]\n","suppLenMean -> \n"," [[0. 0. 0. ... 0. 0. 0.]\n"," [0. 0. 0. ... 0. 0. 0.]\n"," [0. 0. 0. ... 0. 0. 0.]\n"," ...\n"," [0. 0. 0. ... 0. 0. 0.]\n"," [0. 0. 0. ... 0. 0. 0.]\n"," [0. 0. 0. ... 0. 0. 0.]]\n","suppLenStd -> \n"," [[0. 0. 0. ... 0. 0. 0.]\n"," [0. 0. 0. ... 0. 0. 0.]\n"," [0. 0. 0. ... 0. 0. 0.]\n"," ...\n"," [0. 0. 0. ... 0. 0. 0.]\n"," [0. 0. 0. ... 0. 0. 0.]\n"," [0. 0. 0. ... 0. 0. 0.]]\n"]}],"source":["#debug\n","featuresToAvoid = []\n","for key,value in featuresDict.items():\n"," print(key, '->','\\n',value)\n"," if(np.any(np.isnan(value))):\n"," featuresToAvoid.append(key)"]},{"cell_type":"code","execution_count":null,"metadata":{"colab":{"base_uri":"https://localhost:8080/"},"id":"xep0pjAVTd56","outputId":"9c57b5c1-dacc-44ee-a67d-47de2f38100d"},"outputs":[{"data":{"text/plain":["['volt05', 'volt10', 'volt20', 'burstBandPowers']"]},"execution_count":11,"metadata":{"tags":[]},"output_type":"execute_result"}],"source":["featuresToAvoid"]},{"cell_type":"code","execution_count":null,"metadata":{"id":"UJLo-VFvSjqK"},"outputs":[],"source":["# len(featuresDict.keys())"]},{"cell_type":"markdown","metadata":{"id":"ZMvGKT-uU0wH"},"source":["Feature Ranking"]},{"cell_type":"code","execution_count":null,"metadata":{"colab":{"base_uri":"https://localhost:8080/"},"id":"ujfcdr2MU3E3","outputId":"d9542274-ef28-4889-bd2e-f5cfa3f621aa"},"outputs":[{"name":"stdout","output_type":"stream","text":["(80640, 448)\n"]}],"source":["featureMatrix = np.empty((0,80640)) #[14*32 + 1,80640]\n","for key,value in featuresDict.items():\n"," if(key != 'hFD'):\n"," featureMatrix = np.append(featureMatrix,value,axis=0)\n","\n","print(featureMatrix.T.shape)\n","featureMatrix = featureMatrix.astype('float64') "]},{"cell_type":"code","execution_count":null,"metadata":{"id":"SOiVyOAohbV-"},"outputs":[],"source":["# np.isnan(featureMatrix)"]},{"cell_type":"markdown","metadata":{"id":"5Cil58WudlyW"},"source":["(valence, arousal, dominance, liking)"]},{"cell_type":"markdown","metadata":{"id":"txJQzkBJdUDx"},"source":["For Valence "]},{"cell_type":"code","execution_count":null,"metadata":{"colab":{"base_uri":"https://localhost:8080/"},"id":"JT02-69ndlZc","outputId":"29e17bcf-72c6-41bf-ee76-6860f7f9a8fa"},"outputs":[{"data":{"text/plain":["(80640,)"]},"execution_count":59,"metadata":{"tags":[]},"output_type":"execute_result"}],"source":["Y_epoch = np.load(pwd + \"/data_extracted/DEAP_epoched_data.npz\",allow_pickle=True)['arr_1']\n","y = Y_epoch[:,0] #valence\n","y.shape"]},{"cell_type":"code","execution_count":null,"metadata":{"id":"KqZ54_DlNkhs"},"outputs":[],"source":["for i in range(y.shape[0]):\n"," if(y[i] > 5):\n"," y[i] = 1\n"," else:\n"," y[i] = 0\n"]},{"cell_type":"code","execution_count":null,"metadata":{"id":"-s9JjqJ5Kula"},"outputs":[],"source":[]},{"cell_type":"markdown","metadata":{"id":"yA5Pk2sUlFVW"},"source":["https://scikit-learn.org/stable/modules/generated/sklearn.feature_selection.RFE.html"]},{"cell_type":"code","execution_count":null,"metadata":{"id":"MUFI8yMwm7Vh"},"outputs":[],"source":["feature_channel_index = []\n","for i in range(14):\n"," for feature in featuresDict.keys():\n"," if(feature != 'hFD'):\n"," feature_channel_index.append(feature + str(i))\n","\n","X = pd.DataFrame(featureMatrix.T)\n","X = X.fillna(0)\n","X.columns = feature_channel_index\n","y = pd.DataFrame(y)"]},{"cell_type":"code","execution_count":null,"metadata":{"colab":{"base_uri":"https://localhost:8080/"},"id":"nraygS6OOwZT","outputId":"8e2f9e6e-d4cd-46e6-a909-8b4b796fe86a"},"outputs":[{"data":{"text/plain":["(80640, 448)"]},"execution_count":62,"metadata":{"tags":[]},"output_type":"execute_result"}],"source":["X.shape"]},{"cell_type":"markdown","metadata":{"id":"fWfDUwI-kQqG"},"source":["Recursive Feature Elimination"]},{"cell_type":"code","execution_count":null,"metadata":{"id":"7lXMIOJpDR1A"},"outputs":[],"source":["# from sklearn.svm import SVR\n","# from sklearn.feature_selection import RFE\n","\n","# estimator = SVR()\n","# selector = RFE(estimator, n_features_to_select=5, step=1)\n","# selector = selector.fit(X, y)\n","# selector.ranking_"]},{"cell_type":"markdown","metadata":{"id":"PYoU6hnbq22K"},"source":["SelecKBest"]},{"cell_type":"code","execution_count":null,"metadata":{"colab":{"base_uri":"https://localhost:8080/"},"id":"HwbFGlATdl8r","outputId":"a02f6242-1dd0-405c-e4e0-02a9efe60d35"},"outputs":[{"name":"stderr","output_type":"stream","text":["/usr/local/lib/python3.7/dist-packages/sklearn/utils/validation.py:760: DataConversionWarning: A column-vector y was passed when a 1d array was expected. Please change the shape of y to (n_samples, ), for example using ravel().\n"," y = column_or_1d(y, warn=True)\n"]},{"name":"stdout","output_type":"stream","text":[" Specs Score\n","218 burstLenMean6 332.483168\n","217 numBursts6 279.237265\n","358 lyapunov11 277.020529\n","372 volt2011 261.303731\n","203 bandPwr_delta6 259.115337\n","221 numSuppressions6 245.252407\n","91 burstLenStd2 243.809223\n","231 HjorthComp7 238.064302\n","210 volt056 228.533295\n","211 volt106 221.224145\n","204 bandPwr_theta6 204.429706\n","196 ShannonRes_beta6 180.419938\n","213 diffuseSlowing6 178.252987\n","63 suppLenStd1 176.639884\n","77 bandPwr_alpha2 172.538593\n","197 ShannonRes_gamma6 171.239234\n","7 HjorthComp0 165.145109\n","223 suppLenStd6 164.504483\n","216 shortSpikeNum6 161.929355\n","232 HjorthMob7 153.043278\n","90 burstLenMean2 146.967042\n","199 HjorthComp6 134.328164\n","224 shannonEntropy7 132.184407\n","227 ShannonRes_alpha7 124.984355\n","207 bandPwr_gamma6 113.108290\n","202 medianFreq6 112.543975\n","122 burstLenMean3 107.249177\n","84 volt202 104.785458\n","219 burstLenStd6 100.604184\n","100 ShannonRes_beta3 100.029235\n","6 lyapunov0 94.912302\n","116 volt203 94.092922\n"]},{"name":"stderr","output_type":"stream","text":["/usr/local/lib/python3.7/dist-packages/sklearn/feature_selection/_univariate_selection.py:114: UserWarning: Features [126 127 128 129 130 131 132 133 134 135 136 137 138 139 294 295 296 297\n"," 298 299 300 301 302 303 304 305 306 307 308 309 310 311 312 313 314 315\n"," 316 317 318 319 320 321 322 323 324 325 326 327 328 329 330 331 332 333\n"," 334 335 336 339 344 349 378 379 380 381 382 383 384 385 386 387 388 389\n"," 390 391 434 435 436 437 438 439 440 441 442 443 444 445 446 447] are constant.\n"," UserWarning)\n","/usr/local/lib/python3.7/dist-packages/sklearn/feature_selection/_univariate_selection.py:115: RuntimeWarning: invalid value encountered in true_divide\n"," f = msb / msw\n"]}],"source":["from sklearn.feature_selection import chi2\n","from sklearn.feature_selection import SelectKBest, f_classif\n","#apply SelectKBest class to extract top 10 best features\n","\n","bestfeatures = SelectKBest(score_func=f_classif, k=10)\n","fit = bestfeatures.fit(X,y)\n","\n","dfscores = pd.DataFrame(fit.scores_)\n","dfcolumns = pd.DataFrame(X.columns)\n","\n","#concat two dataframes for better visualization \n","featureScores = pd.concat([dfcolumns,dfscores],axis=1)\n","featureScores.columns = ['Specs','Score'] #naming the dataframe columns\n","print(featureScores.nlargest(32,'Score'))\n","bestFeatureColumns = featureScores.nlargest(32,'Score')['Specs']"]},{"cell_type":"code","execution_count":null,"metadata":{"id":"xFy-R5DDRQsJ"},"outputs":[],"source":["bestFeatureColumns = bestFeatureColumns.tolist()\n","# https://github.com/neuromarket/EEG-based-emotion-analysis-using-DEAP-dataset-for-Supervised-Machine-Learning/blob/master/svmClassifier.py"]},{"cell_type":"code","execution_count":null,"metadata":{"id":"xQKxF05TVyPP"},"outputs":[],"source":["from sklearn.model_selection import train_test_split\n","from sklearn.svm import SVC\n","from sklearn.preprocessing import StandardScaler\n","from sklearn.metrics import accuracy_score\n","import xgboost as xgb"]},{"cell_type":"code","execution_count":null,"metadata":{"colab":{"base_uri":"https://localhost:8080/"},"id":"ZNiyvLaPdmBS","outputId":"4e55ca8c-c271-470c-e2ff-449d73ff1f83"},"outputs":[{"name":"stdout","output_type":"stream","text":["[LibSVM]"]}],"source":["# select particular features from data\n","X = X[bestFeatureColumns]\n","# Perform train-test split\n","X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.2, random_state=42)\n","\n","# Normalise-scale data \n","# Feature Scaling\n","\n","sc = StandardScaler()\n","X_train = sc.fit_transform(X_train)\n","X_test = sc.transform(X_test)\n","\n","# Apply classfier\n","clf = SVC(kernel = 'rbf', random_state = 42, verbose = 5)\n","clf.fit(X_train, y_train.values.ravel())\n","y_predict = clf.predict(X_test)"]},{"cell_type":"code","execution_count":null,"metadata":{"id":"qkYBojN9g642"},"outputs":[],"source":["# select particular features from data\n","X = X[bestFeatureColumns]\n","# Perform train-test split\n","X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.2, random_state=42)\n","\n","# Normalise-scale data \n","# Feature Scaling\n","\n","sc = StandardScaler()\n","X_train = sc.fit_transform(X_train)\n","X_test = sc.transform(X_test)\n","\n","# Apply classfier\n","clf = xgb.XGBClassifier(verbose = 5)\n","clf.fit(X_train, y_train.values.ravel())\n","y_predict = clf.predict(X_test)"]},{"cell_type":"code","execution_count":null,"metadata":{"colab":{"base_uri":"https://localhost:8080/"},"id":"1Bwtwup0U3ab","outputId":"546f592a-a1da-4437-d071-9adb8971f1a0"},"outputs":[{"name":"stdout","output_type":"stream","text":["63.467261904761905\n"]}],"source":["print(accuracy_score(y_test, y_predict)*100)\n","# print(featuresDict['hFD'][0])\n","# # np.repeat()"]},{"cell_type":"code","execution_count":null,"metadata":{"colab":{"base_uri":"https://localhost:8080/"},"id":"PTvuROT80QYA","outputId":"cef77b80-5897-43b9-af52-84e3efe97448"},"outputs":[{"name":"stdout","output_type":"stream","text":["Mounted at /gdrive\n","/gdrive/MyDrive/Project_DEAP\n","Collecting dit\n","\u001b[?25l Downloading https://files.pythonhosted.org/packages/ac/29/8172e0ea15be3966196e8a1a4078564f4246803dd4f09b1871881e118467/dit-1.2.3-py2.py3-none-any.whl (397kB)\n","\u001b[K |████████████████████████████████| 399kB 8.3MB/s \n","\u001b[?25hRequirement already satisfied: six>=1.4.0 in /usr/local/lib/python3.7/dist-packages (from dit) (1.15.0)\n","Requirement already satisfied: contextlib2 in /usr/local/lib/python3.7/dist-packages (from dit) (0.5.5)\n","Collecting boltons\n","\u001b[?25l Downloading https://files.pythonhosted.org/packages/71/e1/e7979a4a6d4b296b5935e926549fff540f7670ddaf09bbf137e2b022c039/boltons-20.2.1-py2.py3-none-any.whl (170kB)\n","\u001b[K |████████████████████████████████| 174kB 15.7MB/s \n","\u001b[?25hCollecting debtcollector\n"," Downloading https://files.pythonhosted.org/packages/8e/50/07a7ccf4dbbe90b58e96f97b747ff98aef9d8c841d2616c48cc05b07db33/debtcollector-2.2.0-py3-none-any.whl\n","Requirement already satisfied: networkx in /usr/local/lib/python3.7/dist-packages (from dit) (2.5)\n","Requirement already satisfied: numpy>=1.11 in /usr/local/lib/python3.7/dist-packages (from dit) (1.19.5)\n","Requirement already satisfied: prettytable in /usr/local/lib/python3.7/dist-packages (from dit) (2.1.0)\n","Requirement already satisfied: scipy>=0.15.0 in /usr/local/lib/python3.7/dist-packages (from dit) (1.4.1)\n","Collecting pbr!=2.1.0,>=2.0.0\n","\u001b[?25l Downloading https://files.pythonhosted.org/packages/fb/48/69046506f6ac61c1eaa9a0d42d22d54673b69e176d30ca98e3f61513e980/pbr-5.5.1-py2.py3-none-any.whl (106kB)\n","\u001b[K |████████████████████████████████| 112kB 28.0MB/s \n","\u001b[?25hRequirement already satisfied: wrapt>=1.7.0 in /usr/local/lib/python3.7/dist-packages (from debtcollector->dit) (1.12.1)\n","Requirement already satisfied: decorator>=4.3.0 in /usr/local/lib/python3.7/dist-packages (from networkx->dit) (4.4.2)\n","Requirement already satisfied: wcwidth in /usr/local/lib/python3.7/dist-packages (from prettytable->dit) (0.2.5)\n","Requirement already satisfied: importlib-metadata; python_version < \"3.8\" in /usr/local/lib/python3.7/dist-packages (from prettytable->dit) (3.7.2)\n","Requirement already satisfied: zipp>=0.5 in /usr/local/lib/python3.7/dist-packages (from importlib-metadata; python_version < \"3.8\"->prettytable->dit) (3.4.1)\n","Requirement already satisfied: typing-extensions>=3.6.4; python_version < \"3.8\" in /usr/local/lib/python3.7/dist-packages (from importlib-metadata; python_version < \"3.8\"->prettytable->dit) (3.7.4.3)\n","Installing collected packages: boltons, pbr, debtcollector, dit\n","Successfully installed boltons-20.2.1 debtcollector-2.2.0 dit-1.2.3 pbr-5.5.1\n","Collecting pyinform\n","\u001b[?25l Downloading https://files.pythonhosted.org/packages/6d/4d/915887ddc6e5c46294575a36c7055a1a75b57a867d2f5841ecc942ff8e42/pyinform-0.2.0-py3-none-any.whl (131kB)\n","\u001b[K |████████████████████████████████| 133kB 8.0MB/s \n","\u001b[?25hRequirement already satisfied: numpy in /usr/local/lib/python3.7/dist-packages (from pyinform) (1.19.5)\n","Installing collected packages: pyinform\n","Successfully installed pyinform-0.2.0\n"]},{"name":"stderr","output_type":"stream","text":["/usr/local/lib/python3.7/dist-packages/statsmodels/tools/_testing.py:19: FutureWarning: pandas.util.testing is deprecated. Use the functions in the public API at pandas.testing instead.\n"," import pandas.util.testing as tm\n"]}],"source":["from google.colab import drive\n","drive.mount('/gdrive',force_remount=True)\n","%cd /gdrive/MyDrive/Project_DEAP/\n","\n","!pip install dit\n","!pip install pyinform\n","\n","import EEGExtract as eeg\n","from sklearn.model_selection import train_test_split\n","from sklearn.svm import SVC\n","from sklearn.preprocessing import StandardScaler\n","from sklearn.metrics import accuracy_score\n","import xgboost as xgb\n","from sklearn.feature_selection import chi2\n","from sklearn.feature_selection import SelectKBest, f_classif\n","from sklearn.model_selection import train_test_split\n","from sklearn.preprocessing import StandardScaler\n","from sklearn.metrics import accuracy_score\n","from sklearn.ensemble import RandomForestRegressor\n","\n"]},{"cell_type":"code","execution_count":null,"metadata":{"id":"4DzeKr1sxN2t"},"outputs":[],"source":["# !export CUDA_HOME=/usr/local/cuda"]},{"cell_type":"code","execution_count":null,"metadata":{"id":"d8ieM7vTyNtA"},"outputs":[],"source":["# os.getcwd()"]},{"cell_type":"code","execution_count":null,"metadata":{"id":"iVZfcY93v-oM"},"outputs":[],"source":["# # intall miniconda\n","# !wget -c https://repo.continuum.io/miniconda/Miniconda3-4.5.4-Linux-x86_64.sh\n","# !chmod +x Miniconda3-4.5.4-Linux-x86_64.sh\n","# !bash ./Miniconda3-4.5.4-Linux-x86_64.sh -b -f -p /usr/local\n","\n","\n","# # install RAPIDS packages\n","# !conda install -q -y --prefix /usr/local -c conda-forge \\\n","# -c rapidsai-nightly/label/cuda10.0 -c nvidia/label/cuda10.0 \\\n","# cudf cuml\n","\n","# # set environment vars\n","# import sys, os, shutil\n","# sys.path.append('/usr/local/lib/python3.6/site-packages/')\n","# os.environ['NUMBAPRO_NVVM'] = '/usr/local/cuda/nvvm/lib64/libnvvm.so'\n","# os.environ['NUMBAPRO_LIBDEVICE'] = '/usr/local/cuda/nvvm/libdevice/'\n","\n","# # copy .so files to current working dir\n","# for fn in ['libcudf.so', 'librmm.so']:\n","# shutil.copy('/usr/local/lib/'+fn, os.getcwd())"]},{"cell_type":"code","execution_count":null,"metadata":{"id":"a3XzPTXetuh5"},"outputs":[],"source":["# import numpy as np\n","# from cuml.test.utils import get_handle\n","# from cuml.ensemble import RandomForestRegressor as curfc\n","# from cuml.test.utils import get_handle"]},{"cell_type":"code","execution_count":null,"metadata":{"id":"lf4qxuox8Hs3"},"outputs":[],"source":["import os\n","import glob\n","from scipy import io,signal\n","import numpy as np\n","import pandas as pd\n","from sklearn import preprocessing\n","import pickle\n","from sklearn.metrics import mean_squared_error\n","from sklearn.impute import SimpleImputer\n"]},{"cell_type":"code","execution_count":null,"metadata":{"id":"QzMlUo9AGxR4"},"outputs":[],"source":["%matplotlib inline\n","import matplotlib.pyplot as plt\n"]},{"cell_type":"code","execution_count":null,"metadata":{"id":"e1sY5eOEw0AM"},"outputs":[],"source":["def select_channels(data,channels):\n"," ans = np.empty((data.shape[0],len(channels),data.shape[2]))\n"," for sub in range(data.shape[0]):\n"," ans[sub,:,:] = np.array([data[sub,x,:] for x in channels])\n"," return ans"]},{"cell_type":"code","execution_count":null,"metadata":{"id":"fIsVghXCxLTJ"},"outputs":[],"source":["\"\"\"Epoching Data\"\"\"\n","\n","# nth window end segment + (n-1)step\n","# 8064 = segment + (n-1)step\n","# => (8064-segment)/step + 1 = n\n","\n","def epoch_data(X, Y, Z, window, stride, sfreq):\n"," #X\n"," trials,channels,timepoints = X.shape #1280x40x8064 => 1280*40*63*128\n"," # print(X[1,:,x:x+128].shape)\n"," # return []\n"," segment = int(window*sfreq)\n"," step = int(stride*sfreq)\n"," epochPerTrial = int((timepoints-segment)/step + 1)\n","\n"," X_new = np.empty((trials*epochPerTrial,channels,segment))\n"," Y_new = np.empty((trials*epochPerTrial,4))\n"," Z_new = np.empty((trials*epochPerTrial,2))\n","\n"," count=0\n"," for trial in range(trials):\n"," for epoch in range(epochPerTrial):\n"," X_new[count,:,:] = X[trial,:,epoch*step:(epoch*step)+segment]\n"," Y_new[count,:] = Y[trial,:]\n"," Z_new[count,:] = Z[trial,:]\n"," count = count+1\n","\n"," #Y\n","\n","\n"," #Z \n"," return X_new, Y_new, Z_new\n"]},{"cell_type":"code","execution_count":null,"metadata":{"id":"QK9tk7Lyzzsi"},"outputs":[],"source":["def getFeaturesDict(data,sfreq):\n"," fs = sfreq\n"," ans = data\n"," featuresDict = {'shannonEntropy': None,\n"," 'ShannonRes_delta':None,\n"," 'ShannonRes_theta':None,\n"," 'ShannonRes_alpha':None,\n"," 'ShannonRes_beta':None,\n"," 'ShannonRes_gamma':None,\n"," 'lyapunov':None,\n"," 'hFD':None,\n"," 'HjorthComp':None,\n"," 'HjorthMob':None,\n"," 'falseNearestNeighbor':None,\n"," 'medianFreq':None,\n"," 'bandPwr_delta':None, \n"," 'bandPwr_theta':None, \n"," 'bandPwr_alpha':None, \n"," 'bandPwr_beta':None, \n"," 'bandPwr_gamma':None,\n"," 'stdDev':None,\n"," 'regularity':None,\n"," 'volt05':None, \n"," 'volt10':None, \n"," 'volt20':None,\n"," 'diffuseSlowing':None,\n"," 'spikeNum':None,\n"," 'deltaBurstAfterSpike':None,\n"," 'shortSpikeNum':None,\n"," 'numBursts':None,\n"," 'burstLenMean':None,\n"," 'burstLenStd':None,\n"," 'burstBandPowers':None,\n"," 'numSuppressions':None,\n"," 'suppLenMean':None,\n"," 'suppLenStd':None\n"," }\n","\n"," #Shannon Entropy\n"," ShannonRes = eeg.shannonEntropy(ans, bin_min=-200, bin_max=200, binWidth=2)\n"," # pwd = os.getcwd()\n"," # np.save(pwd + \"/data_extracted/DEAP_shannonEntropy\",ShannonRes)\n"," featuresDict['shannonEntropy'] = ShannonRes\n"," del ShannonRes\n","\n"," # tsalisRes = eeg.tsalisEntropy(ans,bin_min=-200, bin_max=200, binWidth=2, orders=list(range(1,11)))\n","\n"," # tsalisRes.shape\n","\n"," \"\"\"Subband Information Quantity\"\"\"\n","\n"," # delta (0.5–4 Hz)\n"," eegData_delta = eeg.filt_data(ans, 0.5, 4, fs)\n"," ShannonRes_delta = eeg.shannonEntropy(eegData_delta, bin_min=-200, bin_max=200, binWidth=2)\n"," # theta (4–8 Hz)\n"," eegData_theta = eeg.filt_data(ans, 4, 8, fs)\n"," ShannonRes_theta = eeg.shannonEntropy(eegData_theta, bin_min=-200, bin_max=200, binWidth=2)\n"," # alpha (8–12 Hz)\n"," eegData_alpha = eeg.filt_data(ans, 8, 12, fs)\n"," ShannonRes_alpha = eeg.shannonEntropy(eegData_alpha, bin_min=-200, bin_max=200, binWidth=2)\n"," # beta (12–30 Hz)\n"," eegData_beta = eeg.filt_data(ans, 12, 30, fs)\n"," ShannonRes_beta = eeg.shannonEntropy(eegData_beta, bin_min=-200, bin_max=200, binWidth=2)\n"," # gamma (30–63 Hz)\n"," eegData_gamma = eeg.filt_data(ans, 30,45, fs)\n"," ShannonRes_gamma = eeg.shannonEntropy(eegData_gamma, bin_min=-200, bin_max=200, binWidth=2)\n","\n"," # np.savez(pwd + \"/data_extracted/DEAP_subbandInformationQuantity\",ShannonRes_delta, ShannonRes_theta, ShannonRes_alpha, ShannonRes_beta, ShannonRes_gamma)\n"," featuresDict['ShannonRes_delta'] = ShannonRes_delta\n"," featuresDict['ShannonRes_theta'] = ShannonRes_theta\n"," featuresDict['ShannonRes_alpha']= ShannonRes_alpha\n"," featuresDict['ShannonRes_beta'] = ShannonRes_beta\n"," featuresDict['ShannonRes_gamma'] = ShannonRes_gamma\n"," \n"," del ShannonRes_delta, ShannonRes_theta, ShannonRes_alpha, ShannonRes_beta, ShannonRes_gamma\n","\n","\n"," # Lyapunov Exponent\n"," featuresDict['lyapunov'] = eeg.lyapunov(ans)\n"," # np.save(pwd + \"/data_extracted/DEAP_lyapunov\",LyapunovRes)\n","\n"," # Fractal Embedding Dimension\n"," featuresDict['hFD'] = eeg.hFD(ans[0,:,0],3)\n"," # np.save(pwd + \"/data_extracted/DEAP_hFD\",HiguchiFD_Res)\n"," # del HiguchiFD_Res\n","\n"," # Hjorth Mobility\n"," # Hjorth Complexity\n"," HjorthMob, HjorthComp = eeg.hjorthParameters(ans)\n"," # np.save(pwd + \"/data_extracted/DEAP_HjorthMob\",HjorthMob)\n"," # np.save(pwd + \"/data_extracted/DEAP_HjorthComp\",HjorthComp)\n"," featuresDict['HjorthComp'] = HjorthComp\n"," featuresDict['HjorthMob'] = HjorthMob\n"," del HjorthComp\n"," del HjorthMob\n","\n"," # False Nearest Neighbor\n"," featuresDict['falseNearestNeighbor'] = eeg.falseNearestNeighbor(ans)\n"," # np.save(pwd + \"/data_extracted/DEAP_falseNearestNeighbor\",FalseNnRes)\n"," # del FalseNnRes\n","\n"," # Median Frequency\n"," featuresDict['medianFreq'] = eeg.medianFreq(ans,fs)\n"," # np.save(pwd + \"/data_extracted/DEAP_medianFreq\",medianFreqRes)\n"," # del medianFreqRes\n","\n","\n"," # δ band Power\n"," featuresDict['bandPwr_delta'] = eeg.bandPower(ans, 0.5, 4, fs)\n"," # θ band Power\n"," featuresDict['bandPwr_theta'] = eeg.bandPower(ans, 4, 8, fs)\n"," # α band Power\n"," featuresDict['bandPwr_alpha'] = eeg.bandPower(ans, 8, 12, fs)\n"," # β band Power\n"," featuresDict['bandPwr_beta'] = eeg.bandPower(ans, 12, 30, fs)\n"," # γ band Power\n"," featuresDict['bandPwr_gamma'] = eeg.bandPower(ans, 30, 45, fs)\n","\n"," # np.savez(pwd + \"/data_extracted/DEAP_bandPwr\",bandPwr_delta, bandPwr_theta, bandPwr_alpha, bandPwr_beta, bandPwr_gamma)\n"," # del bandPwr_delta, bandPwr_theta, bandPwr_alpha, bandPwr_beta, bandPwr_gamma\n","\n"," # Standard Deviation\n"," featuresDict['stdDev'] = eeg.eegStd(ans)\n"," # np.save(pwd + \"/data_extracted/DEAP_std_res\",std_res)\n"," # del std_res\n","\n"," # Regularity (burst-suppression)\n"," featuresDict['regularity'] = eeg.eegRegularity(ans,fs)\n"," # np.save(pwd + \"/data_extracted/DEAP_regularity_res\",regularity_res)\n"," # del regularity_res\n","\n"," # Voltage < 5μ\n"," featuresDict['volt05'] = eeg.eegVoltage(ans,voltage=5)\n"," # Voltage < 10μ\n"," featuresDict['volt10'] = eeg.eegVoltage(ans,voltage=10)\n"," # Voltage < 20μ\n"," featuresDict['volt20'] = eeg.eegVoltage(ans,voltage=20)\n","\n"," # np.savez(pwd + \"/data_extracted/DEAP_voltage\",volt05_res, volt10_res, volt20_res)\n"," # del volt05_res, volt10_res, volt20_res\n","\n"," # Diffuse Slowing\n"," featuresDict['diffuseSlowing'] = eeg.diffuseSlowing(ans)\n"," # np.save(pwd + \"/data_extracted/DEAP_df_res\",df_res)\n"," # del df_res\n","\n"," # Spikes\n"," minNumSamples = int(70*fs/1000)\n"," featuresDict['spikeNum'] = eeg.spikeNum(ans,minNumSamples)\n"," # np.save(pwd + \"/data_extracted/DEAP_spikeNum_res\",spikeNum_res)\n"," # del spikeNum_res\n","\n"," # Delta burst after Spike\n"," featuresDict['deltaBurstAfterSpike'] = eeg.burstAfterSpike(ans,eegData_delta,minNumSamples=7,stdAway = 3)\n"," # np.save(pwd + \"/data_extracted/DEAP_deltaBurst_res\",deltaBurst_res)\n"," # del deltaBurst_res\n","\n"," # Sharp spike\n"," featuresDict['shortSpikeNum'] = eeg.shortSpikeNum(ans,minNumSamples)\n"," # np.save(pwd + \"/data_extracted/DEAP_sharpSpike_res\",sharpSpike_res)\n"," # del sharpSpike_res\n","\n"," # Number of Bursts\n"," featuresDict['numBursts'] = eeg.numBursts(ans,fs)\n"," # np.save(pwd + \"/data_extracted/DEAP_numBursts_res\",numBursts_res)\n"," # del numBursts_res\n","\n"," # Burst length μ and σ\n"," burstLenMean_res,burstLenStd_res = eeg.burstLengthStats(ans,fs)\n"," # np.save(pwd + \"/data_extracted/DEAP_burstLenMean_res\",burstLenMean_res)\n"," featuresDict['burstLenMean'] = burstLenMean_res\n"," del burstLenMean_res\n"," # np.save(pwd + \"/data_extracted/DEAP_burstLenStd_res\",burstLenStd_res)\n"," featuresDict['burstLenStd'] = burstLenStd_res\n"," del burstLenStd_res\n","\n"," # Burst Band Power for δ\n"," featuresDict['burstBandPowers'] = eeg.burstBandPowers(ans, 0.5, 4, fs)\n"," # np.save(pwd + \"/data_extracted/DEAP_burstBandPwrAlpha\",burstBandPwrAlpha)\n"," # del burstBandPwrAlpha\n","\n"," # Number of Suppressions\n"," featuresDict['numSuppressions'] = eeg.numSuppressions(ans,fs)\n"," # np.save(pwd + \"/data_extracted/DEAP_numSupps_res\",numSupps_res)\n"," # del numSupps_res\n","\n"," # Suppression length μ and σ\n"," suppLenMean_res,suppLenStd_res = eeg.suppressionLengthStats(ans,fs)\n"," \n"," featuresDict['suppLenMean'] = suppLenMean_res\n"," featuresDict['suppLenStd'] = suppLenStd_res\n"," # np.save(pwd + \"/data_extracted/DEAP_suppLenMean_res\",suppLenMean_res)\n"," del suppLenMean_res\n"," # np.save(pwd + \"/data_extracted/DEAP_suppLenStd_res\",suppLenStd_res)\n"," del suppLenStd_res\n","\n"," return featuresDict\n"]},{"cell_type":"code","execution_count":null,"metadata":{"colab":{"base_uri":"https://localhost:8080/"},"id":"TY2dCLnR9O1U","outputId":"0bb51405-f8d3-462d-c538-71dacfe51cfd"},"outputs":[{"name":"stdout","output_type":"stream","text":["/gdrive/My Drive/Project_DEAP\n"]}],"source":["!pwd"]},{"cell_type":"code","execution_count":null,"metadata":{"id":"NLjLjfflpaRk"},"outputs":[],"source":["# def save_obj(obj, name ):\n","# with open('obj/'+ name + '.pkl', 'wb') as f:\n","# pickle.dump(obj, f, pickle.HIGHEST_PROTOCOL)\n","\n","# def load_obj(name ):\n","# with open('obj/' + name + '.pkl', 'rb') as f:\n","# return pickle.load(f)"]},{"cell_type":"code","execution_count":null,"metadata":{"id":"dRkBs3Tgv6Vp"},"outputs":[],"source":["def getEpochedFeatures(window, stride, sfreq, clf, label):\n"," '''\n"," Returns Accuract vs Segment size plot for\n"," window - length of window\n"," stride - step \n"," sfreq - sampling freq\n"," clf - classifier\n"," label - 0-valence, 1-arousal, 2-dominance, 3-liking\n"," '''\n"," fs = sfreq\n"," X = None\n"," Y = None\n"," Z = None\n"," pwd = os.getcwd()\n"," with np.load(pwd + '/data_extracted/DEAP.npz', allow_pickle=True) as data:\n"," X = data['X']\n"," Y = data['Y']\n"," Z = data['Z']\n"," # print(X)\n"," # print(dict(data).keys())\n"," print(\"Data Loaded...\\n\")\n"," ch_names = ['F1', 'AF3', 'F3', 'F7', 'FC5', 'FC1', 'C3', 'T7', 'CP5', 'CP1', 'P3', 'P7', 'PO3', 'O1', 'Oz', 'Pz', 'Fp2', 'AF4', 'Fz', 'F4', 'F8', 'FC6', 'FC2', 'Cz', 'C4', 'T8', 'CP6', 'CP2', 'P4', 'P8', 'PO4', 'O2', 'hEOG','vEOG', 'zEMG','tEMG','GSR','Respiration belt','Plethysmograph','Temperature']\n"," emotiv_channels = ['AF3', 'F3', 'F7', 'FC5', 'T7', 'P7', 'O1','AF4','F4','F8','FC6','T8','P8','O2']\n"," index_arr = [ch_names.index(x) for x in emotiv_channels]\n"," # print(index_arr)\n"," X_new = select_channels(X,index_arr)\n"," \n"," # X_new.shape\n"," del X\n"," print(\"Channel selection done ...\\n\")\n"," '''\n"," # X = (32*40,40,8064)\n"," # Y = (32*40,4)\n"," # Z = (32*40,2)\n","\n"," # X : (nbSegments, nbChannel, nbTimepoints) : Data\n"," # Y : (nbSegments, nbEmotions) : Valence and arousal data\n"," # Z : (nbSegments, 2) : Participant number, and session number\n"," '''\n","\n"," (X_epoch, Y_epoch, Z_epoch) = epoch_data( X_new, Y, Z,window,stride,sfreq)\n"," del X_new\n"," del Y\n"," del Z\n","\n"," print(\"Epoching done ...\\n\")\n"," print(X_epoch.shape, Y_epoch.shape, Z_epoch.shape) #debug\n","\n"," # 1280*63,40,128\n"," # trial, channel, segment\n"," trials, channels, segment = X_epoch.shape\n"," ans = np.empty((channels, segment, trials)) #[chans x ms x epochs] \n"," for i in range(trials):\n"," ans[:,:,i] = X_epoch[i,:,]\n"," del X_epoch\n","\n"," # ans.shape\n"," print(\"Rotation of np.array done ...\\n\")\n"," pwd = os.getcwd()\n"," # np.savez(pwd + \"/data_extracted/DEAP_epoched_data\",ans,Y_epoch, Z_epoch)\n"," featuresDict = getFeaturesDict(ans,sfreq)\n","\n"," with open(pwd + '/data_extracted/featureDicts/'+str(window)+str(stride)+ '.pkl', 'wb') as f:\n"," pickle.dump(featuresDict, f, pickle.HIGHEST_PROTOCOL)\n","\n"," print(\"Feature Extraction done ...\\n\")\n"," return ans, Y_epoch\n"," "]},{"cell_type":"code","execution_count":null,"metadata":{"id":"uSFqxw9OrJ67"},"outputs":[],"source":["def getAccuracySegmentPlot(ans, Y_epoch, window, stride, sfreq, clf, label, num_features, scale=False, pca=False):\n"," \"\"\"Feature Ranking\"\"\"\n"," # Load FeaturesDict from memory\n"," fs = sfreq\n"," pwd = os.getcwd()\n"," with open(pwd+ '/data_extracted/featureDicts/'+str(window)+str(stride)+ '.pkl', 'rb') as f:\n"," featuresDict = pickle.load(f)\n","\n"," print(\"Number of segments are: {}\".format(ans.shape[2]))\n"," featuresToAvoid = ['volt05', 'volt10', 'volt20', 'burstBandPowers','hFD']\n"," featureMatrix = np.empty((0,ans.shape[2])) #[14*32 + 1,80640]\n"," for key,value in featuresDict.items():\n"," if(key not in featuresToAvoid):\n"," featureMatrix = np.append(featureMatrix,value,axis=0)\n","\n"," \n"," print(\"Shape of FeatureMatrix: {}\\n\".format(featureMatrix.T.shape))\n","\n"," featureMatrix = featureMatrix.astype('float64')\n"," \n"," if np.isnan(featureMatrix).any():\n"," featureMatrix = np.nan_to_num(X,nan=0)\n","\n"," y = Y_epoch[:,label] #valence\n"," \n"," feature_channel_index = []\n"," for i in range(14):\n"," for feature in featuresDict.keys():\n"," if(feature not in featuresToAvoid):\n"," feature_channel_index.append(feature + str(i))\n"," \n"," \n"," print(\"Number of Feature-Columns: {}\\n\".format(len(feature_channel_index)))\n","\n"," X = pd.DataFrame(featureMatrix.T)\n"," X = X.replace([np.inf, -np.inf], np.nan)\n"," X = X.fillna(0)\n"," X.columns = feature_channel_index\n"," y = pd.DataFrame(y)\n","\n"," print(\"Features Ready for undergoing selection tests done ...\\n\")\n"," \"\"\"Recursive Feature Elimination\"\"\"\n","\n"," # from sklearn.svm import SVR\n"," # from sklearn.feature_selection import RFE\n","\n"," # estimator = SVR()\n"," # selector = RFE(estimator, n_features_to_select=5, step=1)\n"," # selector = selector.fit(X, y)\n"," # selector.ranking_\n","\n"," \"\"\"SelecKBest\"\"\"\n"," #apply SelectKBest class to extract top 10 best features\n","\n"," bestfeatures = SelectKBest(score_func=f_classif, k=num_features)\n"," fit = bestfeatures.fit(X,y)\n","\n"," dfscores = pd.DataFrame(fit.scores_)\n"," dfcolumns = pd.DataFrame(X.columns)\n","\n"," #concat two dataframes for better visualization \n"," featureScores = pd.concat([dfcolumns,dfscores],axis=1)\n"," featureScores.columns = ['Specs','Score'] #naming the dataframe columns\n"," features_result = featureScores.nlargest(num_features,'Score')\n"," bestFeatureColumns = featureScores.nlargest(num_features,'Score')['Specs']\n","\n"," bestFeatureColumns = bestFeatureColumns.tolist()\n","\n"," # select particular features from data\n"," X = X[bestFeatureColumns]\n"," # Perform train-test split\n"," X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.2, random_state=42)\n","\n"," # Normalise-scale data \n"," # Feature Scaling\n"," if(scale == True):\n"," sc = StandardScaler()\n"," X_train = sc.fit_transform(X_train)\n"," X_test = sc.transform(X_test)\n","\n"," # Apply classfier\n"," # clf = xgb.XGBClassifier(verbose = 5)\n"," clf.fit(X_train, y_train.values.ravel())\n"," y_predict = clf.predict(X_test)\n"," rmse = mean_squared_error(y_test, y_predict, squared=False)\n"," print(\"window: {}, stide: {}, rmse: {}\".format(window,stride,rmse))\n"," return rmse, features_result\n"," # return (accuracy_score(y_test, y_predict)*100), features_result\n"," # print(featuresDict['hFD'][0])\n"," # # np.repeat()\n","\n"]},{"cell_type":"code","execution_count":null,"metadata":{"id":"-PY4MfbXCA27"},"outputs":[],"source":["def getFeatureAccuracySegmentPlot(ans, Y_epoch, window, stride, sfreq, clf, label, num_features, selectFeatures, scale=False, pca=False):\n"," \"\"\"Feature Ranking\"\"\"\n"," # Load FeaturesDict from memory\n"," fs = sfreq\n"," pwd = os.getcwd()\n"," with open(pwd+ '/data_extracted/featureDicts/'+str(window)+str(stride)+ '.pkl', 'rb') as f:\n"," featuresDict = pickle.load(f)\n","\n","\n"," featureMatrix = np.empty((0,ans.shape[2])) #[14*32 + 1,80640]\n"," for key,value in featuresDict.items():\n"," if(key in selectFeatures):\n"," featureMatrix = np.append(featureMatrix,value,axis=0)\n","\n"," # print(featureMatrix.T.shape)\n"," featureMatrix = featureMatrix.astype('float64')\n","\n","\n"," if np.isnan(featureMatrix).any():\n"," featureMatrix = np.nan_to_num(X,nan=0)\n","\n"," print(\"Number of Feature-Columns: {}\\n\".format(len(feature_channel_index)))\n"," \n"," y = Y_epoch[:,label] #valence\n"," \n"," feature_channel_index = []\n"," for i in range(14):\n"," for feature in selectFeatures:\n"," feature_channel_index.append(feature + str(i))\n","\n"," X = pd.DataFrame(featureMatrix.T)\n"," X = X.replace([np.inf, -np.inf], np.nan)\n"," X = X.fillna(0)\n"," X.columns = feature_channel_index\n"," y = pd.DataFrame(y)\n","\n"," print(\"Features Ready for undergoing selection tests done ...\\n\")\n"," \"\"\"Recursive Feature Elimination\"\"\"\n","\n"," # from sklearn.svm import SVR\n"," # from sklearn.feature_selection import RFE\n","\n"," # estimator = SVR()\n"," # selector = RFE(estimator, n_features_to_select=5, step=1)\n"," # selector = selector.fit(X, y)\n"," # selector.ranking_\n","\n"," \"\"\"SelecKBest\"\"\"\n"," #apply SelectKBest class to extract top 10 best features\n","\n"," bestfeatures = SelectKBest(score_func=f_classif, k=num_features)\n"," fit = bestfeatures.fit(X,y)\n","\n"," dfscores = pd.DataFrame(fit.scores_)\n"," dfcolumns = pd.DataFrame(X.columns)\n","\n"," #concat two dataframes for better visualization \n"," featureScores = pd.concat([dfcolumns,dfscores],axis=1)\n"," featureScores.columns = ['Specs','Score'] #naming the dataframe columns\n"," features_result = featureScores.nlargest(num_features,'Score')\n"," print(features_result)\n"," bestFeatureColumns = featureScores.nlargest(num_features,'Score')['Specs']\n","\n"," bestFeatureColumns = bestFeatureColumns.tolist()\n","\n"," # select particular features from data\n"," X = X[bestFeatureColumns]\n"," # Perform train-test split\n"," X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.2, random_state=42)\n","\n"," # Normalise-scale data \n"," # Feature Scaling\n"," if(scale == True):\n"," sc = StandardScaler()\n"," X_train = sc.fit_transform(X_train)\n"," X_test = sc.transform(X_test)\n","\n"," # Apply classfier\n"," # clf = xgb.XGBClassifier(verbose = 5)\n"," clf.fit(X_train, y_train.values.ravel())\n"," y_predict = clf.predict(X_test)\n"," rmse = mean_squared_error(y_test, y_predict,squared=False)\n"," print(\"window: {}, stide: {}, rmse: {}\".format(window,stride,rmse))\n"," return rmse, features_result\n"," # return (accuracy_score(y_test, y_predict)*100), features_result\n"," # print(featuresDict['hFD'][0])\n"," # # np.repeat()\n","\n"]},{"cell_type":"code","execution_count":null,"metadata":{"colab":{"base_uri":"https://localhost:8080/"},"id":"V_vzFoibFIfV","outputId":"2e1e5cd2-73f5-4bd5-9398-d19655231a05"},"outputs":[{"name":"stdout","output_type":"stream","text":["Data Loaded...\n","\n","Channel selection done ...\n","\n","Epoching done ...\n","\n","Rotation of np.array done ...\n","\n"]},{"name":"stderr","output_type":"stream","text":["/gdrive/MyDrive/Project_DEAP/EEGExtract.py:244: FutureWarning: `rcond` parameter will change to the default of machine precision times ``max(M, N)`` where M and N are the input matrix dimensions.\n","To use the future default and silence this warning we advise to pass `rcond=None`, to keep using the old, explicitly pass `rcond=-1`.\n"," (p, r1, r2, s)=np.linalg.lstsq(x, L)\n","/gdrive/MyDrive/Project_DEAP/EEGExtract.py:422: RuntimeWarning: Mean of empty slice\n"," volt_res = np.nanmean(eegFilt,axis=1)\n","/usr/local/lib/python3.7/dist-packages/numpy/core/fromnumeric.py:3373: RuntimeWarning: Mean of empty slice.\n"," out=out, **kwargs)\n","/usr/local/lib/python3.7/dist-packages/numpy/core/_methods.py:234: RuntimeWarning: Degrees of freedom <= 0 for slice\n"," keepdims=keepdims)\n"]}],"source":["clf = RandomForestRegressor()\n","pwd = os.getcwd()\n","for i in range(2,16,2):\n"," X,Y = getEpochedFeatures(i, i//2, 128, clf,0)\n","\n","# while(i!=1):\n","# X,Y = getEpochedFeatures(i, i//2, 128, clf,0)\n","# i = i-1\n"]},{"cell_type":"markdown","metadata":{"id":"pzVaJ6wc2uNN"},"source":["Random Forest Regressor"]},{"cell_type":"code","execution_count":null,"metadata":{"id":"EQQ0CI66B2YF"},"outputs":[],"source":["clf = RandomForestRegressor()\n","pwd = os.getcwd()\n","numb_features = 448\n","for i in range(2,9,2):\n"," X,Y = getEpochedFeatures(i, i//2, 128, clf,0)\n"," rfr_acc, rfr_fr = getAccuracySegmentPlot( X, Y, i,i//2, 128, clf,0,numb_features) #ans, Y_epoch, window, stride, sfreq, clf, label, num_features)\n"," print(i,i//2, rfr_acc, rfr_acc)\n"," with open(pwd+ \"/results.txt\", \"a\") as myfile:\n"," myfile.write(\"\\nRFR=> Window: {},Stride:{}, RMSE: {}, numbFeatures Selected: {}\".format(str(i),str(i//2), str(rfr_acc), numb_features))\n"]},{"cell_type":"markdown","metadata":{"id":"a4EGlh7S2w_E"},"source":["Support Vector Machine"]},{"cell_type":"code","execution_count":null,"metadata":{"id":"KJNC6YNiFqd_"},"outputs":[],"source":["from sklearn.svm import SVR"]},{"cell_type":"code","execution_count":null,"metadata":{"colab":{"base_uri":"https://localhost:8080/"},"id":"pAnqr2mQFwM1","outputId":"20c7dd11-583d-4da3-ae7c-ac47ee40dfe4"},"outputs":[{"name":"stdout","output_type":"stream","text":["Data Loaded...\n","\n","Channel selection done ...\n","\n","Epoching done ...\n","\n","Rotation of np.array done ...\n","\n","Feature Extraction done ...\n","\n","Features Ready for undergoing selection tests done ...\n","\n"]},{"name":"stderr","output_type":"stream","text":["/usr/local/lib/python3.7/dist-packages/sklearn/utils/validation.py:760: DataConversionWarning: A column-vector y was passed when a 1d array was expected. Please change the shape of y to (n_samples, ), for example using ravel().\n"," y = column_or_1d(y, warn=True)\n","/usr/local/lib/python3.7/dist-packages/sklearn/feature_selection/_univariate_selection.py:114: UserWarning: Features [126 127 128 129 130 131 132 133 134 135 136 137 138 139 294 295 296 297\n"," 298 299 300 301 302 303 304 305 306 307 308 309 310 311 312 313 314 315\n"," 316 317 318 319 320 321 322 323 324 325 326 327 328 329 330 331 332 333\n"," 334 335 339 340] are constant.\n"," UserWarning)\n"]},{"name":"stdout","output_type":"stream","text":["[LibSVM]window: 2, stide: 1, mse: 3.904153344762282\n","Data Loaded...\n","\n","Channel selection done ...\n","\n","Epoching done ...\n","\n","Rotation of np.array done ...\n","\n","Feature Extraction done ...\n","\n","Features Ready for undergoing selection tests done ...\n","\n"]},{"name":"stderr","output_type":"stream","text":["/usr/local/lib/python3.7/dist-packages/sklearn/utils/validation.py:760: DataConversionWarning: A column-vector y was passed when a 1d array was expected. Please change the shape of y to (n_samples, ), for example using ravel().\n"," y = column_or_1d(y, warn=True)\n","/usr/local/lib/python3.7/dist-packages/sklearn/feature_selection/_univariate_selection.py:114: UserWarning: Features [126 127 128 129 130 131 132 133 134 135 136 137 138 139 294 295 296 297\n"," 298 299 300 301 302 303 304 305 306 307 308 309 310 311 312 313 314 316\n"," 317 318 319 320 321 322 323 324 325 326 327 328 330 331 332 333 334 335] are constant.\n"," UserWarning)\n"]},{"name":"stdout","output_type":"stream","text":["[LibSVM]window: 4, stide: 2, mse: 3.9096165019652784\n","Data Loaded...\n","\n","Channel selection done ...\n","\n","Epoching done ...\n","\n","Rotation of np.array done ...\n","\n","Feature Extraction done ...\n","\n","Features Ready for undergoing selection tests done ...\n","\n"]},{"name":"stderr","output_type":"stream","text":["/usr/local/lib/python3.7/dist-packages/sklearn/utils/validation.py:760: DataConversionWarning: A column-vector y was passed when a 1d array was expected. Please change the shape of y to (n_samples, ), for example using ravel().\n"," y = column_or_1d(y, warn=True)\n","/usr/local/lib/python3.7/dist-packages/sklearn/feature_selection/_univariate_selection.py:114: UserWarning: Features [126 127 128 129 130 131 132 133 134 135 136 137 138 139 294 295 296 297\n"," 298 299 300 301 302 303 304 305 306 307 308 309 310 311 312 313 314 316\n"," 317 318 319 320 321 322 323 324 325 326 327 328 330 331 332 333 334 335] are constant.\n"," UserWarning)\n"]},{"name":"stdout","output_type":"stream","text":["[LibSVM]window: 6, stide: 3, mse: 3.8928451346517434\n","Data Loaded...\n","\n","Channel selection done ...\n","\n","Epoching done ...\n","\n","Rotation of np.array done ...\n","\n","Feature Extraction done ...\n","\n","Features Ready for undergoing selection tests done ...\n","\n"]},{"name":"stderr","output_type":"stream","text":["/usr/local/lib/python3.7/dist-packages/sklearn/utils/validation.py:760: DataConversionWarning: A column-vector y was passed when a 1d array was expected. Please change the shape of y to (n_samples, ), for example using ravel().\n"," y = column_or_1d(y, warn=True)\n","/usr/local/lib/python3.7/dist-packages/sklearn/feature_selection/_univariate_selection.py:114: UserWarning: Features [126 127 128 129 130 131 132 133 134 135 136 137 138 139 294 295 296 297\n"," 298 299 300 301 302 303 304 305 306 307 308 309 310 311 312 313 314 316\n"," 317 318 319 320 321 322 323 324 325 326 327 328 330 331 332 333 334 335] are constant.\n"," UserWarning)\n"]},{"name":"stdout","output_type":"stream","text":["[LibSVM]window: 8, stide: 4, mse: 3.756730849030575\n"]}],"source":["accuracy = []\n","feature_ranking = []\n","clf = SVR(kernel = 'rbf', verbose=5)\n","# selectFeatures = ['bandPwr_delta', 'bandPwr_theta', 'bandPwr_alpha', 'bandPwr_beta', 'bandPwr_gamma' ]\n","for i in range(2,9,2):\n"," X,Y = getEpochedFeatures(i, i//2, 128, clf,0)\n"," acc, fr = getAccuracySegmentPlot( X, Y, i,i//2, 128, clf,0,448) #ans, Y_epoch, window, stride, sfreq, clf, label, num_features)\n"," accuracy.append(acc)\n"," feature_ranking.append(fr)"]},{"cell_type":"code","execution_count":null,"metadata":{"id":"4N9kAtCY7n28"},"outputs":[],"source":["accuracy = []\n","feature_ranking = []\n","clf = xgb.XGBRegressor(verbose = 5)\n","selectFeatures = ['bandPwr_delta', 'bandPwr_theta', 'bandPwr_alpha', 'bandPwr_beta', 'bandPwr_gamma' ]\n","for i in range(2,9,2):\n"," X,Y = getEpochedFeatures(i, i//2, 128, clf,0)\n"," acc, fr = getBPAccuracySegmentPlot( X, Y, i,i//2, 128, clf,0,len(selectFeatures)*14,selectFeatures) #ans, Y_epoch, window, stride, sfreq, clf, label, num_features)\n"," accuracy.append(acc)\n"," feature_ranking.append(fr)\n"]},{"cell_type":"code","execution_count":null,"metadata":{"colab":{"base_uri":"https://localhost:8080/","height":184},"id":"1majTZ5MRBp3","outputId":"e2a3cd7b-c1f0-4735-d1e4-fac92f4c92a6"},"outputs":[{"ename":"NameError","evalue":"ignored","output_type":"error","traceback":["\u001b[0;31m---------------------------------------------------------------------------\u001b[0m","\u001b[0;31mNameError\u001b[0m Traceback (most recent call last)","\u001b[0;32m\u001b[0m in \u001b[0;36m\u001b[0;34m()\u001b[0m\n\u001b[0;32m----> 1\u001b[0;31m \u001b[0msvr_acc_448\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0maccuracy\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m 2\u001b[0m \u001b[0msvr_fr_448\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mfeature_ranking\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n","\u001b[0;31mNameError\u001b[0m: name 'accuracy' is not defined"]}],"source":["svr_acc_448 = accuracy\n","svr_fr_448 = feature_ranking"]},{"cell_type":"code","execution_count":null,"metadata":{"id":"oL9BpBcfLzHv"},"outputs":[],"source":["svr_acc_100 = accuracy\n","svr_fr_100 = feature_ranking"]},{"cell_type":"code","execution_count":null,"metadata":{"id":"rxs4wXQJFCH3"},"outputs":[],"source":["xgb_acc_bp = accuracy\n","xgb_fr_bp = feature_ranking"]},{"cell_type":"code","execution_count":null,"metadata":{"id":"K1a3cKSh_dzz"},"outputs":[],"source":["#200 features\n","xbgr_acc_200 = accuracy\n","xgbr_fr_200 = feature_ranking\n","\n","\n"]},{"cell_type":"code","execution_count":null,"metadata":{"colab":{"base_uri":"https://localhost:8080/"},"id":"NXcwhs0YBDR3","outputId":"21256fb1-369c-468c-e0ef-4400f912ab52"},"outputs":[{"name":"stdout","output_type":"stream","text":["[3.8924250570376677, 3.9042555230064364, 3.8264269887032, 3.770032057388034] [ Specs Score\n","226 ShannonRes_theta7 140.629776\n","198 lyapunov6 132.384573\n","86 spikeNum2 121.635594\n","58 burstLenMean1 119.467595\n","208 stdDev6 109.337011\n",".. ... ...\n","434 volt0513 0.709948\n","444 burstBandPowers13 0.682521\n","447 suppLenStd13 0.548314\n","441 numBursts13 0.508979\n","439 deltaBurstAfterSpike13 0.501913\n","\n","[390 rows x 2 columns], Specs Score\n","198 lyapunov6 81.573636\n","226 ShannonRes_theta7 77.753574\n","208 stdDev6 68.405879\n","184 shortSpikeNum5 63.314963\n","58 burstLenMean1 61.275193\n",".. ... ...\n","404 volt2012 0.987136\n","403 volt1012 0.893363\n","394 medianFreq12 0.812213\n","396 bandPwr_theta12 0.699974\n","160 shannonEntropy5 0.677763\n","\n","[394 rows x 2 columns], Specs Score\n","198 lyapunov6 59.435376\n","226 ShannonRes_theta7 54.448377\n","184 shortSpikeNum5 52.377686\n","208 stdDev6 50.412717\n","194 ShannonRes_theta6 44.832300\n",".. ... ...\n","392 HjorthMob12 0.607993\n","402 volt0512 0.355405\n","401 regularity12 0.351426\n","165 ShannonRes_gamma5 0.287447\n","394 medianFreq12 0.114061\n","\n","[394 rows x 2 columns], Specs Score\n","198 lyapunov6 44.238685\n","184 shortSpikeNum5 40.439526\n","226 ShannonRes_theta7 39.255752\n","208 stdDev6 37.638456\n","194 ShannonRes_theta6 35.235796\n",".. ... ...\n","404 volt2012 0.358448\n","405 diffuseSlowing12 0.322424\n","399 bandPwr_gamma12 0.239618\n","395 bandPwr_delta12 0.218744\n","394 medianFreq12 0.173991\n","\n","[394 rows x 2 columns]]\n"]}],"source":["print(xbgr_acc,xgbr_fr)\n"]},{"cell_type":"code","execution_count":null,"metadata":{"id":"vX0dJmsE2Y3b"},"outputs":[],"source":["# feature rmse\n","# channel rmse\n","# with pca/with pca\n","# 4 tables with graph"]},{"cell_type":"markdown","metadata":{"id":"C5YFsJOQ3eHl"},"source":["Electrode Ranking Based on RMSE"]},{"cell_type":"code","execution_count":null,"metadata":{"id":"zkyj0l5v3jyy"},"outputs":[],"source":["def electrodeAnalysis(ans, Y_epoch, window, stride, sfreq, clf, label, scale=False, pca=False):\n"," '''\n"," Ranks of features according to rmse computed by regressor passed in clf\n"," Plots electrode v/s rmse graph\n"," \n"," '''\n"," rmseList = []\n"," electrodeList = [\"Electrode{i}\".format(i) for i in range(1,14)]\n"," fs = sfreq\n"," pwd = os.getcwd()\n","\n"," for electrode in range(14):\n"," # Load FeaturesDict from memory\n"," with open(pwd+ '/data_extracted/featureDicts/'+str(window)+str(stride)+ '.pkl', 'rb') as f:\n"," featuresDict = pickle.load(f)\n","\n"," print(\"Number of segments are: {}\".format(ans.shape[2]))\n"," featuresToAvoid = ['volt05', 'volt10', 'volt20', 'burstBandPowers','hFD']\n"," featureMatrix = np.empty((0,ans.shape[2])) #[14*32 + 1,80640]\n","\n"," for key,value in featuresDict.items():\n"," if(key not in featuresToAvoid):\n"," featureMatrix = np.append(featureMatrix,value[electrode,:],axis=0)\n","\n"," # print(featureMatrix.T.shape)\n"," featureMatrix = featureMatrix.astype('float64')\n","\n"," if np.isnan(featureMatrix).any():\n"," featureMatrix = np.nan_to_num(X,nan=0)\n","\n"," y = Y_epoch[:,label] #valence\n","\n"," feature_channel_index = []\n"," for feature in selectFeatures:\n"," feature_channel_index.append(feature + str(electrode))\n","\n"," print(\"Number of Feature-Columns: {}\\n\".format(len(feature_channel_index)))\n","\n"," X = pd.DataFrame(featureMatrix.T)\n"," X = X.replace([np.inf, -np.inf], np.nan)\n"," X = X.fillna(0)\n"," X.columns = feature_channel_index\n"," y = pd.DataFrame(y)\n","\n"," print(\"Features Ready for undergoing selection tests done ...\\n\")\n","\n","\n"," X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.2, random_state=42)\n","\n"," # Normalise-scale data \n"," # Feature Scaling\n"," if(scale == True):\n"," sc = StandardScaler()\n"," X_train = sc.fit_transform(X_train)\n"," X_test = sc.transform(X_test)\n","\n"," # Apply classfier\n"," # clf = xgb.XGBClassifier(verbose = 5)\n"," clf.fit(X_train, y_train.values.ravel())\n"," y_predict = clf.predict(X_test)\n"," rmse = mean_squared_error(y_test, y_predict,squared=False)\n"," print(\"window: {}, stide: {}, rmse: {}\".format(window,stride,rmse))\n"," rmseList.append(rmse)\n"," \n","\n"," \n"," electrode_df = pd.DataFrame(electrodeList)\n"," rmse_df = pd.DataFrame(rmseList)\n"," #concat two dataframes for better visualization \n"," eletrodeRanking = pd.concat([electrodeList, rmseList],axis=1)\n"," eletrodeRanking.columns = ['Electrode','RMSE'] #naming the dataframe columns\n"," features_result = eletrodeRanking.sort_values('1')\n"," print(features_result)\n"," plt.plot(features_result['Electrode'],features_result['RMSE'])\n"," plt.xlabel('Electrode')\n"," plt.ylabel('RMSE')\n"," plt.title(\"Electrode v/s RMSE Plot for Window:{} Stride:{} epoched data\".format(window,stride))\n"," plt.show()\n"," \n","\n"]},{"cell_type":"markdown","metadata":{"id":"nIElrorJ3kEf"},"source":["Feature Ranking Based on RMSE"]},{"cell_type":"code","execution_count":null,"metadata":{"id":"lXcq6XN83j9Y"},"outputs":[],"source":["def featureAnalysis(ans, Y_epoch, window, stride, sfreq, clf, label, scale=False, pca=False):\n"," '''\n"," Ranks of features according to rmse computed by regressor passed in clf\n"," Plots electrode v/s rmse graph\n"," \n"," '''\n"," print(\"Number of segments are: {}\".format(ans.shape[2]))\n"," \n"," rmseList = []\n"," fs = sfreq\n"," pwd = os.getcwd()\n"," \n"," featuresDict = None\n"," with open(pwd+ '/data_extracted/featureDicts/'+str(window)+str(stride)+ '.pkl', 'rb') as f:\n"," featuresDict = pickle.load(f)\n"," featuresToAvoid = ['volt05', 'volt10', 'volt20', 'burstBandPowers','hFD']\n"," featuresList = featuresDict.keys()\n"," \n"," for x in featuresToAvoid:\n"," featuresList.remove(x)\n","\n"," \n","\n"," for feature in featuresList:\n"," # Load FeaturesDict from memory\n"," \n"," \n"," \n"," featureMatrix = featuresDict[feature]\n","\n"," featureMatrix = featureMatrix.astype('float64')\n","\n"," if np.isnan(featureMatrix).any():\n"," featureMatrix = np.nan_to_num(X,nan=0)\n","\n"," y = Y_epoch[:,label] #valence\n","\n"," feature_channel_index = []\n"," \n"," for i in range(14):\n"," feature_channel_index.append(feature + str(i))\n","\n"," print(\"Number of Feature-Columns: {}\\n\".format(len(feature_channel_index)))\n","\n"," X = pd.DataFrame(featureMatrix.T)\n"," X = X.replace([np.inf, -np.inf], np.nan)\n"," X = X.fillna(0)\n"," X.columns = feature_channel_index\n"," y = pd.DataFrame(y)\n","\n"," print(\"Features Ready for undergoing selection tests done ...\\n\")\n","\n","\n"," X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.2, random_state=42)\n","\n"," # Normalise-scale data \n"," # Feature Scaling\n"," if(scale == True):\n"," sc = StandardScaler()\n"," X_train = sc.fit_transform(X_train)\n"," X_test = sc.transform(X_test)\n","\n"," # Apply classfier\n"," # clf = xgb.XGBClassifier(verbose = 5)\n"," clf.fit(X_train, y_train.values.ravel())\n"," y_predict = clf.predict(X_test)\n"," rmse = mean_squared_error(y_test, y_predict,squared=False)\n"," print(\"window: {}, stide: {}, rmse: {}\".format(window,stride,rmse))\n"," rmseList.append(rmse)\n"," \n","\n"," \n"," features_df = pd.DataFrame(featuresList)\n"," rmse_df = pd.DataFrame(rmseList)\n"," #concat two dataframes for better visualization \n"," featureRanking = pd.concat([electrodeList, rmseList],axis=1)\n"," featureRanking.columns = ['Feature','RMSE'] #naming the dataframe columns\n"," features_result = featureRanking.sort_values('1')\n"," print(features_result)\n"," plt.plot(features_result['Feature'],features_result['RMSE'])\n"," plt.xlabel('Feature')\n"," plt.ylabel('RMSE')\n"," plt.title(\"Feature v/s RMSE Plot for Window:{} Stride:{} epoched data\".format(window,stride))\n"," plt.show()\n","\n","\n","\n"]},{"cell_type":"code","execution_count":null,"metadata":{"id":"bIJGseio3kKZ"},"outputs":[],"source":[]},{"cell_type":"markdown","metadata":{"id":"6ZJ2Jxri4lE0"},"source":["## Connectivity features"]},{"cell_type":"code","execution_count":null,"metadata":{"id":"xHs7pa7uXcUD"},"outputs":[],"source":["# 58%\n","# 59.970238095238095\n","\n","# for 20 features -> 58.36607142857143\n","# for 10 features -> 57.36607142857143\n","# for 1 feature -> 56.64682539682539"]},{"cell_type":"code","execution_count":null,"metadata":{"id":"pIUrkcL-QHvH"},"outputs":[],"source":["# # Filter the eegData, midpass filter \n","# #\teegData: 3D np array [chans x ms x epochs] \n","# def filt_data(eegData, lowcut, highcut, fs, order=7):\n","# trials, channels, segment = eegData.shape\n","# nyq = 0.5 * fs\n","# low = lowcut / nyq\n","# high = highcut / nyq\n","# b, a = signal.butter(order, [low, high], btype='band')\n","# # filt_eegData = np.empty((eegData.shape)) \n","# # for trial in range(trials):\n","# filt_eegData = signal.lfilter(b, a, eegData, axis = 2)\n","# return filt_eegData\n"]},{"cell_type":"code","execution_count":null,"metadata":{"id":"OXDYVZTTy_co"},"outputs":[],"source":["# def filt_data(eegData, lowcut, highcut, fs, order=7):\n","# trials, channels, segment = eegData.shape\n","# nyq = 0.5 * fs\n","# low = lowcut / nyq\n","# high = highcut / nyq\n","# b, a = signal.butter(order, [low, high], btype='band')\n","# # filt_eegData = np.empty((eegData.shape)) \n","# # for trial in range(trials):\n","# filt_eegData = signal.lfilter(b, a, eegData, axis = 2)\n","# return filt_eegData\n"]},{"cell_type":"code","execution_count":null,"metadata":{"id":"GAdrYrSSg3Ea"},"outputs":[],"source":["# bandPwr_delta"]},{"cell_type":"code","execution_count":null,"metadata":{"id":"S_OPCEl3jO2Z"},"outputs":[],"source":["'''\n","https://github.com/jordan-bird/eeg-feature-generation/blob/master/code/EEG_feature_extraction.py\n","https://github.com/JoyRabha/Feature-Extraction-EEG/blob/master/Feature%20Extraction.ipynb\n","https://github.com/veraloes/EEG-emotions/blob/master/load_preprocess_data/load_data.py\n","\n","https://realpython.com/python-scipy-fft/\n","\n","https://colab.research.google.com/drive/1tOVI-6mnoGZ5ToFN3pGatoAm_5PgnZZ_#scrollTo=P7mHjC18fz0q\n","'''\n"]},{"cell_type":"code","execution_count":null,"metadata":{"id":"1v67G6XtItTs"},"outputs":[],"source":["import pyeeg\n","from numpy.random import randn"]},{"cell_type":"code","execution_count":null,"metadata":{"id":"unVmMcIDK0EA"},"outputs":[],"source":["band = [0.5, 4, 7, 12, 30]"]},{"cell_type":"code","execution_count":null,"metadata":{"id":"chOoNY-3qgbY"},"outputs":[],"source":["# #Method 1\n","# def feature_fft(matrix, period = 1., mains_f = 50., \n","# \t\t\t\tfilter_mains = True, filter_DC = True,\n","# \t\t\t\tnormalise_signals = True,\n","# \t\t\t\tntop = 10, get_power_spectrum = True):\n","# \t\"\"\"\n","# \tComputes the FFT of each signal. \n","\t\n","# \tParameters:\n","# \t\tmatrix (numpy.ndarray): 2D [nsamples x nsignals] matrix containing the \n","# \t\tvalues of nsignals for a time window of length nsamples\n","# \t\tperiod (float): width (in seconds) of the time window represented by\n","# \t\tmatrix\n","# \t\tmains_f (float): the frequency of mains power supply, in Hz.\n","# \t\tfilter_mains (bool): should the mains frequency (plus/minus 1Hz) be \n","# \t\tfiltered out?\n","# \t\tfilter_DC (bool): should the DC component be removed?\n","# \t\tnormalise_signals (bool): should the signals be normalised to the \n","# \t\tbefore interval [-1, 1] before computing the FFT?\n","# \t\tntop (int): how many of the \"top N\" most energetic frequencies should \n","# \t\talso be returned (in terms of the value of the frequency, not the power)\n","# \t\tget_power_spectrum (bool): should the full power spectrum of each \n","# \t\tsignal be returned (in terms of magnitude of each frequency component)\n","\t\t\n","# \tReturns:\n","# \t\tnumpy.ndarray: 1D array containing the ntop highest-power frequencies \n","# \t\tfor each signal, plus (if get_power_spectrum is True) the magnitude of \n","# \t\teach frequency component, for all signals.\n","# \t\tlist: list containing feature names for the quantities calculated. The \n","# \t\tnames associated with the power spectrum indicate the frequencies down \n","# \t\tto 1 decimal place.\n","# \tAuthor:\n","# \t\tOriginal: [fcampelo]\n","# \t\"\"\"\n","\t\n","# \t# Signal properties\n","# \tN = matrix.shape[0] # number of samples\n","# \tT = period / N # Sampling period\n","\t\n","# \t# Scale all signals to interval [-1, 1] (if requested)\n","# \tif normalise_signals:\n","# \t\tmatrix = -1 + 2 * (matrix - np.min(matrix)) / (np.max(matrix) - np.min(matrix))\n","\tX = None\n","Y = None\n","Z = None\n","with np.load('data_extracted/DEAP.npz', allow_pickle=True) as data:\n"," X = data['X']\n"," Y = data['Y']\n"," Z = data['Z']\n"," print(dict(data).keys())\n","\n","X.shape\n","\n","def select_channels(data,channels):\n"," ans = np.empty((data.shape[0],len(channels),data.shape[2]))\n"," for sub in range(data.shape[0]):\n"," ans[sub,:,:] = np.array([data[sub,x,:] for x in channels])\n"," return ans\n","\n","ch_names = ['F1', 'AF3', 'F3', 'F7', 'FC5', 'FC1', 'C3', 'T7', 'CP5', 'CP1', 'P3', 'P7', 'PO3', 'O1', 'Oz', 'Pz', 'Fp2', 'AF4', 'Fz', 'F4', 'F8', 'FC6', 'FC2', 'Cz', 'C4', 'T8', 'CP6', 'CP2', 'P4', 'P8', 'PO4', 'O2', 'hEOG','vEOG', 'zEMG','tEMG','GSR','Respiration belt','Plethysmograph','Temperature']\n","emotiv_channels = ['AF3', 'F3', 'F7', 'FC5', 'T7', 'P7', 'O1','AF4','F4','F8','FC6','T8','P8','O2']\n","index_arr = [ch_names.index(x) for x in emotiv_channels]\n","# print(index_arr)\n","X_new = select_channels(X,index_arr)\n","\n","X_new.shape\n","del X\n","\n","'''\n","# X = (32*40,40,8064)\n","# Y = (32*40,4)\n","# Z = (32*40,2)\n","\n","# X : (nbSegments, nbChannel, nbTimepoints) : Data\n","# Y : (nbSegments, nbEmotions) : Valence and arousal data\n","# Z : (nbSegments, 2) : Participant number, and session number\n","'''\n","print(X_new.shape, Y.shape, Z.shape)\n","\n","\"\"\"Preprocessing \"\"\"\n","\n","'''\n","Preprocessing already done since deap_preprocessed data\n","preprocessing()\n","'''\n","\n","\"\"\"Normalise Values along channel\"\"\"\n","\n","def normalise(X):\n"," for video in range(X.shape[0]):\n"," for channel in range(X.shape[1]):\n"," min_max_scaler = preprocessing.MinMaxScaler()\n"," arr = X[video,channel,:].reshape(-1,1)\n"," X[video,channel,:] = np.reshape(min_max_scaler.fit_transform(arr), (X.shape[2],))\n"," \n"," return X\n","\n","# X = normalise(X)\n","X_new\n","\n","\"\"\"Epoching Data\"\"\"\n","\n","# nth window end segment + (n-1)step\n","# 8064 = segment + (n-1)step\n","# => (8064-segment)/step + 1 = n\n","\n","def epoch_data(X, Y, Z, window, stride, sfreq):\n"," #X\n"," trials,channels,timepoints = X.shape #1280x40x8064 => 1280*40*63*128\n"," # print(X[1,:,x:x+128].shape)\n"," # return []\n"," segment = int(window*sfreq)\n"," step = int(stride*sfreq)\n"," epochPerTrial = int((timepoints-segment)/step + 1)\n","\n"," X_new = np.empty((trials*epochPerTrial,channels,segment))\n"," Y_new = np.empty((trials*epochPerTrial,4))\n"," Z_new = np.empty((trials*epochPerTrial,2))\n","\n"," count=0\n"," for trial in range(trials):\n"," for epoch in range(epochPerTrial):\n"," X_new[count,:,:] = X[trial,:,epoch*step:(epoch*step)+segment]\n"," Y_new[count,:] = Y[trial,:]\n"," Z_new[count,:] = Z[trial,:]\n"," count = count+1\n","\n"," #Y\n","\n","\n"," #Z \n"," return X_new, Y_new, Z_new\n","\n","(X_epoch, Y_epoch, Z_epoch) = epoch_data( X_new, Y, Z,1,1,128)\n","del X_new\n","del Y\n","del Z\n","\n","print(X_epoch.shape, Y_epoch.shape, Z_epoch.shape)\n","\n","# 1280*63,40,128\n","# trial, channel, segment\n","trials, channels, segment = X_epoch.shape\n","ans = np.empty((channels, segment, trials)) #[chans x ms x epochs] \n","for i in range(trials):\n"," ans[:,:,i] = X_epoch[i,:,]\n","del X_epoch\n","\n","ans.shape\n","\n","pwd = os.getcwd()\n","np.savez(pwd + \"/data_extracted/DEAP_epoched_data\",ans,Y_epoch, Z_epoch)\n","\n","# \t# Compute the (absolute values of the) FFT\n","# \t# Extract only the first half of each FFT vector, since all the information\n","# \t# is contained there (by construction the FFT returns a symmetric vector).\n","# \tfft_values = np.abs(scipy.fft.fft(matrix, axis = 0))[0:N//2] * 2 / N\n","\t\n","# \t# Compute the corresponding frequencies of the FFT components\n","# \tfreqs = np.linspace(0.0, 1.0 / (2.0 * T), N//2)\n","\t\n","# \t# Remove DC component (if requested)\n","# \tif filter_DC:\n","# \t\tfft_values = fft_values[1:]\n","# \t\tfreqs = freqs[1:]\n","\t\t\n","# \t# Remove mains frequency component(s) (if requested)\n","# \tif filter_mains:\n","# \t\tindx = np.where(np.abs(freqs - mains_f) <= 1)\n","# \t\tfft_values = np.delete(fft_values, indx, axis = 0)\n","# \t\tfreqs = np.delete(freqs, indx)\n","\t\n","# \t# Extract top N frequencies for each signal\n","# \tindx = np.argsort(fft_values, axis = 0)[::-1]\n","# \tindx = indx[:ntop]\n","\t\n","# \tret = freqs[indx].flatten(order = 'F')\n","\t\n","# \t# Make feature names\n","# \tnames = []\n","# \tfor i in np.arange(fft_values.shape[1]):\n","# \t\tnames.extend(['topFreq_' + str(j) + \"_\" + str(i) for j in np.arange(1,11)])\n","\t\n","# \tif (get_power_spectrum):\n","# \t\tret = np.hstack([ret, fft_values.flatten(order = 'F')])\n","\t\t\n","# \t\tfor i in np.arange(fft_values.shape[1]):\n","# \t\t\tnames.extend(['freq_' + \"{:03d}\".format(int(j)) + \"_\" + str(i) for j in 10 * np.round(freqs, 1)])\n","\t\n","# \treturn ret, names\n","\n","#Method 2"]},{"cell_type":"markdown","metadata":{"id":"KAlThS9PjHN6"},"source":["Links"]},{"cell_type":"code","execution_count":null,"metadata":{"id":"mGqWfdcmtwN3"},"outputs":[],"source":["# # function for epoching\n","# vido, channel timepoints\n","# (1280,40,8064) => \n","# [chans x ms x epochs] \n","# 1280*40,8064 => [40,128,63] 8064/128*4 = 8064/512 = 12\n","\n","# => (1280*63,40,128)\n","# # y -axis acc\n","# # x-axis epoch size/window\n","\n","# feature importance/ranking\n","# which channels are most important \n","#select model "]},{"cell_type":"code","execution_count":null,"metadata":{"id":"VqGh6DPiu_d_"},"outputs":[],"source":["# # Extract the Shannon Entropy\n","# # threshold the signal and make it discrete, normalize it and then compute entropy\n","# def shannonEntropy(eegData, bin_min, bin_max, binWidth):\n","# H = np.zeros((eegData.shape[0], eegData.shape[1]))\n","# for trial in range(H.shape[0]):\n","# for channel in range(H.shape[1]):\n","# counts, binCenters = np.histogram(eegData[trial,channel,:], bins=np.arange(bin_min+1, bin_max, binWidth))\n","# nz = counts > 0\n","# prob = counts[nz] / np.sum(counts[nz])\n","# H[trial, channel] = -np.dot(prob, np.log2(prob/binWidth))\n","# return H"]},{"cell_type":"code","execution_count":null,"metadata":{"id":"H0QHiFe_4CPV"},"outputs":[],"source":[]},{"cell_type":"code","execution_count":null,"metadata":{"id":"iyaNpTMQ4CX2"},"outputs":[],"source":["arr = np.array([[1,2],[3,4]])\n","l = [0,1]\n","np.array([arr[x,:] for x in l])"]},{"cell_type":"code","execution_count":null,"metadata":{"id":"BjgBCtcZPzjj"},"outputs":[],"source":[]},{"cell_type":"code","execution_count":null,"metadata":{"id":"XnN5oOebdmGI"},"outputs":[],"source":[]},{"cell_type":"code","execution_count":null,"metadata":{"id":"7CcW1C3xB9-M"},"outputs":[],"source":[]}],"metadata":{"colab":{"collapsed_sections":[],"machine_shape":"hm","provenance":[]},"kernelspec":{"display_name":"Python 3","name":"python3"}},"nbformat":4,"nbformat_minor":0} \ No newline at end of file diff --git a/eeg_ml_pipeline/intersection.pkl b/eeg_ml_pipeline/intersection.pkl new file mode 100644 index 0000000..0dc3e26 Binary files /dev/null and b/eeg_ml_pipeline/intersection.pkl differ diff --git a/ml_plots/draw_plots.ipynb b/ml_plots/draw_plots.ipynb new file mode 100644 index 0000000..43a4ea9 --- /dev/null +++ b/ml_plots/draw_plots.ipynb @@ -0,0 +1 @@ +{"nbformat":4,"nbformat_minor":0,"metadata":{"colab":{"name":"draw_plots.ipynb","provenance":[],"collapsed_sections":[],"toc_visible":true,"authorship_tag":"ABX9TyP6+iKDjDq+q0ckEVaczqCV"},"kernelspec":{"name":"python3","display_name":"Python 3"},"language_info":{"name":"python"}},"cells":[{"cell_type":"code","metadata":{"id":"17CtSqjeGxHT","executionInfo":{"status":"ok","timestamp":1656477754515,"user_tz":-330,"elapsed":805,"user":{"displayName":"Rohit Garg","userId":"11252576926243182044"}}},"source":["import pandas as pd\n","import matplotlib.pyplot as plt\n","%matplotlib inline\n","import numpy as np\n","import os"],"execution_count":1,"outputs":[]},{"cell_type":"code","metadata":{"colab":{"base_uri":"https://localhost:8080/"},"id":"1OGtvnMwEDfy","executionInfo":{"status":"ok","timestamp":1656477760975,"user_tz":-330,"elapsed":6474,"user":{"displayName":"Rohit Garg","userId":"11252576926243182044"}},"outputId":"34b395ae-2aad-46ae-c55d-d65167b00393"},"source":["from google.colab import drive\n","drive.mount('/gdrive',force_remount=True)"],"execution_count":2,"outputs":[{"output_type":"stream","name":"stdout","text":["Mounted at /gdrive\n"]}]},{"cell_type":"code","metadata":{"colab":{"base_uri":"https://localhost:8080/"},"id":"aygGIlqnEcEu","executionInfo":{"status":"ok","timestamp":1656477760982,"user_tz":-330,"elapsed":71,"user":{"displayName":"Rohit Garg","userId":"11252576926243182044"}},"outputId":"351ddd54-fe14-499c-eabd-420fdc1747d5"},"source":["%cd /gdrive/MyDrive/Project_DEAP/4.1.2021/"],"execution_count":3,"outputs":[{"output_type":"stream","name":"stdout","text":["/gdrive/MyDrive/Project_DEAP/4.1.2021\n"]}]},{"cell_type":"markdown","source":["# Revised Plots for IEEE Journal"],"metadata":{"id":"bv25DjSjzwmI"}},{"cell_type":"code","source":["plt.clf()"],"metadata":{"colab":{"base_uri":"https://localhost:8080/","height":35},"id":"jINcGP5r1_3p","executionInfo":{"status":"ok","timestamp":1656478712308,"user_tz":-330,"elapsed":495,"user":{"displayName":"Rohit Garg","userId":"11252576926243182044"}},"outputId":"6165525c-b613-43f3-ff16-1b85e2897743"},"execution_count":18,"outputs":[{"output_type":"display_data","data":{"text/plain":["
"]},"metadata":{}}]},{"cell_type":"code","metadata":{"id":"DWweH7FCzYEJ","executionInfo":{"status":"ok","timestamp":1656478713016,"user_tz":-330,"elapsed":20,"user":{"displayName":"Rohit Garg","userId":"11252576926243182044"}}},"source":["def export_legend(legend, filename=\"exp_legend_ieee\", expand=[-5,-5,5,5]):\n"," fig = legend.figure\n"," fig.canvas.draw()\n"," bbox = legend.get_window_extent()\n"," bbox = bbox.from_extents(*(bbox.extents + np.array(expand)))\n"," bbox = bbox.transformed(fig.dpi_scale_trans.inverted())\n"," fig.savefig(\"/gdrive/MyDrive/Project_DEAP/4.1.2021/plots_ieee/\" + filename + '.svg', dpi=\"figure\", bbox_inches=bbox)\n"],"execution_count":19,"outputs":[]},{"cell_type":"code","metadata":{"id":"7A9pxTuxzYEJ","executionInfo":{"status":"ok","timestamp":1656478713605,"user_tz":-330,"elapsed":13,"user":{"displayName":"Rohit Garg","userId":"11252576926243182044"}}},"source":["def va_features_plots(dataset_list):\n"," legendList = [];\n"," for idx,dataset in enumerate(dataset_list):\n","\n"," os.chdir(\"/gdrive/MyDrive/Project_DEAP/4.1.2021/{}/plots/\".format(dataset))\n","\n"," df = pd.read_csv(\"topCommonFeatureFSRegressionRankingSelectKBest11.csv\")\n"," # plt.plot(df['RMSE'])\n"," fig = plt.gcf()\n"," fig.set_size_inches(16, 10)\n"," # X = pd.DataFrame([x for x in range(1,) ])\n"," plt.rcParams.update({'font.size': 40})\n"," plt.xlabel('Top N Features')\n"," plt.ylabel('RMSE') \n"," # df.index += 1\n"," # X = pd.DataFrame([str(x) for x in df.index])\n"," plt.xticks(np.arange(0,25,5),np.arange(1,26,5))\n"," plt.plot(df.loc[:,\"RMSE\"], linewidth=2.0, markersize = 10)\n"," \n"," plt.tight_layout()\n"," # print(pd.DataFrame(df.index))\n"," legendList.append(f'{dataset}(Valence)');\n","\n"," os.chdir(\"/gdrive/MyDrive/Project_DEAP/4.1.2021/{}/arousal_plots/\".format(dataset))\n","\n","\n"," df = pd.read_csv(\"topCommonFeatureFSRegressionRankingSelectKBest11.csv\")\n"," # plt.plot(df['RMSE'])\n"," fig = plt.gcf()\n"," fig.set_size_inches(16, 10)\n"," # X = pd.DataFrame([x for x in range(1,) ])\n"," plt.rcParams.update({'font.size': 40})\n"," plt.xlabel('Top N Features')\n"," plt.ylabel('RMSE')\n"," # df.index += 1\n"," # X = pd.DataFrame([str(x) for x in df.index])\n"," plt.xticks(np.arange(0,25,5),np.arange(1,26,5))\n"," plt.plot(df.loc[:,\"RMSE\"], '-o', color = f'C{idx}', linewidth=2.0, markersize = 10)\n"," plt.tight_layout()\n"," legendList.append(f'{dataset}(Arousal)');\n"," \n","\n"," # legend = plt.legend(legendList, bbox_to_anchor=(0.5, -0.6),fontsize='xx-small')\n"," # legend = plt.legend(legendList,fontsize='xx-small')\n","\n"," # export_legend(legend)\n","\n"," ax = plt.gca()\n"," box = ax.get_position()\n"," # ax.set_position([box.x0, box.y0 + box.height * 0.1,box.width, box.height * 0.9])\n","\n"," # Put a legend below current axis\n"," ax.legend(labels = legendList, loc='upper center', bbox_to_anchor=(0.5, -0.05),fancybox=True, shadow=True, ncol=3)\n"," \n"," export_legend(ax.get_legend())\n","\n"," ax.legend().set_visible(False)\n"," # ax.legend().set_visible(False)\n"," plt.savefig(\"/gdrive/MyDrive/Project_DEAP/4.1.2021/plots_ieee/exp_feature_plot.svg\".format(dataset), bbox_inches='tight', dpi=500)\n","\n"," # plt.savefig(\"verification/DREAMER_Combined_Features_Plots1.svg\", bbox_inches='tight', dpi=500)\n"," plt.show()\n"," plt.clf()\n","\n"," "],"execution_count":20,"outputs":[]},{"cell_type":"code","metadata":{"colab":{"base_uri":"https://localhost:8080/","height":563},"executionInfo":{"status":"ok","timestamp":1656478718488,"user_tz":-330,"elapsed":1649,"user":{"displayName":"Rohit Garg","userId":"11252576926243182044"}},"outputId":"3443f833-8751-4f59-9bff-18068308f363","id":"j73ZfIurzYEK"},"source":["dataset_list = ['DREAMER', 'DEAP', 'OASIS'];\n","va_features_plots(dataset_list)"],"execution_count":21,"outputs":[{"output_type":"stream","name":"stderr","text":["No handles with labels found to put in legend.\n"]},{"output_type":"display_data","data":{"text/plain":["
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\n"},"metadata":{"needs_background":"light"}},{"output_type":"display_data","data":{"text/plain":["
"]},"metadata":{}}]},{"cell_type":"code","metadata":{"id":"CwojWF21zYEK"},"source":["def va_electrodes_plots(dataset_list):\n"," legendList = []\n"," for idx, dataset in enumerate(dataset_list):\n","\n"," os.chdir(\"/gdrive/MyDrive/Project_DEAP/4.1.2021/{}/plots/\".format(dataset))\n","\n"," df = pd.read_csv(\"topCommonElectrodeFSRegressionRankingSelectKBest11.csv\")\n"," # plt.plot(df['RMSE'])\n"," fig = plt.gcf()\n"," fig.set_size_inches(18,10)\n"," # X = pd.DataFrame([x for x in range(1,) ])\n"," plt.rcParams.update({'font.size': 40})\n"," plt.xlabel('Top N Electrodes')\n"," plt.ylabel('RMSE')\n"," # df.index += 1\n"," # X = pd.DataFrame([str(x) for x in df.index])\n"," plt.xticks(np.arange(0,14,2),np.arange(1,15,2))\n"," plt.plot(df.loc[:,\"RMSE\"], linewidth=2.0, markersize = 10)\n"," plt.tight_layout()\n"," legendList.append(f'{dataset}(Valence)');\n","\n","\n"," os.chdir(\"/gdrive/MyDrive/Project_DEAP/4.1.2021/{}/arousal_plots/\".format(dataset))\n","\n"," df = pd.read_csv(\"topCommonElectrodeFSRegressionRankingSelectKBest11.csv\")\n"," # plt.plot(df['RMSE'])\n"," fig = plt.gcf()\n"," fig.set_size_inches(18,10)\n"," # X = pd.DataFrame([x for x in range(1,) ])\n"," plt.rcParams.update({'font.size': 40})\n"," plt.xlabel('Top N Electrodes')\n"," plt.ylabel('RMSE')\n"," # df.index += 1\n"," # X = pd.DataFrame([str(x) for x in df.index])\n"," plt.xticks(np.arange(0,14,2),np.arange(1,15,2))\n"," plt.plot(df.loc[:,\"RMSE\"], '-o', color = f'C{idx}', linewidth=2.0, markersize = 10)\n"," # plt.plot(df.loc[:,\"RMSE\"], color = 'C1')\n"," plt.tight_layout()\n"," legendList.append(f'{dataset}(Arousal)');\n","\n","\n"," legend = plt.legend(legendList, fontsize='xx-small')\n"," export_legend(legend)\n"," ax = plt.gca()\n"," ax.legend().set_visible(False)\n"," plt.savefig(\"/gdrive/MyDrive/Project_DEAP/4.1.2021/plots_ieee/electrode_plot.svg\".format(dataset), bbox_inches='tight', dpi=500)\n"," plt.show()\n"," plt.clf()"],"execution_count":null,"outputs":[]},{"cell_type":"code","metadata":{"colab":{"base_uri":"https://localhost:8080/","height":674},"executionInfo":{"status":"ok","timestamp":1645961029041,"user_tz":-330,"elapsed":5902,"user":{"displayName":"Rohit Garg","photoUrl":"https://lh3.googleusercontent.com/a-/AOh14Gj3Za6nmSY12sPvpCoWOtWQcVR5CMMSPBRGTexH_w=s64","userId":"11252576926243182044"}},"outputId":"51028a2e-8306-45e8-c486-ca6dca693f98","id":"un5NJHr9zYEL"},"source":["dataset_list = ['DREAMER', 'DEAP', 'OASIS'];\n","va_electrodes_plots(dataset_list);"],"execution_count":null,"outputs":[{"output_type":"stream","name":"stderr","text":["No handles with labels found to put in legend.\n"]},{"output_type":"display_data","data":{"image/png":"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s5nFAwAAAAAAoUBABqxFkYiz+uW6ndJFb609l1+QJl5whmhOvCBNnZCmPdvUCSk75Wxnn2vyECOlN5bDsy2+MM2zDY6xkAAAAAAAYE0jIAP6TTwlbfopZwtirTO3mTcwmz5ZXmXzpDRVfp05Jc2edragOdBc0UTt8M3hrcFhWmJwZd4vAAAAAADLFKqAzBiTlvRqST8laYOklKTzkk5KesJa+1IX22Yk/bSkiyVtkzQn6WVJT1prj3WrXehDxpSHbo4Fz3vmKhackCwoPKscn5Cy551hnZOL/PgkR8qBmT9M8xxnNkvRUP2xBAAAAADoAX3/m6gx5rWSbpZ0g6Q3SYo2qfu8pE9I+qS1dm6V2heT9HuSflvSroAqJWPMw5L+0Fr7yGq0CZDkBFUj25xNb2xcLzdbDs1OBA/ndPez56Uz56UzP2zyUCNlNjWYG83TG21glGGdAAAAAICOMdbabrdhxRhjHpd09RIu/ZGkX7PWPtnhJtUwxmyX9D/UWhtLkv6TtfY/LuVZe/futYcPH17KpcDyWeusyNkoPHOPZ89IauHPpFiqdpGBurnRyr3T4gMr/tYAAAAAAGuDMeZJa+3eoHP93oPskoCyoqTvSXpFzvDKDXKGNq7z1LlU0sPGmLdaa1ckVTLGZCQdlPRa36kfSPqhpCE53XbWl8sjkv6DMWbBWvvRlWgTsGKMkdLrne2C1zSuV8xXh3V6h3PWhGknnQUGJl5wtmZSIwHDObfUDu1Mb2RYJwAAAACEXFh+KyxIuk/SpyQ9bK2d9p4sD3O8VdLHJY2Ui4ckfd4Yc6m1dmYF2vTfVBuOvSzpV621j3raNSBn+OUfSnLHk33EGPMta+0/rUCbgO6KxqWR7c7WTHZm8bnRpk9IC+ed7cyzje9lIs7cZ25gltnkHGc2SulN5eNNzn4y09n3CwAAAADoCf0+xPKEpH+UM3/XKy3Uv1zSY6rtTXaHtfYPOtyuvZKe8BRNSnq9tfaFBvX/dznhnevb1tomk0LVY4glQqdUkubHPcM5G4Rps2dav2d80OlxltlcDs021gZohGkAAAAA0LOaDbHs94BsZ7srUxpjfl1O7y7Xc9baSzvcri9IOuAp+lfW2r9oUt9IelzSVZ7id1prP9/qMwnIgAYKufKwzhNOeDZ7xjmeOV3eP13ePy0VFlq/L2EaAAAAAPSU0AZkS2GMSUk6J2nQU3yBtfZUh+4/Kum0qsNbJyRttdY2/c3bGPMrkj7tKfof1tpbWn0uARmwTNZK2emAAG2VwjT3PGEaAAAAACxJmCfpb5u1dsEY85ykKz3FWyV1JCCTtE+1n/s9i4VjZZ+TNKdqcHejMSZhrc11qF0AmjFGSg072/qLmtdtGKaddo7d/dlyoJafkyZfdLbFVMK08lxphGkAAAAAsGwEZMEKvuN4B+/9dt/xo4G1fMrB3ROSrisXDcsZctnS9QBW0ZLDtAYBWqfDtLreaoRpAAAAAMKNgMynPN/Xbl9xp3qPSdKrfcffauPab6gakLn3IiAD1rIVC9POEKYBAAAAQIsIyOpdK2m95/i0pLYm+l+Ed8J/K+loG9f663Z08QAAPW45YVpQgDZzavlhWnqTNLBOGhiVUuuc/Uav0U52xgUAAACAziEgq/cB3/H9tkMrGRhjNsgZGuk6Y63NtnGLl33HFy+/VWvA+BHp65+Qnr5bys1IiYx0xS3Sm98vje3pduuA3tRumJabqR/O6Q3TZss91toN07wSmcVDtEZBW5R/XQEAAABYOfzG4WGM+VlJN3uKrKT/t4OPGPEdn2nzen99//36z/MPSXffKhXzUinvlOWmpW/fJX3309Itd0mX+Kd1A9AWY6TkkLO1HaadkRYmpfnJ6uv8RH3ZwqRzXW5GmvJn/S1IZHzh2UhAwDZa3h+tlqVGCNcAAAAALKonf2swxsQlXeYeW2ufXoVnrpf0177iT1lrn+rgY/wT97SyeqXX/CL36y/jR5xwLD9Xf65UDszuvlV632P0JANWSzthmpc75HOxEK3yOlHdXzhfDdfOH2u/zYkhXy+1Jj3Y3HBtYNQJ1yLR9p8HAAAAYM1pKyAzxny7vGsl3WStPblI/aUGXVslfcfzrBUN8owxUUn/IGm7p/hlSb/X4UelfcftBmT++v771TDG/Kak35SknTt3tvmoHvD1Tzg9x5op5qXH/1y66WOr0yYAS+Md8rmuzT+PSiWn52izEK3Za27a2ZYSriWHy+HZyOLDQL092QjXAAAAgDWl3eDpyvKrlZRoof5ygi7TRt3l+jNJb/Mc5yT9irV2coWf2+7cZv76TT8ja+1fSvpLSdq7d29H5lFbVU/fXR1W2UgpLz31d9J1/07KbFyddgFYXZGIEzilRiRd2N61pZKUnWocnjXtyXbeuTY7JZ1fQruTI7XBmvuayEjRhGeL+14D9mPJxeu4+wRzAAAAQNtWY4jlagZdbTPGfFDS+zxFJUm3WmsfW4HHzfqOB9q83l9/Zhlt6X25Ft9efl76k4uloa3Sltc529YrndehLU7PFQDhFIlUh1WOtnmtG64tOhw0IGhbmJKy552towsht8BEWw/fYouEbXX7ydbrLhbqEeQBAACgh/TkHGSrpTwE8SO+4vdbaz+zQo/0B2SpNq/31+/vgCyRcYZFLcZEpfigNH3c2Z47VD2X3ihtubI2OBvZQWgGYHHecK1dpWI5XAsYDpqfl4o5Z4h4MedshVx131te2Xdfs83PF7KSLUqFeWfrZSbi60nXSpCXKAdvyXJZ0nPOX5Ys140HlCV857xlCf4dAQAAEEKhDciMMb8k6S98xR+01vrLOsk/SGdDm9f7xxAuZdDP2nHFLc5qlc2GWUbi0htvk/b9sTRxVDr+HenEd8vbU84Kez9+yNlcA6PlwMwTnI3udn4ZBoBOiETLq2q2222tA0pFJyhrGLY1CtwWqVtoFM75g7pmz/YGeSWpsOBsvSbqD+T8wVyr55YS5CUD7k2POwAAgJUWyoDMGHOjpL+V5E1E/sRa+9GVfK619owxZkrScLlokzEmYa3NtXiLHb7jn3SudT3oze+Xvvvp5gFZNC5d8ztOuLX+Imd77c3OOWulyRedsOz4U9XQbO6cdOQrzuZKjkhbrqgGZluudO7FLyQA1ppIVEoMShrsdkuaKxYWCds8gVshVw3e3H03BKyEgbn6skXPlUM/b1nJ8/xeYqLthXU1w1z9Peb8YV2iyTWLXB8N5V8lAQBAHwrd32qMMT8j6V7VLjLwSWvtv1mlJvxI0pvc5kjaXS5rxW7f8Q871aieNLZHuuUu6e5bnV9gvEFZJO78Rf2Wu5x6QYyRRnc52+W/4JRZK029Uu1l5gZnMyelFx51Nlc8LV3w2up8ZlteJ224lF8GAKATorHyn6c9FuSVSrW969wgrRLMBYV1vtBuSUFeQFjnPWeLUn7O2XpJZahsUMDmlgcNk21w3Mo1ddf7r3F73dEzHAAAtC5Uv+kbY14v6X7V/m38bkm/tYrN+IGqAZkkXaXWA7KrfMfPdKRFveySt0vve0x6/M+lpz/jTNyfyEhX/LLTc6xRONaIMdLIdmf7qZuq5dMnPUMzy8HZ1MvSsW84myuWkja/pnYhgI2XOX8hBwCsfZGIFElJ8XanCV1B1kqlQn1o1iys85Z5g7fAsC7f4jXeHn3ler08VDYSW7w3nBuqmagT9lU2U92P+M/5zjfcGtWJtniPFp4RWWobGtVp471GYs5nU/Maq14DAMAaY6y1rVc2plTetZJ2W2ubLs1ljLlQ0lH3GmttS+PVlnrdIve8VNKjqp3H65CkX7DWNhnD11nGmF+TM7zT9Ulr7W+0cF1K0llJ6XLRtKQNrQ7P3Lt3rz18+HC7zQ232bPVYZlucDbxQn29SFzafLlnTrMrpc2v7q1frgAAWAmVOe8WC9UaBGzeXnqthnJNAz9PPXSP8QRmkagvQPMdB54PKI9EffeNlQPtWG04V3Pecw/jD/PauHfDa/33bvR+6M0IAL3CGPOktXZv0LlQ9CAzxuyU9JBqw7FHJP3iaoZjZQclFVT97H/RGPMBa+1i/+n1naqGY5L0QBtzl2Ep0huki3/W2VzzE9KJp2sXAjj34+qxy0SlTZfVLgZwwWukRLr+OQAArFW9OuedtbXz2XlDtKBQzZaWudnl1ymtwjMq54vLu75UdO5RKjj73ldbPlcsElRWmEVW6/UOT25wvm7V3yYr/1bu1+R80P0iUXr/AQi1vg/IjDEbJX1RtRPcH5b0Dmvt/Gq3x1o7YYx5QNKBctGopPeqfkXNCmOMkfSvfcV/tzItRFMDo9Ke65zNtTAlnfq+ZyGA70pnf+SUnfq+9FT5H5WJSBteVbsQwAWvlVLDwc8CAABLY4wzdJIpEFaftb4AzQ3PvMeFctBWaByyNbx2kXs3Cu5auXdNmxrdu9Vnu/t5Sbbc+7HXA8MmQV5LgZv72mI4F1vkfE1vQ98w3roefPTSA7B8fT3E0hgzLOlhSW/wFP9A0nXW2nNLuWfAM94j6VOeoq9aa69f5Jq9kp7wFE1KutJa+2KD+v9a0p96ir4taa9t4x8eQyxXWW5WOvWD2oUAzjzr/EXJb+yi2oUALrhCGhxb/TYDAACg80olzwq5ed8Q4mar+bZQp5Bb5B6tnC8PW7bFbn9Sy2Dqh78GDX2tKQsaRusvazZEONpeWc3z22lnkzL/EGBjqs+uvHrmEwQQziGWxpiEpM+rNhw7K+k3JA0ZY4bauN1Za+1Mp9pmrT1sjPkHSb9SLlon6WvGmF+11laWUSzPO/Z7kj7svVzSv20nHEMXJNLSjp92Nld+QTr9g9qFAE4/I43/xNm+f0+17roLq4HZ1iud3mbpDa09e/yI9PVPSE/f7VnU4Bbpze9vf1EDAAAALE8kIkWSTi+sXlYqeoKzpQZ22UXu0Si0axDYlQrlgLFBDz23TNYJIUurPXvOGuJdiKMSoHkW6HDLKguAeAM2X9gWFMDVXL+M+9eVrWSbvKFmJCB0jASEkEE9GBme3C/6NiCTtFXS9b6yDZK+voR7vVfSXy+zPX6/IenVkl5bPt4u6RFjzPcl/VBSRtJeOW32+g/W2n/qcFuwGuIpadsbnc1VyElnfli7EMDJ70mTLzrbs/9YrTu8zbMQQDk4G7qg9hnPPyTdfavzFwv3Lwi5aenbd0nf/bR0y13OyqAAAACAl9sraa0tNOWdG69uOGyjIbLF9soazbvXtMwT5tWUlVQ/LNdf1m7b3Wfbark712CpKMkzv6AkreXOgr3KRFTX8zAwhPP3HAwI4bxhX8NhxUHP8B37e0EGPrPNZ2x7o/Pap5YSkLk9l242xpxdpG5NuGOMubXFZ7TYVWbtstbOGGNuknS3pKs9p15T3vxKkj5qrf1Pq9E+rJJYQtpyhbO5igXp7HO1CwGceFqaesXZfnR/tW5mczUwG9oiPfh/SYWA9R7c/6J2963S+x6jJxkAAAD6g3dYoZh3MFBlbsBSQIDmLfPvexfl8J4v1QZwlfO+gC7oPotdUzlfatBm22abGj3fE6oG9lZ0z7nhpT+Y9ASf7jOKIVhD74MnpchAt1uxYpbag8xI+uMlXPOpRWtV2fI1fctae8wYc62cYZTvk3RhUDU586j9gbX2kdVsH7okGpM2X+5sV77bKSsVnaGTx5/y9DZ7Wpo5JT3/RWdrRTEvPf7n0k0fW7n2AwAAAOgdxji/Y2Bl1C1O4g3XmoVwBU9Y1yCEaxTKrfozyseR/v4eLWWSfveCVsMr7wPaCbzcgGzJk/SvJeWVKq+SdLGc4aHzkl6R9IS19thy788k/X2oVJImX6jOZ/b1P2txclUjbX+TNLTZ6YGW2SxlNnn2N0vpjaz8BQAAAADoK80m6V/qKparKRQB2UojIAuBO9apNo9epoGxang2dIEvRNskZcplA6NMSgkAAAAA6HmdXMXyzg60B8BKSGScCfkXrZeWfu2z0vRJaea0M0yz8lreZs9I8+POdubZ5veLxGt7oQ0F9Uor78f7d7w6AAAAAGDtaisgs9a+d6UaAmCZrrjFWa2y2fLWkbj0undLF765+b1KRWnunCc0O90gUDstZc9LUy8722KSI57gzN8zzROoDa5f3dVRxo9IX/+E9PTdUm7GCRuvuEV68/tZ0AAAAAAAQqCtIZZYuxhiGQLjR6S/+BkpP9e4Tnyw86tY5uak2dO+XmgNArVm4Z2XiTrzoHlDs8CeaZulZGZ57X/+IWd1z2K+tn2RuBSNS7fcJV3y9uU9AwAAAADQdR2bgwxrFwFZSPRy2GOtND9RG6K5+9O+svnx1u8bT9f3QgtagCC90fkMvLoVKgIAAAAAVl0n5yAD0MsuebsT5jz+59LTn/EMF/xl6Zrf6W7IY4w0OOZsmy5rXreQc+ZBm2kwT9q0Z760/Kw0cdTZmjfAGbrpDc1O/UAqLDS/rJh3Ps+bPtbW2wUAAAAArB30IAsJepChL1krZac94VlAoDbtWXhgqat8RmLSa2+pBnwD5dfB9dX9gTEplujo2wMAAAAAdE5P9iAzxqQkXShpVNKUpBPW2olutQfAGmSMlBp2tg0XN69bLHgWHjjthGmf/53WnlMqSN/9+8XrJYakwdFyaLbeE6a5+6P1wVp80HkfAAAAAICuWfWAzBjzTkm/K+kaSQnfue9L+gdJ/4+1dn612wagj0VjztxkQ5urZYf+nZSbXvza+IC074+dudHmzklz4858apX9cec1N+1sky+13q5YytMjbay2R1pdyFYO31Ij3Q3VWPUTAAAAQJ9pKyAzxgxJ+oCn6CfW2s+0eO2wpL+RdMAtCqj2WkmvkfTbxph/bq39djvtA4C2XHGL9O27mq+uGYlLV/6a9Ib/rfm9rJUWzlfDMm9wNnfOtz9R3S8sSNPHna1VkZjTG61mqGdA77SaIaCjUiTa+jMaCVoIIjftfI7f/TSrfgIAAABYk9rtQXajpI+oOpHPr7dykTEmJulzkq5TNRhrNBmQkbRD0j8ZY95irf1+m20EgNa8+f1OqNMsIIvGnQUOFmOMNLDO2VrtRWWts4KmP0Sr653m76k248ypNnumtee4Uusa9E4bbRysxZLV68ePOOFY0KqfpXJgdvetLVR1WwAAIABJREFUrPoZhF53AAAAQE9ra5J+Y8xfSXpv+fCUpJ3W2ia/WVau+/eS/lC1oZgblJ2TVJK0oVxmPecPS7rKspLAsjFJP9BAUI8oyek5Fo33Zo+oQrZJiDYR3GttYXJpz0pkqiHa7Blp6oSaLnZgotLl75R+9t8786vFUs4Q1WgivHOtrcXvGAAAANCHmk3S325A9iNJl8j57ei/Wms/sMglMsZskPSCpAG3SE4g9seS/sxae7xcb0xO+HaHpEFVw7J3W2vvbrmRCERABjQxfkR6/M+lpz/j6d3zy07PsX7p3VMqSvOTAcM9/cNBfYGbLXaoAcYJyuIDUmxAiqc8ryknTKspK796Qzb/a8OyASd46oVAbvyI9Bc/E9zrzhUfpNcdAAAAsAo6soqlMWadJO8ycZ9t8dJb5QReVtXQ67ettZ/0VrLWjkv6mDHmm5L+SVK8fOq9kgjIAKycsT3STR9ztn4ViUrp9c7WKmul7FQ5LJuQPvmzatp7zGvdTim/IOXnpcK8sxJofq55UNRJJhIQsvkDuGYhW1Ao1yTEi8aD2/H1Tzg9x5op5p2Atp+/f0vBsNT28HkBAAAsS8s9yIwxV0l6vHyYkzRsrc21cN0Tkt6o6m9Vj1prr1/kmv9b0u+XD/OShlp5FhqjBxmAZfvo9tZW/UwOSf/ny7VlxYITlOUXyq/lrbBQ++oGapV6C+Vzc76y8mt+rnzeV9axnm8tMtHgYO3U9yVbWvz6WFLa91+c62JJJ3yLJT3BXkB5LCVFIiv/3rqBYant4fNaGkJFAABCpyNDLI0xvyLp7+UEXd9pdEPfNSNy5hgzqvYeu8Vae88i1+2UdNRzzZtY0XJ5CMgALNt9t7e26ucbb+t+b6hivo0Azg3smgVwje5V3l/tQM4VTVTDMjdEi6fqy2qCNn99X+jWSvlKzinHsNT28HktDaEiAACh1JEhlpI2evaPt3jNP5MUUbX3WF7S/YtdZK19yRhzRNJF5aKfkkRABgDd1MlVP1datPxLroZX53luIOcP4D61v7VhpdG49Lp3V3vMFbLO9YWsp4dcQHkx52zZqZV/j36dCNrqylPS4b9y3l8zxZz0yMekGz7sDKc1pvwakeTZN779fsQw3vaxIi8AAAjQTkCW9uy3uhza1Z59K+mwtXahxWt/qGpANtriNQCAlTK2x+lVsVivizD+QukGcilfIPe6d7fW6+4NS+h1Z201KGs1UFu03Ht+kWDOrdsNpYL01N86W1tMC4GaaR601dxjsWCuk/cqD6f1lz/zeefzaPp55Z3vYSLtDAeORKVIrLwfKb/GnHK3rHI+6rkmukrXR1c20CRUXBqGpAIA+lw7AZl3LOZAw1q13IDMHSr5RBvPO+/ZH2rjOgDASrnk7U6vin5f9bNTVrLXnTHlRQJSS2/fUpWKnnDOE6Ll55df/pMv///s3XmYJNdd5vv3REQutfe+d9tqSRZarZYawzXusTUYbGAGSywGM2IxSOZ5hAd0PcMdw3AfA4bBXB4GmwENF/nOsGhsYa4sG66xDAZhGoOQtVqLJdlqqbvVe1d115JVuUTEuX9EZGbkWpnVWVlLfj/Pk09EnNhOpbauV79zTuf9yG6IgkIbSoq3Nqy2JduleD/oeK2JdSUoSl/+2Er3onPG6XEY51Tbjvxd9PdwO2FJeuJPpc1XVCsda4Y0ZxLbTN1xVnIz62uOwGZDUouzUfD69CcZkgoAWBe6CcguJPb3LHaxMcZIepOqq1dKUjeTYKUT+x3McAwA6ItBWPWzV9Zr1Z3jSunh6NNr3SwG8cGjnT/X2ubBWdNAzbYO2urbrV3kWeV2dfjuclsnfZX02Z/prJLPzUhv+2AUEIZhvPWjoKjcFvrxflDd1uz70XvbtnX7zKDuvJ/4WcP24fJyCwrSQx9c+v1OqkmYlpW8dPN2t769TQCXDOKansv0bq5AhqQuHVV3ALCmdBOQlecdM5KuN8ZkrLXtJgk5qKjyK/n/ab/cxfu2JPY7+JMyAACrEFV33bnh3Z0NS73hh7p7bnkYo9ZRVY8kHf2nDofx/qh06AP969elKIeADaFbmAjlugny6kK7T/1YNGR4MW5aOvhTtcOOk9ugSVtyG5akYqmzwHe51AdnbougrV1o99JfLx7C+kXpb39NuuUXo+C/HNC5qXi7jAt7rFZU3XWPQBHACutmFcvtkk6pGnjdbq39ZJvrf0vSf1C1guyotfayjjtmzFFJe+P7v89a+9lO70UjVrEEAKwJrMrYHb6v7vVjRV5ro2Ckq1Ct0KQ9eV+n9yQCutXESdWGZl4mEaClovCuPlTz0nVtmdrzrcI4N93mXW3e16thsfxz2T1WlgXQJ+1Wsew4IIsf9KykqxUFXq9Kutlae6HJdVslvShpQtX5x37XWvu/d/ie3ZKOx4dW0nXW2q913FE0ICADAKwZ/KLUHb6v7gxKeBGGbQK5YudB3D/+Tufv3LQ/+vuwvMKuH29XW1jXiuN1GMg1C+MS9x39snTmebWdJca40v63Sgd/Mp5XLyW58dbx6vbrtpVrveo/52u5Qm9Q/plcDlTdAV3rZUD27yV9TNUqsqcl3WGtfSJxzX5J9ymaoL9cPWYlHbDWfrXD99wu6U/iw7ykcWvtIks0oR0CMgDAmjJ1hGGp3eD76g6hYue6mRfwF15rfq5cURcUagO0oBQHc8VEeyfXlJ+VaPOb3Ff/LL9Y95y6z1pWXsCiEqKV9+vDtLpgreba5bgvFS+Wkaqed9za/S/9pvTsA+1X5L3Uqs71iH+PdY9AEeptQDYk6auSyn/3lMOvo5JOSNok6SpVJ+Uvn/9La+2tXbznryW9Pb73y9baf9VxJ9EUARkAAEACoWJn+jEk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\n","text/plain":["
"]},"metadata":{"needs_background":"light"}},{"output_type":"display_data","data":{"text/plain":["
"]},"metadata":{}}]},{"cell_type":"code","metadata":{"id":"RvDPL2VBbMB0"},"source":["electrode_ranking = ['AF3', 'F7', 'F3', 'FC5', 'T7', 'P7', 'O1', 'O2', 'P8', 'T8', 'FC6', 'F4', 'F8', 'AF4']"],"execution_count":null,"outputs":[]},{"cell_type":"markdown","metadata":{"id":"UWkeZSDaElDI"},"source":["OASIS"]},{"cell_type":"markdown","metadata":{"id":"fAdrnWNfGI0V"},"source":["Method C"]},{"cell_type":"markdown","metadata":{"id":"yzOw6Hstq4dx"},"source":["*********************************************************************"]},{"cell_type":"code","metadata":{"colab":{"base_uri":"https://localhost:8080/"},"id":"4uou35FHsgY0","executionInfo":{"status":"ok","timestamp":1619263792139,"user_tz":-330,"elapsed":1205,"user":{"displayName":"Rohit Garg","photoUrl":"https://lh3.googleusercontent.com/a-/AOh14Gj3Za6nmSY12sPvpCoWOtWQcVR5CMMSPBRGTexH_w=s64","userId":"11252576926243182044"}},"outputId":"fdd05b8f-dff4-4985-a73e-d0b613d5307e"},"source":["%cd /gdrive/MyDrive/Project_DEAP/4.1.2021/DREAMER/arousal_plots/\n","features_result = pd.read_csv(\"CommonFeatureFSRegressionRankingSelectKBest11.csv\")\n","topcolumns = features_result['Specs'].values\n","topfeatures = []\n","topelectrodes = []\n","\n","for col in topcolumns:\n"," feature = col[:-2]\n"," electrode = int(col[-2:])\n"," if(feature not in topfeatures):\n"," topfeatures.append(feature)\n"," \n"," if(electrode not in topelectrodes):\n"," topelectrodes.append(electrode)\n","\n","topfeatures = pd.DataFrame(topfeatures)\n","topfeatures.to_csv(\"FSKB.csv\")"],"execution_count":null,"outputs":[{"output_type":"stream","text":["/gdrive/MyDrive/Project_DEAP/4.1.2021/DREAMER/arousal_plots\n"],"name":"stdout"}]},{"cell_type":"code","metadata":{"colab":{"base_uri":"https://localhost:8080/"},"id":"cR3GB1SOtTGk","executionInfo":{"status":"ok","timestamp":1619263813958,"user_tz":-330,"elapsed":1083,"user":{"displayName":"Rohit Garg","photoUrl":"https://lh3.googleusercontent.com/a-/AOh14Gj3Za6nmSY12sPvpCoWOtWQcVR5CMMSPBRGTexH_w=s64","userId":"11252576926243182044"}},"outputId":"08b7552a-e31b-4a08-b469-1f7aa4735391"},"source":["%cd /gdrive/MyDrive/Project_DEAP/4.1.2021/DREAMER/arousal_plots/\n","features_result = pd.read_csv(\"CommonFeatureFSRegressionRankingRandomForest11.csv\")\n","topcolumns = features_result['Specs'].values\n","topfeatures = []\n","topelectrodes = []\n","\n","for col in topcolumns:\n"," feature = col[:-2]\n"," electrode = int(col[-2:])\n"," if(feature not in topfeatures):\n"," topfeatures.append(feature)\n"," \n"," if(electrode not in topelectrodes):\n"," topelectrodes.append(electrode)\n","\n","topfeatures = pd.DataFrame(topfeatures)\n","topfeatures.to_csv(\"FRF.csv\")"],"execution_count":null,"outputs":[{"output_type":"stream","text":["/gdrive/MyDrive/Project_DEAP/4.1.2021/DREAMER/arousal_plots\n"],"name":"stdout"}]},{"cell_type":"code","metadata":{"colab":{"base_uri":"https://localhost:8080/"},"id":"8rJzbaG7uRLD","executionInfo":{"status":"ok","timestamp":1619263992311,"user_tz":-330,"elapsed":1791,"user":{"displayName":"Rohit Garg","photoUrl":"https://lh3.googleusercontent.com/a-/AOh14Gj3Za6nmSY12sPvpCoWOtWQcVR5CMMSPBRGTexH_w=s64","userId":"11252576926243182044"}},"outputId":"303df8e7-d5d7-472e-d432-e8a0795f5f24"},"source":["%cd /gdrive/MyDrive/Project_DEAP/4.1.2021/DEAP/arousal_plots/\n","features_result = pd.read_csv(\"CommonFeatureFSRegressionRankingSelectKBest11.csv\")\n","topcolumns = features_result['Specs'].values\n","topfeatures = []\n","topelectrodes = []\n","\n","for col in topcolumns:\n"," feature = col[:-2]\n"," electrode = int(col[-2:])\n"," if(feature not in topfeatures):\n"," topfeatures.append(feature)\n"," \n"," if(electrode not in topelectrodes):\n"," topelectrodes.append(electrode)\n","\n","topfeatures = pd.DataFrame(topfeatures)\n","topfeatures.to_csv(\"FSKB.csv\")"],"execution_count":null,"outputs":[{"output_type":"stream","text":["/gdrive/MyDrive/Project_DEAP/4.1.2021/DEAP/arousal_plots\n"],"name":"stdout"}]},{"cell_type":"code","metadata":{"colab":{"base_uri":"https://localhost:8080/"},"id":"nxLussY3uTXb","executionInfo":{"status":"ok","timestamp":1619263994724,"user_tz":-330,"elapsed":1162,"user":{"displayName":"Rohit Garg","photoUrl":"https://lh3.googleusercontent.com/a-/AOh14Gj3Za6nmSY12sPvpCoWOtWQcVR5CMMSPBRGTexH_w=s64","userId":"11252576926243182044"}},"outputId":"27b1a635-3fc6-471d-ca52-dc5fb12648ea"},"source":["%cd /gdrive/MyDrive/Project_DEAP/4.1.2021/DEAP/arousal_plots/\n","features_result = pd.read_csv(\"CommonFeatureFSRegressionRankingRandomForest11.csv\")\n","topcolumns = features_result['Specs'].values\n","topfeatures = []\n","topelectrodes = []\n","\n","for col in topcolumns:\n"," feature = col[:-2]\n"," electrode = int(col[-2:])\n"," if(feature not in topfeatures):\n"," topfeatures.append(feature)\n"," \n"," if(electrode not in topelectrodes):\n"," topelectrodes.append(electrode)\n","\n","topfeatures = pd.DataFrame(topfeatures)\n","topfeatures.to_csv(\"FRF.csv\")"],"execution_count":null,"outputs":[{"output_type":"stream","text":["/gdrive/MyDrive/Project_DEAP/4.1.2021/DEAP/arousal_plots\n"],"name":"stdout"}]},{"cell_type":"code","metadata":{"colab":{"base_uri":"https://localhost:8080/"},"id":"pRlOLdJ0vWgJ","executionInfo":{"status":"ok","timestamp":1619264225567,"user_tz":-330,"elapsed":1544,"user":{"displayName":"Rohit Garg","photoUrl":"https://lh3.googleusercontent.com/a-/AOh14Gj3Za6nmSY12sPvpCoWOtWQcVR5CMMSPBRGTexH_w=s64","userId":"11252576926243182044"}},"outputId":"5825a269-fc2c-421f-bafa-97b767a8a1b0"},"source":["%cd /gdrive/MyDrive/Project_DEAP/4.1.2021/OASIS/arousal_plots/\n","features_result = pd.read_csv(\"CommonFeatureFSRegressionRankingSelectKBest11.csv\")\n","topcolumns = features_result['Specs'].values\n","topfeatures = []\n","topelectrodes = []\n","\n","for col in topcolumns:\n"," feature = col[:-2]\n"," electrode = int(col[-2:])\n"," if(feature not in topfeatures):\n"," topfeatures.append(feature)\n"," \n"," if(electrode not in topelectrodes):\n"," topelectrodes.append(electrode)\n","\n","topfeatures = pd.DataFrame(topfeatures)\n","topfeatures.to_csv(\"FSKB.csv\")"],"execution_count":null,"outputs":[{"output_type":"stream","text":["/gdrive/MyDrive/Project_DEAP/4.1.2021/OASIS/arousal_plots\n"],"name":"stdout"}]},{"cell_type":"code","metadata":{"colab":{"base_uri":"https://localhost:8080/"},"id":"8ZDp66B-vX79","executionInfo":{"status":"ok","timestamp":1619264235478,"user_tz":-330,"elapsed":1112,"user":{"displayName":"Rohit Garg","photoUrl":"https://lh3.googleusercontent.com/a-/AOh14Gj3Za6nmSY12sPvpCoWOtWQcVR5CMMSPBRGTexH_w=s64","userId":"11252576926243182044"}},"outputId":"01815f8b-6db9-4fe8-f269-f076249f3454"},"source":["%cd /gdrive/MyDrive/Project_DEAP/4.1.2021/OASIS/arousal_plots/\n","features_result = pd.read_csv(\"CommonFeatureFSRegressionRankingRandomForest11.csv\")\n","topcolumns = features_result['Specs'].values\n","topfeatures = []\n","topelectrodes = []\n","\n","for col in topcolumns:\n"," feature = col[:-2]\n"," electrode = int(col[-2:])\n"," if(feature not in topfeatures):\n"," topfeatures.append(feature)\n"," \n"," if(electrode not in topelectrodes):\n"," topelectrodes.append(electrode)\n","\n","topfeatures = pd.DataFrame(topfeatures)\n","topfeatures.to_csv(\"FRF.csv\")"],"execution_count":null,"outputs":[{"output_type":"stream","text":["/gdrive/MyDrive/Project_DEAP/4.1.2021/OASIS/arousal_plots\n"],"name":"stdout"}]},{"cell_type":"markdown","metadata":{"id":"CCMexH4_q80O"},"source":["*********************************************************************"]},{"cell_type":"code","metadata":{"colab":{"base_uri":"https://localhost:8080/"},"id":"QFu_QISlZiSA","executionInfo":{"status":"ok","timestamp":1619269124467,"user_tz":-330,"elapsed":1890,"user":{"displayName":"Rohit Garg","photoUrl":"https://lh3.googleusercontent.com/a-/AOh14Gj3Za6nmSY12sPvpCoWOtWQcVR5CMMSPBRGTexH_w=s64","userId":"11252576926243182044"}},"outputId":"53077760-ccce-49c3-bbf4-ffd74ba6544a"},"source":["%cd /gdrive/MyDrive/Project_DEAP/4.1.2021/DREAMER/arousal_plots/\n","features_result = pd.read_csv(\"CommonElectrodeFSRegressionRankingSelectKBest11.csv\")\n","topcolumns = features_result['Specs'].values\n","topfeatures = []\n","topelectrodes = []\n","\n","for col in topcolumns:\n"," feature = col[:-2]\n"," electrode = int(col[-2:])\n"," if(feature not in topfeatures):\n"," topfeatures.append(feature)\n"," \n"," if(electrode not in topelectrodes):\n"," topelectrodes.append(electrode)\n"," \n","print(topelectrodes)\n","print(pd.DataFrame([electrode_ranking[x] for x in topelectrodes]))\n","\n"],"execution_count":null,"outputs":[{"output_type":"stream","text":["/gdrive/MyDrive/Project_DEAP/4.1.2021/DREAMER/arousal_plots\n","[4, 9, 12, 5, 3, 10, 8, 7, 13, 0, 2, 6, 11, 1]\n"," 0\n","0 T7\n","1 T8\n","2 F8\n","3 P7\n","4 FC5\n","5 FC6\n","6 P8\n","7 O2\n","8 AF4\n","9 AF3\n","10 F3\n","11 O1\n","12 F4\n","13 F7\n"],"name":"stdout"}]},{"cell_type":"code","metadata":{"colab":{"base_uri":"https://localhost:8080/"},"id":"rByIFciLcSk2","executionInfo":{"status":"ok","timestamp":1619269250974,"user_tz":-330,"elapsed":1105,"user":{"displayName":"Rohit Garg","photoUrl":"https://lh3.googleusercontent.com/a-/AOh14Gj3Za6nmSY12sPvpCoWOtWQcVR5CMMSPBRGTexH_w=s64","userId":"11252576926243182044"}},"outputId":"23da4af1-b153-45bf-f04d-e323c7008a0c"},"source":["%cd /gdrive/MyDrive/Project_DEAP/4.1.2021/DREAMER/arousal_plots/\n","features_result = pd.read_csv(\"CommonElectrodeFSRegressionRankingRandomForest11.csv\")\n","topcolumns = features_result['Specs'].values\n","topfeatures = []\n","topelectrodes = []\n","\n","for col in topcolumns:\n"," feature = col[:-2]\n"," electrode = int(col[-2:])\n"," if(feature not in topfeatures):\n"," topfeatures.append(feature)\n"," \n"," if(electrode not in topelectrodes):\n"," topelectrodes.append(electrode)\n"," \n","print(topelectrodes)\n","print(pd.DataFrame([electrode_ranking[x] for x in topelectrodes]))\n","\n","print(topfeatures)"],"execution_count":null,"outputs":[{"output_type":"stream","text":["/gdrive/MyDrive/Project_DEAP/4.1.2021/DREAMER/arousal_plots\n","[13, 2, 1, 9, 3, 8, 4, 7, 5, 0, 11, 10, 6, 12]\n"," 0\n","0 AF4\n","1 F3\n","2 F7\n","3 T8\n","4 FC5\n","5 P8\n","6 T7\n","7 O2\n","8 P7\n","9 AF3\n","10 F4\n","11 FC6\n","12 O1\n","13 F8\n","['HjorthMob', 'ShannonRes_gamma', 'HjorthComp', 'stdDev', 'bandPwr_gamma', 'bandPwr_theta', 'bandPwr_beta', 'bandPwr_alpha', 'ShannonRes_beta', 'ShannonRes_delta', 'ShannonRes_alpha', 'bandPwr_delta', 'ShannonRes_theta', 'medianFreq', 'shortSpikeNum']\n"],"name":"stdout"}]},{"cell_type":"code","metadata":{"colab":{"base_uri":"https://localhost:8080/"},"id":"EvXd19BdegoL","executionInfo":{"status":"ok","timestamp":1619269387133,"user_tz":-330,"elapsed":1865,"user":{"displayName":"Rohit Garg","photoUrl":"https://lh3.googleusercontent.com/a-/AOh14Gj3Za6nmSY12sPvpCoWOtWQcVR5CMMSPBRGTexH_w=s64","userId":"11252576926243182044"}},"outputId":"249355fd-d3b6-4766-b5a7-07fedbe2e9d7"},"source":["%cd /gdrive/MyDrive/Project_DEAP/4.1.2021/DEAP/arousal_plots/\n","features_result = pd.read_csv(\"CommonElectrodeFSRegressionRankingSelectKBest11.csv\")\n","topcolumns = features_result['Specs'].values\n","topfeatures = []\n","topelectrodes = []\n","\n","for col in topcolumns:\n"," feature = col[:-2]\n"," electrode = int(col[-2:])\n"," if(feature not in topfeatures):\n"," topfeatures.append(feature)\n"," \n"," if(electrode not in topelectrodes):\n"," topelectrodes.append(electrode)\n"," \n","print(topelectrodes)\n","print(pd.DataFrame([electrode_ranking[x] for x in topelectrodes]))\n","\n","print(topfeatures)\n"],"execution_count":null,"outputs":[{"output_type":"stream","text":["/gdrive/MyDrive/Project_DEAP/4.1.2021/DEAP/arousal_plots\n","[1, 12, 10, 8, 5, 7, 2, 11, 4, 0, 9, 13, 6, 3]\n"," 0\n","0 F7\n","1 F8\n","2 FC6\n","3 P8\n","4 P7\n","5 O2\n","6 F3\n","7 F4\n","8 T7\n","9 AF3\n","10 T8\n","11 AF4\n","12 O1\n","13 FC5\n","['bandPwr_gamma', 'ShannonRes_beta', 'stdDev', 'bandPwr_beta', 'ShannonRes_gamma', 'ShannonRes_alpha', 'ShannonRes_theta', 'ShannonRes_delta', 'HjorthMob', 'bandPwr_alpha', 'bandPwr_theta', 'HjorthComp', 'shortSpikeNum', 'bandPwr_delta', 'medianFreq']\n"],"name":"stdout"}]},{"cell_type":"code","metadata":{"colab":{"base_uri":"https://localhost:8080/"},"id":"QSfQA_hqe5oF","executionInfo":{"status":"ok","timestamp":1619269393329,"user_tz":-330,"elapsed":958,"user":{"displayName":"Rohit Garg","photoUrl":"https://lh3.googleusercontent.com/a-/AOh14Gj3Za6nmSY12sPvpCoWOtWQcVR5CMMSPBRGTexH_w=s64","userId":"11252576926243182044"}},"outputId":"3781d3c9-fd5c-497a-e7b0-d03da4e4ee14"},"source":["%cd /gdrive/MyDrive/Project_DEAP/4.1.2021/DEAP/arousal_plots/\n","features_result = pd.read_csv(\"CommonElectrodeFSRegressionRankingRandomForest11.csv\")\n","topcolumns = features_result['Specs'].values\n","topfeatures = []\n","topelectrodes = []\n","\n","for col in topcolumns:\n"," feature = col[:-2]\n"," electrode = int(col[-2:])\n"," if(feature not in topfeatures):\n"," topfeatures.append(feature)\n"," \n"," if(electrode not in topelectrodes):\n"," topelectrodes.append(electrode)\n"," \n","print(topelectrodes)\n","print(pd.DataFrame([electrode_ranking[x] for x in topelectrodes]))\n","\n","print(topfeatures)\n"],"execution_count":null,"outputs":[{"output_type":"stream","text":["/gdrive/MyDrive/Project_DEAP/4.1.2021/DEAP/arousal_plots\n","[1, 4, 3, 13, 0, 9, 8, 7, 10, 2, 11, 12, 5, 6]\n"," 0\n","0 F7\n","1 T7\n","2 FC5\n","3 AF4\n","4 AF3\n","5 T8\n","6 P8\n","7 O2\n","8 FC6\n","9 F3\n","10 F4\n","11 F8\n","12 P7\n","13 O1\n","['ShannonRes_gamma', 'bandPwr_gamma', 'ShannonRes_beta', 'bandPwr_beta', 'stdDev', 'medianFreq', 'bandPwr_delta', 'HjorthMob', 'HjorthComp', 'ShannonRes_alpha', 'ShannonRes_delta', 'bandPwr_alpha', 'ShannonRes_theta', 'bandPwr_theta', 'shortSpikeNum']\n"],"name":"stdout"}]},{"cell_type":"code","metadata":{"colab":{"base_uri":"https://localhost:8080/"},"id":"auALlKCmfy8t","executionInfo":{"status":"ok","timestamp":1619269632132,"user_tz":-330,"elapsed":1111,"user":{"displayName":"Rohit Garg","photoUrl":"https://lh3.googleusercontent.com/a-/AOh14Gj3Za6nmSY12sPvpCoWOtWQcVR5CMMSPBRGTexH_w=s64","userId":"11252576926243182044"}},"outputId":"d0467c2b-1158-4f59-e969-a9596d6d3372"},"source":["%cd /gdrive/MyDrive/Project_DEAP/4.1.2021/OASIS/arousal_plots/\n","features_result = pd.read_csv(\"CommonElectrodeFSRegressionRankingSelectKBest11.csv\")\n","topcolumns = features_result['Specs'].values\n","topfeatures = []\n","topelectrodes = []\n","\n","for col in topcolumns:\n"," feature = col[:-2]\n"," electrode = int(col[-2:])\n"," if(feature not in topfeatures):\n"," topfeatures.append(feature)\n"," \n"," if(electrode not in topelectrodes):\n"," topelectrodes.append(electrode)\n"," \n","print(topelectrodes)\n","print(pd.DataFrame([electrode_ranking[x] for x in topelectrodes]))\n","\n","print(topfeatures)\n"],"execution_count":null,"outputs":[{"output_type":"stream","text":["/gdrive/MyDrive/Project_DEAP/4.1.2021/OASIS/arousal_plots\n","[1, 9, 4, 12, 2, 11, 0, 13, 10, 3, 6, 5, 8, 7]\n"," 0\n","0 F7\n","1 T8\n","2 T7\n","3 F8\n","4 F3\n","5 F4\n","6 AF3\n","7 AF4\n","8 FC6\n","9 FC5\n","10 O1\n","11 P7\n","12 P8\n","13 O2\n","['HjorthMob', 'ShannonRes_gamma', 'ShannonRes_beta', 'HjorthComp', 'bandPwr_gamma', 'stdDev', 'medianFreq', 'ShannonRes_delta', 'ShannonRes_theta', 'ShannonRes_alpha', 'bandPwr_beta', 'bandPwr_delta', 'shortSpikeNum', 'bandPwr_theta', 'bandPwr_alpha']\n"],"name":"stdout"}]},{"cell_type":"code","metadata":{"colab":{"base_uri":"https://localhost:8080/"},"id":"43lGM_9JgiJR","executionInfo":{"status":"ok","timestamp":1619270987906,"user_tz":-330,"elapsed":941,"user":{"displayName":"Rohit Garg","photoUrl":"https://lh3.googleusercontent.com/a-/AOh14Gj3Za6nmSY12sPvpCoWOtWQcVR5CMMSPBRGTexH_w=s64","userId":"11252576926243182044"}},"outputId":"cd73fcb0-bcaa-4840-8497-f1a04747af2c"},"source":["%cd /gdrive/MyDrive/Project_DEAP/4.1.2021/OASIS/arousal_plots/\n","features_result = pd.read_csv(\"CommonElectrodeFSRegressionRankingRandomForest11.csv\")\n","topcolumns = features_result['Specs'].values\n","topfeatures = []\n","topelectrodes = []\n","\n","for col in topcolumns:\n"," feature = col[:-2]\n"," electrode = int(col[-2:])\n"," if(feature not in topfeatures):\n"," topfeatures.append(feature)\n"," \n"," if(electrode not in topelectrodes):\n"," topelectrodes.append(electrode)\n"," \n","print(topelectrodes)\n","print(pd.DataFrame([electrode_ranking[x] for x in topelectrodes]))\n","\n","print(topfeatures)\n"],"execution_count":null,"outputs":[{"output_type":"stream","text":["/gdrive/MyDrive/Project_DEAP/4.1.2021/OASIS/arousal_plots\n","[9, 2, 4, 6, 8, 3, 5, 11, 0, 7, 10, 1, 12, 13]\n"," 0\n","0 T8\n","1 F3\n","2 T7\n","3 O1\n","4 P8\n","5 FC5\n","6 P7\n","7 F4\n","8 AF3\n","9 O2\n","10 FC6\n","11 F7\n","12 F8\n","13 AF4\n","['ShannonRes_gamma', 'HjorthComp', 'HjorthMob', 'ShannonRes_beta', 'bandPwr_beta', 'stdDev', 'bandPwr_theta', 'bandPwr_delta', 'ShannonRes_alpha', 'ShannonRes_theta', 'bandPwr_gamma', 'medianFreq', 'bandPwr_alpha', 'ShannonRes_delta', 'shortSpikeNum']\n"],"name":"stdout"}]},{"cell_type":"code","metadata":{"colab":{"base_uri":"https://localhost:8080/"},"id":"bvOuTovc9Q0F","executionInfo":{"status":"ok","timestamp":1619075741130,"user_tz":-330,"elapsed":1283,"user":{"displayName":"Rohit Garg","photoUrl":"https://lh3.googleusercontent.com/a-/AOh14Gj3Za6nmSY12sPvpCoWOtWQcVR5CMMSPBRGTexH_w=s64","userId":"11252576926243182044"}},"outputId":"c2b3d50c-249c-4208-f86e-6d3df4cda71b"},"source":["%cd /gdrive/MyDrive/Project_DEAP/4.1.2021/DREAMER/plots/\n","features_result = pd.read_csv(\"CommonElectrodeFSRegressionRankingSelectKBest11.csv\")\n","topcolumns = features_result['Specs'].values\n","topfeatures = []\n","topelectrodes = []\n","\n","for col in topcolumns:\n"," feature = col[:-2]\n"," electrode = int(col[-2:])\n"," if(feature not in topfeatures):\n"," topfeatures.append(feature)\n"," \n"," if(electrode not in topelectrodes):\n"," topelectrodes.append(electrode)\n"," \n","print(topelectrodes)\n","print(pd.DataFrame([electrode_ranking[x] for x in topelectrodes]))\n","\n","print(topfeatures)\n"],"execution_count":null,"outputs":[{"output_type":"stream","text":["/gdrive/MyDrive/Project_DEAP/4.1.2021/DREAMER/plots\n","[9, 5, 7, 10, 12, 8, 4, 6, 3, 2, 13, 1, 0, 11]\n"," 0\n","0 T8\n","1 P7\n","2 O2\n","3 FC6\n","4 F8\n","5 P8\n","6 T7\n","7 O1\n","8 FC5\n","9 F3\n","10 AF4\n","11 F7\n","12 AF3\n","13 F4\n","['ShannonRes_gamma', 'ShannonRes_beta', 'HjorthMob', 'HjorthComp', 'bandPwr_alpha', 'stdDev', 'ShannonRes_alpha', 'bandPwr_beta', 'medianFreq', 'bandPwr_gamma', 'ShannonRes_delta', 'bandPwr_theta', 'ShannonRes_theta', 'bandPwr_delta', 'shortSpikeNum']\n"],"name":"stdout"}]},{"cell_type":"code","metadata":{"colab":{"base_uri":"https://localhost:8080/"},"id":"RG7LLKzd9Q0R","executionInfo":{"status":"ok","timestamp":1619075886539,"user_tz":-330,"elapsed":1214,"user":{"displayName":"Rohit Garg","photoUrl":"https://lh3.googleusercontent.com/a-/AOh14Gj3Za6nmSY12sPvpCoWOtWQcVR5CMMSPBRGTexH_w=s64","userId":"11252576926243182044"}},"outputId":"24d1cbed-8c9e-458e-a75d-1761fbb62c3d"},"source":["%cd /gdrive/MyDrive/Project_DEAP/4.1.2021/DREAMER/plots/\n","features_result = pd.read_csv(\"CommonElectrodeFSRegressionRankingRandomForest11.csv\")\n","topcolumns = features_result['Specs'].values\n","topfeatures = []\n","topelectrodes = []\n","\n","for col in topcolumns:\n"," feature = col[:-2]\n"," electrode = int(col[-2:])\n"," if(feature not in topfeatures):\n"," topfeatures.append(feature)\n"," \n"," if(electrode not in topelectrodes):\n"," topelectrodes.append(electrode)\n"," \n","print(topelectrodes)\n","print(pd.DataFrame([electrode_ranking[x] for x in topelectrodes]))\n","\n","print(topfeatures)"],"execution_count":null,"outputs":[{"output_type":"stream","text":["/gdrive/MyDrive/Project_DEAP/4.1.2021/DREAMER/plots\n","[4, 5, 11, 3, 9, 13, 2, 1, 8, 0, 6, 10, 7, 12]\n"," 0\n","0 T7\n","1 P7\n","2 F4\n","3 FC5\n","4 T8\n","5 AF4\n","6 F3\n","7 F7\n","8 P8\n","9 AF3\n","10 O1\n","11 FC6\n","12 O2\n","13 F8\n","['HjorthMob', 'HjorthComp', 'stdDev', 'ShannonRes_gamma', 'bandPwr_theta', 'bandPwr_gamma', 'bandPwr_beta', 'ShannonRes_beta', 'bandPwr_alpha', 'ShannonRes_alpha', 'bandPwr_delta', 'ShannonRes_theta', 'medianFreq', 'ShannonRes_delta', 'shortSpikeNum']\n"],"name":"stdout"}]},{"cell_type":"code","metadata":{"colab":{"base_uri":"https://localhost:8080/"},"id":"lSjkJncx9Q0R","executionInfo":{"status":"ok","timestamp":1619076472424,"user_tz":-330,"elapsed":2340,"user":{"displayName":"Rohit Garg","photoUrl":"https://lh3.googleusercontent.com/a-/AOh14Gj3Za6nmSY12sPvpCoWOtWQcVR5CMMSPBRGTexH_w=s64","userId":"11252576926243182044"}},"outputId":"3b2af45d-005e-4ffc-f8ce-afd9a8991549"},"source":["%cd /gdrive/MyDrive/Project_DEAP/4.1.2021/DEAP/plots/\n","features_result = pd.read_csv(\"CommonElectrodeFSRegressionRankingSelectKBest11.csv\")\n","topcolumns = features_result['Specs'].values\n","topfeatures = []\n","topelectrodes = []\n","\n","for col in topcolumns:\n"," feature = col[:-2]\n"," electrode = int(col[-2:])\n"," if(feature not in topfeatures):\n"," topfeatures.append(feature)\n"," \n"," if(electrode not in topelectrodes):\n"," topelectrodes.append(electrode)\n"," \n","print(topelectrodes)\n","print(pd.DataFrame([electrode_ranking[x] for x in topelectrodes]))\n","\n","print(topfeatures)\n"],"execution_count":null,"outputs":[{"output_type":"stream","text":["/gdrive/MyDrive/Project_DEAP/4.1.2021/DEAP/plots\n","[1, 12, 5, 10, 8, 11, 6, 0, 7, 13, 2, 9, 4, 3]\n"," 0\n","0 F7\n","1 F8\n","2 P7\n","3 FC6\n","4 P8\n","5 F4\n","6 O1\n","7 AF3\n","8 O2\n","9 AF4\n","10 F3\n","11 T8\n","12 T7\n","13 FC5\n","['stdDev', 'ShannonRes_beta', 'bandPwr_beta', 'ShannonRes_gamma', 'ShannonRes_alpha', 'bandPwr_gamma', 'ShannonRes_theta', 'ShannonRes_delta', 'bandPwr_alpha', 'bandPwr_theta', 'HjorthMob', 'HjorthComp', 'bandPwr_delta', 'shortSpikeNum', 'medianFreq']\n"],"name":"stdout"}]},{"cell_type":"code","metadata":{"colab":{"base_uri":"https://localhost:8080/"},"id":"EcRnEVSV9Q0S","executionInfo":{"status":"ok","timestamp":1619076564540,"user_tz":-330,"elapsed":1608,"user":{"displayName":"Rohit Garg","photoUrl":"https://lh3.googleusercontent.com/a-/AOh14Gj3Za6nmSY12sPvpCoWOtWQcVR5CMMSPBRGTexH_w=s64","userId":"11252576926243182044"}},"outputId":"cced5603-19bf-492a-f7a3-3fe1463df566"},"source":["%cd /gdrive/MyDrive/Project_DEAP/4.1.2021/DEAP/plots/\n","features_result = pd.read_csv(\"CommonElectrodeFSRegressionRankingRandomForest11.csv\")\n","topcolumns = features_result['Specs'].values\n","topfeatures = []\n","topelectrodes = []\n","\n","for col in topcolumns:\n"," feature = col[:-2]\n"," electrode = int(col[-2:])\n"," if(feature not in topfeatures):\n"," topfeatures.append(feature)\n"," \n"," if(electrode not in topelectrodes):\n"," topelectrodes.append(electrode)\n"," \n","print(topelectrodes)\n","print(pd.DataFrame([electrode_ranking[x] for x in topelectrodes]))\n","\n","print(topfeatures)\n"],"execution_count":null,"outputs":[{"output_type":"stream","text":["/gdrive/MyDrive/Project_DEAP/4.1.2021/DEAP/plots\n","[11, 13, 10, 1, 12, 0, 9, 7, 4, 8, 3, 2, 6, 5]\n"," 0\n","0 F4\n","1 AF4\n","2 FC6\n","3 F7\n","4 F8\n","5 AF3\n","6 T8\n","7 O2\n","8 T7\n","9 P8\n","10 FC5\n","11 F3\n","12 O1\n","13 P7\n","['bandPwr_gamma', 'ShannonRes_gamma', 'HjorthMob', 'ShannonRes_beta', 'bandPwr_beta', 'stdDev', 'medianFreq', 'bandPwr_delta', 'HjorthComp', 'ShannonRes_delta', 'bandPwr_alpha', 'ShannonRes_alpha', 'ShannonRes_theta', 'bandPwr_theta', 'shortSpikeNum']\n"],"name":"stdout"}]},{"cell_type":"code","metadata":{"colab":{"base_uri":"https://localhost:8080/"},"id":"KIWDz6lf9Q0S","executionInfo":{"status":"ok","timestamp":1619076819146,"user_tz":-330,"elapsed":2564,"user":{"displayName":"Rohit Garg","photoUrl":"https://lh3.googleusercontent.com/a-/AOh14Gj3Za6nmSY12sPvpCoWOtWQcVR5CMMSPBRGTexH_w=s64","userId":"11252576926243182044"}},"outputId":"c0806e76-df12-4869-f780-9a4511f1e526"},"source":["%cd /gdrive/MyDrive/Project_DEAP/4.1.2021/OASIS/plots/\n","features_result = pd.read_csv(\"CommonElectrodeFSRegressionRankingSelectKBest11.csv\")\n","topcolumns = features_result['Specs'].values\n","topfeatures = []\n","topelectrodes = []\n","\n","for col in topcolumns:\n"," feature = col[:-2]\n"," electrode = int(col[-2:])\n"," if(feature not in topfeatures):\n"," topfeatures.append(feature)\n"," \n"," if(electrode not in topelectrodes):\n"," topelectrodes.append(electrode)\n"," \n","print(topelectrodes)\n","print(pd.DataFrame([electrode_ranking[x] for x in topelectrodes]))\n","\n","print(topfeatures)\n"],"execution_count":null,"outputs":[{"output_type":"stream","text":["/gdrive/MyDrive/Project_DEAP/4.1.2021/OASIS/plots\n","[1, 4, 9, 12, 2, 0, 3, 13, 11, 10, 6, 5, 7, 8]\n"," 0\n","0 F7\n","1 T7\n","2 T8\n","3 F8\n","4 F3\n","5 AF3\n","6 FC5\n","7 AF4\n","8 F4\n","9 FC6\n","10 O1\n","11 P7\n","12 O2\n","13 P8\n","['ShannonRes_gamma', 'HjorthMob', 'ShannonRes_beta', 'HjorthComp', 'medianFreq', 'stdDev', 'ShannonRes_delta', 'ShannonRes_theta', 'bandPwr_beta', 'bandPwr_alpha', 'bandPwr_theta', 'bandPwr_gamma', 'bandPwr_delta', 'ShannonRes_alpha', 'shortSpikeNum']\n"],"name":"stdout"}]},{"cell_type":"code","metadata":{"colab":{"base_uri":"https://localhost:8080/"},"id":"BPmWHQpo9Q0S","executionInfo":{"status":"ok","timestamp":1619076996582,"user_tz":-330,"elapsed":1783,"user":{"displayName":"Rohit Garg","photoUrl":"https://lh3.googleusercontent.com/a-/AOh14Gj3Za6nmSY12sPvpCoWOtWQcVR5CMMSPBRGTexH_w=s64","userId":"11252576926243182044"}},"outputId":"8f98a839-c4fd-496f-ba92-a29657b62d61"},"source":["%cd /gdrive/MyDrive/Project_DEAP/4.1.2021/OASIS/plots/\n","features_result = pd.read_csv(\"CommonElectrodeFSRegressionRankingRandomForest11.csv\")\n","topcolumns = features_result['Specs'].values\n","topfeatures = []\n","topelectrodes = []\n","\n","for col in topcolumns:\n"," feature = col[:-2]\n"," electrode = int(col[-2:])\n"," if(feature not in topfeatures):\n"," topfeatures.append(feature)\n"," \n"," if(electrode not in topelectrodes):\n"," topelectrodes.append(electrode)\n"," \n","print(topelectrodes)\n","print(pd.DataFrame([electrode_ranking[x] for x in topelectrodes]))\n","\n","print(topfeatures)\n"],"execution_count":null,"outputs":[{"output_type":"stream","text":["/gdrive/MyDrive/Project_DEAP/4.1.2021/OASIS/plots\n","[4, 3, 6, 8, 7, 0, 1, 5, 11, 2, 13, 9, 12, 10]\n"," 0\n","0 T7\n","1 FC5\n","2 O1\n","3 P8\n","4 O2\n","5 AF3\n","6 F7\n","7 P7\n","8 F4\n","9 F3\n","10 AF4\n","11 T8\n","12 F8\n","13 FC6\n","['bandPwr_delta', 'HjorthComp', 'HjorthMob', 'ShannonRes_alpha', 'stdDev', 'bandPwr_beta', 'ShannonRes_theta', 'ShannonRes_beta', 'medianFreq', 'ShannonRes_gamma', 'bandPwr_theta', 'bandPwr_gamma', 'bandPwr_alpha', 'ShannonRes_delta', 'shortSpikeNum']\n"],"name":"stdout"}]},{"cell_type":"code","metadata":{"colab":{"base_uri":"https://localhost:8080/"},"id":"NSen1gNIG1V1","executionInfo":{"status":"ok","timestamp":1618651476538,"user_tz":-330,"elapsed":1718,"user":{"displayName":"Rohit Garg","photoUrl":"https://lh3.googleusercontent.com/a-/AOh14Gj3Za6nmSY12sPvpCoWOtWQcVR5CMMSPBRGTexH_w=s64","userId":"11252576926243182044"}},"outputId":"691c82e8-f12c-4877-8aee-e2186860e8f2"},"source":["!ls"],"execution_count":null,"outputs":[{"output_type":"stream","text":["args_eeg.py\t\t\t EpochedFeatures.py\n","before_threshold_removal\t feature_select_main.py\n","Code_before_mergeplots\t\t g1.ipynb\n","Code_Used_before_DASM\t\t g2.ipynb\n","CombinedPlots\t\t\t g3.ipynb\n","compute_dasm_rasm_bandwise_8.4.ipynb ImportUtils.py\n","data_extracted\t\t\t o1.ipynb\n","DEAP\t\t\t\t OASIS\n","DEAP_25GB_RAM.ipynb\t\t OASIS_before_DASM\n","DEAP_before_DASM\t\t __pycache__\n","draw_plots.ipynb\t\t TopNByClassifier.py\n","DREAMER\t\t\t\t TopNByFSMethods.py\n","EEGExtract.py\n"],"name":"stdout"}]},{"cell_type":"code","metadata":{"id":"55gPWzAFKHHD"},"source":["df = pd.read_csv(\"topFeatureFSRegressionRankingRandomForest11.csv\")\n","df"],"execution_count":null,"outputs":[]},{"cell_type":"code","metadata":{"colab":{"base_uri":"https://localhost:8080/"},"id":"9I54ujAtE6sk","executionInfo":{"status":"ok","timestamp":1618578087405,"user_tz":-330,"elapsed":1257,"user":{"displayName":"Rohit Garg","photoUrl":"https://lh3.googleusercontent.com/a-/AOh14Gj3Za6nmSY12sPvpCoWOtWQcVR5CMMSPBRGTexH_w=s64","userId":"11252576926243182044"}},"outputId":"e0b19b68-d633-4bf4-d066-fd40863150d2"},"source":["%cd /gdrive/MyDrive/Project_DEAP/4.1.2021/OASIS/plots/"],"execution_count":null,"outputs":[{"output_type":"stream","text":["/gdrive/MyDrive/Project_DEAP/4.1.2021/OASIS/plots\n"],"name":"stdout"}]},{"cell_type":"markdown","metadata":{"id":"-fz-9gO8JaY2"},"source":["Create Feature Subset"]},{"cell_type":"code","metadata":{"id":"3QEFxfNqJjOA"},"source":["%cd /gdrive/MyDrive/Project_DEAP/4.1.2021/DREAMER/plots/\n","!ls\n","df1 = pd.read_csv(\"FeaturesRegressionRanking11.csv\")\n","list1 = df1['Feature'].to_list()\n","%cd /gdrive/MyDrive/Project_DEAP/4.1.2021/DEAP/plots/\n","!ls\n","df2 = pd.read_csv(\"FeaturesRegressionRanking11.csv\")\n","list2 = df2['Feature'].to_list()\n","%cd /gdrive/MyDrive/Project_DEAP/4.1.2021/OASIS/plots/\n","!ls\n","df3 = pd.read_csv(\"FeaturesRegressionRanking11.csv\")\n","list3 = df3['Feature'].to_list()\n","%cd /gdrive/MyDrive/Project_DEAP/4.1.2021/"],"execution_count":null,"outputs":[]},{"cell_type":"code","metadata":{"colab":{"base_uri":"https://localhost:8080/"},"id":"AtPEJKSqJjyg","executionInfo":{"status":"ok","timestamp":1618652714028,"user_tz":-330,"elapsed":1442,"user":{"displayName":"Rohit Garg","photoUrl":"https://lh3.googleusercontent.com/a-/AOh14Gj3Za6nmSY12sPvpCoWOtWQcVR5CMMSPBRGTexH_w=s64","userId":"11252576926243182044"}},"outputId":"4c687d9d-6573-4ee5-ce74-8317bd59eef6"},"source":["print(len(list1),len(list2),len(list3))"],"execution_count":null,"outputs":[{"output_type":"stream","text":["28 30 27\n"],"name":"stdout"}]},{"cell_type":"code","metadata":{"colab":{"base_uri":"https://localhost:8080/"},"id":"J0286cY8Jj26","executionInfo":{"status":"ok","timestamp":1618652777375,"user_tz":-330,"elapsed":1835,"user":{"displayName":"Rohit Garg","photoUrl":"https://lh3.googleusercontent.com/a-/AOh14Gj3Za6nmSY12sPvpCoWOtWQcVR5CMMSPBRGTexH_w=s64","userId":"11252576926243182044"}},"outputId":"9cb09af7-00da-4e0a-e32e-1bff48964232"},"source":["temp = list(set(list1) & set(list2))\n","subset = list(set(temp) & set(list3))\n","print(len(subset))\n","print(subset)"],"execution_count":null,"outputs":[{"output_type":"stream","text":["25\n","['bandPwr_theta', 'HjorthComp', 'rasm_beta', 'rasm_alpha', 'ShannonRes_gamma', 'dasm_beta', 'ShannonRes_delta', 'ShannonRes_alpha', 'dasm_alpha', 'rasm_theta', 'dasm_gamma', 'shortSpikeNum', 'ShannonRes_theta', 'bandPwr_delta', 'dasm_theta', 'medianFreq', 'rasm_delta', 'dasm_delta', 'stdDev', 'HjorthMob', 'bandPwr_gamma', 'bandPwr_alpha', 'rasm_gamma', 'ShannonRes_beta', 'bandPwr_beta']\n"],"name":"stdout"}]},{"cell_type":"code","metadata":{"colab":{"base_uri":"https://localhost:8080/"},"id":"-BrDYKI3Oxim","executionInfo":{"status":"ok","timestamp":1618652956429,"user_tz":-330,"elapsed":1515,"user":{"displayName":"Rohit Garg","photoUrl":"https://lh3.googleusercontent.com/a-/AOh14Gj3Za6nmSY12sPvpCoWOtWQcVR5CMMSPBRGTexH_w=s64","userId":"11252576926243182044"}},"outputId":"239560fc-b24b-4820-e457-372980a7b934"},"source":["common = []\n","for f in list1:\n"," if f in list2:\n"," if f in list3:\n"," common.append(f)\n","\n","\n","print(len(common),common)"],"execution_count":null,"outputs":[{"output_type":"stream","text":["25 ['HjorthMob', 'HjorthComp', 'stdDev', 'bandPwr_theta', 'ShannonRes_gamma', 'bandPwr_beta', 'bandPwr_gamma', 'bandPwr_alpha', 'ShannonRes_beta', 'dasm_beta', 'rasm_beta', 'dasm_gamma', 'rasm_gamma', 'rasm_theta', 'dasm_theta', 'dasm_alpha', 'rasm_alpha', 'ShannonRes_delta', 'medianFreq', 'ShannonRes_alpha', 'shortSpikeNum', 'ShannonRes_theta', 'bandPwr_delta', 'rasm_delta', 'dasm_delta']\n"],"name":"stdout"}]},{"cell_type":"code","metadata":{"id":"fAWzNuTnQBlt"},"source":["import pickle\n","with open('intersection.pkl', 'wb') as f:\n"," pickle.dump(common, f)\n","\n","\n","# >>> import pickle\n","# >>> with open('parrot.pkl', 'rb') as f:\n","# ... mynewlist = pickle.load(f)"],"execution_count":null,"outputs":[]},{"cell_type":"code","metadata":{"colab":{"base_uri":"https://localhost:8080/","height":372},"id":"Z2FW3Hu7E6m2","executionInfo":{"status":"ok","timestamp":1619102488281,"user_tz":-330,"elapsed":2534,"user":{"displayName":"Rohit Garg","photoUrl":"https://lh3.googleusercontent.com/a-/AOh14Gj3Za6nmSY12sPvpCoWOtWQcVR5CMMSPBRGTexH_w=s64","userId":"11252576926243182044"}},"outputId":"bcb56329-8cf3-43b8-dadf-01b0d2deda2a"},"source":["%cd /gdrive/MyDrive/Project_DEAP/4.1.2021/OASIS/plots/\n","df = pd.read_csv(\"topCommonFeaturesRegressionRanking11.csv\")\n","# plt.plot(df['RMSE'])\n","fig = plt.gcf()\n","fig.set_size_inches(25, 10)\n","# X = pd.DataFrame([x for x in range(1,) ])\n","plt.rcParams.update({'font.size': 30})\n","plt.xlabel('Top N Features')\n","plt.ylabel('RMSE')\n","df.index += 1\n","X = pd.DataFrame([str(x) for x in df.index])\n","plt.plot(X.loc[:,0], df.loc[:,\"RMSE\"])\n","plt.tight_layout()\n","# print(pd.DataFrame(df.index))\n","\n","df = pd.read_csv(\"topCommonFeatureFSRegressionRankingSelectKBest11.csv\")\n","# plt.plot(df['RMSE'])\n","fig = plt.gcf()\n","fig.set_size_inches(25,10)\n","# X = pd.DataFrame([x for x in range(1,) ])\n","plt.rcParams.update({'font.size': 30})\n","plt.xlabel('Top N Features')\n","plt.ylabel('RMSE')\n","df.index += 1\n","X = pd.DataFrame([str(x) for x in df.index])\n","plt.plot(X.loc[:,0], df.loc[:,\"RMSE\"])\n","plt.tight_layout()\n","# print(pd.DataFrame(df.index))\n","\n","df = pd.read_csv(\"topCommonFeatureFSRegressionRankingRandomForest11.csv\")\n","# plt.plot(df['RMSE'])\n","fig = plt.gcf()\n","fig.set_size_inches(25,10)\n","# X = pd.DataFrame([x for x in range(1,) ])\n","plt.rcParams.update({'font.size': 30})\n","plt.xlabel('Top N Features')\n","plt.ylabel('RMSE')\n","df.index += 1\n","X = pd.DataFrame([str(x) for x in df.index])\n","plt.plot(X.loc[:,0], df.loc[:,\"RMSE\"])\n","plt.tight_layout()\n","# print(pd.DataFrame(df.index))\n","plt.legend([\"Method A\",\"Method B\", \"Method C\"])\n","plt.savefig(os.getcwd() + \"/verification/OASIS_Combined_Features_Plots1.svg\", bbox_inches='tight', dpi=500)\n","plt.show()\n","plt.clf()\n"],"execution_count":null,"outputs":[{"output_type":"stream","text":["/gdrive/MyDrive/Project_DEAP/4.1.2021/OASIS/plots\n"],"name":"stdout"},{"output_type":"display_data","data":{"image/png":"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\n","text/plain":["
"]},"metadata":{"tags":[],"needs_background":"light"}},{"output_type":"display_data","data":{"text/plain":["
"]},"metadata":{"tags":[]}}]},{"cell_type":"markdown","metadata":{"id":"7fu506LZNuZd"},"source":["OASIS_ELECTRODES_PLOTS"]},{"cell_type":"code","metadata":{"id":"G1udwQ4-8S0x"},"source":["import os"],"execution_count":null,"outputs":[]},{"cell_type":"code","metadata":{"colab":{"base_uri":"https://localhost:8080/","height":455},"id":"og8uHSG-E6j5","executionInfo":{"status":"ok","timestamp":1619102480299,"user_tz":-330,"elapsed":2438,"user":{"displayName":"Rohit Garg","photoUrl":"https://lh3.googleusercontent.com/a-/AOh14Gj3Za6nmSY12sPvpCoWOtWQcVR5CMMSPBRGTexH_w=s64","userId":"11252576926243182044"}},"outputId":"76ff1550-b376-4b5c-c610-710342699181"},"source":["%cd /gdrive/MyDrive/Project_DEAP/4.1.2021/OASIS/plots/\n","df = pd.read_csv(\"topCommonElectrodeRegressionRanking11.csv\")\n","# plt.plot(df['RMSE'])\n","fig = plt.gcf()\n","fig.set_size_inches(20, 10)\n","# X = pd.DataFrame([x for x in range(1,) ])\n","plt.rcParams.update({'font.size': 30})\n","plt.xlabel('Top N Electrodes')\n","plt.ylabel('RMSE')\n","df.index += 1\n","X = pd.DataFrame([str(x) for x in df.index])\n","plt.plot(X.loc[:,0], df.loc[:,\"RMSE\"])\n","plt.tight_layout()\n","# print(pd.DataFrame(df.index))\n","\n","df = pd.read_csv(\"topCommonElectrodeFSRegressionRankingSelectKBest11.csv\")\n","# plt.plot(df['RMSE'])\n","fig = plt.gcf()\n","fig.set_size_inches(20, 10)\n","# X = pd.DataFrame([x for x in range(1,) ])\n","plt.rcParams.update({'font.size': 30})\n","plt.xlabel('Top N Electrodes')\n","plt.ylabel('RMSE')\n","df.index += 1\n","X = pd.DataFrame([str(x) for x in df.index])\n","plt.plot(X.loc[:,0], df.loc[:,\"RMSE\"])\n","plt.tight_layout()\n","# print(pd.DataFrame(df.index))\n","\n","df = pd.read_csv(\"topCommonElectrodeFSRegressionRankingRandomForest11.csv\")\n","# plt.plot(df['RMSE'])\n","fig = plt.gcf()\n","fig.set_size_inches(20, 10)\n","# X = pd.DataFrame([x for x in range(1,) ])\n","plt.rcParams.update({'font.size': 30})\n","plt.xlabel('Top N Electrodes')\n","plt.ylabel('RMSE')\n","df.index += 1\n","X = pd.DataFrame([str(x) for x in df.index])\n","plt.plot(X.loc[:,0], df.loc[:,\"RMSE\"])\n","plt.tight_layout()\n","# print(pd.DataFrame(df.index))\n","plt.legend([\"Method A\",\"Method B\", \"Method C\"])\n","plt.savefig(os.getcwd() + \"/verification/OASIS_Combined_Electrodes_Plots1.svg\", bbox_inches='tight', dpi=500)\n","plt.show()\n","plt.clf()\n"],"execution_count":null,"outputs":[{"output_type":"stream","text":["/gdrive/MyDrive/Project_DEAP/4.1.2021/OASIS/plots\n"],"name":"stdout"},{"output_type":"display_data","data":{"image/png":"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\n","text/plain":["
"]},"metadata":{"tags":[],"needs_background":"light"}},{"output_type":"display_data","data":{"text/plain":["
"]},"metadata":{"tags":[]}}]},{"cell_type":"markdown","metadata":{"id":"shNxxCx_E6hO"},"source":["DEAP"]},{"cell_type":"code","metadata":{"colab":{"base_uri":"https://localhost:8080/"},"id":"KmR6y9MsE6eF","executionInfo":{"status":"ok","timestamp":1619101429122,"user_tz":-330,"elapsed":937,"user":{"displayName":"Rohit Garg","photoUrl":"https://lh3.googleusercontent.com/a-/AOh14Gj3Za6nmSY12sPvpCoWOtWQcVR5CMMSPBRGTexH_w=s64","userId":"11252576926243182044"}},"outputId":"1ea76223-2e12-4526-d258-90204e4b4b28"},"source":["%cd /gdrive/MyDrive/Project_DEAP/4.1.2021/DEAP/plots/"],"execution_count":null,"outputs":[{"output_type":"stream","text":["/gdrive/MyDrive/Project_DEAP/4.1.2021/DEAP/plots\n"],"name":"stdout"}]},{"cell_type":"code","metadata":{"colab":{"base_uri":"https://localhost:8080/","height":355},"id":"yGJnzbHGE6Wb","executionInfo":{"status":"ok","timestamp":1619101434620,"user_tz":-330,"elapsed":3633,"user":{"displayName":"Rohit Garg","photoUrl":"https://lh3.googleusercontent.com/a-/AOh14Gj3Za6nmSY12sPvpCoWOtWQcVR5CMMSPBRGTexH_w=s64","userId":"11252576926243182044"}},"outputId":"6a438cee-23b1-49fe-a6f8-ea280559965b"},"source":["df = pd.read_csv(\"topCommonFeaturesRegressionRanking11.csv\")\n","# plt.plot(df['RMSE'])\n","fig = plt.gcf()\n","fig.set_size_inches(25, 10)\n","# X = pd.DataFrame([x for x in range(1,) ])\n","plt.rcParams.update({'font.size': 30})\n","plt.xlabel('Top N Features')\n","plt.ylabel('RMSE')\n","df.index += 1\n","X = pd.DataFrame([str(x) for x in df.index])\n","plt.plot(X.loc[:,0], df.loc[:,\"RMSE\"])\n","plt.tight_layout()\n","# print(pd.DataFrame(df.index))\n","\n","df = pd.read_csv(\"topCommonFeatureFSRegressionRankingSelectKBest11.csv\")\n","# plt.plot(df['RMSE'])\n","fig = plt.gcf()\n","fig.set_size_inches(25, 10)\n","# X = pd.DataFrame([x for x in range(1,) ])\n","plt.rcParams.update({'font.size': 30})\n","plt.xlabel('Top N Features')\n","plt.ylabel('RMSE')\n","df.index += 1\n","X = pd.DataFrame([str(x) for x in df.index])\n","plt.plot(X.loc[:,0], df.loc[:,\"RMSE\"])\n","plt.tight_layout()\n","# print(pd.DataFrame(df.index))\n","\n","df = pd.read_csv(\"topCommonFeatureFSRegressionRankingRandomForest11.csv\")\n","# plt.plot(df['RMSE'])\n","fig = plt.gcf()\n","fig.set_size_inches(25, 10)\n","# X = pd.DataFrame([x for x in range(1,) ])\n","plt.rcParams.update({'font.size': 30})\n","plt.xlabel('Top N Features')\n","plt.ylabel('RMSE')\n","df.index += 1\n","X = pd.DataFrame([str(x) for x in df.index])\n","plt.plot(X.loc[:,0], df.loc[:,\"RMSE\"])\n","plt.tight_layout()\n","# print(pd.DataFrame(df.index))\n","plt.legend([\"Method A\",\"Method B\", \"Method C\"])\n","plt.savefig(os.getcwd() + \"/verification/DEAP_Combined_Features_Plots1.svg\", bbox_inches='tight', dpi=500)\n","plt.show()\n","plt.clf()\n"],"execution_count":null,"outputs":[{"output_type":"display_data","data":{"image/png":"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\n","text/plain":["
"]},"metadata":{"tags":[],"needs_background":"light"}},{"output_type":"display_data","data":{"text/plain":["
"]},"metadata":{"tags":[]}}]},{"cell_type":"code","metadata":{"colab":{"base_uri":"https://localhost:8080/","height":438},"id":"iEX0t3fME6QL","executionInfo":{"status":"ok","timestamp":1619101489953,"user_tz":-330,"elapsed":2156,"user":{"displayName":"Rohit Garg","photoUrl":"https://lh3.googleusercontent.com/a-/AOh14Gj3Za6nmSY12sPvpCoWOtWQcVR5CMMSPBRGTexH_w=s64","userId":"11252576926243182044"}},"outputId":"fff63a1c-a611-4af2-9118-d295a69ca4ab"},"source":["df = pd.read_csv(\"topCommonElectrodeRegressionRanking11.csv\")\n","# plt.plot(df['RMSE'])\n","fig = plt.gcf()\n","fig.set_size_inches(20, 10)\n","# X = pd.DataFrame([x for x in range(1,) ])\n","plt.rcParams.update({'font.size': 30})\n","plt.xlabel('Top N Electrodes')\n","plt.ylabel('RMSE')\n","df.index += 1\n","X = pd.DataFrame([str(x) for x in df.index])\n","plt.plot(X.loc[:,0], df.loc[:,\"RMSE\"])\n","plt.tight_layout()\n","# print(pd.DataFrame(df.index))\n","\n","df = pd.read_csv(\"topCommonElectrodeFSRegressionRankingSelectKBest11.csv\")\n","# plt.plot(df['RMSE'])\n","fig = plt.gcf()\n","fig.set_size_inches(20, 10)\n","# X = pd.DataFrame([x for x in range(1,) ])\n","plt.rcParams.update({'font.size': 30})\n","plt.xlabel('Top N Electrodes')\n","plt.ylabel('RMSE')\n","df.index += 1\n","X = pd.DataFrame([str(x) for x in df.index])\n","plt.plot(X.loc[:,0], df.loc[:,\"RMSE\"])\n","plt.tight_layout()\n","# print(pd.DataFrame(df.index))\n","\n","df = pd.read_csv(\"topCommonElectrodeFSRegressionRankingRandomForest11.csv\")\n","# plt.plot(df['RMSE'])\n","fig = plt.gcf()\n","fig.set_size_inches(20, 10)\n","# X = pd.DataFrame([x for x in range(1,) ])\n","plt.rcParams.update({'font.size': 30})\n","plt.xlabel('Top N Electrodes')\n","plt.ylabel('RMSE')\n","df.index += 1\n","X = pd.DataFrame([str(x) for x in df.index])\n","plt.plot(X.loc[:,0], df.loc[:,\"RMSE\"])\n","plt.tight_layout()\n","# print(pd.DataFrame(df.index))\n","plt.legend([\"Method A\",\"Method B\", \"Method C\"])\n","plt.savefig(\"verification/DEAP_Combined_Electrodes_Plots1.svg\", bbox_inches='tight', dpi=500)\n","plt.show()\n","plt.clf()\n"],"execution_count":null,"outputs":[{"output_type":"display_data","data":{"image/png":"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\n","text/plain":["
"]},"metadata":{"tags":[],"needs_background":"light"}},{"output_type":"display_data","data":{"text/plain":["
"]},"metadata":{"tags":[]}}]},{"cell_type":"markdown","metadata":{"id":"2Ad4fDKmcXkV"},"source":["DREAMER"]},{"cell_type":"code","metadata":{"colab":{"base_uri":"https://localhost:8080/"},"id":"VuGXL4SQdRjc","executionInfo":{"status":"ok","timestamp":1619101892651,"user_tz":-330,"elapsed":1328,"user":{"displayName":"Rohit Garg","photoUrl":"https://lh3.googleusercontent.com/a-/AOh14Gj3Za6nmSY12sPvpCoWOtWQcVR5CMMSPBRGTexH_w=s64","userId":"11252576926243182044"}},"outputId":"4119260d-3be3-406b-b13a-4d96921e6a2e"},"source":["%cd /gdrive/MyDrive/Project_DEAP/4.1.2021/DREAMER/plots/"],"execution_count":null,"outputs":[{"output_type":"stream","text":["/gdrive/MyDrive/Project_DEAP/4.1.2021/DREAMER/plots\n"],"name":"stdout"}]},{"cell_type":"markdown","metadata":{"id":"hl2B0hMsdAiO"},"source":["FEATURES"]},{"cell_type":"code","metadata":{"colab":{"base_uri":"https://localhost:8080/","height":355},"id":"MCoMcZEpdAfo","executionInfo":{"status":"ok","timestamp":1619101954660,"user_tz":-330,"elapsed":3601,"user":{"displayName":"Rohit Garg","photoUrl":"https://lh3.googleusercontent.com/a-/AOh14Gj3Za6nmSY12sPvpCoWOtWQcVR5CMMSPBRGTexH_w=s64","userId":"11252576926243182044"}},"outputId":"1acbbfdb-dbef-43e1-aee5-375f2d7efbc4"},"source":["df = pd.read_csv(\"topCommonFeaturesRegressionRanking11.csv\")\n","# plt.plot(df['RMSE'])\n","fig = plt.gcf()\n","fig.set_size_inches(25, 10)\n","# X = pd.DataFrame([x for x in range(1,) ])\n","plt.rcParams.update({'font.size': 30})\n","plt.xlabel('Top N Features')\n","plt.ylabel('RMSE')\n","df.index += 1\n","X = pd.DataFrame([str(x) for x in df.index])\n","plt.plot(X.loc[:,0], df.loc[:,\"RMSE\"])\n","plt.tight_layout()\n","# print(pd.DataFrame(df.index))\n","\n","df = pd.read_csv(\"topCommonFeatureFSRegressionRankingSelectKBest11.csv\")\n","# plt.plot(df['RMSE'])\n","fig = plt.gcf()\n","fig.set_size_inches(25, 10)\n","# X = pd.DataFrame([x for x in range(1,) ])\n","plt.rcParams.update({'font.size': 30})\n","plt.xlabel('Top N Features')\n","plt.ylabel('RMSE')\n","df.index += 1\n","X = pd.DataFrame([str(x) for x in df.index])\n","plt.plot(X.loc[:,0], df.loc[:,\"RMSE\"])\n","plt.tight_layout()\n","# print(pd.DataFrame(df.index))\n","\n","df = pd.read_csv(\"topCommonFeatureFSRegressionRankingRandomForest11.csv\")\n","# plt.plot(df['RMSE'])\n","fig = plt.gcf()\n","fig.set_size_inches(25, 10)\n","# X = pd.DataFrame([x for x in range(1,) ])\n","plt.rcParams.update({'font.size': 30})\n","plt.xlabel('Top N Features')\n","plt.ylabel('RMSE')\n","df.index += 1\n","X = pd.DataFrame([str(x) for x in df.index])\n","plt.plot(X.loc[:,0], df.loc[:,\"RMSE\"])\n","plt.tight_layout()\n","# print(pd.DataFrame(df.index))\n","plt.legend([\"Method A\",\"Method B\", \"Method C\"])\n","plt.savefig(\"verification/DREAMER_Combined_Features_Plots1.svg\", bbox_inches='tight', dpi=500)\n","plt.show()\n","plt.clf()\n"],"execution_count":null,"outputs":[{"output_type":"display_data","data":{"image/png":"iVBORw0KGgoAAAANSUhEUgAABtUAAAKdCAYAAABcYeboAAAABHNCSVQICAgIfAhkiAAAAAlwSFlzAAALEgAACxIB0t1+/AAAADh0RVh0U29mdHdhcmUAbWF0cGxvdGxpYiB2ZXJzaW9uMy4yLjIsIGh0dHA6Ly9tYXRwbG90bGliLm9yZy+WH4yJAAAgAElEQVR4nOzdf5yOZf7//8c540eDYcL4EJOxIZSthlik3bA+1JaSfmxrx7RtrW3X7rRs2cr3TdFutpX0Q/n2zmjFd2vVit6spW33TU2MiDaiYsqSZvwIYc1wfv8YrmbMTzNjLvK4327Xbc/rPI7zOJ7nJftHr47jCMIwRJIkSZIkSZIkSVLpYqIdQJIkSZIkSZIkSTrVWVSTJEmSJEmSJEmSymFRTZIkSZIkSZIkSSqHRTVJkiRJkiRJkiSpHBbVJEmSJEmSJEmSpHJYVJMkSZIkSZIkSZLKUSvaAVQ9mjZtGiYnJ0c7hiRJkiRJkiRJ0mlr5cqVuWEYJpbUZlHtayI5OZmsrKxox5AkSZIkSZIkSTptBUGQXVqb2z9KkiRJkiRJkiRJ5bCoJkmSJEmSJEmSJJXDopokSZIkSZIkSZJUDotqkiRJkiRJkiRJUjksqkmSJEmSJEmSJEnlsKgmSZIkSZIkSZIklcOimiRJkiRJkiRJklQOi2qSJEmSJEmSJElSOSyqSZIkSZIkSZIkSeU47YpqQRDEBkFwYRAEaUEQPB4EwVtBEOwPgiA8+hlbzfOdGwTBT4IgeCEIgveCINgTBMGhIAhygiBYGgTBA0EQnHuCY9YOguD2IAiWBEGwLQiC/wRBsCUIgnlBENwUBEFQne8gSZIkSZIkSZKkqqkV7QCV8CIwuCYmCoLgL8A1QElFrqZHP72Au4MguD8Mw0cqMGYy8DJwyXFNLY9+vgf8OAiCG8Iw3F359JIkSZIkSZIkSaoup2NRLfa47zuBHUC7kzDXhXxVUFsB/B3YAOwBWgHXU1BUqwv8PgiCumEYTihtsCAIEoAFQIejt9YBzwFbgLbAHUAS0A94OQiC/mEY5lf3S0mSJEmSJEmSJOnEnI5FteUUFKNWAivDMNwUBEEaMP0kzHUQeBJ4PAzDD0pofzQIgruASUe/jw2C4KUwDDeUMt5/8VVBbSFwXRiGB481BkHwFLCYglVsVwA/OTq/JEmSJEmSJEmSoui0K6qFYfhQDU7XOwzDXWV1CMPw0SAIelGwaq0W8AMKimdFBEHQDLjz6NcvgWGFC2pHx9oZBEEqsIaCFXL3B0HwdBiGh6v+KpIkSZIkSZIkgMOHD7Nnzx727t3LgQMHOHLkSLQjSaqkmJgY4uLiiI+Pp2HDhsTGHr/hYfU57YpqNam8glohL1FQVAPoXEqfa4E6R69nh2H4eSlzvhcEwetAX6A58G3g9QrmkCRJkiRJkiSV4dChQ2RnZ1OvXj0SEhJo2bIlMTExBEFQ/sOSTilhGHLkyBG+/PJL9u7dS25uLq1bt6ZOnTrlP1wJMSdl1DPP3kLXcaX06V/oemE54xVuH1CpRJIkSZIkSZKkIg4fPkx2djZNmzalZcuWkVUtFtSk01MQBMTGxtKwYUNatmxJ06ZNyc7O5vDhk7MBoEW16nFhoevsCvRZWc54WaU8J0mSJEmSJEmqpD179lCvXj3OPvvsaEeRdBKcffbZ1KtXjz179pyU8S2qVVEQBLWAWwvdeq2EPjHAeUe/Hga2lDNs4cJc+yoFlCRJkiRJkiQBsHfvXuLj46MdQ9JJFB8fz969e8vvWAkW1apuFNDh6PUaSiiqAQ346vy63WEY5pcz5o5C1wlViydJkiRJkiRJAjhw4AD169ePdgxJJ1H9+vU5cODASRnboloVBEFwBfDg0a/5wPAwDI+U0LVBoeuDFRi68J92qf/ZRBAEdwRBkBUEQVZOTk4FhpUkSZIkSZKkM9eRI0eIifFfi0tfZzExMRw5UlKpphrGPimjngGCIOgAvMRXK9DuDcPwrZrMEIbhtDAMu4Zh2DUxMbEmp5YkSZIkSZKk01IQBNGOIOkkOpl/xy2qVUIQBG2AxUCTo7cmh2H4+zIe2Vfo+qwKTBFX6PrkbPwpSZIkSZIkSZKkCrOodoKCIEgCXgdaHr31dBiGd5Xz2D4KtocESAiCoFZZnfmqWAew+8RTSpIkSZIkSZIkqTpZVDsBQRCcQ0FBLfnoreeAO8t77ug5ax8d/RoLtCrnkdaFrjecWEqV6pO34R9lLSiUJEmSJEmSJEkqmUW1CgqCoDkFBbW2R2/NBG4PwzCs4BDvFbruUk7frqU8p6r4cDH8fTxsezfaSSRJkiRJkiRJ0mnGoloFBEGQCCwBzj96609A2tEVaBX110LX/7ecvgMKXS88gTlUlp4/h7MS4PUJ0U4iSZIkSZIkSfqa2rx5M0EQEAQBaWlp0Y5TKcnJyQRBQHJycrSjnFIsqpUjCILGwGKg09FbLwNDwzA8fIJD/QU4dPT6+0EQNCtlvguBPke/fgb84wTnUWnOagS9fgkb/wqfLo92GkmSJEmSJEk6Yx0rOh37jBgxosLPpqenF3v+ZJs8eTJjx45l8uTJJ32ur7sHH3ywyJ/dG2+8Ee1IFXbGFtWCIMgIgiA8+hlbSp9GwCLgm0dvzQVuDsMw/0TnC8MwB3jq6NcGQEYQBGcdN9/ZwPPAsf8HGF+J4p3K0v0nUL8ZLHkAKrxzpyRJkiRJkiTpZJo9ezaHDh0qt19eXh4vvPBCDSQqavLkyYwbN86iWhWFYUhGRkaRe9OnT49OmEqoFe0AJyoIgjbAbcfd/mah6z5BEBz/XnPCMFxViekW8NX5Z1uBWcBV5VS994dhuKiUtnEUbO3YARgIvBMEwbPAvyk4q+0nQNLRvm8A0yqRWWWpUx8uHwUL7oaP34Dzroh2IkmSJEmSJEk6Y9WqVYv8/Hx27NjBvHnzuP7668vsP3/+fHJzc4s8q9PHP//5Tz7++OMi9/785z/zxBNPEB8fH6VUFXfaFdWA1sB9ZbT3Pvop7EOgMkW1HoWuz6HgLLXyZAPJJTWEYbg7CIKBFGwheQnQEfhDCV0XAzeEYZh3QmlVMV3S4M3H4fUH4RvfgRpYGixJkiRJkiRJKu68887jyJEjbNy4kYyMjHKLasdWObVv354gCPjggw9qIKWqS+FVaWlpaWRkZLB//35efPFFbrvt+PVUp54zdvvHaAnDcDPQHbgDeB3YTsFZa1uB14Cbgf5hGO6OVsavvVp14dt3w79XwgcLop1GkiRJkiRJks5ow4YNA2DhwoVs37691H45OTksWLCgyDM6fezbt48///nPAHTu3JmHH36YWrUK1n6dLltAnnZFtTAM3wjDMDjBT0YJ46QVah9bylwnOk8QhmFyBd4hLwzD/zcMw75hGDYPw7BuGIYtwzD8XhiGfwpDD/s66S66BRqfB3+fAEeORDuNJEmSJEmSJJ2xUlNTiYmJIT8/v8zz0mbOnEleXh4xMTGkpqae0By5ublMmDCB3r1707x5c+rUqUNiYiK9e/dm4sSJ7Nu3r8TnkpOTCYKA7OxsALKzswmCoNjn+HPCSpp/7NixdO7cmfj4eOLj40lJSeG3v/0t+/fvr9A7fPrpp4wePZqUlBQaN25M3bp1admyJVdffTUZGRkcPny4wr/Fb37zGzp16kT9+vVp3Lgxl156KY888kiFs1TGiy++yJdffgkU/Jk3a9aM/v37A7Bs2TI2btx40uauLqddUU2qFrG14Ip7Yft78K+Xo51GkiRJkiRJks5YSUlJ9OnTB4AZM2aU2u9YW9++fWnVqlWFx8/IyOAb3/gG999/P0uXLmX79u3k5eWRm5vL0qVLueeee2jbti1vvfVW1V6kFFlZWVx88cWMGzeO9957j3379rFv3z5WrVrFvffeS69evdi5c2eZYzzzzDOcf/75PPzww6xatYpdu3Zx6NAhtm7dyvz587n11lu55JJL2Lx5c5njvPXWW3Ts2JHf/e53rFu3jv3797Nr1y6ysrL49a9/Tbdu3SIFxOp2bDVaTEwMt9xyC0CR4ujpsFrtdDxTTaoeFwyG/50Eb/wWOl1bUGiTJEmSJEmSJNW4tLQ0Fi9ezJo1a3jnnXdISUkp0r5q1SrefffdSN+Keuyxx0hPTwegXr16DBkyhJ49e9KkSRNyc3NZuHAhr776Ktu3b6dfv36sWLGCTp06RZ6fNm0a+/fv54477iAnJ4fExESmTZtWbJ7j8x7z6aefctVVV7Fz505+8IMfcMUVV9CgQQPef/99nnzySXbs2MHq1atJT0/n+eefL3GMZ555huHDh0e+X3311Vx11VUkJCSwYcMGpk+fzqZNm1i7di2XXXYZq1atIjExsdg4H374IQMGDGDPnj1AwRaMqampJCUlsW3bNmbPns3y5cu58cYbycvLq/BvXBEbN25k6dKlQEFR9JxzzgFg0KBBNGrUiC+++ILnn3+e8ePHExNz6q4HC9xp8Ouha9euYVZWVrRjnH7Wvwb/3y1wzROQ8sNop5EkSZIkSZJ0Eq1bt46OHTtGO4aAIAgAOP/881m/fj0HDhygefPm7NmzhxEjRjBlypQi/X/5y18yZcoUGjVqxLZt24iLi6NDhw588MEHAJRU68jKyqJHjx7k5+dz8cUXM3fuXM4999xi/ebPn8/gwYPJy8uje/fuZGZmFuuTnJxMdnY2rVu3Lnc12ObNm2nTpk3ke0JCAgsXLqR79+5F+m3atImUlBR2795NbGwsn3zySaTYVHisTp06ceDAAWJjY5k1axY33nhjkT4HDhzghhtu4LXXXgNgyJAhvPTSS8Vy9evXjyVLlgBw6623Mm3atMiZZlDwG44aNYpJkyZF7lXkfSvivvvu46GHHgLgj3/8I0OHDo20/fjHP+a///u/AViwYAEDBgyo8nxV+bseBMHKMAy7ltR26pb7pJpw/pXQsgv842HI/0+000iSJEmSJEnSGSkuLi5SLJo9e3aRlVJ5eXnMmjULgBtvvJG4uLgKjfnAAw+Qn59PfHw88+fPL7GgBvC9732P0aNHA/D222/z5ptvVuVVipkyZUqxghpAmzZt+NnPfgbA4cOHIwWv4589cOAAACNHjixWUIOC327WrFm0aNECgDlz5hQ7n2z16tWR8du3b8/TTz9dpKAGBYXORx55hG7dulXiLUt35MiRyCq8+vXrc9111xVpP522gHS/O53ZggD63A9/vA5WzoDud0Q7kSRJkiRJkqRTwLh5/+L9rXuiHaNGdTqnIf919QVRmz8tLY1nn32W3Nxc5s2bx+DBgwGYN28eubm5kT4VsWvXrsjKre9///u0bNmyzP5Dhw7lwQcfBGDRokX07Nmzkm9RVGJiYuT8sJL06dOHCRMmAPD+++8Xa3/55ZcBqFWrFiNHjix1nIYNG3LnnXcyZswYwjDklVde4e677460v/LKK5HrESNGUKdOnRLHCYKAkSNHctNNN5X9Yifgb3/7G1u2bAFg8ODB1K9fv0h77969SU5OZvPmzcydO5edO3fSuHHjapu/OllUk75xBbS+DP73EbhkKNSpF+1EkiRJkiRJknTG6dWrF+3atWPjxo3MmDEjUlTLyMgAClZYVbTYtWzZMo4cOQJAbGwsf/nLX8rsX3hl3Lp16yqRvmRdu3YlNja21PbCxb5du3YVafv888/Jzs4G4KKLLqJZs2ZlztW/f3/GjBkDFKy4K2zFihWR6759+5Y5TnntJ+q5556LXBdelXZMEAQMHTqU8ePH85///IdZs2bx85//vFozVBeLalIQQN8x8Nz/heXT4LL0aCeSJEmSJEmSFGXRXLF1Jhs2bBj3338/CxYsICcnhzAMWbBgQaStogqfAzZ16lSmTp1a4WePL25VRdOmTctsr1u3buT64MGDRdq2bdsWuW7fvn25cxXuU/hZgK1bt0au27ZtW+Y4TZo0ISEhgd27d5c7Z3l27drF3LlzgYICYp8+fUrsl5qayvjx44GCLSBP1aKaZ6pJAOd+C9p+F5ZNhoNfRDuNJEmSJEmSJJ2RUlNTiYmJIS8vj5kzZzJz5kzy8/OJiYkpcZVTab74ovL/nvfQoUOVfvZ4MTGVL8Ps3bs3cn38lokladCgQYnPAuzbtw8o2Eaydu3a5Y5VkfkqYtasWfznP/8B4JZbbin192jXrh3f+ta3AHjnnXdYs2ZNtcxf3SyqScf0uR8O7IK3nop2EkmSJEmSJEk6IyUlJUVWM2VkZDBjxgygYEvCVq1aVXicwgWm5557jjAMK/x54403qvWdKis+Pj5y/eWXX5bb/1jh7Phn4avfIz8/v8hWl6WpyHwVMX369Mj173//e4IgKPWTmZlZ4nOnEotq0jHnXAwdr4G3noT9O6OdRpIkSZIkSZLOSGlpaQCsWbMmsmLp2L2KKnxW2ZYtW6orWo1q0aJF5Hrjxo3l9i/c55xzzinSVvj7hx9+WOY4O3bsqJatH9euXcvKlSsr9ewLL7xQoeJfTfNMNamwK+6DdfNg6aPQ/8Fop5EkSZIkSZKkM87gwYNp2LAhe/bsAaBRo0Zcd911JzTG5ZdfThAEhGHIokWLGDNmTJUyHdu2MAzDKo1zIpo1a0br1q3Jzs5m9erV5OTkkJiYWGr/RYsWRa67detWpK1bt26Rs+lef/11OnbsWOo4S5YsqWLyAoVXmw0aNIiLL7643Gf+53/+hxUrVpCTk8P8+fNP+M/9ZLOoJhXWrAN88yZY/v9Cj59BfPNoJ5IkSZIkSZKkM0pcXBzp6en89a9/BWDAgAHExcWd0BjNmjVjwIABLFiwgKVLl7Jo0SL69+9f6UzHtk+srm0RK+r6669n0qRJ5OfnM3nyZCZMmFBiv7179/LUUwVHGwVBUKwYdd111zFu3DgAnnjiCe64444Sz1YLw5BHH320yrmPnYl3LM/jjz9OUlJSuc9dcsklXHvttUBBUe5UK6q5/aN0vO+MhiN58M9Hop1EkiRJkiRJks5I48aNIzMzk8zMTMaOHVupMcaPHx8pHN18880sXLiwzP7Z2dmMGjWKzz//vFhbmzZtgIKtET/55JNK5amMESNGRAqKEydOZM6cOcX6HDx4kKFDh7J161agoBDXrl27In0uuugi+vXrB8D69eu58847OXz4cJE+YRhyzz33FDnbrLJee+01cnJyAPj2t79doYIawJVXXkmTJk0AWLBgAdu3b69ylurkSjXpeI3bwCU/hJUZ0HMEnN062okkSZIkSZIkSScoJSWFqVOncvvtt7Nr1y4GDhxIr169GDhwIG3atKF27drs3LmT9evXs3TpUrKysgBIT08vNlbfvn159dVXgYLtKX/605/SokWLyLaQnTt3LnKOW3VJTk7m0UcfZfjw4eTn5zNkyBAGDRrElVdeSUJCAhs3buS5557j448/BgrOkju2Yu14U6dOpUuXLuzZs4dnn32W5cuXk5qaSlJSEp999hmzZs3i7bffplu3bmzZsiVSpKuMwls//vCHP6zwc7Vr1+amm27iqaeeIj8/nz/+8Y+MGjWq0jmqW1CT+3/q5OnatWt47C+8qsEX/4Ypl0DnG+DaJ6OdRpIkSZIkSVI1WLduXZlnSanmBEEAwPnnn8/69esrNUaHDh344IMPgLLPOps3bx633357hVY9NWnShPXr19O0adMi9/ft20eXLl3YsGFDic9Nnz6dtLQ0ADZv3hxZ2TZs2DAyMjJKna+ifZ9++mnuuusuDh48WOpYF154IfPmzSM5ObnUPm+++SaDBg0iNze3xPYLLriA//mf/+Hyyy8nOzub1q1bs3nz5lLHK8n27dtp1aoV+fn5nHXWWWzfvp2GDRtW+PnMzEx69OgBQKdOnfjXv/51QvND1f6uB0GwMgzDriW1uf2jVJJGLeHSH8O7syB3Y7TTSJIkSZIkSZIq6eqrr2bTpk08/fTTXHPNNSQlJREXF0edOnVITEykR48ejBgxgnnz5rF169ZiBTUoOFMtMzOT++67j5SUFBo1ahRZpVYThg8fzoYNG7jnnnu4+OKLSUhIoE6dOrRo0YIrr7yS6dOns3r16jILagA9e/Zk3bp1jB49mg4dOhAXF0dCQgJdunRh4sSJLF++nHPPPbdKWWfOnEl+fj5Q8NufSEEN4Fvf+lZk+8r333+ft99+u0p5qpMr1b4mXKl2EuzLgccugvb/F26YXn5/SZIkSZIkSac0V6pJZwZXqkk1rUEifOun8K+X4bO10U4jSZIkSZIkSZKiyKKaVJaeI+CsRvD6+GgnkSRJkiRJkiRJUWRRTSpLXAL0/AVsWAifroh2GkmSJEmSJEmSFCUW1aTydB8O9RPh9QeinUSSJEmSJEmSJEWJRTWpPHUbQO+RsOmf8PE/op1GkiRJkiRJkiRFgUU1qSK63AoNW8LrD0IYRjuNJEmSJEmSJEmqYRbVpIqofRZ8+27YsgI2/DXaaSRJkiRJkiRJUg2zqCZV1MU/gLPbwOvj4ciRaKeRJEmSJEmSJEk1yKKaVFGxteGKe2H7Wnj/L9FOI0mSJEmSJEmSapBFNelEXHg9JHaEvz8Eh/OjnUaSJEmSJEmSJNUQi2rSiYiJhT73wY6NsOZP0U4jSZIkSZIkSZJqiEU16UR1+B6ccwn843eQfyjaaSRJkiRJkiRJUg2wqCadqCCAPvfD7k/gnRnRTiNJkiRJkiRJkmqARTWpMs7rC+f2hH8+Aof2RzuNJEmSJEmSJEk6ySyqSZURBNB3DOz7DFY8G+00kiRJkiRJkiTpJLOoJlVW654FK9aWPgoH90Q7jSRJkiRJkiRJOoksqklV0ed+OLATMqdGO4kkSZIkSZIkSTqJLKpJVdEyBTp8D956AvbvjHYaSZIkSZIkSZJ0klhUk6qqz/3wn72w7LFoJ5EkSZIkSZIkSSeJRTWpqpp1hM43wNvPwN7t0U4jSZIkSZIkSZJOAotqUnX4zmg4fAj+9w/RTiJJkiRJkiRJiqLNmzcTBAFBEJCWlhbtOJWSnJxMEAQkJydHO8opxaKaVB2anAeXDIWV02H3p9FOI0mSJEmSJEmntGNFp2OfESNGVPjZ9PT0Ys+fbJMnT2bs2LFMnjz5pM/1dXKsOFfap0GDBpx33nkMGTKE2bNnk5+fH+3IZbKoJlWXb99d8L//eDi6OSRJkiRJkiTpNDN79mwOHTpUbr+8vDxeeOGFGkhU1OTJkxk3bpxFtWr25Zdf8vHHHzNnzhxuueUWUlJS2LRpU7RjlapWtANIXxuNWkHX22D5NOiVDk3bRjuRJEmSJEmSJJ3SatWqRX5+Pjt27GDevHlcf/31ZfafP38+ubm5RZ7V6eGZZ56hWbNmke9Hjhxh9+7dvPPOO8ycOZMvvviCtWvX8t3vfpc1a9ZQr169KKYtmSvVpOrU+1dQqy688VC0k0iSJEmSJEnSKe+8886jXbt2AGRkZJTb/1if9u3bc955553EZKpu/fv359prr418Bg8ezI9+9COeeOIJ3nvvPZKSkgD46KOPmDFjRpTTlsyimlSdGjSD7sPhvTnw2XvRTiNJkiRJkiRJp7xhw4YBsHDhQrZv315qv5ycHBYsWFDkGX09tGrVilGjRkW+/+Mf/4himtJZVJOqW69fQN1G8PcJ0U4iSZIkSZIkSae81NRUYmJiyM/PL/O8tJkzZ5KXl0dMTAypqaknNEdubi4TJkygd+/eNG/enDp16pCYmEjv3r2ZOHEi+/btK/G55ORkgiAgOzsbgOzsbIIgKPYpb5Vdbm4uY8eOpXPnzsTHxxMfH09KSgq//e1v2b9/f4Xe4dNPP2X06NGkpKTQuHFj6tatS8uWLbn66qvJyMjg8OHDFf4tfvOb39CpUyfq169P48aNufTSS3nkkUcqnOVk6NChQ+T6iy++iFqOsnimmlTd4s6GniPg7+NhSxa06hrtRJIkSZIkSZJ0ykpKSqJPnz4sXryYGTNm8Ktf/arEfse2BOzbty+tWrWq8PgZGRn84he/YO/evUXu5+bmsnTpUpYuXcqkSZN45ZVX6NGjR+VfpBRZWVlce+21/Pvf/y5yf9WqVaxatYoXX3yRJUuW0Lhx41LHeOaZZ7jrrrs4cOBAkftbt25l69atzJ8/n0mTJvHqq6+SnJxc6jhvvfUW11xzTeRcOoD9+/eTlZVFVlYWGRkZvPbaa5V70SoqnOncc8+NSobyWFSTToZvDYe3p8LrD0Lq3GinkSRJkiRJkqRTWlpaGosXL2bNmjW88847pKSkFGlftWoV7777bqRvRT322GOkp6cDUK9ePYYMGULPnj1p0qQJubm5LFy4kFdffZXt27fTr18/VqxYQadOnSLPT5s2jf3793PHHXeQk5NDYmIi06ZNKzbP8XmP+fTTT7nqqqvYuXMnP/jBD7jiiito0KAB77//Pk8++SQ7duxg9erVpKen8/zzz5c4xjPPPMPw4cMj36+++mquuuoqEhIS2LBhA9OnT2fTpk2sXbuWyy67jFWrVpGYmFhsnA8//JABAwawZ88eADp37kxqaipJSUls27aN2bNns3z5cm688Uby8vIq/BtXhyNHjjB9+vTI9379+tXo/BUVhGEY7QyqBl27dg2zsrKiHUOFvfUk/PVeGDYf2vSOdhpJkiRJkiTpjLdu3To6duwY7RgCgiAA4Pzzz2f9+vUcOHCA5s2bs2fPHkaMGMGUKVOK9P/lL3/JlClTaNSoEdu2bSMuLo4OHTrwwQcfAFBSrSMrK4sePXqQn5/PxRdfzNy5c0tcATV//nwGDx5MXl4e3bt3JzMzs1if5ORksrOzad26NZs3by7z3TZv3kybNm0i3xMSEli4cCHdu3cv0m/Tpk2kpKSwe/duYmNj+eSTTzjnnHOKjdWpUycOHDhAbGwss2bN4sYbbyzS58CBA9xwww2RFWZDhgzhpZdeKparX79+LFmyBIBbb72VadOmUavWV2uvwjBk1KhRTJo0KXKvIu9blmO/27H3LbyKLgxDvvjiC9555x0eeeSRyHl5V1xxBUuWLIn8M1IZVfm7HgTByjAMS9yCzjPVpJOl620Qf07BajWL15IkSZIkSZJUqri4uEixaPbs2UVWSuXl5TFr1iwAbrzxRuLi4io05gMPPEB+fj7x8Vyy6AgAACAASURBVPHMnz+/1C0Fv/e97zF69GgA3n77bd58882qvEoxU6ZMKVZQA2jTpg0/+9nPADh8+HCk4HX8s8e2fBw5cmSxghoU/HazZs2iRYsWAMyZM4eNGzcW6bN69erI+O3bt+fpp58uUlCDgkLnI488Qrdu3SrxluVr06ZNkXPoYmJiOPvss+nbty8LFizgG9/4Bvfffz8LFy6sUkHtZHL7R+lkqX0WfPvXMP8u2Pg3aN8/2okkSZIkSZIkVdSC0fDZ2minqFnNO8PA30Vt+rS0NJ599llyc3OZN28egwcPBmDevHmR87YquvXjrl27Iiu3vv/979OyZcsy+w8dOpQHH3wQgEWLFtGzZ89KvkVRiYmJ3HLLLaW29+nThwkTJgDw/vvvF2t/+eWXAahVqxYjR44sdZyGDRty5513MmbMGMIw5JVXXuHuu++OtL/yyiuR6xEjRlCnTp0SxwmCgJEjR3LTTTeV/WInQZ06dWjQoAFHjhyp8bkryqKadDJd8kNY9ljBarW2/SDGxaGSJEmSJEmSVJJevXrRrl07Nm7cyIwZMyJFtYyMDKBghVVFi13Lli2LFGdiY2P5y1/+Umb/wivj1q1bV4n0JevatSuxsbGlthcu9u3atatI2+effx7ZOvGiiy6iWbNmZc7Vv39/xowZAxSsuCtsxYoVkeu+ffuWOU557ZX1zDPPFHuH/fv389FHHzFnzhzeffddRo8ezaxZs1i8eHGJ58JFm0U16WSKrQ3f+Q288hNY9ypccG20E0mSJEmSJEmqiCiu2DqTDRs2jPvvv58FCxaQk5NDGIaRs7aGDRtW4XEKnwM2depUpk6dWuFnjy9uVUXTpk3LbK9bt27k+uDBg0Xatm3bFrlu3759uXMV7lP4WYCtW7dGrtu2bVvmOE2aNCEhIYHdu3eXO+eJ6N+/f5Ez1Qq7//77GT16NBMnTmTNmjV8//vfZ/HixdU6f3Vw2Yx0snW+ARI7wN8fgiOHo51GkiRJkiRJkk5ZqampxMTEkJeXx8yZM5k5cyb5+fnExMSQmppa4XG++OKLSmc4dOhQpZ89XkwVdi/bu3dv5Lp+/frl9m/QoEGJzwLs27cPKNhGsnbt2uWOVZH5qlMQBDz00EOcd955ACxZsqTaz7arDhbVpJMtJhauuBdyP4A1L0Y7jSRJkiRJkiSdspKSkujTpw9QsO3jjBkzgIItCVu1alXhcQoXmJ577jnCMKzw54033qjWd6qs+Pj4yPWXX35Zbv9jhbPjn4Wvfo/8/PwiW12WpiLzVbfY2Fj69esX+f63v/2txjOUx6KaVBM6XgMtLoI3fgv51fdfOUiSJEmSJEnS101aWhoAa9asYc2aNUXuVVThs8q2bNlSXdFqVIsWLSLXGzduLLd/4T7nnHNOkbbC3z/88MMyx9mxY0e1b/1YUU2aNIlcF96y8lRhUU2qCUEAfcbA7mxY9cdop5EkSZIkSZKkU9bgwYNp2LBh5HujRo247rrrTmiMyy+/nCAIAFi0aFGVMx3bxjEMwyqPVVHNmjWjdevWAKxevZqcnJwy+xd+z27duhVpK/z99ddfL3OcJUuWnGjUarNjx47IdU1vQVkRFtWkmtK2HyR9C/75e8g7EO00kiRJkiRJknRKiouLIz09ne7du9O9e3fS09OJi4s7oTGaNWvGgAEDAFi6dGmVC2vHtk+s6W0Rr7/+eqBg28bJkyeX2m/v3r089dRTQMH5ZMcXIQt/f+KJJ0rdAjIMQx599NGqxq6Uw4cPs3jx4sj3jh07RiVHWSyqSTUlCKDvGNi7DVb8d7TTSJIkSZIkSdIpa9y4cWRmZpKZmcnYsWMrNcb48eOpXbs2ADfffDMLFy4ss392djajRo3i888/L9bWpk0boGAl1SeffFKpPJUxYsSISEFx4sSJzJkzp1ifgwcPMnTo0Mh2iddffz3t2rUr0ueiiy6KnFe2fv167rzzTg4fPlykTxiG3HPPPWRmZp6MVylTGIbce++9fPTRRwDUq1ePQYMG1XiO8tSKdgDpjJJ8GXzjClg6CboMg7rx5T8jSZIkSZIkSTphKSkpTJ06ldtvv51du3YxcOBAevXqxcCBA2nTpg21a9dm586drF+/nqVLl5KVlQVAenp6sbH69u3Lq6++ChRsT/nTn/6UFi1aRLaF7Ny5c5Fz3KpLcnIyjz76KMOHDyc/P58hQ4YwaNAgrrzyShISEti4cSPPPfccH3/8MVBwltyxFWvHmzp1Kl26dGHPnj08++yzLF++nNTUVJKSkvjss8+YNWsWb7/9Nt26dWPLli3VfqbZokWLaNasWZF7Bw4c4KOPPmLOnDmsXr06cv+hhx4q1vdUYFFNqml9xsCzfSDzafj2r6OdRpIkSZIkSZK+tm677TaaNWvG7bffzvbt21m2bBnLli0rtX+TJk0466yzit3/0Y9+xJNPPsmGDRtYuXIlP/7xj4u0T58+nbS0tOqOD8BPfvITwjDkrrvu4uDBg8ydO5e5c+cW63fhhRcyb948EhMTSxynbdu2LFiwgEGDBpGbm8uaNWsYNWpUkT4XXHABL730EpdffvlJeY/yxMXF8fDDDzNixIhqn786uP2jVNNadYHzr4I3H4cDu6KdRpIkSZIkSZK+1q6++mo2bdrE008/zTXXXENSUhJxcXHUqVOHxMREevTowYgRI5g3bx5bt26ladOmxcZo0KABmZmZ3HfffaSkpNCoUaPIKrWaMHz4cDZs2MA999zDxRdfTEJCAnXq1KFFixZceeWVTJ8+ndWrV5OcnFzmOD179mTdunWMHj2aDh06EBcXR0JCAl26dGHixIksX76cc889t2ZeCqhbty7NmzenT58+jB8/ng8//PCULagBBGEYRjuDqkHXrl3DY0tTdRrY/i+Y2gsuuwv6/Ve000iSJEmSJElnhHXr1tGxY8dox5B0klXl73oQBCvDMOxaUpsr1aRo+D8XwIXXw9tPw77ih15KkiRJkiRJkqRTy2lXVAuCIDYIgguDIEgLguDxIAjeCoJgfxAE4dHP2Gqer14QBD2CIBgRBEFGEATvBUGQX2i+75zAWJsLPVfe543qfA+dgq64F/L/A/87KdpJJEmSJEmSJElSOWpFO0AlvAgMrsH5PgUa1+B8OlM0OQ8uvgWy/ht6/AwSkqKdSJIkSZIkSZIkleJ0LKrFHvd9J7ADaFdD830C1AGaV2HMHOCOcvrkVmF8nS6+fQ+s+RP8cyJc83i000iSJEmSJEmSpFKcjkW15cA6YCWwMgzDTUEQpAHTT9J8c4EPCs2XGwRBBjCsCmPuD8PwL9URTqe5hCTociuseBZ6pResXpMkSZIkSZIkSaec066oFobhQzU8X1WKZ1L5eo+Ed56HN34L1z8b7TSSJEmSJEmSJKkEMdEOIJ3x4v8PdP8JrP0zbH8/2mkkSZIkSZIkSVIJLKpJp4Jev4S68fD3CdFOIkmSJEmSJEmSSmBRLTqaBEGwOAiCz4MgOBQEQU4QBMuDIHg4CAIP1ToT1WsMPX4O6+fDv1dGO40kSZIkSZIkSTqORbXoaAD0BRKB2kBT4FLgbuCDIAh+GwRBbBTzKRq+9VOIawyvj492EkmSJEmSJEmSdJxa0Q5wBtoKLARWA9uBOkBbYDDQGYgFRgMtgLToRFRUnNUQev8KFt0Pm5dBcq9oJ5IkSZIkSZIkSUe5Uq1mDQWSwjC8LQzDx8MwfDEMw5lhGI4Nw/CbwJ3AkaN9hwVB8IOyBguC4I4gCLKCIMjKyck52dlVEy79McS3gNcfhDCMdhpJkiRJkiRJknSURbUaFIbh0jAMj5TRPhUYU+jW/eWMNy0Mw65hGHZNTEysrpiKptpxcPko+OQt+HBJtNNIkiRJkiRJkqSjLKqdeiYBXxy97hAEwTeiGUZRcEkqJJzrajVJkiRJkiRJkk4hFtVOMWEYHgQyC93qEK0sipJadeA7v4Ftq2HdvGinkSRJkiRJkiRJWFQ7Ve0odJ0QtRSKnm/eBE3bw98nwJHD0U4jSZIkSZIkSdIZz6LaqalJoevdUUuh6ImJhSvuhZz1sPbP0U4jSZIkSZIkSdIZz6LaKSYIgrrAtwrd2hCtLIqyjoOgeWd44yE4nBftNJIkSZIkSZIkndEsqp16fgU0Onq9MQzDD6MZRlEUEwN9xsCuzbBqZrTTSJIkSZIkSZJ0Rjtji2pBEGQEQRAe/YytgfnuC4KgYzl9hgMPFro14eSm0imvXX9o1Q3++XvIOxjtNJIkSZIkSZIknbFqRTvAiQqCoA1w23G3v1nouk8QBMe/15wwDFdVcr4+QJ/jbl9S6Pq2IAj6Hdf+SBiGx5+FdgMwPgiCVcA/gHXALqAO0BYYTNH3mBmG4YzKZNbXSBBA3zEw42rIeg563BntRJIkSZIkSZKkMmzevJk2bdoAMGzYMDIyMqIbqBKSk5PJzs6mdevWbN68OdpxThmnXVENaA3cV0Z776Ofwj4EKlVUAy4vZ76hJdx7Fji+qHbMJRQtyh0vH/gdMK5C6fT11+ZyaPNt+N8/QEoq1G0Q7USSJEmSJEmSVCVBEBT5/vOf/5zHH3+8Qs+mp6fz2GOPFbkXhmG1ZSvJ5MmT2b17NwkJCaSnp5/Uub7Odu7cyZw5c1i8eDHvvvsuOTk57Nmzh4YNG9KiRQu6dOnCd7/7Xa677jrq168f7bjFnI5FtdPVDyko0PUALgCaAk0o2IJzJ/A+BSvYngvD8N/RCqlTVN//B57tC28/DZePinYaSZIkSZIkSapWs2fP5g9/+AN16tQps19eXh4vvPBCDaX6yuTJkyMrtyyqnbhDhw4xfvx4Jk+ezN69e4u179y5k507d/Kvf/2L559/ngYNGjBy5Eh+/etfn1LFtdOuqBaG4RtAUF6/CoyTBqRVoN9YYGw1zLcWWAs8WdWxdAZq1RXaD4Q3p8ClP4a4hGgnkiRJkiRJkqQqq1WrFvn5+ezYsYN58+Zx/fXXl9l//vz55ObmFnlWp7bPP/+c6667jjfffDNy78ILL2TAgAG0bduWxo0bs3v3brKzs1m0aBErV65k3759jBs3juTkZNLS0qIX/jinXVFNOmP1uQ+evgzefLzgnDVJkiRJkiRJOs2dd955HDlyhI0bN5KRkVFuUe3Y+WTt27cnCAI++OCDGkipyjp48CADBw7knXfeAeDcc89l6tSpXHnllSX2Hz9+POvXr+e//uu/ePHFF2syaoXERDuApApq3hkuGAyZU2FfTrTTSJIkSZIkSVK1GDZsGAALFy5k+/btpfbLyclhwYIFRZ7RqW3UqFGRglq7du3IzMwstaB2TIcOHfjTn/7E7NmziY+Pr4mYFWZRTTqdXHEv5B+ApZOinUSSJEmSJEmSqkVqaioxMTHk5+eXeV7azJkzycvLIyYmhtTU1BOaIzc3lwkTJtC7d2+aN29OnTp1SExMpHfv3kycOJF9+/aV+FxycjJBEJCdnQ1AdnY2QRAU+xxbQVfW/GPHjqVz587Ex8cTHx9PSkoKv/3tb9m/f3+F3uHTTz9l9OjRpKSk0LhxY+rWrUvLli25+uqrycjI4PDhwxX+LX7zm9/QqVMn6tevT+PGjbn00kt55JFHKpylIj755BOmTZsGQGxsLC+88AItWrSo8PM333xzuSsXa5rbP0qnk6bt4KJbYMV/Q4+fQ6OW0U4kSZIkSZIkSVWSlJREnz59WLx4MTNmzOBXv/pVif1mzJgBQN++fWnVqlWFx8/IyOAXv/gFe/fuLXI/NzeXpUuXsnTpUiZNmsQrr7xCjx49Kv8ipcjKyuLaa6/l3//+d5H7q1atYtWqVbz44ossWbKExo0blzrGM888w1133cWBAweK3N+6dStbt25l/vz5TJo0iVdffZXk5ORSx3nrrbe45pprIufSAezfv5+srCyysrLIyMjgtddeq9yLHufJJ58kLy8PgKuuuopLL720WsaNJotq0unm23fDmj/BPyfC1Y9FO40kSZIkSZIkVVlaWhqLFy9mzZo1vPPOO6SkpBRpX7VqFe+++26kb0U99thjpKenA1CvXj2GDBlCz549adKkCbm5uSxcuJBXX32V7du3069fP1asWEGnTp0iz0+bNo39+/dzxx13kJOTQ2JiYmT1VWHH5z3m008/5aqrrmLnzp384Ac/4IorrqBBgwa8//77PPnkk+zYsYPVq1eTnp7O888/X+IYzzzzDMOHD498v/rqq7nqqqtISEhgw4YNTJ8+nU2bNrF27Vouu+wyVq1aRWJiYrFxPvzwQwYMGMCePXsA6Ny5M6mpqSQlJbFt2zZmz57N8uXLufHGGyPFsKr429/+Frn+4Q9/WOXxTgVBGIbRzqBq0LVr1zArKyvaMVRTXhsFK6fDz1dA429EO40kSZIkSZJ0Wli3bh0dO3aMdgwBQRAAcP7557N+/XoOHDhA8+bN2bNnDyNGjGDKlClF+v/yl79kypQpNGrUiG3bthEXF0eHDh344IMPACip1pGVlUWPHj3Iz8/n4osvZu7cuZx77rnF+s2fP5/BgweTl5dH9+7dyczMLNYnOTmZ7OxsWrduzebNm8t8t82bN9OmTZvI94SEBBYuXEj37t2L9Nu0aRMpKSns3r2b2NhYPvnkE84555xiY3Xq1IkDBw4QGxvLrFmzuPHGG4v0OXDgADfccENkhdmQIUN46aWXiuXq168fS5YsAeDWW29l2rRp1Kr11dqrMAwZNWoUkyZ9dfxQRd63JPv27aNRo0YcOXIEgC1bttCyZc3tvFaVv+tBEKwMw7BrSW2eqSadji4fBTG14Y3fRTuJJEmSJEmSJFVZXFxcpFg0e/bsIiul8vLymDVrFgA33ngjcXFxFRrzgQceID8/n/j4eObPn19iQQ3ge9/7HqNHjwbg7bff5s0336zKqxQzZcqUYgU1gDZt2vCzn/0MgMOHD0cKXsc/e2zLx5EjRxYrqEHBbzdr1qzIeWVz5sxh48aNRfqsXr06Mn779u15+umnixTUoKDQ+cgjj9CtW7dKvGVRn332WaSgdtZZZ9VoQe1kcvtH6XQU3xy63Q5vPg6X3QXN/K9rJEmSJEmSpOr08PKHWb9zfbRj1KgOjTtwT7d7ojZ/Wloazz77LLm5ucybN4/BgwcDMG/evMgZYBXd+nHXrl2RlVvf//73yy3qDB06lAcffBCARYsW0bNnz0q+RVGJiYnccsstpbb36dOHCRMmAPD+++8Xa3/55ZcBqFWrFiNHjix1nIYNG3LnnXcyZswYwjDklVde4e677460v/LKK5HrESNGUKdOnRLHCYKAkSNHctNNN5X9YuXYsWNH5DohIaFKY51KLKpJp6vL7oKs6fD3CXDTzGinkSRJkiRJkqQq6dWrF+3atWPjxo3MmDEjUlTLyMgAClZYVbTYtWzZsshKqdjYWP7yl7+U2b/wyrh169ZVIn3JunbtSmxsbKnthYt9u3btKtL2+eefk52dDcBFF11Es2bNypyrf//+jBkzBihYcVfYihUrItd9+/Ytc5zy2s9kFtWk01W9xtDjZ/CP38HWVXDOJdFOJEmSJEmSJH1tRHPF1pls2LBh3H///SxYsICcnBzCMGTBggWRtooqfA7Y1KlTmTp1aoWfPb64VRVNmzYts71u3bqR64MHDxZp27ZtW+S6ffv25c5VuE/hZwG2bt0auW7btm2Z4zRp0oSEhAR2795d7pxljXFMVcY51XimmnQ66/EziDsbXh8f7SSSJEmSJEmSVGWpqanExMSQl5fHzJkzmTlzJvn5+cTExJCamlrhcb744otKZzh06FClnz1eTEzlyzB79+6NXNevX7/c/g0aNCjxWYB9+/YBBdtI1q5du9yxKjJfWZo3bx5594MHDxYp6p3OLKpJp7OzGkKvdPhwMWS/Fe00kiRJkiRJklQlSUlJ9OnTByjY9nHGjBlAwZaErVq1qvA4hQtMzz33HGEYVvjzxhtvVOs7VVZ8fHzk+ssvvyy3/7HC2fHPwle/R35+fpGtLktTkfnK0qBBA775zW9Gvr/55ptVGu9UYVFNOt11uwMa/B94/UEIw2inkSRJkiRJkqQqSUtLA2DNmjWsWbOmyL2KKnxW2ZYtW6orWo1q0aJF5Hrjxo3l9i/c55xzzinSVvj7hx9+WOY4O3bsqJYtG/v37x+5/uMf/1jl8U4FFtWk012denD5ryF7GXz0erTTSJIkSZIkSVKVDB48mIYNG0a+N2rUiOuuu+6Exrj88ssJggCARYsWVTnTsa0Mwxpc2NCsWTNat24NwOrVq8nJySmzf+H37NatW5G2wt9ff73sf4+8ZMmSE41aojvvvJNatWoB8Nprr5GVlVUt40aTRTXp6yBlGDQ619VqkiRJkiRJkk57cXFxpKen0717d7p37056ejpxcXEnNEazZs0YMGAAAEuXLq1yYe3Y9olV3RbxRF1//fVAwbaNkydPLrXf3r17eeqppwAIgqBYEbLw9yeeeKLULSDDMOTRRx+tamwAWrduzR133AHA4cOHueWWW9i2bVuFn//Tn/7EnDlzqiVLdbGoJn0d1KoD37kHtq6C9a9FO40kSZIkSZIkVcm4cePIzMwkMzOTsWPHVmqM8ePHU7t2bQBuvvlmFi5cWGb/7OxsRo0axeeff16srU2bNkDB1oiffPJJpfJUxogRIyIFxYkTJ5ZYZDp48CBDhw5l69atQEEhrl27dkX6XHTRRfTr1w+A9evXc+edd3L48OEifcIw5J577iEzM7Pa8v/hD3/g4osvBgq2p+zRo0e5fw7r16/n5ptv5uabb2bv3r3VlqU61Ip2AEnV5Js3w9LJ8PcJcP5AiImNdiJJkiRJkiRJipqUlBSmTp3K7bffzq5duxg4cCC9evVi4MCBtGnThtq1a7Nz507Wr1/P0qVLI9sTpqenFxurb9++vPrqq0DB9pQ//elPadGiRWRbyM6dOxc5x626JCcn8+ijjzJ8+HDy8/MZMmQIgwYN4sorryQhIYGNGzfy3HPP8fHHHwMFZ8kdW7F2vKlTp9KlSxf27NnDs88+y/Lly0lNTSUpKYnPPvuMWbNm8fbbb9OtWze2bNkSKdJVxVlnncWCBQu47rrryMzMJDs7m4EDB9K5c2cGDBhAu3btSEhI4IsvviA7O5tFixaRlZXFkSNHqjz3yWBRTfq6iK0FV/wG/vwjeO9l+OYN0U4kSZIkSZIkSVF122230axZM26//Xa2b9/OsmXLWLZsWan9mzRpwllnnVXs/o9+9COefPJJNmzYwMqVK/nxj39cpH369OmkpaVVd3wAfvKTnxCGIXfddRcHDx5k7ty5zJ07t1i/Cy+8kHnz5pGYmFjiOG3btmXBggUMGjSI3Nxc1qxZw6hRo4r0ueCCC3jppZe4/PLLqy1/8+bNeeONN3jggQeYMmUK+/btY+3ataxdu7bUZxo1asSvf/3/s3fnUZaWhZ34v0/vDQ10A81Od19Ewr42W2sVbgExmqjjEkczoI6YTCYz/pKZSSbGiTnZZjLZzphJDDGjUVlcB2NQxOgIjYAEBGVpULS6ge5G1u6maeil6vn9cW9L2XZVV1VX1VvL53POPe9z733u836rz+Efvud9nv+ct771raOWYzTY/hGmkhPfkBx6SvKNP0p6d78nLgAAAADAdPK6170uPT09+fCHP5yf//mfz9FHH5358+dnzpw5Wbx4cc4///z82q/9Wr74xS9m3bp1Ofjgg39qjQULFuTWW2/N+9///px55pk54IADfvyU2nj45V/+5Xzve9/Lb/7mb+b000/PwoULM2fOnBx++OF5zWtek49+9KO56667smzZskHXWbFiRVatWpXf+q3fyvHHH5/58+dn4cKFOeuss/Inf/Inue2227JkyZJRzz937tz84R/+YVavXp0Pf/jDedOb3pTjjjsuixYtyqxZs7Jo0aKcdNJJueSSS3LllVdm/fr1ef/73z/ss/TGWqm1Np2BUbB8+fK689FUprkHvpxc9YvJ6/5XctYlTacBAAAAgAlj1apVOeGEE5qOAYyxvflvvZRyR611+e6+86QaTDXHvTo5cnlyw58kO7Y2nQYAAAAAAKYEpRpMNaUkr/xAsumR5PaPNp0GAAAAAACmBKUaTEXHvCxZ1pWs/NNk27NNpwEAAAAAgElPqQZT1Sv/W/Ls48m3/rbpJAAAAAAAMOkp1WCqOvqc5MUXJd/8y+S5DU2nAQAAAACASU2pBlPZK34neX5j8q0PN50EAAAAAAAmNaUaTGWHn5osfWly/7VNJwEAAAAAgElNqQZT3TEXJI/enWx5qukkAAAAAAAwaSnVYKprdSepyZpvNp0EAAAAAAAmLaUaTHVHnJnM3ifpubHpJAAAAAAAMGkp1WCqmzUnWXJ+0rOy6SQAAAAAADBpKdVgOmh1J4+vSjY/1nQSAAAAAGhUrbXpCMAYGsv/xpVqMB20utrX1Z5WAwAAAGD6mjFjRvr6+pqOAYyhvr6+zJgxNvWXUg2mg8NOS+Ye4Fw1AAAAAKa1+fPn59lnn206BjCGnn322cyfP39M1laqwXQwc1aydIVSDQAAAIBpbb/99sszzzzTdAxgDD3zzDPZb7/9xmRtpRpMF63u5KkfJhsfaToJAAAAADRi//33z5YtW/L00083HQUYA08//XS2bNmS/ffff0zWV6rBdNHqbl97nKsGAAAAwPQ0c+bMLF26NE888UTWrl2bTZs2pbe3N7XWpqMBI1BrTW9vbzZt2pS1a9fmiSeeyNKlSzNz5swxud+sMVkVmHgOOTGZf2CyemVy+tuaTgMAAAAAjZgzZ06OOeaYbNq0KRs2bMj69evT19fXdCxghGbMmJH58+dnv/32y2GHHTZmhVqiVIPpY8aMpNXVPlet1qSUphMBAAAAQCNmzpyZRYsWZdGiRU1HASYR2z/CdLKsK9n4cPJ0T9NJAAAAAABgUlGqwXTSHUaLCQAAIABJREFUuqB9da4aAAAAAAAMi1INppODX5wsOKy9BSQAAAAAADBkSjWYTkr5yXPVAAAAAACAIVGqwXTT6k6efSx54ntNJwEAAAAAgElDqQbTTau7fbUFJAAAAAAADJlSDaabRcuSA5Yo1QAAAAAAYBiUajAdtbqT1SuTvr6mkwAAAAAAwKSgVIPpqNWVPPd08qN7mk4CAAAAAACTglINpqNlXe3r6pXN5gAAAAAAgElCqQbT0QFHJgcd61w1AAAAAAAYIqUaTFfLupI1Nye9O5pOAgAAAAAAE55SDaarVneydVOy/jtNJwEAAAAAgAlPqQbT1c5z1XpuaDYHAAAAAABMAko1mK4WLE4OOTFZvbLpJAAAAAAAMOEp1WA6a3UnD92a7NjWdBIAAAAAAJjQlGownS3rSrZvSdbe0XQSAAAAAACY0JRqMJ0te0mSkvTc2HQSAAAAAACY0JRqMJ3NX5QcfqpSDQAAAAAA9kCpBtNdqzt55LZk+3NNJwEAAAAAgAlLqQbTXeuCpHdb8vC3mk4CAAAAAAATllINprsl5yVlpi0gAQAAAABgEEo1mO7m7pcceVbSs7LpJAAAAAAAMGFNulKtlDKzlHJyKeXSUsqHSim3lFK2lFJq5/XBUb7fPqWU80spv1ZK+Vgp5Z5Syo5+93vZCNacXUp5Tynla6WU9aWUraWUR0opXyylvLWUUkbzb4A9anUna+9Itj7TdBIAAAAAAJiQZjUdYAQ+neSN43i/h5McOFqLlVKWJfl8kjN2+erIzuu1Sf5tKeXNtdYNo3VfGFSrK1n5p8lDtyYv/tmm0wAAAAAAwIQz6Z5USzJzl/dPJfn+ON7voSSPjmShUsrCJF/OC4XaqiT/Ocnbknwg7QIvSV6V5POllMlYejIZHX1uMnNO0nND00kAAAAAAGBCmoylzW1pl1F3JLmj1tpTSrk0yUfH6H5fSPJAv/s9UUr5WJJLRrDW7yY5vjO+Lskbaq3P7/yylPLXSf457dLt5Unem+R/jzw6DNHs+clR5yQ9NzadBAAAAAAAJqRJV6rVWv9onO83kvLsp5RSDkny7zpvn01ySf9CrXOvp0op/ybJd5OUJL9TSvlwrbV3NDLAoFrdyTf+OHnu6WT+oqbTAAAAAADAhDIZt3+crF6fZE5nfFWt9bHdTaq13pPk6523hyW5YByyQbtUS01Wf7PpJAAAAAAAMOEo1cbPhf3G1+1hbv/vXz0GWeCnHXlWMnufZPXKppMAAAAAAMCEo1QbPyf3G9+xh7m3D/A7GDuz5iRLznOuGgAAAAAA7IZSbRyUUmYkeVHnbW+SR/bwkzX9xseNSSjYnWVdyWP3JZsfbzoJAAAAAABMKEq18bEgyazOeEOtdcce5j/Zb7xwbCLBbrQ6R/jZAhIAAAAAAH6CUm18LOg3fn4I85/rN95voEmllMtKKbeXUm5//HFPFjEKDj8tmbu/LSABAAAAAGAXSrVJrNZ6ea11ea11+eLFi5uOw1Qwc1aydIVSDQAAAAAAdqFUGx+b+43nDWH+/H7jZ0Y5Cwyu1Z089YNk49qmkwAAAAAAwIShVBsfm5PsPEdtYSll1mCTkxzUb7xhbCLBAJZ1ta/OVQMAAAAAgB9Tqo2DWmtfkh903s5MctQefrK03/h7YxIKBnLoycn8RUmPUg0AAAAAAHZSqo2fe/qNz9rD3OUD/A7G3owZ7afVem5Iam06DQAAAAAATAhKtfHzlX7ji/Yw99X9xteNQRYYXKs72fhw8vTqppMAAAAAAMCEoFQbP9ck2dYZv62UcsjuJpVSTk7yis7bR5PcMA7Z4Ce1uttX56oBAAAAAECSaVyqlVI+VkqpndcHx/p+tdbHk/x15+2CJB8rpczbJdOiJB9PUjof/UGttXess8FPOfi4ZMGhSc+NTScBAAAAAIAJYVbTAYarlNJK8u5dPj613/gVpZRd/67P1VrvHOH9XpEXnhzb6Yx+43eXUl61y/d/WmvdsJvlfi/trR2PT3Jxkm+XUj6SZG2SY5O8N8nRnbnfSHL5SDLDXiulc67ayva5aqXs+TcAAAAAADCFTbpSLcnSJO8f5Puuzqu/B5OMqFRL0r2H+71jN599JMlPlWq11g2llIuTfD7tYu6EJH+2m9//c5I311q3Dz8ujJJWd3LPZ5Mnvp8sPq7pNAAAAAAA0Khpu/1jU2qtq5Ocm+SyJF9P8qO0z1pbl+TaJL+Y5MIBnnSD8dPqdNM9jvUDAAAAAIBSa206A6Ng+fLl9fbbb286BlNJrclfnpIceWbylo83nQYAAAAAAMZcKeWOWuvy3X3nSTVg90ppbwHZszLp62s6DQAAAAAANEqpBgxsWVfy3FPJY/c2nQQAAAAAABqlVAMG9uNz1VY2mwMAAAAAABqmVAMGdsBRyYHHJD03Np0EAAAAAAAapVQDBtfqTtZ8M+nd0XQSAAAAAABojFINGFyrO9m6KXn0O00nAQAAAACAxijVgMEt23mumi0gAQAAAACYvpRqwOAWHJIsPiHpWdl0EgAAAAAAaIxSDdizVlfy0C3Jjm1NJwEAAAAAgEYo1YA9a3Un27ck677ddBIAAAAAAGiEUg3Ys6UvSVKcqwYAAAAAwLSlVAP2bJ8Dk8NOUaoBAAAAADBtKdWAoWl1Jw/flmx/rukkAAAAAAAw7pRqwNC0Lkh6t7aLNQAAAAAAmGaUasDQLD0/KTNtAQkAAAAAwLSkVAOGZu5+yZFnJqtXNp0EAAAAAADGnVINGLplXcnaO5Ktm5tOAgAAAAAA40qpBgxdqzvp25E8dGvTSQAAAAAAYFwp1YChO/rcZOacpOeGppMAAAAAAMC4UqoBQzdnn+Sos5OeG5tOAgAAAAAA40qpBgxPqzt59LvJc083nQQAAAAAAMaNUg0YnmVdSe1L1tzcdBIAAAAAABg3SjVgeI5ansyan/SsbDoJAAAAAACMG6UaMDyz5iZLznOuGgAAAAAA04pSDRi+Vlfy2L3Js080nQQAAAAAAMaFUg0YvtYF7etqW0ACAAAAADA9KNWA4Tv89GTOfraABAAAAABg2lCqAcM3c1aydIVSDQAAAACAaUOpBoxMqzt58sFk07qmkwAAAAAAwJhTqgEj0+pqX3ucqwYAAAAAwNSnVANG5tBTknkLk9W2gAQAAAAAYOpTqgEjM2NG+2k156oBAAAAADANKNWAkVvWnWx4KHl6ddNJAAAAAABgTCnVgJFrdbevzlUDAAAAAGCKU6oBI7f4Z5J9D7EFJAAAAAAAU55SDRi5Utrnqq1emdTadBoAAAAAABgzSjVg77S6k2fWJ08+2HQSAAAAAAAYM0o1YO8s62pfe25oNgcAAAAAAIwhpRqwdw48Jtn/qKRnZdNJAAAAAABgzCjVgL3T/1y1vr6m0wAAAAAAwJhQqgF7r9WdbHkyeXxV00kAAAAAAGBMKNWAvffjc9VubDYHAAAAAACMEaUasPcWHp0sainVAAAAAACYspRqwOhodServ5n09TadBAAAAAAARp1SDRgdre5k68Zk/XeaTgIAAAAAAKNOqQaMDueqAQAAAAAwhSnVgNGx36HJ4uOT1SubTgIAAAAAAKNOqQaMnmVdyZpbkt7tTScBAAAAAIBRpVQDRk+rO9n+bLL2200nAQAAAACAUaVUA0bPspcmKc5VAwAAAABgylGqAaNnnwOTw05Oem5oOgkAAAAAAIwqpRowuloXJA/flmx/vukkAAAAAAAwapRqwOha1pX0bk0eua3pJAAAAAAAMGqGXKqVUv688zppkDlLOq95e1jrZaWUb5dS7hhOWGASWLoiKTOTnpVNJwEAAAAAgFEznCfV3pfkPyZ50SBzVif5YZIL97DWAUlO77yAqWTe/skRZyQ9NzadBAAAAAAARs1YbP9YxmBNYDJpdSVrb0+2bm46CQAAAAAAjApnqgGjr9Wd9O1IHr616SQAAAAAADAqlGrA6Dv6vGTGbFtAAgAAAAAwZSjVgNE3Z5/kqLOVagAAAAAATBlKNWBstLqT9d9JntvQdBIAAAAAANhrSjVgbLS6ktqXrLm56SQAAAAAALDXlGrA2Djq7GTWvGT1yqaTAAAAAADAXpt0pVopZWYp5eRSyqWllA+VUm4ppWwppdTO64NjdN99Sym/Xkr5ZinlsVLK86WUNaWUT5VSLhriGqv75dzT6xtj8XfAuJk1Nzn6XOeqAQAAAAAwJcxqOsAIfDrJG8fzhqWUM5J8Nskxu3y1pPN6SynliiTvqrVuG89sMKG1upOv/37y7BPJvgc3nQYAAAAAAEZsJKXaH5RS3reXc/bm/67P3OX9U0meTPLivVhzQKWUpUm+nOTQzke3JflkkieSnJLksiQHJXl7kprkl4aw7OOd3w3miZHkhQmldUGS309W35Sc9Pqm0wAAAAAAwIiNpFQ7aZDv6hDm7K3bkqxKckeSO2qtPaWUS5N8dIzu95d5oVD7P0neU2vt67y/qpTyt0luTPuJtXeUUq6utV67hzW31FqvGZu4MIEccXoyZ0F7C0ilGgAAAAAAk9hwS7UyJimGodb6R+N1r1LKaUl2NgEPJfnVfoXazjxrSim/kmRnkfbBfmOY3mbOTpauSFavbDoJAAAAAADsleGUai8fsxQT11v7jS+vtT4/wLwvJ3kwybFJlpdSjqm1/nDM08Fk0OpOvn99sml9sv/hTacBAAAAAIARGXKpVmu9YSyDTFAX9htfN9CkWmstpXwl7VItSV6d5K/HMhhMGsu62tfVK5NT39JsFgAAAAAAGKEZTQeYqEopM5Kc2Hm7I8l39vCT2/uNT97D3INKKf9cSnmslLKtlPJ4KeW2Usr/KKW8aKSZYUI67JRk3sL2uWoAAAAAADBJKdUGdlSS+Z3x2lrrjj3MX9NvfNwe5i5I8soki5PMTnJwkrOT/JckD5RS/riUMnP4kWECmjEzWfZSpRoAAAAAAJPacM5UG5FOOXRwks211mfH+n6jaGG/8RNDmP/kAL/d1bq0t5K8K8mPksxJe9vINyY5JcnMJL+V5PAklw49Lkxgre7k/n9Knl6TLFradBoAAAAAABi2MSvVSildST6QpCvt4iillIeTfCLJH9dat4zVvUfJgn7j54cw/7l+4/0GmPOOJDfXWvt2890HSym/kuSv0n6C8JJSyldrrVcMdMNSymVJLkuSJUuWDCEiNKTV3b6uXqlUAwAAAABgUhry9o+llHmllFs6Z3/9zz3MfW+Sr6e9xeHcJKXzWpLkt5PcVko5eOSxJ6da600DFGo7v/+btIvInX5nD+tdXmtdXmtdvnjx4tGKCaNv8fHJvottAQkAAAAAwKQ1nDPVLkhybpKzktw80KRSyplJPpT2Noa7nZLkhCQfGca9m7C533jeEObP7zd+Zi/u++dJNnbGx5dSjtmLtWBiKCVZ1pX0rExqbToNAAAAAAAM23BKtZd2rpuT/NMg834/7W0la5LHk7wtySFpnzN2Sdpnj5UkryulnDXcwONoQ7/xUJ6qO2iA3w5LrfX5JLf2++j4ka4FE0qrK3lmXfLkD5pOAgAAAAAAwzacUu3MtIuyf661bt/dhFLKoUku6rztTfKqWuunaq1P1Fo31Vo/keQNnXWS5K0jzD0eHskL56QdWUrZ0/lz/Q+K+t5e3vvJfuOFe7kWTAytC9rXnhuazQEAAAAAACMwnFLtxZ3rvwwy55WdNWuSL9Ra79l1Qq31piRfS/tptXOGcf9x1Tn77L7O21lJTtvDT5b3G//U3z1Mo/LUG0woBx6T7H9ksnpl00kAAAAAAGDYhlOqHda5PjLInBX9xv84yLyvda4vHmTORPCVfuOLBppUSim7fH/dSG9YSpmb5Lx+H+3tU28wMThXDQAAAACASWw4pdr8znXLIHP6P6118yDz1nSuBwzj/k34dL/xe0sp8waYd3GSYzvj22utP9yLe/56Xvh3+X6t9cG9WAsmllZ3suWJ5LFVTScBAAAAAIBhGU6p9mznetDuvuycObZzi8SNtdYfDLLWjs51zjDuP6pKKR8rpdTO64O7m1Nr/U6SazpvlyT5q1LKT/yblVKWJPmbfh/tdq1SyvtLKSfsIdMvJ/n9fh/94aB/BEw2ra72tefGZnMAAAAAAMAwzRrG3EeT7JfkzAG+PzvJ3LTPU7ttD2sd2Lk+M4z7J0lKKa0k797l41P7jV/RKfj6+1yt9c7h3qvjfUnOT3Jo574nl1I+keTJJKckeW9eKBqvqLVeO8A6b07yB6WUO5PckGRVkqfTLhaPTfLGXf6OT9Za/2GEmWFiWrgkWbSsXaqd98tNpwEAAAAAgCEbTql2e5LjkryplPJfaq27FmK/2G/8jT2sdXznOtj5bANZmuT9g3zf1Xn192CSEZVqtdY1pZSLk3w2yTFJzu28dnVlkncNYckzOq+B7Ejy35P83jCjwuTQ6k7u+0LS15vMmNl0GgAAAAAAGJLhbP/42c71wCRXl1J+fB5aKeU1SS7rN/cze1hrRdpPtN0/jPs3pvOU26lJfiPJLUmeSLI1ycNp/60X11rfXmvdNsgyv5Tk3ye5IsldaReKz3XWWZ/ka0n+W5JltdYP1Fp3DLQQTGrLupPnNyaPfrfpJAAAAAAAMGTDeVLti0m+m/aWh69OsraUcneSxUlaSUraRdk/DnaeWinl6LS3ikzaBdWw1Fq/0bnXXqm1Xprk0mHMfzbJn3deI7nf3UnuTvK/R/J7mDL6n6t2xGAPbQIAAAAAwMQx5CfVaq29Sd6e9lliJck+Sc5Ju1Db6ckk/2EPS70zL5RiXxtyUmBq2O+w5OCfSXpWNp0EAAAAAACGbDjbP6bWem/a54n9Y9pnf5W8UJB9NclLaq0PD/T7Usq+SX6t8/aHnae3gOmm1ZWsuTnp3d50EgAAAAAAGJLhbP+YJKm19iR5fedMtRclmZl2QfbkEH7el+SlnfEzw703MEW0upN/+Uiy7s7k6HOaTgMAAAAAAHs07FJtp1rrxiTfHuZvnkvywEjvCUwRSzvdes8NSjUAAAAAACaFYW3/CDAq9j0oOfSUpOfGppMAAAAAAMCQKNWAZrS6k4dvS7Y/33QSAAAAAADYI6Ua0IxWV7Lj+eSRf2k6CQAAAAAA7NGQz1QrpfxwDO5fa60vGoN1gYlu6YqkzEhWr2wXbAAAAAAAMIENuVRLsixJTVL6fVb34t5lL38PTGbzDkgOP719rtrLf7vpNAAAAAAAMKjhlGo79SbZMUr3V6rBdNbqTm7538m2Z5M5+zadBgAAAAAABjTSM9VuSPLOJAtrrfP34rXPKP4twGTT6k76ticP3dp0EgAAAAAAGNRwSrV/TPsJtZlJfjbJFUnWlVL+ppRy7liEA6a4JeclM2a3t4AEAAAAAIAJbMilWq319UmOTPIbSb6b9ploi5JcluTmUsqqUspvllKOGJOkwNQzZ9/kqOVKNQAAAAAAJrxhbf9Ya32i1voXtdYzkpyZ5ENJnky7YPuZJH+UZE0p5bpSyltLKXNHPTEwtSzrStbflTy/sekkAAAAAAAwoJGeqZZa61211v+Y5Igkb0x7e8jevLA95JVJ1ne2hzxvNMICU1CrO6l9yZqbm04CAAAAAAADGnGptlOtdUet9Zp+20P+pyR3p/302sK0t4f8Zmd7yPfs7f2AKeaos5NZ85KelU0nAQAAAACAAe11qdZfrfXxWuuf11pPT7I8P7095DtH837AFDB7XnL0Oc5VAwAAAABgQhvVUm0X30ny/5Lc3nlfx/BewGTW6k5+dHfy7JNNJwEAAAAAgN0a9VKtlHJ6KeUvkqxL8rkkF3W+qkm+O9r3A6aA1gXt65qbms0BAAAAAAADmDUai5RSDk7yjiSXJDl158ed6/eT/EOSj9daHxmN+wFTzBFnJLP3bW8BeeIvNJ0GAAAAAAB+yohLtVLKrCSvTXJpkos7a+0s0jYl+XSSj9Vab97LjMBUN3N2snRF0rOy6SQAAAAAALBbwy7VSilnpF2kvS3JQTs/TtKX5GtJPpbk87XW50cnIjAttLqSr/635JlHk/0OazoNAAAAAAD8hCGXaqWU/y/t7R1P2flR52p7R2Dvtbrb156VyalvbjYLAAAAAADsYjhPqv1Zkpp2mbYpyafS3t7xlrEIBkwzh52azDsgWX2jUg0AAAAAgAlnJGeqbU9yZ5Kjk3yglLKH6YOqtdaf25sFgClixsxk6UuTnhubTgIAAAAAAD9lJKXarCTdo3DvkvaTbwBtre7kgWuTDQ8lC5c0nQYAAAAAAH5sxjDnl1F8AfykVlf72rOy2RwAAAAAALCL4Typ1hqzFABJsviEZJ+D21tAnvH2ptMAAAAAAMCPDblUq7WuGcsgAJkxo/202uqVSa3J3p3ZCAAAAAAAo2a42z8CjK1lXcmmtclTP2w6CQAAAAAA/JhSjWljw/MbctPam5qOwZ60Lmhfe25oNgcAAAAAAPTTWKlWSnlNKeXWpu7P9POhOz+U9/2/92Xj1o1NR2EwB70o2e/wpGdl00kAAAAAAODHxr1UK6W8ulOmfTHJ2eN9f6av5QdenK29W3PNg9c0HYXBlJK0ul84Vw0AAAAAACaAvSrVSin7lVIWD3Huq0sptyS5Nu0yrezNvWG47vrBvul9bmmuXHV1+mpf03EYTKs7efbx5PH7m04CAAAAAABJRlCqlVKOLqX8bSllfZINSR4tpWwppXytlPKzu5l/dinlhrTLtHPSLtNKkgeSvHvv4sPQveO8pdn+9PlZ9+wjuWXdLU3HYTDLutrXnhubzQEAAAAAAB3DKtVKKecluSvJv01yaF4oyOYleXmS60op/6Hf/D9IcnOSl/abe0eSNyU5sdb6sb3/E2Bojj5wn3Qd8fKkd0GuWHVV03EYzKKlycKlSjUAAAAAACaMIZdqpZR9klydZFEG3rqxJPmLUsqJpZS/TvJfk8zsfP7/klxYaz271vr5Wh2WxPi75PwXZ+vTZ+emtTdm7ea1TcdhMK3uZPVNSV9v00kAAAAAAGBYT6r96yRLktQk9yd5bZID0n5K7awkn+s39+NJ3pt2mXZ7ku5a6ytrrf88GqFhpLqOPTiHlZel1uQzD3ym6TgMptWdPL8hefTuppMAAAAAAMCwSrXXdq5Ppl2SfanW+kytdVut9c5a65uT/FPaRdoZnbn/K8l5tdabRi8yjNyMGSWXnHNGtm8+IZ9+4HPZ2ru16UgMZOe5aqtXNpsDAAAAAAAyvFLt1LSfUvtErfWJAeb8z37je2ut76u19o04HYyBN511VGZsekme2b4h16++vuk4DGT/w5ODXuxcNQAAAAAAJoThlGoHd653DjKn/3dXDD8OjL0D5s/OLxx/Qeq2g3PFfVc1HYfBtLqTNTcnvdubTgIAAAAAwDQ3nFJtQee6aaAJtdbN/d6uHkkgGA+XrGhl61Pn5d6n7s59T97XdBwG0upOtm1O1t3VdBIAAAAAAKa54ZRqw/XcGK4Ne+X4w/bPqQtfldQ5ufr+q5uOw0B2nqvWc0OzOQAAAAAAmPbGslSDCe1d55+YbRtOzz/98EvZuHVj03HYnX0PSg492blqAAAAAAA0biSlWh3ledCIC086NPtv7872vq255sFrmo7DQJZ1JQ9/K9mxtekkAAAAAABMYyMp1a4ppfQO9OrMKXua13ntGMW/BYZl9swZeccZK9K7ZWmuuO/q9NW+piOxO63uZMfzySP/0nQSAAAAAACmsZFu/1gGedW88JTaYPN2vqAxbzvn6PRuOD/rtzySW9bd0nQcdmfpiqTMSHpWNp0EAAAAAIBpbLil2lCKMIUZk8Yh+8/Lq5b+bNK7b65YdWXTcdid+QuTw09zrhoAAAAAAI0acqlWa50xBq+ZY/nHwVC8c8Wx2fr0OVm5dmXWbl7bdBx2p9Xd3v5x25amkwAAAAAAME2NdPtHmDLOWrooS2e/MqnJZx74TNNx2J1l3Unf9uThW5tOAgAAAADANKVUY9orpeTd552R7ZtPyKce+Gy29m5tOhK7WnJeMmOWLSABAAAAAGiMUg2S/MLpR2b25pdm8/aNuX719U3HYVdzFyRHLk96VjadBAAAAACAaUqpBknmz5mZt5z08vRtOzifuPeqpuOwO62uZN23k+c3Np0EAAAAAIBpSKkGHb90fivbnz4vq56+O/c9eV/TcdhVqzupfcmaW5pOAgAAAADANKRUg45lB++bcxZflPTNyZWrrm46Drs66pxk5txktS0gAQAAAAAYf0o16Ofd55+YbRtPz5d++KVs3GqbwQll9rzk6HOSnhuaTgIAAAAAwDSkVIN+LjhucQ7qe1m21635woNfaDoOu2pdkDx6d7LlqaaTAAAAAAAwzSjVoJ8ZM0reufwl6d2yNJ+496r01b6mI9Ffq6t9XX1TszkAAAAAAJh2lGqwizcvPyp10/l59LlHcsu6W5qOQ39HnJnM3jfpubHpJAAAAAAATDNKNdjFwn3m5LUvenXqjn3zyfuuajoO/c2akyw9P1m9sukkAAAAAABMM0o12I1LVxybbRvOyTfXrcy6zeuajkN/y7qSx+9PnvlR00kAAAAAAJhGlGqwGycdcUBO2PfC1Frzqfs/3XQc+mt1t6+eVgMAAAAAYBxNulKtlDKzlHJyKeXSUsqHSim3lFK2lFJq5/XBMbrvvqWUXy+lfLOU8lgp5flSyppSyqdKKRcNc63ZpZT3lFK+VkpZX0rZWkp5pJTyxVLKW0spZSz+BobnXeedke2bT8inH/hstvVuazoOOx1+WjL3AOeqAQAAAAAwriZdqZbk00nuTvLRJP8+yXlJ5o/lDUspZyT5bpI/S7IiyeIkc5MsSfKWJNeVUj5ZSpkzhLWWJflWksuTvCLJYUnmJDkyyWuTXJ3k+lLKwlH/QxiWi08+PPs8153NOzbmK6u/0nQcdpoxM1n2EqUaAAAAAADjajKWajN3ef/TpdR+AAAgAElEQVRUku+P1c1KKUuTfDnJMZ2PbkvyH5L86yR/nOTJzudvT/L3e1hrYWetMzofrUryn5O8LckHkjzc+fxVST5fSpk1On8FIzFn1oy8/bRXpm/bwfn4vVc1HYf+Wt3J0z3Jhof3PBcAAAAAAEbBZCzVbkvy35O8OckxtdaDkvzRGN7vL5Mc2hn/nyTn11o/VGu9qtb620nOSvJQ5/t3lFJ+bpC1fjfJ8Z3xdUnOrLX+aa316lrrHyQ5Pcmdne9fnuS9o/mHMHxvP3dZdmw4L/c/fXdWPbmq6TjstKyrfXWuGgAAAAAA42TSlWq11j+qtf7XWutna609Y3mvUsppSV7feftQkl+ttfbtkmdNkl/p99EHB1jrkCT/rvP22SSX1Fqf32Wtp5L8myS189HvlFJ2fTKPcXTYAfPyssNfk/TNzifv87TahHHIick+B9kCEgAAAACAcTPpSrVx9tZ+48t3LcH6+XKSBzvj5aWUY3Yz5/Vpn52WJFfVWh/b3UK11nuSfL3z9rAkFwwvMqPtXStOzLaNp+dLPV/Kxq0bm45DksyYkSx7adKzMql1z/MBAAAAAGAvKdUGd2G/8XUDTaq11iRf6ffRq0e61m6+391ajKNzWgfmyBmvzI66NV948AtNx2GnVney6ZHkqR82nQQAAAAAgGlAqTaAUsqMJCd23u5I8p09/OT2fuOTd/N9/8/u2Mu1GEellLz73Jemd8uSfPzeK9P3kzuA0pRW5yFOW0ACAAAAADAOlGoDOyrJ/M54ba11xx7mr+k3Pq7/F52C7kWdt71JHhnpWjTj9acfmZmbX5ofPbc2t667tek4JMlBxyYLDktWr2w6CQAAAAAA04BSbWAL+42fGML8Jwf4bZIsSDKrM94whIJusLVowL5zZ+UNP3Nx6o598w/3XtF0HJKklPYWkM5VAwAAAABgHCjVBrag3/j5Icx/rt94vzFc68dKKZeVUm4vpdz++OOPD2FZ9sal5x+bbRvOzi3rb8q6zeuajkOStLqSZx9LHn+g6SQAAAAAAExxSrVJrNZ6ea11ea11+eLFi5uOM+Uds3hBzlh4cWqtufr+Tzcdh6T9pFriXDUAAAAAAMacUm1gm/uN5w1h/vx+42fGcC0a9J7zz8r2zSfk0w98Ntt6tzUdh0XLkoVLkp4bmk4CAAAAAMAUp1Qb2IZ+44OHMP+gAX6btEu1neeoLSylzMrgBluLBr38+ENywPYL8uyOjbl+zfVNxyFJlnUnq29K+vqaTgIAAAAAwBSmVBvYI3nhbLMjh1CELe03/l7/L2qtfUl+0Hk7M8lRI12LZs2cUXLpGT+bvq0H56N3X9F0HJL2FpDPb0h+dHfTSQAAAAAAmMKUagPoFGH3dd7OSnLaHn6yvN/4nt183/+zs/ZyLRr0i2cvTd+m8/O9Dfdk1ZOrmo5Dq6t97VnZbA4AAAAAAKY0pdrgvtJvfNFAk0opZZfvrxvpWh2v3sNaNGjRvnNy4ZLXpvbNzifuu6rpOOx/RHLQsUnPjU0nAQAAAABgClOqDe7T/cbvLaXMG2DexUmO7Yxvr7X+cDdzrkmyrTN+WynlkN0tVEo5OckrOm8fTXLD8CIzHv7tipOyfePp+XLPtdm4dWPTcWh1J2tuTnp37HkuAAAAAACMwLQt1UopHyul1M7rg7ubU2v9TtplWJIsSfJXpZSf+DcrpSxJ8jf9PhporceT/HXn7YIkH9u1pCulLEry8SSl89Ef1Fp7h/xHMW5OOeqAvGjez2ZH3ZZrvv+FpuOwrCvZ9kyy/q6mkwAAAAAAMEXNajrAcJVSWknevcvHp/Ybv6KUsuvf9bla650jvOX7kpyf5NDOfU8upXwiyZNJTkny3iQHdeZeUWu9dpC1fi/trR2PT/vptm+XUj6SZG3aT7q9N8nRnbnfSHL5CDMzDt5zblfef+uSfPzeq/JLJ70jM8q07aibt2znuWo3JEctH3wuAAAAAACMwKQr1ZIsTfL+Qb7v6rz6ezDJiEq1WuuaUsrFST6b5Jgk53Zeu7oyybv2sNaGzlqfT3JGkhOS/Nlupv5zkjfXWrePJDPj4zWnHJ7f+3pXHtvnity67tasOHJF05GmrwWLk0NOap+r1vUbTacBAAAAAGAK8mjNEHSecjs1yW8kuSXJE0m2Jnk4yWeSXFxrfXutddvAq/x4rdVpl3KXJfl6kh+lfdbauiTXJvnFJBfWWjeM/l/CaJo7a2bedtJrU3fsm4/e/cmm49DqSh76VrJja9NJAAAAAACYgkqttekMjILly5fX22+/vekY0866Dc/l5X//XzL34Bty3b+6LkcsOKLpSNPX/dcmV//r5NIvJcte0nQaAAAAAAAmoVLKHbXW3Z4z5Ek12AtHLJyf8xe/NrUmV636VNNxprelK5KUZPXKppMAAAAAADAFKdVgL1224qxs33x8Pv3A57Ktd487gDJW5i9KDj+tfa4aAAAAAACMMqUa7KXzX3RQFtdXZEvvxly/5vqm40xvre7k4duSbVuaTgIAAAAAwBSjVIO9VErJe866MH1bD87ff+eKpuNMb63upG978vC3mk4CAAAAAMAUo1SDUfDGs5akbF6RBzfdk1VPrmo6zvS15LxkxixbQAIAAAAAMOqUajAKFsydlde1XpfaNzv/cM+VTceZvubulxxxZrJ6ZdNJAAAAAACYYpRqMEre/ZKTs33j6fnKmi9l49aNTceZvlrdydpvJ89vajoJAAAAAABTiFINRsmxhyzISQtenR11W675/heajjN9tbqT2ps8dEvTSQAAAAAAmEKUajCK3nt+d3q3LMnH7rkyfbWv6TjT09HnJDPnOFcNAAAAAIBRpVSDUfTK4w/Jvlu788TWtbl13a1Nx5meZs9Pjj5XqQYAAAAAwKhSqsEomjVzRn7p1J9P34598/ff/WTTcaavVnfy6N3JlqeaTgIAAAAAwBShVINR9vZzjknfxnNy22M3Zf3m9U3HmZ5aFySpyZ2faDoJAAAAAABThFINRtlBC+bmZUf8fGpNPnnf1U3HmZ6OPic5/rXJ134/WfvtptMAAAAAADAFKNVgDLz3JcuzY/Px+ez3PpdtvduajjP9lJL8/IeSBYcmn31X8vymphMBAAAAADDJKdVgDJx+9MIcPfNV2dK7Mdevvr7pONPTPgcm/+ojyYY1ybW/ntTadCIAAAAAACYxpRqMkcvOfnX6th2Uj3znk01Hmb6Wnp+87LeTuz+T3HVl02kAAAAAAJjElGowRl532pGZtfkl+cEz9+b+p+5vOs701fXrybKu5Ev/KXn8e02nAQAAAABgklKqwRiZN3tm3njcG1L7Zufvv+tptcbMmJm88e+S2fOTz74z2f5804kAAAAAAJiElGowht51/onZsem0fHXNl7Np26am40xf+x+evP5vkh/dk3z1A02nAQAAAABgElKqwRg6+sB9cubCn0tvtuVz3/u/TceZ3o67KDnvV5PbLk9W/VPTaQAAAAAAmGSUajDGfmXFBendsiT/cPdV6at9TceZ3l71u8nhpydf+NVkw8NNpwEAAAAAYBJRqsEYe8mLDs7C3pflyW1rc+v6W5uOM73Nmpu86f8kfTuSz78n6d3RdCIAAAAAACYJpRqMsRkzSt51+i+kb8e+ufzOTzQdh4NelLz2L5KHbklu+B9NpwEAAAAAYJJQqsE4eMvyVvLMObnjiW9m/eb1Tcfh1Lckp789ufF/Jj03Np0GAAAAAIBJQKkG42D/ebNz0ZLXp9bk4/de3XQckuTiP0kOOjb5/GXJs080nQYAAAAAgAlOqQbj5JdfcnZ2bD4+n/ve57Ktd1vTcZi7oH2+2pYnk2t+Jam16UQAAAAAAExgSjUYJ8cdul+OnXthnuvbmK+svr7pOCTJ4acmF/5h8v3rk1v/uuk0AAAAAABMYEo1GEe/cu7F6dt2UC6/65NNR2Gnc96T/MzPJV/93WTdnU2nAQAAAABgglKqwTi66KTDM29LV1Zvvjf3P3V/03FIklKSX/irZMGhyWfemTy/qelEAAAAAABMQEo1GEezZs7I2058Y2rf7Pydp9Umjn0OTP7V3yUb1iTX/rrz1QAAAAAA+ClKNRhnl5x3YnqfOT1fe/i6bNrmqagJY+mK5GX/Nbn7M8ldVzadBgAAAACACUapBuNs8X5zc/7Br0tvtuYz9//fpuPQX9dvJMu6ki/9p+SJ7zedBgAAAACACUSpBg341Ze8LL1bluQf7rkyfbWv6TjsNGNm8sbLk1nz2uerbX++6UQAAAAAAEwQSjVowJlLFuaQvDxPb1+XW9fd2nQc+tv/iOQNH05+dHfy1Q80nQYAAAAAgAlCqQYNKKXksjPfkL4d++Zv7vxE03HY1XEXJef9anLb5cn91zadBgAAAACACUCpBg1545nLMvPZc3PXk9/M+s3rm47Drl71u8nhpyXX/Ltk4yNNpwEAAAAAoGFKNWjIvNkz8/+zd+dxctR1/sdf3+pzztw3gXDkvjii3PchyJ2I1+qu6666ul4/BHUXcddFFhVFXUQU8HYFgYCsoNwEFAEJRxIJIQlJCCEXOefqu76/P6q7p7qne65MUnO8n7v16G9961tVn5lMMNPv/n7rwkMXYS38bMXtQZcj5cIxeM/PwM3C4n+GXDboikREREREREREREQkQArVRAL0iRMWkGuZwb1r7yGdSwddjpQbdThc8F3Y+Aw89a2gqxERERERERERERGRAClUEwnQwaNqmVV/Hkl3L39c93DQ5Ugl894L8z8IT34L1j8VdDUiIiIiIiIiIiIiEhCFaiIB+9fjzsNNj+KWl38VdClSzbuv92at3fNxaN0ZdDUiIiIiIiIiIiIiEgCFaiIBO23aOOpTJ7OxbSWrdq0KuhypJFbvPV+tbSf87pNgbdAViYiIiIiIiIiIiMgBplBNJGCOY/jwnMuwboSbX/xl0OVINRPmwTnXwpqH4Nmbg65GRERERERERERERA4whWoi/cCH3zkT23wUT771ME3ppqDLkWre+TGYfj488lXY/FLQ1YiIiIiIiIiIiIjIAaRQTaQfGFYb4dQJF5MjxW9fvSfocqQaY+DiH0D9WLj7o5BqDroiERERERERERERETlAFKqJ9BOfOek0cm0H88tXbse1btDlSDW1I2HRbbB7A9x/uZ6vJiIiIiIiIiIiIjJEKFQT6SdmTmhkcvhM9mQ28+zm54IuRzpzyAlw6pdhxZ2w7PagqxERERERERERERGRA0Chmkg/8i/vWIibreOmF34RdCnSlVOugENOgge+ADvWBF2NiIiIiIiIiIiIiOxnCtVE+pEL5h5MtO04lu/6C1tatgRdjnTGCcGiWyEch7v/ETLJoCsSERERERERERERkf1IoZpIPxIJOSyc+h4scNuy3wRdjnSlcSJccjNsXQGPfDXoakRERERERERERERkP1KoJtLPfOKEBbitM7jv9XtJ59JBlyNdmX4uHPcp+OuPYdUDQVcjIiIiIiIiIiIiIvuJQjWRfmZsY5wjh51Pyu7lgdcfCroc6Y6z/hMmzIf7/hX2bgq6GhERERERERERERHZDxSqifRDnzvxfNz0KG55+ddBlyLdEY7Be34GuQws/hjkskFXJCIiIiIiIiIiIiJ9TKGaSD/0zimjGJ49lU2JlazauSrocqQ7Rh0O598AG/8CT30r6GpEREREREREREREpI8pVBPph4wx/NOR78W6EW584RdBlyPdNf99MP+D8NT1sP5PQVcjIiIiIiIiIiIiIn1IoZpIP/X+Y6ZjWo/iz1sepindFHQ50l3vvh5GHgb3fAxadwZdjYiIiIiIiIiIiIj0EYVqIv1UbTTM2Qddikua//3b4qDLke6K1XvPV2vbCfd9CqwNuiIRERERERERERER6QMK1UT6sc+edDq5xGT+99U7sApnBo4J8+Ccr8PqB+G5HwVdjYiIiIiIiIiIiIj0AYVqIv3YlNF1HBY7h73ZzTz91jNBlyM98c6Pw/R3w8NXw+aXgq5GRERERERERERERPaRQjWRfu4zxy7CzdZx49JfBF2K9IQxcPFNUD8W7v4opJqDrkhERERERERERERE9oFCNZF+7qwZB1GTOp6Ve55ha+vWoMuRnqgdCQtvhd0b4IErgq5GRERERERERERERPaBQjWRfi7kGN4//TIscPMLvw66HOmpKSfCqV+G5XfAy7cHXY2IiIiIiIiIiIiI9JJCNZEB4J+PX4BtncH9G+4lnUsHXY701ClXwCEnwQNfgB1rgq5GRERERERERERERHphQIZqxvM+Y8z9xphNxpiUMWaLMeYxY8w/G2PC++GeFxhj7jDGbDDGJIwxe40xK40x3zXGzOrmNTYYY2w3tyV9/TXIwDW8Nsqxoy4kbZv4v7UPBV2O9JQTgoW3QDgGd/8jZFNBVyQiIiIiIiIiIiIiPTTgQjVjzAjgUeAO4HxgEhAFxgNnALcCzxljDu6j+401xjwG/B54H3AIEAcagZnA54GXjTFf7Iv7iVTz+ZMuwE2P4taXfxV0KdIbwybBJTfD1hXwyFeDrkZEREREREREREREeqjPZ3TtT8aYKHAfcHK+603gFmAtcBDwUbyg62jgj8aY4621Tftwv3rgYWB+vmsH8BNgGd737njgI0AN8E1jTMZa+91uXPpt4ONdjNnRm5pl8Jp30AjGcTqbk3fz6o5VzBw9I+iSpKemnwvHfQqe/SEceirMeHfQFYmIiIiIiIiIiIhINxlrbdA1dJsx5nPA9/K7LwJnWWt3+47Hgd8B78p3fdtae+U+3O+bQGEG2vL8/d4uGzMDWAKMA9LAbGvt2irX24A30+0Na+2U3tZVyYIFC+zSpUv78pLSD92+dBXXrvgAJ40/lx+dd13Q5UhvZFNw21mw9034l6e9GWwiIiIiIiIiIiIi0i8YY16w1i6odGzALP+Yf07aVfldC/y9P1ADsNYmgb8HWvNdnzHGjOrl/SLAJ333+1B5oJa/5yrgM/ndKPAfvbmfSHcsOnIaobZjeGbbwzSlez0JU4IUjsFlP4dcBhb/M+SyQVckIiIiIiIiIiIiIt0wYEI1vOeljcm3H7PWvlJpkLV2O97z1gBiwMW9vN8CoCHfXmatXdHJ2HuAlnz7EmNMTS/vKdKpaNjh/CmLcE2aXy5fHHQ50lujDofzb4CNf4Gnrg+6GhERERERERERERHphoEUqp3jaz/YxVj/8XN7eb+DfO3XOhtorc0B6/K79cApvbynSJc+e9Lp5BKTuX3VHQyk5VulzPz3wfwPwFPfgg1/DroaEREREREREREREenCQArV5vjaL3Qx1v9wsTlVR3XO9PI8gLldHB9ljHnUGLPdGJM2xrxtjPmrMeabxpjD9+G+MgSMHxZnZt25NOU289SmvwRdjuyLd38bRhwKiz8GrTuDrkZEREREREREREREOjGQQrVpvvaGLsZuAnL59lRjTG8Csq1V7t2BMSYEHObrmt7FteuBM/GWs4wAo4F3AF8EXjPGXJe/pkhFnz/+PbjZOm58/pdBlyL7IlYPl/0M2nbAfZ8CzTwUERERERERERER6bcGUqg23Nfe0dlAa20WaMrvhoG6XtxvKZDOt480xnQ24+1SvKCsYHi1gcBm4KfAZ4H3AR8GvgYUntkWAr4M/KQXNcsQcdLhE2jInMBrTc+wpWVL0OXIvpgwH86+BlY/CM/9KOhqRERERERERERERKSKgRSq+UOrZDfGJ3zthp7ezFrbBvxvftcAvzLGjC4fZ4yZBvxPWXdjlct+CJhsrf0na+2N1to7rbW/ttb+p7V2HvApwM2P/QdjzN91VqMx5uPGmKXGmKVvv/12d780GQSMMXx49vuxwI3P/yrocmRfHfsJmHYePPJV2Pxy0NWIiIiIiIiIiIiISAUDKVQLwlVAYRrQkcDK/NKM7zfGfMgY8wPgJWACsM53nksF1to/W2srHssfvxm42tf1lc6Ks9beYq1dYK1dMGbMmG58OTKYfOSdR2PaZvDgxvvI5DJBlyP7whi45IdQOxru/iikmoOuSERERERERERERETKDKRQrcXXjndjfI2v3at3qK21W4CzgbX5rjF4SzPeDvwK+FegFvgZcKPv1N29uV/eDcDefHuGMeawzgbL0FUXC3PiuIvJ0MTi1/4QdDmyr2pHwqLbYPd6eOCKoKsRERERERERERERkTIDKVTb42t3WIbRzxgTpn0JxgzQ2tubWmtfAebiLc34GPB2/prbgPuBC6y1HwVG+E7bug/3SwLP+rpm9PZaMvh94eQLcdOjuG3Zr4MuRfrClBPh1C/B8jvg5duDrkZEREREREREREREfAZSqLba157SxdiDgFC+vdZaa/flxtbapLX2ZmvtWdbasdbaqLV2vLX2QmvtA/lhs3ynPL8v9wN2+trD9/FaMohNHdvIQaEz2JZexSs7Xg26HOkLp1wJh5wED3wBdqzteryIiIiIiIiIiIiIHBADKVT7m699TBdjF1Q5b78wxjjASfldCzy9j5cc5WvvqTpKBPjkgg9g3TDffe7nQZcifcEJwcJbIByDuz8C2VTQFYmIiIiIiIiIiIgIAytUe8jXflcXY8/1tR/cD7WUOw8Yn28/Yq3d2NsLGWNiwHG+rtXVxooAXDjnCKLJY/jr24/SnO7V4wOlvxk2CS75IWxdAY98NehqRERERERERERERISBFao9gfc8M4CzjDGzKw0yxowF3p/fTQL37c+ijDG1wPW+ruurje2my4Fh+fYaa63Wf5NOhRzDxYddhjVpbnvpzqDLkb4y/Tw49pPw3I9g1R+CrkZERERERERERERkyBswoZq1Ngtcm981wC+NMSP8Y4wxceAXQF2+6wfWWv/zyfxjf26MsfntP6vd1xhzSifHxgG/B2bmu35urX20ytirjDEzKx3zjfkX4Bpf17XVxor4feak03ETB3Pn6t+yj48QlP7k7K/B+Hlw36dg71tBVyMiIiIiIiIiIiIypIWDLqCHbgYWAScDRwPLjDE/BtYCBwH/RHvAtRL4eh/c8w/GmG3AH4DlwG5gBHAscBnQmB/3BPDpTq5zGfB1Y8xLwJPAq/lrRYEjgIXAPN/4X1trf9EH9csQMLIuyrzG8/hb5sc8sfFpzjjkpK5Pkv4vHIP3/Ax+fArc8zH4h997z1wTERERERERERERkQNuQIVq1tq0MeZi4G7gDGAylYOzF4FLrbV7++jWh1E9MHOBW4DLrbWJblzrqPxWTRb4BvC1HlUoQ94XTryMf3j019z4/C8Uqg0mo4+AC26Aez8BT10Pp3056IpEREREREREREREhqQBFaoBWGt3G2POAt4LfBgvoBqNN+vrFeAO4Gf55SL7wvuAs4ETgEn5e7UAm4BHgF9aa5d34zofBk4Bjgdm568zCm8Jzl14M+ueBH5qrdU6b9JjCw4Zx0j3JNa2PMyWli1MqJ8QdEnSV+a/H9YtgSe/CVNO8jYREREREREREREROaCMnr80OCxYsMAuXbo06DIkYD95dinfXfVR3j35Q3zrzC8GXY70pVSLtwxkJgGffBpqRwZdkYiIiIiIiIiIiMigY4x5wVq7oNIx50AXIyL7z98dcxROciaPbrqPTC4TdDnSl2L18J6fQtsO+N2nQB+IEBERERERERERETmgFKqJDCLxSIgzJl5ChiZ+u/L+oMuRvjbxSDj7Glj9R3jux0FXIyIiIiIiIiIiIjKkKFQTGWS+cPJFuOlR/HTFb4IuRfaHYz8B086DR66GLcuCrkZERERERERERERkyFCoJjLITB5Rx6Gxs3g7s4oV218Nuhzpa8bAxTdB7Wi46x8h1Rx0RSIiIiIiIiIiIiJDgkI1kUHos+/4INYN851nfx50KbI/1I2CRbfC7vXwhyuDrkZERERERERERERkSFCoJjIInT3jUGrSC3hx12M0pzWTaVCachKc+iVYdjssuyPoakREREREREREREQGPYVqIoOQMYbLpr0Pa1L8cOlvgy5H9pdTroRDToT7L4cda4OuRkRERERERERERGRQU6gmMkh98vhTscnJ3LP2Tqy1QZcj+4MTgoW3QjgKd/8jZFNBVyQiIiIiIiIiIiIyaClUExmkGuIRFoy8gDa7hfvXPqZgbbAaNgkuuRm2LodH/iPoakREREREREREREQGLaM32geHBQsW2KVLlwZdhvQzK7fs4LI/XIgTbqEmNIzDGqZx1PjZzB87mxkjZ3Bw48E4Rtn6oPDHL8FzP4IP3AHTzwu6GhEREREREREREZEByRjzgrV2QcVjCtUGB4VqUs3VDzzJ/WseI2E24sQ348S2Y0wOgIiJM6nucOaOmcWC8XOYOWomRww/gkgoEnDV0mPZFNx2Fux8HU74NBz/rxAfFnRVIiIiIiIiIiIiIgOKQrUhQKGadGV7c5KVm5tYsXknSzevYvXuVezKrseJbSYU34Jx0gAYQoyLH8L0ETN456R5zB0zi+kjplMbqQ34K5Au7d0ED/07rLwPakbAiZ+Hd34covqzExEREREREREREekOhWpDgEI16Y22dJbXtjbzyuY9PL9pDSt3rmRz4nXcyFverLZwa36kYXhkAkcMm84x4+dw9Pg5zBg1g5HxkYHWL1Vsfgke/zqsfRTqx8EpV8LR/wDhaNCViYiIiIiIiIiIiPRrCtWGAIVq0ldyruWNna28snkvSzdtYPn2V3ijZQ1tZiOh2Gac6J7i2FpnJIfUT2X+uNm8c+JcZo2eycS6iRhjAvwKpGjD0/D4NbDxGRh+MJz2bzDvfeCEgq5MREREREREREREpF9SqDYEKFST/W1HS4pXtzTx0qa3WLrlFdbufY3d2Q2Y2Fs40bcxxvtvScTUMbHmcGaNnslxk+Yxb8wspgybQtgJB/wVDFHWwtrH4LGvwdblMHo6nHEVzLwIFH6KiIiIiIiIiIiIlFCoNgQoVJMgJJ+XiBQAACAASURBVDM51mxrYdlb23hu00pe3fUqW5PrcCObcGJbMU4WAIcoY6KHMHXEDN4xcQ7vmDCXqSOmEg/HA/4KhhDXhVf/D564FnashglHwhlXwxFnKlwTERERERERERERyVOoNgQoVJP+wnUtm3YnWLF5F89ufJXlO1ayqWWtt3xkfDMmlPQGWodh4Ukc2jiNo8fP5viD5jNr9Awao43BfgGDXS4LK+6EJ66DvRvh4BPgzKvhkBOCrkxEREREREREREQkcArVhgCFatLf7WlL88rmvTy7cQ0vbXuFdU2r2VNYPjLSXBxXa8ZwUN1U5oyZyYmT53PkuNmMqRmj57T1tWwKXvwlPHU9tGyDI86GM74CE48MujIRERERERERERGRwChUGwIUqslAlMrmWLu9hb9ufIPn3/obr+1Zxfbk67jRt3CiO4vjIjQyPn4400fO4PhJc3nnpLkc3HgwjnECrH6QSLfBX2+Bp78Hid0w62I4/SoYMz3oykREREREREREREQOOIVqQ4BCNRksrLW8tSfBS5u28peNK3hl50realtLwryJE9uGMS4Ajo0zKjKFw4dP45jxczj54PlMGzmVSCgS8FcwQCX3wjM3eVumDeZ/AE79Eow4JOjKRERERERERERERA4YhWpDgEI1GeyakhlWvLWTP2/4Gy9vf4U3WtawN7cBE9uCcdLeIBumMXQQB9dP5ahxs/nI/AsZWzc22MIHmtYd8Ofvwl9vBevCMR+BU66AhvFBVyYiIiIiIiIiIiKy3ylUGwIUqslQlMm5rNnexNMbVvH8lhWs3bOaHel15CKbcMJtYB2On3ASfzfrvZw46UTCTjjokgeOvW95z1t76VfgRODYT8CJn4PakUFXJiIiIiIiIiIiIrLfKFQbAhSqiXistWzdm+T2l5fyk5fvwjQsxYSbGVs7lkuOuIRLj7iUgxoOCrrMgWPn67DkG7DiLog1wAmfheP+xWuLiIiIiIiIiIiIDDIK1YYAhWoiHb25q40vLn6R57c/zbiJy2hxXsHF5bgJx7Fo6iLOOPgMoqFo0GUODNtWwhPXwqr7oXYUnPwFWPBPEIkHXZmIiIiIiIiIiIhIn1GoNgQoVBOpzHUt//vcG1z3x1WEwns5ZcF61rQ9zpbWLQyPDefCwy9k4RELOWLEEUGXOjBsegEe/y9YtwQaJ8GpX4Qj/w5CkaArExEREREREREREdlnCtWGAIVqIp3buLONLy5exrPrdnHKtFFcdnKCJW/dz+NvPk7WzTJ/zHwWTV3Eu6a8i9pIbdDl9n/rn4LHroFNf4URh8LpV8GcReA4QVcmIiIiIiIiIiIi0msK1YYAhWoiXXNdy6+efYNv/HEV4ZDh6gtmcebsWu5fdz+L1yxm/d711EXqOO/Q81g0dRGzR83GGBN02f2XtbD6IXj8Gtj2Nxg7G874Ckw/D/R9ExERERERERERkQFIodoQoFBNpPve2NnKlXcv56/rd3H69DFct3Ae4xpjvPz2yyxevZiHNjxEMpdk+ojpLJy6kPMPO59hsWFBl91/uS6svBcevxZ2vQ6TjoEzvwqHnRZ0ZSIiIiIiIiIiIiI9olBtCFCoJtIzrmv5xTMb+OaDq4iEHP7jwtksOnoSxhia0838cf0fWbxmMSt3riTqRDl7ytksmrqIBeMWaPZaNbksLPsNLPkmNG2CQ0+BM74Kk98RdGUiIiIiIiIiIiIi3aJQbQhQqCbSOxt2tHLl3ct4fsNuzpgxlusWzmVcY7x4/NWdr3LPmnt4YN0DNGeaObjhYBZOXcjFR1zM6JrRAVbej2WS8MLP4U/fhta3Ydp53rKQ4+cEXZmIiIiIiIiIiIhIpxSqDQEK1UR6z3UtP/vLBq5/aBXRkMN/XjSbS4+aVDIjLZFN8Ogbj7J4zWJe2PYCIRPi1INOZdG0RZw48URCTijAr6CfSrXAcz+Cp/8HUk0wZxGc/u8w6vCgKxMRERERERERERGpSKHaEKBQTWTfrd/RypV3LWPpG7s5a+ZY/vvSuYz1zVorjtu7nnvX3st9a+9jV3IXY2vHcukRl3Lp1EuZVD8pgMr7ucRu+MuN8OzNkE3BUX8Hp34Jhh0UdGUiIiIiIiIiIiIiJRSqDQEK1UT6Rs61/Ozp9Vz/0GvEIyG+dtFsLj5yYsXnqGVyGZ7c9CSL1yzm6beeBuD4icezcOpCTp98OtFQ9ECX37+1bIc/fQeW/tTbf8c/w0mXQ/2YYOsSERERERERERERyVOoNgQoVBPpW6+/3cKVdy3jxY17OHvWOK69dA5jGzrOWivY0rKF3639HfeuvZctrVsYERvBhYdfyKKpizhs+GEHsPIBYM+b8OQ34eX/hXANHPdJOOEzUDM86MpERERERERERERkiFOoNgQoVBPpeznX8pM/r+PbD6+mNurNWrtofuVZa+3n5Hh2y7MsXrOYJzY+QdZmOWrsUSycupBzDjmH2kjtAfwK+rkda+CJ/4ZX7oH4MDjx83DsJyBaF3RlIiIiIiIiIiIiMkQpVBsCFKqJ7D9rt7dwxV3LePnNPbxr9ji+fslcxjTEujxvZ2Inv3/99yxes5gNTRuoi9Tx7kPfzaJpi5g1clan4dyQsmU5PHEtrH4Q6sbCKVfAMR+BcNffYxEREREREREREZG+pFBtCFCoJrJ/5VzLrX9axw2PrKYuGuK/Lp7DBfMmdCsYs9by0vaXWLxmMQ9veJhkLsmMkTNYOHUh5x92Po3RxgPwFQwAb/4VHvsv2PAnGDYZTvsyzHs/hMJBVyYiIiIiIiIiIiJDhEK1IUChmsiBsWZbM1fcvZxlb+7hvDnjueaSOYyu7/6MqqZ0E39c90cWr1nMq7teJRaKcc4h57Bw6kKOGXeMZq9ZC+uWeOHa5hdh1FQ4/d9h1iXgOEFXJyIiIiIiIiIiIoOcQrUhQKGayIGTzbnc8qd1fO+RNdTHw1xz8RzOnzehx9dZuXMl96y5hwfWPUBLpoUpjVO4dOqlXHT4RYyuGb0fKh9ArIVVD8DjX4e3X4Xxc+GMr8LUs2GoB49DjbWQ2A2718Ou9eDmYNq7oGZ40JWJiIiIiIiIiMggpFBtCFCoJnLgrd7WzBV3LWP5pr2cP3cC/3XxbEb1YNZaQSKb4JE3HmHx6sW8uP1FwibMaZNPY+HUhZww8QRCTmg/VD9AuDn422LvmWu7N8DkY+HMr8KUk4KuTPqSm4Omze3BWcnrBkjtLR0fisGMd8P8D8DhZ0AoEkjZIiIiIiIiIiIy+ChUGwIUqokEI5tz+fFT6/jeo6tpjEf4+iVzOG9uz2etFazbu45719zL/73+f+xK7mJ83XguPeJSLjniEibWT+zDygeYXAZe+hU8eT00b/aClDOuhklHB12ZdFcm4QWjuzd0DM72bIRcun2sE4HhB8PIQ2HEoaWvmTZYfiesuAvadkLdGJjzHpj/fpgwXzMZRURERERERERknyhUGwIUqokE67Wt3qy1FW/t5YJ5E/ivi+cwsi7a6+tlchmWbFrC4tWL+cvmvwBwwsQTWDh1IadPPp3IUJ2Zk0nA8z+BP30HErtgxgVwxldg7MygKxNroW2XF5JVCs6at5SOjzXCiCmVg7PGSdDVDM1cBtY+Cstuh9f+6IVyY2Z64dq890LjEA6hRURERERERESk1xSqDQEK1USCl8m5/GjJ6/zP42sYVhPh65fM5dw54/f5uptbNvO7tb/j3rX3srV1KyPjI7no8Iu4dOqlHDbssD6ofABKNsGzN8MzP4BUM4yfA7WjoXaUbxtZtp/vC/d8iU7Jc3Owd1OV4GwDpJpKxzdM6BiYFV5rRvTdrLLEbnjlXlh2B7z5HGDgsNO85SFnXgDRur65j4iIiIiIiIiIDHoK1YYAhWoi/cerW5q44q5lvLK5iYvmT+RrF81mxD7MWivIuTme2fIMi1cvZsmbS8jaLEePPZqFUxdyzpRzqAnX9EH1A0zbLnj2h7BlubcUYGKX95rcW/2caENZ4DaykxBulBf+DKWZgem2/DKNFYKzPRvBzbSPdSIw4pDKwdmIQyASwM/kzte95SGX3Q573oBoPcy8yJvBNuVkcJwDX5OIiIiIiIiIiAwYCtWGAIVqIv1LJufywyde58bH1zC8Nsp/XzqHc2bv+6y1gh2JHfz+9d9zz5p72NC0gfpIPecfdj6XTbuM6SOn99l9Bqxcxpu91Lazwrarcl+6pfr14sMqz3qrFMLVjvLGd7V8YVCs9b7mQlhWHpy1bC0dHxsGI6dUDs4aJ/bvr3Pjs7DsN/DK77xZdI0HeUtDzv8AjJkWdIUiIiIiIiIiItIPKVQbAhSqifRPKzd7s9ZWbmnikiMn8p8XzWZ47b7PWiuw1vLi9hdZvHoxD7/xMFk3y5XvuJIPzvggpq+W1hsqMsn2mW5dBXBtu6BtB2STVS5mvBlu3QrhRrYHcX31Z5bLQtOmKsHZBkg3l45vmOgLzKaUBmd9uUxjUDIJeO0P3vKQax8Dm4OJR3vh2pxFUDcq6ApFRERERERkqLEW3Kz3wWA34/0u72Z8fRWOVdz3j+vlecbxVpuJ1FZ57caxUHTgv38gkqdQbQhQqCbSf6WzLjc9sZabnljLiLoo/33pXM6eNa7P77M3tZevPP0Vlry5hIVTF3LVsVcRDfVdgCcVpNu6EcKV9fuXT/RzwlBTaVnK8gAu3x9rhOYt7c8zKz7brLBMY7b92qEoDD+k8rPNhh8CkfgB+Xb1C83b4G93e8tDbl3hfd+nvstbHnLau/TMPRERERERkcHIzUG61be1eK+ZtvZ2YasaUHUWWPVinP/39v3NCXuPcAhFvHYokt/P91vX++Bwps37YGrVDxF3wjidhHD7ENaV9/XXFXP6QqWgNZeu/nNVPFYenlYJUgt9x33S+xC1VKVQbQhQqCbS/72yeS9fuHMZq7Y2s/CoSfzHhbMZVtu3z+pyrctNL9/ELctv4aixR3HDaTcwumZ0n95D9oG1kGruPIBL7Op4zLpdXzs+rPISjSMPhYYJg/sfnb219W+w/A5Yfpe37GV8uDdzbf4H4KAF+oSdiIiIiIhIELLpjkFXyX532mX72UQPizAdg6eK+xUCqg79nY07AOc5oZ7/fuvm8iFboj1o6/Dqa6dbO/ZVGlfS19q99zvKhWI9C+Gq9sUrhFiZyuFoLl09KC0Z7z+WrhyoVr1W+sAFrZ95EUYdfmDuNUApVBsCFKqJDAzprMsPHl/DTUteZ1RdlOsWzuXMmX0/a+3BDQ9y9Z+vZnh8ON8//fvMGjWrz+8hB4jrQmpvx6AtuRcaxnvB2Ygp3gw26R03B+uWeMtDvvp775etkYd74dq898KIQ4KuUEREREREpP+xtj1Q6U3QVe1YtRVeKgnXQLQuv9X72uX73WnXtS9j6Dj77/smHmu9YKlbIVxXAV0nfT0OVDthnPbg0hdi2g7hZxRbCD2dSHs7P8bmx1kTzp8fwebDUFs47oSxThjrRL2xTiS/H8aaCDYUxTqh/PXCuKZQSwjXiebHRrAmhJuvwzURXOMwcUQt0bA+fN0ZhWpDgEI1kYHlb2/t5Yq7vFlri44+iK9eOIthNX07a+3Vna/y2Sc+y57kHq458RrOPfTcPr2+yKCUbPKCtWW3w4Y/eX2HnOQtDznrYog3BlufiIiIiOx/1nozNNJt3kyKkte29hkZNtc+vv3k6n0l/ZX6qNLXk/P39f5djDWOtxR9fJi30kN8mPdv5PgwbwvHteLDQOC6kGqC5B7vA5uFLVG2n27pIgRrpeTno1OmB6FXD0KwIbgqi7WWtJsmmU2SyCZIZpMkc8nq+/l2xf2cN95iqQnVEA/HvS3kvdaEa6gJ1xT7/O3C8Ur7USeKOQD/LcjmXJqTWZqSGZoSWZqTmWK7KZmhKZmluS1FItFGKtFCKtFCNtlKNtWKm2oj61qyhMkQIm3DZHDIECZjQ2QIkbVh0oTIEiJtQ7g4WGuLP/X7L1rJgpPGOBnv1fjaThpM/rW4n8EU2k4GY3zH8ufjFMZkuPX0ezju4Kn7q/hBQaHaEKBQTWTgSWVz3PjYWm5+8nVG10f5xsJ5nD5jbJ/eY0diB5cvuZyXtr/Ex+Z+jE8f9Wkco09biXTLno2w/LfeDLada703CGZc4M1gO+w079NnIiIiIhKMYvDV6nsuUmchWFsnY9s69vdmSTLxZvjEfCFbeehWCOOqjYnWK5TrDmu9gKtSEJbc2zEsKx+XaqLzMMx4fyaxxp6HYJHayuMiNUPizzbjZoqBlT+0KgRdxdCrfL9KKOYPwPzjbbfDzHaF4KtSQAaQyqVKQjd/LT3lGKc0aPPfOxwvBngRJ4ZDFGwU3AjWjeDmomRzYbLZMOlMiFQmRDIVIpkO0ZZyaEk4tCYdmhPQlu76+9AQC9NYE6Eh7r02xsM0xr39cMh7j8zg/XgaYyj+lBowGK+/0JXvA5esTZOzKbKk8q9JsjZFzk23t22SjE2RtSmy1uvL2CRZN122771mbIqsm8Ql16Pvd8hEiJg4Ucf7nnpbnKiJe6/5LRLy+r504keZPKxv34McbBSqDQEK1UQGruWb9nDFXctYva2Fy445iK9c0Lez1jK5DF9/7uvcs+YeTpt8GteddB310fo+u77IoGctvPWCN3vtb4shsRvqx8Hcy7yAbfycoCsUERER6Z8KS8Nl2vIzXCqEXBUDrkpjK4RgPXpT2fje8M+/6V9oR+ryr7WlYwr90TrfmMJrjffMIv/1i03TSZ+vv1Jft87v5Jr7+/5u1ntOdDGsqTDbKdVUFuj49rtahs04XrjWIXSrsFUK5mKNA2PmUuHvRtUgbE+VwMy331XwG23wvi81wyt8/9r7MtF6ErEaEuEYbaEIiVCYhHFIuilytvSN/cL7yP5Ap7yvJOwpTJz0jy+Mq3St9hO6vE+la5XU2tl9ysb7j+VsjlQ21bsALJska3v+TKywEy6dJeYLnTrMAvOFUx32O7lGLBTr9YesXeuSyqU6BIVtmQR7kq3sTrSyJ9HK3mQbTak2mtNttKTbaE23kcgmSWSTpHIJ0rkUaTeVD5tSuKSxxVlVGYzp+YcZQkQIOzGiToxYqH1mXW2khvqotxXDwwrf14ybIZFNdL5lEsWw0d/fEwZTrK0mXENNxNcOVeirsMXDcWrDtRX7w44+9NvXFKoNAQrVRAa2VDbH9x9dw4+efJ2xDXG+sWgup03vu0+MWGv5zarfcP3z1zOlcQo3nnEjkxsn99n1RYaMbArWPOzNXlv9kLfW/7i53vKQcy+Dhr5/RqKIiIgEwHW9YCfV7Nuaum6n2ypfr+LsjK5Cki7s0zX7eFwuXSUEa6NHwZdxOgZcxZCrQthV3l8xKMu/alnC/iGb9oVueyoHb50Fc+nmru9RCNu6nDFXPma4NybUzQ+5ZlMdA6/uzhpL7vX+3nQmUtsefMUbScQaScTqScRqaYvUkIjESETiJEIREqEQCSdEwjG0AQnrknBTxTf/27JtxWCguJ9vZ92eh0BDRSEI6Wq2VbWlEbsTiMXCMSJO3z4OpDustSQyOVpSWdpS3mtTouPSiU2JjG95xdJ2SyqL28V/4uMRpzgrzJslVq0dpi5qiEYzxCI5IpEcoVAGa9LFQK9k1l+lWYCZRIe+8iUvU7lU1VojTqRyoBWpKYZZhT/nzgKw2nBtydKZNeEaYqHYAVkOU/qOQrUhQKGayOCw7E1v1tqa7S28b8FkrrpgJo3xvvvH1bNbnuWKJ6/AWst3TvsOx004rs+uLTLktO3yZq4tuwPeWuq9CXT4mV7ANuN879PLIiIicmC5uY5hWLKpLATzh2GV+vNbd8KgSB3EGrwt3ui9Cd4dFd+L6eq5Wr0ZewDGgTdjq0P41UkIVm1sOKbgSzqXy7b/3e0QVFUL5vaUHu/q77YvzCqGbsZ0DMzKZqpkgIRjSBiHhDEkQhHaahpJROtJRGu8LRzzwrBQmDYnRMLJjzXQhkvC5ki4GRK5FG2+WTE9Db7ioXjFYMD/pn93jodM+6y/QiBQXBzPP5GR0mP+8MBUCOfLr+Uf05P7dHWt9ktUr6v8WgZTDEQiTqTfBCGua2nL5GhLZWlJZWnNB2Ft6fb91lSW1nSW1lSWllSOtmI7fzy/X2h3JxYoWToxHqGxJkxDPL+EYhchWUM8QjTcvx5B4lq3JHCLOJHin7dme4mfQrUhQKGayOCRzOT43qNruOWp1xnXGOebi+ZxyrQxfXb9N5ve5LNPfJb1e9dz5Tuu5IMzPthv/pEoMmDtWOOFa8t/C3vf9H75nnWxtzzkwceD079+kRARkT6Qy3qzDDpsGW/2Qi6T30954YQT9m2h/FbeFwYTKt0vOeYM3sDBzXUSfBWCsWqzxnz76Zbu3S/a0B6GlWyN+SXkKvSXvDZ4s6L0jFWRgSU/C9Um9pBM7KC1dTutrdtpSeykNbGLltQeWlN7aU0305JppTWboCWboM1YLyRzHC8IAxLG0mZzxSAsa3v2DKTOgq+KoVcnx2vDtcVj8VCc0EBYAnOQy7k2H2rl8qFWIfDqGH61FsOx/DHfWG8mWZbWdPd/vuIRh/pYmLpYmNpomPpYiLr8fl3Ua9dXODasJDyLUB8LE3IG6b87RLqgUG0IUKgmMvi8tHE3V9y1jNffbuUD75zMv797Jg19NGutNdPKl//0ZZa8uYRFUxdx1bFXEenuEhciUp3rwht/9gK2lfd5b+wNPxjmvd+bwTbq8KArFBEZGFzXW2K3PJwqtEv6KwVa+XYuVaW/7LxslXCss3t19Syb/aUQtFUM38LeBznKwzpTIaCrGNpVu2aFENBUuI9/jHG8pf/KQ7Lk3sqzwjKt3fjiTZWgq8J+STBWNi5aPzCeuSQiHWTdLK2Z1pKtJdNCS6aF1nRpn/9YW6atOKZwrPx5YZWETZj6aH1piBXpJPTq5LiCr75hrcW13qvF+9yKxRZnXfn3LaXjsOAW90vPz/8/OdfmQ61ccZZXh8Ar325J5wOvQnDmC80Sme6HYLUlQVdpu74sDKt4rCQ0UxAm0hcUqg0BCtVEBqdkJsd3H1nNrX9ax4RhNXxj0VxOnto3s9Zc63LTyzdxy/JbOGrsUdxw2g2MrhndJ9cWEbzniax6AJbdDuuWeG++HvROL1ybfSnUjgy6QhHZ3zIJaNvp23ZB6w6vndgNbhbvHZzC72Tlbar093WbA3d961YIyMqDrFT+e9PHnDCEoqVbuNCOQCjma0e9ZegK7Yrn+LdK5/jaxngzsdys7zULtkJfpXFu1vve+ffdXMcxbs53zWrXKr9/+TUq3MeWjekp41QJwarMAKs2ayxSp9nfIgOQtZZkLumFXOkWWrOtJeFW4bV4vCwUKwZimVYSZUsuVlMbrqU+Uk9dtI66cB110TpvP9L+WmxXOxatJ+pEu72yS861ZF03/2rJ5fKvZf3ZXNm4fF+3xrmWXM4t2ffOLe0ruVb+2hk3Py5XZVx+P+O7nq0SXrUHWN4x1/pDLKBKqFVoF/6Z035e+3g6C8X6AWOgLpoPsqLtoVZh5pcXeoXys8B8oVfUF4jF2gOx2kgIRyGYSL+jUG0IUKgmMri9mJ+1tu7tVj547MH8+7tnUh/rm6VmHlz/IFc/fTXD48P5/unfZ9aoWX1yXRHxadoMK+6Cl2+Ht1/13mCddq63POQRZ3lv0IpI/5bLeKFYMSDb0R6UlQRnvr5MW5WLGe/5LKH8331jaH/oR3fa9HB8P7q+MaVBVDhWFmp1EU51FoR1uFa09DyFMX3D2nzAVyHQKw8KI7X5mWF1g3fZSpFBzFpLIpugOd1Mc7q5OOurfMZXpRlj5WFYT2aFdRV8+Y9XOlYbri2ZBWatJZHJ0ZL0Zh0Vt6Q3s8jrb5+V5D/WnPRmJiWzubKQrGMgFfRbrJGQIeQYwo6TfzXtr6HS/nDIEHKc0jHFV4dIyOA4Bsd4TxwzhvyrKf4zwWAwBhxf2+QPto/3HcP3/LN8v5M/p9J1K51PfpxjysaUXbfS+XQ4r+N1jYGQMdTmg7H20Kw9OIuHFYKJDAUK1YYAhWoig18yk+M7D7/GbX9ez8RhNXzrPfM48Yi+mVm2cudKPvfE59iT3MM1J17DuYee2yfXFZEy1sLW5d7ykCvugta3oXYUzHmPN4Nt4lF601HkQHBdSO5pD8EKs8fKQzH/fmpv9evFGr2/yyXbyAp9+a1muJaeExGR/aoQiLVkWmhJt9CcaS6+Nqfz7UJQVtbfkvGOdTcM888Kq4/UUxup7TDzqz5aX3HGWHF82awway1t+eX3mlPZ9sCrLAhrSWVoTeWK4VchFCtvu914+9MxUB9rn11UHw8X92Nhh3DI6Ty4cpx8WJUPrhxDKFQpuCoNt4r7ocrjKt4zVDrO8QVLIiKy7xSqDQEK1USGjhfe2MUVdy1n/Y5WPnTcwfzbeTOp64NZazsSO7h8yeW8tP0lPjb3Y3z6qE/jGH2iW2S/yWXg9Se85SFXPeAtdzZ6uheuzXsvDDso6ApFBgZrvecxFcOw8oCsQkiW2F39eVjhGqgbXSUUq9BXM1KzTUVkUCks1VeYndRhy3Tsy9kc8VCcWDhGLBQjHooTDUWJh+PF/cKxSvuxUKxkbOHcofr7iLWWVC5VDLeKIVimNPQqhGIl4ZivnbWdL9fqGIf6SD0N0QYv4Ip67YZIA/XR+vZj0XoaIg00RBuoi9QVX8tnhVUMwpKl4Vaz75lU5UFYi++c1nTPg7D6ePvyev5wrCHePtuoIVY2Ju7NQGqIRYhHHAVTIiICKFQbEhSqiQwtiXSObz/8Gj99ej2Thnuz1k44fN9nraVzaa597lruWXMPp00+jetOuo76aH0fVCwinUrsgZW/82awbXwGMHDoyd7ykDMvglgP/h5am1+CK9O+JFeusDRXpov9Ql/OC/38y3qV7xf7fPcq3+/y3hXu47+XE/KegeSEy9rV+vZ1aS+HhwAAIABJREFUf3+dU6HPOJqVWE3hOWStnS2vWNbvZipfy4l0Y+ZYWX+09sB+vSIifcy1Lm2ZNprTzTSlm0qCl+K+b2ZSU7qpGMYUtq7CmLATpjHaSGO0kfpIPSEnRCqXIplNksql2rdsqstrdSbiRLodwPmPRUNR7zzf2PJzqx3riyAvlUt1mPVVHoSVB2BN6aaSWWPZLp5faDAlQZg/APMHZbXheuKhOmJOLRGnjqipJWJqCVODsTHSOUsqmyOddUllXVIZl1TOJZXJkcq67f35MYlMrj0U84dgqe4HYSHHUBcN0RCPFJfUq6sShFULyAp9CsJERGR/UKg2BChUExmant+wiyvvWsaGnW38/fGH8KVzZ+zzrDVrLb9Z9Ruuf/56pjRO4cYzbmRy4+Q+qlhEurRrHSy/05vBtnuD9zyaUYd3EUCVBVlBKAZGES9ACkVK951wvi+U76uw74Qh5AuerNvxWT19tV9tllIg3zNf6GZCFZ5DBcVnURXaxWPVnoFV4ZxOz+/uOfvpPpnW7j2HrEMw1kVQFmtUeCkiA07WzdKaaS0GYIVwprifKdsvC81a0i1YOn+vpyZcU5x5VJydFG0ohmQl+xWOx0KxbgcZWTdbDNzSuTTJXLJiAFcpkKs21n+sw34fBHnVQrdYOFYM62KhmPe8sbLZYy3pFtJuusv7xEO11ITqiYVqiTl1RB0v7PICr1pCtgaHGoytATeGcWuwuTi5/JbNhkln6RB6ecFYjnTOa/fF237RkEMs7BCLOMTCoZIgrCEepi5aulSiv19BmIiIDESDLlQz3v/yvhf4MHAkMAbYBawEbgd+bu0+/Auq8j0vAD4EHAeMA9LAW8BDwK3W2pU9uFYE+AjwfmAWMBJ4G3gJ+DVwp+3hH4xCNZGhK5HO8a2HVvHzv2xg4rAaPnfmVC49ehKR0L59wvLZLc9yxZNXYK3lO6d9h+MmHNdHFYtIt1gLbz7nBWzNW6qEUWFfX7jKvm8LRarsdxJ0dedeTmjgBReuC7anwVxvwrzeBH6FfwbmX631tWk/5h/X6TlVxnXrnE7Or3hOF/esNi5S23lIVjca4sP0HDIRGRDSuXS3lkys2Jdupi1b7cMF7fzBV32knsZoY/t+tGy/wvGIEzkA34ngZN1se4CXD9wK+23pBHtTCZpTCVpSbbSkk7RmkrRlErRmkiQySZKFkC6bIpVLknbTZNwUGTdN1qbI2jSuzYCNghsHN45147jZOLlcjFw2TjYbw+biWDcGuRqsG8/vx8GNAV3/vuYYiIVDRMPtoZYXcIXa25EQsbDTPiYcyr86vn7/eG+/0K52jUI7GnJwnAH27zwREZF9NKhCNWPMCOBu4IxOhr0IXGqt3dgH9xuLF9R1dr8M8BVr7be6cb0pwD3AUZ0MexS4zFq7p7t1KlQTkb+u38XXH1jJ8k17OWRULZ87cyoXHzmJ0D78AvRm05t85vHPsKFpA1e+40o+OOOD+kShiIj0C9ZaXOvi4nqv+S1nc1hrydlcSX/5sc7GlPTh4rr5c7Hk3Fzxnr26nm8LUlD/e24wGGOKrw7tsxUc45QcL9n3jS2e29n4avtl98a037ew5Fun+4aOdRT2O7mvxWJtfsP72bWF/8v3u7jF44V9LMWxhZ+ZTvctHa9TuFf5vTvbL6ujYu2+/ap1+c4v/Nz771H4O1bpeHm7cE3/datex9fucmz5Pcu+zs7uWel4JpfpcpZSyIRKnlFVPhus8Eyrkn3fVheuKz7DajAqLDGYSOdoS2eL7UQmR1u6vJ0ttpP5V3+7fWy22M7kevY+mGOgNhqmJhqiJhKiNhoinn+NVQusKoRT5QFXrIvx0bBD2DH6/UtERCQAgyZUM8ZE8QKnk/NdbwK3AGuBg4CPAjPzx1YCx1trm/bhfvXAn4H5+a4dwE+AZUAYOB5vxllN/vjl1trvdnK94cAzwIx816vAT4FNwBHAx4HCGmtPAOd0d8adQjURAe8Nxkdf3c4Nj6zm1S1NHDamjs+dOZUL5k3sdbjWkm7h3/70byzZtIRFUxdx1bFXEQkN7k+2ioj0J9ZasjZLzs2Rs/kt38662eJ+yRjfWP+YSvuVzt+XMdXqcq3b6fk9Cr8IPpQSkZ7xB5sOTnvbOMVg0jFOSYjpDyb9/f7rVDqvO/cpP17pPhXP847iLWtrwBpsvtda71h9pIHaSD114Xrqwg3URerz+1475sSLQYn/HZnSt2dsxf7yd3BKj1U5pxtjenzNkvGWrGs7hFyJ/NaWaW8Xj2Wy+dCsYwCW7c5DuXxCjqE2EvJCL1/w5bW9MMx/vKSdH18TDfvaoZJ2NKSlCkVERIaawRSqfQ74Xn73ReAsa+1u3/E48DvgXfmub1trr9yH+30T+GJ+d3n+fm+XjZkBLKF9ScjZ1tq1Va73XeDz+d0H8WbTJX3HR+KFhoVZbJ+21t7UnVoVqomIn+taHl65le8+sobXtjUzbVw9nz9rGufOHt+rpTtc6/KDl37ArStu5aixR3HDaTcwumb0fqhcRGTwyrk5diZ3sr1tO9vatrGtdRvb27YXt21t29id2t0hdOov4VHYhAk5IUImRMgJETZhHOMU24VjYSdcMibkhHCMU/F8/zFjDCETKr6pXdhCJlT1WOF4cZ/un9uj61Y4x5A/1/Hu29X1yq9ZeBM/CF09a2m/3rtsBhW0z3DqdNZWT2Zb9cNZX0CHmXL9fXZdV3VUOt/7M/bCJtcF14JrDbmcJWch61qyOTf/asm6btmrLY7J5Cw5t3RMoS+Tc/PHbNmx0vPKx2fyY8v7CvtZt8r4nCXjtvdJz0RCpjizqzYaLrYrBVj+kKs0HCudIVabP14T9WZ2iYiIiPSlQRGqGWPCwGa856dZYK619pUK48YC64A6IAVMstbu7MX9IsBOoCF/v/nW2hVVxl4G3Jnf/bW19sNV6noTiAKtwGHW2u0Vxs3BC/AMsBU4yFqb66pehWoiUonrWh5YsYXvPbqa199uZcb4Bv7f2dM4Z9a4Xn3a8sH1D3L101czPD6c75/+fWaNmrUfqhYRGXiS2WR7WNZWFpa1en07EjvIlf2zLmzCjKkdw9jasYyrHceI+AgiTqRDMNVVeFXY7yy88o+pFn75+x3jlLT1KX2RjnKupSWZpTmVoTmZpSWVpTnptQtbSypDKuPmA6ZCOOe1XVtYytT6jlO2b/MBVVfj/ccrjHc7ju94r06u5XY9PijGQMRxCIcMIccQCTneq2MIh7wl9LxjDpHCmErjQ4aw03F8OD827OS38vG+80KOKXnMaEnbF6RX+0+q/7+1pqS/bFyVa1U/pzvju753ta8nHDL5QCxcDMcKAdi+PutZRERE5EAbLKHaOcBD+d1HrbVndzL2NuCf8rv/ZK39aS/udzzwl/zuy9baqs9AM8aEgD1APdACjLXWJsrGfBz4cX73Nmvtxzq53qPAmfndM621j3dVr0I1EelMzrX8ftlmvv/YGtbvaGXOpEYuP3sap08f2+M3SVfuXMnnnvgce5J7uObEazj30HP3U9UiIsGz1rI3tbcYlPkDs61tW4vtvam9Hc6ti9QVw7LCa6E9ts7bHxkfWZxZIiIHjrWWZMb1ArBUIQDLeAFZMpvv8+9nfCFZ+7HWdJeffyTkGGJhh5DxwhbHMTjG4BjySwqS388f9/WZSsecwjH/ud25lu+4Uxhf6V6dXT/f53RvfLgs1PJCKV8IFWofE8kHUv7x7eGYQyhkSsb4w7HerMQgIiIiIlJNZ6Fa+EAXsw/O8bUf7GLsg7SHaufiPbespw7ytV/rbKC1NmeMWQfMwwvWTqE9ACzoaf2FUO1coMtQTUSkMyHHcMlRk7hg3gTufekt/ufxNXz050s5cvJwLj97GidPHd3tcG3WqFncfv7tXL7kcq586kpW717Np4/6tN4UFpEBJ+Nm2JnYydbWrSUzy/xh2fa27aRyqZLzDIaR8ZGMqxvHpPpJHD326PawzBei1UfrA/rKRAa3bM7NB1tZmnzBV3GWWNWQLN+XytKSzHbruU210RAN8TD1sTAN8QgN8TATh8dpiEWoj4eLxxrzx7y+SL7P26+JhDTTU0RERERkkBhIodocX/uFLsb6p2zNqTqqc/vyW89cOoZqB7p+EZEOwiGHyxZM5pKjJnH3C5u48bE1/P1P/8qCQ0Zw+dnTOOGI7j0nbXTNaG475zaufe5abl1xK2v2rOG6k67TG8gi0m+0Zdo6LMXoD8+2tW1jZ2Jnh2dLRZxIMRSbM2oO4w5uD8vG1oxlTO04RsRGETJhXOs9e8e13nK7rrXk8kusNbVZdre0FZdEy7m2uERazm1fhi2XX9ot53pjLJaw483MCBdnZBTa3uwNx3+s+Np+jmZsVGat9/yldNYlk3NJZ11SWZd0vl3oS2ddUjmXjO9Y2t/29WV8fakKfd6rzY/P+e5lyWRdjPH+t9kxpX/eIccQMv59h5Ch/c+5yphwfgZU2DGEQu3XDBn/fvsSdaGyn6X2n63SMf5xoQo/cx2v5eA4lI4JeXVYoCW/JGJTMusLxDL5kKzQlykGZ82+JRVbklkSma5nh4UdQ0O8PQirj4WZNLyGhnhDh5CsoRiORUpCsrpYiLCWrRMREREREZ+BtPzjOuDQ/O6h1toNnYwNA0kgBGSBqO3hF2qMOQV4Mr/7krX26E7G+pd/hLLlHY0xDt7z3cJADohba7OdXO9QvOfCAbxurT2iq3q1/KOI9EYqm+PO5/8/e3ceJ0dV7n/88/SsmZnsmbAGsgdBWUUWQcKOCopAQLkXDS543UEv14u44PUqPxdWcUUxXFSUsCkgyBo2WQUERJIQkhAgkIVsk2T25/fHqUnXTGamZzLVU9Pd3/fr1a+pqj5dfZ6unu7T9dQ5ZxlX3PcSb65v4sDJY/jy0TN416QxfXq8u/P7F3/PD5/4IZNGTuLywy9nwogJea61iBSzdZtaeGDhSh5bvJpNzW20tzttsfl82trbaPIGGv0tmvwtmnwNzbxFM2tpYQ0ttpZWW0O7NW61b2uvIdM+kkzbSKx9JLSOwtpG4q0j8NaReOtI2lpr8FjCqyNh1hbNHVQIzOicTInN/9NjMq7MuknsZLopHyV4Mmx9fzdJm60TLhm6Jo/KM0ZbO9lEVw9Jq64JrY71llhCa8tjuinX3Nae6DEsyxiVZRkqyzNUlGWoKg/LlWUZKsqz91WWl1FZZlvu6yjfMcdQa3t7lFRtp7U9JF1b28P7fev19i3rbbHb1utbl4svD3Uh4VWe/Rslv0bEkmEd93VNnHUsV5VrHkAREREREdk2xTKn2lvA6Gh1uLs3JFm+m8fXAGuASsCBPd39+R7KngLMjW263t1nxe4fAXRMtLHa3XvtCtLf8qCkmogMTGNLG9c+/go/uW8RqxqaOHTaOM4+ajr77To694OBR5c/ylfmfQUz40eH/YgDdzgwzzUWkW3h7rR7O+20Z5e9HSe27E47seU+3N+x3nVbX56jrb2dJasbeP61tTz3+loWr2rA3RlWCdXVG/HydZBZR3vZOtoza2nLrAPr0kvFjXJGUuGjqGA0ldGtitFUZ8aEvzaGyrLqaC4jormNLEoORctRAsqi+zvmPSrLZOcoKstk5xMq65hfqGOfW+ZJ2voxFt2ffd5QpuvzGka7dyRA2mlt65wQ6djeXRKmrd2j8ltvjz+2031tnbe3u/f4nJ2fIyQaw3r7VuWTlDE6JaSyyaqQmIpvq+pm29YJr8776Lrfiti+untMx3OUFWiPQI+SxfHEW/cJvOxxb23z2Puy63p7N++J7Hp4n2XfP8CWIRKHV5czvCNJ1tFbrLJcvS1FRERERCRVxZJUawYqotWK3np6ReVfA3aMVnd09+Xb8JxXAWdGq88AR7v7qi5lpgPzgB1im+9092NjZXYEXotWX3P3+Hxt3T1vBdAcrTa7e1UP5c4CzgLYZZdd9lu6dGlfwhIR6dHm5jZ+++hSfn7/IlZvbGbmjHrOOWo6e00YlfOxy9Yv4wv3foEl65dw7v7ncvpup+sKcZEBaGlr4Z5l93Djght5teHVASW84mULzbDyYVvNVTa+Zjzb12y/ZXnssLGUZwppVPPi1pekTUjmhftb2jz0+irPUBH16KoqK9uyruH3REREREREZDApqbbtSbUdCPOfdSTMVgK/Bv5BGMrxQELSrYYwXOPkqNwd7v7e2H7yklSLU081EUnSxqZW/u+RpfzigUWs3dTCUW/bjnOOnsYeO47s9XENzQ2c9+B5zHt1HidPO5nzDzifirKKXh8jIp0t27CMGxbcwE0v3cRbjW+xU91O7FW/F2VWhpmRsQwZy2BYt9u2LJuRIbZsGTJkei4fLfeprMWem9zPhxuLV23iH6+u45lX1vPSyo14uzFiWAX7ThjNfruOZb9dxzC6pmrLc2Qsw9hhYxleMVwJehEREREREREZNMWSVBvU4R9j+9kDuBnobV6z3wDPApdE69e6++mxfWj4RxEpSBsaW5jz8BKufPBl1je2ctwe23PO0dOZsf3wHh/T7u1c8fQVXPnclewzfh8unnkx44bl/BgTKWmt7a3cv+x+5i6Yy8OvP0yZlXHYzocxa8YsDt7xYDJWeD11VjU08cCCldy/YCUPLFjJmk0tmMHeE0Zx2PR6Zs4Yzzt2GlmwQ+iJiIiIiIiISHEqlqTay8CkaHWSuy/ppWw50AiUAS1AlQ8gUDOrJvRIOxnYExgFvAU8Afzc3W8zs28D34wecom7fzn2+AzQROjd1gZU99bTzswmEXq+ASxy994SeoCSaiKSX+s2t3DVQ4u56qHFNDS38v537MDZR01j6viek2t3LL6Dbzz8DUZVj+Kywy9j97G7D2KNRQrD8obl3LDwBm5aeBMrNq9gu5rtOHn6yZw09SS2q90u7er1S1u788yyNdw/fyXzFqzkudfW4Q7j6ip5z7R6DptRz3um1TO6tjLtqoqIiIiIiIiI9KhYkmp3AB3zlB3u7vN6KTsRWByt/svd834m18zmAqdEq6e7+7Vd7n8RmBGt5koKzgTui1Zvd/f35Xp+JdVEZDCs3dTMlQ++zG8eXsLmljY+uNeOfOmo6UwaV9tt+RdWv8AX7/0i65rW8Z13f4fjJh03yDUWGXra2tt46LWHmLtgLg++9iDuziE7HcKpM07lkJ0OKai5wVZsaOSBBauYN38FDy5cxbrNLWQM9t1l9JbeaHvsOIKMeqOJiIiIiIiISIHoLalWOGdt4HmySbX9gHm9lI0H+3y+KtQh6ol2SLTqwMPdFHuebFJtP2BJL7sc1PqLiPTVqJpKzj12Nz7+7kn88oGXufqRJdzy7HI+tM9OfPGIaewytqZT+d3H7s4fjv8D59x3Duc+cC4L1izg8/t8viCHshMZqBWbVnDjwhu5ceGNLN+4nHHDxvHJd3ySk6edzI51O+bewRDQ2tbOU6+s5f4FK5g3fyX/fH09APXDqzh69+2YOaOeQ6fWM7JGcymKiIiIiIiISPEppJ5qRwN3Rqt3ufsxvZT9FfCJaPUT7n5Vnuv2fuDWaPVOdz+2mzKfAn4ZrV7p7mf1sr+7gSOj1SPd/d5cdVBPNRFJw8oNTfz8/kX89tGltLU7s965M587fCo7j+6cXGtua+a7j32XGxfeyOETDufCQy+ktqL73m0ixaTd23n09Ue5bsF1zFs2jzZv46AdDuLUGady2ITDqMgM/eTTG+saeWDBSuYtCL3RNjS2UpYx9ttlNIfNqGfmjHp232EEZuqNJiIiIiIiIiKFr1iGfywHXgfqCb3B3uHu/+ym3HjCfGS1hHnVdnb31XmsVw3wJPC2aNPR7n53N+XqgVeBSqABmOLuK7op93bgWcCANwj1b8tVDyXVRCRNb65v5Kf3vcS1jy/DcU7bfwKfO3wqO4wctqWMu/P7F3/PD5/4IZNGTuLywy9nwogJKdZaJH9WbV7FzS/dzA0LbuDVhlcZUz2GD079IKdMO4VdRuySdvV61dLWzpNL1nD/gpXMm7+CF9/YAMD2I6qjIR3refe0cYyoHvoJQRERERERERGR/iqKpBqAmX0JuDRafQo4yt3XxO6vBm4COibt+ZG7n9vDvuYAH4tWv+3uF/RQ7j3u/kAP920H/B44Ito0x93P7KX+lwBnR6u3Aye5e2Ps/tHAPcA+0abPu/tPetpfnJJqIjIUvL52M1fc9xLXPbGMTMY4/V278NmZUxg/onpLmUeXP8pX5n0FM+NHh/2IA3c4MMUaiyTH3XnijSe4bsF13PPKPbS2t7L/9vsza/osjtzlSCrLKtOuYo9eX7t5SxLt4ZdW09DUSnnGeOfE0cycMZ6ZM+qZsd1w9UYTERERERERkaJXTEm1SuBu4NBo0zLgF8BLwM6EIR87eoy9ABzs7ut62Ncc+pZUawDeBP5C6EG2BhgNHADMAkZERe8DTnD3jb3UfxTwCLBbtOlfwK+A14CpwKeBjm4b84Bj3L2lp/3FKakmIkPJsrc2ccW9L3H9U69SnjHOOHBX/mPmFMbVVYX71y/jC/d+gSXrl3Du/udy+m6n62S9FKy1jWv506I/cf2C61myfgkjKkeEXmnTT2HyyMlpV69bTa1tnXqjLXizAYAdR1ZzWJREO3jKWIarN5qIiIiIiIiIlJiiSarBlt5c15PtHdadp4APufsrvexnDn1PqvU28U87Ya60L7v75l7KdexvInAj2d5o3bkbmOXua3Ptr4OSaiIyFC1ZtZHL713IzU+/RlV5GR87eCJnvWcyY2oraWhu4LwHz2Peq/M4edrJnH/A+VSU6QS+FAZ35+kVT3Pdguu4a8ldNLc3s8/4fZg1fRZH73o01eXVuXcyyJa9tSlKoq3kb4tWsam5jYoy412TxjBzekikTR1fpwS3iIiIiIiIiJS0okqqAVg423MqcAYhOTWO0IPsn8AfgN+4e2uOfcyhb0m19wNHAwcDO0XP1UCYH+0u4P/c/dl+1r8CmA18GNiD0PNtFfA0cA1wnffzwCipJiJD2aKVDVx290JuefZ1airK+Pghk/jkIZMZPqyMK56+giufu5J9xu/DxTMvZtywcWlXV6RH65vXc8uiW5g7fy6L1i2irqKOE6acwKzps5g2elra1euksaWNxxe/taU32qKVoTP9zqOHMXNGPTOnj+egKWOprSpPuaYiIiIiIiIiIkNH0SXVZGtKqolIIVjw5gYuu3shtz23nOHV5XzykMmcechEHl5+N998+JuMqh7FZYdfxu5jd0+7qiJbuDvPrXqOuQvmcsfiO2hsa+TtY9/OqTNO5diJx1JTUZN2FbdYunrjlt5ojyxazeaWNirLMxwwaQwzZ4znsOn1TKmvVW80EREREREREZEeKKlWApRUE5FC8q/l67nkrgXc+cKbjBxWwVnvmcyBu23mqw+dw7qmdXznkO9w3MTj0q6mlLiNLRu57eXbmLtgLi++9SLDyofx/snvZ9b0WUMm8dvY0sajL69m3vyV3L9gJYtXhd5ou46tYeb0eg6bUc+Bk8dSU6neaCIiIiIiIiIifaGkWglQUk1ECtFzr67j0rsXcM+LKxhTW8kZ7x7Dk5sv4dlV/+BT7/gUn9/n82Qsk3Y1pcS8sPoF5i6Yy19e/gubWjex25jdmDV9Fu+b9D7qKutSrZu7s3hVtjfaoy+vpqm1naryDAdNGcth0+uZOWM8k8b1Nh2siIiIiIiIiIj0REm1EqCkmogUsqdfWcMldy/kgQUrGVuXYerud/LChrs5fMLhXHjohdRWKEEg+bWpZRN3LLmDufPn8vzq56kuq+a4Scdx6vRTefu4t6c6XGJDUyuPLw690ebNX8krb20CYPK4Wg6bUc9h00NvtOqKstTqKCIiIiIiIiJSLJRUKwFKqolIMXhyyVtccvcCHn5pFWN2eJzWUX9i0shJXHHEj5kwYkLa1ZMitGDNAubOn8utL99KQ0sDU0dN5ZTpp3DClBMYUTli0Orh7qxqaOalFQ0sWtmw5e+iFQ28vq4RgGEVZRw8ZSyHzahn5vTx7DJ26MzlJiIiIiIiIiJSLJRUKwFKqolIMXn05dVcfOcC/r7iMWomXEt1eRmXHH4Rh+x8UNpVkyLQ2NrIXUvv4rr51/HMymeozFRyzMRjmDV9FvuM3yevvdLa2p1X12zqlDwLyxtZt7llS7mayjKm1Ncxpb6WqePr2GvCKPafOEa90URERERERERE8kxJtRKgpJqIFBt352+LVnPhXQ/yctkVlFWu4tgdPsX3jvwMleVKLEj/vbzuZebOn8ufF/2Z9c3rmThiIqdMP4UPTvkgo6pHJfpcjS1tvLxyYzZxFvU6e3nVRppb27eUG1dXGZJn4+uYWl/H1PFheYcR1WQy6Q05KSIiIiIiIiJSqpRUKwFKqolIsXJ37nhhCRc8ej6byp+javNBnLvfeZy8766Ul2XSrp4Mcc1tzdzzyj1cN/86nnzzScoz5Ry1y1HMmj6L/bfff8C90tZuau7U46yj19myNZvoaGKZwYTRNVt6nXXcptTXMaqmMoEoRUREREREREQkKUqqlQAl1USk2LW1t3Hu3T/gruW/p3XTrmy36SzOOXI/jt9zR8rUo0e6WLZ+GXMXzuXmhTezpmkNO9XtxKzpszhx6omMHTa2X/tyd15f18iiFZ17nS1a2cCqhuYt5SrLM0weV9u511l9HZPrazVso4iIiIiIiIhIgVBSrQQoqSYipeK2l2/j6w99E2+tZf3Sf2fKyBkcv+eOjB9RxfjhVYwfXk398CrG1VUWTU+2tvY2Wr2VlrYWWttbaWlvoc3bqC6rprailoqyirSrOCS0tLcwb9k85s6fyyPLH6HMypg5YSanTj+VA3c8kIz1/n5obm3nlbc2btXrbNHKBjY1t20pN3JYRZQwq+3U62zn0TVK8IqIiIiIiIiIFDgl1UqAkmoiUkr+ufqffOneL/FDTgbOAAAgAElEQVTW5rXUbfg3Xlk2fasyZjC2tpJxdVWMH1HN+OFVjK0rZ2xdGWPryhlVm2F0TRkja42KspCQaWnPJq1a26MkVpTMavGWTkmtrcp28zf++B7L9GE/7d7ezauQVZmppLailpqKGmoraqmrqNuyXFtRS015DXWVddSW91ym41ZdVj3gIREH2+sNr3P9guu56aWbWLV5FdvXbs/J007mpGknMb5m/FblNzS28PLKjZ16nb20soFXVm+itT3bLtpxZDVTooRZPHk2rq6y4F4jERERERERERHpGyXVSoCSaiJSalZtXsU5953DMyufYfroGbS0tdHY2kxzWyvNbS20RkmpNlpxb8VpA8vfd55hVGQqKM+UU1FWQbl1/rvlvm7+9nhfl8fFl8usjMa2Rja2bKShpYFNLZvY2LJxq9umlk3h/tZNfYojY5ktybe6irpOybqut1zJupryGsoy+Rn2sLW9lQdffZC5C+by0GsPYWYcutOhzJo+i0N2OoSMZVjZ0BR6m8V6nb20ooE31jdu2U95xpg4rrbzfGf1w5lcX0ttVXle6i4iIiIiIiIiIkOXkmolQEk1ESlFzW3NXPHMFSxcs7BPyakyK6e1LUNTC2xugk3NsKkJGhqdDY3Ohs3Ohs3trN3YRmOLgZfhXgbRrczKGVM7jLG1w6ivq6G+rpbxw2vYfngt24+ooX546BVXX1dFZfnQGnqy3dvZ3Lo5ZxJuY8tGNrVuoqE5JOJ6StS1emufnndY+bBOSbjeesltSeCV11JXWbelfMdjKsoqeHPjm9y48EZuWHgDb256k/ph9Ryx0wnMqDmS1etrt/Q6W7SigfWN2TrWVpZt6Wk2JdbrbNexNVQUyTChIiIiIiIiIiIycEqqlQAl1UREkrWxqZWVG5pYsaGJFRsas8vrm1jZ0MSK9WHb6o3N3T5+VE1Fpznexg+voj66jR9ezfgRYXl4VXnBDSXo7jS1NW1JsG1s3dhrEi7eY64jaRe/r6mtqU/PW24VtHkbTjuj7R2w/kDeWD6Z5rbs61c/vIqp9XVMGV/L1Po6po4fzpTxtWw/ovCGtRQRERERERERkcHXW1JN4xqJiIh0o7aqnNqqciaOq+21XEtbO6sbmrtJvDWyYn1YX7xqIys3NNHctvXcaNUVmZBk25Jwy/Z2qx+RTcaNra2iLJNMUqit3Wlubae5tZ2mtrYty81t7dnl1naaovWm1vj2tk7lmrYsV9HcWkFz2/BO++v02C376/ycLW2tkGnCMk1YWVN2OdN5uSnTBF5B2/p9GDt8AlPH1/HeybGeZ+PqGFlTkchrJCIiIiIiIiIi0pWSaiIiIgNQUZZh+5HVbD+yutdy7s66zS2der+tWN/UaX3Bmxt46KVVbGjcemjFjMG4umzibWxdFe5Eiaq2bBKrpXNyrKlrsqytnbb25HqpV5ZnqCrLUFkeu5V1Xh5eXU5Vt/eVbVmu6uZxne6L/g6vrmCXMTVUV+RnrjYREREREREREZGeKKkmIiIyCMyMUTWVjKqpZNp2w3st29jSFiXbGmPDTXYehvJfyzdQlrFuE1B11eVbJ6V6SGZtlRTrJkFWVb71/VXlZVSUmYZUFBERERERERGRkqGkmoiIyBBTXVHGhDE1TBhTk3ZVREREREREREREJJJJuwIiIiIiIiIiIiIiIiIiQ52SaiIiIiIiIiIiIiIiIiI5KKkmIiIiIiIiIiIiIiIikoOSaiIiIiIiIiIiIiIiIiI5KKkmIiIiIiIiIiIiIiIikoOSaiIiIiIiIiIiIiIiIiI5KKkmIiIiIiIiIiIiIiIikoOSaiIiIiIiIiIiIiIiIiI5KKkmIiIiIiIiIiIiIiIikoOSaiIiIiIiIiIiIiIiIiI5KKkmIiIiIiIiIiIiIiIikoOSaiIiIiIiIiIiIiIiIiI5KKkmIiIiIiIiIiIiIiIikoOSaiIiIiIiIiIiIiIiIiI5KKkmIiIiIiIiIiIiIiIikoOSaiIiIiIiIiIiIiIiIiI5KKkmIiIiIiIiIiIiIiIikoOSaiIiIiIiIiIiIiIiIiI5KKkmIiIiIiIiIiIiIiIikoOSaiIiIiIiIiIiIiIiIiI5KKkmIiIiIiIiIiIiIiIikoOSaiIiIiIiIiIiIiIiIiI5KKkmIiIiIiIiIiIiIiIikoOSaiIiIiIiIiIiIiIiIiI5KKkmIiIiIiIiIiIiIiIikoO5e9p1kASY2Upgadr1KADjgFVpVyIFirt0lGLMoLhLSSnGDIq7lJRizKC4S0kpxgyKu5SUYsyguEtJKcYMiruUlGLMoLhLSSnGvC12dff67u5QUk1Kipk96e7vTLseg01xl45SjBkUd9r1GEylGDMo7rTrMZhKMWZQ3GnXYzCVYsyguNOux2AqxZhBcaddj8FUijGD4k67HoOpFGMGxZ12PQZTKcacNA3/KCIiIiIiIiIiIiIiIpKDkmoiIiIiIiIiIiIiIiIiOSipJqXml2lXICWKu3SUYsyguEtJKcYMiruUlGLMoLhLSSnGDIq7lJRizKC4S0kpxgyKu5SUYsyguEtJKcacKM2pJiIiIiIiIiIiIiIiIpKDeqqJiIiIiIiIiIiIiIiI5KCkmoiIiIiIiIiIiIiIiEgOSqpJUTKzMjN7u5nNNrMfm9kjZrbJzDy6XZB2HfPBzEaa2alm9jMze8zMVptZi5mtMbN/mNlPzWz/tOuZFAvebWZnm9nvzOwpM1tmZpuj4/2qmd1uZp83s1Fp13ewmNlfY+91N7PZadcpKWY2r0tsvd2WpF3ffIje81eY2fNm9lb0fl9qZg+Z2ffM7JC06zhQZnZBP45z/DYn7bonwcz2jr67njaztWbWGv191sx+WQzHuCdmtmf0XfVPM1sfvb9fjj7jj0m7fn2RjzaImR1nZn+M/tcbzWyFmT1sZueYWW0ewui3pOKOvtunm9npZnZR9Lm/fqj+nycY9zAzO8HMLok+z1eYWXMU+7/M7DdmdmSew+mTBGPe18w+a2ZXmdkTZrbEzBqi9/hyM7vHzP7bzLbPc0h9ko//7W6e45ddvtcGvM8E6pTU8Z7Th+/xLbc8h9WX+ubleFv4nvuBhe/4lWbWZOE3y2NmdrGZvTfhUPpTtwHHHD12W9pw8/IfYY91TvRYm9lUM/t+dEzfsvB7fL2ZvWhm15jZ+/IUSr/kIe7JZvaj6L29JvosX2pmN5nZKXkKo18sD+dLrDDaaInEbUFBtNESjLlg2meQaNyF1kbL+7lQG5pttKSOd0G10YYMd9dNt6K7ATcA3svtgrTrmIeY/wtozBF3x+0aoCbtOicQc3Uf43VgBfDBtOs8CK/Jx7qJfXba9Uowvnn9OOZL0q5vwrGPA+b2Ie5n0q5rArFe0I/jHL99M+26DzDuDHAZ0N6HWK8FqtOuc4KxlwOX9CHuPwz1uJNsgwBV0bHubX8vAXsWS9zARTn2MyftWJOOG/g3YEMfP+duB+oLPeZoP2/0MeYNwFnFcKxz7H9mN5//A9rnUIobmNPH4+2AF0vcsf3VAD8H2nLsd20hxwzM7s9xjt2uKuS4Y/v6b6C5D/HeA4wplvc48FWgpQ8xj00x3kTPl1AgbbQk46ZA2mhJxUzhtc+SPNYF00ZL+n+7h+eYyRBroyV8vOf0cT8OeJpxD6VbOSLFqazL+lvAamBaCnUZLNMJDTuAl4G7gWeAVcBo4EjgZMJr8+/AeDN7r7u3p1DXpL0GPAY8CywlfLHXALsBswjHvR64IYr5rrQqmk9mNh64OFrdCAyJK+Py6EM57t80KLUYBGa2HeGH6B7Rpn8BNwMLgAZgLPB2ILWrmxP2B8LnVy4jCQ1ACI3cq/NVoUFyMfDF2PothETy68B44CDCZ1oZ8OHo76mDW8W8+TnwiWi5Bfgt8ADhh8Lbovt2Ak4DqszsJI9+AQxBSbZBribETLSPXwLPEZLs/w68C5gC3GFmB7j7sm2qcTKSirvrfjYAy4Ddt7Fe+ZZE3JOAumh5OXAX8AThgqBa4FDgI4SLiY4D7jazg9w9re+5JN/jq4BHgX8Ai4F1QCUwFTgR2Ifw2vzCzFrd/aptrXQC8vb7wsyGAVcCxtBrw+Uj7k8T3t9DWWJxm1kdcCtwWLTpFUJC43lgPaE9sxvh/3vnbaxvEpKI+V5yt9EhXEj0W2BYtP6bfjxH0hI51mZ2NnBhbNMDwG2E77DRhM+zMwi/2Y8AbjOzQ9y9bRvrPVBJxf114DvRqhPe23cSPs+nEC76nEE25sPdffMA6r2tkj5fUihttCTjLpQ2WlIxF1r7LOn3eKG00fJ6LnQIt9HyFXchtNGGhrSzerrplo8b8DVCg/YUYFK0bTZD5IqCPMV8JdGPtV7KHErnK23OTLveA4w5A+yeo0wZ8NNYzP9Ku955fD3+GMX4FOFKlI6YZ6ddtwRjnEeJXR1DaLzdH8XdCnweyPRSfkLadR7E1+Y/Yu/zu9KuzwBjmUj26vVW4Jgeyu3b5XN877TrnkDs743Fsx7Yv5sywwknpzrKfTTtevcSTyJtEOCDsccsBXbpcn8GuCpWZm6RxH0WIcF8OuEknBGuDu3Yz5y0j3HScQNfBx4CjgfKeiizOyHB3rHfbxdyzNFj9gAsR5nzYvtdA1QVetw97Pv70T5ejd7/Q+Y3S4LHe07sMRPTjmswjzchedTxuO8Clb2UTa0dl8/3eDfPdVxsvwsK/VgTLuiMt88+3kO5SdH/eUe5Ews87j3Itl9bgOO7KVMBXBfbbyojS5Dg+RIKqI2WcNwF0UZLKmYKr32W5LEumDZaknH38Ngh2UZL+HjPiZWZmGZchXRLvQK66TZYN4o/qTa6j+U+H3sd7k+73oP02lQQrtboiHty2nXKQ4wfiGJrA97Z5Utxdtr1SzDOeR1xpV2XQYw5njg6O+36DKUb8HjstTk97foMMJZPxmK5LkfZH8XKfiHtuicQ++2xeD7fS7mdgSayJzB6/aE3lG7b0gYBno495n09lBkWvRYd5d6edqwDjbuH/cyM7WdO2nElHXc/2nDHx/a7NO0483Gse9j3P2L7PiLtWJOOm3CxRGu0j5PoPARyoq9lmnFTBCdstjHuePLo0rRjGIyY+7jfeJLla2nHOdC4gaNi5R/PUTb+e/yitGMdYNw/i5X/US/lagk9H5wwysaIFGJL7HwJBdRGSzLuHh43M/a4OWnEmK+Y+7GfIdE+y/ex7mFfqbfR8hk3Q7iNlvBn2pxYmYlpxlVItwwiUhTcfU0fi86NLb8jH3UZaty9BVgY2zQkJlNNipmNIPTGA7jC3Z9Msz6SHDMz4CvR6iLg8hSrM6SY2R5Ax6S7a4EbU6xOEsbHlhf2WCpYEFseKsNPbBMzy5AdDsuB3/VU1t1fJQwrBbALcEh+a5ceM5sG7B2tLnT3v3RXzsPwSVfGNhXLcKAlpR9tuNsJw84A7BJ9/5eCF2LLxdaGKwd+RRhZ4c/uXujfZbK1c6O/Gwi9HkqemY0hXBAI4YLAQh++G0q0HUcY0rHDNT0VcveNhGEhIcT8wXxWqoc6JHK+pNDaaKV4niipmAutfZbSsU69jZavuId6G60U/7eHGiXVRErPhtjysB5LFZHopO3E2KY3UqpKvvyAMNfQq+jHerE5lDBmOcDvvTjmQEzKx2PL17p7Y2o1ScabseVcc1nE7/9XHuoymMaS/S5a0YcfB/ETUe/LT5WGhGNjy3/NUfaO2PJxeaiLDBEe5t2Jz9NREu04wpw0HYqtDfefhDlJGghXEksRMbNdgcOj1ZvdvSHN+gwh/0Z2Hpg73f21NCuTkFJtx8XnAJyfo2yhtOFynS8p1jZayZ0nIqGYC7B9luSxLqQ2Wn/jLpY2Win+bw8KJdVESs/bY8tLU6vFIIl6+vwv2atmnnH3l1OsUqLM7D2Esc0hDJu2obfyxcTMbjOz5WbWbGarzewZM/uxme2d+9EF4z2x5cfNLGNmZ5rZ/Wa2yswazWypmV1rZsekVstBFl019u+xTWlNipyk24HmaPkkMzu6u0Jmti9h8mAIV0J3e3VsAbEBPLaYr7SLf1f/PUfZZwhX+gPsHn3vSREys/FAfbS6CViZYnUGhZn9B9leyW8CD6dYnURFvR2+Fa1+3d2XpVmfQXalmb1iZk1mttbMXjCzK6N2bTE5lOz33OMAZnaSmd1uZm9E7bjXzOxmMzu1hD6/z4wtF0MbDsJn06poeX8zO7O7QmY2kTAPEcBqwnx7hWxb37NDuQ2X63xJsbbRSuo8USSRmAuwfZZU3IXWRutz3EXWRuvv8S6VNtqAladdAREZdGfFlm9LrRZ5YGbHAdXRag2hh89JwF7RttXAJ1KoWl6YWTVhSAkDbnL3P6VcpcEWv8JxTHTbC/i8mf0G+Fw07EYhe2dsuQG4n62HvNslun3YzK4HPubumyhux5MdZufZYhjy1N1fN7OvApcQhpi408xuAe4jTH49HjgYmBXd/wJhcvuWlKqclLcIE9tXAPVmNsrd1/ZSfnpseUZea5aueJxLeivo7q1m9hrhc6CWbM9lKT7xNtwdxdR7OfqhPiZarSKMMHA82e+8zYTJ1ZsGv3bJi06s/orQbn0SuCLdGg26o2LLlcBI4G3AJ83sNuCj7v5WKjVLVrwdt8LMbiD8NonbkTAU3gcJbdiT3H0VRcrM9iJc+Q8hCfXnFKuTGHdvNLPPANcSzrNdZWazgVuBZcBowtw8ZxA+414DTnL31enUODFvAJOi5enAs72UjbdtppuZeTSZzxCT63xJsbbRivY8US+SirnQ2mf9iruI2mh9irsI22j9fZ+XShttwJRUEykhZnYw2SsDGwknb4vJHGC7brY3E36w/Ze7Lx7UGuXXtwiN+g3AF1Kuy2BaTRhq4++EZIORbdgdHJU5kzCe+XHu3ppGJRMSH5f8F4TjvZbQyHuakIh4D+EHegVwCqHhM+jzFAyy+NW/v0mtFglz90vN7A3g+4Qf3ydEt7iVwPnA74oheRqdbHiM8KMsQxgS6ifdlTWzneg8d8eo/NcwNfHY+nJydTXhPdPx2KF6wka2kZlNJtu7wYH/l2J18uEHwAHdbG8D7gbOc/enB7dKeXUW4fu7DTgrGjqqFGwA7iL02lpGiH9n4JjoBvB+4H4ze7e7r0+llsmJt+O+Q2jHNRLaLo8A7YSr/D9JOOF+KHB7FHszxSk+fPfviilOd7/ezNYAPyacgHwPnUedgDDv0vnAb4rkpOTDZJNqZ5CdQ7ATM6uhc0K5gnAR7Mbuyqelj+dLiq6NVgLnibaSVMyF1j7bxrgLvo3Wz7iLpo3Wz7hLrY02YEqqiZQIM9seuI7ssK/fcPch16DLkxcJX/Yr0q5IUqIhDv8zWj2/SOYi6IvzgCd76J1zoZl9iDCMSg1wJPBV4LuDWL+kxX+0TQdeAg7v8r97tZn9gtAAGgF8wMxOc/c/DmI9B42ZbUe2l2IzhT9sTlc3EHpuXUa4mrWreuC/gFaKJ6F4JdkrHb9nZo+6e6fhdMysDvgdIWncIZVJwAdJXWy5L/MFxnvlDk+4LpIyM6sFbiJ8twH81N2fSLFKg2kpcCfwStoVSUp0gcAPotXLhvqJqAT9mDCKQHcn0S8ys0OB6wk9s98OXAR8ahDrlw9d23GrCO2452Pbf2dmVwDzCN/77wTOIVxgU1TMrJJw8UyHYhn6Me4+4EvApcDu3dxfC3wZKDOzHw7Rnlr9cSXZIdnPNrN73f32eAEzqwB+TXaUiQ4jGEJJtX6cLymqNlopnidKKuZCa5/l4VgXRButP3EXUxutn8e7FNtoA6Y51URKQPRl/yeyJ2hvI3wIFhV3397djfDZNhJ4N/AzYA/g58BjZjall10UBDMrI/wwKQeeoIdeHcXI3R/pbbg7d7+Jzl/u55pZVU/lC0DX7+nZ3TWE3P1xwlWvHb6U11ql6wyyFwXdUkxDJEWfT88QGqzNwEeBHQiJpB2i9cWEoW2vMrMLU6pq0n4H3BstjwD+Zma/NrOPmdlpZnYBYbjLw4D4nJhDfWgVkQGLvvN/D+wZbXqK7EU1RcPdD3R3i9pxdcB+hKu9dyC0WZ8xs/3SrGOCfkr4rFsKfDPlugwad/97DydrOu5/kNCTpSPJcGZ0cquQdW3Hnd0loQaAu78E/Eds0xfzWqv0fAAYGy3/3d17Gyqw4ETzKs0jnGSuBz4H7Epox9UDJwP/IAz5+X3gGjMr6HNy7v4AcHW0Wg7camZ/NLNPRPME/jdhSMgP07kNB0OoHVcq50u6KsW4k4q50NpnA4m7kNto2xB3UbTR+ht3ibbRBqygv8BFJLdo3q0/A++KNj0MnFYEV8X1yIP17v43d/8soYtyGyG5dlf0BVPIvkIYk78V+FQBjNk9qNz998D8aLUjuVqoNsSWX3D33ib//Q2hhxPAu6KePcWoGCe3x8x2BB4lXNX8EvBOd7/G3d9w95bo7zWEK9gXRQ/7bzN7f0pVTkw0pMbJQMeVzZWE4aHmAH8gDHU7gTCmfXxM+DWDV8tB1xBbru6xVNaw2PKGHktJQYlOts4hnIiG8N32Xnfvy5XxBcvdN7r7U+5+HuE7fANh+Jm7o8/KgmVmp5I9nj1dEVyyonbOndFqGXBsitVJQvzzeB3Q2ygCtxGGNQfY0czelrdapSc+9GPRtOFgywnMBwlDeK4GDnD3n7r7K1E7bpW73wgcSBj6E0Kvvc+kU+NEfZpsYi0DnEoYqv6PwIXAboS27aldHjck2nHbcL6kKNpopXieKKmYC619luSxLqQ2Wn/jLpY2Wr7+t4uwjTZgSqqJFLFoiI0byc5B8zjwvkL9cthW7v5XQqMHwpjvH02vNgNjZlOBC6LVS9z9HylWZyibF1veLa1KJGBtbPnvPZYiNHDJJhPLCPPMFRUzO4DsUDqvEebWKxZfB8Z1LPc0z0a0/euxTUUxn6K7r3X39xHmkLueMN9EE+F/4BHg88BBdL6q+Y3Brucgiv/vj+uxVNbY2PLaHktJwYgmSf8F2WG1FgFHunvRDGXdF9GwOx3D8IyigHtim9kYwvA6AHPdvS+TxZeiebHlQm7DQefP42d7m+c3Otn1VGxTwY+uERedbO2Yk6WR0MOjmHyOMMQnwA97msc7Oun+5dimgm/HuXuTu88mzEF0DWFUhc2Ek+1PA18D9gbibds1Q2E+vW08X1LwbbRSPE+UVMyF1j7L57Eeym20/sZdLG20QfjfnhdbLvQ22oBpTjWRIhWNXT4XeG+06WnguBKeTPIO4BPR8kzCsJCF6N8IV7s50GpmX++h3J6x5RPMbOdo+c5oqMBitzq2PKrHUkPffLINonV9KB8vMzL56qQufoXz/xXypMHdiPc4uztH2fj97+qxVAFy91uBW3u638zi85MM2TkLErAAODxanthbQTMrJzu0x0ZCwlkK3xXAJ6PlpcARJTR/ald3AN+JlmemWI+BOoHsnEIre2nDvSe+HCv3mLvflbfaDR3F0oaD7MVOoHbcxwgXfQHc5O5DIrmQoP604x4j9HaqA2aY2Yhi+I0eDQ/2YE/3D7U23ADOlxR0G60UzxMlHHPBtM8G6VgPuTbaNsZd8G20QTrexdRGGzAl1USKUNR4u5Zs1+XngKPdfUgMsZCS+FALhfzhb7G/5/XxMSdFNwg/4EohqTbkrgjcRvG5JvpyciVepi8nbwqGmQ0DTott+k1adcmT+HAZuRq+8WNb6MPZ9tdhseUeT9wUgficO/uR7W3dnb3Jnqh8oZiH7SkVZnYp8Nlo9VXCCZshPQl8nhVbGw6yxzeXw8mevL0MKIWkWrG04UDtuLjZseWiGvox0ud2nLu7ma0nJNUgtOWKNqERM2TacAM8X1KwbbRSPE+UZMyF1D4bxGM9pNpoA4i7oNtog3i8i6mNNmAa/lGkyEQTpv6WMD8NwAvAUe6+uudHlYSpseVVqdVCBkv8R9uC1GoxcLfHlnud/Deay2FGtNpCGHqlmJxM9mTTg+6+MM3K5EH8ZMqEHGV3jS2XzGe7mY0Djo9W1xKGtihW8aFNc41Xf1xs+Y481EUGkZn9kOzwOcsJJ2xeTrFKQ4HacKWlWNpwAA8QeqcA7Bmd9OpWNKTYPrFNhR77FmZ2CNmhEZcC96RYnXzpczsuulCsPrap2yG/i4mZVQEfiVbbyM7BlkZdBnq+pCDbaKV4nijJmAupfTbIx3rItNFK8T0Ogx53MbXRBkxJNZEiEk2YehXZ3hzzGcLjOw+W6HX5RGzT39Kqy0C5+wXubrludP6hcmbsvkvTqvtgMbOPkB3feQPwUIrVGRB3X0p2MvPdzezdvRQ/E6iIlh8qwjHxz4wtF+MVzvGrXj+co2z8/ifzUJeh6iKyk73/xN03pVmZfIqSxk9Hq9PM7L3dlYsmov5UbNN1+a6b5I+Z/S/wn9Hqm4QTNsV2AcG2OCu2XMhtuDl9bMN9O/awb8fuOzutug+WqJ3TcZK6nQKfOzX6nvpztDqSzj3uu3o/2WHiFrt7MZ2sirfh5qTdWydP+tOOO5lsm/05d2/KT5WGlK8BHdMR/MHdl6VRiSTOlxRiG60UzxMlGXMhtc9SONZDoo020LgLtY02mMe72NpoSVBSTaRIxCZM/Wi06SXCl/0b6dUqv8zsbDM7MEeZ4YQJkzuu/HwL+EO+6ybJM7MvmtkBOcqcCPwqtumiaELwQhYfz3uOme3UtYCZ7Q98N7bph3mv1SAys4lkh1bYQBgrvNhcG1v+hpkd2V2haPv5sU3X5LVWg8TMDoyuYu7uviozu5js99uLwP8OWuXSE//R9jMz2yV+Z/Qj6idAx/br3T1+Uk8KSDQnQ8f/9krCD+IXU6xSXpnZbDM7Jmq/9lSm0sx+RHYom2Y6f8dLgTCzj5rZ0TmO9yGEHsgdZf4vrRPvCfs20BotX2pme3QtYGZT6Dzfc9G046KRFE6NVp3iG767Q7wd9wkz+7cwkJoAABJnSURBVPfuCpnZnkD8IseCb8eZ2b7Rb+7u7suY2VeAb0SbVgJpnXxO8nxJwbTRSvQ8UWIxF1L7LKm4C62NVorvcUj0eJdyG21ANKeaFCUzm0TnnkkAe8aWj+hm+I0b3P1pCtd3yU6Y2kIY0/ddvXwudrizgK/2nwlcYmYLgXsJVwiuIgwpUQ/sC3wIGBOVbwU+Wezdv4vYEcBlZjafMGzMPwlD3xlhkugTgINj5e8DLhzkOibO3e81s58BnyEMr/C8mV1JuEKygjBZ7kfJXvF6pbvf3u3OCtdssg2464qwFx7Ar4GPA/sD1cCdZnYzcCfhfT4WOAY4kexFUXcA1w9+VfPi68DBZvYXwryPy4EaYHfCybiJUbnXgA8M5WR5Um0Qd/+Tmf2RcOXhrsBTZvYLwhj5Ywn/9++Kii8HvpxQCNskqbjNbBTZq4E7xIc83Se6YjjuXne/t791TkIScZvZWWQneQe4gnD1+7QcT/+Quw/6UDsJHeu9CSfXXzWzuwhzT60gnJQZE+3vQ3Sep+g/3X1+AiFskxL9fZFU3PsShs1aZmZ/JXyOrSS02XcmfL8dQ/a7/p/AOclEsG0S/Cyfb2bnA98HxgFPmtlVhJEI2gmf458gO7/WXwknyQZdnt7js8jGdm80CsOQkkTc7n5H1G7raKddY2ZnALcSvqNHEIbNOg3ouIjoH4TP+1QkeLw/Dsw2szsI7+tXCb9LpgGnENpyEOYJ/EAa31uRxM6XFFgbLbG4C6iNlkjMhdY+I7ljXWhttFI8FwrJxV1wbbQhw911063oboRki/fzNjvteg8w5nnbELMDE9Ou+wBivrkfcS4ijCucer0H6bWZUyzv7W043u2EkxE1adc5wdgzwI+j2HqL/XKgLO36Jhy7AUtiMR6cdp3yGOtYQqKsL+/z64C6tOucYOy39iHme4FJade1D7Ek1gYhnHi7NsdjXwL2LJa4CQnU/u7ngkKOm87f2f25zSzgmC/tx2PfBD5cLO/xPjzPBUPhvZ3i8b4RGFsMcXfZ39cIJyR7e/x1pNh+zcd7nDCvXEfZ09M+rvmMmzBE9TV9fPy9wHZFEvcVfXjcU8DeKcc7bxvidXo4X0KBtNGSjJsCaaMlFTOF1z5LKu6CaqMl/b+d47kuSPO9PQSO95Boow2Vm3qqiUghOxM4mtBTZ29gMuGkdIYwRNwyQm+ePwO3untzSvWUZHyFcOL9QGAvYDzhit9yYC1hotSHgN94cc1Dgbu3A18ws98RriidSfbKsNeA+4GfuftT6dQwr44gewXkfHcv2Pl0cvHQi/Y4MzsKOB04gHB1WC2wEXiFcAXw1e7+cGoVzY9vAk8QruKeBGxHuDpuOWF8/j968fXAzMnDPCsfMbOrCVeCH0j47NsALCQMhfpLL87em1Lczif0Op9JuEJ2KuE7vQJoIPzvPwPcTugZofd4YfshYQ7QgwjHezvC8a4m9F5ZTPis/78ibcvg7t+LejKdRbjie2fC+/0N4GHgKk+px22+mNlU4NBodS3hZFzRcvfNwBlm9mPgY4QRNCYCw4HNwOvAY4REzB0enc0sApcTfnfPJPRO247we/xNwv/99YTP8ba0KpgPaqNJEVMbrbSUfBttW1nxfI+LiIiIiIiIiIiIiIiI5EcmdxERERERERERERERERGR0qakmoiIiIiIiIiIiIiIiEgOSqqJiIiIiIiIiIiIiIiI5KCkmoiIiIiIiIiIiIiIiEgOSqqJiIiIiIiIiIiIiIiI5KCkmoiIiIiIiIiIiIiIiEgOSqqJiIiIiIiIiIiIiIiI5KCkmoiIiIiIiIiIiIiIiEgOSqqJiIiIiIiIiIiIiIiI5KCkmoiIiIiIiIiIiIiIiEgOSqqJiIiIiEhBMbOJZuYJ3WanHU9/mNmSLvU/rQ+POTBWfk7Cz5/rdvNAni/fzOxEM7sguo1Kuz4iIiIiIjK0KakmIiIiIiJSuP7HzMrSrkQBOxH4VnRTUk1ERERERHpVnnYFRERERERE+mkF8KFe7j8C+EK0fB9weS9ln0qqUimZDpwJ/CqF5/404Vj0ZvlgVERERERERGQwKKkmIiIiIiIFxd03AT0OK9hlGL9X3H1ID0G4jdqBFqAK+JaZ/dbdGwe5Dne6+5JBfk4REREREZHUaPhHERERERGRwtMC/DRa3hn4bIp1ERERERERKQlKqomIiIiISEkyswlm9v/M7Ckze8vMmszsNTO7xcxm55qrzMzmmZmbmUfr5Wb2GTN7yMxWmtlmM1tgZpeZ2YQ8hPA9YEO0fJ6ZDc/Dc+SFme1iZt81s8ej16rZzN4ws7ui17Ayx+PLzexYM7soer1XRPvYEL3mc8zsPb08fk503D4W27y443jGbnNij5nY3fYe9p+zrJktie5fEq1Xm9kXo3jeNLN2M5vXzeMyZnaqmf3RzBab2aYo7hfN7Gdm9o7e6hbtY5SZfdXM7o+9duvN7GUze8TMfmJm7zUzy7UvEREREZFSouEfRURERESk5JjZp4FLgGFd7toxuh0PfNnMPtCXIQ7NbDRwC/DuLndNi25nmtmH3f0vA617B3dfZWYXA98CxgFfAS5Iav/5YmbnEepc1eWu7aLbUcA5Zna8uy/oYTd3ATO72V5B9jX/mJldDZzl7s1J1D1fzGwS4f2zR45yU4Drgb27uXtGdDvLzL7r7t/sYR/7A7cC47vcVQEMByYBBxJ6P44G1vY9EhERERGR4qakmoiIiIiIlJQoofbz2KZbgNsIyYPpwJmExMI7gIfMbB93X5ljt1cREmovAFcDS4HtgY8ABxCSFTea2SHu/mSC4VwEfI6QVPuymV3h7qsS3H+izOwS4OxodS3wB+AJQo+7HYATgcMJSbH7o9f+jW52NQxoAO4B/g4sARqjfewB/BtQS+iJtjb2nB0uJ8zL98Xo+QA+DazoUu6VbQizv6qAGwn1fgi4AXgdqCckGYEtCbVHCceaqOythPdaGbAfMJuQCPuGmbW7+wXxJzKzGuAmsgm1B6J9vEKYp28c8HbgSEKCTkREREREYpRUExERERGRkmFmEwk91ADagNPd/bouZX4EzAXeD+xEmLtsVo5dnwj8Hpjt7i2xfV0OfB84l5A8ucrM9nJ3H3AwgLtvMLMLCcm14cDXgC8nse+kmdkHySa37gY+7O6ruxS7PJb03B64FPhwN7s7H/ibu2/u4bm+RkiaHQJ8wcwuc/fFHfe7+1PAU2Z2Yuxhd/alV2IebB/dvuzul3RXwMwywHWEpFcTcIa7z+1S7Hdm9n3gDkJPtm+Y2Vx3/2eszPsI72mAn7l7j3PxmdmBQLevr4iIiIhIqdKcaiIiIiIiUkq+SHbIx4u6JtQAokTN6cDyaNPJZjYtx34XA5+IJ9SifTnwVUIPIwi9347Zxrr35KfAq9HyZ/M0f1t3upuDLH6b16X8/0R/lwEndpNQA8DdfwFcE62e0l087n5PTwm16P7VZOdLyxB6rg1lN/WUUIucCOwbLZ/bTUINAHd/EziNkDDOAF/qUmRqbPnK3irk7o+6e1OvtRYRERERKTFKqomIiIiISCk5KfrbSujd1S13X09IVgEY8KEc+/2puzf2sC8HLo5tyrWvfome99vRahVhvrIhxcz2AvaMVn/m7htzPOS30d8ywlCE/ebuLwMdQ0cesC37GEQ/znH/GdHf9eROhi0AHo9WuyZwN8WWe52/TUREREREtqbhH0VEREREpCSY2Xhg12j1H+7edf6sru4EvhMt50rK3JPj/ntjy/vnKLst5hCGmJwOzDazH7r7/Dw8T1x3c5DFxed2OzS2XNVl2MXu7BRbflt3BcxsBKEH2vsIPQDHEeZR687OOZ4vTW3AIznKdLx+y4HjzKwv+wTY1cyGxXr13Q04IVH882iett+7+8L+V1tEREREpPQoqSYiIiIiIqVih9jygj6Uj5fZocdSwUu93enuq81sLTAK2LEPz90v7t5qZt8A/kjo3fUd4NSkn6eL/sxBNjG23N+edKO7bjCzwwlz2G3fx32M6OdzDqbVPfVyBDCzOmBstDoDuKmf+x9NNDeau79gZv8POI+QgLwAuMDMlgF/Ax4AbnP3pf18DhERERGRkqDhH0VEREREpFQMjy3nGn4QoKGHx3ZnU477489Z14ey22Iu8HS0fIqZ7dtb4UE2cgCPrYyvRPPb3UY2oTYfuBT4HPARwvCaHbeVUZmyATx/vvU4N1xkIK8ddHn93P1rhGFQH4ttnkCYi+0nhLny/mJm0wf4vCIiIiIiRUc91UREREREpFRsiC33NExgXDz5taHHUkFNH8p0PGdDr6W2kbu7mX0NuJ0wvN/3gOPy8VzbIB7zEe5+3wD2dR4wLFr+LvCNaN66rZhZr/OP5VGSF7DGX7sH3P2wge7Q3W8CbjKzHQlDSx4MzCTMe2fAe4GDzewgd//XQJ9PRERERKRYqKeaiIiIiIiUiuWx5Wl9KB8v83qOslN7u9PMxhKGfuzLvraZu99BGMIP4FgzG3ACJiGvxZYHOr/ZUdHfFcA3e0moDQfGDPC54ppiy5U9lgrGJfWk7r6ObGIt0bnh3P11d/+ju3/J3fcizMl3d3T3SLJzCoqIiIiICEqqiYiIiIhIiXD3FUDHXFF7m1l9joccE1t+PEfZI3Lcf3hs+YkcZQfqvNjy9/L8XH11f2z5mB5L9c120d/F7t7eS7mjyP2bN/54y1F2bWw517x4B+S4v786EqWTzazXBO5AuPtC4BSgLdp0SL6eS0RERESkECmpJiIiIiIipeSG6G85cHZPhaJeTp+NVh24Kcd+P2tmVb3cf05s+cZclRwId/8bcGu0ejBwQj6fr4+eBP4ZLZ9mZnsMYF8d89dNNrNuE2FmVgZ8rQ/7ig+t2OuQoO6+GVgSre5vZt3OjWdmFcBn+vDc/XF1bPl/Et53J1HPuDXRqqaMEBERERGJUVJNRERERERKyY+BzdHyf5nZyV0LmFk18FuyvZFuiHrw9GYycKWZdUpCWPA9QnIL4Fngrm2tfD+cT0gGAnxpEJ6vV9EQjR096CqAv5jZ/r09xsx2N7OfdXNXR0+/erpJjEZJrSuBd/ahaotjy/v2ofwd0d8a4NvdPHc58AvgbX3YV39cTzbuj5jZpWbW4xCUZjbMzGab2Ye7bP+imZ0cvUY9PXYW2eEr/zHQiouIiIiIFBNddSYiIiIiIiXD3ZeY2TnAzwm/h643sz8BfyEM7zcN+DghSQZhLrDPdrevLm4GzgD2MbOrgVcIwxR+BDgoKtMEfKKnOcCS5O7Pmtm1wOnk6IE1WNz9FjP7H+CbwC7AY2Z2J2EOr1cJScCxwB7ATGB3wjCEXXt9/Rg4Olq+2MxmAn8FVhOO30ejv/dFf3ubh+ye2PIPoiFB5wOt0bbX3P25WJnLCe+PSuDLZrYboefhBsK8eh8FZgB/ADoltAbC3dujBPAjwE6EROmpZjaXkPhaB9QRXtd3AkcSjvs3uuxqX+AyYE302v+d8B5vB7YnDM15bMfTAhcmFYOIiIiISDFQUk1EREREREqKu/8iGjbwEqAa+GB06+p54AR3X9mH3Z5J6Dn1buCH3dy/AfiIuz+5bbXeJt8EZhF6hg0J7v4tM1sGXASMICRwju3lIa92s49bzOxCsj3fPhDd4h4GTiPH/HWx5ONHCEnQH3UpcjUwO1b+X2b2WeCXhJFf3hfd4n5FSEYlllSLnntZ1LvvGkLSbAfgi708pA14o+tuor+jCa/PaT08diPwGXe/e9trLCIiIiJSfDT8o4iIiIiIlBx3/zkwHfg+8Ayhl1ozsJzQa+1MYG93X9LH/a0FDgc+B/yN0GuqCVhE6Fm1h7vflmwUOeu0CPj1YD5nX7j7r4Bdga8AdwKvE16rJkIS6AFCYvJIsj0Gu+7ja8B7gduAVUAL4djdC3wKmNnHZCiEHoafAeZF+2rtrbC7/5rQ+/C66DlbonrfBhzv7p8i9PxKnLsvd/ejCD35fkGYp24tIYG2HngB+P/t3DERAkEMhtF/hdBiAz2ooMcGFS4osIILZnLFUtClOg6Y9xRs0n6TvWbOs3vt+t0xc2/nzEu+R+ben5mz35Ockuyr6rLGDAAA8MvGB34eAQAA+DtjjFuSQ5JU1dj2NQAAAKzNpRoAAAAAAAA0RDUAAAAAAABoiGoAAAAAAADQENUAAAAAAACgIaoBAAAAAABAY1TV1m8AAAAAAACAr+ZSDQAAAAAAABqiGgAAAAAAADRENQAAAAAAAGiIagAAAAAAANAQ1QAAAAAAAKAhqgEAAAAAAEBjAVsoec2KRMSFAAAAAElFTkSuQmCC\n","text/plain":["
"]},"metadata":{"tags":[],"needs_background":"light"}},{"output_type":"display_data","data":{"text/plain":["
"]},"metadata":{"tags":[]}}]},{"cell_type":"code","metadata":{"id":"Hxit5I8ZdAc7"},"source":[""],"execution_count":null,"outputs":[]},{"cell_type":"markdown","metadata":{"id":"CyVxCpjKdAab"},"source":["ELECTRODES"]},{"cell_type":"code","metadata":{"colab":{"base_uri":"https://localhost:8080/","height":438},"id":"izYOVwpLdAYG","executionInfo":{"status":"ok","timestamp":1619101987489,"user_tz":-330,"elapsed":3037,"user":{"displayName":"Rohit Garg","photoUrl":"https://lh3.googleusercontent.com/a-/AOh14Gj3Za6nmSY12sPvpCoWOtWQcVR5CMMSPBRGTexH_w=s64","userId":"11252576926243182044"}},"outputId":"da9ff754-d3ac-480b-8092-8fc57da9f967"},"source":["df = pd.read_csv(\"topCommonElectrodeRegressionRanking11.csv\")\n","# plt.plot(df['RMSE'])\n","fig = plt.gcf()\n","fig.set_size_inches(20, 10)\n","# X = pd.DataFrame([x for x in range(1,) ])\n","plt.rcParams.update({'font.size': 30})\n","plt.xlabel('Top N Electrodes')\n","plt.ylabel('RMSE')\n","df.index += 1\n","X = pd.DataFrame([str(x) for x in df.index])\n","plt.plot(X.loc[:,0], df.loc[:,\"RMSE\"])\n","plt.tight_layout()\n","# print(pd.DataFrame(df.index))\n","\n","df = pd.read_csv(\"topCommonElectrodeFSRegressionRankingSelectKBest11.csv\")\n","# plt.plot(df['RMSE'])\n","fig = plt.gcf()\n","fig.set_size_inches(20, 10)\n","# X = pd.DataFrame([x for x in range(1,) ])\n","plt.rcParams.update({'font.size': 30})\n","plt.xlabel('Top N Electrodes')\n","plt.ylabel('RMSE')\n","df.index += 1\n","X = pd.DataFrame([str(x) for x in df.index])\n","plt.plot(X.loc[:,0], df.loc[:,\"RMSE\"])\n","plt.tight_layout()\n","# print(pd.DataFrame(df.index))\n","\n","df = pd.read_csv(\"topCommonElectrodeFSRegressionRankingRandomForest11.csv\")\n","# plt.plot(df['RMSE'])\n","fig = plt.gcf()\n","fig.set_size_inches(20, 10)\n","# X = pd.DataFrame([x for x in range(1,) ])\n","plt.rcParams.update({'font.size': 30})\n","plt.xlabel('Top N Electrodes')\n","plt.ylabel('RMSE')\n","df.index += 1\n","X = pd.DataFrame([str(x) for x in df.index])\n","plt.plot(X.loc[:,0], df.loc[:,\"RMSE\"])\n","plt.tight_layout()\n","# print(pd.DataFrame(df.index))\n","plt.legend([\"Method A\",\"Method B\", \"Method C\"])\n","plt.savefig(\"verification/DREAMER_Combined_Electrodes_Plots1.svg\", bbox_inches='tight', dpi=500)\n","plt.show()\n","plt.clf()\n"],"execution_count":null,"outputs":[{"output_type":"display_data","data":{"image/png":"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\n","text/plain":["
"]},"metadata":{"tags":[],"needs_background":"light"}},{"output_type":"display_data","data":{"text/plain":["
"]},"metadata":{"tags":[]}}]},{"cell_type":"markdown","metadata":{"id":"q3tFbBgcdAV7"},"source":["## Draw plots for valence and arousal"]},{"cell_type":"markdown","metadata":{"id":"gZVM2QgRdATi"},"source":["DREAMER [electrodes]"]},{"cell_type":"code","metadata":{"id":"Ww20l9ZXdARJ","colab":{"base_uri":"https://localhost:8080/","height":715},"executionInfo":{"status":"ok","timestamp":1619276163959,"user_tz":-330,"elapsed":4369,"user":{"displayName":"Rohit Garg","photoUrl":"https://lh3.googleusercontent.com/a-/AOh14Gj3Za6nmSY12sPvpCoWOtWQcVR5CMMSPBRGTexH_w=s64","userId":"11252576926243182044"}},"outputId":"225f3282-be5f-4fcf-a786-c0aade22a712"},"source":["%cd /gdrive/MyDrive/Project_DEAP/4.1.2021/DREAMER/plots/\n","df = pd.read_csv(\"topCommonElectrodeRegressionRanking11.csv\")\n","# plt.plot(df['RMSE'])\n","fig = plt.gcf()\n","fig.set_size_inches(15,10)\n","# X = pd.DataFrame([x for x in range(1,) ])\n","plt.rcParams.update({'font.size': 40})\n","plt.xlabel('Top N Electrodes')\n","plt.ylabel('RMSE')\n","# df.index += 1\n","# X = pd.DataFrame([str(x) for x in df.index])\n","# plt.plot(X.loc[:,0], df.loc[:,\"RMSE\"])\n","plt.plot(df.loc[:,\"RMSE\"])\n","plt.tight_layout()\n","# print(pd.DataFrame(df.index))\n","\n","df = pd.read_csv(\"topCommonElectrodeFSRegressionRankingSelectKBest11.csv\")\n","# plt.plot(df['RMSE'])\n","fig = plt.gcf()\n","fig.set_size_inches(15,10)\n","# X = pd.DataFrame([x for x in range(1,) ])\n","plt.rcParams.update({'font.size': 40})\n","plt.xlabel('Top N Electrodes')\n","plt.ylabel('RMSE')\n","# df.index += 1\n","# X = pd.DataFrame([str(x) for x in df.index])\n","plt.plot(df.loc[:,\"RMSE\"])\n","plt.tight_layout()\n","# print(pd.DataFrame(df.index))\n","\n","df = pd.read_csv(\"topCommonElectrodeFSRegressionRankingRandomForest11.csv\")\n","# plt.plot(df['RMSE'])\n","fig = plt.gcf()\n","fig.set_size_inches(15,10)\n","# X = pd.DataFrame([x for x in range(1,) ])\n","plt.rcParams.update({'font.size': 40})\n","plt.xlabel('Top N Electrodes')\n","plt.ylabel('RMSE')\n","# df.index += 1\n","# X = pd.DataFrame([str(x) for x in df.index])\n","plt.plot(df.loc[:,\"RMSE\"])\n","plt.tight_layout()\n","# print(pd.DataFrame(df.index))\n","\n","%cd /gdrive/MyDrive/Project_DEAP/4.1.2021/DREAMER/arousal_plots/\n","df = pd.read_csv(\"topCommonElectrodeRegressionRanking11.csv\")\n","# plt.plot(df['RMSE'])\n","fig = plt.gcf()\n","fig.set_size_inches(15,10)\n","# X = pd.DataFrame([x for x in range(1,) ])\n","plt.rcParams.update({'font.size': 40})\n","plt.xlabel('Top N Electrodes')\n","plt.ylabel('RMSE')\n","# df.index += 1\n","# X = pd.DataFrame([str(x) for x in df.index])\n","plt.plot(df.loc[:,\"RMSE\"], 'o', color = 'C0')\n","plt.tight_layout()\n","# print(pd.DataFrame(df.index))\n","\n","df = pd.read_csv(\"topCommonElectrodeFSRegressionRankingSelectKBest11.csv\")\n","# plt.plot(df['RMSE'])\n","fig = plt.gcf()\n","fig.set_size_inches(15,10)\n","# X = pd.DataFrame([x for x in range(1,) ])\n","plt.rcParams.update({'font.size': 40})\n","plt.xlabel('Top N Electrodes')\n","plt.ylabel('RMSE')\n","# df.index += 1\n","# X = pd.DataFrame([str(x) for x in df.index])\n","plt.plot(df.loc[:,\"RMSE\"], 'o', color = 'C1')\n","# plt.plot(df.loc[:,\"RMSE\"], color = 'C1')\n","plt.tight_layout()\n","# print(pd.DataFrame(df.index))\n","\n","df = pd.read_csv(\"topCommonElectrodeFSRegressionRankingRandomForest11.csv\")\n","# plt.plot(df['RMSE'])\n","fig = plt.gcf()\n","fig.set_size_inches(15,10)\n","# X = pd.DataFrame([x for x in range(1,) ])\n","plt.rcParams.update({'font.size': 40})\n","plt.xlabel('Top N Electrodes')\n","plt.ylabel('RMSE')\n","# df.index += 1\n","# X = pd.DataFrame([str(x) for x in df.index])\n","plt.plot(df.loc[:,\"RMSE\"], 'o', color = 'C2')\n","# plt.plot(df.loc[:,\"RMSE\"], color = 'C2')\n","plt.tight_layout()\n","# print(pd.DataFrame(df.index))\n","plt.legend([\"Method A(Valence)\",\"Method B(Valence)\", \"Method C(Valence)\",\"Method A(Arousal)\",\"Method B(Arousal)\", \"Method C(Arousal)\"], fontsize='xx-small')\n","plt.savefig(\"/gdrive/MyDrive/Project_DEAP/4.1.2021/VA_plots/DREAMER_Electrodes_Plots.svg\", bbox_inches='tight', dpi=500)\n","plt.show()\n","plt.clf()"],"execution_count":null,"outputs":[{"output_type":"stream","text":["/gdrive/MyDrive/Project_DEAP/4.1.2021/DREAMER/plots\n","/gdrive/MyDrive/Project_DEAP/4.1.2021/DREAMER/arousal_plots\n"],"name":"stdout"},{"output_type":"display_data","data":{"image/png":"iVBORw0KGgoAAAANSUhEUgAAA+8AAAKHCAYAAADwqnmmAAAABHNCSVQICAgIfAhkiAAAAAlwSFlzAAALEgAACxIB0t1+/AAAADh0RVh0U29mdHdhcmUAbWF0cGxvdGxpYiB2ZXJzaW9uMy4yLjIsIGh0dHA6Ly9tYXRwbG90bGliLm9yZy+WH4yJAAAgAElEQVR4nOzdeXhN1/748fdOxElMEUNIEBERJAgZRCVIaioaqsYUjamToTr+6tY89fbqbQ1XBzqIKYZSVUNxVcy3hqBEhqLSmkI0SAQZ1++PfM9pjuRkIHoS+bye5zzPsfdaa3/2Prvqs/caNKUUQgghhBBCCCGEKL0szB2AEEIIIYQQQgghCibJuxBCCCGEEEIIUcpJ8i6EEEIIIYQQQpRykrwLIYQQQgghhBClnCTvQgghhBBCCCFEKVfB3AGIv0etWrWUs7OzucMQQgghhBBCCFGAyMjIG0qp2g9ul+S9nHB2dubYsWPmDkMIIYQQQgghRAE0Tfs9v+3SbV4IIYQQQgghhCjlJHkXQgghhBBCCCFKOUnehRBCCCGEEEKIUk6SdyGEEEIIIYQQopST5F0IIYQQQgghhCjlJHkXQgghhBBCCCFKOUnehRBCCCGEEEKIUk6SdyGEEEIIIYQQopST5F0IIYQQQgghhCjlJHkXQgghhBBCCCFKuQrmDkAIIYQQQojiyM7O5ubNm9y5c4f79++TnZ1t7pCEEMKIpaUlVatWpUaNGuh0uhJpU5J3IYQQQghRZmRmZnLx4kUqVKhAjRo1qFSpEhYWFmiaZu7QhBACAKUUGRkZJCcn88cff+Dk5FQiCbwk70IIIYQQosxISkpCp9Ph4OAgCbsQolTSNI2KFStSq1YtIOfvLQcHh0duV8a8CyGEEEKIMuP27dvUrFlTEnchRJlQrVo1UlJSSqQtSd6FEEIIIUSZkZmZScWKFc0dhhBCFImVlRVZWVkl0pYk70IIIYQQokyRt+5CiLKiJP++kuRdCCGEEEIIIYQo5SR5F0IIIYQQQgghSjlJ3oUQQgghhBBCiFJOknchhBBCCCGEEKKUk+RdCCGEEEIIIYQo5SR5F+anFGSmmTsKIYQQQghhRoGBgWiaRlhYmLlDMZg+fTqapjF8+HCzHP/evXs4OTnh5OREWpp5/71s7mthLgcPHkTTNEaPHm3uUCR5F2aWlQFLe8BPM80diRBCCCHEE0efEGuaRps2bQosm5mZiYODg6F8SSZpYWFhTJ8+nZMnT5ZYm2XJ/PnzDdd18uTJRa7373//m4sXLzJ58mR0Oh0Aw4cPR9M0goKCitRGRkYG9vb2aJrGggULHir+8szf359u3bqxdOlSTp06ZdZYJHkX5mVpBbWawOEv4MY5c0cjhBBCCPHEOnnyZIHJx/bt20lISHgsxw4LC2PGjBnlNnlfunSp4fvy5cvJzs4utM7169eZO3cuTk5OjBgxwrBd/33v3r3Ex8cX2s7WrVtJTEzEysqKIUOGFD94wbRp08jOzubdd981axySvAvze3oKVLCBnUV/CimEEEIIIYquWbNmAAV2Sdfva968+d8QUflx/PhxTp06hZWVFZUrV+bixYv89NNPhdb78ssvuXPnDqGhoVhZWRm2d+zYERcXF5RSLF++vNB2li1bBkBwcDC1atV6+BMpx9q3b0+zZs3YuXMnsbGxZotDkndhflXsoeM78OuPcH63uaMRQgghhHjiDBo0CGtra1atWkVmZmae/UlJSWzevJl69erRpUsXM0T45NK/de/Rowf9+vUz2mZKdnY2S5YsAWDo0KFG+3IPaVi+fDlKKZPt3Lhxg61btwKUu7HqJW3YsGEAfPHFF2aLQZJ3UTq0ew3snGH7+5CV938oQgghhBDi4VWvXp0+ffpw/fp1tm/fnmf/6tWrSU9PZ9iwYVhYFJwiZGRksHjxYgIDA6lZsyY6nY6GDRsycuRIzp49a1R2z549aJrG3r17gZwu3/qx3wWNq79x4wbjxo3DyckJnU6Hk5MTEyZM4NatWybjunHjBhMnTsTDw4NKlSpRtWpVvLy8+OCDD0hNTTVZLzExkbFjx9KgQQOsra1xdnZm/PjxJCUlFXgdiiItLY3w8HAAXnzxRV588UUAvv/+e27fvm2y3oEDB/jjjz9o1qwZbm5uefaHhoZiYWHB+fPnOXDggMl2wsPDycjIoE6dOvTo0QOAS5cusWDBAp555hkaN26MjY0N1apVw9vbm9mzZ5OSkvJQ55qamspHH32En58f1atXx9raGldXVyZMmMCVK1fyrePs7IymaezZs4cLFy4QGhqKg4MDOp0OV1dXpk6dWuBEfampqXz88cf4+/tTo0YNw+/Xu3dvk70SYmNjefnll2ncuDHW1tbY2tri7+/PkiVLyMrKMnmsPn36ADn/rZiNUko+5eDj7e2tSr0zm5SaVk2pI1+aOxIhhBBClFLR0dHmDqFM6dSpkwLUvHnz1LZt2xSg+vXrl6ecj4+PAlRMTIyaMGGCAlRoaGiecomJicrPz08BStM0Vb9+fdW6dWtVuXJlBajKlSurHTt2GMofP35c+fv7q2rVqilANWnSRPn7+xs+c+bMyRPrrFmzVP369ZWVlZVq3bq1cnFxUZqmKUD5+Pio9PT0PHFFRUUpR0dHBagKFSooT09P5e7ubqjn7u6uLl++nKfehQsXVIMGDRSgLCwsVKtWrZSHh4fSNE25uLio119/3eS1KIp169YpQNnZ2am0tDSVlZVlON4XX3xhst6MGTMUoIYPH26yTJcuXRSgRo4cabJMmzZtFKDeeecdw7a3335bAcrGxkY1atRI+fr6KhcXF2VpaWm4VklJSXnamjZtmslrcf78eeXm5qYAZWlpqRo1aqRatmypdDqdAlTt2rXViRMn8tRr2LChAtT8+fOVra2tsrGxUd7e3qp+/foKUIDq06dPvucWFxenXF1dDeUaNWqkfHx8VJ06dQzbHvTNN98oKysrw73aqlUr5eTkZLhPevbsme/9pZRS2dnZytbWVgEqKirK1CXPV3H/3gKOqXxyugqP++GAEEXWPBicO8DuOdCiP9hUN3dEQgghhChjZmw+Q/SVZHOH8dDcHasxLdjjsbTdrVs3HB0d2bx5M0lJSdSoUQOA6Ohojh07hp+fn2FsvCmDBw/m8OHDBAUF8fnnn9O0aVMA0tPTmTlzJnPmzCEkJIS4uDhq1apFmzZtOHDgAIGBgezdu5f333+/0O7bM2fOpHv37nzzzTfUrl0byJmcrVevXhw7dozly5czatQoQ/m0tDT69evHlStXCAgIYM2aNdSrV89wbn379iU6OpqhQ4eye7fxEM1hw4Zx8eJFPD09+e6773BxcQEgJiaG3r178/nnnxf9AudD3z1+0KBBVKxYEcjpBv/Pf/6TpUuX8sorr+Rbb//+/QD4+vqabHvEiBHs2rWLb7/9lv/85z9UqlTJaP/p06c5ceIEYNxlvkePHgQHBxMQEIClpaVh+8WLFxk3bhw//PAD//jHP4rcPfz+/fv07t2bX3/9lQEDBjB//nwcHR0BSElJ4Y033uCbb76hf//+xMTEGI3f13v33XcJDQ1l3rx5VKlSBYB169YxePBgNm3axE8//UTnzp0N5e/evcuzzz7LuXPn8PX15ZtvvqFFixaG/X/88Qdff/210TH27t3L6NGjsbKy4rPPPuOll16iQoWcdPj48eOEhISwbds2Zs2axcyZeVfC0jQNX19fdu3axb59+/DweDz/nRZEus2L0kPToPsHcO8m7PvI3NEIIYQQQjxRLC0tGTp0KOnp6axZs8awXT9RXWFJ9fbt2/npp59wcXFh06ZNhsQdoGLFisyePZs+ffqQlJTEV1999dBx2tnZER4ebkjcATp16mRI2Lds2WJUft26dcTFxWFjY8P69esNiTuAu7u7odt6RESEURfz/fv3c+DAATRNY/Xq1YbEHXIm7QsLCyMjI+Ohz+PKlSvs3LkTwNBdHnK6vAMcPnzY5ORn+lnkc5/Lg/r27YutrS0pKSl89913efbrf1dfX1+jRLNz58506tTJKHEHaNCgAatXr8bKyopVq1YV2IX8weOcOXOGp556ivDwcEPiDlC1alWWLFmCl5cX58+fZ8OGDfm24ebmxhdffGFI3AEGDhxIcHAwkPc3X7JkCWfPnsXR0ZEdO3YYJe4ATk5OzJgxw2jbe++9R3Z2Np988gmvvfaaIXEH8PLyYvXq1Ybl9Ex11def24ULFwq7LI+FvHkXpYtDK/AalrN0nPcIqOVq7oiEEEIIUYY8rrfWT4rQ0FDmzp1LWFgYY8aMISsri5UrV6LT6Rg8eHCBddevXw/kvDmuWrVqvmX69u3Lpk2b2L17NxMnTnyoGENCQvJtv127dixcuJBz54yXF9ZPyDZkyBDq1KmTp563tzdBQUFERESwbds2AgICANi2bRsAXbt2zXeGfX9/f3x9fTl69OhDncfy5cvJysqiSZMmPPXUU4btTZs2pW3bthw5coSlS5fyr3/9K0/d69evAxh6R+THxsaGwYMHs3jxYsLCwowmtsvMzGTVqlUARsvM6SUnJ7N27VoOHTrElStXuHv3Ljm9tcHCwoI7d+5w9uzZQntiwF/3Re432blZWlrSu3dvjh8/zu7du/O9z0aPHp3nYQLk/OY//PBDnt9c/7Di1Vdfxc7OrtAYL126xOHDh9HpdPleD8hJ4Bs2bEh8fDyRkZG0b98+T5maNWsCOfMkmIMk76L0eXoKRG3MWTruhTWFlxdCCCGEEEXi7u5uSEhjYmKIj4/n6tWrDBw4kOrVCx6y+MsvvwCwdu1ak0ud6SeUu3Tp0kPH2KRJk3y36xPzO3fuGG2Pi4sDyPP2NbeWLVsSERFh9KZb/72g7s8eHh4Pnbzr33zrZynPLTQ0lCNHjrBixQo++OCDPInr/fv3AbC2ti7wGCNGjGDx4sVERERw8eJFGjRoAOT0krh27RrW1taEhIQY1dm3bx8DBgwwPCAw5c8//yxwv57+vpg/f36erup6165dA0zfF8X9zc+cOQNg9FCkKDFaWFjQtWtXk+X052wqTv3vce/evSIdt6RJ8i5KH/3Scbum5Swd1/hpc0ckhBBCCPHEGD58OEePHmXZsmWG7r9FWUZMn5jHxcUZEmZT7t69+9DxVa5cOd/t+lnw9W+I9fSzo9etW9dkmw4ODkZlc3/P7229XkH7CnLo0CHi4uLQNC3f5H3w4MG8+eabXL16le3bt9OrVy+j/TVr1uTq1avcvHmzwOP4+fnRvHlzYmJiWL58OZMmTQL+enDQp08fo4cyycnJ9O/fn8TERDp37szEiRNp1aoVdnZ2hrHoTk5OXLx4schDBvT3xalTpwota+q+KO5vnpycM69FYQ+cHozx3r17HDx48KHj1K9AUKtWrSIdt6TJmHdROsnScUIIIYQQj0VISAg6nY5ly5axadMmHBwc6NatW6H19OORw8PDC13pSD9m+++g72KfkJBgsszVq1eNyub+rn8rnJ+C9hVEP1GdUopGjRoZLY+naRo1a9YkPT0d+CvRzk3/0KAob7/13cD1S6MlJSWxefNmo31627ZtIzExkQYNGrB582a6dOmCvb29IXFXShX6wOBB+vvi0KFDhd4Xe/bsKVbbplSrVg2gwOX28ovRzc2tSCt1mXqYpU/e7e3tH/0kHoIk76J0qqCDrrMgMQaOh5k7GiGEEEKIJ4adnR3BwcEkJCSQlpbG0KFD8x1v/KCWLVsCObOYF5emacWuU1T6cdlRUVEmy+hjzj2GW/89OjraZD199+ziuHv3LuvWrQNyxqzXqVMn34/+7e0PP/yQJ0lv06ZNobHpDRs2DEtLS3799VcOHTrE6tWrSU9Pp379+nm6iOt7Wvj6+mJjY5OnraioqDxd1AvzKPfFw9IPkTh06FCRyutjjI+PL/b55aY/R29v74du41FI8i5Kr9xLx927Ze5ohBBCCCGeGGPHjqVz58507tyZkSNHFqnOgAEDgJy3yvo3kEWlTxQfx1hhfZfzVatW5fum/Pjx40RERBiVhZwl0wD++9//5jsM4H//+99DjXffsGEDycnJWFtb89tvv5GQkJDv58qVK9SuXZv09HTDjPh6nTp1AnJmpC9M3bp1DecSFhZmNNZe3+1cT7+cnL4nwoM++qj4Kz7p74tFixaZnKW9pPXv3x+AxYsXF+ntu4uLC15eXqSnp7Nw4cKHOuatW7eIi4vD0tKSDh06PFQbj0qSd1F65V46bu9cc0cjhBBCCPHECAwMZNeuXezatatIM4oDBAcH06VLFxISEujcuTNHjhzJUyYmJoZp06bx/fffG21v3LgxAHv27MkzfvlRDRw4kKZNm3Lv3j0GDhzIlStXDPtiY2N54YUXAAgKCsLf39+wr2PHjrRv357s7GxCQkKMuvrHxsYyfPjwfNckL4y+y/xzzz2Hra2tyXJWVlaG2B7sOt+1a1csLS05fPhwkRJifff4lStXcuzYMSD/eQw6duwI5DyYWLJkiWF7eno6U6ZMYdWqVYb16IvqpZdewsPDg9OnT9OzZ09iYmKM9iuliIyM5K233uLnn38uVtumjBo1Cjc3Ny5fvkz37t3z9FC4ePEi06ZNM9r28ccfY2lpydSpU5k9e7bR/AcAqampbNy40eTDrP3796OUon379oZu+383Sd5F6aZfOu7IYrhxrvDyQgghhBDisVm3bh1BQUGcPHkSPz8/HB0d8fPzw8vLixo1auDu7s7MmTO5ceOGUb2QkBA0TWPdunU0bNiQDh06EBgYyIcffvjIMVWsWJENGzbg4ODAvn37aNiwIW3atKFFixa4u7sTFxdH8+bNWblyZZ66K1asoF69epw4cQJXV1c8PT1p2bIl7u7uZGZm8uqrrxYrlvj4eMO47qJMAqgvc/z4caMJ3xwdHQkODiY5Odkwfr0gwcHB1KpVy9Czwd/fHzc3tzzl2rRpY5hA75VXXqFevXr4+vpib2/P7NmzmTlzpmFyv6KytrZm27ZteHp6snv3btzd3XF2dqZdu3Z4enpSrVo1fHx8mDdv3iN1Wc+tUqVKbN68GRcXFw4fPoyHhweurq60bdsWBwcHnJycmDlzplGdwMBAVqxYQcWKFZkyZQq1atWiVatWtGvXDjc3N2xtbXn++efZuXNnvsfU9454+eWXS+QcHoYk76L0e3oKVLDJWTpOCCGEEEKYjZ2dHbt27WLNmjX06tULpRQnTpwgLi4Oe3t7RowYwffff8+QIUOM6rVv355vv/2WgIAAbt++zcGDB9m7d6/R0m2PwsPDg1OnTvHee+/h6upKbGws8fHxeHp6Mnv2bI4cOYKjo2Oeei4uLkRGRvLaa69Rt25dYmNjSU5OZuzYsRw9erTAddbzExYWhlKKevXqFbgkmV7r1q3x9PQE/npjr/faa68Bf01EVxArKyuja17QgwP92vJNmzYlMTGR8+fP4+XlxcaNGw2z1ReXk5MTR44cYfHixXTu3JnU1FSOHTvGb7/9RqNGjRgzZgw7d+40DAcoCW5ubpw8eZJ//vOf+Pr6kpiYyOnTp7GxsaFPnz75PqwJCQkhJiaGt99+m6ZNm3LhwgUiIyO5ffs2HTp04MMPP2T37t156qWkpLBp0yZq1qxpGCZgDlpJd1sRpZOPj4/Sd6Epkw7Mz1k6bthGWTpOCCGEKMdiYmJo3ry5ucMQ4m8REBDAzz//TFRUVJGHN4iSN3fuXN577z0++ugj3nnnnWLXL+7fW5qmRSqlfB7cLm/eRdkgS8cJIYQQQohy5pNPPiE7O5vp06ebO5RyKyUlhblz5+Li4sLrr79u1lgkeRdlQwUddJstS8cJIYQQQohyo23btixZsgR3d/e/bSZ3YSw+Pp5x48axbNmyYk/mV9IqmPXoQhRHs2f/WjquRT+wsTN3REIIIYQQQjxWo0ePNncI5VrLli0N68Sbm7x5F2WH0dJxxV+DUgghhBBCCCHKKkneRdni0Aq8XpSl44QQQgghhBDliiTvolQo1qoHT0+WpeOEEEIIIYQQ5Yok78KsMrIzmHRgEkvPLC28sF4Ve+j4Dvz6I5zPuw6jEEIIIYQQQjxpJHkXZmVlYUVyWjJfnvqSm/dvFr2iLB0nhBBCCCGEKEckeRdm94b3G9zNvMuSU0uKXkmWjhNCCCGEEEKUI5K8C7NrXL0xfV37siZuDReTLxa9Yu6l4+4V4629EEIIIYQQQpQxkryLUmFs67FYWVix8MTColeSpeOEEEIIIYQQ5YQk76JUqF2pNqEeoWyP387pxNNFryhLxwkhhBBCCCHKAUneRakx3GM4Naxr8EnkJw+5dNykxxecEEIIIYQQQpiRJO+i1KhsVZkxnmM4du0Y+y7tK3pFw9Jx2+HcT48vQCGEEEIIIYQwE0nehVllZmWzaPdZNp64BMDzbs/jXM2ZeZHzyMwuxhJw+qXjdkySpeOEEEIIIYQQTxxJ3oVZWVpoRMQl8sG2WFLTMrGysOINrzc4f/s8m85tKnpDuZeOi1z6+AIWQgghhBCPRWBgIJqmERYWZu5QDKZPn46maQwfPtwsx7937x5OTk44OTmRlpZmlhj0zH0tzOXgwYNomsbo0aPNHYok78K8NE1jUq/mJKaksXjveQCednqa1rVb8+nJT7mbcbfojemXjov4QJaOE0IIIYTgr4RY0zTatGlTYNnMzEwcHBwM5UsySQsLC2P69OmcPHmyxNoszYYPH264jrk/VlZWODg48Oyzz7Jx48ZC2/n3v//NxYsXmTx5MjqdzqjtoKCgIsWSkZGBvb09mqaxYMGCRzqv8sjf359u3bqxdOlSTp06ZdZYJHkXZuflZEewpyNL9v/G1dv30DSNt33eJvFeIiuiVxS9IU2DZ/4pS8cJIYQQQuTj5MmTBSYf27dvJyEh4bEcOywsjBkzZpSb5F3P3t4ef39/w8fd3Z3U1FS2bt3K888/X+Db3OvXrzN37lycnJwYMWKEYbv++969e4mPjy80hq1bt5KYmIiVlRVDhgx55HMqj6ZNm0Z2djbvvvuuWeOQ5F2UCv+ve1OyFfx7x68AtLZvTRenLnwT9Q1/3vuz6A3VbZlr6bizjylaIYQQQoiypVmzZgAFdknX72vevPnfEFH50KNHDw4cOGD4/PLLLyQmJjJr1iwAvv76a3bs2JFv3S+//JI7d+4QGhqKlZWVYXvHjh1xcXFBKcXy5csLjWHZsmUABAcHU6tWrRI4q/Knffv2NGvWjJ07dxIbG2u2OCR5NzNN06w0TfPQNG2gpmmva5o2SdO0CZqmDdU0rYWmaeXiN2pQoxIj/J357sQloi7fBmCC1wTSstL44pcviteYYem4yY8hUiGEEEKIsmfQoEFYW1uzatUqMjPzTu6blJTE5s2bqVevHl26dDFDhOWHTqdj8uTJeHt7A7Bz5848ZbKzs1myZAkAQ4cONdqXe0jD8uXLC1xi+caNG2zduhWg3I1VL2nDhg0D4IsvipmblKBykRgCaJpm8X9J8ghN0z7TNO2opmlpmqapXJ/hf1MsDv+XqG8DbgJRwFpgATAbmA+sAE4DiZqmzdU0rd7fEZs5jQ1yxa5SRWZvjUYphbOtM/3d+rP+1/XE344vekOydJwQQgghhJHq1avTp08frl+/zvbt2/PsX716Nenp6QwbNgwLi4JThIyMDBYvXkxgYCA1a9ZEp9PRsGFDRo4cydmzxj0f9+zZg6Zp7N27F8jp8p17DLiphPLGjRuMGzcOJycndDodTk5OTJgwgVu3bpmM68aNG0ycOBEPDw8qVapE1apV8fLy4oMPPiA1NdVkvcTERMaOHUuDBg2wtrbG2dmZ8ePHk5SUVOB1eFTOzs4ApKen59l34MAB/vjjD5o1a4abm1ue/aGhoVhYWHD+/HkOHDhg8hjh4eFkZGRQp04devToAcClS5dYsGABzzzzDI0bN8bGxoZq1arh7e3N7NmzSUlJeajzSU1N5aOPPsLPz4/q1atjbW2Nq6srEyZM4MqVK/nWcXZ2RtM09uzZw4ULFwgNDcXBwQGdToerqytTp04tcKK+1NRUPv74Y/z9/alRo4bh9+vdu7fJXgmxsbG8/PLLNG7cGGtra2xtbfH392fJkiVkZWWZPFafPn2AnP9WzEYp9UR/gP7AHiAFUIV8hv8N8cwFsooQy4OfW8CQhz2ut7e3KguWHbqgGr63Re08k6CUUirxbqJqu7KtejPizeI1lHFfqfmtlFrkp1RmxmOIVAghhBDmEB0dbe4QypROnTopQM2bN09t27ZNAapfv355yvn4+ChAxcTEqAkTJihAhYaG5imXmJio/Pz8FKA0TVP169dXrVu3VpUrV1aAqly5stqxY4eh/PHjx5W/v7+qVq2aAlSTJk2Uv7+/4TNnzpw8sc6aNUvVr19fWVlZqdatWysXFxelaZoClI+Pj0pPT88TV1RUlHJ0dFSAqlChgvL09FTu7u6Geu7u7ury5ct56l24cEE1aNBAAcrCwkK1atVKeXh4KE3TlIuLi3r99ddNXouChIaGFlgvPT1dubi4KEAtWrQoz/4ZM2YoQA0fPtzkMbp06aIANXLkSJNl2rRpowD1zjvvGLa9/fbbClA2NjaqUaNGytfXV7m4uChLS0vDtUpKSsrT1rRp00ye0/nz55Wbm5sClKWlpWrUqJFq2bKl0ul0ClC1a9dWJ06cyFOvYcOGClDz589Xtra2ysbGRnl7e6v69esb8qA+ffrke25xcXHK1dXVUK5Ro0bKx8dH1alTx7DtQd98842ysrIy3KutWrVSTk5OhvukZ8+e+d5fSimVnZ2tbG1tFaCioqJMXfJ8FffvLeCYyi+XzG/jk/Qh5y12URPkvyN5X2/i2GeBnUA48APwh4lyrzzMcctK8p6emaWe/neECvooQqVnZimllPr85OeqRVgLdeJa3v/gCxT9g1LTqil1eMljiFQIIYQQ5iDJe/HkTt4zMzOVo6Ojqlixovrzzz8NZc6cOaMA5efnp5RSBSbvnTt3VoAKCgpSsbGxhu1paWlq0qRJClA1atRQiYmJ+caxdOnSQmO1srJSzz77rLp+/bph3549ewwPCL766iujevfv31dNmzZVgKdAqYUAACAASURBVAoICFCXLl0yOjd9UhkUFJTnmAEBAQpQnp6e6vz584bt0dHRytXV1ZDolVTynpKSoo4dO6aee+45BSgnJyeVnJycp74+Mf/0009NHmPVqlUKUFWrVlWpqal59p86dcqQQ+RONnft2qX27NmjMjMzjcr/8ccfqnfv3gpQr7zySp72TCXv9+7dUx4eHgpQAwYMMHpIkpycrEaOHKkA1bhx4zyJsT55t7KyUqNHj1YpKSmGfWvXrjUk1bt27TKql5qaqpo0aaIA5evrq06fPm20//fff1dTp0412rZnzx5lYWGhdDqd+uyzz1RGxl8v+CIjIw33yZQpU/Kcu57+d/nss89MlslPSSXvFSi/bgN3AHN2Rz8CfAlsU0rl6UuiaVoQsAhwz7X5M03TIpVSx/6mGP9WVpYWvN+zOaOWHSP88B+EtnfmRfcXWRu3lk8iP2HZM8vQNK1ojeVeOq5lf7Cxe7zBCyGEEML8fpwICafNHcXDq9sSenz4WJq2tLRk6NChzJ07lzVr1jBmzBjgr4nqChsTvX37dn766SdcXFzYtGkTVatWNeyrWLEis2fPJioqik2bNvHVV18xceLEh4rTzs6O8PBwo/Y7derEqFGjWLhwIVu2bGHUqFGGfevWrSMuLg4bGxvWr19PnTp1DPvc3d0JDw/Hx8eHiIgIDhw4QEBAAAD79+/nwIEDaJrG6tWrcXFxMdRr3rw5YWFhhrIPa9myZYYJ43KztLRkzJgxTJkyxeg89fSzyNerZzpV6du3L7a2tty+fZvvvvsuz9h4/e/q6+uLh4eHYXvnzp3zba9BgwasXr2a6tWrs2rVKj799FMsLS0LO0XCwsI4c+YMTz31FOHh4VSo8FeKWbVqVZYsWcLJkyc5fvw4GzZsYPDgwXnacHNz44svvjA63sCBA1m1ahU//PADW7ZsMYp7yZIlnD17FkdHR3bs2IGdnfG/852cnJgxY4bRtvfee4/s7Gw++eQTXnvtNaN9Xl5erF69Gh8fHxYsWMCkSZMMS/Pl5ujoCMCFCxcKvS6PQ3kZ834P+B+wEBgGNAPsgK/MFM8uoK1Syk8p9VV+iTuAUioC8AMic222AJ7oddCebmZP+8Y1mb/rV27fy6CSVSXGth7Liesn2H1xd9EbkqXjhBBCCCGMhIaGAn8ldllZWaxcuRKdTpdvUpXb+vXrgZwJ1PJLOCEnoQTYvbsY/2Z7QEhISL7tt2vXDoBz584ZbddPyDZkyBCjxF3P29vbsCb6tm3bDNv137t27ZrvDPv+/v74+vo+5FnkeHCpOF9fX+rUqUNWVharV682Ofv/9evXAahRo4bJtm1sbAy/2YPtZGZmsmrVKgCjZeb0kpOT+fLLLxkxYgTdu3enQ4cOBAQE0K1bNywsLLhz506e+QtM0d8XL730klHirmdpaUnv3r0B0/fF6NGj831QYOo3/+677wB49dVX8yTu+bl06RKHDx9Gp9Plez0gJ4Fv2LAhycnJREZG5lumZs2aQM48CeZQHt68zwHeUUrlmVazyG9wS9ZbSqk/ilpYKXVH07Sh5Exqp7+jO2ma5mgq6S/rNE1jUq/mPPufA3wacY73ezbnOdfnWBG9gvmR8+lYvyNWFlaFNwTGS8f5jIBaTR5v8EIIIYQwr8f01vpJ4e7ujq+vL0ePHiUmJob4+HiuXr3KwIEDqV69eoF1f/nlFwDWrl3LTz/lPymwfkK5S5cuPXSMTZrk/+81fWJ+584do+1xcXEAtGjRwmSbLVu2JCIiwmiZL/333G+lH+Th4cHRo0eLFng+evTokW+Cvn//foYMGcI//vEP7t27l+ct8f379wGwtrYusP0RI0awePFiIiIiuHjxIg0aNAByeklcu3YNa2trQkJCjOrs27ePAQMGGB4QmPLnn0Vbrll/X8yfP5+vv/463zLXrl0DTN8Xxf3Nz5w5A8BTTz1VrBgtLCzo2rWryXL6czYVp/73uHfvXpGOW9Ke+ORdKWWexyImFCdxz1UnVtO0fUDQ/23SgA7kzFD/RPJwtKW/V33CDsYzrF1DGtSoxJvebzJ+93g2nt3IwKYDi97Y05Mh6rucpeNeeGIvmRBCCCFEkQwfPpyjR4+ybNkyQ/ffoiwjpk/M4+LiDAmzKXfv3n3o+CpXrpzvdv0s+DlDgv+inx29bt26Jtt0cHAwKpv7e35v6/UK2vcoOnTowMKFC+nbty9z585l3Lhx1K5d27C/Zs2aXL16lZs3bxbYjp+fH82bNycmJobly5czadIk4K838X369DF6KJOcnEz//v1JTEykc+fOTJw4kVatWmFnZ2dYS97JyYmLFy+SkZFRpHPR3xenTp0qtKyp+6K4v3lycjJAoQ+cHozx3r17HDx48KHj1K9AUKtWrSIdt6SVl27zT4JfHvizo1mi+Bu9070plhYaH27PeSraqX4nvOt48+nJT0nNML3cRx5V7KHTu7J0nBBCCCEEOd3SdTody5YtY9OmTTg4ONCtW7dC61WpUgXIWX4sv8m0cn/0Y7b/Dvou9gkJCSbLXL161ahs7u/6t8L5KWjfo+rQoQOQ85Zd/2ZYT//QoChvv/XdwPVLoyUlJbF582ajfXrbtm0jMTGRBg0asHnzZrp06YK9vb0hcVdKFfrA4EH6++LQoUOF3hd79uwpVtumVKtWDYDbt28XK0Y3N7ciTfZt6mGWPnm3t7d/9JN4CJK8lx0PdvsvYr/xsqtONWte7ujC1lNXifz9Jpqm8bb32yTdT2LZmbwTfxTI71Wwc4YdkyArzwgKIYQQQohyw87OjuDgYBISEkhLS2Po0KFFmpisZcuWAJw+XfwJAR/ncNVmzZoBEBUVZbKMPmZ92dzfo6OjTdbTd89+HLKzsw3fH0zS27RpAxQcm96wYcOwtLTk119/5dChQ6xevZr09HTq16+fp4u4vqeFr68vNjY2edqKiorK00W9MI9yXzws/RCJQ4cOFam8Psb4+Phin19u+nP09vZ+6DYehSTvZUfjB/78+B4DliKvdHLBvqqO2VujUUrRsnZLujt3J+xMGIl3izEiooIOus2GxBiIXPr4AhZCCCGEKAPGjh1L586d6dy5MyNHjixSnQEDBgCwdOlSwxvIotInio9jrHCvXr0AWLVqVb5vyo8fP05ERIRRWcgZjw7w3//+N99hAP/73/8eabx7Yfbv32/43rix8T/1O3XqBMDhw4cLbadu3bqGcwkLCzN0mR82bJih27lepUqVgL96Ijzoo4+KP8mz/r5YtGgRaWlpxa7/MPr37w/A4sWLi/T23cXFBS8vL9LT01m4cOFDHfPWrVvExcVhaWlp6DXxd5PkvQzQNK0S8ODMCk/kUnEPqlSxAu90a8qJP26x5VTOXzIT2kwgIzuDz375rHiN5V467l7xugMJIYQQQjxJAgMD2bVrF7t27TJ6G12Q4OBgunTpQkJCAp07d+bIkSN5ysTExDBt2jS+//57o+365HTPnj15xi8/qoEDB9K0aVPu3bvHwIEDuXLlrzmdY2NjeeGFFwAICgrC39/fsK9jx460b9+e7OxsQkJCjLr6x8bGMnz4cEN38pK2d+9eXn/9dSDnrfCDb3K7du2KpaUlhw8fLlJCrO8ev3LlSo4dy0kT8uv63bFjRyDnwcSSJUsM29PT05kyZQqrVq2iYsWKxTqXl156CQ8PD06fPk3Pnj2JiYkx2q+UIjIykrfeeouff/65WG2bMmrUKNzc3Lh8+TLdu3fP00Ph4sWLTJs2zWjbxx9/jKWlJVOnTmX27NlG8x8ApKamsnHjRpMPs/bv349Sivbt2xu67f/dJHkvG0YAVXL9+YJS6vH14Sll+nnXp7lDNf61PZb7GVk0qNaAQU0H8d3Z7/jt1m9Fb8ho6bi5jy9gIYQQQogn1Lp16wgKCuLkyZP4+fnh6OiIn58fXl5e1KhRA3d3d2bOnMmNGzeM6oWEhKBpGuvWraNhw4Z06NCBwMBAPvzw0VcHqFixIhs2bMDBwYF9+/bRsGFD2rRpQ4sWLXB3dycuLo7mzZuzcuXKPHVXrFhBvXr1OHHiBK6urnh6etKyZUvc3d3JzMzk1VdffaTYfvzxRwICAgyftm3bUrduXQIDA7l8+TL16tVj7dq1eYYVODo6EhwcTHJysmH8ekGCg4OpVauWoWeDv78/bm5uecq1adOGYcOGAfDKK69Qr149fH19sbe3Z/bs2cycOdMwuV9RWVtbs23bNjw9Pdm9ezfu7u44OzvTrl07PD09qVatGj4+PsybN++RuqznVqlSJTZv3oyLiwuHDx/Gw8MDV1dX2rZti4ODA05OTsycOdOoTmBgICtWrKBixYpMmTKFWrVq0apVK9q1a4ebmxu2trY8//zz7Ny5M99jhoeHA/Dyyy+XyDk8DEneSzlN0+yBGQ9snm+OWMzF0kJjcq/mXLp5j2WH4gF4udXLVKpQiXnH5xWvMcPScUvgRtHWrhRCCCGEEDns7OzYtWsXa9asoVevXiilOHHiBHFxcdjb2zNixAi+//57hgwZYlSvffv2fPvttwQEBHD79m0OHjzI3r17jZZuexQeHh6cOnWK9957D1dXV2JjY4mPj8fT05PZs2dz5MgRHB3zzvfs4uJCZGQkr732GnXr1iU2Npbk5GTGjh3L0aNHC1xnvSiuX7/OwYMHDZ/IyEjS0tLw8/Nj9uzZnDlzJt815gFee+014K+J6ApiZWVldM0LWj1g6dKl/Otf/6Jp06YkJiZy/vx5vLy82Lhxo2G2+uJycnLiyJEjLF68mM6dO5OamsqxY8f47bffaNSoEWPGjGHnzp2G4QAlwc3NjZMnT/LPf/4TX19fEhMTOX36NDY2NvTp0yffhzUhISHExMTw9ttv07RpUy5cuEBkZCS3b9+mQ4cOfPjhh/muRZ+SksKmTZuoWbOmYZiAOWgl3W2lLNE0bTqQuz/FCKVUmHmiyUvLeQS3BeiZa3Ms0EopVbS1G/6Pj4+P0nehKatGhh3laHwSe98Nokblinx1+isWHF/A0u5L8anrU/SG7lyHhV7g7C9LxwkhhBBlTExMjMlkR4gnTUBAAD///DNRUVFFHt4gSt7cuXN57733+Oijj3jnnXeKXb+4f29pmhaplMqT4Mib99JtKsaJeyY5DxiKlLhrmvaypmnHNE07lphYqpa7fyjv92zG3fQsFuz6FYAhzYdgX8meTyI/Kd7YKVk6TgghhBBClAGffPIJ2dnZTJ8+3dyhlFspKSnMnTsXFxcXwzwF5iLJeymladpgjHsFALyvlCryLA9KqSVKKR+llE/t2rVLNkAzcLWvSkjbBqw8/Afnrt/BpoIN41qP4/SN0+z8Pf+xKSYZlo57X5aOE0IIIYQQpVLbtm1ZsmQJ7u7uf9tM7sJYfHw848aNY9myZcWezK+kSfJeCmma9jSwDMg9c8VnSqnir93whHmjixuVrCz58MecWSx7N+6Na3VXFhxfQEZWMUYSGJaOi5Wl44QQQgghRKk1evRopk6dik6nM3co5VLLli2ZPn06AQEB5g5FkvfSRtM0b+B7IPdjndXAePNEVLrUqqJjTJAru2Kuc+j8DSwtLHnL+y0uplxk3a/riteYLB0nhBBCCCGEKCMkeS9FNE1rCvwIVM21eRsQqpTKNk9Upc8If2fqVbdh9pYYsrIVAfUC8Kvrx+JfFpOSnlJ4A3qydJwQQgghhBCijJDkvZTQNM0J+C+Qe3D6fqB/cWeWf9JZW1ny/55pSvTVZL47fglN03jT501upt1kaVQxu8DL0nFCCCGEEEKIMkCS91JA07Q6wC6gQa7Nx4FnlVL3zBNV6dbb05HWDarz751x3E3PxKOmBz0b9WR59HISUhOK19jTU6CCDeyc/HiCFUIIIYQQQohHJMm7mWmaVh3YCTTJtTkW6K6USjZPVKWfpmlMebY515LT+HLfBQDGtxlPtsrms5OfFa+xKrVl6TghhBBCCCFEqSbJuxlpmlYJ2Aq0yrU5HuiqlLphlqDKEO+GNejZsi6L953nevJ96letT0izEDad38SvN38tXmOydJwQQgghhBCiFJPk3Uw0TasIbATa59qcAHRRSl0yT1Rlz3vPNCMjK5uPd+Yk6y+3epnKVpWZHzm/eA3J0nFCCCGEEEKIUkySdzPQNM0CWAl0y7U5iZw37ufNE1XZ1LBmZUKfcmZd5EWiryRjq7PlpZYvsf/yfg5fPVy8xmTpOCGEEEIIIUQpJcn7I9I0LVDTNJXrE1+Eal8AA3L9OQV4RikV9ViCfMKNf7oJtjZWfLAtBqUULzR/AYfKDnwS+QnZxVlhT5aOE0IIIYQQQpRS5SJ51zTNOb8PUP2BorVMlK1bgrHMAV7KtSkbGA8kmorTxKdWScVU1tlWsmJC5yYcOHeDPXGJ6Cx1jG8znug/o9l+YXvxGpOl44QQQgghhBClULlI3oELJj4THij3kYlya0owliEP/NkCCCsgRlOff5dgTGXeEL+GNKpVmTnbYsjMyqaXSy+a2jVl4YmFpGelF68x/dJxOyY9nmCFEEIIIYQQopjKS/IunnAVK1gwsUczzl2/w5qjF7HQLHjL5y0u37nMmthiPnvRLx13dgec2/V4AhZCCCGEEEKIYpDkXTwxurnXwa9RDeb991dS7mfQ3rE97R3bs/jUYm6n3S5eY36vgl2jnLfvsnScEEIIIYQQwszKRfKulNIe8RNYQNt7HijrXEgsziUQj6aUGl7Cl6nM0zSNyb3c+TM1nc/25Eza/6b3m6Skp/B11NfFa0yWjhNCCCGE+FsFBgaiaRphYWHmDsVg+vTpaJrG8OHDzRZD//79sbS0JCrKvHNb79mzB03TcHZ2Nmscf7eUlBRq1qxJ8+bNycw070u9cpG8i/KjZX1bnm9Tj68PXODSzbs0q9GM4MbBrIpexdU7V4vXWLNe/7d03BxZOk4IIYQQZZI+IdY0jTZt2hRYNjMzEwcHB0P5kkxYw8LCmD59OidPniyxNsuKy5cvM23aNPz9/albty4VK1bE1taWVq1a8corr7Bnzx6Tdffv38+GDRsYNGgQLVq0AHKupaZpVK1aldTU1CLF8Nxzz6FpGn379i2JUypXqlatyjvvvENsbCxffPGFWWOR5F08cd7p3hQN+GhHHADj24wHYNHJRcVrSL903P3bsnScEEIIIcq8kydPcurUKZP7t2/fTkJCwmM5dlhYGDNmzCh3yfuHH35I48aNmTlzJocOHUKn09G6dWvq1avHuXPnWLJkCUFBQXTq1Cnf+m+//TaapjFt2jTDtgEDBlClShXu3LnD+vXrC40hMTGRbdu2ATBixIiSObFyZvz48djZ2TFjxgxSUlLMFock7+KJ41jdhpc6uLDp5BVOXrxF3cp1Geo+lM3nNxObFFu8xnIvHZf46+MJWAghhBDiMWvWrBlAgV3S9fuaN2/+N0T05Bs/fjz/+Mc/yMjIYMKECfz+++/8/vvvHDlyhOjoaJKSkvj+++/x9fVl3759eeofPHiQo0ePEhAQQNOmTQ3bK1euzIABAwBYtmxZoXGEh4eTkZFBnTp16NmzZ8mdYDlSpUoVBg0axI0bN1i1apXZ4pDkXTyRXg1sTK0qOmZviUYpxaiWo6imq8a8yHnFbyxocs7ScTsnl3ygQgghhBB/g0GDBmFtbc2qVavyHbeblJTE5s2bqVevHl26dDFDhE+WdevWsWhRTq/PlStXMn/+fJycnIzKWFtb06dPHw4fPsycOXPytPH5558DMGzYsDz79G/Q9+zZw++//15gLPqHMkOGDKFChQrFPheRQ/87LF682GwxSPIunkhVdBV4u5sbx36/yfaoBKpVrMYrrV7h0JVDHLp8qJiNydJxQgghhCjbqlevTp8+fbh+/Trbt2/Ps3/16tWkp6czbNgwLCwKThEyMjJYvHgxgYGB1KxZE51OR8OGDRk5ciRnz541Kquf5Gzv3r1ATtKpH1Nf0Lj6GzduMG7cOJycnNDpdDg5OTFhwgRu3bplMq4bN24wceJEPDw8qFSpElWrVsXLy4sPPvigwLHhiYmJjB07lgYNGmBtbY2zszPjx48nKSmpwOtgSnZ2NlOnTgXgxRdfJCQkpMDymqbx/vvvG21LS0tjw4YNAPTu3TtPnQ4dOuDq6opSiuXLl5ts+9SpU4ahCvqE/86dO6xcuZKQkBCaN2+Ora0tNjY2NGnShDFjxnDhwoWin2wuSinWrl1Ljx49qF27NhUrVsTBwYFBgwYRGRmZb53hw4ejaRrTp08nNTWVSZMm4erqirW1NXXr1mXEiBFcvny5wGOuW7eOZ599lrp166LT6XBwcKBjx458/PHH3L6dd8WppKQkpkyZQuvWralatSqVKlXCw8ODyZMnF3h/tW/fntq1a3Py5Emio6OLf4FKglJKPuXg4+3trcqbzKxs1e2Tvarj3N0qLSNLpWWmqe7ru6t+m/qprOys4jWWcV+p+Z5KLWqrVGbG4wlYCCGEEIWKjo42dwhlSqdOnRSg5s2bp7Zt26YA1a9fvzzlfHx8FKBiYmLUhAkTFKBCQ0PzlEtMTFR+fn4KUJqmqfr166vWrVurypUrK0BVrlxZ7dixw1D++PHjyt/fX1WrVk0BqkmTJsrf39/wmTNnTp5YZ82aperXr6+srKxU69atlYuLi9I0TQHKx8dHpaen54krKipKOTo6KkBVqFBBeXp6Knd3d0M9d3d3dfny5Tz1Lly4oBo0aKAAZWFhoVq1aqU8PDyUpmnKxcVFvf766yavhSlHjhxRgAJUZGRkkevltnfvXgUoZ2dnk2Vmz56tANW4cWOTZd58803DddPbvHmz4TrVq1dPeXt7q2bNmikbGxsFKFtbW3X48OE8bUVERChANWzYMM++u3fvqmeffdZw3nXr1lVt2rRRtra2hmMtW7YsT73Q0FAFqDfffFO1atVKWVhYKA8PD9WsWTNlaWlpuAY3b97MU/fOnTuqR48ehmPa29srX19f5ezsbKgbERFhVOfo0aOqTp06ClBWVlbKzc1NNW/eXFWoUMFwLX///XeT1zM4OFgBatGiRSbL5Ke4f28Bx1Q+OZ30mxBPLEsLjfd7NSf0myMs/188ozu4MMFrAv9v3/9j629bCW4cXPTG9EvHrR2Ss3Rc25ceW9xCCCGEeHj/OvKv4s9xU4o0q9GM99q+91ja7tatG46OjmzevJmkpCRq1KgBQHR0NMeOHcPPz88wNt6UwYMHc/jwYYKCgvj8888NY7HT09OZOXMmc+bMISQkhLi4OGrVqkWbNm04cOAAgYGB7N27l/fff7/QWexnzpxJ9+7d+eabb6hduzYAe/fupVevXhw7dozly5czatQoQ/m0tDT69evHlStXCAgIYM2aNdSrV89wbn379iU6OpqhQ4eye/duo2MNGzaMixcv4unpyXfffYeLiwsAMTEx9O7d29B1vTgOHDgAgK2tbaEz/Juyf/9+AHx9fU2WefHFF5k6dSrnz59n//79dOjQwWh/ZmamYXx27mvepEkT1q9fT/fu3alSpYphe0pKCh999BGzZs1i+PDhnDlzBk3TihTvuHHj2LJlC56ennz55ZeGuJVSfPrpp7zxxhu89NJL+Pr65junwqJFi/Dy8uL8+fOGpehOnz5N9+7diY+P5+OPP2bWrFlGdV5++WV+/PFHateuzdKlS+nZs6ch3pSUFFatWkWdOnUM5RMTEwkODubatWuMHTuWWbNmYWdnB8C1a9cYNWoUW7duZejQofnOQQDg5+fH5s2b2bdvH2PHji3StSlJ0m1ePNE6udWmo1tt/rP7HLfuptPduTseNT1YeGIhaVlpxWtMlo4TQgghRBlmaWnJ0KFDSU9PZ82aNYbt+jHRhSXV27dv56effsLFxYVNmzYZTaJWsWJFZs+eTZ8+fUhKSuKrr7566Djt7OwIDw83JO4AnTp1MiTsW7ZsMSq/bt064uLisLGxYf369YbEHcDd3Z3w8HAAIiIiDIk15CTIBw4cQNM0Vq9ebUjcIWfSvrCwMDIyMood/6VLlwBo1KhRkZPfB8XHxwMYncuDGjRoYJifIL+J63788UeuX7+OTqfjhRdeMGxv2rQp/fr1M0rcIWdJtJkzZ+Lv709MTAxHjhwpUqwxMTEsXboUOzs7tmzZYvTAQdM0xo0bx7hx40hPT2fBggX5tmFhYcHatWuN1pBv2bIl7777LpD3Nz958iTh4eFomsYPP/xAr169jK511apVefXVV40eFHz88cckJCQwYMAAFi1aZEjcAerUqcOaNWuoX78++/fv5+eff843TkdHR4CHHlrwqOTNu3jiTerZnB4L9rHgp7NMC/bgLe+3GLVzFOEx4YxoUYzlMvRLxy3umLN03DP/fHxBCyGEEOKhPK631k+K0NBQ5s6dS1hYGGPGjCErK4uVK1ei0+kYPHhwgXX1y5INHTqUqlWr5lumb9++bNq0id27dzNx4sSHijEkJCTf9tu1a8fChQs5d+6c0fatW7cCOROy5X7Tquft7U1QUBARERFs27aNgIAAAMPyaV27ds33bbC/vz++vr4cPXq0WPEnJycD5EmOi+P69esAht4RpowYMYKdO3fy7bffsnDhQipVqmTYp38o06dPH6NEFSArK4stW7awa9cufvvtN1JSUsjOzgYwzFtw4sQJ/Pz8Co11w4YNKKUIDg6mfv36+Zbp27cvCxYsyNPzQe+ZZ56hYcOGeba3a9cOIM9v/t133wEQFBRkKFMY/f372muv5bu/SpUqdOnShbCwMHbv3p1vuzVr1gRy3uKbgyTv4onXtG5VBvk6seJ/v/PiU860dWhLx/od+fLUl/R17Ut16+pFbyz30nHeI6C22+MLXAghhBCihLm7uxsS0piYGOLj47l69SoDBw6kevWC/030yy+/ALB27Vp++umnfMvoJ/zSv31+GE2aNMl3uz4xv3PnjtH2sJ3LJwAAIABJREFUuLg4AFq0aGGyzZYtWxIREUFs7F9DKvTfPTw8TNbz8PAodvJerVq1fOMsjvv37wM5M9IX5LnnnqN69ercunWLjRs3MmTIECBnUjb92+oH13a/evUqvXr14sSJEwW2/eeffxYpVv19sXfvXsODkQfpz8fUfVHc3/zMmTMAPPXUU0WKMTU1lfPnzwMwceJErKys8i2nn7nfVJz63+PevXtFOm5Jk+RdlAtvdXXjh5OX+fDHGBYP8+ENrzfov7k/X57+knd93y1eY0GT4fSGnKXjhqx7PAELIYQQQjwmw4cP5+jRoyxbtszQ/bewLvPwV2IeFxdnSJhNuXv37kPHV7ly5Xy362fBz5nP6y8pKSkA1K1b12SbDg4ORmVzf8/vbb1eQftM0Xd1j4+PRyn1UF3n9W94b94seKimtbU1ISEhfP7554SFhRmS9/DwcNLT03F0dKRr165GdUaMGMGJEydwcXFhzpw5tG/fnjp16qDT6YCcsfQrVqwo8pAB/X2hX8e+IKaS3sJ+8wfpezcU9sDpwRiBIg0HMHX/6lcgqFWrVpGOW9JkzLsoF2pX1fFaYGN2nLnG4d/+pIldE55zfY7Vsau5lFLMJ8OydJwQQgghyrCQkBB0Oh3Lli1j06ZNODg40K1bt0Lr6buBh4eHF7rSkX7M9t9B38U+ISHBZJmrV68alc39/dq1aybrFbTPFP3b51u3bhX6dtsU/UODorz91r9Z3717t+GNsX4M/IsvvoilpaWhbEJCAjt27ADghx9+YPDgwYbl+PSK+sZdT39ffPDBB0VaBask6Hs35LcUXEExAly5cqXQGPVDDh6kT97t7e0f7QQekiTvotwY3cEFR1tr5myLITtbMcZzDJaaJf858Z/iN+b3Ktg1gh2TICuz5IMVQgghhHhM7OzsCA4OJiEhgbS0NIYOHWqU4JnSsmVLIGcW8OJ62InbikI/Q35UVJTJMvqYc8+mr/9e0Jrd+u7ZxeHz/9m787Cqyu2B498NMooD4gQoIA4MioiAA4NCDuWUZTmQkENWmqnVrevYFc28Zl5v+SsrqysOKGleNU3NIQcgRRzQZHIkEUREUkQRQc7vD+45eWSePCjr8zznifbe77vX2SC6zjssDw/NZn4lbdBWFvUu9eWpJ+7p6UnHjh0pKChg9erVxMbGcuzYMaDojAr1TIsmTZoUu1wgPz9f07a8qvJzUVnqJRK//fZbua5v1KgRrVu3BqoWp7qtu7t7pfuoCkneRZ1hbKDPB885cPrKLbaeSqFF/RYEOQex49IOYm9U8BezunTc9YTC0nFCCCGEEE+QyZMn06dPH/r06cP48ePL1Wb48OEArFy5UjMCWV4mJiZAzawVHjRoEAChoaHFjpSfOHGC/fv3a10LMGDAAAD27NlT7DKAw4cPV3i9OxRO9Q4ODgZg9erVrF+/vtTrVSoV//yn9kbIvXv3BuD48ePk55c9UKQefV+1apVm1Lhnz55aFQEAzYZ2WVlZxU4NX716tWazvPJ6+eWXURSFzZs3c/HixQq1rayXXnoJKKwgUN5d8dU/v0uXLq30DAD1LvR+fn6Val9VkryLOmWoqzWdWzXi012J3Mt7wPhO4zE3MmfpsUr8IZbScUIIIYR4Qvn5+bF371727t1bZm13tSFDhtC3b1/S0tLo06dPsUlTfHw8c+fOZcuWLVrH27ZtC8CBAweqbeq02ogRI3BwcCAnJ4cRI0aQmpqqOZeQkKApk+bv74+3t7fmXK9evfDy8qKgoICAgACtqf4JCQmMHTu2xI3NyjJq1CgmTpwIFO7O/+6775KcnKx1TW5uLtu3b6dnz57MmjVL65ytrS1OTk7k5OSU6wOEwMBA6tWrx9mzZ/nyyy+BohvVQeEGfE2bNiU/P5+3335bs5EcFO7GPmXKlDI3yXtU586dGT9+PPfu3aNPnz7s3r27yDVJSUksWbKEFStWVKjv0u45evRoVCoVzz//PDt37tQ6f/v2bb755hvi4+M1x6ZPn46lpSW//PILAQEBRb4fDx48IDw8nAkTJnD58uUi9/zzzz85c+YMpqam9OrVq1reR0VJ8i7qFD09hdkDnUi9dY/vIy5hZmjGRNeJHE07SkRKRNkdPExR4LlFcO8WHPikZgIWQgghhKhFNmzYgL+/PzExMXTv3h0rKyu6d+9O165dadKkCc7OzsyfP5+MjAytdgEBASiKwoYNG7C1tcXX1xc/Pz8WLVpU5ZgMDQ3ZtGkTlpaWHDp0CFtbW9zc3OjUqRPOzs4kJibi5OTE2rVri7Rds2YN1tbWnDx5knbt2uHq6oqLiwvOzs7k5+drEvDKWL58OR999BH16tXjs88+w8bGhjZt2tC9e3c6depEkyZNGDJkCFFRUTzzzDNF2r/55ptA4YyCsrRo0YKBAwcChbMbTExMGDlyZJHr6tWrxyefFP67deXKlbRs2RIPDw9atWrF8OHD8fX15eWXX67we/3yyy8ZOXIkSUlJPPvsszRr1oxu3brh4eFBixYtaNOmDR988EGxSXFlffPNNzz77LNcu3aNgQMH0rJlS7p164a9vT1NmjRh4sSJWjMxmjdvzs6dO7G1teWHH37A1taWDh060LNnT1xcXDAzM6NXr158//333L9/v8j9NmzYQH5+fomlDB8HSd5FndPd3oL+zi1Yvv8812/nMrzDcGwa2LD0+FIeFDyoWGctOxWWjov+Fq6frZmAhRBCCCFqCXNzc/bu3UtYWBiDBg1CpVJx8uRJEhMTad68OePGjWPLli2aXc/VvLy82LhxIz4+Pty6dYvIyEgOHjyoVbqtKjp27Mjp06eZPn067dq1IyEhgaSkJFxdXVmwYAFHjx7FysqqSDt7e3uOHz/OpEmTaNmyJQkJCWRlZTF58mSio6PLrLNeGkVRmDNnDhcuXODDDz+kZ8+e3LlzhxMnTnD58mXatm3LxIkTiYiIKLb03pgxYzA1NSUsLKxcO78/PNI+bNgwzaZujxo/fjybN2+mZ8+e3L9/n4SEBJo2bcqnn37K9u3by7X/waOMjIwICwtj586dvPzyyxgbG3Pq1CliY2Np0KABo0aNYt26dfz973+vcN8lqV+/Pjt27GDt2rX069eP/Px8YmJiyM3NxcvLi6VLl9K1a1etNq6urvz+++8sWbIELy8vMjIyiI6OJjk5mY4dO/L+++8TERGhmSnysDVr1gBU6QOdqlKqe9qKqJ08PDxUFd184ml2KeMO/ZYeZIRnaxa+6MLupN387eDfmO81nxfbv1ixzrKvwzI3sPWS0nFCCCFEDYuPj8fJyUnXYQjxWMyePZuFCxeyYsUKXn/9dV2HU2cdPXqU7t27M2jQILZv317h9hX9vaUoynGVSuXx6HEZeRd1Upum9QnqaUvY0cskpt2mn20/OjftzBcxX5CTX8GNVKR0nBBCCCGEqAEzZ86kRYsWLFiwoNip3OLx+PDDD6lXrx5LlizRaRySvIs6a1qf9pgZ1WPhjngUReE9j/dIv5tOaHzZ64qKUJeO2zVLSscJIYQQQohqYWZmxtq1axk3bhx//PGHrsOpk27fvo2XlxcrV64s9+aONUWSd1FnNTY1ZGqf9hw8e51DZ6/j3sId/9b+fPf7d2Teq1j5E03puIxEKR0nhBBCCCGqTd++fQkODqZ9+/a6DqVOatCgAXPnziUwMFDXoUjyLuq2oJ622DQx5eOf43lQoOId93e4l3+PFacrUcZCSscJIYQQQgghaogk76JOM6qnz8wBjiReu82GY8nYN7JnWPth/JDwA5ezKljKQkrHCSGEEEIIIWqIJO+iznuuU0s87cz51+6zZOfm81aXtzDQN2DZyWUV70xKxwkhhBBCCCFqgCTvos5TFIXZg5zJyM7lm4MXaGrSlLEdx/JL0i+cvn664h36zwEDU9g9p/qDFUIIIYQQQtRJkrwLAXRp3ZjnXa34NvwiV2/lMKbjGCyMLVh6fCkqlapinZk1g15SOk4IIYQQQghRfSR5F+J//v6cAwUq+PSXROob1OetLm9x/NpxDl45WPHOur8ppeOEEEIIIYQQ1UaSdyH+p5W5Ka/5tOG/J1L4/cotXmz/InYN7fj38X+TX1DBBFxKxwkhhBBCCCGqkSTvQjzkLb+2WNQ3ZMHPcdRT6vGO+ztcvHWRLee3VLwzx0HQpldh6bi7FawbL4QQQgghhBAPkeRdiIc0MDbgnX4diLqUyZ64azzT+hncmrvxZcyX3M27W7HOFAWe/Wdh6biDi2smYCGEEEIIIUSdIMm7EI8I8GxNu+ZmLNqZQH6Bivfc3yMjJ4NVcasq3pmUjhNCCCGEEEJUA0nehXhEPX09Zg904mLGHUKP/EGX5l3oZ9uPlWdWkpGTUfEONaXjZld/sEIIIYQQQog6QZJ3IYrh59AMn3ZN+WzfOW7dzWOq21TyHuTx9amvK96ZpnTcbjgnpeOEEEIIIYQQFSfJuxDFUBSFWQOduJWTxxf7z2HXyI6XO7zMj2d/5NKtSxXvUF067hcpHSeEEEIIURw/Pz8URSEkJETXoWgEBwejKApjx47Vyf1zcnKwsbHBxsaG3NxcncTwtBo7diyKohAcHKx1PDIyEkVRmDBhgm4CK4Uk70KUwNmqIcPdW7Hqtz+4fOMuE10nYqRvxOcnPq94Zw+Xjjv2n+oPVgghhBCiGOqEWFEU3NzcSr02Pz8fS0tLzfXVmbCGhIQQHBxMTExMtfX5JPnss880z3XOnDnlbrdkyRKSk5OZM2cORkZGpV77zjvvaO7x3XffVTXkOsvb25v+/fuzcuVKTp8+retwtEjyLkQp/tbfAX09hU92JWBhYsH4TuPZd3kfJ9NPVrwzdem4AwuldJwQQgghHruYmJhSk5Fdu3aRlpZWI/cOCQlh3rx5dTZ5X7lypebr1atXU1BQUGab9PR0Fi9ejI2NDePGjSv12ry8PEJDQzX/X5tmLzyJ5s6dS0FBAR988IGuQ9EiybsQpWjR0JiJvdvy8+9XOf5HJkHOQTQzaca/jv0LlUpVsc6kdJwQQgghdMTR0REoPalTn3NycnoMEdUdJ06c4PTp0xgYGFC/fn2Sk5PZt29fme2+/fZbsrOzGTNmDAYGBqVeu23bNjIyMmjUqBGKohAZGcm5c+eq6y3UOV5eXjg6OrJ7924SEhJ0HY6GJO9ClOH1Xm1o0dCIj7bHY1LPhMldJnPq+in2XS77l24RUjpOCCGEEDowcuRIjI2NCQ0NJT+/6P47mZmZbNu2DWtra/r27auDCJ9e6lH3AQMG8NJLL2kdK0lBQQErVqwAIDAwsNz3GDNmDL6+vuW6hyhdUFAQAF9/XYkNq2uIJO9ClMHUsB7v93cgJvkm205fZWi7obRt1JbPTnxGXkFexTtUl477ZRZUdPReCCGEEKISGjduzNChQ0lPT2fXrl1Fzq9fv5779+8TFBSEnl7pKUJeXh7ffPMNfn5+WFhYYGRkhK2tLePHjy8y2nvgwAEUReHgwYMAjBs3TrMuu7R19RkZGbz99tvY2NhgZGSEjY0N06ZN4+bNmyXGlZGRwYwZM+jYsSOmpqY0aNCArl27snDhQu7cuVNiu+vXrzN58mRat26NsbExdnZ2TJkyhczMqi9zzM3NZd26dQC8+uqrvPrqqwBs2bKFW7duldguIiKCy5cv4+joSIcOHUq9R1pamuZ7+vA91qxZU+r0fPX3ICkpifDwcIYMGULz5s3R09PTmqFx584dFi5cSNeuXWnQoAGmpqZ07NiRGTNmcOPGjWL7trOzQ1EUDhw4UOz5kJAQFEXBz8+vyLmrV6/yzjvv4OjoiImJCcbGxrRq1QpfX1/mzZtX5Gfg7NmzLFq0CH9/f2xtbTE2NqZx48Z4eXmxbNky7t+/X8rTK9nQoUOBwj8btYUk70KUw0tdW+Fs2ZBPdiaQ/0DhXfd3+SPrDzad3VTxzsyaQe/pcH4PrHkRbqVUf8BCCCGEqFZbTqbgvehX2sz4Ge9Fv7Ll5JP39/eYMWOA4qfOq4+prylJRkYGvr6+TJw4kUOHDmFqaoqzszM3btxg5cqVuLm5sXv3bs31jRo1wtvbm4YNGwLQvn17vL29Na/iEtMrV67g5ubGihUrsLCwoFWrVly5coVly5bRr18/8vKKDp7Exsbi6urKJ598wtmzZ+nQoQM2NjbExMQwe/ZsunXrRmpqapF2SUlJuLu7s3z5clJTU3FwcMDMzIwvv/wST09P/vzzz1KfR1l++uknMjMzMTc3Z8iQIfj7+9O6dWtycnIICwsrsZ066e3Ro0eZ91izZg35+fl07NgRd3d3hg8fjomJCVeuXGHPnj1ltg8LC8PPz4/w8HBsbW2xs7PTnEtNTcXT05PZs2cTExODjY0NHTp0IDExkU8++YTOnTsTFxdX5j3KKzk5ma5du/L5559z4cIF7OzscHFxQaVScfjwYYKDg0lKStJqM2vWLGbOnMmxY8cwNDSkc+fONG7cmMOHDzNt2jT69+9fqQTe2dmZRo0akZ6eTmxsbDW9w6qR5F2IctDTU5gzyImUmzmE/JZEr1a98GjhwVenvuJOXsmf5Jao52QYtBSSo+CrnnB6o4zCCyGEELXUlpMpzPzv76TczEEFpNzMYeZ/f3/iEvj+/ftjZWXFtm3btEaV4+LiOHbsGN27d9esjS/JqFGjiIqKwt/fn/j4eJKTkzl58iSZmZnMnj2bO3fuEBAQQEZGBgBubm5ERERodrqfNWsWERERmtesWbOK3GP+/Pl06dKFlJQUTp48yYULF9i/fz/169fn2LFjrF69Wuv63NxcXnrpJVJTU/Hx8SEpKYmYmBhiY2M5c+YMHTp0IC4urtjp50FBQSQnJ+Pq6sq5c+c4deoUZ86cITY2Fj09Pb766qsKP+eHqaeujxw5EkNDQ/T09DRxlDatPTw8HABPT89y30M9zbthw4a88MILZd5Dbc6cOcycOZP09HSio6O5ePEiI0eOBGD06NHEx8fj5OREXFwcsbGxxMTEcOnSJXr06EFqairDhg2r9Oj2o5YsWUJaWhp9+vQhNTWV+Ph4oqOjSUlJ4fr165oPdB4WFBREVFQUWVlZnDt3jqNHj5KUlER8fDw9evTg4MGDLF26tMKxKIqief6HDh2qlvdXVZK8C1FOXu2a0tepOV/+ep7MO/d5z/09Mu9lsvJMJdYTKQp4vgYTI6CpA/x3AmwcK7vQCyGEELXQp78kkpP3QOtYTt4DPv0lUUcRVY6+vj6BgYHcv39fa9RXPepeVmm4Xbt2sW/fPuzt7dm6dSsODg6ac4aGhixYsIChQ4eSmZlZpVJl5ubmrFu3jmbNmmmO9e7dm9deew2A7du3a12/YcMGEhMTMTEx4ccff8Ta2lpzztnZWTNtff/+/URERGjOhYeHExERgaIorF+/Hnt7e805JycnQkJCih3lL6/U1FTNLAT1VHb4a3ZDVFRUiZuhqUeXH34vxYmKiiI+Pl7rQ4GH77F169ZSlxoAPPfccyxYsABDQ0PNMRMTE8LDwzXLHsLCwrQ+2GndujWbNm3CyMiIxMRENmzYUOo9yisxsfDP1JQpU7S+/1D4c/H666/TunVrreNDhw6lW7duKIqiddzR0ZE1a9YAsGrVqkrFY2VlBcClS5cq1b66SfIuRAXMGODE3bwHfLb3HC7NXHjO7jlWx60m/W565Tq0aAvjdkKff0DCz7C8B5z9pXqDFkIIIUSVpN7MqdDx2uzRqfMPHjxg7dq1GBkZMWrUqFLb/vjjj0DhBmoNGjQo9poXX3wRgF9//bXSMQYEBBTbv3oK+fnz57WO//zzz0DhKHGLFi2KtHN3d8ff3x+AHTt2aI6rv+7Xr1+xO+x7e3uXa+S7JKtXr+bBgwe0b9+enj17ao47ODjQrVs3oOSR8fT0wn9bNmnSpNR7qNs/88wzWol+3759sbS05N69e2Wu2S7pQxv1c+3Tpw+dO3cuct7KyopXXnkF0H6uVWFrawsU/qxVZDQ/PT2dZcuWERgYSL9+/fD19cXHx0fz3hITE8nJqfifV/Uo//Xr1yvctiZI8i5EBbRrbsbo7jasO3qZ8+m3mdp1KnkFeSyPWV75TvXrge/f4PVfwdQC1o2An6ZC7u3qC1wIIYQQlWbV2KRCx2szZ2dnPD09iY6OJj4+nt27d3P16lWGDh1K48aNS2176tQpAH744Qd8fHyKfX366adA4br1ymrfvn2xx9WJeXZ2ttZx9Whtp06dSuzTxcUFQGukW/11x44dS2xX2rmyqD8gUU9nf5j6Q5Q1a9bw4MGDIufv3bsHgLGxcYn9P7xu/uGRffhrlgWUPXW+pNKAlX2uVTFt2jSMjY1Zu3YtVlZWjB49muXLl/P777+X2ObHH3+kbdu2TJs2jdDQUPbu3UtERASRkZFERkYCoFKpKrUBofr5VybxrwmSvAtRQdP6tMfUQJ9/7kigdYPWjHIYxebzm7lw80LVOrbsDG8cAO9pcGI1fOUNf/xWHSELIYQQogo+eNYBEwN9rWMmBvp88KxDCS1qN/Vo5KpVq8o9ZR7QTL9OTEzUJEaPvtQbe929e7fS8dWvX7/Y4+pd8FWP7BN0+3bhgEfLli1L7NPS0lLr2oe/Lm60Xq20c6X57bffSExMRFGUYpP3UaNGYWhoyNWrV4vd/V894lvahnmbN2/m1q1bmJmZMWzYsCLn1R8QREdHl7rhWknPu7LPtSqcnZ05fPgwL7zwAnfu3GHdunVMnjyZzp070759e0JDQ7WuT0pKIjAwkOzsbEaMGEFERAQZGRnk5eWhUqm0PhipzBIIdcLftGnTqr2xaiLJuxAVZGFmxNvPtGNfQjq/nc/gzc5vUr9efT47/lnVO69nBP3mF06lB1g5EHZ/CPm5Ve9bCCGEEJXygps1/xzmgnVjExTAurEJ/xzmwgtupa9Hrq0CAgIwMjJi1apVbN26FUtLS/r3719mOzMzMwDWrVuHSqUq9fXojuA1ST3FPi0trcRrrl69qnXtw19fu3atxHalnSuNerRbpVLRpk0brfJ4iqJgYWGhmRZe3O7/6g8NSirF9vA9srOzMTMzK3KPh0fMi7tHWSr7XAHN+vNHP2hRK610X5cuXdi8eTM3b97k0KFDLFy4EE9PT86fP09gYCCbNv1V7SksLIzc3Fy6devG+vXr8fb2xsLCgnr16gGlP7/yUCfvzZs3r1I/1UWSdyEqYYyXHa3MTVjwczwNDBvxmstrHLhygOi06Oq5gW1PmBQJ7mPgt2Wwwg+unq6evoUQQghRYS+4WRM54xkuLRpE5IxnntjEHdCULUtLSyM3N5fAwED09fXLbKeeIl3aFOaSPLqZWHVSb6R25syZEq9Rx/zwpmvqr0srdVaZEmF3797VbODWpEkTWrRoUexLPZr7008/FUky1bvzlxTb5cuXNfsKNGvWrMR7mJubA3+Vk6uIyj5X+Gs0v6QPP86dO1fm/Y2MjPD19WXmzJkcPXqUCRMmAGhVAFBvJOfj46OZmfGwI0eOlHmf0qjfn7u7e5X6qS6SvAtRCcYG+kx/zpG4q1lsOnGF0U6jaWHagqXHlpb4CWOFGTWAIZ/DKxvh7g349hkI/xc8qNgvXiGEEEKIR02ePJk+ffrQp08fxo8fX642w4cPBwpHfCu6ftjEpHB/gJpYOzxo0CAAQkNDi00WT5w4wf79+7WuBRgwYAAAe/bs0azvftjhw4eJjq74wMymTZvIysrC2NiYixcvkpaWVuwrNTWVZs2acf/+fc2O+Gq9e/cGCneTL86qVasoKCjA2tq6xP7T0tJITEzEwMCAa9eusXPnzgq9D/Wz2rdvH6dPFx1Eunr1qmYzvIefK/y1b0Fx8WdlZZW5iV5xvL29gcJd/NVMTU01sTxKpVKxZMmSCt9H7ebNmyQmJqKvr4+vr2+l+6lOkrwLUUmDO1viZtOYJb8kUlBQjyluUzhz4wy//FHNu8V36A9vHQHHQbBvPqwcADequL5eCCGEEHWan58fe/fuZe/evWXWdlcbMmQIffv21dThPnr0aJFr4uPjmTt3Llu2bNE63rZtWwAOHDhQfQMd/zNixAgcHBzIyclhxIgRWsldQkKCZkd0f39/TQII0KtXL7y8vCgoKCAgIEBrqn9CQgJjx47FwMCgwvGop7O/8MILNGrUqMTrDAwMNLE9Oq29X79+6OvrExUVRW6u9vJJlUqltRlecSPOas2aNWPgwIHF3qMsPj4++Pn5oVKpCAgI0PqAIyUlheHDh3Pv3j0cHBw0H+yoDR48GIDvvvtOU7MeCndtHz16dIlr+d98803Wrl1LVlaW1vHk5GS++OILADw8PDTH1R9ybNy4UbM7PhSuwZ8wYUKxP6PlFR4ejkqlwsvLi4YNG1a6n+okybsQlaQoCnMGOZN+O5cVhy4y2H4wHcw78Pnxz8l7UPmaoMUybQLDQ2DYd5CRCF/7QPR3UM1/+QkhhBBClGbDhg34+/sTExND9+7dsbKyonv37nTt2pUmTZrg7OzM/PnzycjI0GoXEBCAoihs2LABW1tbfH198fPzY9GiRVWOydDQkE2bNmFpacmhQ4ewtbXFzc2NTp064ezsTGJiIk5OTqxdu7ZI2zVr1mBtbc3Jkydp164drq6uuLi44OzsTH5+PhMnTqxQLElJSRw4cAAo3yaA6mtOnDihNbptZWXFkCFDyMrKYtu2bVptDh06xMWLFyt8j23bthX5vpQlNDQUR0dH4uLicHJywsXFBTc3N+zs7IiMjMTKyopNmzZp1YiHwt3vPTw8yM7Opnfv3rRr1w43Nzesra2JiYnhH//4R7H3i4qKIigoCHNzc9q3b0+PHj1wdHTEzs6O48ePY2try8KFCzXXP//88/j5+ZGfn8/gwYOxt7fHw8ODli1bEhISwooVKyr0fh+mng3xxhtvVLqP6ibJuxBV4G5rzqDOlnxz8CINQoICAAAgAElEQVQZ2Xm85/4eV7KvsOHshuq/maJA5+GFo/A2PeDnv8HalyArtey2QgghhBDVwNzcnL179xIWFsagQYNQqVScPHmSxMREmjdvzrhx49iyZQujR4/Waufl5cXGjRvx8fHh1q1bREZGcvDgwWorMdaxY0dOnz7N9OnTadeuHQkJCSQlJeHq6sqCBQs4evQoVlZWRdrZ29tz/PhxJk2aRMuWLUlISCArK4vJkycTHR1dZp31R4WEhKBSqbC2tqZfv35lXt+lSxdcXV2BoiXdJk2aBBTWi3+Y+roePXrg4FB2xYNBgwbRrFkz8vLyiuzWXhYrKyuio6NZsGABrq6uXLp0iYSEBNq1a8f06dM5depUseX0DAwM2LNnD1OnTqVVq1ZcvnyZ69evM378eE6cOEGrVq2Kvd9nn33Ge++9h7u7O9nZ2Zw4cYKUlBRcXV2ZO3cuMTExWm319PTYsWMHM2bMoE2bNly5coXLly/j7+/Pr7/+WuxO/+Vx+/Zttm7dioWFRZFZBbqkVPe0FVE7eXh4qI4dO6brMJ5KyZl36fOvg7zgZsUnL3XmjT1vkJCZwI5hO2hg2KDsDipDpSoced/9IdQzhEFLweXlmrmXEEIIUYvEx8eXWJdaiKeNj48PR44c4cyZM+Ve3iCqbvHixUyfPp1PP/2U999/v8r9VfT3lqIox1Uqlcejx2XkXYgqat3ElLHedmw8foW4q1m85/4eN3Nv8p8z/6m5myoKdHu9cEf6ph1g02uwcRzcrdjmMUIIIYQQovZaunQpBQUFBAcH6zqUOuP27dssXrwYe3t7pk6dqutwtEjyLkQ1mOzfjsYmBizcEY9jE0cG2w9mTdwa0u6UXBezWli0hXG74JkPIf4nWN4Tzu2p2XsKIYQQQojHolu3bqxYsQJnZ+ciG9eJmpGUlMTbb7/NqlWriqzl1zWZNl9HyLT5mrfqtyTm/hTLf8Z64NjqAYM3D2aQ/SA+8v7o8QRw9RRsngjpceA+DvovACOzx3NvIYQQ4jGRafNCiCeNTJsXopZ5pbsN9s3q8/HP8TQzaclop9FsPb+Vs3+efTwBWLrC6/vBayocD4GvveHykcdzbyGEEEIIIUSNkuRdiGpioK/HzAFOXLh+h7Cjl5ngMoEGhg349/F/P8YgjKH/RzBuR+GmdisHwJ65kC/TrIQQQgghhHiSSfIuRDXq69ScHvZN+PfecygqU97o/AYRKREcufqYR8BtvQo3s3MLgsjPYIU/pP3+eGMQQgghhBBCVBtJ3oWoRoqiMGeQM3/evc/y/RcY5TgKq/pWLD22lAJVweMNxqgBPL8MAn6AO9cLE/jwpVDw4PHGIYQQQgghhKgySd6FqGadrBsxzK0V/4m8RPqtB0zpOoX4zHh2Xtqpm4AcnoO3joDjQNg3r3AqfeZF3cQihBBCCCGEqBRJ3oWoAR8864CeAp/+ksjANgNxauLEshPLuP/gvm4Cqm8Bw1fBsO/gegJ85QPH/lO4Ll4IIYQQQghR60nyLkQNaNnImDd87fnpVCqnkm/xnsd7pN5JZX3Cet0FpSjQeThMOgytPWH7uxD6MmRd1V1MQgghhBBCiHKR5F2IGvJm77Y0a2DEgp/j6d6yO97W3qw4vYJbubd0G1gjawjcDAOXQFIkLO8BZzbpNiYhhBBCCCFEqSR5F6KG1Deqx/v9O3D8jz/ZeSaNd7u+y+37t/n+9+91HRro6UG312FiBFi0gx/HF77uZuo6MiGEEEIIIUQxJHkXoga97N4ax5YNWLQzAbuG7Xi+7fOExoeSmp2q69AKNW0H43+BZ+ZA3FZY3hPO7dV1VEIIIYQQQohHSPIuRA3S11OYPciJy5l3WXP4D952extFUfji5Be6Du0v+vWg1wfw+q9gYg6hLxWuh79/R9eRCSGEEEIIIf5Hknchaphv+2b4OTRj2b5zGNGEQKdAtl/cTvyNeF2Hps3SFd44AF5T4NhK+MobLkfpOiohhBBCCCEEkrwL8VjMGuhEdm4+n+87x2sur9HIqBFLjy9FVdtKtRkYQ/8FMPZnUD2Alc/B3nmQr6MSd0IIIYQQQghAknchHosOLRoQ0M2GtUf+4PothYmuEzly9QjbL27XdWjFs/OGiZHQZTRELIVvn4FrsbqOSgghhBBPMT8/PxRFISQkRNehaAQHB6MoCmPHjtXJ/XNycrCxscHGxobc3FydxPC0Gjt2LIqiEBwcrHU8MjISRVGYMGGCbgIrRZ1J3hVF0VMUpaOiKOMURVmuKEq0oii5iqKoHnqN1UFcNoqivKQoyieKovyqKMqtR2IKedwxiZrxbr8OGBvos2hnAqMcRtG1eVc+jvqYK7ev6Dq04hk3hKFfQEAYZF+DFX4Q8RkUPNB1ZEIIIYQoJ3VCrCgKbm5upV6bn5+PpaWl5vrqTFhDQkIIDg4mJiam2vqszdSJ4aMvAwMDLC0tGTx4MJs3by6znyVLlpCcnMycOXMwMjIq9dp33nlHc5/vvvuuut5KnePt7U3//v1ZuXIlp0+f1nU4Wp765F1RlJcVRTkA3ALOAP8BJgEegKGOYnJRFGWboihpwB/Aj8DfAX+goS5iEjWvqZkRk/zasjvuGtFJN1nouxAFhVkRs8gvyNd1eCVzGABvHYEOz8HeuRAyCDIv6ToqIYQQQlRQTExMqcnIrl27SEtLq5F7h4SEMG/evDqTvKs1b94cb29vzcvZ2Zk7d+7w888/M2zYsFJHd9PT01m8eDE2NjaMGzeu1Pvk5eURGhqq+f/aNHvhSTR37lwKCgr44IMPdB2Klqc+eQd8gN6Ama4DeUhbYDDQQteBiMfrNZ82WDc2YcHPcViaWjG7x2xOpp/ku99r+aej9S1gxGp4cQVciyvczO7YSqhta/aFEEIIUSxHR0eg9KROfc7JyekxRFQ3DBgwgIiICM3r1KlTXL9+nY8++giA77//nl9++aXYtt9++y3Z2dmMGTMGAwODUu+zbds2MjIyaNSoEYqiEBkZyblz56r9/dQVXl5eODo6snv3bhISEnQdjkZdSN5LcgtI0XUQj8gFLug6CFFzjA30+eBZB86kZLElJoXB9oMZ2GYgX5/6mlPXT+k6vNIpCriOhLd+g1YesP0dWDcCbtfMJ/RCCCGEqD4jR47E2NiY0NBQ8vOLzvjLzMxk27ZtWFtb07dvXx1EWHcYGRkxZ84c3N3dAdi9e3eRawoKClixYgUAgYGBZfa5cuVKAMaMGYOvr6/WMVE5QUFBAHz99dc6juQvdSV5zwEOA8uAIMARMAd0OdxZAMQCK4G3AE8Kp8zXvp0RRLV63tUK11aN+PSXRG7l5DG7x2xamLZgZvhM7uQ9AbXVG7WCoC0w4FO4FA7Le0Bs2Wu2hBBCiCfa6Q3w704Q3Ljwv6c36DqiCmncuDFDhw4lPT2dXbt2FTm/fv167t+/T1BQEHp6pacIeXl5fPPNN/j5+WFhYYGRkRG2traMHz++yGjvgQMHUBSFgwcPAjBu3DitNeAlravPyMjg7bffxsbGBiMjI2xsbJg2bRo3b94sMa6MjAxmzJhBx44dMTU1pUGDBnTt2pWFCxdy507J/8a6fv06kydPpnXr1hgbG2NnZ8eUKVPIzMws9TlUlZ2dHQD37xet6hMREcHly5dxdHSkQ4cOpfaTlpam+Z6++uqrvPrqqwCsWbOGgoKCEtupvwdJSUmEh4czZMgQmjdvjp6entYMjTt37rBw4UK6du1KgwYNMDU1pWPHjsyYMYMbN26U+N4UReHAgQPFng8JCUFRFPz8/Iqcu3r1Ku+88w6Ojo6YmJhgbGxMq1at8PX1Zd68eUV+Bs6ePcuiRYvw9/fH1tYWY2NjGjdujJeXF8uWLSv2+ZbH0KFDgcI/G7VFXUjePwYaqlQqL5VKNU2lUq1VqVSJKt3W6Nr3v5g6qVSq8SqV6iuVSnVMpVJJPa46QE9PIfj5jmRk5zI59AQm+mYs9F1ISnYKi44u0nV45aOnB93fgInh0MQeNo6FTRMg509dRyaEEEJUv9MbYNtUuJUMqAr/u23qE5fAjxkzBih+6rz6mPqakmRkZODr68vEiRM5dOgQpqamODs7c+PGDVauXImbm5vWSHKjRo3w9vamYcPCbZ3at2+vtQa8uMT0ypUruLm5sWLFCiwsLGjVqhVXrlxh2bJl9OvXj7y8vCJtYmNjcXV15ZNPPuHs2bN06NABGxsbYmJimD17Nt26dSM1NbVIu6SkJNzd3Vm+fDmpqak4ODhgZmbGl19+iaenJ3/+WTP/tsnLy+PkyZPAX0saHqZOenv06FFmX2vWrCE/P5+OHTvi7u7O8OHDMTEx4cqVK+zZs6fM9mFhYfj5+REeHo6tra3mQwWA1NRUPD09mT17NjExMdjY2NChQwcSExP55JNP6Ny5M3FxceV70+WQnJxM165d+fzzz7lw4QJ2dna4uLigUqk4fPgwwcHBJCUlabWZNWsWM2fO5NixYxgaGtK5c2caN27M4cOHmTZtGv37969UAu/s7EyjRo1IT08nNrZ2VF166pN3lUp1XaVS1ardwFQq1W2VSvUEDLGKmuJmY87CF12IOJ/B3J9i6dq8K691eo0t57ewO6no1Klaq2l7GL8b/OcUjr4v94Lz+3QdlRBCCFG99s2HvBztY3k5hcefIP3798fKyopt27ZpjSrHxcVx7NgxunfvXmwi+bBRo0YRFRWFv78/8fHxJCcnc/LkSTIzM5k9ezZ37twhICCAjIwMANzc3IiIiNDsdD9r1iytNeCzZs0qco/58+fTpUsXUlJSOHnyJBcuXGD//v3Ur1+fY8eOsXr1aq3rc3Nzeemll0hNTcXHx4ekpCRiYmKIjY3lzJkzdOjQgbi4uGKnnwcFBZGcnIyrqyvnzp3j1KlTnDlzhtjYWPT09Pjqq68q/JxLk52dzfHjxxkxYgQXL17ExsZGM1L+sPDwcAA8PT3L7FM9PV49zbthw4a88MILWudKM2fOHGbOnEl6ejrR0dFcvHiRkSNHAjB69Gji4+NxcnIiLi6O2NhYYmJiuHTpEj169CA1NZVhw4ZVenT7UUuWLCEtLY0+ffqQmppKfHw80dHRpKSkcP36dc0HOg8LCgoiKiqKrKwszp07x9GjR0lKSiI+Pp4ePXpw8OBBli5dWuFYFEXRPP9Dhw5Vy/urqqc+eReithru0ZrJ/m1ZF3WZ7yMuManLJDpZdGLe4Xmk3XmC1pHr14PeH8CEfYXl5dYOg5//Bvfl8ykhhBBPiVsllHUt6Xgtpa+vT2BgIPfv3ycsLExzXD3qXlZpuF27drFv3z7s7e3ZunUrDg4OmnOGhoYsWLCAoUOHkpmZWaVSZebm5qxbt45mzZppjvXu3ZvXXnsNgO3bt2tdv2HDBhITEzExMeHHH3/E2tpac87Z2Zl169YBsH//fiIiIjTnwsPDiYiIQFEU1q9fj729veack5MTISEhxY7yV8SqVau0lgk0aNAADw8Ptm3bxltvvUVUVBQNGjQo0k49uvzweylOVFQU8fHx6OnpaX04oZ5BsXXr1lKXGgA899xzLFiwAEPDvwpxmZiYEB4erln2EBYWpvXBTuvWrdm0aRNGRkYkJiayYUP1zEJJTEwEYMqUKVrffyj8uXj99ddp3bq11vGhQ4fSrVs3FEXROu7o6MiaNWuAwu9DZVhZWQFw6VLtqLQkybsQOvS3fg4MdGnJxzvi2R9/g0W9FpFXkMeciDkUqEpeo1QrWXWBNw5Cz7ch+nv42heSo3UdlRBCCFF1jVpV7Hgt9ujU+QcPHrB27VqMjIwYNWpUqW1//PFHoHADteISToAXX3wRgF9//bXSMQYEBBTbv3oK+fnz57WO//zzz0DhKHGLFkWLObm7u+Pv7w/Ajh07NMfVX/fr16/YHfa9vb3LNfJdmkdLxXl6etKiRQsePHjA+vXrS9z9Pz09HYAmTZqU2r96ZP2ZZ57RSvT79u2LpaUl9+7dK3PNdkkf2qifa58+fejcuXOR81ZWVrzyyiuA9nOtCltbW6DwZ60io/np6eksW7aMwMBA+vXrh6+vLz4+Ppr3lpiYSE5OTumdFEM9yn/9+vUKt60J9XQdgBB1mZ6ewtIRXUi5eYRpYTFsnNiTGd1mMPe3uayOXc3YTmN1HWLFGBjDsx8X1oTf8hb8pz/4vAe9p0M9w7LbCyGEELVRn38UrnF/eOq8gUnh8SeMs7Mznp6eREdHEx8fT1JSElevXmXEiBE0bty41LanThVWxvnhhx/Yt6/4ZXLqUd4rVyo/K6F9+/bFHlcn5tnZ2VrH1aO1nTp1KrFPFxcX9u/fr1X2S/11x44dS2zXsWNHoqMrPxgxYMCAYhP08PBwRo8ezcyZM8nJyWHevHla5+/duweAsbFxiX3n5ORoZlA8OvVePcvi008/ZeXKlUyaNKnEfkoqDVje5wpUWzm1adOmsXr1atauXcvOnTt59tln8fb2xtfXV3OvR/3444+MGzeuyM/Fw1QqFZmZmWXOZHiU+vlXJvGvCTLyLoSOGRvo8+2r7jSpb8hrq6Lp2WwAfWz68PnJz4m/Ea/r8CqnjS9MioQur0D4EvjumcL68EIIIcSTqPMIGLIMGrUGlML/DllWePwJpB6NXLVqVbmnzMNfiXliYiKRkZHFvtQbe929e7fS8dWvX7/Y4+pd8B/dd/r27dsAtGzZssQ+LS0tta59+OviRuvVSjtXFb6+vixbtgyAxYsXFxnZVY/4lrZh3ubNm7l16xZmZmYMGzasyHn1LIvo6OhSN1wr6XlX9rlWhbOzM4cPH+aFF17gzp07rFu3jsmTJ9O5c2fat29PaGio1vVJSUkEBgaSnZ3NiBEjiIiIICMjg7y8PFQqFQ8ePNBcW5klEOq9IZo2bVq1N1ZNJHkXohZo3sCY78d6cCf3ARNWH+Pv7nNoYtSE6eHTycmvHZ/0VZhxQxj6JYxaX1gLfkVviFwGBQ/KbiuEEELUNp1HwLtnIPhm4X+f0MQdCqelGxkZsWrVKrZu3YqlpSX9+/cvs52ZmRkA69atQ6VSlfp6dEfwmqSeYp+WVvKeQVevXtW69uGvr127VmK70s5Vlboe+7179zSzGtTUHxqUVIoN/poyn52djZmZmdbaekVRtEbMS5qeX5rKPldAs/68pAJfpZXu69KlC5s3b+bmzZscOnSIhQsX4unpyfnz5wkMDGTTpk2aa8PCwsjNzaVbt26sX78eb29vLCwsqFevcIJ5ac+vPNTJe/PmzavUT3WR5F2IWsKxZUP+7xU34q9m8Y/NScz3XsClW5f417F/6Tq0qnEcCG8dgfb9Yc+HhUn8+X2g02qNQgghRN1lbm7OkCFDSEtLIzc3l8DAQPT19ctsp562/Pvvv1f4no9uJlad1BupnTlzpsRr1DE/vOma+uvSSp3VZImwh2uwP5pkqnfnLym2y5cva/YVaNasGS1atCj2ZW5uDvxVTq4iKvtc4a/R/JI+/Dh37lyZ9zcyMsLX15eZM2dy9OhRJkyYAKBVAUC9kZyPj49mZsbDjhw5UuZ9SqN+f+7u7lXqp7pI8i5ELeLv0Jzg5zuyN/4aB2Ia8arzq/yQ+AMHkw/qOrSqqd8URq6Fl/8D924V7ki/+nlIOa7ryIQQQog6afLkyfTp04c+ffowfvz4crUZPnw4UDji+3CpufIwMTEBambt8KBBgwAIDQ0tNlk8ceIE+/fv17oWCtejA+zZs0ezvvthhw8frtJ697Koy8EBtG3bVutc7969gcLd5IuzatUqCgoKsLa2Ji0trcRXYmIiBgYGXLt2jZ07d1YoPvWz2rdvH6dPny5y/urVq5rN8B5+rvDXvgXFxZ+VlVXmJnrF8fb2Bgprz6uZmppqYnmUSqViyZIlFb6P2s2bN0lMTERfX18zS0LXJHl/iimK8oaiKMcURTlWW3ZIFGV7tacdY73s+Db8Ei3yh9HBvAP/+O0fZORk6Dq0qlEU6PQSvH0MnvsErsXCt8/AhjFw44KuoxNCCCHqFD8/P/bu3cvevXvLrO2uNmTIEPr27aupw3306NEi18THxzN37ly2bNmidVydnB44cKDEqdSVNWLECBwcHMjJyWHEiBFayV1CQoJmR3R/f39NAgjQq1cvvLy8KCgoICAgQGuqf0JCAmPHjsXAwKBaY1U7ePAgU6dOBQpnNDw6stuvXz/09fWJiooiNzdX65xKpdJMgw8KCip2xFmtWbNmDBw4EKj41HkfHx/8/PxQqVQEBARofcCRkpLC8OHDuXfvHg4ODpoPdtQGDx4MwHfffaf1IcX169cZPXp0iWv533zzTdauXUtWVpbW8eTkZL744gsAPDw8NMfVH3Js3LhRszs+FK7BnzBhQrE/o+UVHh6OSqXCy8uLhg0bVrqf6iTJ+1NMpVKtUKlUHiqVyuPROomidpszyAl/h2bM25bISNsZ3Mm7w4eRH1b7X3Y6Uc8IekyEqTGFu9Cf2wNfeML2dwvXxgshhBCi1tqwYQP+/v7ExMTQvXt3rKys6N69O127dqVJkyY4Ozszf/58MjK0Bx0CAgJQFIUNGzZga2uLr68vfn5+LFq0qMoxGRoasmnTJiwtLTl06BC2tra4ubnRqVMnnJ2dSUxMxMnJibVr1xZpu2bNGqytrTl58iTt2rXD1dUVFxcXnJ2dyc/PZ+LEiVWKbefOnfj4+Ghe3bp1o2XLlvj5+ZGSkoK1tTU//PBDkWUFVlZWDBkyhKysLLZt26Z17tChQ1y8eBEo30aD6mu2bdtW5PtSltDQUBwdHYmLi8PJyQkXFxfc3Nyws7MjMjISKysrNm3apFUjHgp3v/fw8CA7O5vevXvTrl073NzcsLa2JiYmhn/8o/hKDVFRUQQFBWFubk779u3p0aMHjo6O2NnZcfz4cWxtbVm4cKHm+ueffx4/Pz/y8/MZPHgw9vb2eHh40LJlS0JCQlixYkWF3u/D1q1bB8Abb7xR6T6qmyTvQtRC9fT1+L9XutK+uRkfbc4kqMNkIlIiWJ9Q8SlGtZZxQ/CfBdNiwGM8nFgNy9xg30eFU+uFEEIIUeuYm5uzd+9ewsLCGDRoECqVipMnT5KYmEjz5s0ZN24cW7ZsYfTo0VrtvLy82LhxIz4+Pty6dYvIyEgOHjxYbSXGOnbsyOnTp5k+fTrt2rUjISGBpKQkXF1dWbBgAUePHsXKyqpIO3t7e44fP86kSZNo2bIlCQkJZGVlMXnyZKKjo8uss16W9PR0rd34jx8/Tm5uLt27d2fBggXExsaWWKpNXd5t9erVWsfVG9X16NEDBweHMmMYNGgQzZo1Iy8vr8hu7WWxsrIiOjqaBQsW4OrqyqVLl0hISKBdu3ZMnz6dU6dOFVtqz8DAgD179jB16lRatWrF5cuXuX79OuPHj+fEiRO0atWq2Pt99tlnvPfee7i7u5Odnc2JEydISUnB1dWVuXPnEhMTo9VWT0+PHTt2MGPGDNq0acOVK1e4fPky/v7+/PrrrwQFBVXo/ardvn2brVu3YmFhUWRWgS4pT8VIXiUpihIMzH3o0DiVShWim2gKKYriB+x/6NAqlUo1tqr9enh4qI4dO1bVbsRjlnIzh6FfRGJsqODoupGY68cIGxRGO/N2ug6t+t24APs/hjObwKQJ9HofPCcUjtQLIYQQ/xMfH19isiPE08bHx4cjR45w5syZci9vEFW3ePFipk+fzqeffsr7779f5f4q+ntLUZTjKpXK49HjMvIuRC1m3diE78d4kHH7PukXhmJaz5Tp4dO5/+C+rkOrfhZtCze0e+MgWLrCL7Pg/zwgZr2UlxNCCCFEnbR06VIKCgoIDg7WdSh1xu3bt1m8eDH29vaafQlqC0nehajlXFs35t8junD6cgGtHozl7J9nWXZima7DqjlWXeDVLRC0BUybwJaJ8LUvnP1FyssJIYQQok7p1q0bK1aswNnZucjGdaJmJCUl8fbbb7Nq1aoia/l1rZ6uAxBClG2AiyV/f86BxbvA0/05VsWtwtvam55WPXUdWs1p6w9tekPcFvj1I1g3Amy9oe88aO2p6+iEEEIIIR4LdX1z8Xi4uLjg4uKi6zCKJSPvQjwhJvVuy3D3VkSf8KKpUWvmRMzh5r2bug6rZunpQadhMPkoDPoXZJyD7/tC2Gi4flbX0QkhhBBCCPHYSPIuxBNCURQ+ftGFHm1aknp2GDfuZRJ8OPjpKB9XFn2Dws3rpp4E/zlw8SAs7w4/TYGs1LLbCyGEEEII8YST5F2IJ4hhPT2+DnTH2rQdqswB7Lu8j83nN+s6rMfHyAx6f1BYXq77RDgVVlhebs8/IOdPXUcnhBBCCCFEjZHkXYgnTGNTQ74f6wlZvTC434F/Rv2TP7L+0HVYj1f9pvDcP+HtY+D8AkQug89dIfJzyMvRdXRCCCGEEEJUO0neq0hRFD9FUVQPvZJ0HZN4+rVpWp8VgZ7cvvISefl6TD80nbyCPF2H9fiZ28Kwb2BiBLTuXjgC/3/ucGINPMjXdXRCCCGEEEJUmzqRvCuKYlfcC2j8yKVNS7i2ZQ3E1LKEmB69l1kp8Ys6rLu9BYue70V2yovE3ojlq5ivdB2S7rTsBKM3wtifoYEl/PQ2fOUFCT9LeTkhhBBCCPFUqCul4i6V87pP//d61EHAr9qiKRQG9C7HdS/971UcpfrCEU+il9xbcSnjRb5LSOC737/H29ob9xbuug5Ld+x8YMJeSNgOe+dB2CuFI/J9g8HWS9fRCSGEEEIIUWl1YuRdiKfZe/060NvidR7cN+edX/9O1v0sXYekW4oCTkPgrSMw5HO4eRlWDoB1I+FanK6jE0IIIYQQolIkeRfiCaenp/D5yB60zp/An7kZfPBrsK5Dqh3064H7WJhyonDk/Y/DhVPpN0+Cm8k6Dk4IIYQQQoiKqRPJu0qlUqr48iul7wOPXGtXzpj8qhpXdT0f8eQzNtBnTRf8mQ0AACAASURBVOBwjLOf47dre1h7pg6VjyuLoSn4vFtYXs5rCpzZVLip3S+z4W6mrqMTQgghhBCiXOpE8i5EXdCsgRGrh81Adc+OxccWcu7GZV2HVLuYNoH+H8HUE+AyHI4sLywvd2gJ3L+j6+iEEEIIIYQolSTvQjxFnK3MCe6xgIICFWO2v8P9fCmXVkSjVvDClzDpN7DzhV8/gmVucOw/8KAOltsTQgghhBBPBEnehXjKvOzqymDrydzmHGP+u0jX4dRezZ0gYB2M/wXM28D2d2F5D4jdIuXlhBBCCB3w8/NDURRCQkJ0HYpGcHAwiqIwduxYncXw8ssvo6+vz5kzZ3QWw9MoJCQERVHw8/PTOn779m0sLCxwcnIiv5YNhEnyLsRT6J/9x9LKwIvf725k8f7dug6ndrPpAeN3QUAY6BnAxjHw7TNw6ZCuIxNCCCGqTJ0QK4qCm5tbqdfm5+djaWmpub46E9aQkBCCg4OJiYmptj6fFCkpKcydOxdvb29atmyJoaEhjRo1onPnzrz55pscOHCgxLbh4eFs2rSJkSNH0qlTp1Lvs2XLFs33LjAwsJrfRd3RoEED3n//fRISEvj66691HY4WSd6FeAopisK6YYsxwpxV5xeyO17Wv5dKUcBhAEyKhKHLITsdVg2BNcPg6mldRyeEEEJUi5iYGE6fLvnvtV27dpGWllYj9w4JCWHevHl1LnlftGgRbdu2Zf78+fz2228YGRnRpUsXrK2tOX/+PCtWrMDf35/evXsX2/5vf/sbiqIwd+7cMu+1cuVKzdebN28mK6uOlw+ugilTpmBubs68efO4ffu2rsPRkORdiKeUuXEjPuvzCXoGmfxt7zzOXqs9v3hqLT19cBsNU45D/wWQegK+8YVNr8OfSbqOTgghhKg0R0dHgFKnpKvPOTk5PYaInn5Tpkxh5syZ5OXlMW3aNP744w/++OMPjh49SlxcHJmZmWzZsgVPT08OHSo64y8yMpLo6Gh8fHxwcHAo9V7Xrl1jx44dADRu3Ji7d+/yww8/1Mj7qgvMzMwYOXIkGRkZhIaG6jocDUnehXiK+bbuzsgOY6DhUYLWf0dGdq6uQ3oyGBgXlpWbGgM+70H8Nvg/D9jxd8i+ruvoaj+VCu5lQUGBriMRQgjxPyNHjsTY2JjQ0NBi1/FmZmaybds2rK2t6du3rw4ifLps2LCBL774AoC1a9fy2WefYWNjo3WNsbExQ4cOJSoqio8//rhIH1999RUAQUFBZd5v7dq15Ofn07lzZ6ZOnQpoj8SLilM/92+++UbHkfxFknchnnLTu0+lTQNH7jQMY9yaPdzLe6DrkJ4cJo2h71yYerJwRD76O1jWBQ4sgtw6PJPh/l3IOAcX9sPJtYXPY+vbsOZF+KIbLLSGRa3hEzsIGQy7P4Qz/4XMS7IZoBBC6Ejjxo0ZOnQo6enp7Nq1q8j59evXc//+fYKCgtDTKz1FyMvL45tvvsHPzw8LCwuMjIywtbVl/PjxnDv3/+zdeVxU1f/H8ddlX0VQEFxwwTTFDcV918pKM22xLPfym0tl2V6apv6ytNRss7Rw3yrTUitzzR0XUHHfdwQUUJB9zu+PywCDrDowIJ/n4zEPZ+69597PTIa855x7zkmTYzdv3oymaWzZsgWAwYMHZ9yXndd99VFRUbz66qv4+vpib2+Pr68vo0aNIiYmJte6oqKieP/99/H398fJyQlXV1eaNm3Kp59+Snx87svCRkZGMnLkSKpVq4aDgwM1atTgtdde48aNG3l+DrkxGAx8/PHHAAwYMIC+ffvmebymaXz44Ycm25KSkvjtt98A6NmzZ77XNAb1AQMGMGDAAAB27tzJiRMncm2TdYLA8+fP8/LLL+Pr64utra3JfxelFIsWLaJLly54eHhk/PcYNGgQR44cyfHcgwYNQtM0xo8fn+P+c+fOZfwdyC4xMZGpU6fSokULypUrh52dHZUqVSIgIIBRo0Zx9OhRk+OjoqKYPXs2vXr1ok6dOjg7O+Ps7EyDBg149913iYiIyOujy1WbNm3w9PQkNDQ01/dZ3GwsXYAQomjZWtsys+tUnvrjGU4l/sTbv/jwdd+mOf6wFLko5wNPfAWtX4UNE2DzZD3Id3gXmg0CGztLV2g+aSlw8wrcvAyxlyH2Yubzm5f0PxNy+GXGpRKUqwKedcCvC7hWgpiLcCUEds+CtGT9OIfyULkJVA7IfLhV0+cdEEKIEmzNmTV8tf8rwuPD8Xb2ZlTTUXSv1d3SZRXKwIEDWbZsGXPnzqVHjx4m+4xD5gcOHJjnJF1RUVH06NGD3bt3o2kaVapUwdfXl5MnTxIUFMTy5ctZsWIFjzzyCABubm60bduWQ4cOcfPmTR544AG8vLwyzlenTp07rnHp0iUCAgK4du0a/v7+2NracvbsWWbOnMmOHTvYsWMHtra2Jm0OHz7MI488wpUrV7CxscHf35+UlBRCQ0MJCQlh0aJF/Pvvv1SuXNmk3blz5+jQoQMXL17EysqKBg0akJaWxrfffsvatWvv+JwKYt++fRw/fhyAUaNGFbo9wO7du0lMTKRGjRpUqlQpz2P37NnD4cOHsba25sUXX8Tb25u2bduyfft2goKCmDx5cp7tT5w4wejRo4mLi8Pf35/y5ctjbW0N6JMYPv/88xlfJPj6+uLn58exY8eYN28eS5YsYcGCBfTp0+eu3md2aWlpPPLII2zduhWAGjVq4OnpSVRUFEePHiU0NJSaNWua3NqxdOlSXnvtNezs7PD29qZ+/frExsZy4sQJDh8+zKJFi9i2bRs1a9YsdD2tWrXizz//ZNOmTdSvX98s7/GeKKXkUQYezZo1U6Js++X4L6rB3Aaq9ufvqi/XHbd0OaXbxT1KBXVXalw5pWY0UurgL0qlpVm6qvylpSl186pSF/cqdXilUju+UeqvD5Ra1l+pH7so9UVdpca56e8r62Oyr1LftVFq4bNK/fmGUlumKhW6VKmzW5W6fkaplMS8r5uSpNTlEKX2Bin1x+tKzWqv1CcVMs//WQ2l5vdWav0EpY78qVTMRaUMhmL5SIQQpc+RI0eK/ZqrT69WgQsCVYO5DTIegQsC1erTq4u9lsLq2LGjAtT06dNVamqqqly5srKzs1PXr1/POObw4cMKUC1btlRKKTVq1CgFqIEDB95xvq5duypAde7cWR07dixje1JSkvroo48UoDw8PFRkZGSOdQQFBeVbq62trerRo4eKiIjI2Ld582bl7OysADVnzhyTdomJiapu3boKUO3atVOXLl0yeW916tTJqDm7du3aKUA1btxYnT59OmP7kSNHVO3atZWtrW2un0Vupk2bpgDl5uamDHf579mkSZMUoJ599tl8jx0+fLgC1KOPPpqx7YcfflCAqly5skpNTc2xnfHztra2Vo8//rjJ53379m2llFLjx49XgHJxcVGrV2f+fY+Pj1dDhw5VgHJwcDD5u6CUUgMHDlSAGjduXI7XPnv2rAKUHkczrVixQgGqSpUq6sCBAyb7kpKS1MqVK9XmzZtNtu/evVutXbtWJSaa/j4SGRmZUWO3bt3uqCEoKEgBqmPHjjnWqFTmf4c+ffrkekxBFPbnFrBX5ZDppOddiDLi6QeeZuulrWxiHd9sq03Nik70Dqhq6bJKp6qBMPBPOLUB1o+H316C7V/Bw5/ovc6WoBQkROfSY258fRUMKabtbJ30HnO3KuDXFdyq6s/LVdGfl6sC9i73VpuNXXpvexN9pAJAahJcO6z3zF8JgSuhsG06qPTbOpw9TXvnfZroIyCEEMICvtr/FYlpiSbbEtMS+Wr/V6Wq993a2pp+/foxZcoUli5dyogRI4DMXvf8lob7+++/2bBhA7Vq1WLVqlW4urpm7LOzs2PSpEmEhYWxatUq5syZw/vvv39Xdbq7u7N48WKT83fs2JGXXnqJmTNnsnr1al566aWMfcuXL+f48eM4Ojry66+/mvRU169fn8WLFxMYGMimTZvYtm0b7dq1A/Rl2LZt24amaSxZsoRatWpltKtXrx5z587NOLYwLl26BEDNmjXveqTjuXPnAKhSpUqexyUmJrJkyRKAjOHyAH369GHUqFFcuXKFf//9l0cffTTXc1SsWJFly5bh4pL5772joyPx8fFMmzYNgE8//ZTu3TP/rjs5OfHDDz+we/duDh48yNSpU5kzZ06h32d2xhELzz77LI0aNTLZZ2dnx5NPPnlHmxYtWuT6vn788UfWrl3LunXrCA8Px9vbu1D1GEdqnD17tlDtioqEdyHKCE3TGN9mPAf/eBq7Gr/w3m+VqOruRPMaHpYurXTSNHjgIT2sh/0KGyfq93zX7AgPjYcqTc17veT4LEPXL5kOY4+9pAf0lNumbaxsoFxlKFcVqrXMDONZ/3R0t8yQdRt7/TPK+jmlJGQL9CFwaj2o9InvXLxNA33lJuDilfP5hRDCjMLjc14+LbftJdnAgQOZMmUKc+fOZcSIEaSlpbFw4ULs7e15/vnn82z766+/AtCvXz+TYJ1V7969WbVqFRs3brzr8N63b98cz9+qVStmzpzJqVOnTLavWbMGgBdffDHHIebNmjWjc+fObNq0ibVr12YEcuPs7A8//HCOM+y3bduW5s2bs2fPnkLVb1yiLWsYLizjfdoeHnn/nrZy5UpiYmIoV64cvXr1ythevnx5evbsyfLlywkKCsozvD/zzDM51rp161Zu3ryJq6srQ4cOvWO/pmm89dZbDBw4MOOzvFfVq1cH4N9//yUyMhJPT88CtUtMTGTFihVs2bKF8+fPEx8fj96BDbdu3UIpRWhoaJ6fQ04qVKgA6PMilAQS3oUoQ9wd3Pm/tpN4Zf0ruFX9h//Nd2DlyLZUr+Bs6dJKLysraNQH6j8Je4PgvykwuzP494YuY6GCX/7nSE2GW1eyBPFLWXrM03vNE7NP0KPp95m7VQGvevDAI6Y95m5VwdlLr6+0sHXURzVUDczclhwP4WGmgf7E3+gj7dDfrzHI+6T/6VzRIuULIe5f3s7eXI2/muP20qZ+/foZgfTo0aOcO3eOq1ev0qdPH8qXL59n2wMHDgCwbNkyNmzYkOMxxgnljL3Pd+OBBx7IcbsxmMfFxZlsN/bWNmjQINdzNmzYkE2bNnHs2LGMbcbn/v7+ubbz9/cvdHgvV65cjnUWRmKiPtLDwcEhz+OME9U988wzODo6muwbOHAgy5cvZ9WqVURHR+Pu7p7jOXJbGtD4ufr5+eVaR8OGDQG4evUqN2/ezHjvd6tXr17UrVuXw4cPU61aNbp27Ur79u1p27YtrVq1umOuA4CjR4/SvXv3fHvHr1+/Xuh6jO87ISGh0G2LgoR3IcqYNlXa0L9+fxYcWYCVQx0Gz7Xj9+FtcXO684ehKAQbe2g1DJq8ADu/gR3f6EvMNR0ILV/RZ6ePvZgllF/K7DGPiyAjjBo5uus95m5VwbflnT3mrj7310R5ubFz1t+/b8vMbUm3IPyQaaA/tjpzv5tv5jB945B7JxlhIoS4e6OajmL8jvEmQ+cdrB0Y1fTuJiOztEGDBrFnzx7mzZuXEXjyGzIPmcH8+PHjGcEuN7dv385zf16cnXPuVDDOgm/sUTW6dUtfASavIdE+Pj4mx2Z9nteEcPlNFpcT41D3c+fOoZS6q6Hzxh7f6OjoXI+5dOkS69evB0yHzBt169YNb29vwsPDWbx4MSNHjszxPLl93oX5XI3H32t4d3R05L///mPChAksXbqUtWvXZvTqu7u78+qrrzJmzBjs7PTfgQwGA08//TRnz54lICCATz75hGbNmlGxYsWMYzp06MDWrVtJSUnJ9bq5Ma44ULFiyegYkPAuRBk0qukodl3dxTXb37h4zIfhi/Yxb0gLbK1LUS9tSeVQDjp/CM1fhi1TYF8Q7P3J9BhbZ72X3K0qVPI3DeVuVfWh7nYyGiJX9q5QvY3+MEqMhasHM8P81VA4+kfmfvcamUG+cgD4NNaXAhRCiAIw3tde2mebN+rbty+jR49m3rx5REdH4+PjkzE7fF6MQ6sXL16c7/Jnxck4xD48PPfbGK5evWpybNbn165dy7VdXvtyYxyWHxMTQ0hICE2bFv5WOuOXBnn1Fs+bNw+DQb+1rFOnTnmeb+7cubmG99wU5nPNejyQ8YVF9i9ajPJaus/Ly4tvvvmGr7/+mkOHDrF9+3b++usv1qxZw8SJE4mOjubrr78GIDg4mKNHj+Lo6Mi6detyDNl30+NuZAzvWVdIsCQJ70KUQfbW9nze/nOeX/08DZv8w47g3oz5PYzPnm4oS8iZi4sXdP8CWo+As1vB1TtzYjiH8rI0mrk5uEHN9vrDKCEarh7InBDv8n44/Hvmfg8/02XrvBvpX74IIUQOutfqXmrDenbu7u488cQTJvewG5cGy0vDhg0JDQ3l0KFDhQ7vRfn7xYMPPkhoaChhYWG5HnPo0KGMY7O2A/Jcw/vw4cOFricwMJC6dety/PhxvvrqK+bNm1focwQEBORbm3GiQTc3tzyH11+7do29e/cSFhaW560F2Rk/n9OnT5OYmJjjNYyfq4+Pj0mvu7E3P7cvP06ePJnv9TVNo1GjRjRq1Ijhw4ezaNEi+vXrx+zZs5kxYwbW1tYZI0fq1auXY3CPjo7Oc637/BjfX7Nmze76HOYk3WxClFEPuD/A6MDRnLwVzMMtT7Ns70V+/O+Mpcu6/3jUgmYDoU438G5guQniyiJHd6jVCdq9CX3mwRsH4d2z0G+FPh9BpfpwMRjWjYG53eEzX/imOfw2FHZ+B+d3QtLd368ohBAl2ciRI+natStdu3ZlyJAhBWrz7LPPAvp91sYeyYIy3o9dFPcOG2dBX7RoUY5hcf/+/WzatMnkWIDHHnsM0CdHy+k2gJ07dxb6fnfQh/ePHz8egPnz52fMBp8bpdQda7F37NgR0NeMT01NvaPN1q1bMybu2717N+Hh4bk+GjduDGTeH19Q7dq1o1y5cty6dYvZs2fnWLdxNvqsnytkzlsQHByc47lnzZpVqFpAn0AQICkpKaM33cnJCdC/JMipl3/69Ok5fn4FtWvXLiD/kQ3FRcK7EGXYCw++QNsqbQmJX0DnhgY++/sYf4eVvplzhSgwJw+o3RU6vA3PLYQ3w+DtU/Dir/rtDhUegHPb4J8PIOhR+KwafNsSfh8Gu3/Qw37y3d/HKYQQJUWnTp1Yv34969evN+mNzssTTzzBQw89RHh4OF27ds0xmB09epRx48axcuVKk+1+fvoErps3b851KPXd6tOnD3Xr1iUhIYE+ffpw5cqVjH3Hjh3jhRdeAKBz584ZARD0e6HbtGmDwWCgb9++GcuzGdsNGjQoxwnSCuL5559n2LBhgD6y4c033+TixYsmxyQlJbF69Wpat27Nhx9+aLKvevXq1KtXj4SEhBy/QDAG8VatWlG3bt08azHOZ7Bo0aJCBVlnZ2dGjx4NwEcffcRff/2VsS8hIYERI0Zw4MABHBwcePvtt03adu/eHU3TCAkJYcaMGRnbk5OT+fjjj9m4cWOO15w2bRpffPHFHZ9VXFwcEydOBPTPxjgLfevWrbG1teXy5ct8/PHHpKXpS84aDAa+/fZbPv3003wn/ctNdHQ0YWFhODk50aFDh7s6h9nltPi7PO6/R7NmzZQQOYm8HanaL2mvnlr1tHry202q7pi16uDFGEuXJYRl3byq1LG/lNo0WalFfZSaUlupceX0x3h3pb5trdTvI5Ta/aNSF/cqlZxg6YqFKDOOHDli6RJKlY4dOypATZ8+vcBtRo0apQA1cODAO/bduHFDde7cWaHPtKp8fHxUixYtVEBAgHJ3d8/YPnv2bJN227dvV5qmKUBVq1ZNtWvXTnXs2FFNnjz5jlqDgoJyrGvTpk0KUNWrV79jX1hYmPLx8VGAsrGxUU2aNFH+/v4Z16xXr566fPnyHe1Onz6tqlSpogBlbW2tGjVqpBo0aKA0TVO1atVSr732Wq6fRX4MBoOaOHGisrOzy/hcatSooVq0aKH8/f2Vk5NTxvYuXbrc0X7GjBkKUCNHjjTZHhcXp1xcXBSgZs2alW8dERERytbWVgFq1apVGdvz+7yVUiolJUU99dRTJvU3b95cubq6KkDZ2tqqZcuW5dj29ddfz2hXqVIlFRgYqNzc3JS9vb2aNWtWxr6sjH/3AFWlShXVvHlz1bBhw4zPysHBQa1bt86kzdixYzPaeHp6qsDAQOXp6akA9fLLL+f6PoOCghSgOnbsmGP9xhpfeuml3D/cAirszy1gr8oh00nPuxBlXEXHikxoO4ET0cdp1iSYCs72vDRvD1diSsaSGEJYhKs31H0UOr0PLyyDt0/A6KPw/GJo/5Y+qeCJv2Ht2zCnC0yuArPawV/vw/XTlq5eCCGKjLu7O+vXr2fp0qV0794dpRQhISEcP34cLy8vBg8ezMqVK3nxxRdN2rVp04ZffvmFdu3aERsby/bt29myZYvJ0m33wt/fn4MHD/Lee+9Ru3Ztjh07xrlz52jcuDGTJk0iODiYypUr39GuVq1a7Nu3j+HDh+Pt7c2xY8e4efMmI0eOZM+ePfmus54XTdMYM2YMp0+fZuzYsbRu3Zr4+Hj279/PhQsX8PPzY9iwYWzbti3HpfcGDhyIk5MTS5cuNZkp/ZdffiEuLg4HBweee+65fOvw9PTk8ccfBwo/dN7GxoZffvmFBQsW0KlTJ2JiYggNDcXNzY0BAwYQEhJCnz59cmw7Y8YMpk+fTv369YmOjubMmTN07NiRHTt20K1btxzbDB8+nIkTJ9K5c2esra05dOgQJ06coEqVKrzyyiscOHCAhx9+2KTNhAkTmDNnDk2aNOHmzZucOHECPz8/5syZk+Nw/4JasGABQMYIipJA04O9uN8FBgaqvXv3WroMUYJN3DmR5SeW83HgTD5Znkw1Dyd+HdYaZ3uZ11KIHCmlL/d3NVSfFO/yfji/HdJSoO7j0OY18G0lcxwIYWZHjx7NdV1qIe43H330EZ9++ik//vgjQ4cOtXQ5ZUZwcDAtW7ake/furF69Ov8G+Sjszy1N0/YppQLv2C7hvWyQ8C7yk5CaQJ8/+3A79Taj6//A64tO0KmOJz8OCMTaSsKHEAVy6xrsmQ175uiz3VdpBq1fhXo9wVq+CBPCHCS8i7IkLi6O2rVrY29vz8mTJzPWLhdFq1u3bmzcuJFDhw4VeE6IvJgrvMuweSEEAI42jnze4XNuJN5gQ+R3jHuiPhuORfB/a45aujQhSg/XStBlDLx5GB7/Qg/wvw6GrwNg1/eQdMvSFQohhChFXFxcWLhwIYMHD+b8+fOWLqdMuHXrFm3atCEoKMgswd2cpOe9jJCed1FQP4f9zPR905nQZgIHj9UhaPs5JvZqQP9W1S1dmhCljyENjv8FO7+BCzvB3g0CB0HLYfp980KIQpOedyFEaSM970KIIjHIfxAtvFswOXgyA9q70OVBL8b/cZgtJyItXZoQpY+VNdTrAUP+hpc3gF9n2PE1zGgIK16B8EOWrlAIIYQQpYSEdyGECSvNiv9r93/YWNkwZvuHTHuuAXUqufLqov0cD5chv0LctaqB0GcevB4CzYfC0T/1Gern9YST6/UJ8IQQQgghciHhXQhxB29nb8a1HsfBqIMsPPYTPw0MxNHOmiFz9xB5K8nS5QlRurnXgMc+g9GH4aHxEHUCFj0N37WG/QsgVf4fE0IIIcSdJLwLIXLUrUY3evr1ZPah2YQnHWXOwECuxyfxvwV7SUxJs3R5QpR+ju7Q7k0YdRB6zdKH2P/xqj6k/r+pcPuGpSsUQgghRAki4V0IkasPWnxAZefKfLjtQ2p6WTPjuQBCL8bw9i8HMBhkiK8QZmFjB036wrBt0H8leDeEjZNgWn1Y8xZcP23pCoUQQghRAkh4F0LkysXOhcntJxMeH86nuz/l0QbevPfog6w+eJXp609Yujwh7i+apk9o1+83GL4TGjwN++fD181g6YtwYZfcFy9EOlktSQhRWpjz51WJDO+aptlqmtbI+LB0PUKUZU28mvBKo1dYfWY1a8+s5ZUOtXgusBpfbzzFb/suWbo8Ie5PlepDr2/hjUPQfjSc2wY/d4M5D8HhlfoSdEKUUTY2NiQnJ1u6DCGEKJCUlBSsra3Ncq5CrfOuadr+9KcK6K6UCs/neFsgY0E7pdTBAl6nOnAms5myKXCRIkeyzru4F6mGVAb9PYgzMWf4teevVHTwZlBQMHvO3WDhSy1pWauCpUsU4v6WHA+hi2HntxB9FspXh1YjIKAf2LtYujohilVERASpqan4+PigaZqlyxFCiDxFRUWRkpKCj49PgduYa533JlkedgU4vjIQkv7Yn8+x2WlZHkIIC7KxsmFy+8kYMPDB1g+wtlJ8/2Izqnk48crCfZyLird0iULc3+ycocVQeG0f9FkArt7w93swvT6sHw83r1q6QiGKjYeHB0lJSVy6dIlbt26RlpYmw+iFECWKUork5GSioqKIjo7Gw8PDLOctbM+7wVgPUFMpdSGf46sDZ41tlFIFGi9wt+1E7qTnXZjDH6f/4KNtHzGq6Shebvgy56Li6f3ddtyd7Ph9RFvcnGwtXaIQZcfFPbDza329eM0aGj4DrV8F7waWrkyIImcwGIiOjiY+Pp6EhAQMBkP+jYQQohhZW1vj6uqKh4cH9vb2hWqbW8+7hPcyQsK7MAelFO/+9y7rz69nweMLaFCxAcFnb9Bvzm6aVXdn3pAW2NmUyKk0hLh/3TgLu76HkIWQEg+1OkObV8Gvqz4JnhBCCCFKFXMNmxdClGGapjGm1RgqOlXk/a3vczvlNi1qevD5Mw3ZeeY6Y1YekqGLQhQ3j5rw+BQYfRi6joOIo7Dwafi+LYQsgtQkS1cohBBCCDOQ8C6EKBQ3ezc+bfcpF25eYMqeKQD0DqjK611qs3zvJX744n2GSAAAIABJREFU70w+ZxBCFAlHd31m+jcOQa/v9W2rRsCMhvDfF3D7hmXrE0IIIcQ9kfAuhCi05t7NGdJgCL+d/I0N5zcA8ObDdXiicWU+++sYf4fJ5FlCWIyNHTR5AYZvh34roJI/bJwI0/1h7TtwQ75gE0IIIUojCe9CiLsysslI6nnUY9zOcUTcjkDTNKY+04gA3/K8sSyUg5diLF2iEGWbpkHtrtD/dxi+A/x7w94gmNkUlvWDi8GWrlAIIYQQhSDhXQhxV2ytbfm8w+ckpSYxZtsYDMqAg601P/YPpKKLPS/N28uVmARLlymEAL33vdd3+pD6dm/C2a3w08Mw52E4sgoMaZauUAghhBD5kPAuhLhrNd1q8m6Ld9l5dScLjywEwNPVnp8HNScxOY0hc/cQl5Rq4SqFEBnK+cBD4+DNw/DYVIi7BssHwNdNYfePkBxv6QqFEEIIkQsJ70KIe/LMA8/QuVpnZuyfwfEbxwGoU8mVb15sysmIOF5fEkKaQWagF6JEsXeBlv+D10Ogz3xw9oK/3oFp9WH9J3Ar3NIVCiGEECIbCe9CiHuiaRrj24zHzd6N97e+T2JqIgAd63gyvqc/G49FMGnNEQtXKYTIkZU11H8SXv4XhqyDmu1h23SY3gBWjoBr8v+uEEIIUVJIeBdC3DMPBw8mtZ3EqZhTTN83PWN7/1bVGdK2JkHbzzF/5zmL1SeEKADflvDcQnh9PwQOhsO/w/etYcFTcHojKBlBI4QQQliShHchhFm0rdKWfvX6sfjYYrZe2pqx/aPu9ej6oBfj/zjM5uMRFqxQCFEgHrXg8an6ffFdxsK1MFjQG2a1g9DFkJps6QqFEEKIMklThfgmXdM0A2Bs8A4QlU+TisAX6c8VMLiAlzJpp5SyLnCRIkeBgYFq7969li5D3OeS0pJ4fvXzRCdG81vP36jgWAGA+KRUnp21kws3bvPLsNbU8yln4UqFEAWWmgSHfoGd30LEEXD1gRb/03vnHd0tXZ0QQghx39E0bZ9SKvCO7XcZ3jUyQ3y+zbI8L+yYOw0J72Yh4V0UlxPRJ+i7ui+tK7fm6y5fo2n6j4CrsQn0+nY7SakGggY1J8BXfukXolRRCk5vgB3fwJlNYOsMTftDq+HgXsPS1QkhhBD3DXOHdzAN5XnJeoGCtjG2k/BuJhLeRXFaeGQhn+/5nLGtxtKnbp+M7eevx9P/p2Ci4pKY1a8ZHep4WrBKIcRdCz+k98Qf+hUMqeDqrffIl6usP0yeV9aXqLNztnTVQgghRKlgzvBe3CS8m4GEd1GcDMrAiPUj2HdtH8ueWEYtt1oZ+yJuJjLg52BOR8Yx/bkm9GhU2YKVCiHuyc0rcGAJ3DijP795FW5dgcTYO4+1d0sP9D6ZgT5ruHetDE4VwEqm4xFCCFG2mSu8B5m1qgJSShX0XnmRi5Ia3tdsHstXZ34n3Aq8DTCqVm+6d5po6bKEGUTejuTpP57G29mbRY8vwtbaNmNfbEIKL8/bw97z0Ux4sgH9W1W3YKVCCLNLjs8M8jevws3LcOuqHvCNf8ZdA5WtT8DaLr0Xv3K2XnwfKFdFf+7qAzZ2lnlfQgghRDEwS3gXpVdJDO9rNo9l/NnfSbTKvJvCwaAYX1MC/P1i44WNjNo0isENBjO62WiTfQnJaYxcvJ+NxyIY/XAdXutSO+P+eCFEGZCWCvER6T32WUJ91ue3rkLK7TvbOlXMZYi+T+afDm4gP1OEEEKUQhLey7iSGN4f+bkBV63v/MXKJ02xbkiYBSoSReGTnZ/w24nfmPPIHFr4tDDZl5Jm4L1fD7Ii5DKD2tTg4x71sbKSX7aFEOmUgsSYLL34V7L16F/Rn9++fmdbW+f04fg53H9v7Nl38QIruTNPCCFEyZJbeLexRDFCAITncltjbttF6fRO4DvsDd/LB9s+YEXPFbjZu2Xss7W24otnG1PeyY6ft58l5nYyU59tjK21/CUQQqD3nDu6649K9XM/LjUp55574/PzO/WQb0jNdn5rcKl05/33xiH6xl58O6eifZ9CCCFEAUh4FxbjbYCrOXR4eFtiWkRRZJxsnfisw2f0W9OPT3Z+wpcdvzQZHm9lpTG2Rz0quNgx9Z/jxCak8N2LzXC0k94wIUQB2djry9XltWSdwQC3o7KE+8vpvfjpzyNPwJktkHTzzrYO5U3vv3etDF714IFHwN6lqN6VEEIIYULCu7CYUbV653jP+6havS1YlSgK/hX8GRkwkq/2f8X8I/MZUH+ASYDXNI2RnWtT3smWMSvD6P/Tbn4a2Bw3J9s8ziqEEIVgZaUPk3fxAprkflzSLdOh+dmH618Lg7gIQIGNA9R+COr3grqPgr1rcb0bIYQQZZDF7nnXNM0BqA64AzeBq0qpaIsUUwaUxHveQWabL0vSDGmM3jyajRc30tOvJ2NbjcXBxuGO49YeusobS0Op5enMvCEtqFTuzmOEEMKi0lLg0h44vBKO/qH33lvbpwf5J6HuY+BQztJVCiGEKKVKzIR1mqb1Al4HWgPZ13oJA5YCM5RSCcVa2H2upIZ3UbYYlIEfDvzA9we+p65HXaZ1mkY112p3HLftZBT/W7CXCi52LBjSkhoVnS1QrRBCFIDBABd3w5FV+uPWFX3JO7+u4N8L6jwKjuUtXaUQQohSxFzrvLsCr2XZdFoptayAbcsBC4Aexk25HKqAS0BvpdT+Ahcn8iThXZQk/136j/e3vg/AZ+0/o0PVDnccE3oxhsFBwVhbWTFvSHP8K7vdcYwQQpQoBoPeI39kpR7kb14GK1vw66IH+bqPS5AXQgiRL3OF92eA5egBG+BlpVRQAdrZAOuAjmSG9twubNwfA3RQSsmaYWYg4V2UNBdvXWT05tEcv3GcYY2HMazxMKw001nmT0Xcov9PwcQlpjJnYCAta1WwULVCCFFIBgNc3pcZ5GMvpgf5zvrQ+ge767PoCyGEENmYK7z/BAxOf3kN8FVKpRSg3RhgAqaB3RjSrwMGoGL6NpVl/16gpZLF6O+ZhHdREiWkJjBp1yT+OP0H7au0Z3L7ySZLyQFciUmg/0+7uRSdwLcvNOWh+pUsVK0QQtwlpTKD/OFVEHsBrGygVid9srsHu4OTh6WrFEIIUUKYK7wfBx5AD9jfKaVey6cJmqZVBM4BjsZN6GF9KvC1UupK+nEe6F8MjAecyAzyfZVSywtcpMiRhHdRUimlWH58OZ/t+YxKTpWY0XkGD3o8aHLMjfhkBgcFE3blJp8/3YhnmlW1ULVCCHGPlIIr+/XJ7o6shJj0IF+zQ3qQ7wHOMspICCHKsnsO75qmlUfvJTeG6i5KqS0FaDca+CK9jbHtK0qpObkc3w5YDxjXiFqnlHqsQEWKXEl4FyXdgcgDjN48mtikWD5u/TE9/Xqa7I9LSuWVBXvZfuo6Y7rX4+X2tSxUqRBCmIlScDU0M8hHnwPNOj3IPwn1ngDnipauUgghRDEzR3hvCexMf5kMlFNKJReg3R6gGZnD4bcqpTrl0+Yz4N30lymAa0GuJXIn4V2UBtcTrvPOf++wJ3wPz9V9jnebv4uddeaiFEmpaby5LJS1h8IZ0cmPd7rVNVkvXgghSi2l4OqB9FnrV8KNM3qQr9FOn+zuwSfAxdPSVQohhCgG5gjvzwOL0UN4SE4ny6GNG5m99cZe9z5Kqd/yaecLnM3SprnMPH9vJLyL0iLVkMrM/TMJOhxEo4qN+LLTl3g7e2fsTzMoxq4KY/HuC/RtUY1JvRpibSUBXghxH1EKwg+l3yO/Em6cBs0KqrfVg3y9nuDiZekqhRBCFJHcwrtVTgfnIuvXvVcK2KZdtmukAGvya6SUugCcybLpwdyOFULcX2ysbBgdOJppnaZxKuYUz61+juCrwRn7ra00/q9XA17tXJslwRcZuWg/iSlpFqxYCCHMTNPApxF0/Rhe2wfDtkP7t+BWOKx5C76sC3N7QPBsuHXN0tUKIYQoJoUJ785ZnscUsE2rLM8VsFcplVjAtseyPJe1VIQoYx6u/jBLeizBzd6Nof8OJSgsCONIIU3TeLtbXcZ0r8ffh8MZMncPcUmpFq5YCCGKgKaBdwPoMgZe3QPDd0CHdyAuAta+rQf5oMdh9496uBdCCHHfKkx4zzq+3jHXo0wZw7txTOueQlwvNstz10K0E0LcJ2q51WJJ9yV09e3KtH3TGL15NHHJcRn7X25fiy+fbczuszd4YfYursclWbBaIYQoYpoGlfyh84fwajCM2AUd34Pb1+Gvd+DLB+HnR2HXLLhZ0EGSQgghSovChPfoLM/zXadJ02eRaoFp6C/MTdd2WZ4bCtFOCHEfcbZ15suOX/JWs7fYeHEjL6x9gTMxmXfVPN2sKj/2b8bx8Fs8+8NOLsckWLBaIYQoRl71oPMHMHI3jNgNnT6AxFj4+z2YVg9+6gY7v4PYy5auVAghhBkUZsK6HsAf6S9vAxWUUrl2c2ma1hzYjekScX5KqXMFvN5GoFN6u1eVUt8XqNBSTNO0RkA99C9H0oBLwAGl1Ml7PbdMWCfuB8FXg3nnv3dITE1kQtsJdKvRLXPf2Ru8NG8PLvY2LHipBbW9ZMCOEKKMijyROWv9tTB9W9UW+mR39Z8Et3z7YIQQQliQOWabrwRcJbMnvZ9Sakkex08F3iIzvJ9XStUsRMHngWrp7Z9SSq0qaNtczmeFHoxbAM3TH40w7eEfrJSaey/XucvahgKjAP9cDtkNfK6U+v1uryHhXdwvwuPDeWvLWxyMPMgg/0GMajoKGysbAI5cucmAn4NJNRgIGtScAF+ZLkMIUcZFnYIjv8PhVXDtkL6tSmBmkC/va9n6hBBC3OGew3v6ScLQA7AGnAOaKaWiczjOEzgOuJHZ6z5TKfVmAa9TBbiY/lIBDZRSRwtcqOm5ngFeRV9r3iWfw4s1vGuaVh5YAPQoYJPZwEilVEphryXhXdxPUtJSmLJnCkuPL6W5d3OmdJhCRceKAJy/Hk//n4KJikvih/7NaP+ArIsshBAAXD+dufxc+EF9W+WmmUHevYZFyxNCCKEzx1JxAD+QGcarAxs0TWua7UK1gFVA+Wxtgwpxnc5ZnicB9zJsvB3QkfyDe7HSNM0aWM6dwf00sBJ9Sb3ss80MBb4t+uqEKNlsrW35qNVH/F+7/+Ng5EGe+/M5QiNCAahewZlfh7XG18OJIXP3sObgVQtXK4QQJUQFP33JuWFb4bX98NB4UAb492P4qjH82Am2zYAbZy1cqBBCiJwUNrzPQQ+XRk2APZqmndE0baumaYeBE0BLTO91/1MpdbAQ1xmQ/qdxebmiWAMqFrDkDC6TgIezvI4FegMPKKV6K6V6oH9B8gqQnOW4oZqmDSm+MoUouXr69WTh4wuxs7Zj8D+DWXpsKUopvMo5sOyV1jSpVp5Xl+xn4a7zli5VCCFKlgp+0O5NeGULvB4KD30CaLB+HMxsAj90gK3T4MaZfE8lhBCieBRq2DyApmmtgPVkLhdnXAZOZXme9fUNILAQE9VVB7L+S/GpUmpsoYo0Pd8M4H9AKPpSdcbHCWBc+sOoWIbNp98WcApwSN+UArRRSuU4rl3TtN7AiiybrqBP/pdY0GvKsHlxP4tNiuWDrR+w9fJWnqj1BGNbj8XRxpGE5DRGLt7PxmMRvPVwHV7tUht9IQwhhBA5ij6fOdnd5X36Nu+GUL8X+PfWQ78QQogiZZZ73rOcrD3wG1AR06XgTA4DrqNPNre1EOf+Hr232XjeVkqpwqwPn/18nkB0Tr33mqaNxzLh/Wv0+/CNpiil3sunzXLg2Syb3lBKfVXQa0p4F/c7gzLww8Ef+D70e+q412F6p+lUK1eNlDQD7/16kBUhlxnctgZju9fHykoCvBBC5CvmAhz5Qw/yl9J/FStfHWq2h5odoUZ7KOdj2RqFEOI+ZNbwnn5CN+ADoA9QI9vuKGAZMFkplf2+7bzOWRM4Btimb7qolKp+VwUW7HrjKebwnj7rfThgnEUrDfDN73NKH/GwM8umPUqpFgW9bkkN7ytDLjP1n+NciUmgcnlH3ulWl14BVSxdlijFtl7ayvtb30eh+Kz9Z3So2gGDQTFpzVF+3n6W3gFVmPJMI2ytC3vXkBBClGGxl+DYGjj7H5zbqq8nD1ChNtTsoAf5Gu3BRSYJFUKIe2X28J7t5JUBb/TQHaGUuquZTjRNc0bvzTe6rZSKvOcCc7/eeIo/vLcBtmfZtF4p9XBux2drewowjldTQBWlVIFm4yqJ4X1lyGU+WHGIhJS0jG2OttZMfqqhBHhxTy7eusjozaM5duMYwxoPY1ijYVhpVny3+TRT/zlOlwe9+PaFpjjaWVu6VCGEKH0MaRB+SA/xZ/+D8zsgOU7f51VfD/E1O0CNtuAoS3YKIURh5Rbebcxx8vRe4wL3sOdxnngg/t4rKtGyB/UC31IAbCMzvGvAQ+hLzZVKU/85bhLcARJS0pj6z3EJ7+KeVHOtxoLHFjBx10RmHZjFoahDfN7+c0Z2rk15J1vGrAyj/0+7+Wlgc9ycbPM/oRBCiExW1lC5if5o8xqkpcLVUDi7Bc5uhf3zITh9gSKfRplh3rc1OJSzdPVCCFFqmSW8i0Lxz/Y6uBBtdwED8zhXqXIlJqFQ24UoDAcbBya1nURjz8ZMDp7Mc6ufY3qn6bzYsh7lHe14Y1kIz/24k/lDWuBVziH/EwohhMiZtQ1UDdQf7d+C1CR9sruz6T3zwT/Czm9As4bKAen3zHeAaq3AzsnS1QshRKkhN30Wv7rZXhdmDZbstyNkP1epUrm8Y6G2C1FYmqbRp24f5j06j1RDKv3/6s+qU6vo3siHoEEtuHDjNk/P2sH56/f7gB8hhChGNvZQvQ10eg8Gr4H3L8CAP/Sl6aysYcfXsKA3fOYLPz8Gmz7Vg35KgRfREUKIMknCe/HLvsbKxUK0vZTtde17rMWi3ulWF0db03uOHW2teadbqf5OQpRAjTwbsazHMhp7NmbM9jFM3DmRFrXKsXhoK+ISU3n6+50cuXLT0mUKIcT9ydYRanWErmPhpXXw3nl48TdoNRxSE+C/qTCvB3xeHeY9ob++sBvSUixduRBClChmmbCutCruCes0TbMGsi5ZF6+UcilEey/gWpZNF5VSvgVpWxInrAOZbV4Ur1RDKjNDZhIUFkTDig2Z1mkacfHO9P8pmLikVH4a2JwWNT0sXaYQQpQtCTH6pHfGCfCuhenbbZ2heuv0e+bbg08TvedeCCHuc2aZbV7TtPpmraqAlFJHiuK8FgjvbkBMlk3XlVIVczs+h/auQNbuwWilVIGSRkkN70JYwr/n/2XMtjHYW9szteNUqjo2ov9Pu7kcncC3LzTlofqVLF2iEEKUXfHX4fw2Pcif3QpRx/Xt9m76cPyaHfQw7+UPVjKIVAhx/zFXeDegL1FWnJRSqkgm1rNAeK8MXM6y6bJSqmoh2tsCyVk2JSul7PM4/n/A/wB8fX2bnT9/vpAViyJxcDlsmKCvmetWFbp+DI36WLqqMudM7Bne3PQm526eY1TTUTxZ40WGzN1D2JWbTHm6EU83K/D/mkIIIYrSrXA4Zwzz/0F0+hRAjh5Qo116mO8AFeuAplm2ViGEMANzhvfippRSRTJGqgSE90tKqWqFaG8DZL0BLEUpZVeQttLzXkIcXA5/vg4pWWbUt3WEJ2ZKgLeA+JR4Pt7+MevOr+Mh34d4v/k4Ri89xvZT1xnTvR4vt69l6RKFEEJkF3spcyb7c1shNn36IJdKmWG+RnvwqCVhXghRKplznfesaV9+IhZO9imtCzutevbj4+6hFmEJGyaYBnfQX2+YIOHdApxtnfmi4xfMPzKf6fumcyrmFFN6f8nMv2yZtOYoN+KTeadbXTT55U8IIUoOt6rQpK/+UErviT+7NfOe+bDf9OPKVdWH1xvvmS9foGmChBCixLrb4ega+vDtf4Eo85Vz38se3gu7uHT24yW8lzax2RcMyGe7KHKapjHQfyD1K9Tn7S1vM/DvfoxvM4HyTr58t/k00beTmdSrIdZWEuCFEKLE0TS9h92jFjQbqIf5qJNwdose5k+ugwNL9GPda6QH+Y56mHf1tmjpQghRWHcb3hVgCzwM/A3MB/5USsmaHnlQSqVqmnYbcErf5KxpmqNSKiGvdll4Znsda77qRLFwq5o5vC/7dmFRzb2bs7zHct7a8hbvbX2HAfUHMMKpO99tPkvM7RRmPN8EexuZ5VgIIUo0TQPPOvqjxVAwGCDiSHqv/FY48geELNCPrVgns1e+RntwLvAcwkIIYRGFDe/HAeMi3MYA/0T6I0bTtGXAfKXULvOVeN85DTTM8roqcLKAbbPfH3/aLBWJ4tP145zvee/6seVqEhkqOVciqFsQU/ZMYf6R+QRWOsKb3V5j+j9XuTl3Dz/0D8TFvkjmzxRCCFEUrKzAu4H+aDUcDGkQfjBzJvuDy2DvT/qxXv6ZM9lXbwuO5S1buxBCZFOo9TWUUvWAlsB3wA304fPGhzvwCrBd07QTmqaN0TSthlmrvT8cy/a6MDNi1cznXKKka9RHn5zOrRqg6X/KZHUliq21LR+1+ohP231KWFQYf0S+w6jutuw6c4MXZu/ielySpUsUQghxt6ysoXIAtB0F/X6F987BS/9Cl7F6z/u+IFj6AnxeA37oCOvGwIVd+nB8IYSwsELNNm/SUJ/5vDswEHgcyDrrucry53ZgHvCLUurW3ZdqfsU923z6NccB47NsGqeUmlDAtkHAoCybBiql5hekrcw2L0ThHb9xnDc2vUF4fDg9qw1j6YZqVHF3YsFLLalSvrDzTQohhCjxUpPg0t7MmewvBoMhBaq1hLZvQJ1HZW15IUSRy222+bv+6aOUSlVKrVJKPQVUBl4Dgo3XS39YAe2A2UC4pmlLNU17XNO0svxTb3221+0L0TbrsSqHcwkhzKiuR12W9lhK2yptWXHhGzq0XU9k3C2e+X4HpyJK1HeRQgghzMHGHmq0hc4fwOC1es/8Y1Ph5lVY2he+bw2hiyFNpnkSQhQ/s4RopdQNpdS3SqlWwIPAZ8CF9N3GIO8IPAv8CVzWNO1LTdOamOP6pcxOIDLL687p67/nSdO0VoBflk17lVJXzF2cEMKUm70bM7vMZGSTkeyOWI9vg59J1iJ4dtZOQi/GWLo8IYQQRcneBVr+D17fD0/NBs0aVg6Hr5rAzu8gSRb+EUIUH7P3gCulTiilPlRK1QC6os9Eb/zJZgzylYA3gH2aph3UNO0tTdN8zF1LSaSUMgDLsmyyBkYVoOnobK8Xma0oIUSerDQrhjUexncPfUdMcgS21WZiX+4YL8zexbaTslqmEELc96xt9flphm+HF37R14z/5wOY0QA2fQrx1y1doRCiDCjS4etKqU1KqUGAN/q98esBQ/puY5BvAEwBzmua1rQo6ykKmqZ10jRNZXmcK0Czz4Css169qWnaHfc0ZLlGL/RRC0ZXgR/vqmAhxF1rV6Udy3osw7dcNeLdZ1O+8kYGz93FmoNXLV2aEEKI4qBpUOcRGPIXDFkHvq1hy+cw3R/WvgsxF/I/hxBC3KViufdcKXVbKbVAKfUI4At8ABwx7kYP8daAQ1FcX9O0Gjk9gOxrgFTM5Vhvc9ajlLoMTM+yyRb4V9O0J7PVbaNp2lBMe+oBxhZibXghhBlVda3K/Mfm86Tfk9xy/IsKfgt4ddlWFu46b+nShBBCFCffltB3CYzYDQ2e0pec+6oJrHgFrh3Jv70QQhTSXc82f08X1TRfYAD6UHA39PCugPZKqR1FcL17fZNblFKdcjl3J2BTlk3n028ZyK8mG+Av4KFsu04Bh9ADfQBQJdv+OUqpoQWqOguZbV4I81JK8evJX5m8ezJamhvR5/oyukNnRnaujaZpli5PCCFEcYu9BDu/hX3zICVen5m+7RtQvbWlKxNClDJmn23+Lgpw0TRtsKZpm4AzwCfowb1MUkqlog+FX5NtV22gN9CDHII7MKLoqxNC5EfTNJ6t8yzzHp2Hu7M1rjVnMWP3YiauPorBIOsBCyFEmeNWFR6dDG+GQacP9WXmgh6Fn7rB8b/AYMj/HEIIkYciDe+a7lFN0xYD19DDZ4f062pZHnvRJ20LLcp6ShqlVIxSqgfwPzJvI8hJMPCUUmqoUkrWJhGiBGno2ZDlTywn0DsAx8q/suj0l7y5fC8pafJLmhBClElOHtDpPT3EPzYFbl6BJc/D920gdIksMyeEuGtFMmxe07TGQH/gBfSZ5UEP6VldRJ8xfb5S6pjZiyiF0pfOq4fe454GXAIOKKVO3Ou5Zdi8EEUr1ZDK1/u/5ufDP5OWUJUAh1HMeeFhHO2sLV2aEEIIS0pLgbAVsH0GRBwBt2rQ+lVo2h/snC1dnRCiBMpt2LzZwrumaZWAfuihvaFxc7bD4oAV6MvHbVKWuOG+jJLwLkTxWH9+Pe/99yFJyVZUSx3K0gEDcHO0tXRZQgghLE0pOLkOtk2HCzvB0QNa/E9/OFewdHVCiBKkSMK7pmkO6PdnD0Bf093YxZQ1tBuADeiB/Xel1O27vqC4axLehSg+Z2PP8vLfr3Et4QJuCT1Z8cJHVHJztHRZQgghSooLu2DbDDjxF9g6QdMBem98+WqWrkwIUQKYNbynz7A+AHgacDFuznbYYfTAvlApJYsgW5iEdyGK1+2U24z45z32Xd+MllCHqvatqO/emADvutT2cqG2lwsVXOwtXaYQQghLijgK27+CQ7/orxs+C21HgVc9y9YlhLAos4R3TdMmAS+ir9UOdwb2CGAJ+n3sIXdZqygCEt6FKH5KKabumsPSE0GkcAsAQ6oTaQnVMdyugZPyo1a5B6nj5Y6fp0tGqK9S3hErK1luTgghyoyYi/oyc/vnQcptqPMYtHsDfFtZujIhhAWYK7wb0Ndjz/pbZSLwB7AA+FsplXaPtYoiIOFdWNyVzJ0ZAAAgAElEQVTB5bBhgr4OrltV6PoxNOpj6aqKhVKKC7cusC98P9sv7yE0IoSIxEsAaMoGknxJjPMl7XYN0hJ8sbdyoVZ6mPfzdE7/04WaFZ1xsJUJ8IQQ4r51+wYE/wi7f4CEG1CtFbR7Ex54BKyKbYVnIYSFFUV4Twb+ApYDsWaqM0dKqbVFef6yQMK7sKiDy+HP1yElIXObrSM8MbPMBPjsridcJzQilP0R+wmNCOXw9cOkpX/36WZdDbtUP+JvViMysjKGlPKAhpUG1TycMnvpPV3w83Kmtqcrbk4yKZ4QQtw3kuNh/wLY8TXcvARe9fXh9A2eBmv5eS/E/a4owntxzRSvlFI2xXSt+5aEd2FR0xtA7MU7t7tV09fBFSSkJhAWFcb+a/sJiQghNDKU+JR4ACrYe1HZsR7OqjbJ8dUJj3TnbFQCyamZa8lXdLHLCPVZh+D7uDmgaTIEXwghSqW0FAj7TZ/cLvKoLDMnRBlh7vBusvkea8uPUkrJONF7JOFdWNT48uT8fZ8G42OKu5pSIc2QxsmYk4REhBByLYR9EfuIuB0BgLOtM409G1PLtQFuWh3SEqpxPiqF05HxnIqIIzYhJeM8TnbWWUJ95hD86hWcsbORIZhCCFEqGAz6MnPbZ2QuM9fyFX2ZOScPS1cnhDAzc4b34ibh3QwkvAuLkp73e6aU4mr8VfZH7CfkWgghkSGcij6FQmGj2VCvQj0CvAII8ArA18mfG7fsOBURx6mIOE5HxnE6Io4rsYkZ57O20qheIfsQfD3guzrIkEwhhCixzu/UQ/yJv9OXmRsIrUfKMnNC3EfMFd7HmbWqAlJKfWKJ695PJLwLi5J73otEbFIsByIPEBIRwv5r+wmLCiPZkAxA9XLVCfAKoKlXUwK8Aqherjq3k9M4ExnPqchbnI7Qe+lPRcZxLiqeVEPmvwWVytmbBPra6QHf09VehuALIURJce1I5jJzmgYN+6QvM/egpSsTQtwjs67zLkofCe/C4srwbPPFJTktmSPXj+hhPkK/dz42SZ9P1MPBI6NnPsArgHoe9bBNn/QoJc3AhRu3M3rpT0XoPfWnI+OJS0rNOL+rgw1+nqb31Pt5OuPr4YSNtQzBF0IIi4i5kL7M3Hx9mbm6j0PbN8C3paUrE0LcJQnvZZyEdyHKHoMycC72XEaQ339tP5fi9CXqHKwdaOjZMKN3vpFnI1ztXE3aK6W4djMpI9BnDfcRt5IyjrOztqJGRac7Jsyr6+2KrYR6IYQoHvHX9WXmgn+AhGjwbaOvFf/AI3rPvBCi1JDwXsZJeBdCAETejtQnwUvvnT924xgGZUBDo457HT3MV9KH2ns7e+d6ntiEFM4YQ31kHKcj4jkdGcf56/EYR+A72VnTvIYHbWtXoI1fRer5lMPaSn6BFEKIIpUcr/fC7/gmfZk5//Rl5p6SZeaEKCVKbXjXNK2rUmqDpeso7SS8CyFyEp8Sz8HIgxlrzh+IPEBCqj43QWXnygRUCiDAM4CASgHULl8bKy3vnvSk1DTOX7/NiWu3CD57gx2nr3MqIg4AN0dbWtXyoG3tirTxq4Cfp4vcQy+EEEUlLQUO/apPbhd5DNx8oc2rENAf7JwsXZ0QIg+lLrxrmvYYMBZoKbPN3zsJ70KIgkg1pHI8+jgh1zLvm49KiALA1c6VJp5NaFqpKU08m9CgYgMcbBzyPWfEzUR2nL7OjtNRbD91ncsx+pcDXq72tPHTe+Vb+1Wgmof8MimEEGZnMMDJf2DbdLi4G5wqQMth0PxlWWZOiBKq1IR3TdOeBMYATdHXkJel4sxAwrsQ4m4opbgUdynjnvmQiBDOxJ4BwMbKBv8K/hkz2jfxaoK7g3u+57xw/TY7TkelB/rrRMXp98/7ejjRxq8CrdMfXq75fzEghBCiEM7v1EP8yX/A1hmapS8z51bV0pUJIbL4f/buPDzO8r73//ueRZrRYkmWbMsbeANvILCPQxL4JbSQ2G1ceshyaJs2bdOWNudKgrqEJlwJIEjSJOW01CSnaUt7TvLLlfxOHeCQUjfHSaAkOSwNxjY2NhiCjLFlS7K1WdtIs9y/P56Z0cyjkayRZjSjmc/riq7Rc8/zzHzHEbY+z70VfXg3xtwGfBa4KtEUf1R4zwGFdxHJlb5QH4e7Dyfnzr/c8zKRmLMq/dq6tckwf3XT1Vy+6HK8nqn/CrfW8nr3EM/+3Anzz7f3cDHkvNaVy2qSvfLvWNtIXZXmaoqI5ETXsfg2c49omzmRIpS38G6M2QrcCKwGGoAQ8CbwE2vtwRlc/2GcnvaNpAT2xNNAt7V26lWTZEYU3kUkX0KREMd6jiV75w93H2YwPAhAla+KTYs3saVxC1sat7C1ceu0gT4asxw7O5DslX/hZC+j4SgeA1etrOOd8WH2b1vTQFWFbz4/pohI6ek7NbHNXGQUNu52VqhffV2hKxMpazkP78aYXcCXgZZpTjsEfMJa+3yG638R2ANsJXNoPw38FfCwtXZ0VkVKksK7iMyXmI3xRv8bHOs5xrELxzjee5wTvScYizrD44O+IJsXb55RoB+PxDh8ut8ZZv/zHg6d7iMctfi9hm2rG+JhvpFtlzVQ4dO2dCIiszJ8wdlm7j/+HkL9sPrtToBvvAKarnAeq5u05ZzIPMlpeDfG3Al8CSdkp/5XbF3HAOPAr1lrvxe/tgL4G+CPEi/nuu414CvAt6y1kayLk4wU3kWkkCKxCO0D7RzvOc7xnuMc6zk2q0A/Mh7hwJt9yQXwXu4YIGYh6PeyY00D169v4oYNjWxdUadt6UREsjU25PTCH/42XHgNouMTzwXqnBDfuAGaNkwE+8Xrwa81SkRyKWfh3RizG3gifpjaU+6W+twwsBm4AHwfeDeTQ/shnBsCj9pimYhfQhTeRaTYuAP98R6nhz4UDQGTA/2Wxi2sWbQmLdAPjIT5j5M9yTD/WpezLV1twMc71jm98jdsaOKKpdqWTkQkK7Eo9L8FPT+HC687jz2vw4Wfw+DZlBMN1K92Qn2yp36D81i7AjwaFSWSrZyEd2OMB2gHLiM9eL8Q/+oDFgHXAjcw0TNvgYfj399OerB/DrjfWrs/608lM6bwLiILwVwD/fnBMZ5r7+G5+LZ0b/WOANBUU8E71zdxQ3zO/OrFQYV5EZHZGhuKh/mfp4T7eLAPD0+c56+CxvUpPfYpj5W1hatfpMjlKry/D/hXJsL3SeDXrbWTUqExZiPwHWBb/PxhIAAkVhh6C7jDWvsvWXwOmSWFdxFZqCKxCCcHTnKs59iUgX7T4k1sbdw6KdCf7h3hufae5Gr23YPOMP2V9UFu2DCxx/yyRRryKSIyZ9bC4LmUnvqUYN//FtjYxLk1zRNhPjXY118OXi1IKuUtV+H9IeAT8cMh4Bpr7clpzl8MvASsSDThBPmfAB+w1vbN+M1lThTeRaSUJAK9ew69O9An5s9vadzC5bWX82ZPKLn43XPtPQyMhgFYv6SaGzY0cf36Rt6xrpH6qopCfjwRkdITGYPe9szBfjQlEnj8sHhdhmB/BVQ3Fq5+kXmUq/D+DPBOnAD+NWtt6wyu+TPgASaG2fcBa6y1gzN+Y5kzhXcRKXXuQH+85ziv9r6aMdBvadzCpobNhEYaeb7dWQDvZyd7GRmPYgxsWb6IGzY4vfLXrVlMdaV6gURE8ma4J2VO/esTwb63HWLhifOCDSnz6tenLJq3DnyVhatfJMdyFd7P4PSiW+AWa+2/zeCaLcDLTAy1/wtr7d0zflPJCYV3ESlHmQL9ib4TjEacHUhTA/3Ghs14w6toP1vN8+19HDzVz3g0hs9juGZ1PTesb+Sd65vYdlk9AX/mfepFRCSHohHoPwU9b0wO9kOdE+cZD9Rflr5gXnLRvOXa4k4WnFyF90GgGieIX22tPT6Da6pwhtgn3mi3tfb/zPhNJScU3kVEHNFY1An0vcedfegzBPqNDRvZ2LCZQOxyevuWcuxUgKNnLhKzUOnz8LY1i5N7zF+9sg6fV6spi4jMq9DFeG99arB/3TkOj0ycV1GT3kufCPaNG6CypnD1i0wjV+E9scqEBdZaa9+axXVXWWtfmfGbSk4ovIuITC010B/vcUK9O9BvqLuSeu9aQsMrOHW2gTfOVgMeait9vH3d4mSvfPOiAE01lVT4FOhFROZdLOZsZZe6xV1y0bzTTPQn4mxll7pnfd1qqFkK1U1QvVThXgqmmML7jK+T3FF4FxHJjjvQJ+bQJwJ9wBtgWWAdnvBqzl9YQteFJmJjSwBnSP3i6gqW1FSydFElS2oqWbKokqW1AZbUVrI0/rWktpKaSp+2rRMRmQ/hUeg9OXkIfs/rEBqYfL6/CqqXOF81S9Mfk9/Hw36wQcPzJWcU3sucwruIyNxFY1HevPhm2rZ1qYG+whNgaeU66jxrqYheTiy0isGhei4Mhjk/OMZ4NDbpNYN+bzLgTzw6IX8i6AdYXF2B16NfDEVEcs5aGL4AFzucx+FuGOqG4fPOV+L7oW4YuZC+5V2Cxx8P9U0Tob4mHvKT3y+dOMejtVNkagrvZU7hXUQkPxKBPrFl3bELx9JWua/2Vzsr3C/ewtpFm1hSsR4TbuLC8BjdF8c4PzhG9+AY3YOh5PeDocik9/F6DI3VFWmBfkmtE/iXJoO+06YF9URE8iQWg9HeeKDvdsJ+8vvzMHQ+Hv7jj9HxDC9ioKoxfYj+pB79lEetpF928hHe11hrT+fzOskdhXcRkfkTiUVoH2jn2IVjyV76V3tfJRzf8qi2opatjVudrybncXn18uTw+VA4Gg/yE4F+IuiH6B50vr8wNEYswz/jtQFfWqBPfu8aul8X9GvIvohIvlgLYxcngry7Fz+tZ/8CjE+xk3ZlXUovvmu4vruXv6JGw/dLQC7De+KCDmBy10Bma+KPNsvrAKy1dn0W50sGCu8iIoUVjob5ef/Pnd75eA/9632vE7HOP4kNlQ1sadrC1satXNV4FVubtrK0aum0rxmNWXqHx9MC/fnBMbovhjg/5AT+RK9+KDx5mGeF15Mcnj+pRz8e9pfUVtJUU4lfK+qLiOTX+MhEoE+G+kQvvqtttC/za/iC6UP0E99n6uXXPP2ilevwPp//L1trrcb/zZHCu4hI8RmLjvFa72sTgb7nGG/0v0EsPp9ySXAJWxu3JkP91satNAYbs34fay1DY5GUIfoTvfjnL46lBP0QfSPhSdcbA4urKlxBf6JHvzbgo6bSR3XlxGN1pZdKn/75FhHJi2h4Yn5+2nD97pR5++cnhvbb6OTX8Pig/jK4oRW2fUTz8ItIPnre54NB4T0nFN5FRBaG0cgoJ3pPJHvnj/Uc4+TASWz8n9/m6uaJIfeNW9nSuIX6QH3O3n88EuPCUOa5+N3xoH8+3rMfjk7/K4Hfa5wgX5EI9d60gD+prWJye3Wlj5oK59in3n8RkezFYk5Pfaah+2/+XzjzM1i6BXZ+Hja8p9DVCrmf8z6fFN5zQOFdRGThGg4P80rPK2mB/q3BibVfV9as5Kqmq5KBfnPjZmoravNak7WW/pEw54fGGAyFGRqLMjwWYWgswnD8K9E2PJ5oi7qejzA8HiWaaeJ+BpU+T0rPvo+aSQHf1ZYyCsA9MqDK78Wj1ftFpNxZC8e/Bz+6F/rehPU3w84vwLItha6srOUqvF+e06pmyFp7qhDvW0oU3kVESsvA2ACv9L6Stihex1BH8vk1i9awpXFLclG8zYs3U+WvKmDFmVlrGYvEkqF+KB7yU28EJNvG3TcHJp87PJ5haOgUqiq86T39Fb6MNwcmtcVvFAT8HgJ+LwG/l2D8Udv5iciCFBmDnz0MP/lLGBt0htH/4mehdlmhKytLOQnvsnApvIuIlL6+UF/alnXHeo7RNdIFgMGwrm4dW5u2JkP9psWbCPgCBa46t2Ixy2g4mhbuJ4J96g2BqOsmQMq54xPtmRb6m06F15MM9cEKLwGfl0CFl4DPkzwOVnjjod+TDP3B+HHqzQDnvMk3CIJ+L5U+j0YOiEjujfTCj/8SXngYfAG44Y/hnR+HiuK7+VvKFN7LnMK7iEh5Oj9yfiLQ9xzj5Qsv0xvqBcBrvKyvX5825P6Khiuo8FYUuOriEYnGGB53B/0oI+MRQpEYofEooUiU0fEoo+EooXCMUDhKKJw4jjKa0pZoHx2PMRb/PjLDaQNula4bAu7jSTcDMtwgcG4yeJI3GTLdTKj0ebSloEi56XkDfngPvPqvsGgl3HQ3tPwaeLT2yHxQeC9zCu8iIgLOMPWuka5k73wi2PeP9QPg8/i4suHKtH3o19evx+/xF7jy0hWOJsK9O/jH4kE/ylj8BsFUNwMS505qS7l2NBxlNvcJjIGAz0t1pZd1TTVsWl7L5uWL2Lx8ERuX1RKs0NJEIiXrzWfgB5+Fs4dg+TWw84uw9l2FrqrkKbyXOYV3ERGZirWWs8NnOXbhGC/3vMzxC8c53nOcwfAgABWeCjYt3uQMt29y9qFfW7cWr7YVWlCstYSjllAk6owYSNwcSAn8Y67gnzw3EuPiaJjXu4d49dzF5NoCxsDaxup4mHdC/abli1hRF1BvvUipiMXg5UfgR/fBxTOwcTe8935o2lDoykqWwnuZU3gXEZFsxGyM04Onk3Pnj/Uc45WeVxiJjAAQ9AXZvHgzWxq3cM3Sa9i2ZBvLqrWwUTmIxSxn+kY5fu4ir5y7yKudF3nl3CBv9Y4kz6kL+tnUXJsW6q9cVkvArxs+IgtWeBSe/1v46YMQGYUdvw83fhqqGwtdWclReC9zCu8iIjJX0ViUUxdPJcP8sQvHeLX3VULREOBsWbdt6bbk1/r69XiM5keWi8FQmNe6Bjl+bpBX4sH+ROcgI/Feeo+BdUtqkqF+S3zo/bJFleqlF1lIhrrh6S/Bi9+Ailp496fg7X8EvspCV1YyFN7LnMK7iIjkQzgW5kTvCQ52HeRQ9yEOdR+iJ9QDQG1FbVqYv6rpKiq9+uWunMRilrd6R5Jh/pVOJ9if6RtNntNQ5WdT86K0XvoNS2vUSy9S7LpfgR/cDT//IdRfDu9pg63vd+bTyJwovJc5hXcREZkP1lpOD57mYPdBDncf5mD3QU4OnATA7/GztXFrMsxfu/RaGgINBa5YCuFiKMyr5wbjQ+4vcvzcICc6Lya35vN6DOuXOHPpnWBfy5bli1hSq156kaLzxlOw/3PQfQxWXQe7vgirryt0VQuawnuZU3gvHvva97Hn4B46hztprm6mdXsru9ftLnRZIiJ50xfq43D3YQ51H+Jg90GO9RwjEosAsLZuLduXbmfb0m1sX7qdVbWrFM7KVDRmOdUzzCspw+5f7Ryko3+il76xusJZ7T7ZU+/00lf4ND1DpKBiUTj8bXjqCzDU5fTAv6cNGtYUuLCFSeG9zCm8F4d97ftoe7YtOT8UIOAN0HZ9mwK8iJSNUCTEsZ5jTpjvOsjh84cZHHdWtm8MNLJ92fZk7/zGxRu1TV2ZGxgJ80q8h/7Vc4O80unMpR+LOL30Po9hw9KaeC/9xDZ2S2o1RUNk3o0NwbMPwTMPgY06c+Hf9SkI1he6sgVF4b3MKbwXh52P7OTc8LlJ7curl/ODD/2gABWJiBRezMZ4o/+N5Jz5Q92H6BjqAJxV7VuaWrh26bVsX7qdliUt1FTUFLhiKbRINMabrl76V84N0nlx4uZ4U01lcg594nH9khr8XvXSi+TdxbNOL/zh70CwAX7hM7Dj98Crm7EzofBe5hTei0PLN1uwTP5vzmA48jtHClCRiEhx6hru4tD5QxzqcsL8ib4TxGwMj/FwZcOVyWH21y69lubq5kKXK0Wib3g83ks/mNzG7rWuIcbjvfR+r2HD0trkHPrEfPrGGvXSi+TFuSPwg8/CyZ9A4wZnf/iN79Oidpeg8F7mFN6Lg3reRURmZzg8zEvnX0r2zB85f4TRiDMXekX1CrYtmwjzG+o3aIs6SYpEY7RfGE72zid66rsHx5LnLK2tdIbdx0P95uWLWNdUjU+99CJzZy28th9+eDdceA3WvAt2fgFWXFvoyoqWwnuZU3gvDprzLiKSG+FYmNd6X0sugneo+xAXRi8AzhZ11y65Nm2LuoAvUOCKpdj0DI3xanzruuPx+fSvdw8Sjjq/G1f4PFyxtIYrl9VyeWMVaxqrubyxissbq2mo8mthRZFsRcPO3vBPfwlGeuGaX4eb7oa6lYWurOgovJc5hffiodXmRURyz1rLmaEzE4vgdR/mjYE3APB5fGxp3JJc1X7b0m3aok4yCkdjvHF+KLk43vFzF2k/P8zZgVFSf2WuDfhSwrwT6Nc0VrOmsUrb2YlcSmgAfvrX8PzXwXjg+k/ADa1QWVvoyoqGwnuZU3gXEZFy0x/q5/D5w8mh9i9feJlwLAzAmkVr0la1v6z2MgUumVIoHOVM3winekZ4s2eEUz3DvNkzwls9w5zuGyUam/h9Ouj3JkP9msZqLkvptV9eF8Tr0c+ZCAB9p+DJ++HlR6B6Kdz0Wdj2EfB4C11ZwSm8lzmFdxERKXdj0TGO9xznYNfBZKC/OH4RgMWBxck589uXbmdT4yZtUSczEo7GONs/mgz1p1LDfe9IcrE8gAqvh1WLg8kwnxruVzUEtRK+lKczB2D/Z+H087B0C+z8PGx4T6GrKiiF9zKn8C4iIpIuZmOcHDjJwW5nmP3BroOcGToDOOuRtCyZ2KLumiXXaIs6yVosZum8GOLNeKh/s2eYt1J670fGo8lzvR7DyvpgWq/95fGQf9niKgJ+9UZKCbMWXvkX+OG90HcS1t/sLGq3bEuhKysIhfcyp/AuIiJyad0j3RzqPuSE+e6DnOg9QdRG8RgPV9Rf4WxRt2w76+rWUeGtwO/x4/f48Xl8+L3+5LHXeDUMX6ZlreX80Fi8p36it/5UzzBvXhjmYiiSdv7yuoAT7BdXc3lT+gJ6NZW+An0KkRyLjMEL/wg//gqMDTrD6H/xs1C7rNCVzSuF9zKn8C4iIpK9kfAIRy4c4VCXs6r9kfNHGImMXPI6g3ECvcefFuqTQT9x7M3QNkW7+waB+3UmvUaGa9Ku082GotY/Mp4S5kc41TsxJP/C0HjauU01Fcle+tRQv6axivqqigJ9ApE5GOmFH/8lvPAw+AJwwx/DOz8OFVWFrmxeKLyXOYX34vH4oQ4e2H+Cs/2jrKgPcueujdy6TVtkiIgsBJFYhNf6XuPM4BkisQjhWDj5mPyKhrNuT21Lfh8NE7GR5HXhWJiojV66yFmY6maD+4ZAlb+KKl8VQX+QKl8V1f7qZFu1v5qgL5h2XOWrSjv2e7WOQC4MjUWS8+vf7BnmVEq4PzcQSju3LuhPC/OXp6ySv6RGK+NLket5A354D7z6r7BopbO1XMuvgae014dQeC9zCu/F4fFDHdz12FFGwxO/fAX9Xr70gasV4EVE5JKisSgRG3ECfkqonyrsT9eeepNgJu3haJjRyCgjkRFGwiOMREYYDg8zGhmdcf0+jy9jsK/yV2UM/UFfMONNgMRzVf4qLSzoEgpHeat3hDcvDDuPKSG/o2+UlIXxqarwOmF+cVXaUPxV9VVU+DwYA8aAxxgM8UcDxhg8qY+YifNSzk+cKzJnbz4DP/gsnD0Ey6+BnV+Ete8qdFV5o/Be5hTei8MNX36Kjv7Jv+SsrA/yzGduKkBFIiIicxOzMUKRUDLMpwb7kcgIo+HRSc+5H1OfG42MZnVDoMJTccmbAJkeU0cNuB+9JbpV1XgkRkf/aLy3fmJF/Dd7hjndO0I4mvtcYAyZgz8TNwAyB//E8+ltiePUaxOvn/oeBqdzdsprMWk3JzzG4PEYfB6Dd9Kjx3n0Osc+jwefd5rzPCbt+cnneya/j3ei3eed5jyPJ1lHor1sbpDEYs62cj+6Dy6egY274b33Q9OGQleWc1OFd61uITKPzmYI7tO1i4iIFDuP8SRDc1OwKSevGY1F03r5hyNOuB+NjE66CTDVcz2hHuf7+PFYdGzG7x/wBpK9+/WV9TRXNztfVc0T31c30xRswudZOL9OV/g8rG2qZm1TNWxMfy4as5ztH+Wt3hE6+p2962PWOj318Ucbf4zFO/9i1mItznPEv49ZbMpziWssiddItGW41qY8Ej8vNsW1kKxpqmsTx857uNsS9UKUWPzzOn8OkZglGovFHy2RqCUSi008F7UTz8ViaaMZCsE73Q2HS9xICPi9rGuq5opltVy5rJYrl9UU7zoJHg+03Aabb4Hn/xZ++iD87dthx+/DjZ+G6sZCV5h3C+dvG5ESsKI+mLHnfUV9sADViIiIFCevx0tNRU1Ot+eLxCKTe/0z3ARIjBYYDg8zHBmmL9THyYGTPHf2uUmLFXqMhyXBJVOG++bqZhYHFuMxxT8/1+sxrF5cxerF5bEgWC7FYpaotWnhPjwp7Kccp94giN8ISD3OeF78OBxNOY5Ofd7UdaSfNzAa5pEXzzCcsm3hktpKrlxWwxVLJwL9FctqqQsWyRQVfxDe9Wew7bfh6b9wFrV76X/Buz8Fb/8j8FUWusK80bD5MqFh88VBc95FREQWJmstQ+EhOoc7na+RzuT3XcNdyWN3D7/P42NZ1TKWVS1LD/YpQb++sr58hj5L0bHW0tE/yutdQ7zePchrXUO83jXI691DjKSE+mWLKrlyWW081DuB/oplNSwKFDjUd78KP7wbXv8B1F8G77kPtr7fmROxQGnOe5lTeC8eWm1eRESkNFlr6R/rzxjwO4c76Rrpomuki0gsfQ/3gDfAsuplNFc1O48ZAn5tRW2BPpWUq1gsHurjgf61rsFkwA+FY8nzltcFnGH3S2uccB8P9jWV8zzI+41/hx98DrpehlXXwa4vwurr5reGHFF4L3MK7yIiIiKFF7MxekZ7Mob7zhGnF//86HliNpZ2XbW/Oi3MTwr7Vc1U+TXkXfIvFrOc6Rvlta5BXlcGFFkAACAASURBVOt2Av1rXYP8vHuIscjEz+3K+iBXLIsH+niw37C0hup8hvpYFA5/B576Agx1Oj3w72mDhjX5e888UHgvcwrvIiIiIgtDJBbhwuiFScE+9bgn1DPpukUVi6aefx8P+hXeIl2MTBa8aMxyunfE6aHvdgL9a11DvHF+iPGUUL+qIZjsob9yqfO4YWkNVRU5DPVjQ/DsQ/DMQ2Cjzlz4d30KgvW5e488UngvcwrvIiIiIqVjPDpO10hX2pB8d9gfGBuYdN3iwOIpF9drrmpmSdWSnKygb62NrxIfc1aIJ/5oY/HV5SfaEt8nzk29LuNruK6b9Lrx79MeU19ritf3GA9+rx+/x0+Fp2LS9z6Pzzn2VuD3+PEar9YqmIFINMZbvSPJufSvdTuP7eeHGY86od6YeKhfWhtf+X6ipz7gn8PWjRfPwlNfhMPfhmAD/MJnYMfvgbdIFt+bgsJ7mVN4FxERESkvI+GR9FA/MrG4Xtew0z4UHkq7xmM8NFQ24DXeZKAGZ7h/phCcCMvJQB1/rhwYTDLIJ7+86Y8zuQng/j7tddzPedNfc9pzPX68njkE3zyLRGOc6h1xAn3KnPr2C0OEo05GNQYuW1yVXCQv0WO/fkmWof7cEWc+/MkfQ+MG2PlF2PhLefpkc6fwnsIY4wOuB9YAy4GLwBngOWvthQLVtCpe0zKgGugETgPPWGtDc319hXcRERERcRsaH5o0LP/CqPPrsDEGDx6MMRgMHuP6PqUtcTzpezwZr0ucl9hGz2M8E++V4X3TznO/Bh4wZH6vmbxG/L1iNsZ4dJxwLDzxFU3/fjw2nvF79/nTvY77ucSxeyHDXPAab8aQn7gJ4PP4kn8OBoPzP2c0QVp7/OdhUlvKNe7XSb5GSrv7fPfrAmANI+NRBsciDIUiDMa/hsaiONHVOa+m0s+iQPwr6KcuWMGioB+fxzPptRNXmYEzcPpnbFt6Lbv/8zdy/uedK1OF97La590YUwXcDXwUJyS7hY0x3wc+Z609Og/1GOCDwGeBa6c47aIx5hHgbmvt2XzXJCIiIiLlo6aihg0VG9jQsKHQpZQ9a+3kmwCx8amDf+pNg9TnXDcWIrFI2uu4X9Nicf7njKSA+Pep7fHnLJZYbGJkRWp76vmJz5N6rfv81OOpzscPVX5LsMbSZC3haJTxaIxwNEY4GqUnaukajMGgTVbk8xi8XoPPA16PwecxeDwTf8a2thb/sivYnef/P/OhbMK7MWYr8AiwaZrT/MCvAjuNMX9irf27PNZTC/wz8MuXOHUR8HvArcaY37fWPp6vmkREREREpDCMcYbhV3grnFQiMzIWiXLywvDEnPr48Ps3e4aJxTO9z2NY01Tt7E+/tJZ3Lm0sbNGzVBbh3RizHNgPuDfTfhFoBxqBtwGJDTQDwNeNMRettd/JQz1VwNPAdtdTPcB/AAPACuCdQGJJ0MXAd40xv2yt/VGuaxIREREREVloKn1eNjUvYlPzorT2UDhK+/nh+D71zrz642cv8v2XO+kdHucd6xZegC/58B4fmv4o6cH9KPBb1tojKefVA58HPpFy3j8ZY16y1h7LcVl/S3pwHwH+DPhHa21ysosxZjHwZeD2eJMPeMQY02KtfSvHNYmIiIiIiJSEgN/LlhWL2LJicqgPhaMFqmpuPIUuYB58AKcHO+EkcGNqcAew1vZbaz8JPJTSHMAJ9DljjNkB/E5KUwz4z9bav0sN7vGaeq21fwj8dUpzHXBfLmsSkTw5shcevAra6p3HI3sLXZGIiIhIWQv4vdRXVVz6xCJUDuH9Xtfxx621fdOcfxdwKuX4/caYqRaTm41PuY6/NoNh8HcBJ1KOP2KMuTKHNYlIrh3ZC0/cAQOnAes8PnGHAryIiIiIzEpJh3djzNXA1SlNr1hrvz/dNdbaEcC9UN2Hc1SPB9jlat5zqeusteM4Q+0TvLmqSUTy5Mn7ITya3hYeddpFRERERLJU0uEduMV1/O0ZXuc+71dzUAvAVUB9yvEb1tr2GV7r7p1/f25KEpG8GDiTXbuIiIiIyDRKPby/13X805lcZK09TfrQ+Y3GmNU5qGeF6/iVLK59FWd+fEJLfBV9ESlGdauyaxcRERERmUaph/etKd/HgANZXPv8NK81W4tdxwMzvdBaGwOGXM25qElE8uHme8AfTG/zB512EREREZEslWx4N8Y0AEtSmrri89ln6qTreOPcq2LMdVyZ5fXu8zfPoRYRyaeW2+CWh6BuNWCcx1sectpFRERERLJUyvu8r3cdn87yevfE1A1zqCXBvcp980wvjN+McIf3K+ZckYjkT8ttCusiIiIikhMl2/OOsx96qvNZXu8+3/16s3HCdbzNGOOf4bVvy9CWi5pERERERESkyJVyeK9xHYeyvN61x9Ok18uatbaD9OH41UxeEX8qv5Ghbc41iYiIiIiISPEr5fBe7TrONry7z3e/3mz9s+v488aYaee+G2OuAX4zw1PThndjzB8aYw4YYw6cP5/twAMREREREREpFqUc3t3sHM83Oarjq6QvXLcF+P+MMYFMJxtj1gPfAzINr5/2M1lr/8Fau8Nau2PJkiXTnSoiIiIiIiJFrJTD+7DrOJjxrKm5z3dv0zYr1tqzwKdcze8HjhljWo0xO4wxVxpjbjTG/CXwEnB5/Dz3Inr9uahJREREREREilsprzbvDu8Ze7an4T4/J+EdwFr7NWPMRuATKc3rgL+Z5rKngH3AX6W0KbyLiIiIiIiUgVLueR9wHTdleb17nLn79ebEWvtJ4ONA7wxO/zrwK0weDdCVy5pERERERESkOJVyz/sbruPVWV7vPt/9enNmrf1bY8x3gI8AvwRchXPTIIyzL/3TwD9Zaw8BGGNWul7iUK5rEhERERERkeJTsuHdWttrjDnPRA96szGmylo7MsOXWOs6fjV31U2w1vbjLGL31RmcfrXr+EDuKxIREREREZFiU8rD5gGOpXzvAXZkce3bXcfH517O7MW3k7smpemktda9gJ2IiIiIiIiUoFIP7z9yHb9rJhcZY1YDa1KaTlhr38pVUbP0PqA25fgbBapDRERERERE5lmph/d/cR3/5gyvc5/nfp1CaE35PobCu4iIiIiISNko6fBurT0KvJzStNkY88vTXWOMCQIfczV/J9e1ZcMY81vAjSlN/1QEIwFERERERERknpR0eI9rcx1/zRjTMM35XwIuTzl+3Fp7eKqTjTG/a4yxKV9PX6ogY8yMFwo0xvwS8I8pTd3Ap2d6vUix2Ne+j52P7KTlmy3sfGQn+9r3FbokEREREZEFoxzC+2PAcynH64AfG2PSVm43xtQZY75K+vD0EPC5PNR0pzFmnzHmNmPMokwnGGPWGWO+DuwDKuPNMeB2a21fHmoSyZt97ftoe7aNc8PnsFjODZ+j7dk2BXgRERERkRkq2a3iEqy11hjzIeAFYEW8+WrgJWPMi0A70AhcR/qCcAB/YK09Ru55cRagex8QNca8irOP/CDQgHODYZPrmhjwMWttMcy/F8nKnoN7CEVDaW2haIg9B/ewe93uAlUlIiIiIrJwlHx4B7DWnjXG7AIeATbGmw3O1nGZto8LAX9qrf32PJTnBbbGv6bSDfyutfb781CPSM51Dndm1S4iIiIiIunKYdg8ANbal4HtwFdwwnAmYZyV5a+z1n49j+U8GX+foUucdxa4D7hSwV0Wsubq5qzaRUREREQknbHWFrqGeRdfMO4GYC3QDFwEzgDPWWvPz2MdXpwh/JtwhvRXA+M4of0l4KjN0f9BO3bssAcOHMjFS4lkLTHnPXXofMAboO36Ng2bFxERERFJYYx50Vo7aYR4WQybd7PWRoAfx78KWUcUOBz/EilZiYC+5+AeOoc7aa5upnV7q4K7iIiIiMgMlWV4F5H5t3vdboV1EREREZFZKps57yIiIiIiIiILlcK7iIiIiIiISJFTeBcREREREREpcgrvIiIiIiIiIkVO4V1ERERERESkyCm8i4iIiIiIiBQ5hXcRERERERGRIqfwLiIiIiIiIlLkFN5FREREREREipzCu4iIiIiIiEiRU3gXERERERERKXIK7yIiIiIiIiJFTuFdREREREREpMgpvIuIiIiIiIgUOYV3ERERERERkSKn8C4iIiIiIiJS5BTeRUTyZF/7PnY+spOWb7aw85Gd7GvfV+iSRERERGSB8hW6ABGRUrSvfR9tz7YRioYAODd8jrZn2wDYvW53ASsTERERkYVIPe8iInmw5+CeZHBPCEVD7Dm4p0AViYiIiMhCpvAuIpIHncOdWbWLiIiIiExH4V1EJA+aq5uzahcRERERmY7Cu4hIHrRubyXgDaS1BbwBWre3FqgiEREREVnItGCdiEgeJBal23NwD53DnTRXN9O6vVWL1YmIiIjIrCi8i4jkye51uxXWRURERCQnNGxeREREREREpMip511E5sXjhzp4YP8JzvaPsqI+yJ27NnLrtpWFLktEREREZEFQeBeRvHv8UAd3PXaU0XAUgI7+Ue567CiAAryIiIiIyAxo2LyI5N0D+08kg3vCaDjKA/tPFKgiEREREZGFReFdRPLubP9oVu0iIiIiIpJO4V1E8m5FfTCrdhERERERSafwLiJ5d+eujQT93rS2oN/Lnbs2FqgiEREREZGFRQvWiUjeJRal02rzIiIiIiKzo/AuIvPi1m0rFdZFRERERGZJw+ZFREREREREipzCu4iIiIiIiEiRU3gXEZHcOrIXHrwK2uqdxyN7C12RiIiIyIKnOe8iIpI7R/bCE3dAeNQ5HjjtHAO03Fa4ukREREQWOPW8i4hI7jx5/0RwTwiPOu0iIiIiMmsK7yIikjsDZ7JrFxEREZEZUXgXEZHcqVuVXbuIiIiIzIjCu4iI5M7N94A/mN7mDzrtIiIiIjJrCu8iIpI7LbfBLQ9B3WrAOI+3PKTF6kRERETmSKvNi4hIbrXcprAuIiIikmPqeRcREREREREpcgrvIiIiIiIiIkVO4V1ERERERESkyCm8i4iIzNWRvfDgVdBW7zwe2VvoikRERKTEaME6ERGRuTiyF564A8KjzvHAaecYtHCfiIiI5Ix63kVERObiyfsngntCeNRpFxEREckRhXcREZG5GDiTXbuIiIjILCi8i4iIzEXdquzaS4nm+ouIiMwbhXcREZG5uPke8AfT2/xBp72UJeb6D5wG7MRcfwV4ERGRvNCCdSIiefL4oQ4e2H+Cs/2jrKgPcueujdy6bWWhy5JcSyxK9+T9zlD5ulVOcC/1xeqmm+tf6p9dRESkABTeRUTy4PFDHdz12FFGw1EAOvpHueuxowAK8KWo5bbyC6ya6y8iIjKvNGxeRCQPHth/IhncE0bDUR7Yf6JAFYnkWDnP9RcRESkAhXcRkTw42z+aVXsp2de+j52P7KTlmy3sfGQn+9r3FbokyYdynesvIiJSIArvIiJ5sKI+mFV7qdjXvo+2Z9s4N3wOi+Xc8Dnanm1TgC9FLbfBLQ9B3WrAOI+3PFR+0wdERETmiea8i4jkwZ27NqbNeQcI+r3cuWtjAavKvz0H9xCKhtLaQtEQew7uYfe63QWqSvKmHOf6i4iIFIjCu4hIHiQWpSu31eY7hzuzahdZcI7sLb+dBUREpCgovIuI5Mmt21aWfFh3a65u5tzwuYztIgteYm/7xBZ5ib3tQQFeRETyTnPeRUQkZ1q3txLwBtLaAt4ArdtbC1SRSA5Nt7e9iIhInqnnXUREciYxr33PwT10DnfSXN1M6/bWkp/vvq99X9l95rKkve1FRKSAFN5FRCSndq/bXVbBNbHCfmKhvsQK+0BZ/TmUhbpVzlD5TO0iIiJ5pmHzIiIiczDdCvtSYrS3vYiIFJDCu4iIyBxohf0yor3tRUSkgMpy2LwxxgdcD6wBlgMXgTPAc9baCwWqqQm4DrgMaAAiQB9wAjhgrR2d5nIRESkQrbBfZrS3vYiIFEhZhXdjTBVwN/BRYFmGU8LGmO8Dn7PWHp2nmm4F/gR49zSnjRtjHgG+Yq09Mh91iYjM1uOHOspqf/vW7a1pc95BK+yLiIhI7pXNsHljzFbgReAzZA7uAH7gV4GfGWM+lud6qo0xjwL/m+mDO0AF8GHggDHmznzWJSIyF48f6uCux47S0T+KBTr6R7nrsaM8fqij0KXlze51u2m7vo3l1csxGJZXL6ft+jYtViciIiI5Zay1ha4h74wxy4EXAHfXz4tAO9AIvA2odT3/m9ba7+ShHh+wH7jJ9VQoXmcHzo2EK4CrAeM678+ttQ9k8547duywBw4cmF3BIiIzdMOXn6Kjf/Isn5X1QZ75jPuvPBERERFxM8a8aK3d4W4v+Z53Y4wBHiU9uB8FrrHW7rDW3matvRlnrvnXXJf/U7zHPtc+SXpwt8B/A5Zba99trf0Na+2HrLXXAJuBJ13X/4UxZnMe6hIRmZOzGYL7dO0iIiIiMjMlH96BDwDvTDk+Cdzonjture231n4SeCilOQB8PpfFxG8m/Jmr+V5r7Z3W2n73+dbaE8AvAU+nNPuAT+WyLhGRXFhRH8yqXUQWiCN74cGroK3eeTyyt9AViYiUnXII7/e6jj9ure2b5vy7gFMpx+83xlybw3paSB8FcA74ynQXWGsjwB+7mt+Xw5pERHLizl0bCfq9aW1Bv5c7d20sUEUiMmdH9sITd8DAacA6j0/coQAvIjLPSjq8G2OuxpkznvCKtfb7011jrR0B/s7V/OEclrXGdfxDa+34pS6y1r6Es51dQnN89XwRkaJx67aVfOkDV7OyPojBmev+pQ9cXdKrzYuUvCfvh7Br6kt41GkXEZF5U+pbxd3iOv72DK/7NvCllONfBf48JxVBtev4TMazMjsNrEo5rgdG5lyRiEgO3bptpcK6SCkZmOJXlanaRUQkL0q65x14r+v4pzO5yFp7mvSh8xuNMatzVFOX6zibiaCp51pg0hx5ERERkZyqW5Vdu4iI5EWph/fUleJjQDZ7pT0/zWvNxQEgdZj8jObTG2OCwKaUphPxIf4iIiIi+XPzPeB39TX4g067iIjMm5IN78aYBmBJSlNXlmH3pOs4J6stWWsHgH9OabpxhtvR/QHO6vcJM50CICIiIjJ7LbfBLQ9B3WrAOI+3POS0i4jIvCnlOe/rXcens7zePZFrwxxqcfs0sBNYhnMD5bvGmPdaazsynWyMeQ/pK9K/BezJYT0iIiIiU2u5TWFdRKTASjm817mOz2d5vft89+vNmrX2XDyQfw9YB2wGjhpj/g74IdAB+IErgA8Bvw4k9l7qBn7FWjuYq3pERERERESkuJVyeK9xHYeyvN61J8qk15sTa+3LxphrgE8CH8UJ6nfFvzKJAnuBT1lrz+ayFhERkdnY176PPQf30DncSXN1M63bW9m9bnehyxIRESlJpRze3VuyZRve3ee7Xy8XEr3pY5c4LwZ8DXgwm+BujPlD4A8BLrvsslkVKCIiksm+9n20PdtGKOr8c3lu+Bxtz7YBKMCLiIjkQckuWJeBneP5JleFABhj3ge8DvwFcNUlTvcArcDPjTFfja88f0nW2n+w1u6w1u5YsmTJpS8QERGZoT0H9ySDe0IoGmLPQS3JIiIikg+lHN6HXcfZ7Kee6fyhOdSSxhjzfuBfSF8N/wXgt4G1OKvK1wItwJ8Did52H/AJ4CljTG2u6hEREclW53BnVu0iIiIyN+UU3gMZz5qa+/ychHdjzErgfzIxZB7gL4F3WGu/Za1901o7Zq0dstYetdY+gLPH/NMp578D+Ptc1CMiIjIbzdXNWbWLiIjI3JRyeB9wHTdleb17nLn79WbrT0hfuf7frLWfttbGprrAWtsPvB/oSmn+DWPM23JUk4iISFZat7cS8Kbf5w54A7Ruby1QRSIiIqWtlMP7G67j1Vle7z7f/Xqz9UHX8ZdmclE8wP93V/Nv5aQiERGRLO1et5u269tYXr0cg2F59XLarm/TYnUiIiJ5UrKrzVtre40x55noQW82xlRZa0dm+BJrXcevzrUmY0wNsCalaQx4LouXeNp1rJ53EREpmN3rdiusS+k6sheevB8GzkDdKrj5Hmi5rdBViUgZK+Wed4BjKd97gB1ZXPt21/HxuZeTNlweoMdaG83i+i7XcbZTAURERGQO9rXvY+cjO2n5Zgs7H9nJvvZ9hS5J8uHIXnjiDhg4DVjn8Yk7nHYRkQIp9fD+I9fxu2ZykTFmNek95CestW/loJ6LruNs9453n5+zFfBFRERkeom97c8Nn8Nik3vbK8CXoCfvh/Boelt41GkXESmQUg/v/+I6/s0ZXuc+z/06s2KtHSQ9cNcZYy7L4iWudR27e+JFREQkT7S3fRkZOJNdu4jIPCjp8G6tPQq8nNK02Rjzy9NdY4wJAh9zNX8nh2X91HX80SyudZ/7zBxrERERkRnS3vZlpG5Vdu0iIvOgpMN7XJvr+GvGmIZpzv8ScHnK8ePW2sNTnWyM+V1jjE35evoS9TzqOv70TLZ8M8a0kj7s3wLfu9R1IiIikhva276M3HwP+IPpbf6g0y4iUiDlEN4fI31F93XAj40xV6eeZIypM8Z8FUjdoDYEfC7H9fy/pG87FwT+3RjzcWNMwH2yMabZGPPfgb9xPfXd+MgCERERmQfa276MtNwGtzwEdasB4zze8pBWmxeRgirZreISrLXWGPMh4AVgRbz5auAlY8yLQDvQCFwH1Lou/wNr7TFyyFobNsb8GvBjJhagqwa+BnzFGPMzoBPw42xXdy3gdb3M68AnclmXiIjM3uOHOnhg/wnO9o+yoj7Inbs2cuu2lYUuS3IssS3enoN76BzupLm6mdbtrdour1S13KawLiJFpeTDO4C19qwxZhfwCLAx3mxwto7LtH1cCPhTa+2381TPi8aY9+LMpV+T8lQ18IuXuPz/Ar9prT2fj9pERCQ7jx/q4K7HjjIadnb+7Ogf5a7HnIFRCvClR3vbi4hIoZTDsHkArLUvA9uBrwDdU5wWxllZ/jpr7dfzXM9zQAvw56QPo5/KfwC/A9yYo23rREQkBx7YfyIZ3BNGw1Ee2H+iQBWJiIhIKSqLnvcEa+0I8BljzOeAG3CGpTfj7L9+Bngu2x5ta+03gG/Msp5B4AHggfiWcTvi9dQBMaAfOAW8YK3tmc17iIhIfp3tH82qvZSU43SBcvzMUkaO7HX2sh8446ysf/M95TF1oFw/tyw4ZRXeE6y1EZw55z8udC0J8d509aiLiCwwK+qDdGQI6ivqgxnOLh3lOF2gHD+zlJEje+GJOyAc//ts4LRzDKUdZMv1c8uCVDbD5kVERPLhzl0bCfrT1xUN+r3cuWvjFFeUhnKcLlCOn1nKyJP3TwTYhPCo017KyvVzy4JUlj3vIiIiuZLocS23odTlOF2gHD+zlJGBM9m1l4py/dyyICm8i4iIzNGt21aWfFh3K8fpAuX4maWM1K1yhoxnai9l5fq5ZUHSsHkRERHJWjlOFyjHzyxl5OZ7wO+6EeUPOu2lrFw/tyxI6nkXERGRrJXjdIFy/MxSRhKLs5Xbquvl+rllQTLW2kLXIPNgx44d9sCBA4UuQ0RERERERKZhjHnRWrvD3a6edxERERGZ1n1PfYtHTz5MzNuHJ9rAB9fezr03faTQZeXV44c6NMpCRIqK5ryLiIiIyJTue+pbfPfUg1hfH8aA9fXx3VMPct9T3yp0aXnz+KEO7nrsKB39o1igo3+Uux47yuOHOgpdmoiUMYV3EREREZnSoycfxnjCaW3GE+bRkw8XqKL8e2D/CUbD0bS20XCUB/afKFBFIiIK7yIiIiIyjZi3L6v2UnA2w5aA07WLiMwHzXkXERERkSl5og1Y3+Sg7ok2FKCa+bGiPkhX7Fkql+zH+Pux4XrGzu9imef6QpcmImVMPe8iIiIiMqUPrr0dG/OntdmYnw+uvb1AFeXfzus6CCx/DE9FP8aAp6KfwPLH2Hmd5ryLSOEovIuIiIjIlO696SP8l8v/BBNpwFowkQb+y+V/UtKrzT/T+62M8/yf6S3dRfqkzBzZCw9eBW31zuORvYWuSGZAw+ZFREREZFr33vQR7qV0w7pb53BnVu2lQtvjlYkje+GJOyAcX8Nh4LRzDNByW+HqkktSz7uIiIiISIrm6uas2kuBtscrI0/ePxHcE8KjTrsUNYV3EREREZEUrdtbCXgDaW0Bb4DW7a0Fqij/tD1eGRk4k127FA0NmxcRERERSbF73W4A9hzcQ+dwJ83VzbRub022lyJtj1dG6lY5Q+UztUtRU3gXEREREXHZvW53SYd1txX1QToyBPUV9cECVCN5dfM96XPeAfxBp12KmobNi4iIiIiUuTt3bSTo96a1Bf1e7ty1sUAVzZ/HD3Vww5efYu1n9nHDl58q/Xn+LbfBLQ9B3WrAOI+3PKTF6hYA9byLiIiIiJS5xKry5bbafGKhvsR8/8RCfUBpf/aW28ozrB/Z6yzMN3DGmSZw8z0L6s9B4V1ERERERLh128rSDqwZPLD/BOHgAaov24/x92PD9Yyd38UD+yvK7s+i5B3ZS+R7n8QXDTnHA6edY1gwAV7D5kVEREREhH3t+9j5yE5avtnCzkd2sq99X6FLyrvu2LMElj+Gp6IfY8BT0U9g+WN0x54tdGmSYyPfv4f9AQ87V62gZc1qdq5awf6Ah5HvL5y5/up5FxEREREpc/va99H2bBuheK/kueFztD3bBlDSC/cFl/0A6wmntRlPmOCyHwCfK0xR8+C+p77FoycfJubtwxNt4INrb+femz5S6LLy6knPRe5vWkzI4/Rfn/P7aGtaTOxCH7cUuLaZUs+7iIiIiEiZ23NwTzK4J4SiIfYc3FOgiuaH9fVn1V4K7nvqW3z31INYXx/GgPX18d1TD3LfU98qdGl59TcNDcngnhDyePibhoYCVZQ9hXcRg/aLqwAAIABJREFUERERkTLXOdyZVXupWF7dnFV7KXj05MOYDKMNHj35cIEqmh/dvszRd6r2YrRwKhURERERkbxoniKsTtVeKlq3txLwBtLaAt4ArdtbC1RR/sW8fVm1l4q6iqVZtRcjhXcRERERkTJXjiEWnPn8bde3sbx6OQbD8urltF3fVtLz/D3RzMPEp2ovFXe940/xm8q0Nr+p5K53/GmBKsqeFqwTERERESlzibC65+AeOoc7aa5upnV7a0mH2ITd63aXxedM+ODa2/nuqQfThs7bmJ8Prb29gFXlXyn8jBtrbaFrkHmwY8cOe+DAgUKXISIiIiIiBVaOq80vJMaYF621Oya1K7yXB4V3ERERERGR4jdVeNecdxEREREREZEip/AuIiIiIiIiUuQU3kVERERERESKnMK7iIiIiIiISJFTeBcREREREREpcgrvIiIiIiIiIkVO4V1ERERERESkyCm8i4iIiIiIiBQ5hXcRERERERGRIqfwLiIiIiIiIlLkFN5FREREREREipzCu4iIiIiIiEiRU3gXERERERERKXIK7yIiIiIiIiJFTuFdREREREREpMgpvIuIiIiIiIgUOYV3ERERERERkSKn8C4iIiIiIiJS5BTeRURERERERIqcwruIiIiIiIhIkTPW2kLXIPPAGHMeOFXoOqbRBFwodBFS8vRzJvmmnzGZD/o5k3zTz5jMB/2cTe1ya+0Sd6PCuxQFY8wBa+2OQtchpU0/Z5Jv+hmT+aCfM8k3/YzJfNDPWfY0bF5ERERERESkyCm8i4iIiIiIiBQ5hXcpFv9Q6AKkLOjnTPJNP2MyH/RzJvmmnzGZD/o5y5LmvIuIiIiIiIgUOfW8i4iIiIiIiBQ5X6ELkIXFGOMDrgfWAMuBi8AZ4DlrbUG2ejDGGOA6YAOwEhiJ1/SitfZ0IWqS2THGVANbgU0424cEgAGgE3jBWvtWAcsTyTtjTAuwGVgFRHH+LnvJWvt6QQsTkZJgjFkN7MD5fakK6ABex/k3tmDDcY0xS4B34Pzdtwg4B7wJPGOtjRaqLsmOMWYNcBVwGVAHjAN9wAnggLV2rGDFlQgNm5cZMcZUAXcDHwWWZTglDHwf+Jy19ug81eQD/gz4GM7NBLcY8O/A/dban8xHTZI9Y8zVwIeAncDbAO80p78OfA34R2vtSB5ragPuneXlw9bamhyWI3NkjPkG8DuzvPyYtfaqHJaTkTHmdqAV5+ZVJv8BfMVa+7/zXYuIzB9jjAfnht11OP8Gvg1oASpSTvuotfYbc3yfG4F7gF8g88jbk8DfAX81n2E5fsPy88AvA/4Mp3QC38D5XW50vuoqNfn6OYt3uvwq8CvAzWTOCAljwGM4P2MvZvM+s2GMmUvIvdNa+99yVkwOadi8XJIxZivwIvAZpv6P0o/zH+/PjDEfm4eaVgE/Bb5M5uAOzs/3zcC/G2Puz3dNkj1jzHPAEZxfKN7B9MEd4ApgD3DQGPOf8lyeSN4ZY+qNMU/gLNozVXAHeDvwmDHmH4wxmX7BlQIxxjxtjLG5+MpxXW1zqGUol7XIZMaYDxljnsYZXfYy8D+A/4rTK14xzaXZvo8xxnwBeAq4ial/918LfAX4iTFmZa7e/xK1fRz4Gc7vj1P9vdaM8/vni8aYLfNRVynJ58+ZMeYDQDfwHeDDTB/cASqB38DJCl+Jd8JJlvSHJtMyxiwH9uMMr0r1ItAONOLcvauNtweArxtjLlprv5OnmmqAfwOudj11DHg1Xst/itcGzj9UdxtjQtbav8hHTTJrV2RoiwJHcYbyDeAMn78OqE85ZyPOTZmbrLUH8l6lSB4YY7zAXuC9rqfewPlvwA9sA1akPHd7/PEP816gzLdQoQuQefX/ADfOw/vcA3zW1XYB5/e4YZxpaqmh+HrgX40xN+R5hNvv4IykS3UReAHoBdYD21Oe2wz8H2PM26y1XfmqqwTl8+dsBc7UC7cu4JX4ox/nd72rABN/3gP8ObDKGPNbhZyusRApvMuU4nPJHyU9uB8FfstaeyTlvHqcIU+fSDnvn4wxL1lrj+WhtIdJD+5ngA9ba3+aUlMQZ0j9/Uz8ZfEFY8zPrLU/ykNNMjcR4F+B/wn8u7V2MPXJ+N3Z3wb+GmcOFTg3ab5njNlorc13L9G7cH7OZiKWz0IkJ9Zmce543qqAL5Ae3AeA3wW+l/hlJv6z/3vAV5noJbndGPO8tfZ/5LE2mX+PF7oAKQoDwBCTO02yZoz5JdKngFngc8BfW2tDKefdCHw75T2vxRlC/9tzrWGKulqAv3c1fxW421o7kHLeNfG6EqOSVgPfBd6dj7rKTM5+zuJOAf8IPGatPe5+0hhzxf/f3n3H21GV+x//fCXUEHqTIhGRYgMUEPECQbGLIEgRUYpiV8CrFyleQC5e8aeC7aKIEAtSFERFUSyAiEAAAWkiKEEECZFeQyDP748128xeZ/ezy5yc7/v12q9k1lkz85wze8+eZ82atYDPk3pZ1OwFXAUc36cYWjkb+HgX9e8fVCDj5eTdWtkFeEVp+XZgu4h4oFwpIh4EPiJpAfDRongpUkK/Sz8DkrQ5sGep6EFgm4iYncX0BClZf4yU8EFK4o8j3ZW3aphP6i786Yi4q1mliHgaOEXS5cClLLwLvyapkeboAcf5j/w9ZhNXFY5l0S31oFLRfGCHvCdJ8d4/SdJc0rOCNcdI+n75AtxGZk/Sd143RBrHYNVS2bf7FlFjboSsnieAa0l3m2uvv5AS7l7HXQH+fQPmOBbewAA4OCK+lNeNiIslbQNcw8IG8r0lfSEirhtPHE0cS+pCXXNCRBzcIK7rJG1L6iUwvSjeRtJbIuInA4hrUTWw91mxnSOAsyOi6XmjGHR1J0lfB95X+tGRkk4pN9oMyKNV+O7vByfv1kr+gf5QnrhnDgV2AtYtlt8qadOIuHaAMR3W5sN4ArAH6XlRgJdK2ikiftzHmKx3L+9mBPmIuEnSJ0i9L2r2YvDJu1m/fZL6hO/4Vo+ARMSPJP0A2K0oWpN0ATTmQtyGKyLu6XYdSa+iPnG/G/hV34JqzI2Q1XIs8PGiga5OyrvHbRfSgGQ1lwNfblY5Im6XdBjwtVoYwFHAW/sRTE0xXs2bS0WzgcNaxHV/8Wz8z0rFRwNO3jszyPfZOcDXG227hY+SBiiu9YBbHngjcPp4g5ksPGCdNVSMAF7umn5zRJzfap3i2aivZ8V79TGmFYHXl4oeIHWzbhVTkBL4snf0KyYbnx6nfvseaTrAmg0ktRskxawyilF/9ygVPUNnSfgXs2WfyyaufPaD73k6rMklIuZ2mfR0K7/+OqGDZ4tPIfVorHmTpOWbVe5RHtfX240iHxE/J001VrOpB6/rzCDfZxFxd7fbjoinSLMHlPkxiC44ebdmdsyWT+twvbzeWxrW6s0bqO8tcnaHXUbPpT7Ze52kvo3kasNVHPO/ZMVrNqprVlFbUX/X9cKIuLvdShFxOWkwu5rNi0FFbQIpBl3dNSsedJd5m0QkLUm6u1nzGB2MqVB8v5Yfz1mcdO3VT1W8vrThyh/F8DVcF5y8WzP56MeXNKyViYg7SYNW1GwoaZ0Rx/Qk6fmemuVY2I3eJqa8pddTZ9lE0tO5rPD70v8F7DD+cGzIdgWmlpavajTAk9k4bAUsW1qeFRHzOlw3Px/l56ueSVqX+llmbo+ITsdhGFhcNnS+hhsHJ+/WTHm+4QWk0SA7dXmLbY1Hvp1ZXaw7qJhsyIpBePLRwj1tjE0kPpdNbnmXed91t36r6jlmPHHNIo2W32xbNnE8L1v2NVwXnLzbGMWz5eUunXO6nOvz9mx5w/FHNWY70WA/rQwqJhu+bYCVS8v3Ar08O9+NIyT9QdIcSU9Juk/SLZLOkvRBSSu334RVhaQvS7pK0tzieM6VdJOk70jar+jWPEj5+edvXazrc9kEJuk5wIxS0VPA90cTjS3CxnOOmd1mW+PRc1zFdWg5yVt9AM/j23DsnC13c4OwV5tIOkPSbZIekfSEpDslXSbpc5JmDCGGvnDybo3kLWJ3drl+3gVq/XHEAoCkVUjd3WvmdtEFbCAx2ch8JFv+WQeD8IzXu0nTJq5G6t61ErABaeTvrwF3FCf/bqeLstH4CGnKyFVIx3MVYGPgnaQBm2ZLOqQYWG4QxnOO9blsYnsn9VN3nRcRw5pP2I2Qk0fP55jiUcN/lYpW6ON7o3LXlzZcxYDYM0pFAZw3hF1vShoo9nmkR0qWAtYmPWLyCeBCSbOKKRMrzcm7NZK3ZM7tcv28fj9aRqsYkw2ZpFcDbysVBS2mvhmiqaST/x+KO2s2sa0MfBb4ZdETqW8kLUb9886PtRtpOeNz2cT2rmx5mF3m3Qg5eVT1mqmqcdkQFI89fpX6BsxzI+KOJqsM2xbAbyUdPOpAWvE879ZI3mW0kxHdy/IL0X50Qa1iTDZERcv/zKz41Ii4doC7vR34KWnAw1tIU+gsRRoZdRtgX2CtUv3NgJ9LekVEPDLAuKw3N5Fa+K8GbgMeJiXSzwG2Jx3PcrK+A3C2pNf2caodn8smKUmvICXLNXOBllOwDlmtEXIHSTv3OJWnVUNVzzNVjcuG47+onxbuSeCQAe9zHnAh8GvgT8A9xX5XBF5Cmv1gRxY2KEwBvijpoYg4ZcCx9cTJuzUyNVvu9uSa18+314sqxmRDUtytPIPUxanmH8B/DmiXs4BXRcSFTX5+Pemu7DHA0dR/+byQ1LKcD0plo/ML4CsRcXWTn18H/FTSkaRjV747uj3wKeDIPsXic9nklZ8TTouI+UPYrxshJ5+qnmeqGpcNWNFz8tis+IiIuHWAuz0UOKnFo0mzgJMlvRQ4i/rHOk6UdGlE3DLA+HribvPWiW6fJ87rq2Gt8aliTDY4X6F+SqyngD0j4sFB7Cwift4icS/XmxcRnyR9QZTtLWnjQcRm3YuIM1ok7uV6j0TEPsA3sh8dPMDngX0umwSKebf3yIoH3WW+1gi5XkQcGBHfi4grI+LWiLg+In4ZEUeQLliPy9atNULaoqGq55mqxmV9JGkj4AfAYqXi84AvDnK/EfHZTsYUiYg/AltTP/jxEsBRAwptXJy8WyOPZctLd7l+Xv/RccRSU8WYbAgkHQ58oFS0AHhXRFw6opDGiIjPAn8oFT0L2H9E4dj4fRQoP4M3DdizT9v2uWxyeguwQmn5TwN+5MeNkJNbVc8zVY3LBkTSmqTeb+VH0maRbsAMerDhjkXEvcABWfFukpZrVH+UnLxbI/nJtdvBa/L6g0jeqxCTDZik9wL/kxV/OCLOHEU8beQtyDs0rGWVFxFPkXp7lPXrePpcNjnlXeZnjiKIVtwIuUip6nmmqnHZABQDvv4SWLdUfBPwxojI3wsjFxEXADeUihYjPTpXKU7erZGHsuVVulx/1Ww5314vqhiTDZCk3YATs+LDIyIvq4pfZ8svGkkU1i/58XxxPzZaDHz3eKloqqRu7j75XDbBSFodeF2p6GngtBGF044bIRcNVb1mqmpc1meSpgI/o/5aaDbw2oi4byRBdWYg3/395OTdGvlrtrxOl+vn9fPtdS0i5pJGhq5ZTdISo4zJBkfS64DvUX+O+nxEfGZEIbUVEQ+RBoKqmSJphWb1rfJmZ8vdXmS2kp9/1m5YqzGfyyaed1A/QPAvii6aVeRGyEVDz9dxxfgM5ST5wT4mW5W7vrT+K67Pf0SamrLmHuA1EXHXaKLq2OxsuZ/f/X3h5N3GKAZ3KM+luYakZbrYxHOz5T+PPyogjZJbowb7aWVQMVmfSXolcA5psJCakyPiEyMKqRv5NDbdPs9n1THIY5mff9brYl2fyyaevMv8MOd274obIRcZ/TzH9HO07Z7jKq5DVysVzRnUoLXWu2J2oNOB15SKHyDdcb9tNFF1pfLXcU7erZkbS/9/FrB5F+u+PFu+afzhAPUxNdpPK4OKyfpI0makblblxqKzgPeNJqLOSRKQj0he5a5h1lre2t7PY+lz2SQhaRPSXMI19wM/GVE4nar8xau1VdVzzHji2oL6vMXnvooproO+BexSKn6U9Iz79aOJqmuD/O7vCyfv1kzedW6bTlaStA4wvVR0S0T8vUn1YcW0FOmkX/MIcHmfYrI+kbQhaWCT5UvF5wN7R8SC0UTVlZdQ31vgvmLgM5uYtsiW7+7jtns6lzWoGw22ZdWS33U/o8rnBTdCLjIup34wty2L7vCdyM9HF/QnJIiIO4Dy3dfnSlqrw9UHFpf1zfHUn/PmATtHxES65h7kd39fOHm3ZvI7A+/ocL28Xj/vMPycNNBPza5FYt7OzsDU0vIvqnzxNBlJeg7wK+qfs/sdsGtEzB9NVF3L53D+3UiisH4Z5PG8jPpHk7YvptNpSdJWpDm5a66KiMpdWFgiaQpjvxMr22W+4EbIRUBEzKM+uZ1KuhZqqbim2rVUNJ/UiN5P+XXh3h2uN8jrSxsnSUcBB5aKngb2iIjfjCai7klaibGDdFbuWs7JuzVUdG8pT5ewsaQ3tFqnGDH5/Vnx9/sY0wOkuSJrVgT2axOTgIOy4qqO8jspSVqVdJFRHojmKmDHiMi7b1aSpOnAh7Pinw0/EusHSVsyNnnv2/EsepKUpztcjPqLnmY+li37XFZtr6f+Gd2bI2LWqILpkBshFx359ddBxTVRK/sD5TEOflaMg9BPp2fL728344akNwIblYqujQh3m68ISR8FjiwVBbBvRPx4RCH16mhg2dLynRHxp1EF04yTd2vlqGz5q8Wcjc38L/VzOZ4bEdc2qyxpX0lRel3UQUxHZ8ufkbRuw5rJgdQ/U/VH3FpbGZKWIzXIbFgqvhF4fUQ83HitrvfR1ftM0t6SVmtVJ6u/FvBTYFqp+O/Ad3sK2PpK0gGSprWv+e/6LyCNklv+fry81d0DSTOy99jsDnb1WVKXwpqDJTUdW0TSzsBupaJ/Aid1sB8bnQkzUB24EXIRdA5Qfs54K+CjzSoXx788o0sw9pqr0Xqzs/PfjFb1I+Iq4LxSUb7ffPsrAl/Nio9qF5cNh6R3AidkxR+KiL42LnfzPpO0gaQ3dbn9Axl7/qvkDEdO3q2Vc0jdO2vWAy6WVDfnoaTlJX2F+jtHTwJH9Dug4qR/RqloBeD3kuqehZK0lKTDqZ+zNoBDIiL6HZd1r5hK5MfAS0vF/wIOAKZJmt7Fa9mGO+nNe4DbJX1L0g7NnhOUtIykD5IahMrTKQVwoLuaVsbhwGxJX5a0ddGVeQxJK0o6FLgCKHdhn0dnd8W7UkyXc3ypaHHgV5J2yuKaIukA6u/UA3xqovRMmYyKhGPHUtEC0vSXvW7PjZCLoGbfadTf/QZYpUndNZptu7jW+S/Sd1LN8ZIOzR85lLQtcAn1Y86c1uoGzDgdTn3j5UGSvlQ06Jfj2oTU+6M8Av4lE/CO7kgN6n1W9Ig4hTQDVM3XgPO7vIbrZrrUTqwJnCfpGkkHtbrJJ2lTSecwtgFiFmnwvcqR8xhrRek5zCupv5gN4Grgb6SBbbak/gsf0iBjLVvdJO0LnFoqujgiZnQQ07LAH4AXZz+6gTQNybKk0fHzESOPiIhj223fhqP44ri9T5vbLyJmNtnPvnTxPisuircrFc0njWp7F2kKpSWBtUiNDkvk6wMfj4gvdB66DVJxF7z8xf0k6VxxD/AQaWaDdYFNSN3Xy54B9oqIs9rsYwZwYanojoiY3kFsU0jPk+bP2N1GumO2OLAZ6f1WdnJEHNBu+zY6kt4PnFgquiAiXjeO7e1L9+exLUiN3aeTEp55DeotA+xL6vJaTvYD2CUizu01ZmtP0ngvwtteN0k6krF3queSHk97nNQd/YXZz68F/iMiHmsXQINz7PYRcVEH6+0DzMyKHyYlTfeTxvd4WfbzO4EtI+Kedtu3hQb1PpM0k7E9jHrR9juzm/dZg+9kgDmka7kHSDNqrEga46NRw8Ffga0j4t72oQ9fwzsQZjURcbek1wE/ZGHXZpGS40ZdPJ8EPtbv7jJZTI8W3WHOInUDq3kR9XdAaxYAn3Hibj1anJTYbdKm3v3Au32xW3lL0dnUl3eSEvffDyqQiHha0m6kO7LlLn7rF69GTgY+OKiYrG/yC9qZI4hhGdIzzPsD8yV10wj5CZ/LFhmfJh3jT7Kwx+2qQLNxjP4A7N5J4j4eEfFtpUeaPk96PwIsx9jGzJqbgbc5cbcerV682jkHeE8xzlYludu8tRURN5C+4I8DmrVCzSc9S75lRJzYpE4/Y7qTNG3IJ4E7mlUDfktqnfvUoGOyRcaXSHer7uqw/q3AIcDzfLFbSceQHs/4Vwd1A7gO+ACw0SAT93/vMOLBiHgz8F5az1s8i3Qn9IAJNAPDpCRpA+oblh8GRn1uqDVCvhHYizSi+FaMTdzvB97q3kOLjkgOB15FuhvZ7C7sbNI11bbFYz3DiO2rpN6bP6V+NqGyOaQxQjb3IHXWoZtJ43BdQf3jGc08Tkrat4uIXaucuIO7zVuXim6eryQ9f7QG6aLkH8BlETG31boDjEmkQenWJ3Xvf4KUeF1ZJPlmPSmeGX0B8BzSYxhLkxqqHiB1u77SdwEmDknrkHoQrUN65GcpUm+hB0jnjCtG/aUtaVNgY9Jd0WdI59frIuIvo4zLOifpWOCwUtG4H3Poodv8W4HdSY3cncyjfSupV8dJEfFg75Fa1SlNzbo56X2xNGke69tI57+RJQXF9+1WpG7My5G+Y28HLo2IZom9WUuSFid9pz6X9J6fRmrMfJj03f9n4JqJ9B5z8m5mZma2iHIjpJnZosPJu5mZmZmZmVnF+Zl3MzMzMzMzs4pz8m5mZmZmZmZWcU7ezczMzMzMzCrOybuZmZmZmZlZxTl5NzMzMzMzM6s4J+9mZmZmZmZmFefk3czMzMzMzKzinLybmZmZmZmZVZyTdzMzMzMzM7OKc/JuZmZmZmZmVnFO3s3MzMzMzMwqzsm7mZmZjZSkKL1mjjoeGy5Js0vH/6JRx2NmVlVO3s3MrC1J07MEaxCvo0b9ew6LpIsa/P5X9LCdo7JtvLnPcfbzuO/cz9jMzMwmGyfvZmZm1bClpLeMOggbDEkzssaMfUcdk5mZTSxO3s3MzKrj05I06iDMzMyseqaMOgAzM5sQ/gE8t8O6ZwAvLy2/Hbi8g/Ue7DaoRdAmwG7AWaMOpIm7gP/ocd17+xmImZnZZOPk3czM2oqIp4HZndSV9GRWdE9EdLSuAXC0pLMj4plRB9LA0z6WZmZmo+Fu82ZmZqP309L/NwL2HlUgZmZmVk1O3s3MzEbvSODp8rKkxUcVjJmZmVWPu82bmVllFAnr1sB6wKqkhPZe4IaIuLbP+1oW2AZYB1gJmAPcBMyKiOjnvjpwK/AdYP9i+bnAu4GvDzmOCUvShsCmwGrAssC/gDuASyLiiT5sfzHgZcDzSe/NqcAjpMdJrouIO8a7j/GQtBawBfBs0vv5PuD0iHioxTrLkT4DawErk36fOcAVEfH3PsS0MjADWBtYnDR2xo0Rcf14t91gXwM5/qXtrlFs92ngUeBO0uf2zyM4X5jZJOXk3czMRk7SmsDRwO7Ack3q3A18A/h8RDzewTZnABeWivaLiJmSVgeOIQ2kt2yDVe+QdHREnNrdbzFunyZ1l1+iWD5c0qkRMW/IcUwYkpYGDgIOoPmAik9KOgc4vJfn9SWtDxwB7Aws36LebcAPgBMj4s5S+Wxg3QarnCqp2Xvs4oiYkW1/JrBPbTkiVJS/kvTemcHYHpWXA2MavSRtTvoMvJqUVDf6fa4HPktqAOgqOZW0DnA8sBMNrjUlXQMcFxFndrPdBtsZyPEvGhE/CnwAeF6b6g9J+g3w9Yj4VUeBm5n1yN3mzcxspCTtRrqD9R6aJO6FWoL/Z0kv6XFfLyElMwfQOHGHlGidIulcSUs0qdN3xZ3bk0tFa5OSB2tA0tbAbcBnaD0TwlLAXqT3zdu72L4kHQPcTEqamybuhfWBQ4H/7HQf4yXpE8DFwKvo4Jqu+J0+B8wCXk+TxL3wYuA04EJJK3YR02uAG4FdaX6TaDPgDElf63S7DfYzkOMvaVXgCuDztE/cIb0vdgE+1EFdM7Nx8Z13MzMbGUn7AKcwNvG4BvgrKbl4EfUX0esAv5O0Q0Rc1cXuVgXOJ3V/hdT19TJS99rVgFcAy5Tq7wScKWmXIXaL/R9gP2DpYvlQSd+MiMeGtP8JQdKOpOn0lsp+9GfgL6RjuzppysJaI82SwGmSpkTEd9tsfzHSlIdva/DjW0hJ40OkxqbnAxsA6umX6ZGkPYDPlYr+Snrs43FSQ9eWDVb7JulxjLKnSHfo7wZWADYHVin9fDvS523biHigTUxbAz9m4fu35kbS302kARk3Lso/KOmuVttssp+BHH9JAs4hNS6UzQGuJ50rFpAS9ueRGmx8LW1mwxMRfvnll19++dW3F3AREKXXjCb1NiIlGuW6vwKe36DudqSL/3LdW4GpLeKYkdV/oPj3KeAwYOms/lTSnf2ns/XeN4S/0bKln30++9mhLbZzVFb3zX2Oc3q2/dkDes+U9zGzTd3nk57NLq/zLeC5DeouSboTPq9U91Fggzb7ODbb/oJm+yjqr0RKimcBJ2Q/W7v4O+6ZbfPjRXmj1xoN9jEzW7/2N7gC2LJB/ZWBaaXlfRr8TicAK2TrTSl+l4ey+qe3+ZstQ2pAKK9zNbBZg7qbkxroap/HR0vrXDSq4w+8KdvuraRHC9Sk/lRSI98ZwA8G8dnwyy+//Cq/Rh6AX3755Zdfi9aLzpP3X2f1zgEWa7HdVUh31srrHNui/oysbi1h2b1N/Ptl6zwILDfgv1E5eV8lS07uA5Zvsp2jsu0MOnn/B80Tzlav1drsp5vk/bKs/rs7+D12oL5RpmmiRRr0bUGp7nxgry7+Zqt3+H7ct8tjMbPB+/m3ZI1QTdZdtngfl9c9uM0QmJCfAAALo0lEQVQ6Lwcey9Z5TYv6R2Z1r6B149o04KoGv9NFozr+wInZcW/YWNNk3aX68Znzyy+//Gr18jPvZmY2dJJeSLqjVTOHNKDcM83WiYh/Ae8kJVY175WUd51t5TsRcVarCpEGqju7VLQ8Q5x3vfg9TygVrQR8bFj7b2Mt4PYeXif1Y+eStge2KhWdGBHfardeRPyaNIBazVslPadJ9cOo7wJ/bER8v9MYI2JOp3XH6XFgn+hsJPV9qH9m/xcRcXyzygARcQXw31nxgY3qSpoCvK9U9BTwzmjxuEdEPEL6PM9vFUe2n0Ef/3LZtRFxe6exRcSTndY1M+uVk3czMxuFd2TLX4wWU1rVRMSVwE9KRauQBt7q1DEd1vt0tpzHO2hfIN0prTm4mHZrsisP4Pc06TGHTn2l9P/FaPC+kbQaqRt0zRzSgGhVdFaURrVvI3//Htnhel8G5paW3yhppQb1XkWaoq4c21/abTwibgZ+2GEsMODjn1m1i22bmQ2Fk3czMxuFrbPl07tYN78Lmm+rmT9GxF87qRgRfyJ10a952ZBHnn+Q9Ox7zTTgkGHtv8JmlP7/+27uckeat7w8F/srG1Tbjvq77t+JiKe6inB4ftK+CkhakjQ/fc3fImJWJ+tGxHzS9Hf/3hz1d75rXpEtt+zdkulmurgZpf8P4vjfUvr/upI8gryZVYpHyDQzs1EoJxP/7OIOIqSRsZttq5Uru9hHrf5Gxf+XBF5IGmRrWL5E6qZcuwP4IUlfjIh7hhhD7o6ImD6KHUvagPq7oXdI6jaWB1g453qjdV+eLV/S5faHacz87U28CCg3PF3R5X4uBz5YWn4Z8POszkuz5W4+ax3VHdLxPwM4uLT8VUk7A6cC50eb0fbNzAbNybuZmQ1V8Yx6eY71W7tZPyLulPQEC6ej6rR7a0d33Utuy5ZX63L9cYmIRyV9ltSFHtJo3ocDHxlmHBWydra8T/HqVaPu32tkyzePY/uDNrd9FWDs56Orzxv1d6MbbQ/StGw1j3fTwBQRd2ef52YGfvwjYpak/6O+sWKH4rVA0vXApcDvgAsj4t5x7N/MrGvuNm9mZsO2Qrb8cA/bKD8fv2KH63S7n/wZ/DzuYfg/0vzbNQdIWmcEcVRBo2R7PJZtUJaPK/BggzqVEBGPdlh1vJ+3/HPQ6PNW3sd4P8/NDOP4A3yY1Ej2eFb+LGATUmJ/BvBPSRdKelsxP7yZ2cA5eTczM6uoYgTrY0tFSzJ2BPDJYvE+b6+ThCv6vE/r3VCOfySfAdYjjTNxGWlwvNyzSM/g/wC4WNKzG9QxM+srJ+9mZjZs+d3M5XrYRnnaq06fQ+12P8tny6O6C3syMLu0vK+k9UcUyyjdny1/LiI0jtf0DvYxit4W/Tbez1v+OWj0eSvvY7yf52aGcfz/LSLmRMTnImJrUm+DVwNHARczNpnfBvhFMTigmdnAOHk3M7OhKu4ml7v8dpWISlqb+udjO33293nd7IexcY3k+dZitPPy1HVTSEnEZJP//VcZwD7yZ7U3HsA+hi3/fHTb8LNBm+1BmlKvZhlJ+dgBTUlak/bPu8Nwjn9DEfFoRPw2Io6OiBmkafGOAJ4oVXsJsP+wYjKzycnJu5mZjcLVpf+vWSTkncqnqrq6Ya2xtuhiH3n9ecCNXa7fT98ByvNmv13SC0YVzIjcADxWWs5Hhu+Hy7Llbfq47VF1wb8BKE93t2WX63fyeftjttzNZ63TusM4/h2JiH9FxLHAAdmPdhxFPGY2eTh5NzOzUfhDtrxHF+vulS3nCVczL+20u7mkl7BwmjiAq0c533dEPAMcWSp6FvV34xd5xZzjF5eKXijphX3ezcXAgtLyuyT161nrednyEg1r9VlEzKM+4V5fUkfTK0qaAuxW3hyNp5rLP4O7dxFiR5/9IR3/bp1J/XGdPqI4zGyScPJuZmaj8P1s+WBJzUZ//jdJLwV2KhXdB5zfxX6P6LBePijcaV3sY1DOBK4vLe/C2Pm1F3XfyJaP6+dI3xExFzi3VLQG8Mk+bT4fUb3jruV9kH/eOh308MPUT5F4fkTc16Deb4F/lpZ3L+Zlb0nSRsDbOowFBnz8uxURT1PfG2BkDXxmNjk4eTczs6GLiBuAC0tFawHflNT0e0nSysD3qP/u+mbxDH2n3iVpt1YVJO0L7FoqeqjY70hFRFCfdIlJ1k03In5CfRftNwHHS1qs021ImiJpr+KuciP/S30X9/+W1HHPEEmrN/nR36gf6Gz7TrfZB9+mvvHgLZI+2KwygKQtgGOy4i83qlsksSeVipYAvitpaovtL0v6XHXcs2HQx1/SByQ1mse+2bbeQP0Udrd0uq6ZWS+cvJuZ2ah8GCgn3nsC50laL68oaVvgUuoHEPsb9dOotfMgKeE9TdJhkuoGyZI0VdJRpNHdyw6JiF7mru67iDgXuHKEIUyRNL3H12rtN9+Rd1A/l/iBwCWSXt8siSsStq0kHQfcTupJ0TB5j4irgM+UiqYAp0s6SdL0JttfSdL+kmYBhzbZ7jzqu5zPkHSypFdLen72t+rrXfmIeAT4WFb8FUn/T1LdSO/F32o/4FfUz4V+VkT8ssVuPkf6TNZsSZpCbbO8oqTNgd8BLwPmU3/3up1BHv9DgL9LOk3SzpKmtdjeu4DTsx+NvJHPzBZtSg35ZmZm/SHpImC7UtH2EXFRk7r7k5LlctfXIN1d+xvprtyLGDtC9sPAayJiVos4ZlB/d/8Q4CDSSNEAj5Ce1b0PWBV4BZDfKTwX2CX6/GXZ4G80LSIebVI9X/d1wC+a/HjHiDhvnOGV9zWdlOz0w48jYucm+yn/fb8dEfu2ieu1wNnUJ5eQjukfSSOTzydNQbYm8AIgn8Zr6Wa9Nook8Ezqe2DU3AzcRnoPLgc8nzQie+2GyJci4qAm290DOKPV71a4uBjVvLzuTGCf2nJEdN1dPN9GYR7pc/BP0tR4WzB2JPebgG0iIp+uLd/+K0lJfz56/A2ku9IijSVRHmzxU8B7gHWL5TG/e4P9DOT4S5pdigPSuegvpM9AbYq8ZwObMXZ6u3Mj4q2t4jYzG69mXcbMzMwGLiJOkfQ4cAoLL/hFuiPXbFCtu0hJ6jVd7u5e4I3ABaRkfRrw2hb1fwLs0e/Efbwi4peSLqG/I6FPKBFxgaStgB9Q3xtjGvWNIs08TP3AdPn2n5G0O6kL/cep76m4MT1OIRcRZ0p6OXBwL+v3wX6k+dIPYmGD2ZLAjBbr/B7YqV3iDhARl0raGfgh6VjUvKh45U4i9Z55T9vI6/cz0ONfImDD4tXKmYxtFDEz6zt3mzczs5GKiDNIdy+/Rbpz1sw/gaOBDXtI3Gv7uhbYFDgVeLxJtTuAd0fETqMcYb6NTgfeW2RFxI3Ai4F3kh4laJeMPQj8CHgX8Ox2xzYiFkTEIcU+zqR91+5bSO/P49ps92Okac6+VsR9H0Ma6CySj5Gmf7uA+mfwczeS/lbbdpK4l/ZxASlRP7vF9v8E7B0R7+u1cWxAx38n0jG8nHTnvpUFwG+AN0XEnsVjEWZmA+Vu82ZmVhnFtFyvBNYj3R1/mnTH/Ebgmm4u9Bt0m98vImaWfj4N2BZYB1ixtJ8rqna33dqTtCKwNalb88qkGxQPA3eTurrfWky51+v2lyi2P5303ly82P7twHUR8Y/xxD8KxfPu25IGjFwJeBSYQ/oMzO7D9lchDcy3Nqm3513AjRFx3Xi33WBffT3+kpZi4SM7a5C66M8nDfx3G/DHbho1zMz6wcm7mZktktol72ZmZmYTibvNm5mZmZmZmVWck3czMzMzMzOzinPybmZmZmZmZlZxTt7NzMzMzMzMKs7Ju5mZmZmZmVnFOXk3MzMzMzMzqzgn72ZmZmZmZmYV53nezczMzMzMzCrOd97NzMzMzMzMKs7Ju5mZmZmZmVnFOXk3MzMzMzMzqzgn72ZmZmZmZmYV5+TdzMzMzMzMrOL+Pwzchhc3fUdgAAAAAElFTkSuQmCC\n","text/plain":["
"]},"metadata":{"tags":[],"needs_background":"light"}},{"output_type":"display_data","data":{"text/plain":["
"]},"metadata":{"tags":[]}}]},{"cell_type":"code","metadata":{"id":"pZGSlLfohNwX"},"source":["# def export_legend(legend, filename=\"legend.png\"):\n","# fig = legend.figure\n","# fig.canvas.draw()\n","# bbox = legend.get_window_extent().transformed(fig.dpi_scale_trans.inverted())\n","# fig.savefig(filename, dpi=\"figure\", bbox_inches=bbox)\n"],"execution_count":null,"outputs":[]},{"cell_type":"code","metadata":{"id":"_TaD4dwWhhQ8"},"source":["def export_legend(legend, filename=\"legend_new.png\", expand=[-5,-5,5,5]):\n"," fig = legend.figure\n"," fig.canvas.draw()\n"," bbox = legend.get_window_extent()\n"," bbox = bbox.from_extents(*(bbox.extents + np.array(expand)))\n"," bbox = bbox.transformed(fig.dpi_scale_trans.inverted())\n"," fig.savefig(\"/gdrive/MyDrive/Project_DEAP/4.1.2021/plots_ieee/\" + filename + '.svg', dpi=\"figure\", bbox_inches=bbox)\n"],"execution_count":null,"outputs":[]},{"cell_type":"code","metadata":{"id":"1FizC1t1dANZ"},"source":["def va_features_plots(dataset):\n"," os.chdir(\"/gdrive/MyDrive/Project_DEAP/4.1.2021/{}/plots/\".format(dataset))\n","\n"," '''\n"," df = pd.read_csv(\"topCommonFeaturesRegressionRanking11.csv\")\n"," # plt.plot(df['RMSE'])\n"," fig = plt.gcf()\n"," fig.set_size_inches(16, 10)\n"," # X = pd.DataFrame([x for x in range(1,) ])\n"," plt.rcParams.update({'font.size': 40})\n"," plt.xlabel('Top N Features')\n"," plt.ylabel('RMSE')\n"," # df.index += 1\n"," # X = pd.DataFrame([str(x) for x in df.index])\n"," plt.xticks(np.arange(0,25,5),np.arange(1,26,5))\n"," plt.plot(df.loc[:,\"RMSE\"], linewidth=2.0, markersize = 10)\n"," plt.tight_layout()\n"," # print(pd.DataFrame(df.index))\n"," '''\n","\n"," df = pd.read_csv(\"topCommonFeatureFSRegressionRankingSelectKBest11.csv\")\n"," # plt.plot(df['RMSE'])\n"," fig = plt.gcf()\n"," fig.set_size_inches(16, 10)\n"," # X = pd.DataFrame([x for x in range(1,) ])\n"," plt.rcParams.update({'font.size': 40})\n"," plt.xlabel('Top N Features')\n"," plt.ylabel('RMSE')\n"," # df.index += 1\n"," # X = pd.DataFrame([str(x) for x in df.index])\n"," plt.xticks(np.arange(0,25,5),np.arange(1,26,5))\n"," plt.plot(df.loc[:,\"RMSE\"], linewidth=2.0, markersize = 10)\n"," \n"," plt.tight_layout()\n"," # print(pd.DataFrame(df.index))\n"," '''\n"," df = pd.read_csv(\"topCommonFeatureFSRegressionRankingRandomForest11.csv\")\n"," # plt.plot(df['RMSE'])\n"," fig = plt.gcf()\n"," fig.set_size_inches(16, 10)\n"," # X = pd.DataFrame([x for x in range(1,) ])\n"," plt.rcParams.update({'font.size': 40})\n"," plt.xlabel('Top N Features')\n"," plt.ylabel('RMSE')\n"," # df.index += 1\n"," # X = pd.DataFrame([str(x) for x in df.index])\n"," plt.xticks(np.arange(0,25,5),np.arange(1,26,5))\n"," plt.plot(df.loc[:,\"RMSE\"], linewidth=2.0, markersize = 10)\n"," plt.tight_layout()\n"," '''\n"," os.chdir(\"/gdrive/MyDrive/Project_DEAP/4.1.2021/{}/arousal_plots/\".format(dataset))\n","\n"," '''\n"," df = pd.read_csv(\"topCommonFeaturesRegressionRanking11.csv\")\n"," # plt.plot(df['RMSE'])\n"," fig = plt.gcf()\n"," fig.set_size_inches(16, 10)\n"," # X = pd.DataFrame([x for x in range(1,) ])\n"," plt.rcParams.update({'font.size': 40})\n"," plt.xlabel('Top N Features')\n"," plt.ylabel('RMSE')\n"," # df.index += 1\n"," # X = pd.DataFrame([str(x) for x in df.index])\n"," plt.xticks(np.arange(0,25,5),np.arange(1,26,5))\n"," plt.plot(df.loc[:,\"RMSE\"], '-o', color = 'C0', linewidth=2.0, markersize = 10)\n"," plt.tight_layout()\n"," # print(pd.DataFrame(df.index))\n"," '''\n","\n"," df = pd.read_csv(\"topCommonFeatureFSRegressionRankingSelectKBest11.csv\")\n"," # plt.plot(df['RMSE'])\n"," fig = plt.gcf()\n"," fig.set_size_inches(16, 10)\n"," # X = pd.DataFrame([x for x in range(1,) ])\n"," plt.rcParams.update({'font.size': 40})\n"," plt.xlabel('Top N Features')\n"," plt.ylabel('RMSE')\n"," # df.index += 1\n"," # X = pd.DataFrame([str(x) for x in df.index])\n"," plt.xticks(np.arange(0,25,5),np.arange(1,26,5))\n"," plt.plot(df.loc[:,\"RMSE\"], '-o', color = 'C1', linewidth=2.0, markersize = 10)\n"," plt.tight_layout()\n"," # print(pd.DataFrame(df.index))\n","\n"," '''\n"," df = pd.read_csv(\"topCommonFeatureFSRegressionRankingRandomForest11.csv\")\n"," # plt.plot(df['RMSE'])\n"," fig = plt.gcf()\n"," fig.set_size_inches(16, 10)\n"," # X = pd.DataFrame([x for x in range(1,) ])\n"," plt.rcParams.update({'font.size': 40})\n"," plt.xlabel('Top N Features')\n"," plt.ylabel('RMSE')\n"," # df.index += 1\n"," # X = pd.DataFrame([str(x) for x in df.index])\n"," plt.xticks(np.arange(0,25,5),np.arange(1,26,5))\n"," plt.plot(df.loc[:,\"RMSE\"], linestyle='-', marker='o', color='C2', linewidth=2.0, markersize = 10)\n"," plt.tight_layout()\n"," '''\n"," \n"," # print(pd.DataFrame(df.index))\n"," legend = plt.legend([\"Method A(Valence)\",\"Method B(Valence)\", \"Method C(Valence)\",\"Method A(Arousal)\",\"Method B(Arousal)\", \"Method C(Arousal)\"], bbox_to_anchor=(0.5, -0.6),fontsize='xx-small')\n","\n","\n"," export_legend(legend)\n"," ax = plt.gca()\n"," ax.legend().set_visible(False)\n"," plt.savefig(\"/gdrive/MyDrive/Project_DEAP/4.1.2021/plots_top_va_latest/{}_feature_plot.svg\".format(dataset), bbox_inches='tight', dpi=500)\n","\n"," # plt.savefig(\"verification/DREAMER_Combined_Features_Plots1.svg\", bbox_inches='tight', dpi=500)\n"," plt.show()\n"," plt.clf()\n","\n"," "],"execution_count":null,"outputs":[]},{"cell_type":"code","metadata":{"id":"8JAI-_3hdAER","colab":{"base_uri":"https://localhost:8080/","height":1000},"executionInfo":{"status":"ok","timestamp":1620628163358,"user_tz":-330,"elapsed":4094,"user":{"displayName":"Rohit Garg","photoUrl":"https://lh3.googleusercontent.com/a-/AOh14Gj3Za6nmSY12sPvpCoWOtWQcVR5CMMSPBRGTexH_w=s64","userId":"11252576926243182044"}},"outputId":"fc35f48d-df27-4c8f-9942-d37d1f553676"},"source":["va_features_plots('DEAP')\n","va_features_plots('DREAMER')\n","va_features_plots('OASIS')"],"execution_count":null,"outputs":[{"output_type":"stream","text":["No handles with labels found to put in legend.\n"],"name":"stderr"},{"output_type":"display_data","data":{"image/png":"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\n","text/plain":["
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\n","text/plain":["
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\n","text/plain":["
"]},"metadata":{"tags":[],"needs_background":"light"}},{"output_type":"display_data","data":{"text/plain":["
"]},"metadata":{"tags":[]}}]},{"cell_type":"code","metadata":{"id":"mrYa7pZraVPJ"},"source":["def va_electrodes_plots(dataset):\n","\n"," os.chdir(\"/gdrive/MyDrive/Project_DEAP/4.1.2021/{}/plots/\".format(dataset))\n"," df = pd.read_csv(\"topCommonElectrodeRegressionRanking11.csv\")\n"," # plt.plot(df['RMSE'])\n"," fig = plt.gcf()\n"," fig.set_size_inches(18,10)\n"," # X = pd.DataFrame([x for x in range(1,) ])\n"," plt.rcParams.update({'font.size': 40})\n"," plt.xlabel('Top N Electrodes')\n"," plt.ylabel('RMSE')\n"," # df.index += 1\n"," # X = pd.DataFrame([str(x) for x in df.index])\n"," # plt.plot(X.loc[:,0], df.loc[:,\"RMSE\"])\n"," plt.xticks(np.arange(0,14,2),np.arange(1,15,2))\n"," plt.plot(df.loc[:,\"RMSE\"], linewidth=2.0, markersize = 10)\n"," plt.tight_layout()\n"," # print(pd.DataFrame(df.index))\n","\n"," df = pd.read_csv(\"topCommonElectrodeFSRegressionRankingSelectKBest11.csv\")\n"," # plt.plot(df['RMSE'])\n"," fig = plt.gcf()\n"," fig.set_size_inches(18,10)\n"," # X = pd.DataFrame([x for x in range(1,) ])\n"," plt.rcParams.update({'font.size': 40})\n"," plt.xlabel('Top N Electrodes')\n"," plt.ylabel('RMSE')\n"," # df.index += 1\n"," # X = pd.DataFrame([str(x) for x in df.index])\n"," plt.xticks(np.arange(0,14,2),np.arange(1,15,2))\n"," plt.plot(df.loc[:,\"RMSE\"], linewidth=2.0, markersize = 10)\n"," plt.tight_layout()\n"," # print(pd.DataFrame(df.index))\n","\n"," df = pd.read_csv(\"topCommonElectrodeFSRegressionRankingRandomForest11.csv\")\n"," # plt.plot(df['RMSE'])\n"," fig = plt.gcf()\n"," fig.set_size_inches(18,10)\n"," # X = pd.DataFrame([x for x in range(1,) ])\n"," plt.rcParams.update({'font.size': 40})\n"," plt.xlabel('Top N Electrodes')\n"," plt.ylabel('RMSE')\n"," # df.index += 1\n"," # X = pd.DataFrame([str(x) for x in df.index])\n"," plt.xticks(np.arange(0,14,2),np.arange(1,15,2))\n"," plt.plot(df.loc[:,\"RMSE\"], linewidth=2.0, markersize = 10)\n"," plt.tight_layout()\n"," # print(pd.DataFrame(df.index))\n","\n"," os.chdir(\"/gdrive/MyDrive/Project_DEAP/4.1.2021/{}/arousal_plots/\".format(dataset))\n"," df = pd.read_csv(\"topCommonElectrodeRegressionRanking11.csv\")\n"," # plt.plot(df['RMSE'])\n"," fig = plt.gcf()\n"," fig.set_size_inches(18,10)\n"," # X = pd.DataFrame([x for x in range(1,) ])\n"," plt.rcParams.update({'font.size': 40})\n"," plt.xlabel('Top N Electrodes')\n"," plt.ylabel('RMSE')\n"," # df.index += 1\n"," # X = pd.DataFrame([str(x) for x in df.index])\n"," plt.xticks(np.arange(0,14,2),np.arange(1,15,2))\n"," plt.plot(df.loc[:,\"RMSE\"], '-o', color = 'C0', linewidth=2.0, markersize = 10)\n"," plt.tight_layout()\n"," # print(pd.DataFrame(df.index))\n","\n"," df = pd.read_csv(\"topCommonElectrodeFSRegressionRankingSelectKBest11.csv\")\n"," # plt.plot(df['RMSE'])\n"," fig = plt.gcf()\n"," fig.set_size_inches(18,10)\n"," # X = pd.DataFrame([x for x in range(1,) ])\n"," plt.rcParams.update({'font.size': 40})\n"," plt.xlabel('Top N Electrodes')\n"," plt.ylabel('RMSE')\n"," # df.index += 1\n"," # X = pd.DataFrame([str(x) for x in df.index])\n"," plt.xticks(np.arange(0,14,2),np.arange(1,15,2))\n"," plt.plot(df.loc[:,\"RMSE\"], '-o', color = 'C1', linewidth=2.0, markersize = 10)\n"," # plt.plot(df.loc[:,\"RMSE\"], color = 'C1')\n"," plt.tight_layout()\n"," # print(pd.DataFrame(df.index))\n","\n"," df = pd.read_csv(\"topCommonElectrodeFSRegressionRankingRandomForest11.csv\")\n"," # plt.plot(df['RMSE'])\n"," fig = plt.gcf()\n"," fig.set_size_inches(18,10)\n"," # X = pd.DataFrame([x for x in range(1,) ])\n"," plt.rcParams.update({'font.size': 40})\n"," plt.xlabel('Top N Electrodes')\n"," plt.ylabel('RMSE')\n"," # df.index += 1\n"," # X = pd.DataFrame([str(x) for x in df.index])\n"," plt.xticks(np.arange(0,14,2),np.arange(1,15,2))\n"," plt.plot(df.loc[:,\"RMSE\"], '-o', color = 'C2', linewidth=2.0, markersize = 10)\n"," # plt.plot(df.loc[:,\"RMSE\"], color = 'C2')\n"," plt.tight_layout()\n"," # print(pd.DataFrame(df.index))\n"," legend = plt.legend([\"Method A(Valence)\",\"Method B(Valence)\", \"Method C(Valence)\",\"Method A(Arousal)\",\"Method B(Arousal)\", \"Method C(Arousal)\"], fontsize='xx-small')\n"," export_legend(legend)\n"," ax = plt.gca()\n"," ax.legend().set_visible(False)\n"," plt.savefig(\"/gdrive/MyDrive/Project_DEAP/4.1.2021/plots_top_va_latest/{}_electrode_plot.svg\".format(dataset), bbox_inches='tight', dpi=500)\n"," plt.show()\n"," plt.clf()"],"execution_count":null,"outputs":[]},{"cell_type":"code","metadata":{"colab":{"base_uri":"https://localhost:8080/","height":1000},"id":"-CXYPbfXdKKw","executionInfo":{"status":"ok","timestamp":1620628125716,"user_tz":-330,"elapsed":4634,"user":{"displayName":"Rohit Garg","photoUrl":"https://lh3.googleusercontent.com/a-/AOh14Gj3Za6nmSY12sPvpCoWOtWQcVR5CMMSPBRGTexH_w=s64","userId":"11252576926243182044"}},"outputId":"56d42265-3bf0-418d-eb37-d494d100395d"},"source":["va_electrodes_plots('DEAP')\n","va_electrodes_plots('DREAMER')\n","va_electrodes_plots('OASIS')"],"execution_count":null,"outputs":[{"output_type":"stream","text":["No handles with labels found to put in legend.\n"],"name":"stderr"},{"output_type":"display_data","data":{"image/png":"iVBORw0KGgoAAAANSUhEUgAABMgAAAKHCAYAAAB0EHSMAAAABHNCSVQICAgIfAhkiAAAAAlwSFlzAAALEgAACxIB0t1+/AAAADh0RVh0U29mdHdhcmUAbWF0cGxvdGxpYiB2ZXJzaW9uMy4yLjIsIGh0dHA6Ly9tYXRwbG90bGliLm9yZy+WH4yJAAAgAElEQVR4nOzdZ3RVZdqH8Wuf9AQIvffepAvYG4oUxVHHDggq4tgL6ljGccY6goqMdbCXsYwdEBURuygggvQiSO+EkJB69vvhOFheCzAJIcn1W8u18uzznHvftx90rf/a+zlBGIZIkiRJkiRJ5VWkpBuQJEmSJEmSSpIBmSRJkiRJkso1AzJJkiRJkiSVawZkkiRJkiRJKtcMyCRJkiRJklSuxZd0A9o7qlevHjZu3Lik25AkSZIkSSoR06dP3xiGYY1f+syArJxo3Lgx06ZNK+k2JEmSJEmSSkQQBMt/7TNfsZQkSZIkSVK5ZkAmSZIkSZKkcs2ATJIkSZIkSeWaAZkkSZIkSZLKNQMySZIkSZIklWsGZJIkSZIkSSrXDMgkSZIkSZJUrhmQSZIkSZIkqVwzIJMkSZIkSVK5ZkAmSZIkSZKkcs2ATJIkSZIkSeWaAZkkSZIkSZLKNQMySZIkSZIklWsGZJIkSZIkSSrXDMgkSZIkSZJUrhmQSZIkSZIkqVwzIJMkSZIkSVK5ZkAmSZIkSZKkcs2ATJIkSZIkSeWaAZkkSZIkSZLKNQMySZIkSZIklWsGZCo9Fr8H+TtKugtJkiRJklTGGJCpdJjzKjx7Mjx3CuRllXQ3kiRJkiSpDDEgU+lQow2kVodvP4Rn/wi5mSXdkSRJkiRJKiMMyFQ61GwNQyZAxTqw/BN45iTI2VbSXUmSJEmSpDLAgEylR/UWcPZ4qFQfVkyFp0+AHVtLuitJkiRJklTKGZCpdKnWDIaMh8oNYdV0eOp4yN5c0l1JkiRJkqRSzIBMpU+VxnD2BKjSBNZ8DU8eD1kbS7orSZIkSZJUShmQqXSq3CB2Jlm15rBuNjzRH7avL+muJEmSJElSKWRAptKrUt3Yk2Q1WsOGefBEP9i2pqS7kiRJkiRJpYwBmUq3irVg8Dio2Q42LoQn+kLGypLuSpIkSZIklSIGZCr9KtSAs8dB7Q6weSk83he2LC/priRJkiRJUilhQKayIbUqDH4D6naBrctjr1tu/raku5IkSZIkSaWAAZnKjpQqMOg1qN8dMlbEniTbtKSku5IkSZIkSfs4AzKVLcnpMPAVaHgAZK6OhWQbFpZ0V5IkSZIkaR9mQKayJ6kinPUyND4Etq+NHdy/bm5JdyVJkiRJkvZRBmQqmxLT4IwXoekRkLUBnuwPa2eXdFeSJEmSJGkfZECmsisxFU5/HlocA9mb4In+sPqrku5KkiRJkiTtYwzIVLYlJMOpz0CrvpCzFZ4cACunlXRXkiRJkiRpH2JAprIvPgn++CS0OR5yM+CpE+C7z0u6K0mSJEmStI8wIFOpkFeYx8A3Lmbq6pl7ViA+EU5+HNqfBHmZ8PSJsOyTom1SkiRJkiSVSgZkKhWue+9BZm6ZwnnvnMukZR/tWZG4ePjDI9DhNMjPgmdOgqVTirRPSZIkSZJU+hiQqVS4oPPZJGTvTxjkcvmUi3llwbg9KxQXDyc8AJ3PgoId8NypsHhS0TYrSZIkSZJKFQMylQrNaqTz6in3kZR1OASF3PTZdTw265k9KxaJg+PGQNchUJAD/z4dFkws0n4lSZIkSVLpYUCmUqNRtQq8fvqdpGw/DoKQe766k5Ff3EcYhrtfLBKB/vdA9/OhMA9eOAvm7eFTaZIkSZIkqVQL9ihcKKWCIEgD2gGtgepAMpABrAW+DMPwuxJsr1h169YtnDZtWkm3USTWZuTwh6fvIbPCvwmCkAFN/8jNB11PXCRu94uFIbxzA3z2T4jEw0ljod0fir5pSZIkSZJUooIgmB6GYbdf+ix+bzeztwVBsB9wMnAMsD/wqylKEASLgH8CY8MwzN5L/TUHun/fW3egM5Dyoy03h2H4173RS2lROz2Z1wddzolPprG1wuO8vvQlMnK3MOqIO0mMS9y9YkEAx9wCcQnw8T3wn6FQWAAd/lg8zUuSJEmSpH1OmX7FMgiCz4BZwF+AnvxGOPa9FsBoYEYQBF2Lsa/DgyB4OwiCzcAi4FngMuBAfhqO6VfUrJTMa0POp/r2iwgLk5iyahLnvX0BWflZu18sCOCom+CwayCMwqvDYOZzRd+0JEmSJEnaJ5XpgIxY4PVzhcBMYDzwHPAOsPVne1oB7wdB8IuP3RWBTsSeaKtSTPXLheoVknhl6EBqZV9BtKACMzZ8waAJQ9mSs2X3iwUBHHEdHHFDLCR77U8w/cmib1qSJEmSJO1zynpA9l8FwGvAAKBKGIadwzDsH4bhmWEY9gZqAOcQO4/svyoCrwdBUGEv9pkFLN+L9yv1qqYl8vLQk6m/YwTRvKos3DqXM8YNZM32NXtW8LAR0OtmIIQ3L4EvxxZpv5IkSZIkad9T1gOyfOARoHEYhn8Iw/CNMAwzf74pDMOCMAwfI/aK44+fJqsLXFmMvc0AHiYWznUA0oG/FtP9yqzKqYm8eM5xNM6/hsKc2qzMWs7p489kydYle1bw4Mug922xv8dfCZ8/WHTNSpIkSZKkfU5ZD8h6hGF4fhiGq3ZlcxiGc4ERP7t8RtG3xZNAxTAMu4ZhODwMw8fCMJwdhmFhMdyrXEhPSeDfQ4+mReHVFGQ3ZlPOBs6aMJCvN3y9ZwUPuBD6joz9PfFa+OS+omtWkiRJkiTtU8p0QBaG4Xd78LVngB//gmXLIAhqFVFLAIRhuCUMw9yirCmolJzAs+ccQbvgKgoy27A9P5Nz3j6Xj1d9vGcFu58H/e+N/f3ujfDhyKJrVpIkSZIk7TPKdEC2J8IwzAEW/uxy3ZLoRbuvQlI8Tw09iA6Jl5C/tSu5hTlc9N5FjF86fs8KdhsCA+4HApj8d3j/dgjDIu1ZkiRJkiSVLAOyX1bws3VCiXShPZKaGM8TZx9A17Th5G06lMKwkGs/upZn5z27ZwU7nwV/eBiCCHxwRywoMySTJEmSJKnMMCD7mSAIAqDJzy6vK4letOdSEuN4dPD+9Kw8mJx1fQC444s7GPPVGMI9Cbc6ngonjYUgDj4aFXvl0pBMkiRJkqQywYDs/zsEqPaj9XpgT84yUwlLTojjkUFdObTmH9mx+mQIIzwy6xH+/vnfKYzuwe8htD8J/vgEROLh0zGxw/sNySRJkiRJKvUMyP6/i3+2Hh/u0SNH2hckxcfx4FldOap+P7JXngXReF5a+BIjPhxBXmHe7hdsezyc+gzEJcLUh2D8lRCNFn3jkiRJkiRprzEg+5EgCI4CTv7RpRC4r4TaURFJjI/wzzO6cGyTo8j+7hzCwmTeXf4uf5r0J7bnbd/9gq36wGnPQVwSTHsU3rzEkEySJEmSpFLMgOx7QRBUA5742eXHwzCcWQLtqIglxEUYfVon+rU8iOzl5xMWVGTq2qkMfXsom3Zs2v2CLY6GM16A+BT46ml4/U+wJ69tSpIkSZKkEmdABgRBEAc8D9T/0eWVwJUl01HRCIJgWBAE04IgmLZhw4aSbqfExcdFuOfUTpzQthtZy4YT5lVj3uZ5DJ44mFXbV+1+wWZHwJkvQUIafP1veGUYFP78B1AlSZIkSdK+zoAsZgzQ60frPOC0MAy3llA/RSIMw0fCMOwWhmG3GjVqlHQ7+4S4SMBdf+zIHzt2JGvZcKI5dVm+bTkDJwxk0ZZFu1+wySFw1suQWAG++Q+8PBQK84u+cUmSJEmSVGzKfUAWBMH1wAU/uhQFBoVh+EkJtaRiFhcJuOPEDpzerR1Zy4cRzW7Khh0bGDxxMF+t/2r3CzY6AAa+BkmVYO7r8OJgKMgt+sYlSZIkSVKxKNcBWRAEw4Bbfnb5ojAMXyiJfrT3RCIBt57QnkE9WpH13RAKt7cjMy+TYe8M48OVH+5+wQb7w6DXITkdFoyHFwZCfk7RNy5JkiRJkopcuQ3IgiD4I/Dgzy5fH4bhz6+pjAqCgJuPb8fQA1uSveIMCrbuT05hDpdMvoQ3l7y5+wXrdYHBb0JKVVj0Njx/OuTvKPrGJUmSJElSkSqXAVkQBL2BZ/jp/CPDMLythFpSCQmCgBv7t+H8Q1uwY82J5G86gsKwkOs+vo6n5jy1+wXrdISzx0FqdVgyGZ47BfKyir5xSZIkSZJUZMpdQBYEwUHAK0Dijy6PDcNwRAm1pBIWBAHX9mnNRUe0IGd9b3LX9QPgrml3ce/0ewnDcPcK1moHZ4+HCrXg2w/hmZMhN7MYOpckSZIkSUWhXAVkQRB0BsYDqT+6/CJwfsl0pH1FEARceUxLLuvVgrzNh5Cz+hQCIjz6zaP89bO/UhAt2L2CNVvD2ROgYl347lN4+kTIySie5iVJkiRJ0v+k3ARkQRC0At4G0n90+S3grDAMoyXTlfYlQRBwWa+WXHVMS/IzupC9YhDxQRKvLHqFK6dcSW7hbv4yZfXmMGQ8pDeAlV/A03+AHVuKp3lJkiRJkrTHykVAFgRBQ+BdoMaPLn8InBSGYX7JdKV91UVHtuDaPq0p2N6abd8OISmSxuQVkxn+7nAy83bzVcmqTWOvW1ZuCKumw1MDIHtz8TQuSZIkSZL2SJkPyIIgqAG8AzT40eVpwHFhGPoTg/pFww9rxg392lC4ozGbF59HWlxVpq2bxtC3h7Jxx8bdK1alEQx5C6o0gTVfw5PHQdZu1pAkSZIkScWmTAdkQRBUAiYCrX50eQ5wbBiG24roHmcHQRD+6J8pRVFXJe/cQ5py8/HtiObWZt2Cc6kcX5f5m+cz6K1BrMhcsXvF0uvDkAlQrQWs+wae6AeZ64qncUmSJEmStFviS7qB4hIEQSLwOtDlR5c3AucBFYMgqLgb5TaGYbi9iPurzy//+6/+s3XlIAga/8K+gjAMVxZlT/r/Bh/YmPi4gOtf/YaVc4fSuP1zrMhcyqC3BvFQr4doVbXV7xf5r0p1Y69bPnU8bJgfC8kGvwmV6hTfAJIkSZIk6XcFYRiWdA/F4vtQ6dsiKjckDMMnfuU+ZwOP/+jSB2EYHv57BYMgWAY0+h96Wh6GYeNd3dytW7dw2rRp/8PtyrcXv1zBNa/MIgxyaNXhZVbnzqZiQkXGHDWGrrW67l6xrI2xs8jWfRM7o2zwm7EnzCRJkiRJUrEJgmB6GIbdfumzMv2KpVRUTtm/ASNP7kgkTGbB16fSOLknmfmZnP/u+UxZMWX3iqVVj4VitTvA5qXweF/YsrxY+pYkSZIkSb/PgEzaRSd1rc89p3YiQjyzvzqeFilHk1uYy2XvX8Zri1/bvWKpVWHwG1C3C2xdHnvdcvPS4mlckiRJkiT9pjL7iqV+ylcsi874WWu45PmvKIxG6dllGnN2vAzAFV2vYEj7IbtXLCcDnjkZVn4BFevGniyr3rwYupYkSZIkqXzzFUupCPXrUIf7z+hCQlyEz2fsT8fUwQDcPf1uRk0bxW6FzsnpMPAVaHggZK6GJ/rChgXF1LkkSZIkSfolBmTSHji2fW0ePLMriXERPp7ehq4pFxIfxPPEnCe48ZMbKYgW7HqxpIpw1n+g8SGwfV3sTLJ1c4uveUmSJEmS9BMGZNIe6tW2Fg8P6kpifIQpMxrQOekKkuOSeX3J61z+/uXkFOTserHENDjjRWh2JGRvjJ1JtmZW8TUvSZIkSZJ2MiCT/gdHtKrJ2EHdSIqPMPmrqnSMv5b0xHSmrJzC+e+ez7a8bbteLDEVTvs3tDgGdmyGJ4+D1V8VX/OSJEmSJAkwIJP+Z4e2rMHjZ+9PSkIck2Ym04Y/Uyu1FjPWz2DIxCFsyN6w68USkuHUZ6BVP8jZCk8OgJX+uIIkSZIkScXJgEwqAgc2r84TQ/YnNTGOd7+GJvnX0LhSYxZuWcjAtwby3bbvdr1YfBKc8iS0HQC5GfDUCfDd58XXvCRJkiRJ5ZwBmVREejStxtPndKdCUjzvzsqj1vYraVetHau2r2LQW4OYt2nerheLS4CTHoP2J0NeJjx9Iiz7uPialyRJkiSpHDMgk4pQ10ZVefqc7lRMjmfSnCxSN11Ej9o92ZSziaFvD+XLtV/uerG4eDjxEehwGuRnwTMnw9Ipxda7JEmSJEnllQGZVMQ6N6zCs+f2ID0lgcnzMihYM4ReDY9he/52hr87nPe+e2/Xi0Xi4IQHoPNZULADnjsVFk0qvuYlSZIkSSqHDMikYtChfmWeO68HVVITmDJ/Cxu//SMntziFvGgeV0y5glcWvbLrxSJxcNwY6DoECnLg+dNhwcTia16SJEmSpHLGgEwqJu3qpvPvYT2plpbIRws3sWBuL85rP5xoGOWmT29i7OyxhGG4a8UiEeh/D3Q/Hwrz4IWzYN6bxTuAJEmSJEnlhAGZVIxa167E88N6Ur1CEp8u3swn07pwVddrCQgYPWM0I6eNJBpGd61YEECfO+GAiyCaDy8Ohm9240k0SZIkSZL0iwzIpGLWolZFXji/J7UqJTH1282M+7gJfzvwduIj8Tw19ylu+PgG8qP5u1YsCOCYW+DgyyEshJfPgVkvFu8AkiRJkiSVcQZk0l7QrEYFXhh2AHXSk/ly2RaefLcydx1yHynxKby59E0ue/8ydhTs2LViQQBH3QSHXQNhFF4ZBjOfK94BJEmSJEkqwwzIpL2kcfU0Xhh2APUqp/DVd1sZMy5g9GEPUzmpMh+u/JBh7wwjIzdj14oFARxxHRxxAxDCa3+C6U8UZ/uSJEmSJJVZBmTSXtSwWiovnN+TBlVT+HplBre8sp0xh42ldlptZm6YydkTz2Zd1rpdL3jYCOh1MxDCm5fCF/8qtt4lSZIkSSqrDMikvax+lVReGHYAjaulMmf1Nq59YR33HfooTdObsnjrYga9NYhlGct2veDBl0Hv22N/T7gKPnugWPqWJEmSJKmsMiCTSkDdyim8cP4BNK2Rxvy1mVz67LeMOvgROlTvwOqs1QyeOJg5m+bsesED/gR9R8b+fvvP8Mno4mlckiRJkqQyyIBMKiG1KiXz/LCetKhZgYXrtnP+k/O47YB/clDdg9ics5lz3j6HqWum7nrB7ufBcaOBAN79C3x4V7H1LkmSJElSWWJAJpWgmhWT+fewnrSuXZElG7I4+7Gvua7bP+jTpA9Z+VlcMOkC3l3+7q4X7Ho2DLgfCGDyLTD5VgjD4mpfkiRJkqQywYBMKmHVKyTx3Hk9aVunEt9uzOKsf03n4v1u4ozWZ5AfzefKKVfy0sKXdr1g5zPhxEcgiMCH/4B/nwaZa4tvAEmSJEmSSjkDMmkfUDUtkefO68F+9dL5bnM2pz8ylTObX8JFnS4iJORvn/2NR2Y9QrirT4N1OAVOeQqS0mHhRLi/B8z+j0+TSZIkSZL0CwzIpH1E5dREnjm3B50aVGbllh2c9sjnHFv/LG7seSMBAWO+GsOdX95JNIzuWsE2x8GFn0PzXpCzFV4+B14aDFkbi3cQSZIkSZJKGQMyaR+SnpLA0+d0p2ujKqzOyOGUhz+jW9W+jDxsJAmRBJ6d9yx//ujP5Bfm71rBSnXhzP/EDu9PrABzX489TTbvzeIdRJIkSZKkUsSATNrHVExO4Kmh3enepCrrtuVy6iOf0zT1AB7o9QCp8alM+HYCF79/Mdn52btWMAhih/df8Ck0PgSyN8ILZ8Erw2DHlmKdRZIkSZKk0sCATNoHpSXF88SQ/TmwWTU2ZOZy2iOfUyVox2PHPkbV5Kp8suoTznv3PDJyM3a9aJVGMOgN6PMPiE+BWS/AAwfAot34lUxJkiRJksogAzJpH5WaGM+jg/fnkBbV2bg9j9P/9TlBbgOePPZJ6qbVZdaGWQx+azBrs3bjFyojEehxPlzwCdTvDplr4NmT4Y2LIWdb8Q0jSZIkSdI+zIBM2oelJMbxr0HdOKJVDTZn5XHG2M/Zvr0KT/V5iuaVm7MkYwkD3xrI0oylu1e4WjMYOhGO/hvEJcKMp+DBg+DbD4tnEEmSJEmS9mEGZNI+LjkhjocGdqVXm1pszc7njH99ztrNSTxx7BN0qtGJtVlrGfzWYL7Z+M3uFY7EwUGXwvkfQp1OkPEdPHkcTLga8rKKZxhJkiRJkvZBBmRSKZAUH8cDZ3bh2Ha12ZZTwFljp7J4bZRHjnmEQ+odwtbcrQx9eyifrv5094vXbAPnToLDr4NIPHzxMDx0MHw3tegHkSRJkiRpH2RAJpUSifERxpzRmX771SEzt4BBj07lm5U7GH3kaPo37c+Ogh1c+N6FTFw2cfeLxyXA4dfAeZOhZlvYvBQe6w3v3Aj5OUU/jCRJkiRJ+xADMqkUSYiLMPq0TgzoVJesvEIGP/YF077dxq0H38rAtgMpiBZw9QdX88L8F/bsBnU6wrApcPAVEATw6X3wyGGwakZRjiFJkiRJ0j7FgEwqZeLjItx9SidO6lKf7LxChjzxBZ8u3syIbiO4tMulhITcMvUWHpz5IGEY7sENkqDXTXDOu1CtBWyYD2N7wfu3QUFe0Q8kSZIkSVIJMyCTSqG4SMBdJ3fg1G4NyMmPcs6TX/Lhoo2cu9+5/PWAvxIJIjzw9QPcOvVW8gr3MNSq3w2GfwQ9L4QwCh/cCWOPhHVzinYYSZIkSZJKmAGZVEpFIgG3n7gfZ/ZoSG5BlPOenMbk+es4qeVJ3H3Y3SRGEnlhwQsc/9rxTFw2cc+eJktIgWNvg7PHQ+VGsHY2PHwYfDQKCguKfihJkiRJkkqAAZlUikUiAbec0J6zD2xMXmGU85+ezjtz1nJUo6MY23sszdKbsWr7KkZ8MIKBbw1k5vqZe3ajxgfBBZ9Ct6EQzYf3/gaPHQMbFhbtQJIkSZIklYBgj54qUanTrVu3cNq0aSXdhopJGIbcOn4eYz/+lvhIwH2nd6bvfnUoiBbwyqJXuH/m/WzO2QxA78a9ubTLpTSo2GDPbrb4PXjjYti2CuKT4ai/QI8LIGLeLkmSJEnadwVBMD0Mw26/+JkBWflgQFb2hWHInRMX8NAHS4iLBNx7aieO61gXgKz8LB6d/ShPzX2K3MJcEiIJnNH6DM7rcB7pSem7f7MdW+Ht62Dms7F1o4NgwP1QtUkRTiRJkiRJUtExIJMBWTkRhiF3v7uQMZMXEwlg1Ckd+UPn+js/X5u1ljFfjeGNJW8AkJ6UzvAOwzm11akkxCXs/g0XvAVvXAJZ6yEhDY75e+w1zCAoqpEkSZIkSSoSBmQyICtnRk9axD2TYueDndilHlf3bk3t9OSdn8/dNJeR00by5dovAWhYsSFXdL2CIxseSbC74Vb2ZphwFXzzcmzd9AgY8E9Ir//b35MkSZIkaS8yIJMBWTk09qOl/GPiAvIKo6QkxDHs0Kacf1hTUhPjgdjTZh+s/IBR00axbNsyALrU7MKI/UfQvnr73b/hN6/A+Cthx2ZIqgTH3gGdzvBpMkmSJEnSPsGATAZk5dTyTVncOXE+E2avBaBWpSRG9G7NiZ3rEYnEgqv8aD7/WfgfHpz5IFtytwDQt0lfLu1yKXUr1N29G25fD29eBgvGx9Ytj4XjRkPF2kU2kyRJkiRJe8KATAZk5dyXyzbz93FzmbUyA4D29SpxQ7+29GxabeeezLxMxs4eyzNznyEvmkdiJJGz2p7FufudS8XEirt+szCEWS/AhKshNwNSqkDfkdD+JJ8mkyRJkiSVGAMyGZCJaDTk9a9X8Y+JC1iTkQNA73a1uLZPG5pUT9u5b9X2VYyeMZq3vn0LgCpJVbig0wWc3PJkEiK7cZB/xip442JY8l5s3fYE6Hc3pFX77e9JkiRJklQMDMhkQKadduQVMvajpTz4wRKy8wpJiAsYdEBjLjmyBempPwRgszfMZuS0kcxYPwOAxpUac0XXKzi8weG7fpB/GMKMJ+Ht6yFvO6TVgP73Qpv+xTGaJEmSJEm/yoBMBmT6f9Zvy2HUOwt5cfoKwhAqpyZw6VEtOKtnIxLiIkDsIP/J303m7ul3813mdwDsX3t/rup2FW2rtd31m21ZDq9fCMs+iq07nAZ97oi9filJkiRJ0l5gQCYDMv2qOaszuGXcPD5bugmAptXTuK5vG45qU3Pnk2L5hfm8sOAFHpr1EBm5sXPMjmt6HJd0uYTaabt4AH80Cl88ApP+CgU7oGIdOP6f0KJXcYwlSZIkSdJPGJDJgEy/KQxD3pu3ntsmzGPpxiwADmxWjev7taFd3fSd+zJyM/jXrH/x3PznyI/mkxSXxKC2gzhnv3NIS0j7tfI/tXExvDYcVn4ZW3cZDL1vhaTd+CEASZIkSZJ2kwGZDMi0S/IKojw7dTn3TlpExo58ggBO6dqAK49pSc1KyTv3rchcwegZo3l72dsAVE2uyoWdLuTEFicSH4n//RtFC+HTMfD+rVCYB5UbwoD7ocmhxTWaJEmSJKmcMyCTAZl2y9bsPMZMXsyTny6jIBqSmhjHBYc149xDmpKSGLdz38z1M7lr2l3M2jALgGbpzbiy25UcXO/gXTvIf93c2NNka76OrXsMh6NugsTU4hhLkiRJklSOGZDJgEx75NuNWdw+YR7vzF0HQJ30ZK45tjXHd6xLJBILwMIw5J3l73DP9HtYtX0VAD3r9OSqblfRqmqr379JYT58dDd8+A+IFkDVZnDCg9CwR7HNJUmSJEkqfwzIZECm/8lnSzZxy/i5zFm9DYCO9dO5oX9b9m9cdeeevMI8/j3/3zw862Ey8zIJCBjQfAAXd76Ymqk1f/8mq2fCq8NhwzwIInDgxXD4dZCQ/PvflSRJkiTpdxiQyYBM/7NoNOTlGSu56+0FrM/MBaDffnW45tjWNKz2wyuRW3O28vCsh3l+/vMUhAWkxKdwdruzObvd2aQm/M6rkwW5MOV2+GQ0hFGo0Rr+8BDU7Vyco0mSJEmSygEDMhmQqchk5xXw8AdLefjDJeTkR0mMizDkoMZceGRzKiUn7Ny3fNty7p1+L5O+mwRAjZQaXNT5IgY0G0BcJO7Xyses+DJ2NtmmxRDEwaFXwSFXQXxicY4mSZIkSSrDDMhkQKYityZjB3e9vYBXZsTOHaualsjlvd8l/YYAACAASURBVFpweveGxMdFdu6bvm46I78cyTebvgGgZZWWXNntSg6se+Bv3yAvGyb/HT5/ILau3SH2NFmtdsUyjyRJkiSpbDMgkwGZis2slVu5Zfw8vvh2MwDNa1bg+n5tOLxljZ2/ZBkNo0z8diKjZ4xmddZqAA6qdxBXdb2K5lWa//YNln0Mr/0Jti6HSAIccR0ceAnExRfrXJIkSZKkssWATAZkKlZhGPL2nHXc/tY8lm/KBuCQFtW5oV9bWtWuuHNfbmEuz8x9hrGzx7I9fzuRIMKJLU7kwk4XUj2l+q/fIDcT3rkRpj8eW9frFnuarHqL4hxLkiRJklSGGJDJgEx7RW5BIU9/tpzR7y0iM6eASACndW/I5b1aUqNi0s59m3M28+DMB3lp4UsUhoWkxqcytP1QBrUbREp8yq/fYPEkeP1iyFwN8clw1E3QYzhEIr/+HUmSJEmSMCATBmTauzZn5XHfe4t4+vPlFEZDKiTF86cjmjH0oCYkJ/xwQP/SjKXcM/0epqyYAkDN1Jpc0vkSjmt2HJHgV0KvHVth4p/h6+di60YHwYD7oWqTYp5KkiRJklSaGZDJgEwlYvH67dw+YR7vzV8PQL3KKVzbpzX9O9TZeT4ZwBdrvmDktJHM2zwPgDZV23BVt6voXqf7rxefPwHevBSy1kNCGhzzd+g2FH5UV5IkSZKk/zIgkwGZStTHizZyy/i5zF+bCUDnhpW5sX9bujSssnNPNIwybuk4Rs8YzfrsWKB2WP3DuKLbFTRNb/rLhbM2wYSrYM4rsXWzI+H4MZBev1jnkSRJkiSVPgZkMiBTiSuMhrw0bQUj31nIxu25ABzfsS5XH9uK+lVSd+7bUbCDp+c+zaOzHyW7IJu4II6TW57MBR0voFpKtV8u/s0rMP5K2LEZkipBnzuh4+k+TSZJkiRJ2smATAZk2mdszy3goSlL+NdHS8ktiJIYH+Hcg5twweHNqJicsHPfxh0beWDmA7y86GWiYZS0hDTO3e9czmpzFsnxyf+/cOY6GHcZLJgQW7fsA8eNhoq19tJkkiRJkqR9mQGZDMi0z1m1dQf/mDif12euBqB6hUSuOLoVp+7fgLjID09+Ld6ymFHTR/Hxqo8BqJNWh0u6XELfJn3//0H+YQhfPw9vXQO5GZBSBfqNgvYn7bW5JEmSJEn7JgMyGZBpn/XVd1u4Zfw8pi/fAkDr2hW5vl8bDmlR4yf7Pl39KaOmjWLhloUAtK/Wniu7XUm32r/w37aMVfDGRbBkcmzd7g/QdxSk/cormpIkSZKkMs+ATAZk2qeFYcj42Wu44635rNyyA4AjWtXg+n5taF6z4s59hdFC3ljyBmO+GsOGHRsAOLLBkVze9XIapzf+eVGY/ji8fQPkZ0Fajdgrl6377a2xJEmSJEn7EAMyGZCpVMjJL+SJT5fxz8mL2Z5bQFwk4MweDbmsV0uqpiXu3Jedn82Tc57k8TmPs6NgB/FBPKe2PpXhHYZTObnyT4tu/hZevwiWx17RpOPpcOwdkPKzfZIkSZKkMs2ATAZkKlU2bs/lnncX8u8vviMaQsXkeC4+sjmDD2xMUnzczn3rs9dz/8z7eXXRq4SEVEyoyLAOwzijzRkkxv0QqBGNwhcPw6S/QkEOVKwLA8ZA8157fzhJkiRJUokwIJMBmUqlhesyuWX8PD5cGHudsmHVVP7cpzXHtq9NEPxwkP+CzQsYNW0Un635DIB6FepxWZfL6N2490/2sXERvHYBrPwytu56NhxzCyT98BqnJEmSJKlsMiCTAZlKtSkL1nPr+HksWr8dgP0bV+GGfm3p2OCH1yTDMOST1Z8watooFm9dDECHGh0Y0W0EnWp2+qFYtBA+vQ/evw0K86ByQxjwADQ5ZK/OJEmSJEnauwzIZECmUq+gMMrzX67gnncXsikrD4A/dK7H1ce2ok56yg/7ogW8uvhV7v/qfjblbALgmEbHcFmXy2hQqcEPBdfNhVfPh7WzYuseF8BRf4HE1L02kyRJkiRp7zEgkwGZyoxtOfnc//5iHv94GXmFUZITIgw7pCnnH9aMtKT4nfuy8rN47JvHeGrOU+QU5hAfieeM1mcwrMMw0pPSY5sK8+HDkfDRSIgWQNVmcMID0LBnCU0nSZIkSSouBmQyIFOZs2JzNndMnM/4WWsAqFExiRHHtOKkrvWJi/xw7tjarLWM+WoMby55k5CQSomVGN5xOKe1Oo2EuITYptVfwasXwIZ5sXXHM+Dom6FCzb09liRJkiSpmBiQyYBMZdb05Zv527h5fL1iKwBt61Tihv5tOLBZ9Z/sm7dpHiOnjeSLtV8A0KBiAy7vejm9GvaKHeRfkAsf3gWfjI6dTZZUCQ7/M3Q/D/4bpEmSJEmSSi0DMhmQqUyLRkPenLWaO9+az+qMHAB6tanFdX1b07RGhZ37wjDkw5UfMmr6KL7N+BaALjW7cFW3q9ivxn6xTZuWwMRrYdE7sXXNttDnHx7iL0mSJEmlnAGZDMhULuTkFzL2o6U8MGUJ2XmFxEcCBh7QiEuPakHl1MSd+/Kj+byy8BUe+PoBNudsBqBP4z5c2vVS6lWoF9u0YCJMvAa2LIut258ER/8d0uvt5akkSZIkSUXBgEwGZCpX1mfmcPc7C3lh2grCENJTErjkqBYM7NmIxPjIzn2ZeZk8OvtRnp77NHnRPBIjiQxsO5BhHYaRmpAK+Tnw6Rj4aBQU7ICENDhsBPS8EOITf6MDSZIkSdK+xoBMBmQql+au3satE+byyeJNADSpnsaf+7Tm6La1YueOfW/19tXc99V9jF86HoA6aXW4Zv9rOLLhkbF9W7+Dt6+HeW/EvlCtOfS5E5r32uszSZIkSZL2jAGZDMhUboVhyOT567l1wjyWbsgCoGfTqtzQry3t66X/ZO+sDbO45fNbmLc59muWB9c7mD93/zMNKzWMbVgyGSZcDZsWxdat+0Pv26BKo702jyRJkiRpzxiQyYBM5V5+YZTnpn7HPZMWsjU7nyCAk7rUZ0TvVtSqlLxzX2G0kBcXvsiYGWPIzM8kMZLIOfudw9D2Q0mOT4aCPJj6IEy5E/KzID4ZDr4CDroEElJKcEJJkiRJ0m8xIJMBmfS9jOx8xkxexJOfLSO/MCQlIY7hhzXjvEObkJoYv3Pfxh0buWf6PbyxJPZaZf0K9flzjz9zaP1DYxu2rYZ3/wKzX4qtKzeCY++AVn3gR69vSpIkSZL2DQZkMiCTfmbZxizueGs+E+esBaB2pWRG9G7FHzrXIxL5IeCavm46t3x+C4u3LgbgyAZHck33a6hboe73hT6OvXa5fk5s3fzo2Plk1Zrt1XkkSZIkSb/NgEwGZNKv+HzpJm4ZP5dvVm0DoFG1VPp3qEP/DnVpXbsiQRCQH83nuXnP8cDMB8guyCY5LpnzO57P4LaDSYhLgMICmPYoTL4VcjMgLhEOuAgOvQoS00p4QkmSJEkSGJAJAzLpt0SjIa9+tYqR7yxgTUbOzuvNaqTRv0NdjutYh+Y1K7Iuax0jp41k4rKJADSu1JjrelzHAXUPiH1h+3qYdDPMfCa2rlQPet8KbU/wtUtJkiRJKmEGZDIgk3ZBYTRk6rebGDdrDRO/WcvmrLydn7WuXZF++9Whf8e6rM2bxW1Tb2PZtmUAHNv4WK7qdhW10mrFNq/4EiZcCWu+jq2bHAp97oKarffyRJIkSZKk/zIgkwGZtJvyC6N8tmQT42atZuI3a9mWU7Dzs3Z1K9FnvxrkpE7mhUWPk1OYQ2p8Kn/q9CfOaHMGCZEEiBbCjKfgvZthxxaIxEOP4XDYNZBcqQQnkyRJkqTyyYBMBmTS/yCvIMrHizcw7us1vDN3HdtzfwjL2jYsJLHGGyzJngpA88rNuaHnDXSt1TW2IXszTL4Fpj0GhFChFhz9N+hwqq9dSpIkSdJeZEAmAzKpiOTkF/LBwg2Mm7WG9+atIzuvEIC4CvNJrzeO/MhGAI5vdjyXd72c6inVY19cPRMmXAUrv4ytG/SEvndBnQ4lMYYkSZIklTsGZDIgk4rBjrxCJs9fz/jZq3lv3npyC3NJrDaFxGofEEQKSIqkMbzDhQzZ7wziInEQjcKs5+Hdv0DWBggi0O0cOPJ6SKlS0uNIkiRJUplmQCYDMqmYZeUWMGneOsbNWsMHS+cRV+N14issACAlbMhpTS5l6P6HUzk1EXIyYModMPVhCAshtRocdRN0HgiRSAlPIkmSJEllkwGZDMikvWhbTj7vfLOWZ2aPZ1HhM0QSMgjDgMKM/ela6Uz+0KElR7erRaWMRTBhBCz/OPbFul2g70io37VkB5AkSZKkMsiATAZkUglZsy2Dv344ms82vkIYFBItSCVvw7EEmd05rFUt+u9Xm958RvLkv0DmaiCALgNjT5SlVS/p9iVJkiSpzDAgkwGZVMKWZizl5k9uYcaG2CH9hTsakrNmANHceiTFR+jTsiIXxb9Gs8VPEETzITkdjrwRug6BuPgS7l6SJEmSSj8DMhmQSfuAMAyZuGwid315Fxt2bCAgQnr+oaxYehhEUwBom7iOURWeo0329792WWu/2K9dNjqgBDuXJEmSpNLPgEwGZNI+ZHvedh74+gGem/cchWEhlZOq0r3SIBZ/24qvV2QAIcdEpnFTwjPUCzYAUNj+FOJ6/x0q1i7Z5iVJkiSplDIgkwGZtA9asHkBt029jRnrZwDQtVZXzml9JXOWpzJu1moWr9rA8Pg3uSDuTZKCfHIiqazqeAmN+lxBfGJSCXcvSZIkSaWLAZkMyKR9VBiGvLHkDe6efjebczYTF8RxVpuzuKDTBazPgPGzVjPtq684c+tDHB03HYCl1Of9plfR5qDj6NGkGnGRoISnkCRJkqR9nwGZDMikfVxGbgZjvhrDiwteJCSkZkpNRnQfQe9GvQmCgMXrM5n9/n/Yf/6d1A/XADCusAcPJg6la4f29O9Ql26NqhAxLJMkSZKkX2RAJgMyqZSYs2kOt35+K7M3zgagZ52eXNfjOpqkNwEgzM9hw7t3U3naaBKjOWSHSfyzYABjC/tRtVJF+u5Xh/4d69C5QWWCwLBMkiRJkv7LgEwGZFIpEg2jvLzoZe6dfi/b8rYRH4lnSLshnNfhPFLiY792ScZKwnduIJjzKgArgjrcmHsWU6KdAahXOYX+HerQv0Nd2terZFgmSZIkqdwzIJMBmVQKbcnZwr0z7uWVRa8AUDetLtd0v4YjGhzxQ+C19AN462rYMB+AhekHc232GczIrLyzTqNqqTvDsta1KxqWSZIkSSqXDMhkQCaVYjPXz+TWqbcyf3MsBDu0/qFc2/1aGlRsENtQmA9TH4Ypd0BeJmFcEqvbn8/jDOC1OVvZuD13Z61mNdLo36Eux3WsQ/OaFUtiHEmSJEkqEQZkMiCTSrmCaAEvLHiBf371T7bnbycpLolz9juHoe2HkhSXFNuUuRbevQlmPR9bpzeksPetTE08gHGz1zLxm7VszsrbWbN17Yr0268O/TvWpUn1tBKYSpIkSZL2HgMyGZBJZcTGHRsZNW0U45aOA6BBxQZc1+M6Dq538A+bln8GE0bAuthB/zQ7Evr8g4Iqzfh0ySbGzVrNxG/Wsi2nYOdX2tWtRP8OdenfoQ4NqqbuzZEkSZIkaa8wIJMBmVTGfLn2S279/FaWZCwBoFfDXlzT/Rpqp9WObSgsgOmPw+S/Q04GRBLggD/BoVdDUgXyCqJ8vHgD475ewztz17E994ewrGODyhzXoQ5996tD3copJTGeJEmSJBU5AzIZkEllUH40n2fnPssDXz/AjoIdpMSncH6H8xnUdhAJcQmxTVkb4b2bYcbTQAgV68Axt0D7k+D7w/pz8gv5cOEGxs1aw6R568jOK9x5j26NqtD/+7CsZqXkEphSkiRJkoqGAZkMyKQybG3WWu768i7eWf4OAE3Tm3J9j+vpXqf7D5tWTocJV8HqGbF1o4Oh711Qq+1Pau3IK+T9BesZN2s1781bT25BFIhlaT2aVKV/h7r0aV+bahWS9spskiRJklRUDMhkQCaVA5+u+pTbvriN5duWA9CnSR9GdBtBjdQasQ3RKMx8Bib9FbI3QRAH3YfB4ddCSuX/Vy8rt4BJ89YxbtYaPliwgbzCWFgWFwk4sFk1+neoQ+92tamcmri3RpQkSZKkPWZAJgMyqZzIK8zjiTlP8MisR8gtzCUtIY0LO13I6a1PJz4SH9u0Ywu8fxt8ORbCKKTVgF43Q8fTIRL5xbrbcvJ5d846xs1azUeLNlIQjf2/IzEuwuADG3HxUS2olJywt8aUJEmSpN1mQCYDMqmcWbV9FXd8cQdTVkwBoGWVltzQ8wY61+z8w6a1s2O/dvndZ7F1/e6x1y7rdvrN2luz83h7zlrGzVrDx4s3EoZQvUIiV/duzcld6xOJBMU0lSRJkiTtOQMyGZBJ5dQHKz7g9i9uZ9X2VQAMaDaAy7teTrWUarENYQizXoR3b4Tt64AAug2BI2+E1Kq/W3/2ygxufnMO05ZvAWC/eun89fi2dG30+9+VJEmSpL3JgEwGZFI5llOQw9jZY3nsm8fIj+ZTMbEil3a+lJNbnkxcJO77Tdvggzth6kMQLYCUKnDUX6DLYPjvnl8RhiFvfL2a2yfMZ+22HAAGdKrLtX1aUyc9pbjHkyRJkqRdYkAmAzJJLN+2nNun3s4nqz8BoF21dtzQ8wbaV2//w6b18+GtEfDth7F1nY7QdyQ06P4LFX8qO6+Ah6Ys4aEPl5JXECUlIY4Lj2jGuYc0JTnht0M2SZIkSSpuBmQyIJMExJ72mvTdJO784k7WZa8jIODklidzaZdLSU9K/+8mmPs6vH09bFsZu9bpTOj1V6hQ83fvsWJzNre/NY8Js9cCUL9KCtf3bcOx7WsTBJ5PJkmSJKlkGJDJgEzST2TnZ/PQrId4es7TFIQFVEmqwuVdL2dA8wFEgu9/yTIvCz4aBZ+OgcI8SEqHI66D/c+FuPjfvcenSzbytzfnMn9tJgAHNK3GTce3pXXtSsU5miRJkiT9IgMyGZBJ+kVLti7h1qm38uXaLwHoVKMT1/e8ntZVW/+wadMSeOsaWPxubF2zHfT9BzQ++HfrFxRG+feXKxj1zgK2ZucTCeDMHo244uiWVElLLI6RJEmSJOkXGZDJgEzSrwrDkAnfTmDktJFs3LGRSBDh9Nanc2GnC6mYWPG/m2DhxFhQtnV57Fr7k+GYv0Olur97j63Zedw7aRFPf76cwmhIekoCVxzdkjN7NCQ+LlKM00mSJElSjAGZDMgk/a7MvEwemPkAz81/jmgYpXpKda7sdiX9mvT74eyw/B2xVy4/GgUFOZCQBl0HQ/dhULXJ795jwdpM/jZuDp8s3gRAy1oVuOm4dhzUvHpxjiZJkiRJBmQyIJO06+Zvns8tn9/C1xu+BmD/2vtzfY/raVa52Q+btiyHt6+D+eO+vxBA637Q8wJodBD8xmH8YRjyztx13Dp+Ht9tzgagd7taXN+3LQ2rpRbXWJIkSZLKOQMyGZBJ2i3RMMrri1/nnun3sCV3C/FBPAPbDmR4x+GkJvwoxFrzNXz+EHzzn9hB/gC19osFZe1PgoTkX71HTn4hj378Lfe/v5jsvEIS4yOcd0gT/nR4c9KSfv9HACRJkiRpdxiQyYBM0h7JyM3gvhn38dLClwgJqZVai6v3v5qjGx39w2uXAJnrYNpjMO1RyNoQu5ZWA7qdA/ufAxVq/uo91m3L4c635vPKV6sAqFUpiWv7tOaETvV+eg9JkiRJ+h8YkMmATNL/5Jv/Y+/Ow+Muy/2Pv7+zZWYyk32StGm6J22SQlP2pS2IUFRQEBBZXFBRccEjHpdz1HPcUH/neNwRUJRNdkVlcSsoSEs3oE2hSdombdMlbfY9mX2e3x/fmclMZtJO2sl+v64rVybPd3umLE0+uZ/76djFnVvupLazFoAL5l7AV8/9KguyFsSf6PfArqdhyz3Q+pY+ZrToDf3Puw3mrBz1GdsPdfOtZ2vZeaQXgDPm5/CNd1exsjRnXN6TEEIIIYQQYnaRgExIQCaEOGXBUJCnG57mJ9t/Qr+vH7PBzC1Vt/CORe9gSfYSjAbj8MlKwcFX9aBs95+B8N81C1brQdmyd0Hs+WGhkOLp7Uf4n7/toWPAC8D7zpzHl96xjELn6Ms1hRBCCCGEEOJEJCAL0zQtE6gClgMFgBXoBVqA15RShyZxbhpwDrAUKAGGgCPAG0qpw6d6fwnIhBDp0unu5Mdv/Jhn9j0THXOYHax0rWRl4UpWFa7itILTyDRn6ge7DsC2X8H234KvXx/LWQDnfhJWfQCs2QnP6Pf4ueulRu7feAB/UOHIMHH7JUu55cKFZJgSgzUhhBBCCCGEOJFZHZBpmnYacB2wDjgbON5PVg3AXcCvlVJDEzA9NE0zAf8O3AYsTHJKCHgJ+LZS6pWTfY4EZEKIdNvRtoPHdz9OTVsNxwaPxR0zaAbKc8updlVTXah/zDU60HY+Blvvhe4m/USLQw/JzvkE5C9JeMaBjkG+++d6XqxvBWBhvp3/urKSS5YXSn8yIYQQQgghxJjM2oBM07TNwHknceke4Gal1BtpnlIcTdPmAb8jtTmGgO8qpf77ZJ4lAZkQYjy1DLaws30nNW011LTVsLtrNwEViDun0FbIysKVVBecziqPh+W7nsPctDF8VINl79R3v1y4BkaEX//a2853nq+jsW0AgLXlLv77ykqWFjom4u0JIYQQQgghZoDZHJB1APkjhoPAW0Az+vLKAvSljSO7QPcDlyilxiVV0jTNAWwCThtxqBbYDTiBM0mc/9eUUt8b6/MkIBNCTCR3wM2ujl3DoVl7Db3e3rhzMowZVGUtYtXQINVH3mTl0CC5oRAUrYBzb4PT3gfm4b5j/mCI324+yI9f3Eu/J4DJoPHhCxbyubeXkW0zT/RbFEIIIYQQQkwzEpBBAHgeeAB4SSnVP+I8E/Ah4EdAbDOco8AypdTAOMztceCGmKEjwE1KqQ0x59jQl19+G4iUUyhgnVLqxbE8TwIyIcRkCqkQTX1N7GzbyY62HdS013Cg90DCeQsDIardQ1R7vKzS7CysvgXDObeCszh6TueAlx++sJfHtx1CKcjPtPDFy5dx/VmlGA2y7FIIIYQQQgiR3GwOyI4Bz6L372pO4fxK4FXiq8m+qZT6VprndRbwWsxQD7BKKdU0yvl3oId3EduVUmeO5ZkSkAkhppoeT49eYdauL8vc1bELT9ATd05WMMhKr59VOeVUn/YBqpZdhd1sB6D2aC/feraObU1dAFTOyeKb76ninEV5E/5ehBBCCCGEEFPfbA7I5o91Z0pN024F7osZ2quUWpbmeT0HXBkz9Gml1D3HOV8DNgPnxgxfrZR6ZpRLEkhAJoSY6vwhP3u69lDTVqNXmR3bSpsvflmmUcHyzBKq56+luvAMVrpW8sZ+xff+XM/RXj1cu/L0OfznuyooybFNxtsQQgghhBBCTFGzNiA7GZqmWYFOwB4zXKyUak3T/XOBNsAUHuoG5iqlPKNfBZqm3QA8HjP0O6XU9ak+VwIyIcR0o5SiZbCFHQfWU7P7aWp6GthjNhIa0cC/OLOY0/JX0t87jw27MvEMFGE1m7ntoiV8cu0SbJbjbV4shBBCCCGEmC0kIBsjTdN2ANUxQ2copXak6d43AY/GDP1aKfXxFK4bGdz1AS6llC+V50pAJoSY9rz9DG1/kLe2/5oafxc7MjJ405pBv8EQd5oBC77BeQTdC8gxlPGli9Zx3apyNE36kwkhhBBCCDGbSUA2RpqmvQbE/oGdq5TalqZ7PwDcEjP0YaXUwyle+zJwUczQ2tim/scjAZkQYsYIBaFhPWy5m9CBV9hvNrPDmkGNaxE1GRYOedoTLrGE5rC69CzetvAcql3VLMhaIIGZEEIIIYQQs4wEZGMQ7vfVjr77ZcRCpdTBNN1/G3B2zFCFUmp3itf+P+ArMUOfUkrdm8q1EpAJIWakll2w9V548ykIegHoLFzOzsp17LA5efHgGxwZ2gtaIO6y3IxcVhaupNpVzarCVVTmV2I1WSfjHQghhBBCCCEmiARkY6Bp2lrgXzFDbeg9yNLyB6VpWi+QFf5SATallDfFaz8JxAZiP1FK3ZHKtRKQCSFmtMEOeP0BeO0+GAi3jLTnw1kfpaPiRr6z5S3+3rgFrE2Y7YfA1B93uclgojKvkurCav3DVY3L7pqENyKEEEIIIYQYLxKQjYGmab8DrosZekAp9dE03bsAvTotok0pVTSG668Ano8Zel4p9e5UrpWATAgxKwR8UPtH2PILOLZTHzOYoeq9HF72Yb62zcIre9vQzF3MLWrl9KXdtPn20NDdgCL+78MSR0k0LFtVuIqlOUsxGqThvxBCCCGEENOVBGQp0jTt7cCLMUMKvUF/TZruvwRojBmqVUqtGMP15wBbY4Y2KKXWpnKtBGRCiFlFKTi0BbbcDbufBxXSh0vPY1fpTXy+poR9XXrx7qUVRXxh3Xx6Qo3UtNdQ01bDmx1vMugfjLul3WTndNfprCpcRbWrmtNcp+G0OCf8rQkhhBBCCCFOjgRkKdA0LR+oAebFDN+vlPpYGp+xMvyMiFH/wYxy/WnAmzFDO5RSZ6RyrQRkQohZq/ugvvTyjYfB2wuAyprHFtd13NFwOi0+KxajgY+uXsRnL1mKI8NEMBSksaeRmrYadrTvoKathuaB5rjbamiU5ZZR7aqOLs2c55gnzf+FEEIIIYSYoiQgOwFN04zA34BLY4aPAKcppXrS+JwLgFdjhl5VSq0ew/VlwN6Yob1KqWXHOf8TwCcA5s+ff+bBg2nZZ0AIIaYn7wDsfBy23ANd+wAIme1sdl7O14+t5oCag8uZwVfesZxrVpVgMMQHXe1D7dEKs5r2Guo66wiE4pv/51vzqS7Ul2SudK2kMr8Si9EyYW9RIkTofgAAIABJREFUCCGEEEIIMToJyE5A07S7gU/FDPmAS5RSr45yyck+Z2RAtlEptWYM1y8FGmKGGpRS5alcKxVkQggRFgpB4wv68sv9L0eHX7eczU8GLmVjaAUrS3P55rsrWTU/d9TbeAIe6jrrqGmvYUfbDna27aTb2x13jsVgoaqgimpXdXTXzHxb/ih3FEIIIYQQQownCciOQ9O0rwF3xgyFgJuUUk+Ow7NkiaUQQkwlrXWw9R548ykIeADYp5Vyn+9y/hhczRWrFvGVdy6nKMt6wlsppTjUf4gdbfqSzJ3tO2nsaUw4b1H2IlaXrGZNyRrOLDpTKsyEEEIIIYSYIBKQjSK8BPGXI4Y/rZS6Z5yeN7ICbKxN+s8GtsUMzY4m/V37YdNd+g+wvgGwOOD06+GCz0Le4smenRBiJhjshDcegG33wUALAN3KyaPBS/i94R1cf8k5fPTCRVjNY9vFstfby5vtb0aXZr7V8RbugDt63GaycW7xuawuWc3qeaspcZSk9W0JIYQQQgghhklAloSmae8DngAMMcNfU0p9bxyf6QLaYoZalVLFY7j+XcCfY4aeV0q9O5Vrp21A1vACh5++hYcyM3jeYWNI07ArxZUDbj486KX02geh7LLJnqUQYqYI+KDuGdjyCzi6AwC/MvLn0Ln81X41177nKi6rLDrpRvz+kJ+dbTvZ2LyRDc0b2Nu9N+744uzFelhWslqqy4QQQgghhEgzCchG0DTtcuBZIPYnj/9TSn1pAp7dC2SFv1SAVSnlS/HaTwL3xgz9VCn1+VSunZYBWdd+NjxwMV/IcxLQNAIxP5CalMKkFD/q6mfNR16WSjIhRHopBYe3wZa7UfXPoqkQAG+EythUcD2Xv+/jlM8ZvT9ZqloHW3n16KtsbN7I5qObGfAPRI/ZTDbOnXMua0rWsLpkNXMdc0/5eUIIIYQQQsxm0y4g0zTNDFREvlZKvXmc08d67wuB9YA9ZvjXSqmPp+sZJ3j+NuDsmKHlSqk9KV77/4CvxAx9Sil172jnx5qOAdnhZ27jmq4NeAyGUc+xhkL8IX8tpe8Zl1WxQggBPYcIbf0V/tceJCPQD8BRlU9tyfs557ovkJ3nSstj/CE/NW01bGzeyMbmjUmry9aUrGH1vNWcUXiGVJcJIYQQQggxRmkLyDRN2x5+qYArlFItJzj/pIIuTdMWAPuHL1OmlCd5/PuuAl4CsmOGnwJuVCpcHjDONE17ALglZujDSqmHU7z2JeDimKGLlFKvpHLtdAzI7vxFGU9nZsRVjo1kUorrBr187TMNo54jhBBp4RtkcNsjDG24C5f3EABuMjhUehVL3/1FjIXL0vq4lsEWXm0OV5cd28ygfzB6TKrLhBBCCCGEGLt0BmSREEkBi5RSh05w/kkFXeHrDsRcN7auyMnvuQzYAMT+qv+vwFVKKf+p3n8M87gZeCRmKKXqNU3TrEAHkBke6gcKUl2eOR0DsvMeqGLwONVjEY5QiM0fqZ2AGQkhBBAKcei15+j+509Y6d0eHe6eexG5l/wbLLkETrJH2Wgi1WUbmjewsXkjDd3xvxRYkr0k2uhfqsuEEEIIIYRIbrIDsjEHXekOyDRNmw9sBEpjhl8B3qGUcie/anxompaL3qg/EhZ2A3OVUp4TXHcD8HjM0O+UUten+tzpGJCd/uAKVAo/ZGpK8eYtuyZgRkIIMUwpxcZNG+j85895R+AlrJr+uxZ/bhnmCz8Dp78fLPYT3OXkRKrLNjRvYMuxLaNWl60pWcMcx5xxmYMQQgghhBDTzawOyMI7R24AYte+vA68XSnVd7L3PRWapj0HXBkz9Gml1KhNtDR9u7TNwLkxw1crpZ5J9ZnTMSA776FqBgme8DyHZmLzh3ZMwIyEECKRxx/kt//YTv+mX3Ojtp45WhcAypaLduYtcPbHIbtk3J7vD/qpaT9xddmaeWs4o/AMzEbzuM1FCCGEEEKIqWzWBmSapmWh9xw7I2a4Fr13V+fJ3DPJM24BHogZ+pdS6uITXHMW8FrMUA9QrZQ6OMr5nwd+HDO0HThLjeEf3nQMyO58+cs83fSXE/cgW3gFX7v4fyZwZkIIkehYr5v//csuAm/9iY+Z/kq1YR8ASjOiVV0N530a5iX9uzitWgZboo3+R1aX2U12zp1zrh6YSXWZEEIIIYSYZWZlQKZpmgX4O/FN7TuA9wDHxni7DqXUwCjPuYUxBmTh6x4HbogZOgLcpJTaEHOOFfh34DtAJCVSwDql1ItjeQPTMSA73HeYa565Ck9o9BZxFgz86b3PU5pVOuo5QggxkV5v6uJbz9VhOvo6HzX9lXcZt2Ek/NfnvLPhtOvBtQwKysFZnPZ+ZbFiq8s2HNlAY09j3PGlOUv13mUlq6W6TAghhBBCzHizNSBbGHOPU/URpdSDozznFk4uIHMAm4DTRhzaBewGHMBZQMGI419XSn03pVnHmI4BGcCGIxu446U7CAS9BGN+htQUKA2ygkH+uObHFJZdPnmTFEKIEUIhxe/fOML//n035oFjfND0ArdkvIQ92B9/osUJBWV6WFawNPy5HPIWgykj7fOKrS7bfHQzQ4Gh6DG7yc55c85j9Ty9uqw4szjtzxdCCCGEEGIySUB26tIekIWvLQWeAs5L4fQQ8D2l1H+lcu+RpmtA9tKeNj79xHrIfgVj1nYw+CBkIdhXzTzbTlqsHk73h3jgfX/Hkj1vsqcrhBBx+jx+fv6PBh54tQlzyM31GVt5X9ExlhiOYu1pRPP0JL9QM0DuQj0sy48JzgrKITM/LXPzB/3saNvBxuaNbGhOXl22pmQNq0tWs6pwlVSXCSGEEEKIaU8CslM3LgFZ+HoT+jLKTwELkpyi0PuofUsp9Uqq9x1pOgZkBzsHecdPNuD2J2/UbzL2ULL4f+gyKa5VDr7xgVfQTPIDnBBi6tnXPsCdz9fx0p726NjCPBtXL7NyeXE/y4wtGDr3QkcDdOyFnoOgQslvZstLrDgrKIecBWA0Jb8mBS2DLXqj/yN67zKpLhNCCCGEEDPNrAzIppvwTpXnAkuBuYAbaAZeU0odPtX7T8eA7Ot/eosnth0mEBr939FC226C8x/AZ9D4r6yVXP/eRyZwhkIIMTavNnbwTE0zL9a30TXoi47nZ1q4tKKIdVVFXLi0AKsWgK79eljWEROcdTSAL2lLTDCYIX9JYsVZwVKwZo9pnpHqssjOmFJdJoQQQgghZgIJyMS0DMhWfOPvDHgDJz4v/y8cLHwFk1LcX/FJVp17+wTMTgghTl4wpHjjYDfra1tYX9fKoa6Yai2LkYvKXayrKuKSZUVk22PCJ6WgvyUxOOtshN7j/C7FURzudRbpdxb+nDUPDIYTzvfYwDE2Ht3IhiMb2Hpsa1x1WaY5U68uCzf7l+oyIYQQQggxVUlAJqZlQLboP/5MKv92ahrcdt6jPNLzFvnBEE+u+w1F81Jp6yaEEJNPKcWe1n7W17ayvq6FXc190WNGg8Z5i/NYV1nMZZVFzM2xjX4j36AelEWrzfZCRyN0NkDAk/waky1ccTYiOMtfChZ70kv8QT/b27ZHm/0nrS6bt4Y1JWuoLqzGbJDqMiGEEEIIMTVIQCamZUCWagWZI8NEzX9fwicfW8u20ACnBw08cOPLWGy5EzBLIYRIr+YeNy/W6WHZlv1dBGOWma8oyWJdZTHrqopYVuREX51/AqGQXl0WG5x1NuqfB1pHvy67NDE4KygHR5H+m4mwYwPHoksxtxzbgjvgjh6LVJetKVnDhSUXSnWZEEIIIYSYVBKQiWkZkKXSg8xk0LjxnPl85+oVdPUe4oY/XMExA1xjcvHNG19ES2HpkBBCTFU9Qz5e2tPG+tpW/rW3nSHf8KYl8/PsrKssYl1VMWcuyMVoSCEsG8ndMxyWRZdsNkDXPgiN8gsKizN5cJa3CJ+m6dVlR/Tqsn29++IuLcstY3XJaqkuE0IIIYQQkyLdAVnkgi8BHSe4pAD4v/BrBXwkxUfFXScB2ambjgHZiXaxBLCZjfzt82tYkJ8JQF3jX/nQxi/h1TS+XngR73/nXRM1XSGEGFcef5BXGztYX9vKi/WtdMY0+c/LtHBpRSHrKotZXVaA1XyKf20G/dB9MFxtFrNBQPse8PQkv0YzQO7CuODsqCOfjd42NrRvZ+uxrXHVZQ6zI653WVFm0anNWQghhBBCiBMYj4BMg5TaQxE+NyL1hw1fKwFZGkzHgAzgpT1tfPqR7fiDoaSVZGctzOWpT5yPIaZy4vlXvsl/Hngak1L8+owvc+bpH5rIKQshxLgLhhQ7DnWzvq6Vv9e2cLBzuGm+zaw3+b+ssoi3VxSSY7ek78FKwVBn4s6aHXuh5yCoUPLrbHn4CsrYnu1io1mxwdvKfndb3CnlueXRsEyqy4QQQgghxHgYrwqyVNdyxD5gLOs/okGcBGSnbroGZKBXkv16wwH+uKOZQV+ATIuJi5e5eGl3G4O+IB+9cBH//e7KuGv+93dX8duh/eSFFE9e8STFhVWTNHshhBhfSika2gaiO2K+eaQ3esxo0DhnYR7rqoq4rLKIebnJG++nhd8DXfvjK84in30DCacfNRnZaHewITuPrSaFm+FwzWHO5Lw557Nm3hounHuhVJcJIYQQQoi0GI8eZBNJArI0mM4B2Wg27evglvtfwxcM8fUrKrh1zeLosYDfzW2PrmGr5mWFsvDgTa+QYcmcxNkKIcTEONrj5sX6VtbXtrJlf2dc9W3V3OEm/8uLU2zyf6qUgv5jiRVnHQ3QdwQAH/CGNYONdhsbbTb2W+Krx4pNDiqyl1BRfCaVRWdQkV9Bob1w/OcuhBBCCCFmlHQGZA+kbVZjoJRKtXeZGMVMDMgAnqlp5t+eqAHg5zeu4t0r50aPdXfs4YZnr+WoUeNq23y+/b7nJ+aHQSGEmCJ63X5eDjf5f3mPXnUbMS/XFg3LzlqQi8k4CZua+AbDmwQ0xG0U0Nyzn40WAxttVrbZrAwl2XClwJBBReY8KlwrqCy5kIrClczJnCP/nxdCCCGEEKNKW0Ampq+ZGpAB/PJf+/j+X3djMRp46KPncP6S/Oix3bue5IOvfRuPwcBX51/JjW/7/iTOVAghJo/HH2Tzvk7W17XwQl0rHQPDTf5z7WbeXlHEusoi1pS5sFkmuXA7FILew9DRQLCtjoMtO6jv3k29u416s4F6i4X+JIFeNkYqrC4qcsupnHMOFaVrKc1egEGTHY2FEEIIIYQEZIKZHZAppfjWc3U8uKkJp9XE05+6gPIiZ/T4X/7+eb7S8g9MSnHf+Xdy1rKrJ3G2Qggx+YIhRc3hbtbX6k3+m2Ka/FvNBtaWuVhXVczblxeSm5nGJv+nKhSE7iZUay1HmrdR37GT+v7D1IcGqbOY6TYmBnuZSmO5KYsK53wqC1dSseBtLCw+A5PBNAlvQAghhBBCTCYJyMSMDshA/2HvM49u52+1LczJtvLHT19IcbZVP6gU//fYZTwUaCUvBE9e9SeK85ZM7oSFEGKKUErR2DbA+rpW1te2sDOmyb9Bg3MW5XFZZTHrKosozRvHJv+nwu9Bte+m9chW6lu2Ud/dSL23gzpjiDZTYhBmVYpyzUqFrZjKvOVUlJzP0gWXYLbnTsLkhRBCCCHERJGATMz4gAz05UMf+PVWXj/YzfJiJ0/ddj5ZVr3Rc8DdzW2PXcxWU4gVhkwevPFlMkzWSZ6xEEJMPS29Hl6o18Oyzfvim/xXzMliXWUR66qKqJyTNfX7fQ110XFkK7uPbKS+o5b6wWbqQoM0mxIrzUxKURbUqLTkUpG9mIqiVZTPvxhrYRUYpdpMCCGEEGImkIBMzIqADKB70Me1925if/sgFyzJ58GPnIPFpPee6Wl+nRv+9iGaTUbek7WcO69+aur/cCeEEJMo2uS/rpWXd8c3+S/JsbGuqoh1lcWcvXCSmvyfDKXoba9j98GXqG95g7q+A9T7umgyJH4/ZFSKRf4AlQY7FZklVORXsbz0QjLnrIKsEpC/Q4QQQgghppUpGZBpmmYFFgC5QB9wTCnVPSmTmQVmS0AGcLhriGvu2UR7v5erqufy4+urMRj0H2L2vHYPH9h1Fx6Dgf8ou4GbL/jaJM9WCCGmB28gyKZ9nayvbeXF+lba+73RYzl2M29frleWrZ0KTf5PwoC7iz1NL1F/dDP1XbupG2phv/ISGpGBaUqxwB+gIqiozCigIqeM5XPOInvOmVBYAbacyXkDQgghhBDihKZUQKZp2tXA54DzgZGdf3cBTwA/UUq5J3RiM9xsCsgAdjX38v5fbmbQF+S2i5bwH+9cHj321z/dwpd738CoFPet/RFnL143iTMVQojpJxRS1BzpYX2tvhRzf8dg9FiGycCaMhfrqop4+/JC8h0ZkzjTU+MOuGloraH+0CvUt9VQN3CIBn8fgSSFYyX+AJU+HxWalQrHfCpcp5M/5wwoqoSCcjBN3z8HIYQQQoiZIm0BmaZpTuD2mKF9SqknU7w2C/gtcGVkaJRTFXAEeK9SanvKkxPHNdsCMoBX9rbz0QdfIxBSfPuqKj50/kL9QNDPjx5eywOGAfKUgSeueZ45WaWTOlchhJjO9Cb/LayvbaXmcE903KDBWQvz9L5llcXMz5+iTf7HwB/009jdQP2xrdQd3UZ9z172ejrwEEo4tzAQoNLro8IfoMJSQEXeMoqKqtGKq6CwEnIWgGGaLE0VQgghhJgB0hmQXQc8hR5iAdyqlHoghetMwHrgIoaDsdEeHDneA6xVSu1KeYJiVLMxIAP4/RtH+OLvdqJpcM/NZ/KOFcUABPua+dST69hsMVBpzuah61/EKk37hRDilLX2eXihrpX1da1s3teBPzj81/3yYifrqvQdMavmToMm/ykKhAI09TZR31lL3dGt1He8xe6BIwyqQMK5ecEgFV4fFT4fFUEDFc4FzHOtQCteoYdmRVWQWTAJ70IIIYQQYuZLZ0D2G+Aj4S9bgflKKX8K130d+DbxoVjku+JOIAQUhMdUzPHXgXOV7CRwymZrQAbw83808MMX9pJhMvDYx8/lzAV5APQ2vsD7X/4czWYT78mv5s4rHp4xP6wJIcRU0Ofx8/Kedl6oa+Wl3W0MeIcDo5IcG5dVFrGusoizF+Vhni5N/lMUUiEO9x+mvrOeuvY3qW/dTl3vPvqCnoRzncGQHpj5fHp4ZnSwIH85xqJIaFYJrgqwTP8KPCGEEEKIyZTOgGwPUIYeYt2tlLr9BJegaVoB0ATYIkPogdgPgJ8rpY6Gz8tDD9++CdgZDstuVEo9lfIkRVKzOSBTSvHVP+7i8W2HyLGbefpTF7DE5QBgz8vf4YMHnsBtMPAflR/l5rPvmOTZCiHEzOQNBNmyv4v1tS28UNdKW0yT/2ybmbcvL9Sb/Je7sFtMkzjT8aOU4tjgMT0066qjvu1N6jrr6PT3JZxrC4VY7vNR4fWHgzM/i50lmAurhkOzwirIWwzGmfnnJYQQQgiRbmkJyDRNy0Gv9ooEV5copf6VwnVfAP4vfE3k2k8qpX49yvmrgRcBc3hovVLqnSlNUoxqNgdkAIFgiNseeYMX69uYl2vjD5++gEKnFZTib09czZd8+zEquO+SX3D2/LWTPV0hhJjRQiHFziM9rK/Tm/zvax/Z5L+AdZXFXFJRSME0bvKfqrahtuHQrKOe+s5dtLjbE86zhBTlfl90iWal109ZUMPiWhYfmhVVgnMOSFW0EEIIIUScdAVk5wKbw1/6gCyllC+F614DzmR46eQGpdTFJ7jm/wFfDn/pB5ypPEuMbrYHZABDvgA33reVnYd7WFGSxROfOB9Hhgm8A/z4oTXcnxEgFxNPXvNn5jjnTvZ0hRBi1tjXPqD3LattYfuh4Sb/mgZnzs+lujSH8mIny4udlBU6sVmMkzjbidHl6WJ35249NOusp76zjsMDRxLOMynFUp9/eHmmz0e5z4/dkgWFy8G1XA/PCpfryzQdhRKcCSGEEGLWSldAdgPwGHrQtWO0G464JpvhqrNI9dj1SqmnT3DdfOBAzDVny46Wp0YCMl3ngJdr79lEU+cQa8td/ObDZ2E2Ggi21fOZP17Nq1YLFRkuHr7uL9K0XwghJkFbn4cX69tYX9fCpsZOfMH43SE1Debn2VlW5GRZcfijyMnCgswZ18dspD5fH3u69lDXWUd9Vz31nfUc6D2AGrHvkaYU8wIBynx+lvr8lPn9lPt8zPcHMNnyoLAiHJxVhF9XQGb+JL0rIYQQQoiJk66A7Hbgp+iB1Z+VUu9J4ZorgOcYXl7pA3KUUokdahOvbQCWhK/9oFLqsZQmKpKSgGxYU8cg196zic5BH9edOY8fXHc6mqbRu/Mxbnjt2xwxm7my6Fy+d/l90rRfCCEmUb/Hz9b9Xexp7Wd3Sz97W/rZ1z5AIJT4vYvFaGCxK5NlxU7Ki/Rqs/IiJyU5NgyGmfv/8iH/EHu790ZDs7rOOvb37CeQZAdNs1IsDgdmS30+ynx+yn1+ioJBtMzC4SqzwnDVmWs52HIm4V0JIYQQQoyP4wVkY+nqmhnzumfUs+KdF/NaAa+nEo6F7UYPyAByU7xGiBNaWJDJb245mxt/tYXfv3GEudlWvrBuGdkrb+KnhzfzgfZ/8nzrVipr7uGDqz492dMVQohZy2k1c2llEZdWFkXHfIEQBzoG2dPaz56WPva0DLC3tZ9DXUPsbtGDtFiZFiPl4SqzSLVZebFzxvQ2s5vtVBdWU11YHR3zB/009TXR0N1AY08jDd0NNPQ00DzQzJ4MC3syLMR+W+cMhfTArO8tyjq2s/RNP2U+H9khpfcyi1SZRYOzZZDhnIR3K4QQQggxfsYSkMX+utY26lnxIgFZZKnka2N4Xm/Ma/kuTKRVdWkOd920io8//Do/+2cjc3Js3HjOfMrf+WO+8/ClfJFOfrjzHpYVVnNOyQWTPV0hhBBhFpMhurSSlcP9Ige9ARraBtgbDsn2hqvOOga87DjUw45D8b/bK3BYKC+KqTYLV5w5Mqb/jpBmo5my3DLKcsvixgf9g8OBWUx41u3tZofVyg5rfGuBwkCQMp+Psq7tlLVupcznY7HfT4YCsktHLNNcrgdnlkyEEEIIIaajsSyx/ARwL3rQtU0pdf4JztfQK80cDAdkH1JKPZri854Crgtf959Kqf9NaaIiKVlimdzj2w7xn394C6NB474Pnckly4ugv5WfPPI2fmM3kqtZeOKa55jrkKb9QggxHXUOeNnbOqBXm7Xq1WZ7W/rp9yYuQQSYl2uL629WXuRkicuBxTQz+5sppej0dEZDs4Ye/fO+nn14golF/wYF8wMBPTgLL9Ms8/mZFwhgRIPcBeFqs5jgrKAczNLXUwghhBCTL109yK4Eng1/OQTkK6W8xzn/bGArw/3HFLBEKdWU4vP+CVwcvu6zSql7UpqoSEoCstH9aP0efvbPRmxmI0984jxWluYQbHqVz/ztFl61Wamwz+Gh9z6DzZRq4aQQQoipTCnF0V5PQrXZvraBhE0BAEwGjUUFmXFLNJcXOynNtc/Y/mbBUJDmgea40Kyhp4GDfQcJqcQ/I6uCxbGhmV9fplkQDKFpBshbnLgxQP5SMFkm4d0JIYQQYrZKV0BWBBxjeKnlB5RSjx/n/B8A/85wQHZQKbVoDJM+CJSGr79GKfVMqteKRBKQjU4pxZd+/ya/f+MI+ZkWnv7UBSwsyKR344+5cfcvOWw2c8XctXz/0rukab8QQsxggWCIps4h9rT0sydcabantZ+mzkGSfbtkMxspK3Ik7KjpcmbM2L8vvEEvB3oPJFSctQ61Jj0/JwRLfV7KvD7K/L7ozpoOpcBg0kOykcFZ3mIwTv+lrkIIIYSYetISkIVvtAuoQA+8moAzlVLdSc5zAXuAbIarx36mlLojxeeUAIfDXypghVKqPuWJigQSkB2fPxjiYw+9zit721mYb+fpT11AfqaFhqdu5Oaht3AbDHxp1ef40Okfn+ypCiGEmGBuX5B97QNx1WZ7W/pp6Uu+71CO3RwNzSI9zsqKnGTbzBM884nT6+1lX8+++Iqz7gb6/f1Jz58bgjKPO7qbZpnPzyK/HzOA0aIvy3Qtj9lZswJyF4LBOJFvSwghhBAzTDoDstuBnzJcRbYTuFUptT3mnMXAI+gN+mOXV65SSr2Z4nM+ADwc/tIDZCmVZL9ykTIJyE5swBvghl9tZldzHytLc3ji4+dhU25eeOAivmDzYQB+edmvOG/ucdvvCSGEmCV6h/z6bpqRarOWfna39NHnSf4ty5xs6/AyzXCAtrTQgdU8M0MfpRStQ60Ju2nu79mPL+RLON8ELAxAmWeQpTHLNOcGghgATFY9OCusjA/OskvBMDN7xAkhhBAivdIZkNmAN4HFkSH08Osg0AzkAcvC47HHn1NKXT2G56wHLg1f+6pSam3KkxRJSUCWmrZ+D9fcvYkj3W4urSjk3g+ciamrkZ89+S7uc9rIMVh54r1/osRRMtlTFUIIMQUppWjt80ZDs0jV2d7WfryBxN5dBg0WFmRGQ7PIjpoL8uyYjDMz9AmEAhzqP5Swm+bh/sMoEr8vtaOx1B+izDNImc+nh2c+P3mh8J+nOVPfQTN2mWbhcsgqgRm61FUIIYQQJydtAVn4ZucBLwKRjuWR7zxUzOvYr7uAs8bQnH8BsD9m6HtKqf8a0yRFAgnIUtfYNsB1926iZ8jPTefO57tXryBU+0c+u+HLbLTbWO4o5eGrnpam/UIIIVIWDCkOdYX7m4VDsz2t/RzoGCQYSvxezGIyUFboiG4KEKk8m5NtnbH9zYb8QxzoPcDe7r3RZZqNPY10uDuSnp+PQQ/L3IOUh0OzxX4/9sj3thlZw8s0CyuHe505iiQ4E0IIIWaptAZk4RuuAZ4GCiDJr/rCpwGd6A32N4zh3vcAn4y573lKqdfGPEkRRwKysXm9qYt86zRVAAAgAElEQVSbf70VbyDEF9eV89lLyuj765e5sflZDpnNvHPe2/ifS346Y39IEUIIMTE8/iD72weHe5u16gFac4876flOqyluJ83yIj04y82cubtBdnu6E3bTbOxuZCgwlHCuBsxTJsq8Hpa6Bynz+yn3+ZjvDxBt+2/NGbFMczlkusDigAyn/iG9zoQQQogZKe0BWfim2cB/AtcDC0cc7gCeBL6vlDo6hnsuAnYDkS62h5VSC05qgiKOBGRj97ddLXzq0TdQCv7vfSu5rrqYxoffyc0cZchg4Itn3MGHT/voZE9TCCHEDNTv8bO3dSAamEV21uwaTOzdBVDozIjrb1ZW5KCsyIkjY2buBhlSIY4NHkvYTbOpt4lAkra1ZjQWKyNl7iGWegYp8/kp9/kpCgZJ+qsusz0cmIVDM4sz5nVMkBZ9fZzzjDN3cwYhhBBiuhmXgGzEA+YCxejBVptS6sBJ3icTvSotYkgp1X7KExQSkJ2khzY18Y1nazEZNO6/5WzWzgnx4gMXcUe2GQNw72W/4nxp2i+EEGICKKXoGPDF7aS5J9zfbMgXTHpNSY6NsiKHHpoV6p+XFjrInKHBmT/op6mvKa7SrKGngeaB5qTnOzGyVBmYEwji8gco8Hso8LpxBQO4gkEKgkGcIZU8REuVyRofpFmcMa9jwrZoqObQl4cmu8Y0cysFhRBCiIkw7gGZmPokIDt53/9rPb/8134yLUae/OT5rAjW8/M/3sCvcpxkG208cdUfmOecN9nTFEIIMUuFQormHne0yizS42x/+yC+YOLGAADzcm3RSrPyQr3qbElhJnbLzAzOBnwDNPY0xu2m2dDdQI+354TXZhjMFJizKDBn4jLaKTBYcGkmCpSBgqDCFQrh8nvJ9Xow+gfB2w/eAfD1669V8n8GJ8VoOU6olmKlWzRsy5BebEIIIWYdCciEBGSnIBRSfP7JGp7deRSXM4M/fOoCSvY8yO01P+IVu41lzoU8/O4nsZvtkz1VIYQQIioQDHGwa4iG1v7ocs3GtgH2tQ/gDyZ+/6dp4eCs0ElZkZPyouGKM6t55vXkUkrR6emksaeRtqE22ofa6XB30O7WP3e4O2gfak/a6ywZg2Ygz5qHy+aiwFaAy+6iwFqAKyObAqOdApMNl2ahACMZAa8eonn7wDeQGKp5B5KPhxKXj540g+k4oVqySres4fOs2fpmB/Z86dcmhBBiWpGATEhAdoq8gSC33P8am/d3ssSVydO3nY/hz7dyU99rHDSbecf8S/nfi38kTfuFEEJMeYFgiKbOmOCsrZ+GcMVZIMmOmpoG8/PslBUOh2ZlRQ6WuGZmcDbSkH8oGpq1u9vpGBoRooXHur3dKd8zy5Klh2g2FwX2gmioNnLMYXYMf2+hFAS8elDmCwdn3v6YIC3yeiDmnJFhW8z5weT97MZEM+obHDgK9cDMWaR/dhTrY87i4WOWzFN/nhBCCHGKJCATEpClQZ/Hz/X3bmZ3Sz9nLcjlkQ9V0fzQpdxkG2LIYOALZ9zBR6RpvxBCiGnKHwzR1DEYrTZraNMDtKaO5MGZQYMF+ZnR3maRXmeLXZlkmGZ+cDaSP+in09OZUIkWCdAirzvdnUk3EkjGarQOB2d213CANmIsz5qHQTOMbcIBXzgs6ztB2NY3IngbAHc3DLTCUGfqz7M4R4RmxcmDNXs+GMb4XoQQQogUSUAmJCBLk2O9bq65exPHej28o6qYX1zu5OVHLufz+Q4MaNxz2b1cMPeCyZ6mEEIIkTa+QIgDHYN6aBZTdXawc4jgKMHZwvzM4c0Bwss1Fxc4sJgk+AipED3eHtqH9LCs3d0+HKYNxVemuQPulO5p1IzkW/MpsBfEhWiRarTYMYsxjY3+g34YaNPDsuhHG/S3xI/1t0LQm9o9I1Vp0dAs/BFbjRb5sEh7CyGEEGOTtoBM07TKtM1qDJRSdZPx3JlEArL02dPSz3X3bqLfE+CWCxbyjSUN/OKFz/HL3GyyTHaeeM/vKXWWTvY0hRBCiHHlDQTZ3z4Y7W2mB2gDNHUOkiQ3w2jQWJhvjwvNyoucLMzPlOBsFIP+wYTQLNkyz1Q2G4jIzsjGZXORb8vHZXPF90yLWeaZac5MX+sIpcDTGw7TWpKHaJExd1fq983IOk41WsyHVKUJIYQIS2dAFgImuuRMKaVm5pZKE0gCsvTavK+TD9+/DV8wxFfftZxb3ffzuf1P8i+7jbKsRTxy5RPStF8IIcSs5PHrwZm+RFOvOGto7edg1xDJvu00GTQWFWTGLdMsL3KwID8Ts1FCjVT4g/7EDQbc7QnhWqe7k6AKpnRPm8kWDcuiYVo4RJuTOYeK/AqyLFnpfzMBHwy2DVeeRcKzuGAt/HWqfdQ0Y3z1WbIQLTJmtqX/PQkhhJgy0h2QTTSllJp9jSzSTAKy9Ht251E+9/gOAH52/QretvM2bgocoMli5vIFl/GDi34oTfuFEEKIMI8/SGPbQLS3WWS55uHu5MGZ2agHZ2VFTsrDGwSUFTlZmG/HJMHZSQmpEN2e7qS7dcZ+3eHuSGl554KsBVTmV7IifwVVBVVU5FVM3C8IlQJPT2JolixYc6e+gQIZ2aNXo8V+bcuTqjQhhJiGxrOCbCJ++peALA0kIBsf972yn+/+pR6zUeOxGxeT/+LV3JRtZNBg4I4z7+CjK6RpvxBCCHE8bl8wukRT31FTf32kO3lAYzEaWOyKBGeO6HLNBfmZGA3yi6l0UErpyztHhGiRYO1Q3yF2d+3GF4qv4DJoBhZnL6Yqv4qqgipW5K9gWd6y9PY9OxkBbzgsiwnREoK1cNVaqlVpBhNkFsaEZjFLPZ3F8cGa2Tq+708IIUTKxiMg0wAf8ALQkY5JHo9S6iPj/YyZTgKy8aGU4tvP1/HAq004M0w8/14z+1/4AJ8rzEdD455L7+HCkgsne5pCCCHEtDPoDbCvfSCm2kyvOGvuGSU4MxlY4nJEe5tFdtcszbNLcDYO/CE/jd2N1HbWsqtjF3WddTR0NyTs0GkymCjLKWNFwQqq8qtYUbCCxTmLMRvMkzTz41AqvENnsl5pI8Y8qfd9IyMbchdAQXn4o0z/nL9ElnQKIcQEG88KMj/wN+Bh4DmllP9UJirGjwRk4ycUUnz28e385a0WirOs/P2COh6t+QH35GbjNGXy5Lt/R2mWNO0XQggh0mHAG4jZFGB4uebRXk/S8zNMBpYWOiiLVpvpFWeluXYMEpyllSfgYW/3XnZ17KK2s5bajlr29+5HjWhhnGHMYHne8mhgVlVQxcKshRi0abRkMeBNsmtnsl5prRAa7UckDXLmxwRnS4dfZ7pAWnUIIUTapTMgqweWhb8ceWEP8CTwsFJqy8lMVIwfCcjGl8cf5IO/2cprTd0sK3TwzNwH+FLXBl7OtLM0ezGPXvG4NO0XQgghxlG/x09D23Bvs8iumi19yYMzqzkSnDnJsZuxmY1YzcbwZwMZ0dfDY9bw11azIXrMajZKhdpxDPoHqe+sjwZmuzp3cbj/cMJ5meZMKvMro8szq/KrmOeYN/37uYZCelVa137o2Bv+aNA/d+2H0TZNsGYnVpwVlEPuQjBOweo7IYSYJtIWkIVvdjbwYeD9QP6Iw5Gb7UOvKntEKdU0pgeIcSEB2fjrGfJx3b2baWwb4KKFNn4e+Ao3Z/TTZDFz2YLL+KE07RdCCCEmXK/bT2NbfGjW0NZPa583bc+wGA1xAdrIQC0xYBses1mMWE1GrBYjVpN+TmTMZjGQYQp/bdaPz4QNCnq9vdHALLJEs3WoNeG87IxsPTCL6WlWlFk0CTMeJwEfdDclBmcdDeDtTX6NwQR5i8NLNJfGh2i2nAmdvhBCTEdpDchibmoCrkAPy94FxHbfVDGfXwUeAn6nlOo/qYeJUyYB2cQ40j3ENXdvoq3fy8cqAtzcejs3u5wMGAz82xn/xq2n3TrZUxRCCCEE0Dvkp6Gtn33tA/R7Anj8QTz+EB5/EHfM6+Gvk42FcPtHqQAaJ2ajNhyoxVayxYRswwGbkQyzIfo6eixJUBd5nRFzT/MEhnEd7o64wKy2s5YuT1fCeS6bK67KrKqgijxr3oTNc0IoBYPtSYKzvdBzmMSFPGGZhYkVZwVlkF0qO24KIUTYuARkIx6QB9wIfBA4Z8ThyAM8wHPolWV/U0qFTvnBImUSkE2c2qO9vP+XWxjwBvjhioPkN3+b24tdaGjcfendrC5ZPdlTFEIIIUSaKKXwBkJJAjb9szcconliAjVPzMfIMC72nMTrg4RO/Vv3lJkMWnRJaWyg5sgwMTfHRkmOlbk5tpgPK3aLKS3PVkrRMtjCrs5d0aWZdR119PsTf98+N3NuNDBbUbCCyvxKnBZnWuYx5fiGoGtfYnDW0QiB5BtYYLKGq81GBGf5S8GSObHzF0KISTbuAdmIh5UDtwA3AfNHHI48rA14DPitUqomrRMQSUlANrE2NLTzkQdeIxBSPLfs72zs/SN35+bgNDt44sonmZ818j8NIYQQQojjU0rhC4aSVrINB2ox1W2+IJ7AiMo3XxBPQD/mDQTjzokdc59kGJdrN0cDs5LwRyQ8K8mxUeDIOOnNEUIqxOH+w9HArLajlvquetxJgqGFWQvjQrNluctmdj/YUAj6mpMEZw36xgGjyS6NCc5iAjRHkWwSIISYkSY0IBvx4LehL8G8BnCMOBx5cC36EszHlFLHxm0ys5wEZBPv6TeO8O+/24lJC7J53s+4UzvAPzPtLM1ewqNXPDazv0kTQgghxLSmlMIfVOHwLIjHF8ITDs963X6O9bpp7nbT3OPhaI+bo71ujvV48AWPv0jEbNSYk60HZnPjArThirSxVKEFQ0H29+6PLs2s66xjd9du/CN2jjRoBpbkLNEDs3x958zy3HIsRssod55BPL16hVk0NAsHZ137R99h0+JMHpzlLQbTLPgzE0LMWJMWkMVMwA5ci74E8xJg5CJ4BQSB85RS28d9QrOQBGST4xcvNfKDv+9hjqmPP2d/g1tyDByQpv1CCCGEmIFCIUXHoJejkdCsx01z+PPRHg/NPW66Bn0nvM/IKrS5MUs5S3JsuE5QheYP+mnoaYj2MqvtqKWxp5HgiB0jTQYTy3KXxS3NXJKzBJMhPctEp7xgAHoOJgZn7XvA05P8Gs2o76SZrNeZfYb1ghNCzEiTHpCNmMxc9KDsg0AlejimhT+vUUptmtAJzRISkE0OpRRf/9MuHt16iLXW/XzT8h1unuNiwGDgc6s+x8dP//hkT1EIIYQQYsK4fUGO9rpjArThMC0SpKVShVacbWVudngZZ258FdqcbBuZGfEhlzvgZk/XnmhgtqtzF029TagRDe+tRivL85ZHA7MVBStYkLUAgzaLmtwrBUOdyTcJ6D7IqJsE2POTB2c5C8BgnNC3IIQQo5lSARmApmnzgQ8BXwCykYBs3ElANnmCIcUnf/sGL9a38m+Of3KG+RFuL3KBZuCut9/F2nlrJ3uKQgghhBBTQiik6Bz0xVWgxVahHe1x05lCFVqO3czcbFvSzQRKcmwUOjMYCgxS31UfrTTb1bGL5oHmhHs5zA4q8yvjeprNzZw7O1cC+D0xmwQ0xodo/sHk1xgtiZsERL7OmKGbKQghpqwpEZBpmuYA3ocejK1BD8Wih5GAbFxJQDa53L4gN963hZrD3Tzo/BV7MndwV7hp/+NXPsGCrAWTPUUhhBBCiGnB4w/GBWbRAK13eCmnLzC2KrRIeJad6WWQJtp8+9jbU0dtZy1tQ20J1+dm5FJZUBntabaiYAUuu2u83vLUpxT0HU2+SUD/0dGvc85NrDgrKIesubJJgBBiXExmk34NuBw9FLsKsEYOjTj1deBh4H6l1NC4TWgWk4Bs8nUOeLn2nk20dnbxd8c3+VHeEP/ItLMkewmPXvEomWbZZlsIIYQQ4lQppegY8I3og+aJCdHcdAycuAot26b3QnPluMnIPErQdJje0H6OuhsYCPQmnF9oK4yrMqvKryLHmjMeb3F68faHQ7MG6GwYDs46GyE4yj8Hkw2cRZBZCI5CfVdNR8zr6HghmG0T+36EENPahAdkmqatRO8xdhNQFBkecdph4FHgYaXU7rRPQsSRgGxqONg5yLX3bMI5eJDf2/+bjxU72WexcOn8S/nRxT+anaX6QgghhBATzOMPcqw3XIHWPdYqNIVm7sZsa8aZ3YLJ1ozXeJAg7oQzSxwlVOVXsTR3KaXOUuY751PqLCUnI0e+7wsFw5sENCT2OxvqTP0+GVkxwZkrMUxzFOqBWqZLduAUQkxMQKZpWhHwAfRg7LTI8IjTBoA/oFeLvaQmowHaLCUB2dSx83APN/xqC2uDm/mq/S5uLCmm32Dg9lW384nTPzHZ0xNCCCGEmPWUGtkLLX4zgeaEKrQQmqUTo/UIRtsRDNYjGK1H0Qz+pPc3YcdhLCLLVEyOeQ75GXNxWedSZCsh31ZApsVMhtmI1WTEajZgNRvDHwasJiM2i5EMk2HmhmyeXhhoh4FWGGyDgchHq/45diyU/M84KVtufGgWF6bFVKdlFsjGAkLMUOMWkGmaZgXei76E8u1A5P8isf+nDgH/QA/F/ihLKCeHBGRTy0u727j14df5kuExKp0v8Flp2i+EEEIIMa3EVaGN2IlTHxvAb2zFYD2CwdKBwdKpf5g70YzeUe+rQmZCvjxC/nyUL4+Qr4CQP4+QLx/lz2H4Ry7IMBmGg7NIoGYxYk02Hhe0xX5tCB83kmE2YBt5TuSYyYDBMMUCOaXA3T0cng2GQ7WB1sSAbbAd1PF700VpBrAXjAjOItVpReCIeW3NAcMs2uVUiGku7QGZpmkXo4di1wKOyPCI02rRQ7FHlFLHxvwQkVYSkE09T2w7xNf+UMNvLd9nV+4Rfp6Xg8Ps4PErHmdh9sLJnp4QQgghhDgFSim6Bn0c7fHQ7/Xj9Yfw+IMM+QJ0ebppdTfT7mmmy3uMHv9R+gKtDARb8NF/nJsaIKCHZQGv/jnkz0P5Cgj5c0GZx/U9JQ3kzAa92s1sxJYkkMsIn29LFrrFnG+zxFfIWc1pruAKBWGoazhAi4ZpSarTxrLE02CK74mW0CctpkotI0s2HxBikqUtINM07U7gZmB+ZGjEKW3A4+h9xXacxFzFOJGAbGr60Qt7eewfr/N8xlf5nyIjL2baWZy9mEff9SgOi+PENxBCCCGEEDNKv6+fw/2H4z4O9R3icP9hWodaR71OQyPPWojLOpeCjLnkWuaQbSrGaSrGrhWhQhm4/UE8/hBefxBP+LUnMPza7Q+Gj8WPe/xBvCfYGTTdLCYD2TZz3EeOzUzWiLFsm5lse/zxUw7Xgn4Y7BixpDM2TGsbrk7zJG7YMCpjxijLOpNUp1lkAy8hxkM6A7IQoIgPxjzAs8Bvgb8ppYKnMFcxTiQgm5qUUnzl6TdpfOOf3G+9k4/MLaDRYuGS0kv48dt+jEGTcm0hhBBCCKHzBDw0DzRzqO8Qh/oPxYVoRweOEjzOj2J51ry4jQJKs0qjX6eyaUAopPAG9LBMD8/Cr2MDNd/IY8Pne/0h3NHjsceGA7tIgOf2BfEFTz6Qy0gSrmWHw7Mce5KALeb4mMM1vydcjdY2omfaiOq0wXbwDaR+X4vj+JsORMK0zEIwW8c2ZyFmsfEKyHzAX4GngDHE5mOnlPrLeN5/NpCAbOryB0Pc+tDrLNz3CB+zPcoN4ab9n6n+DLetvG2ypyeEEEIIIaYBf8hPy0BLNDiLBmh9eoDmC/lGvdZpdjLPOY/5WfOjodk85zzmO+fjsrsm/Je2Sik8/hC9bn/yjyHfKMcC9Ln9pxSuWc2J4VrWiCq2bHvy4xmmE4RrvsHECrRomBa75LMVgqP3qUucdLbeM82Wm/qHNRuMppP+cxJiuhqvgGyidqBUSin5L/cUSUA2tQ16A7z/l5u4tf37FDi28+miQtA0fn7Jz7mo9KLJnp4QQgghhJjGQipE21BbwpLNSJA26B8c9doMYwalztJoYBatQssqZU7mHEyGqfWjmlIKtz8YE6bFh2h9bj89ScK1vvBnf/Dkf8yNhGs5NktCsKZ/mMi268dHHrOYYkJIpcDbl1iBNnIDgsjyz1Dg5CackQ22nNFDNHtekmAtB0yWk/4zEmKyjUdAFjd8CnNLhVJKyR67p0gCsqmvrd/DTb/4J3cNfZmNub38NNy0/7ErHmNR9qLJnp4QQgghhJiBlFJ0e7ujwdmR/iMc6teXcB7pP0KXp2vUa02aibmOufqSzfBHpAptnnMeGcaMCXwnpy4SrvUMjR6g9br9ccdjxwOhkw/XbGZjQk+14y0FjS4VtRox+/r0jQXc3WP46OGka14sjnBgdpxwLdmH2XbSfz5CpEu6A7KJJgFZGkhANj3sax/gC3f/nodD/8G3ijJ5IdPOouxFPPaux6RpvxBCCCHE/2fvvsOsqu79j7/3adMrzMDM0AQB6VIdUSxBYgESW0CaLXZRiYnJTXJTb0y88WqUqKhgVESKmp8F1NhFRBApMkhH6jD06f2U9fvjTDszwxSmHGb4vJ5nnnPOPmvv/d0YIXz8rrWk1eWX5gdO2azShVbfpgGJ4YkVgVnA+mdRXdvd/7c1xlBY6q0zQKsI2Gr5ztuEcC3cZQ8I0MrDs9hwV+3Hw1zEhNqJohBbSVlgVtiIcO1Ulx13hNYSnMVCWC2dalV/XBHa/VOaTXMGZH9otqoawRjzp2Dctz1RQNZ2rNuXxb/mzeZRxz+YltyZXS4nl3a9lCcufUKL9ouIiIjIaaN804DywKy862x/3v4GbxoQsOZZWZgWFxJX76YB7YkxhoKycC2n0E12UWmtwVpOkafGGmy5xZ5TDtcsC6JDa9+4oGqYFh3w2UGMvZgwTy5WUfbJu9OKsqAoM/C49+Tr4NXJ5qynO+0knWwh0WDT358kULMFZNJ2KSBrWz7YfJi9i37BVaHvMzkliTybxT1D7uHuc+8OdmkiIiIiIvUq3zSgavdZeYB2IO8AJXUsQh9qD8Vld+G0OXHYHJWvdicOq9pr2fdVx9Y4r7GvtVy/rvs4bI6g/YdsYwz5JZ6KzrWqa6xVdrKV1pweWugmr+QU1y4DXHZbteCs5vTQyuDNRUyog1inmxjycZbm1D/9s+pnd+GpFWnZ/GumnWxdtYiO/p1CIxLLdgxN8IdqZ1A4eyZSQCYKyNqgV1buotd/ZmAid3NPp0R8Fsy+dDaXdrs02KWJiIiIiJwyn/FxrPBYwLTNqhsH5Lvzg11io9kt+6kFcCcJ4up7rf7eZXcR6ggl1B7qf3WEEuYII8QeQqgjFKfNWaNmj9dHbnF5uBa4M2h5kFb+6g/eSiuOlXhOffWliPIpoeEuYsIcFZsaxIY7q4VuZVNEnV5irXwifXnYik/WtVZLwFaadwr/IEPKQrOOEFkWnJX/RCaWhWplx8M7aCfQNkgBmSgga6Nmv/MVP1k3jfdi3TwRH0eEM4KF4xfSM6ZnsEsTEREREWl2xhiKPEW4fW7cPjcenwe3143buHF73XiM/7PH56n8vhGvAddo4LVO9r7q6+nOYTkIdYRWBGZVw7NQRyhh9jBCHCGE2v3flY8Nc4RVhG4hjhDC7P7vbLhwe+yUuh2UlNopLrVTWGyRX0yV0K1yzbXciuCtlFNdbs1mUbH7Z2zFZgWBIVtM1Y62EIi3FRJt8gnx5AROCS084d8ZtOC4fyfQgmP+3UHr2NG1JsvfkRaRWEegVuWzK/zUHlyaVZsOyCzLGmuM+STYdbR1CsjaJp/PMPvlV7l77/38NjGWDyIj6BHdg4XjFxLligp2eSIiIiIiZzxjDB7jCQzOmhDmVX/fkGuVeksp9hZT7Cn78Qa+1rUeXHOysGoGcOVdbWWvDisEyzjBuMA48fmceD0OPB4HpWWhW3GpjaISO0UlNvKLbBQUW+QX28A4MT4nGAfQ8GmtLrstIDyLDXfSMTKEhKiyn7L3iaFeEmy5hJVmQX5ZcFZw1B+kVXwu+ynMpFE7gboiAzvQIhOqTPGsFrCFxmr9tBbSJgMyy7KuBH4HnKddLJtOAVnbVerxsfCp3zIpZw7TkpPY5XJwSZdLePIHT2rRfhERERERqZMxBo/PExCgFXmLKPGUUOwtpshTRIm3xH/cU0Sxp5gSb4n/fdk5FZ/LQrcSTwlF3rKxVd67fe5Wey6n5cJhC8FuubDhwjIuMC6Mz4HX68DrceL2OCh12/F6HWXBmv97jAvjDcd4Iv2v3giMN4zy0C3CZa8Mz6oEaJWfQ0mIsNPBloez6HhlB1pBtZ/8o5Vdao3ZpMDmgPCOdYRoVT6HdwSHq2V+kduhNhWQWZb1Y+C/gWGABRgFZE2ngKxtyy0qZe3j19PbfMmk5GTy7RZ3D7mbe869J9iliYiIiIiIAOD1eWuEawGdbbV0t5W/Vg3qqod4Nb73Fjd/8cbCMuF43RH4vOEYT0RZcBYR+L5KsIZxER/hqiVAC6FjlMsfpEWFkBDpItZWhK3weJWutKqhWtUuteNQktO42kNja18nrbaAzRV5Rm9E0CYCMsuyJgG/BQaWHyp7VUDWDBSQtX1Hjp8g/+lLOBJ6lLs6JWIsePLSJ/lBtx8EuzQREREREZFW4zM+/7TSsrCsIV1wVYO6Qk8h2SXZZBdnk1WSRVZxFrmluY2uw/ic1QK0asFale9sJpIOYbEkRoXV3pVW5ViEzQOFxwM70E4WqBUeB9OITRMcYdU2IqgaqFXfiCAebO0rjmnRgMyyrAHAxUBXIA4oBvYCXxhj1jfg/Kn4O8b6UiUUK/8aOGqM6dykIkUBWTuxZ/tGOi66nNdjHPwjPo5wRziLxi+iZ6wW7RcRERERETlVbp+bnJIcsoqzyC7JJrM4MyBAK38t/y6rOKvRU0qNsTDesHo700JsUcSHxpMYHk+nqOiTBmodIkJw2YCizCpTOk8yxbM8YGWHTxkAACAASURBVPMUNbxgy+bfrbO8A23SfAiLbdwv7GmmRQIyy7IuBx4BBtcxbAMw0xizupbzLwWeBAZQezB2AHgMmGuMacQ/QamNArL2Y/vni+jz+V38IqEjH0aGa9F+ERERERGRVla+42pmcWZloFaS7Q/TqgVq5cdyShs5dZLyLrXq66VFYDzhGG8k4Y5oYl1xdAiLo1NkB5IiO9ApOqxGZ1pcuAubBZQW1LLxwEm61IqyAov53XGwO5vnFzBImj0gsyzrIeBv+IOsqpNXTbXPAKXAZGPM22XnuoAngDvLL1ftvB3A/wKvGGNO//1y2wgFZO3LroU/J3nnC0xJSmZ3iJ2Lu1zM7B/M1qL9IiIiIiIipymPz1PRpZZVklVroJZVnMWJoixOFGeSU5KFxzSlSy28olPN8vnDtGhXLPEhcSSEx9M5sgMp0R1Jjo4lMTq0IlCLcNmxLAu87rLw7BgUnoBel7bQr0zradaAzLKs8cDSso9VO76qq/pdAdAPOA68D1xEzWBsA/7Q7d/mdFkYrR1RQNbOeD1kPHUFvrz1XJ+cQoEd7hx8JzOHzgx2ZSIiIiIiItIMyrvUskqyyC7OrtGtdqIok2OFmRwv9E/5zHXnUOTNozKOaeB9fI6AaZ82E0mYPZpIRwwxIbF0CIunU0QHfnXJ5cSGh7XMw7aSZgvILMuyAbuBbgSGW9+U/WQB0cC5wAVUdpgZYG7Z+9sJDM9WAX82xnzQqKeSRlFA1g7lHyP3yfPZbM/lzs6dMBY8cckTjO0+NtiViYiIiIiISBB4fB5yS3NrdKYdL8rkUN4Jjhac4ERRJjml2eS5cyjy5uCjYV1qn/3kSzqGx7TwE7SsugIyRyOvdQWV4Rj4w7IbjDE1khfLsvoCC4GhZYemAqHlXwP7gfuNMe80sgYRAYhMIHLGQkb+60oezMzisQ5x/HrFb1gUs5Besb2CXZ2IiIiIiIi0MofNQXxoPPGh8Q0+p8hTVBGmZRZlcqQgk/ScYxzOP8GxwhNkFmeR786jQ1h0C1YefKcSkIE/4MoHxhlj9tQ20Biz3bKsccBGIBmIoLKb7AvgWmNMVm3nikjD2LqNwnv5X7npg1+yKSSUDyPhro/u5vzkVD7c9yGF7kLCneFM6DmBm/rfRNforsEuWURERERERE4jYY4wwiLDSI5MDnYpQdXYFb2Hl70a4MWThWPljDGZ+Bfkr7pGWTbwI4VjIs3DmXoH7v7X85fjx0l0Gw4XHuLNXW9S4C7AYChwF/Da9je45p1rWZG+ItjlioiIiIiIiJx2GhuQda/yvqFrhr1f5b0BnjHG5DXyviJyMpaF6+rZHOrQmxx77WsKGryUeIuZ9dmDHMg90MoFioiIiIiIiJzeGhuQVV2NbW8Dz6k+bmUj7yki9XFFMLf3hXit2jaUrVTqLeHp9S+0UlEiIiIiIiIibUNjA7KIKu/zG3KCMaaw7G3539z3NfKeItIAHxxegaeegAzL8OG+Za1TkIiIiIiIiEgb0diArDkUBOGeIu2e25Q06zgRERERERGRM0UwAjIRaQHhxtegcRENHCciIiIiIiJyplBAJtJOTMgrwGFqX6S/gjFcUlBY9xgRERERERGRM0xTArJ6/ibe7OeJSB2m5LvrD8gsi+Xh4XyV8VXrFCUiIiIiIiLSBjhO4Zzyv4GvtCzLcwrnN/Y8Y4zpdQr3ETmjJPa5jr/vep1fdorHY1kBC/Y7jMFhDGeXuvkuNIS7P76bB4Y9wC0DbsGqb2F/ERERERERkXbOMvV1nFQdbFk+/AFZa/6N2hhj7K14v3ZpxIgRZu3atcEuQ1pS5m68T48mw3IzPzqKZVERFFgWEcYwIa+AG3PzSPF4eDY2hjlxMQBc3uNy/jz6z4Q7w4NcvIiIiIiIiEjLsixrnTFmRK3fnWJA1losFJA1CwVkZ4idH+FbMgOfx42DykZNDw5sDidb+9xJ2OYl7I3I5jcJHSiw2egd05Mnf/AUXaO7BrFwERERERERkZZVV0B2KmuQWa34IyKN0Xsctnu+wjHyZgiJAsuCkCgcI2/Gds9XDJj0J3Zc8wFpueN5+eBxepS62ZmzmxveuZaV6SuCXb2IiIiIiIhIUDS2g6x7C9ZyUsaYfcG4b3uiDjKp6sPNh/n7wnd5yPEvlnU6zOcR4dgM3N93Crem/lrrkomIiIiIiEi702xTLKXtUkAm1X22/Sh3vrKWH/q+pHfnN5gXEwLAD0OT+Z8JCwiPSAhyhSIiIiIiIiLNp7mnWIpIO3Bp30T+ddMoPraP4YWMv3BXfi8ifT4+LM5g2uJLOfDtK8EuUURERERERKRVKCATOYNd2LsjL90yCo8rmkcP3M6l5i7O8tnY5bCYvP4Rvnx1ImQfCHaZIiIiIiIiIi1KAZnIGS61Zwfm3zqKyBAHC3f0pJPtSS6J6EGe3cY97j3Mm38xZuVs8LqDXaqIiIiIiIhIi1BAJiKM6BHPgtvOIzrUwUdbC8jP/2/u6DMdY1k8GRPBzzc+SeHzF8OBNcEuVURERERERKTZKSATEQDO7RrLwttTiQ138tm246zZcgmPXTSbSHsoH0WEM81xgv0vXwlLH4CirGCXKyIiIiIiItJsFJCJSIWBKTEsviOVDhEuVuw8zosfhfHiFYvpGd2DXS4XNyR3ZsWWxfDPEbBxCWgXXBEREREREWkHFJCJSIBzOkez+I5UEqJCWLX7BL974wjPXTafH3T9AXl2G/d2SmSusxTz5h3w8kQ4vjPYJYuIiIiIiIg0iQIyEamhd6coltyRSufoUL7Zm8Vd87fwx9S/M/PcmWBZzI6P5cGkZAr2fQlzRsOnD4O7KNhli4iIiIiIiJwSBWQiUqueCZG8duf5pMSG8e2BbG584Rsm976Fp8Y+RZQzio9DHUzr2Zd9lg+++Ds8cz7s+iTYZYuIiIiIiIg0mgIyETmpbh3CWXJnKt3iw9l0MIcbnl/NgNjzWDRhEb1ievG9r5ApPXrxRefekLUHFlwLr98CeYeDXbqIiIiIiIhIgykgE5E6dYkL57U7z6dnxwi2Hc7jhudXE2Z14tXxr3JZt8vI8xYxM6yU54Zfjc8RBpv/Hzw1EtbMBZ832OWLiIiIiIiI1EsBmYjUq3NMKIvvTKV3YiQ7j+Zzw3OrySu08/glj3P/0PsBeCpzPQ+O/BEFZ18GJbnw3i9g3ljI+DbI1YuIiIiIiIjUTQGZiDRIYlQoi+9I5ZzOUew+XsDk51eRkVPM7YNvr1iX7JPDq5ga6WHvj56A6BTI2ABzL4X3fwXFucF+BBEREREREZFaKSATkQbrEBnCottTGZgSzb4ThUx6dhX7TxRyUZeLWDxhMWfHns3unN1M2fY8y69+HM6fCVjw9bP+aZeb3wRjgv0YIiIiIiIiIgEUkIlIo8RFuHj1tlTO7RrLwewiJj+/ij3HC+gW3Y0FVy1gXPdx5LvzmbniIeZ07oLv9k8hZQTkH4bXb4ZXr4fMPcF+DBEREREREZEKCshEpNFiwpwsuO08RvaI41BOMZOeW8Wuo3lEOCN47OLHeGDYA1hYPPPtM8za9i/yZ7wJ4x+HkBjY9TE8kwpf/B94SoP9KCIiIiIiIiIKyETk1ESGOHj51lGc37MDx/JKmPzcarYdzsWyLG4bdBvPXPYMUa4oPjvwGVP/M509fX4A962FQZPAUwyf/g88eyHs/TLYjyIiIiIiIiJnOAVkInLKwl0O/nXzSMb07siJglKmPL+a7w7mAHBhyoUsHu9fl2xPzh6mvjuVzzI3w3Vz4ca3Ib4XHN8OL42HN++GguNBfhoRERERERE5UykgE5EmCXPZmXvjCH5wTiJZhW6mzl3NtweyAegW3Y1Xr3q1Yl2y+z+7nznfzsF31kVw91dwyW/AHgIbF8I/h8O6l8HnC/ITiYiIiIiIyJlGAZmINFmo086z04dz+YBO5BZ7mD7va9btywQg3BnOYxc/xqxhs/zrkm18hgc+e4A844ZLfgX3rIKel0BxNiy9H168Eo5sDurziIiIiIiIyJlFAZmINAuXw8ZTU4cxYXAS+SUeZrywhtW7TwBgWRY/HfRT5lw2hyhXFJ8f+Jyp705ld85u6NALZrwF170AEYlwYDU8dxF89HsoLQjyU4mIiIiIiMiZQAGZiDQbp93GE5PP5dqhKRSWern5xTV8ubNybbELUi5gyfgl9I7rzd7cvUx9dyqf7v8ULAsGXQ8zv4GRt4HPCyufhKfPg+3vB/GJRERERERE5EyggExEmpXDbuPRnwxh0oguFLt93PryN3y2/WjF912ju7LgygVc3uNyCtwFPPDZAzz97dP4jA/CYmH8Y3DbJ9B5EOQcgEU3wOJpkJMexKcSERERERGR9kwBmYg0O7vN4pFrBzM9tRulHh93zl/HR1uOVHwf7gzn0Yse5WfDf4bNsvHsxme5/9P7ySvN8w/oMhxu/xyueARckbBtGTw1Cr56Crye4DyUiIiIiIiItFsKyESkRdhsFv/z44HcckEPSr0+7l6wjvc2Har43rIsbh14K3PGziHaFc3y9OX+dcmyd/sH2B2Qerd/2mW/H4G7AD78LTx/CRz4JjgPJSIiIiIiIu2SZYwJdg0CWJaVAFwIJAExwDHgILDSGJPb1OuPGDHCrF27tqmXEWk0YwyP/Gcbzy3fjd1m8fikIfz43JSAMQfyDjDrs1nsyNpBuCOcv475K2O7jQ280I4P4b2fQ/Z+wILhN8Nlf4CwuFZ7FhEREREREWm7LMtaZ4wZUet3Z0pAZlmWDegHjAJGlv0MBlxVht1ijHmplesaC/wRuACwahlSDCwFfm+M2Xaq91FAJsFkjOEfH+1g9qe7sCx49PohXD+8S8CYQnchf/jqD/xn738AuHPwndxz7j3YrCqNrqWF8MWj8NVs8HkgIgF++DAMnuRf6F9ERERERETkJM7ogMyyrOuBmcBwILKe4a0WkFmW5QSeA25p4ClFwM+MMc+dyv0UkMnp4J+f7OSxj3ZgWfDXawYxZVS3gO+NMby8+WX+sf4f+IyPi7pcxN/G/I1oV3TghY5uhWUPwv6v/J97jIEJ/4COvVvpSURERERERKStqSsgOxPWILsQuJj6w7FWU9bN9hY1w7F84ENgEfAxUFDluzDgWcuybm6NGkVawn1je/PrK8/BGPj1/9vE/FV7A763LIubB97MnMvmEBMSwxfpXzD13al8n/194IUS+8Et78GPn4GweNi7AuaMhs/+Cu7iVnseERERERERaR/OhIDsZHLwr/EVDL8Hrqry2Vt2LMEYc7kxZqoxZhyQCDwMVG3ze96yrFrTTpG24M6Le/H7Cf0B+P3bm5m3YneNMaOTR7N4/GL6xvVlX+4+pr47lY/3fRw4yLJg6DS4bx0MnQ7eUlj+v/BMKuz6pDUeRURERERERNqJMyUgKwJWAbOBGcA5QBwwr7ULsSwrGfh1tcO3G2P+xxgT0PpijCk0xvw38ECVw07g7y1cpkiLuvXCs/jL1QMB+Mu7W3n6s101xnSJ6sIrV73ClWddSaGnkJ99/jNmr5+N1+cNHBgeDz9+Gm55HxL6QdYeWHAtvHEr5B1ujccRERERERGRNu5MCMgeBqKNMaONMQ8YYxYYY7ab4C2+dj+BGwO8Y4x5sa4TjDH/BD6rcujSssX9Rdqs6and+ft1g/2L9n+wnSc+3kH1fy3DHGH875j/5RcjfoHNsjF301zu+/Q+cktr2di1+2i48wu47I/gCIPv/g1PjYQ1c6F6qCYiIiIiIiJSRbsPyIwxx4wxnmDXUcVV1T4/0cDzqo+b0Qy1iATVpJFdeXzSEGwWPPHxTh79YHuNkMyyLG4acBPPjXuO2JBYVhxcwZRlU9iVVbPrDIcLLvwZ3Ps19L4cSnLhvV/AvMsg49tWeioRERERERFpa9p9QHY6sSwrBhhY5VAxsKKBp38K+Kp8nmhZlr25ahMJlmuGduHJG4Zit1k88/n3PPzu1hohGUBqUiqLJyzmnPhz2J+3n6nvTeWjfR/VftG47jB1CUxeAFHJkLEe5l4K7/8XFNfSfSYiIiIiIiJnNAVkrSsJsKp83tXQ7jZjTD5woMqheGBUM9YmEjQThyTz9NRhOO0W877cwx/e2YzPVzMkS4lMYf6V87nqrKso8hTx4OcP8uT6J2uuSwb+Rfz7TYSZa+D8mYAFX8+Bp0fB5rcgaLOsRURERERE5HSjgKx1xVf7nNPI86uPH9CEWkROK1cM7Myz04fjstuYv2ofv3lzU60hWZgjjEfGPFKxLtm8TfO499N7ySk5yb9OIVFw+cNwx+eQMhzyDsHrN8GrP4HMPf4xmbth2YPw1y7wx1j/67IH/cdFRERERESk3VNA1rpKqn0OaeT5odU+92tCLSKnnbH9OjHvphGEOGws/uYAD72RhreWkKx8XbLnxz1PbEgsKw+uZMq7U9iZtfPkF08aDD/9CMY/DiExsOsjeCYV3rwL5oyG9fOhNA8w/tf182HOBbDzJNM4RUREREREpN1QQNa6sqp97tzI8ztV+9y7CbWInJYu6pPAizePJMxp59/r0/nZkm/xeH21jj0v6TyWTFhCv/h+HMg7wLT3pvHB3g9OfnGbHUb+FGZ+A4MmgacYNi4CdxH43IFjfW5wF8JrN6qTTEREREREpJ1TQNa6DgCFVT53sSwrqSEnWpbVB4ipdrj6Z5F2YfTZHXn51lFEuOy8szGD+xZtoNRTe0iWHJnMy1e+zPie4ynyFPGL5b/giXVP1L4uWbmoTnDdXOhzZf3FeN2w6ulTfBIRERERERFpCxSQtSJjjBv4utrhKQ08vbZxkU2rSOT0NeqseF657TyiQh28/91h7nl1HSWe2kOvMEcYf7vwb/xy5C+xW3Ze+O4F7v2kjnXJyu39sv5CfG5IW3IKTyAiIiIiIiJthQKy1lf9b9q/siyrQ10nWJaVDMyq5as6AzLLsu6wLGutZVlrjx071sgyRYJvWLc4Ft6WSkyYk4+3HuWO+esodtceklmWxYz+M3h+3PPEhcSxMmMlNyy7gR1ZO05+g9L8hhXS0HEiIiIiIiLSJikga33zgaNVPicCSy3LiqttsGVZCcAyILaWr2uuXl71S2OeN8aMMMaMSEhIONV6RYJqUJcYFt2eSnyEi+U7jvHTl7+hsNRz0vGjkkaxeMJi+sX3Iz0/nenvTec/e/9T+2BXA5swbQ7IPXQK1YuIiIiIiEhboICslRljioDbqh0+H9hmWdZvLMtKtSyrt2VZoy3L+h2wFRhaNi692nnZLVyuyGmhf3I0i+9IpWNkCCt3neDmF78hv+TkIVlyZDLzr5zPxJ4TKfIU8dDyh3h83eM11yUbPAlszvoL8LrhySHw3kOQc7CJTyMiIiIiIiKnGwVkQWCMWQr8ksAOsETgYWAVsANYCfwZKJ9++R3wX9UupYBMzhh9OkWx5M5UOkWHsGZPJje+8DW5xe6Tjg91hPLwhQ/zX6P+C7tl58XvXuSeT+4JXJds9Eyw1xOQOUKh11jwlsCa52H2ubDsZ5C9v5meTERERERERIJNAVmQGGMeBSYBDWlH+TdwcS3HjzRrUSKnuV4JkSy543ySY0JZvz+bGfO+Jqfw5CGZZVlM6zeNuT+cS3xoPF9lfMXkZZPZnrndPyC+J0yaD87wmp1kNqf/+OQFMOP/wd2rYMC1/m6ytf+C2UPhnfsga2/LPbCIiIiIiIi0CgVkQWSMeQM4G/gp8P+APUA+UABsB14CLjHGXG+MyQRSql1iQ+tVK3J66NExgiV3nk/X+DA2pucwZe5qMgtK6zxnZOeRLB6/mP4d+nMw/yAz3p/B+3ve93/ZexzcvRKG3wQhUWBZ/tfhN/mP9x7nH9epP/zkRbhnNQz6CRgfrJ8Ps4fBW/fCie9b+MlFRERERESkpSggCzJjTLEx5l/GmOuMMT2NMVHGmEhjzDnGmFuMMcurDB9U7fS1rVmryOmia3w4S+44n7M6RrDlUC5Tnl/NsbySOs9Jikzi5Ste5ke9fkSRp4hffvFLHl/7OB6fhwMOJ3/pEEdq9y4M7tGN1O5d+EuHOA44apl+mXgOXDcP7l0DQ6YABr5dAE+NhDfvguO7WuahRUREREREpMVYxtS5EWK7ZlnWH4E/VDl0izHmpeBUUz/LsnYAvcs+FgCJxpjChpw7YsQIs3at8jRpX47mFjN13tfsOppPr4QIFt6eSqfo0DrPMcawcNtCHv3mUbzGS9+4vuzL3YfH58FjKhf+d1gOHHYHj1/8OGO6jDn5BU98Dyseh42LwHjBssHA62DML/xhmoiIiIiIiJwWLMtaZ4wZUdt36iBrIyzLGkplOAbwWkPDMZH2KjE6lMV3pHJO5yi+P1bA5OdWkZFdVOc5Vdcli3HFsD1rO8Xe4oBwDMBjPBR7inlw+YMcyD1w8gt26AVXPw33r4dhN/kDsk2vwzOp8PrNcGRzMzypiIiIiIiItCQFZG3HA9U+vxCUKkROMx0jQ1h0eyoDkqPZe6KQyc+v4kBm/dnxyM4juajLRfWO83g9zN8yv/5C4nrAj2bD/RtgxK1gc8DmN2HOaFgyAw5vasDTiIiIiIiISDAoIGsDLMsaA9xY5dBHxpiVwapH5HQTF+Fi4W2pDOkay4HMIiY/t4q9xwvqPe/TA5/WO8ZjPCzbvazhxcR2gwn/gAe+hVF3gD0Etr4Dz14Ii6ZCxrcNv5aIiIiIiIi0CgVkTWRZ1iWWZZkqP3sbcI6jEdcfBrwNWGWHioG7T6lYkXYsJtzJgp+OYnj3ODJyipn8/Cp2Hc2v85xCd8NmKRe46w/bahbUBa56FB7YCKn3gCMUtr8Lz18MCydD+rrGX1NERERERERaxBkRkFmW1aO2HyC22tCOJxnbuZlLmmxZ1heWZd1iWVbHk9ScbFnWX4BVQFyVrx4yxnzfzPWItAtRoU7m3zqK886K50huCTc8v5rth/NOOj7cGd6g6zZ0XK2ik+CKv8EDaXD+THCGw47/wLwfwILr4MCaU7+2iIiIiIiINIszIiAD9pzkp/q6Xo+eZNziZq7HAsYA/wKOWpa1w7KsZZZlLbAs6x3Lsr4F0oHfAq4q5/3FGPNUM9ci0q5EhDh46ZZRXHh2R47nlzBl7mq2ZOTWOnZCzwk4GtDQ6TM+3tz5Jh6fp96xJxXVCS5/2B+UXTALnBGw62N4YRzM/zHsW3Xq1xYREREREZEmOVMCstOZhX93yvHANGAiMITKKZUA+cAtxpjftX55Im1PmMvOvJtGcEnfBDILSpkydzVp6dk1xt3U/yYc9roDMguLIk8Rv//q91z3znV8su8TjDGnXlxkAoz7E8zaBGN+Aa4o2P05vHgFvDQB9qw49WuLiIiIiIjIKVFAFhzrgCVAVj3jMoEngN7GmJdauiiR9iTUaee5GcMZ178TOUVups39mvX7A/+V6xrdlccvfpxQR2iNTjKH5SDUEcpTY5/ikTGP0CWyC7tzdjPr81lMe28aaw41cWpkRAcY+zuYlQYX/wpCYmDvCnh5Arx4lT80a0oQJyIiIiIiIg1mNakTQprEsiwL6Af0B7oAkYAHOAp8B6wzxnib414jRowwa9eubY5LibQpbq+PBxZv4L1Nh4lw2XnxllGMOis+YMyB3APM3zKfZbuXUeAuIMIZwYSeE7ix/410je5adh03/975b57d+Cwnik8AMDp5NA8Me4D+Hfo3vdCibFjzPKx6GorLut26ngcX/xJ6jQXLqvt8ERERERERqZNlWeuMMSNq/U4B2ZlBAZmcyTxeHz9/fSNvf5tBmNPOCzeNYPTZte6PUa9CdyELti7gxe9eJN/t3yXzih5XMHPoTLpHd296scW5ZUHZU1BU1vGWMtzfZdb7hwrKRERERERETpECMlFAJmc8r8/wq3+n8ca6dEIcNp6/cQQX90k45etlF2fzwncvsHDrQkp9pdgtO9f2vpa7htxFYnhi0wsuyYNvXoCv/gmFx/3Hks71B2V9r1RQJiIiIiIi0kgKyEQBmQjg8xl++9Z3LFqzH5fdxpzpwxjbr1OTrnm44DBzNs7hrV1v4TM+Qu2hTOs3jVsG3kJMSEzTiy4tgLX/gpWzoeCo/1jnQXDRL+GcCWDTUpIiIiIiIiINoYBMFJCJlDHG8Md3NvPyqn047Rb/nDKMfklRzF2xm7c2ZFBQ4iEixMHVQ5O5fUxPuneIaNB1d+fs5qkNT/HRvo8AiHJF8dOBP2Vqv6mEOcKaXnhpIax/Gb58AvIP+48lDoCLH4J+P1ZQJiIiIiIiUg8FZKKATKQKYwx/fW8rc1fswWaBw2bDZwweX+Xvhw6bhdNu45npw7i0b8OnTG46tokn1z/J14e/BiAxLJG7zr2Lq8++GqfN2fTi3cWwfj58+Q/Iy/AfSzgHLnoIBlwDNnvT7yEiIiIiItIOKSATBWQi1Rhj+N3b37Fg9f46x4U57fxn1pgGd5KVX3vVoVU8se4JtmZuBaB7dHdmDp3JD7v/EJvVDN1enhLYsMAflOUc8B/r0NsflA28DuyOpt9DRERERESkHakrINOcHBE5I1mWBQZs9ax17/b6mLdiT6OvPTp5NIsnLOb/Lv4/ukd3Z1/uPh5a/hA3LLuBrw5+RZP/44QjBEb+FO5bDxNnQ2w3OLET3rwDnh4JG14Fr7tp9xARERERETlDqIPsDKEOMpGaBv7hA/JLPPWOiwxx8N2fLj/l+7h9bt7e9TZzvp3D0SL/QvujOo9i1rBZDEoYdMrXDeB1Q9oS+OL/IKss0IvrAWN+DoNvAIeree4jIiIiIiLSRmmKpSggE6nFWf/1Lg35HdCyYM/fxjf5fkWeIhZtW8S8TfPIK80D4LJul3Hf0PvoGduzydcHwOuB796ALx6FE7v8x2K6wZifwbnT/J1nIiIiIiIiZyBNsRQRqUVESMPW6bKAl1bu4WhecZPuF+YI49aBt/L+te9z26DbsXN0CAAAIABJREFUCLWH8vH+j7nmnWv4/crfc7jgcJOuD/jXHhtyA9y7Bq6dBx37Qs5+WPYzmD0U1sz1L/QvIiIiIiIiFdRBdoZQB5lITf/91iYWrzkQsHtlXWwWpPbswITByVw5sDNxEU2btni08CjPbXyOf+/8N17jxWVzMeWcKdw26DZiQ2ObdO0KPi9sedvfUXZ0i/9YVBJcMAuG3wTOsOa5j4iIiIiIyGlOUyxFAZlILfadKOCKJ1ZQ5PaedEyo08ZDP+zLqt2ZLN9xFLfX/3umw2ZxYe+OTByczLgBnYgOdZ56Hbn7eHrD07y/930AIp2R3DzgZmb0n0G4M/yUrxvA54NtS2H53+HId/5jEYlwwQMw4hZwNXyXThERERERkbZIAZkoIBM5ic+2H+WeBetxe30BnWQOm4XTbuOZ6cO4tG8iADlFbj7cfJilaYdYues43rLxLoeNS/okMHFIMmP7JRLuatjUzeq2nNjC7PWzWZmxEoAOoR24c8idXN/7epz2Uw/gAvh8sON9WP6/cGij/1h4Rxh9H4y8DUIim+c+IiIiIiIipxkFZKKATKQO+04UMG/FHt7ccJCCUg8RLgfXDE3htjFn0b1D7Z1VJ/JLeP+7wyzdmMGavZmU/1Ya5rQztl8iE4ckc3GfBEKd9kbXs+bQGp5c/yRpx9MASIlMYebQmVx11lXYrGZaOtIY2PkhfP4IZKwvKz4eRs+EkbdDaHTz3EdEREREROQ0oYBMFJCJtKAjucW8m3aIpWkZbNifXXE8KsTBDwd0ZuKQJC44uyNOe8PDLWMMn+7/lNkbZrM7ZzcAfeL68MCwBxiTMgbLspqneGNg1yew/BFI/8Z/LDQWzp8J590BoTHNcx8REREREZEgU0AmCshEWsmBzEKWpR1iWVoGmzNyK47HhTu5YmASE4ckcd5ZHbDbGhZweXweln6/lGc2PlOxy+WwxGHMGj6LoYlDm69wY2D35/6pl/tX+Y+FxEDq3ZB6F4TFNd+9REREREREgkABmSggEwmC74/ls2yjv7Ns19H8iuMJUSGMH+QPy4Z2jcPWgLCsxFvCkm1LmLtpLtkl/i61S7pcwn3D7qNPXJ/mK9oY2LvCv5j/3hX+Y64oOO9OOP9eCI+HzN3w1VOQ9hqU5oMrEgZP8k/PjO/ZfLWIiIiIiIg0IwVkooBMJIiMMWw/ksfSjRks3XiI/ZmFFd+lxIYxYXASE4ckMyA5ut6pk/ml+by85WVe3vwyRZ4iLCwm9prIPefeQ0pkSvMWvnclfPF3f2cZ+IOwsy+DHR+AzwM+d+VYmxPsTpg0H3qPa946REREREREmoECMlFAJnKaMMaQlp7DsrQMlqUd4lBOccV3Z3WMqAjL+nSKqvM6x4uOMzdtLq/teA2Pz4PD5mBy38ncPuh2OoR1aN6i93/tn3r5/Sf1j3WGw90r1UkmIiIiIiKnHQVkooBM5DTk8xnW7c9i6cYM3tt0iOP5pRXf9e0UxcQhSUwYnEyPjrXvpAmQnpfOM98+w7LdyzAYwh3h3DTgJm7sfyORrsjmLXjJdNi6DKjjzw2bE4bfBOMfa957i4iIiIiINJECMlFAJnKa83h9fL0nk6UbM3j/u8PkFFVOXxyUEsPEIUmMH5xMSmxYredvz9zOPzf8k+XpywGIC4nj9sG3M7nvZFx2V/MU+dcuUJpX/zhHGMxKg8jE5rmviIiIiIhIM1BAJgrIRNqQUo+PL3cdY9nGQ3y45Qj5JZ6K70Z0j2PC4CSuGpxEYlRojXPXH1nPE+ufYMPRDQAkRSRx77n3MqHnBOw2e9MK+2MsdXaPVRffE7qdD91S/a8dzoZ61lgTERERERFpKQrIRAGZSBtV7Pby+fajLN14iE+2HaHY7QPAZkFqzw5MHJLMFQM6ExdR2SVmjOGL9C94Yv0T7MreBUCvmF7cP+x+Lu16ab0bAZxUQzvILDs4QsBdGHg8vGNZWFYWmHUeDI5m6m4TERERERGphwIyUUAm0g4UlHj4eOsRlm48xPIdR3F7/b9/O2wWF/buyMTByYwb0InoUCcAXp+X9/a8x9PfPs3B/IMADE4YzKxhsxjZeWTjC1j2IKyfH7h7ZXXla5Bd8Qgc3gT7V8P+Vf7XgqOBYx1h0GVEZWjWZRSERje+LhERERERkQZQQCYKyETamZxCNx9sOczSjRl89f0JvD7/7+Uuh41L+yYwYXAyY/slEu5yUOot5Y0db/Bc2nNkFmcCcEHKBcwaNotz4s9p+E0zd8OcC2p2hlV1sl0sjYGsPYGB2fEdgWMsG3QaUDkts2sqxKQ0vD4REREREZE6KCATBWQi7diJ/BLe/84flq3Zm0n5b+thTjuX9e/ExMFJXNw3Aa8pYf6W+by0+SUK3AUAXHnWlcw8dybdors17GY7P4LXbgSvO7CTzOYEuxMmzYfe4xp2rYLjcODrysAsYwP4PIFjYroFTstMOAdstoZdX0REREREpAoFZKKATOQMcSS3mHfTDrE0LYMN+7MrjkeFOvhh/85MHJJE/y52Xt7yLxZtW4Tb58ZhObiuz3XcOfhOEsIT6r9J5m5Y9TSkLYHSfHBFwuDJcP69NTvHGqO0EDLWVwZmB9ZASW7gmNAYf2dZeWCWPBScNTcrEBERERERqU4BmSggEzkDHcgsZFnaIZZuzGDLocqgKS7cyZWDkhjdx87qrEUs3f0OPuMjzBHG9H7TuXngzUS7ToO1wHxeOLolcFpm7sHAMXYXJA+rDMy6joLw+ODUKyIiIiIipzUFZKKATOQM9/2xfJZtPMQ7Gw/y/bGCiuMJUSGM6e8l0/U2646vACDaFc1tg25jyjlTCHWcZt1Z2QcCA7OjW4Bqf44l9KsyLTMVYrvDqe7cKSIiIiIi7YYCMlFAJiIAGGPYdjiPZWkZLN14iP2ZlQvud0o4QmTShxx1bwYgMTyRe4bcw4/P/jEOmyNYJdetKAsOfFMZmB1cB96SwDFRSZUdZt1SodNAsNmDU6+IiIiIiASNAjJRQCYiNRhjSEvPYenGDJalHeJwbjFgsEfsJCrpQ7zOdAB6RPfgvqH3Ma77OCzLYvX+7Ty88ln2FK8AqwRMCGeFjuG3F9xFare+wX0oTwlkfFtlHbPV/hCtKlcUdB1ZGZilDAdXRHDqFRERERGRVqOATBSQiUidfD7Duv1ZLN2YwXubDnE8vxhH1CZCEj/E5joBwNkx/Tgn+nyW7nsFLC+W5as43xgbGDt39/sT96ZODNZj1OTzwfEdlYHZ/lWQvS9wjM0BSUMqA7OuqRDZgM0KRERERESkTVFAJgrIRKTBPF4fq3dnsiwtg/e+O0hRyFe4Ej7B5sir91zjczJv7KLgd5LVJfeQv7OsPDA7vAmML3BMh7OrLPyfCh16aR0zEREREZE2TgGZKCATkVNS6vHx5a5jvPXtHj7O/jO20AN15kTG2OgZMpZ3pjzeekU2VUkepH9TGZilrwV3YeCY8I5V1jE7H5IGg93Z8Htk7oavnoK016A0H1yRMHgSjJ4J8T2b93lERERERKRWCshEAZmINNnAF0dg2UrqHWe8Ibx+5af0TozEYbe1QmXNzOuGw2mBu2UWHAsc4wz3r11WPi2zy0gIja79ejs/gtdu9F/X5648bnP6Q7ZJ86H3uJZ7HhERERERARSQCQrIRKTpBr40qEGzDI2xyN/2N0IcNgYkRzO4SyyDUmIY3CWGngmR2G1tbKqiMf4OsP2ryn6+hhM7A8dYNv/umOWBWbdUiE72nzfngpodaVU5w+HuleokExERERFpYQrIRAGZiDRZgzvIDIQVXMXxjGEYb2TAd+EuOwOTYxjUxR+YDUqJoUeHCGxtLTTLPwYHvq7sMDv0Lfg8gWNiu/u7xLJ211zjrCqbE4bfBOMfa9maRURERETOcArIRAGZiDTZxEU/Y0/JpwG7V1ZnTOVa9i6bi+Edf0CK7YccPhZPWnoOB7OLapwTFeJgQEpgp1m3+HCstrQofmkhHFxXOS3zwBoorX9TgwohUfDr9JarT0REREREFJCJAjIRabrV+7dz2ydTsGzuk44xPic/H/Yb1h9fzvL05Rj8f8aM6jyKaf2mMTAulS0Z+WxKzyHtYA6b0nM4nFtc4zoxYU4GpZR1mpW9psSGtZ3QzOeFI5vhuTENPMGCOz6HTgMat/i/iIiIiIg0mAIyUUAmIs3i6dVLmbP1D2B5AzrJjLGBsXN3vz9xb+pEAPbn7mfhtoW8ufNNCj3+Nbi6RHZhWr9pXH321US6/NMvj+YWs+lgDmnpOWWv2RzPL61x7/gIV0WHmf81lk7RIad3aPbXLo3rJHOEQtIQSBkBKcP8GwHE9aBBi7+JiIiIiEidFJCJAjIRaTar92/nryufY3fxCrBKwITQM3QMv7ngTlK79a0xPq80jzd3vsnCbQs5mH8QgAhnBNecfQ1Tz5lK1+iuAeONMRzOLfYHZhWdZtlkFdbsXEuICqnoMPMHZ7EkRIW0zIOfimUPwvr5gbtXVmfZIO4s//vM72t+H97BH5SljCh7HQbh8S1Tr4iIiIhIO6aATBSQiUjQeX1ePk//nFe3vso3h78BwMLi4q4XM6PfDEZ2HnnSbjBjDOlZRVU6zbJJS88hr9hTY2xSTGhlp1nZumbxEa4WfbaTauwuloWZkLEe0tf51zQ7uBYKT9Q8J75nZWDWZQR0HgSO0ygYFBERERE5DSkgEwVkInJa2Za5jVe3vsq7u9/FXdZd1SeuD9P7TeeqnlcRYq8/7DHGsO9EYUWHWVp6Dt8dzKGg1FtjbJe4sIoOs8FdYhiYHENMeCut9bXzI3jtRvC6AzvJbE7/emOT5kPvcbWfawxk74P0tXBwvT8wO7QRPNXWbbM5/SFZeWCWMhzie4HN1nLPJSIiIiLSxiggEwVkInJaOl50nNd3vM6SbUs4UezvlIoLieMnfX/C5L6TSQxPbNT1fD7D7uMFFR1mm9Jz2JyRS5G7ZmjWo0M4g7rEVkzRHJAcTVRoC4Vmmbth1dOQtgRK88EVCYMnw/n3+rvBGsPrhqNbAkOzY9uBan+eh8ZA8rDA0Cyycb+eIiIiIiLtiQIyUUAmIqe1Um8pH+z9gFe2vMLWzK0AOGwOLu9xOTP6zWBAxwGnfG2P18f3xwpIS89m00H/RgCbM3Ip9fgCxlkW9OwYweCyaZmDu8TQPzmacJejSc/WKopzIWND2bTMdf7wLP9wzXEx3fxrmJUHZknngiu89esVEREREQkCBWSigExE2gRjDBuObmDB1gV8sv8TfMYfYg1NHMq0ftMY220sDlvTAyu318fOI/mVnWYHc9h6KBe3N/DPRJsFvROjqmwCEEO/pGhCnfZG3W/fiQLmrtjNWxsyKCjxEBHi4Oqhydw+pifdO0Q0+XlqlXOwMjA7uM4foJXmB46x7JDYH7oMr9wIIKEv2Br3fCIiIiIibYECMlFAJiJtzsH8gyzetph/7/g3ee48ADpHdGbKOVO4rvd1xITENOv9SjxedhzOJ+1gtn/3zPQcth/Jw+sL/HPSYbPo0ymqbBOAGAanxNKncyQhjtpDpc+2H+WeBetxe314qlzLYbNw2m08M30Yl/ZthamPPq9/Kmb54v8H18GRLWCqTT91RULyUH+nWflGADEpLV+fiIiIiEgLU0AmCshEpM0qdBfy9vdvs3DrQvbm7gUgzBHGj3r9iKn9ptIzppFreDVCsdvL1kO5lbtnpuew82ge1TIzXHYbfTuXdZqVrWnWp1MUGdlFXPHEilrXQCsX5rTzn1ljWq6TrC6lBXAorTIwO7gOsvfXHBeVVNZhVhaaJQ+F0OjWr1dEREREpAkUkIkCMhFp83zGx5cHv+TVra/yVcZXFccvSLmA6f2mMzp5NDar5XdtLCz1sCUjt2JqZlp6NruPF1D9j1OXw0ZUiIPMgtLqy+cHcNgspozqxv9cPbBF626w/KOVi/+Xh2bFOdUGWf6pmCnDK386DfDvyikiIiIicppSQCYKyESkXdmVtYtXt73Ksu+XUewtBuCsmLOY3m86E3pOINzZugvP5xW72ZyR65+aeTCHTenZ7D1R2ODzI0McfPeny1uwwibw+fy7cJYHZulr4fAm8LkDxzlCIWlI2bTMst0z43r4dz8QERERETkNKCATBWQi0i5lF2fzxs43WLxtMUcKjwAQ5Yri+j7XM6XvFJIik4JWW06hmyF//rDB468dlkL/pGgGJMfQPymamPDTuBvLU+IPyarumpn5fc1x4R0qF/8vn6IZHt+we2Tuhq+egrTX/JsLuCJh8CQYPRPiW25arYiIiIi0XwrIRAGZiLRrbp+bT/Z9witbXyHtWBoAdsvO2G5jmdF/BkMShmAFoZNp4B8+IL/Ec0rndokLY0ByNP2TYvyvydEkxYQG5TkapDATMtb7p2emr/V3nBWeqDkuvmdlYNZlBHQaCM7QwDE7P4LXbgSvO7BTzeb0T+OcNB96j2vZ5xERERGRdkcBmSggE5EzRtqxNBZsXcBHez/CY/zh1IAOA5jefzqXd78cZyuuk/Xfb21i8ZoDAbtXVuewWYzrn8gFZyewOSOXLYdy2XYolxKPr8bYuHCnv8MsOZoBZT9ndYzEbjsNQzNjIHtfWYdZWafZoW/BUxw4zuaEzoMq1zKL6gyLp4K7jimqznC4e6U6yURERESkURSQiQIyETnjHCk4wpLtS3h9x+tkl2QDkBCWwOS+k/lJ358QH9rAqX5NsO9EwSntYunx+th9vIAtGblszshhy6FcNmfkkl3ornF+qNPGOZ2jq4RmMZzTOYpQp71FnqlJvG44uiUwNDu2DercxqAWNgcMuwkmPN4iZbZJmpIqIiIiUi8FZKKATETOWMWeYt7d/S4Lti5gV/YuAFw2F+N7jmdav2n0je/bovf/bPtR7lmwHrfXF9BJ5rBZOO02npk+jEv7JtZ7HWMMGTnFFaHZ5oxctmTkcjC7qMZYmwW9EiIrpmaWr2sWF+Fq1mdrFsW5/s6y9LJNALa9S4MDM5sTXOHgjCh7DQdXBDjDqryv+lrb2CrHnWGVx5xhbWeDAU1JFREREWkQBWSigExEznjGGFYfWs2rW19lefryiuOjOo9ier/pXNTlIuy2lum62neigHkr9vDmhoMUlHqIcDm4ZmgKt405K6Bz7FRkF5aypWxqZnlotutYPt5apnUmx4TSv9oUzZTYsNNrXbM/xtLojrIWYVUJz+oI2RoSvNU4HgHN9b+1zN0w5wJNSRURERFpAAVkooBMRKSKvTl7WbhtIW/teosij78Dq2tUV6aeM5Wrz76aSFdkkCtsmmK3l+2H88pCsxy2ZOSy9VBerVM9Y8Kc9E8KnKLZKyECh90WhMqBv3aB0rz6x4VEwUPfQ2mBPxxyF1W+Ly0Ed0G118KTfF9+vChwTPW10pqb3dW47raTBXJfPwfb3gNTx2YQNicMvwnGP9ayzyQiIiJymlNAJgrIRERqkVuay5s732TRtkUczD8IQIQzgmvOvoap/abSNaprkCtsPl6f+f/s3XucnGV9///XNefd2WN2c9ycIYkEhAQRqZgqWojVaBERRZHUqm1F2lK09qCtWrG/trR8/baK31ZrJaIiFsQaD4gKGBUEIeEsCYYkm+NmN5s9zO6cr98f9+zszOzM7uzuHHZn3k8e85i577nvez6bDJud936u6+LF3lBWaPbM0UFOhaITjvV5XLxkSXNqFc0WNi5r5aylzTT6POUvdOeN8PiO7KGCuSoR+CQTk4RpefZP59hoiIp3ybl98Oq/hGAnNHZCcGHqcQcEWufPcFIRERGRWVBAJgrIREQmkUgmeKD7Ab7y3Fd47MRjABgMr1nxGt698d1csPiCuTUMsUSstZwYjGQFZs8eG+TQqYnD9YyBNZ3B9HxmY/ObdTb5S1tUPQwZtNbpUMsK1ybrbiv0/Cgc+sXs63F5M4KzjowAbexxanvs+UCbAjURERGZlxSQiQIyEZEiPdf3HLc/dzvff/H7xFJdTOvb13PNWdfwhrVvwO8ucSA0Bw2Mxnju2GA6NHvm6AAv9AxnLTIwZnGLPys0O3tZKysWzHJeM006X7xih6R6/PCKD0CoF0Z6s++jw9N7TZdnPDhr7JgYoE3oUGsDV5WG7E5GK3+KiIjUHQVkooBMRGSaekd7+ebz3+SO5+/gVPgUAAsCC3jb+rfx9g1vZ2HjwipXWFmReIJ9J4azVtF87tggoejEec2a/R7OSg3PHAvNzlzUhM9TfEhyZP8zdH/3Xzi79/sECRMiwDOdv8uKN36YrrVnl/JLm99KMSQ1Fh4PyyYEaCch1Jf9fDGBXCbjLtChli9g66xMoKYQVkREpC4pIBMFZCIiMxRNRPnBgR9w+7O389yp5wDwuDy8fvXruWbjNZzdUb9hTTJpOXhqJGuI5jNHB+kdjkw41ud2sW5xU3pes7O7WjlraQtN/onzmt3/fA/X3f44sUQyq2vN4zJ43S5uveZ8LtmwqKxf27xRjSGpsTCM9BUI0E46z2UGbZHB6V3fuMeDs/T9JMM+G9qnF6jVwzBeERERyUsBmSggExGZJWstj514jK8+91V+0v0TkjYJwOZFm7nmrGt47crX4nFVYBL7eaBnKOzMZ3Z0MN1xdqAvfxixuqPRGaKZmtOstcHDu77wSN4VN8c0eN384IYtrOoIlutLmF/mejdUPDIemuUL0HK71sID07u+cUHDggJdabkBWyf85NOw+yvVXwhCREREKk4BmSggExEpocNDh/n6r7/O3fvuZjjmzN+0NLiUq19yNVesu4JWf2vW8d2D3dz27G3s3L+TkdgIjd5Gtq3dxvaN21nRUjsrZU5mOBLnuWODPHNkILWS5iB7TwwRS0z/5xCPy3D1hSv51OXnlKHSeerUfnjoc/DkNzLm03o7/NYH518XVDya0aGWb9hnzuPw6fLU4W+Gvz5cnmuLiIhIVSggEwVkIiJlEIqF+PYL3+Zrv/4aBwcPAtDgaeDNZ7yZd531Lta0rmHX4V3c+OCNxBNx4jaePtdjPHjcHm559S1sWb6lWl9CVUXjSV7oGXaGaKZCs0dePFXUuU1+D09/cmuZK5R5IRGbvCstt2tttL/4a199ByzbDM1Lyle/iIiIVIwCMlFAJiJSRkmb5GdHfsZXnv0KDx97OL3/ZYtfxlMnnyKajBY8N+AJcPeb7q6bTrKprPmr71LsTybrFjWxeWUbm1a0s2lFG+sXN+Fxz8HVEmVu+Yeu6a/c2bQElm1ywrKlm5zHCs1ERETmHQVkooBMRKRC9vXv46vPfZWd+3cSSUycrD6Xx3i4cv2VfPSij1agurnvnI/fy3AkPvWBeTT63Ly0q5VNK9vYvMIJzpa0Bkpcocx7xaz8adywaCM0tMGxJ/IvNNC0xAnMlm1KhWaboXlx+eoWERGRWVNAJgrIREQqrD/cz+u++Tpik30IT2nyNvHQOx+qQFVz38fueYo7HunOWr0yl8dluOqCFVx5wXL2HDrN7u7T7Onup/vU6IRjl7YG2LSiLX176fJWGn1aTKGuTXcVy2QS+l+Eo7vh2B44uqdwaNa8dDwsGwvOFJqJiIjMGQrIRAGZiEgVnHvbudgiBgwaDE9uf7ICFc19B/tCvP4zu2a0imXvcIQnuk+zp/s0uw+d5onu0wzldKO5XYYNi5vTXWabV7axtrMJl8uU5euROWq2K39mhmZHdzuB2dE9EB2aeGzzsowus1R41rSo9F+TiIiITEkBmSggExGpgou+dhGhWGjK4wyG7WdvZ+vqrZzdcTbG1HdYc//zPVx3++PEEsmsTjKPy+B1u7j1mvO5ZMPUAUMyadnfO8zusS6zQ6d5/sQQiZzutOaAh/OWOx1mzpxmbXQ0+Uv+dckcU+qVP5NJ55rH9qSCs1Sn2WShWeacZgrNREREyk4BmSggExGpgpsevom79t6VtXrlVLqaurhs1WVsXb2VjR0b6zYsO9gX4ou7XuRbu48QisYJ+jy8ZXMX79uyZkLn2HSMROM8fWSQ3Yf62ZPqNjs2EJ5w3IoFDWxa0e7MZbayjbOXteD3uGfzJUk9GgvNcodn5gvNWrrGwzKFZiIiImWhgEwUkImIVEH3YDdXfOcKwvGJAcyYgDvA31/89+zp2cN9B+/j5OjJ9HNdTV1sXb2Vy1ZfxsYF9RuWldvxgTB7uvvTXWZPHRlgJJo9xNPrNmxc2sLmle3p+cxWdTTq70SmL5mEU79JhWV7xodo5ltZMx2aZcxp1rSw8jWLiIjUCAVkooBMRKRKdh3exY0P3kg8Ec/qJPMYDx63h1tefQtblm8BIJFMsLtnNz88+EPuO3gfvaO96eOXNy1Ph2VnLThLwUwZxRNJ9vU4QzP3dDudZvt6hsn9kam90ZsKy9rZtLKNTcvbaG30Vqdomd/Sodnu8eCsYGi2PHvlzGWbINhZ+ZpFRETmIQVkooBMRKSKuge72fHsDnbu30koFiLoDbJt7Tau3XgtK1pW5D0nkUzweM/j/PCAE5b1hfvSz61oXuGEZasu4yULXqKwrAKGwjGePDyQXgBgT3c/vcPRCcetXRh05jJLBWcvWdqM1+2qQsUy7yWT0PdC9pxmx5+cPDRbtgmWKjQTEREpRAGZKCATEZnHxsKyew/cy30H7+NU+FT6uZXNK9m6eitbV29lfft6hWUVYq3lcP9oeh6z3Yf6efroINF4Mus4v8fFS7taUwsAOJ1my1oD+nuSmUkmnNAsPTwz1WmWbzGQ1hWw9LyMxQA2Q7Cj8jWLiIjMIQrIRAGZiEiNSCQTPHbiMe49cC8/OvSjrLBsVcuq9AT/CssqLxpP8uvjgxldZqd5sXdicLGw2Z+1Yua5y9to8nuqULHUhMzQbGwxgGNPThGabR7vNis2NDu1H37xWXjyzoxVP6+CV14/s1U/RUREqkABmSggExGpQfFkPB2W/fjQj7PCstUtq7lstROWrWv71mA3AAAgAElEQVRbp7CsSk6PRDO6zJz7gdFY1jEuA+sWNacDs00r21i3qBm3q/i/s4N9Ib6waz/37D5KKBIn6Pdw+eZlvH/L2lmt+inzVDIBvfsyuszGOs1GJh7buhKWnZcxp9lmaFyQfcy+++DOayERg2TG+9flBbcXrtoB6y4t79ckIiJSAgrIRAGZiEiNiyfj/OrEr5yw7OCP6Y/0p59b07om3Vl2ZtuZCsuqyFrLgb4RZ9XMVGD27NFB4snsn8eCPjfnLnfCsrE5zRa1BPJe8/7ne7ju9seJJZJZ1/G4DF63i1uvOZ9LNiwq69cl80BWaJYxp1nB0Cw1p1nTEvjuhyA+Wvja3kb4wM/VSSYiInOeAjJRQCYiUkfiyTiPHn803Vl2OnI6/dza1rXpCf7PbD+zilXKmHAswTNHB9l9qD/dbXa4f2IY0dXWkFo10wnOXtrVyonBMK//zC5GY4mC12/wuvnBDVvUSSYTJRPQuzd7TrNCodlkXF542XZ447+Wp04REZESUUAmCshEROpULBnj0eOP8sMDP5wQlp3ReoYTlq2+jDPazqhilZLr5FAkFZY5odkT3QMMR+JZx3hchpYGD/2hGJP9NOdxGa6+cCWfuvyc8hYttSEzNDu6Gx79Atjk1Oe5/fDWL0DnBqeTzOMrf60iIiLTpIBMFJCJiIgTlh17lHsP3suPDv6Iwehg+rkz28505ixbtZW1bRomNdckkpbfnBxmz6HT7E51mT1/fJBkkT/GNXjd3H3dK1nU7Ke90YdrGvObSZ37RBtMGsHmYdywYI0Tli1cD53rnced6yDQUpYyRUREiqGATBSQiYhIllgyxiPHHkkPw8wNy8Y6y9a2Kiybq0KROOd8/N7pRhd4XIbOJj8Lm/0sanbusx8H0o8DXndZapd55B+WQ3Ro6uPcPlh7CfQ+D/0HKRiqNS9NBWbrYeGG8cfNS0DzI4qISJkpIBMFZCIiUlAsEeOXx3+ZDsuGMj4Mr2tfx9ZVTli2pnVNFauUfM75+L0Thl7m43YZ1nYGOTkc4fRIbMrjxzQHPHmDs/H7AAub/bQ3erX4Q63aeSM8viN79cpcuXOQxUah7zdOWHZyrzNks3evs0hAIpL/Gv6WjOBsrONsPbSvBren5F+WiIjUJwVkooBMRESKEkvEePjYw9x74F5+cugnDMXGw7L17evTE/yvbl1dvSIl7WP3PMUdj3RPWAUzU+4cZJF4gt7hKD2DYU4ORTg5HKFnMPv+5GCYk8MRYonifk70up2utMyOtIWp8CwzVOtsUlfavHNqP3z+4skn7i92FctkAk4fGg/MTj4/fh8+nf8clxc6zpjYcda5DnxaeEJERKZHAZkoIBMRkWmLJWI8dOwh7j1wL/cfuj8rLHvJgpdw2arLuGz1ZaxqWVXFKuvbwb5Q2VaxtNZyeiSWEZw5gVpumNYzGGYwPHUX25jWBm/2kM4mP4tasjvSFjX7aW0ob1fawb4QX9i1n3t2HyUUiRP0e7h88zLev2WtVvzMte8+uPNaSMSyO8lcXnB74aodsO7SmV/fWgj1Oh1nvXuzu84Guguf17oiIzhbl5rzbAM0dmi4poiI5KWATBSQiYjIrEQT0azOsuHYcPq5sxacxWWrL+OyVZexsmVlFausT/c/38N1tz9OLJHM6iTzuAxet4tbrzmfSzYsKmsN4VhiQjfayaEIJ4dSodrQ2HZk0m63TD63i4XNfjrzhWlNfha1BNL7fB7XtOqdC39m886p/fDQ5+DJb0B0GHxNcO7b4bc+OHXn2GxEhqHvheyOs969zhDOQsM+G9rHFwVYuGH8cdtKcKmDUUSknikgEwVkIiJSMtFElIeOpjrLuu/PG5ZtXbWVFS0rqlhlfTnYF+KLu17kW7uPEIrGCfo8vGVzF+/bsmZOdUMlk5bTozF6hrK70cZDtHA6TBuaRldaW6M3J0QL5IRpfhY2BWhp8HDo1EjZuu6kghIxZzGArK6z1JxnhRYV8ASgY11GcJbqOus4E7yBytYvIiJVoYBMFJCJiEhZRBIRfnHkF/zw4A+5v/t+QrFQ+rmNHRvTc5Ytb15exSplPhqNJugdjoyHaRldaD3p+zC9w1ESxXaleVx4XYZQtHA4BuA28DsbF/Mnr11HwOum0eemweumwefG73HV5YIE82ZIqrUwdDwVnO1LdZ2lHg8dK3CSgfZVqSGaY3OcpR43tM+8llP74RefhSfvzOi6uwpeeX15u+5ERKQgBWSigExERMoukojw8yM/d8KyQ/czEh+f1PvsjrOdsGz1ZXQ1dVWxSqk1yaTl1Eh0QnCWud2belzMip9TcRnSYVnDWHDmHX/c6POMh2q+7OfGj3FPvEbG+W7X3ArgamZIangAel9IdZqlQrPe5+HUi2ALhKbBhTnBWerWunzyec7KPW+biIjMiAIyUUAmIiIVFY6H+fnRn/PDAz/kge4HssKyczrOSYdly5qW5T2/e7Cb2569jZ37dzISG6HR28i2tdvYvnG7hm7KjI1E45z9d/dS7E+/G5e2EI4lGIkmGI0lGI0miCaSZa0RnPnXMsO0QCpAG3vcmCdwyzzGec4z8bkZdMGVcyGIOSMecUKysSGavXvHu84Krd7pDaaGaK5PhWepFTYXrIXBw6Vb+VNEREpKAZkoIBMRkaoJx8P8/MjPuffgvTzQ/QCj8dH0cy/tfClbV2/l0lWXpsOyXYd3ceODNxJPxInb8Y4fj/HgcXu45dW3sGX5lop/HVIbzvn4vUV1kjX5PTz9ya0T9scTSScsiyUIR5OMxOKMRp3wbDQjTEsHaxnh2kh0bH88dY0ko2OPU8eOxBKU+8dzk+qCyw3csjvfPDT4XDx+8DS/Pj7IZKNYPS7D1Reu5FOXn1PewistmYTBIznDNVMBWuhk/nOMG3xBiAzBZFGsywsv2w5v/NeylC4iIvkpIBMFZCIiMieE42F+duRnTmfZ4eyw7NzOc7lwyYV85bmvEElECl4j4Alw95vuVieZzMjH7nmKOx7pnnQ1zWoGPtZaIvHkhM610azALc5oNBXUpQK28fAtO5SbcH4sQTRe+i44r9vwF1s3sLojyJrOICsWNBLw1vCKkSOnxsOyzOCs/yCTBmOZ3H74vc9B2wpoXQHNS7TKpohImSkgEwVkIiIy54zGR9Nh2YOHH8wKyybjMR6uXH8lH73oo2WuUGpRXQwZnEI8kSQcT+Z0vsUnhGnhWIK/+/YzM3oNY2BZawNrOoOs7mxMB2erO4OsaG/E53GV+KuaI2Kj8OmlFB2SZXJ5oGUZtK4cD83aVjjznbWudO612qaIyKwoIBMFZCIiMqeNxkfZdXgXH/npR0gUmiw7Q8Ad4I5td7A0uJRGb2MFKpRaUjOTzldAsUNS/R4XV1+4khd7QxzoC3G4f7Tg6qJul6GrrYHVnUHWdgZZ3dHI6k4nQOtqa8Djnufh2T8sh+jQ1Me5fbDhDTDQDae7IdQz9TnBRePhWetyaFuZEaStgIa22dcvIlLDFJCJAjIREZkXzr3tXOw0Oy8WBBawvGk5y5qW0dXUxbKmZentZU3L8Ll9ZapW5rODfSG+uOtFvrX7CKFonKDPw1s2d/G+LWtqtnNsJmY6JDUaT3K4f4QXe0Pp0OxAr7N9dGC04DxrXrdhRbsTmDldZ+OPl7U1zLkVPvPaeSM8viN79cpc+eYgi4Vh4DAMHHLuT3ePh2cDh2DwKCSnCCv9LamOsxU5XWipDrSmxeCa5wGkiMgsKCATBWQiIjIvXPS1iwjFQlMe5zZuljcv5+jwUWKTfQgFFjUsoqu5Kx2gZYZpi4OL8bq8pSpfpOaUY0hqOJag+9RIOjh7sXeEA6nHxwbCBc/zeVysXNDImlS32eoOZ/jmms4gi5sDuOZKeHZqf3lWsUwmYOh4dmh2ujsVqqX2TfX90+2Dlq7x0Cw9hDMVpLUsB49+qSAitUsBmSggExGReeGmh2/irr13Za1emStzDrKkTXJy5CRHQ0c5PHSYo8NHOTJ8JH07Hjo+6ZBNt3GzuHFxOjDraupywrTgMpY3L2dhw0LcmjRb6lwlh6SORhOpbrMQL6buD/SO8GJfiJNDkyze4XU5gVlHMDVcc3zes4XNfoypcHi27z6481pIxLI7yVxecHvhqh2w7tLSvqa1MNoPpw85gVm6C+3QeDfaSN8UFzHOYgETutBWjg/rDLSUtu4xp/bDLz4LT94J0WHwNcG5V8Err59ekCgiMgkFZKKATERE5oXuwW6u+M4VhOOFu0ims4plPBmnZ6QnKzQ7OuyEaUeGj9Az0jPpkE6Py8PS4NLx8KypKytM62zorPwHb5EqmAtDUocj8XSn2YHeVOdZ6nFfKFrwvKDPzaqOIGsWBlmTE6AtCPrK9//wqf3w0OfgyW9kBD5vh9/6YPUCn2gIBo5kh2Zj9wOHYfAI2ClWOQ20Fl5IoG0FBBc6qzRMRzUCRRGpSwrIRAGZiIjMG7sO7+LGB28knohndZJ5jAeP28Mtr76FLcu3lOS1YokYx0LHssOz4fFOtN7R3knP97v96bnOModujt3a/G0K0EQqYGA0lg7PXuwd60Bzhm4OjBYeht0c8GQM18zuPGtrnN1Qw4N9Ib6waz/37D5KKBIn6Pdw+eZlvH/L2rk7z10iDkNH84RnGY8n+QUGAJ7AeAdavoUEWpY5odeYcg1JFRHJQwGZKCATEZF5pXuwmx3P7mDn/p2EYiGC3iDb1m7j2o3XFtU5VirheJijoaMcGToyYfjm0eGj9Ef6Jz2/0dOYPzxLzYnW4ivNUKXuwW5ue/Y2du7fyUhshEZvI9vWbmP7xu0V/fMSmYv6Q9GM4ZrjwdmB3hBDk6zQ2dbodeY7S4Vnq9OPG2kOTD53Yc2ulGothHoLLyRwuhvCpye/hnFB89Lx0OzkXjjx1OSda/kWNRARmQEFZKKATEREpAxCsdCE4OzI0JF0qDYUG5r0/GZf84Shm5lhWqO3ccoaKtlxJ1JLrLX0Dkezus4yFw2YbGGCziZfRtfZ+IIBqzuC9A5HSr6wwbwSGco//9nYvqFjMM3VigHwN8NfHy55uSJSXxSQiQIyERGRKhiMDqa7zw4PH053no2FaaPx0UnPb/e3j4dnzV10BbuyVuTsCfWUdM42EXFYa+kZimQM1xxfMOBAX4hIvHC3U8DrIhybfB4vj8tw9YUr+dTl55S69LkvHnXmOhvrPPv2dcWfe+alsGwTLNsMSzc5wzU1jF1EpkEBmSggExERmWOstfRH+rPnPRs6wpHQkXSoFk0WnngcIOAOEE5MPh9Q5qqfIjJ7yaTl2GA4tVBAKGvus0OnRoglivt8ZYDVnUGaAx5aAl5aGpz78W1nX7N//PHY/qDPXTvzG/7DcohO3m1bUHBRdmC2bDO0LC1tfSJSUxSQiQIyERGReSZpk/SN9k1cgTMVph0bPpY1pHIyHpeHG86/geXNy1netJwVzSuKGr4pItOTSFrO/JvvzWQA4bS4DDQXCtXGthu8tKTux55vTT3fFPDgds2RgG3njSQfuw3XJN/PksaD66VvhQ1vgKO74dge5z48MPHgpsVOUJYZmjUvLuMXICLziQIyUUAmIiJSYxLJBJu/shk7w4/iHYEOljc7YdmK5hVZjzsCHbXTnSJSYed8/F6GJ5n8f0zQ7+Z/r38Vg6MxBsNxhsIxBkfjDIZjDI7GGAqPP859fiRaeH6zYjX5PekAbbJQLd/zzQEvPo9r1jUAHNn/DO23XUKjiRQ8ZsT66d9+P11rzx7faS30vwhH92SEZk9AJE9o1rw0OzBbtgma5uEiCSIya5MFZJ5KFyMiIiIis+d2uWn0NhKKhaY81ufyceX6Kzk8fJjuoW6ODB2hL9xHX7iPJ04+MeH4Bk8DXU1dE4Kz5U3L6WrqwuuefAU/kXp2+eZl3PFId9bqlbk8LsMVm5dzxsKmGb1GLJFkOB2gOfe5AdvghOfjqf3O4+GIczs6MPkw7UICXldGgOZJdbQVCtgywrfUPr/HhTGGzz+Z5Gj8Bj7r+Qwe4vjMePgXtW7ieLg+fgNdT1o+tTajAGNgwVrnds4Vzr5kMhWajQVme+DYE87CAM8fg+e/N35+S9fE0CzYOaM/CxGpDeogqxPqIBMREak9Nz18E3ftvWvSoZb55iBL2iQ9Iz10D3VzeOhw9v3wYU5HThe8nsu4WNK4JB2cLW9enhWitfhaSvo1isw3B/tCc34Vy0TSMhyZrGstT+iWFbLFSUwSABbD53bRHPBwaiSKtbDSnOC97u9xhftnBAkTIsDdiVfxX4k3cMgupsnv4elPbp3+CyWTcGp/9tDMY09AdHjisa0rYOl540M0l22GxgWz+jpFZG7REEtRQCYiIlKDuge7y7KK5VB0KCs0GwvODg8d5ljoGElbeIW+Fl9L3mGby5uWszi4GJcpzbAskbns/ud7uO72x4klklmdZB6Xwet2ces153PJhvk7xM9ay0g0kTMMNDtAy+5iyxkmOhojmph8pc98PnjJGaxf3Mz6xc2sXRjE73HP7AtIJqHvhfHAbKzTLF9HbtvK7C6zpZsUmonMYwrIRAGZiIhIjdp1eBc3Pngj8UQ8q5PMYzx43B5uefUtbFm+pWSvF0vEOBo6mh2eDR2me9i5H42PFjzX6/LS1dQ1ITgbC9MCnkDJ6hSptoN9Ib6460W+tfsIoWicoM/DWzZ38b4ta6rWOTaXhGNOwPbqm++f0ZxqbpdhdUdjOjBbv7iZDUuaWNURxOueQRCfTEDvvuzQ7PiTEBuZeGz76pzQ7DxoaJ/+a4pIxSkgEwVkIiIiNax7sJsdz+5g5/6dhGIhgt4g29Zu49qN106rc2y2rLX0hfsmDNsc60DrHe2d9PxFDYuyhmxmBmnt/vaSLhzQPdjNbc/exs79OxmJjdDobWTb2m1s37i9on9mIvXuY/c8NeWcbW4XvPKMTjataGPviSH2nhjmYF+IfKd43YYzFjaxbnEzGxaP3TezYkHj9FfuTCagd+94YHZ0Nxx/CvL9IqB9zXhgtmyzE5oFWqf3eiJSdgrIRAGZiIiIVN1IbCQ9VDN36OaR4SPEk4XnUgt6g1ndZun7phUsaVqC11X8wgGV7roTkcJmOmdbOJbghZ5h9vU4gdne40Ps7Rmi+1T+Lla/x8W6xU2sX9TM+iXNrF/cxLpFzXS1NeCaTnCWiEPv89mh2YmnId9Q9wVnZIdmS86FgOZpFKkmBWSigExERETmtEQywfGR4xOHbqbuh2JDBc91GzdLg0uzh25mPA56xz9Ul2veNhGZuVLO2RaKxHmhZ5jnTwyx78QQz58YZt+JIY4VWK0z6HNzZqrbLHO45uIWf/Fdq4kYnPz1eGA2FpolojkHGug4M6PLbBMsPRf8zcW9Tq5T++EXn4Un73QWHfA1wblXwSuvd1b3FJEJFJCJAjIRERGZt6y1DEYHJwRnYx1oJ0InsBT+mbbd354OzQ4MHODXp35NksIThOdb+VNEyqvcc7YNjMZ4oWeI548Pp4ZpOp1nvcORvMe3BDxOWLakmfWLmlJdZ810NvmLe8F4FE4+Nx6aHdsDJ57JH5p1rp8Ymvmm+Jr33Qd3XuuEc8nY+H6XF9xeuGoHrLu0uFpF6ogCMlFAJiIiIjUrkohwZPhIVsdZ+vHwYSKJ/B+AJ+Nz+fjYRR+jPdBOe6CdBf4FtAfaCXqDJZ0LTUSq61QomhGYDbH3uNN9NjAay3t8R9DHusVNbFjc7MxvtqSZ9YuaaW0sYph3PAo9z44HZkd3w4lnswMuAONKhWabx0OzJS8FX2Oq6P3w+YvzLyAwxtsIH/i5OslEciggEwVkIiIiUpeSNsnJkZMcHnYCs7/9+d/O6npel9cJzAILaPO3pR+3+9vHH2eEai3+FlxmBivqiUjVWGs5ORRh74nMoZpD7DsxzHAk/1yJi1v8GUM0neGa6xY30+T3TP5i8YjTWZYZmvU8B7lzMhoXLHyJE5j17YfDj4CdZPVPlxdeth3e+K/T/OpFapsCMlFAJiIiIgJc9LWLCMVCUx7ndXn53TW/S3+437lF+jkVPsVovtXrJuE2blr9rePBWSpIG3ucFailQjePa4oP1FWgVT9FnODs6EA41WmWWhzgxBD7eoYIx/IP2+5qa3ACs1Sn2YYlzZy5qImA1134hWJhJzQ7tnt8MYCe5yYPxPLxBeFP90CgDTy+6Z0rUqMUkIkCMhERERHgpodv4q69d2WtXplrsjnIwvEw/eF+TkVOpcOzU+FTWSFaZqg2FC28uEAhLb6WCYHahI61jHu/u8g5kWZIq37OjELF+pFMWrr7R9KB2d4TQzx/fIj9J0NEExODM2Ng1YJGZ4jm4mZnyOaSZtZ0BvF7CgRnsVE4/rTTZfa9D0+/SG8jBFqdsCzQCg1t49uZj9PPZTz2NTlFz3da1EBQQCYoIBMRERGByq9iGUvGOB0+7QRnkZxALU+odjpyetIFB/Jp9DRmD+/M6UzL7FpbEFhAo6ex6HnUtOrnzChUFIB4IsmBvhH2nRjKCs/294ZIJCf+f+52GdZ0BtNDNMeGa67uCOJxjw/VTn66C1dseMrXtxhM4wIYPT397rNMxj0xVJs0ZGvLCNlanEUDqk2LGkiKAjJRQCYiIiKSMpfDi0QywWB0cDxIyxOqZXav9Uf6iefOVTQFn8s3Yb60fMM92/3tfOnpL/Gd33xnxh139Uih4szUU8ddNJ7kxd7Q+Pxmx4fY1zPMgb4Q+T6e+9wu1i4MpgOzsx7/JFuGvofPFA69otbN452/x0V/8t9gLURDED7thGXhAedxeCC1nfl4YOJxky0GUAxf0+QdapN1snkbZ9+9pkUNJIMCMlFAJiIiIpKhe7CbHc/uYOf+nYRiIYLeINvWbuPajdfOqw/j1lqGYkPjgVmerrSsQC3cTzhROLiZKbdx87qVr0t3ppnUf6mN8X15ns/sZsvcl+9x+rjM5zOum3mdrNfKeJx5jUK1GEx23fmez1eLMfz08E95ru+5STsBXbi4uOti3r3x3fjdfvweP35X6t7t3AKeAD6Xry5WTZ3LoXUlhWMJXugZ6zQb7zg73J899+FKc4If+P6KRlN4hd4R6+et3Mz3P7l99oXFo6mwLDM8yw3WJgnZptkVm8XlnaR7rYiQzeWGnTfC4zvodllua2lmZ3OQEWNotJZtQyG2Dw6xImm0qEGdUEAmCshEREREBICR2EjB4Z65oVr3UHe1y617frcfn9tHwB1I32cGaVk3jz/rOJ/b5wRt0zzf6/JWLJhTx93UhiNxJzg77gRmX/zZi7zGtYdbvf8XD/GsTrKodRPHw3WxP+OB5CZaG7wsbPazsMnv3I/dcrYXNPpwucrwd55MQnRo8iBtsucmeV8Uxd8CkSF2Nfi5cVEncWOIZ7y3PdbisZZbenrZkvDAXx1yQjWpWQrIRAGZiIiIiExbsat++t1+brr4JgAslrHPGDb1H0Dm547MfUU/n++4As9ndm9l1lLU8zn15zsu39eHdbY//8Tnp/zzGvOKJa8gkohMvMUjhBNhYplzJVWQwUwM5jyBvIFaUftSHXH5jr11z618b//3NIx3Gs75+L0MR+J0+Z5nbcfd7G3pZ9QYGqxl/WA7+/uu4Eh0w7Su6XYZOpt8E8OzJj8LmwNZYVrQ565cZ2MsPEWQVqiTbQAiAwB0ezxc0bWEsMtV8GUCySR3HznOikQSGjuhaTE0LUzdL3Lug4vGHzctgob22li8oM5MFpDNvTWkRURERERkTti2dltRq36+5cy38Po1r69gZXPXjmd3FBUqNnmb+OLWL056TNImiSQiRBNRwvGwc58IZwVpueFaOB4mmsw+Pus+Hs4byKXPT4SJJ+OEE2HCiTCDDJbqj2bG4jbON/d+k2OhYwQ8AQLuQPZ96vFYB1yDpyEdyhV67HXNgYnjZ+jyzcu485n7GFh2O7tNAmOc4GfEGHa3DkHL7fiOXsNVGy/lxss2cHIo4tyGw5wcitAzGOHkcCRjf4TTIzFODEY4MVh42OaYBq+7YCfawiY/i1qcxx1BPz5P4VCqKN6Ac2tePP1zkwmIDHLbly7M6hrLJ24MO1qa+eipfgj1OLcTU1zf5XWCsmBmkLYof6jmb1aYNg8oIJsjjDEtwMuBM4D21O5+YD/wiLW2+v8yiYiIiEhd2b5xO9/+zbeJxycJyNwert14bQWrmtuKDRW3rd025bVcxkWDp4EGTwOt/tZSljmpRDIxHsxlBGz5ArnMjrfcIG4sqJsskDsWOlZcTTbBg4cfLNnX6DGedKCWGbb53X4aPA15Hwc8ARrcDXnPKxTc+d1+XGaWIVGObef7uKf/doxrYoehMUkwSXzLbmfby97IgqCPBUEfG5Y0T3rNSDxB33A0KzRLP87Y7hkKMxpLcOjUCIdOTT15f3ujNys862zy0dnspb3JxYJGD61BF62NLhp9kLBxYskYsWSMeDLncaLA/tTjWCJG3OY/bmdTA/Ep5kGLG8O3Wlt52Rs/S4u1tMaitMSitEaGaRo5jWukF4ZPwHBP6v6k06E2eMS5TcXTkNGRtrhwqBZcBL7Gqa8nZVE3QyyNE6ufBVyIE0S9HDgX8GUc9h5r7ZcrXNdrgA8DvwsU+s6ZBL4L3Gyt3TWT19EQSxERERGZCU2gPj2aU2t6ih3GG3AH+Off/mensy0eHr/PfFxg31iANxofJZKIMBofJWmTFfjqHJlDTLM62ooJ2/KEd1977mv85ND9JCm8iqULN1tXX8Z7znnPxEBpkjBpwnF5AqnRWJRQNMJILMpoLMJoLEokESOSiKauGSdh4yRsDEwSQwJMHEzC2TbzK4NwGRfNvmZafC20+lpp9bfS4muhxRukBTetGFoTcSdQi4ZpCQ/ROnqa1pF+/MM9Tqg2nZVAfc0ZodnC7PAsM1QLLgKPb+rrzcap/fCLz8KTd0J02FmR9CjmtpwAACAASURBVNyr4JXXz9sVP+t6DjJjzJXA9cDLgKYpDq9YQGaMcQP/DnxgGqdZ4DPAh62d3nd0BWQiIiIiMlO1supnpShULN5ND99UVMddKecgs9aODyMtFLJNErqNhWxjj8PxMKOJ0QlB3Njzks1Z99UD1oO1LmzSTTLpcrZxgXWnnxt7jHVhU489Lg8Bj5cGr59Gr4+gz0+Tz09zIECL309LIEBbQwNtDQH8Hh+ffvjTRJPRKevyGA+XrLyEwcggg9FBBiIDDEQHigpwC/G7/bT6WmnxBWlxN9BqvLTgotVCSzxGayxCa3SUltFBWkdO0zLSR2ssQlMySVFLBTS053SkFZg7Ldg5/cUH9t3Hwf/Zzm1BP99rbkyv/PmGoRG2hyKsuvI2WHfpTP5YqqreA7LPAH9W5OGVDMh2AO/O2R0DHgcO4HSTrQY2M3Eo7K3W2g9O5/UUkImIiIiIVI5CxeLUesfd2Dxyk3W7pQO3VLfbaCInfMsI3Ebjozze83jRr7+hfQNelxePy4PX7cXr8o5v5z52T/JcKa7h9uIxHtx5gppwLEFfKJqaIy1ccIjnyaEIkXjxvSILgj5ibf+DbX7YGX5agLUulrku4dbX34Tf4yLgdeP3uvB7XBiTYDg2zGBkkIHoAAORgXSANhgddPbn7BsL1+LJwsHvZAyGJk+AVleAFuNxutSSSSdUi4ZpiYRoDQ/SkkjQkkzSmkg698kkAWuZMNuZcaUWH8gY0pk3VFvshG79L/Lgf72GD3c2F1z58196h3j1ex+Yd51kCsjyB2QDwDDQlbGvIgGZMeYtwN05u3cAf2mtPZ5z7HLgZuAdOcdfaq39UbGvqYBMRERERETmInXcTU+xw1KbvE089M6HKlBR5VhrGYrEs8OzAnOm9Q1HSFow3j6Caz+Td8629HWTXkL7b8DGOiY8Zwz4PS78Hvd4eOZx4fe6CHjGgjQ3Ae/4MWM3tzsG7lGSZoSkK0ScEAlGiNphoslhIslhwslhRuNDhOJDjMSHGIoNEooNz/jPyIeLFuN2utQSCVrjUVqi4XSA1pJw7luTyaxwrTmZdDpzXF4Oev1cuaRtypU/b2t6JRvf9oUZ11oNWsUSRoE9wKMZt73Ax1O3SvuLnO0vWWvfm+9Aa+1h4GpjTBy4JuOpvwaKDshERERERETmoi3Lt3D3m+5Wx12RSrkQxHxjjKEl4KUl4OWMhZPPoJRIWk6Folz46R8xevgaGpbfDiaR1Uk2Noxz9PA12FgHaxcGicSSROIJIrEk4XiCWMISjiUJx2Y7b50LaE7dplqVMwHuMG73KD5fBJ8vjNcbxu0dxeUexeUJg2sEXKNYV4gEIeKMECdElBi9NkkvgBtwe8A/1WxTjuakpSWRIGQM4SJW/vzm8QeqEqiUSz10kC0E+q2d+N3DGPMJsgOysneQGWPagV7GJ+SPAF3W2r4pzlsEdDO+qEAMWGCtLSpaVgeZiIiIiIjI/Ffrw1JL7ZyP38twJI7x9uFbsAtv625wRSHpIzawmeipLdhYB01+D09/cuuE8xNJSzSeJBxLEImnwrPM7Vj2c+FYgWNSoduE5+JJIunjEoQztuPJGeQ1JoZxj2DcoxiXc497dHxfxn6TsR9XeNoLKASTSR5+zzPTr7GK6rqDzFp7sto15FhJ9mqVv5wqHAOw1vYYYx4BXpXa5U1d69nSlygiIiIiIiJz0YqWFdzy6lumHJaqcMxx+eZl3PFIN/FYB5ETlxM5cfmEYzwuw1s2d+U5G9wuQ4PPTYNvmpPcl0A8kUyHaFkBXSw7YMsb0GWFchkhXG5AN+ocF47FiSRDRBLDeFb9ozO2dAojRRwzn9R8QDYHBXO2D0/j3O6c7bZZ1iIiIiIiIiLzjIalFu/9W9Zy12NHiCcTBY/xul28b8uaClZVHI/bhcftIuiv7Ou+7L9vJjrJogZjvLbyoWE5KSCrvBM52w3TODf32P5Z1iIiIiIiIiLz0IqWFXz0oo/y0Ys+Wu1S5rRVHUFuveZ8rrv9cWKJ7GGLHpfB63Zx6zXns6ojt5elfq31voIX4r/IWr0yl8da1novqmBV5Vd4SQIpl/1A5kqVm6Zx7uaMx8M4Cw2IiIiIiIiISAGXbFjED27YwtUXrqTJ78EYaPJ7uPrClfzghi1csmFRtUucUz702x8iaSfvp0paDx/67RsrVFFlqIOswqy11hjzBeBvU7vWGGN+11r7/cnOM8a8CViVsesb1trCPaIiIiIiIiIiAjidZJ+6/Bw+dfk51S5lzrto5Qb+8KxP8f+e+ztcJk4yo5HMZZ1w7A/P+nsuWrmhekWWgTrIquMfgecytr9kjNlY6GBjzHnAlzJ2DQKfKE9pIiIiIiIiIlLPPnjRm/jC6+5glf9SbDKAtQabDLDKfylfeN0dfPCiN1W7xJJTB1kVWGtHjDGXAv8LnA8sAR41xnwJ2AkcTB26Bngz8PtAILVvGLjCWjudyf1FRERERERERIp20coN/O/KW6pdRsUoIKsSa+0RY8xvAe8F/gg4D7g+dSvk+8CfW2ufr0CJIiIiIiIiIiJ1QUMsq8uVukUAO8WxtwMfnk44Zoz5Q2PMr4wxvzp58uQsyhQRERERERERqV0KyKrEGHMh8AzwWeBCoPD6qY5rgKeNMV8zxiwo5jWstf9prb3AWnvBwoULZ1ewiIiIiIiIiEiNUkBWBcaYi4CfAGszdv8a+GNgPdAIBIENwHXA3rFTgauBXxpjllWsYBERERERERGRGqaArMKMMUHgDpwAbMxXgU3W2v+w1u6z1o5aa0estXuttZ/HmZ/sjozjzwS+YYzR35+IiIiIiIiIyCwpYKm89wCrMrafAN5jrY0UOsFaGwa2A09l7H4V8NayVCgiIiIiIiIiUkcUkFVebqj1L9ba2FQnWWujwM05u68pWVUiIiIiIiIiInVKAVnlvTRn+/5pnPtAzvbLZ1eKiIiIiIiIiIgoIKu81pztnmmceyJnu3OWtYiIiIiIiIiI1D0FZJU3mLMdzHtUfrnHDs+yFhERERERERGRuqeArPKO5myfO41zN+Vs53aUiYiIiIiIiIjINCkgq7xdOdvvmca5ucf+fJa1iIiIiIiIiIjUPQVks2SMeY0xxmbcDkxxyl0529caY95cxOu8BXhXzu57plGqiIiIiIiIiIjk4al2AZVgjFld4Km2nO3OAseGrbXHS1GLtfbHxpifAr+d2uUC7jLGfAr4jLU2a44yY0wrcCPwN2QHmo9Ya3eWoiYRERERERERkXpWFwEZ8GKRx92cuuV6EHhNyaqBa4GHgSWpbQ/wSeBvjDG/Ag4DBlgOXAD4cs4/CVxTwnpEREREREREROpWvQRkc4q19qAx5hLgDuC8jKf8wMVTnP4McLW1dl+56hMRERERERERqSeag6xKrLW/Bi4E/hh4sohTngH+BLjAWvtUOWsTEREREREREaknddFBZq01Zbz2AzjDIWdybhT4D+A/jDGLcQKzLsbnRhsAjgCPWmuPzb5aERERERERERHJZay11a5BKsAYcxI4WO06SqAT6K12EVLT9B6TctN7TMpN7zEpN73HpNz0HpNy03usfq2y1i7M94QCMplXjDG/stZeUO06pHbpPSblpveYlJveY1Jueo9Juek9JuWm95jkoznIRERERERERESkrikgExERERERERGRuqaATOab/6x2AVLz9B6TctN7TMpN7zEpN73HpNz0HpNy03tMJtAcZCIiIiIiIiIiUtfUQSYiIiIiIiIiInXNU+0CRESqwRizCDgLWImzzHMjEAFOA/uAx621Q9WrUEREZO4wxqwDXg4sBALAUeAA8JC1Nl7F0kRESsYY4wVeAqwBlgPNOLnJANADPA78xmooXk3SEEsRqQupf+xuAF4FvAJYPMUpSeAHwP+11v6wzOWJiIjMOcYYD/Ae4CPAmQUOOwl8FfiEtXagUrWJSP0xxrhwfsF9IU5g/3LgXMCXcdh7rLVfnuZ1LwLeBVwMnJ1zvXyOAF8CPmOtPTWd15K5TQGZzDnl+sYn9c0Y0wb0z/D0O4D3WWtDJSxJapAx5svA9hme/oy19pwSliM1yBjzAPDqUlzLWmtKcR2pTcaYpcD/AhcUecoh4J3W2p+XryqZyyrxM7wxpj3j2mOvszTjkIPW2tUzvb7MTcaYK4HrgZcBTVMcPpOA7DPAn82gtBPAH1hrvzeDc2UO0hBLmTOm+Y1PpBR6gL04v/0O4bzvzgA2Au6M494BLDXGbLXWRipepYhI6YWrXYDMXcaYxcBDwKqcp44Cj+H8m7kaJ6AYm9N4JfB9Y8zF1tqnKlSqzAHl/hneGBMA/gvn/Vaok1Fq26so0S+HihQFfgO8iDO00oUzJct5qfsxi4F7jDFvtdZ+p4L1SZkoIJO5pNLf+KT+9AI7cYZO7rLWHs13kDFmCfDnwIcYD8peDfwN8PEK1CkiUm73VLsAmZtSXUBfJzsc6wWuA/4nc94dY8xy4LPA76V2NQM7jTHnaB7PulLun+EDwDvLeH2ZvwaAYaBrltdJAg8D3wYeBH5lrY3lHpT6/ngp8K84QzEBvMB/G2NeYq3tnWUdUmUKyGQ+KNU3PqlvA8ASa21iqgOttceBvzTGPAncnvHUh4wx/2itHS1XkVJz1kzj2GjZqpBa8g6cD4vTYYBf4kyuPua2klUkteYK4JKM7WHgtfm6wqy1h40xV+BMRfC21O6VwI3AJ8tdqMx55f4Z3gLP40yoLrVvFNgDPJpx24vzy+vZ/gL7I8UsNmKtTQL3GmN+gROkbU491QF8APjULOuQKlNAJnNNOb/xSR1L/cZ7ynAs55yvGmPey/gHhSDwWuC7JS5PapS19kC1a5Dakgrwp8UY81qyw7GjwH0lK0pqzYdztv9usiGT1tqkMeaPgNcBC1K7P2SM+Tdr7Uzn/pT5pxI/wx9KXfeR1P1j1tpBY4wm1a59nwY+nC/EMmb202lOdyVea+2QMeYGnJBszJtRQDbvKSCTuaSs3/hEZuhesn+TvrZahYiIzFDuwhG3F9NNK/XHGNOBM8/TmBDwxanOs9b2G2Nuw5meAJyhlr8HfLnUNcqcVO6f4YeAxdbanlJcTOYfa+3JateQx8+AEaAxta3PCDXANfUhIpVhrT053fRepAJyf/utBSREZN4wxjQBb83ZreGVUsjFOENyxzw8jbnEfpSz/ZbSlCRzXbl/hrfWJhSOyVyTGm45kLFLnxFqgAIyEZHJrcjZPlaVKkREZuatOMPDx/zKWvtstYqROW9ZzvZz0zg39311qTHGnfdIEZF5zhjTQPaKlvqMUAMUkImIFGCM8QJX5ezeVY1aRERmKHd4pbrHZDILcrYH8h6VX+6xDUxvoRIRkfnkbTgrWI75abUKkdJRQCYikocxxgN8DlifsXuntfY3VSpJRGRajDErgddk7IoCX6tONTJPRHK2/dM4N9/qqmfNohYRkTnJGLMZuCVjlwX+vUrlSAlpkn4RkRRjTBBYBfw28EHgnIynj6f2iRTNGPNvwCtx3letOB0WJ4FfAfcD37TWDlevQqlx7yZ7Pqmd1tpT1SpG5oXceTeXTOPcfMeum0UtIiJzQuoX5wuAc4ErgT8gu3vs/7PWPlqN2qS0FJCJSN0yxhwHFhdx6B7g7dbaQ2UuSWrPn+Rsd6ZuZ+GEFzcbY24Gbk5N9ipSStfmbGt4pUzl1znbF+Y9Kr+X59nXOotaRESqwhjzx8Dnizg0BnzCWvsPZS5JKkRDLEVECnsUuBq4wFq7t9rFSE3qAP4RuNcY017tYqR2GGN+i+wh4ieB71epHJk/fgWEM7bXG2POL/Lcq/Ps06puIlKLwjhDLNcrHKstCshERAq7ALge2FbtQmTeeRb4Z+DtwMtwhhltAt4M/B8mDmP6HeCuVAu/SCnkTs7/VWttrCqVyLxhrY0C9+Ts/uepzjPGvIHs+e7GKCATkVoUAH4f+FNjTDGjUWSeUEAmIvXs5TgrbK0BzgDOx1mR5vPAEM7cPRcD9xhjvm6Mmc5kxVKffoDTcXi2tfYvrbV3Wmsft9a+YK19wlr7HWvtjThzku3IOfcS4G8rXrHUnNT3qrfn7NbwSinWv+BMOD3mdcaYzxpj3PkONsa8HLi9wLVsgf0iInPZVxn/jLAGZ2qM3wb+HHgkdcyC1PbTxpjXVqNIKT0FZCJSt6y13dbaA6nbfmvtbmvt/1hrr8P5x/A7GYe/A+cfS5GCrLV3WGsfK+K4IWvtduA/cp76c2NMR3mqkzryZqAtY/tJa+2eahUj80vqe9gtObs/CDxmjHm/MWazMWaDMeZSY8zngV8AY0PED+ecd7rM5YqIlFzq57QDGbdfW2t3WWs/Y619Bc5E/UOpwzuBncaY6czZKHOUAjIRkTystX3AFcCPM3a/1RjzjiqVJLXpT4GDGdvNOGGsyGzkDq/8cjWKkHntr4Fv5ew7D/hP4HGcyfx/CPwx44t+7QC+mXOOAjIRqTnW2ruAtzDeJdsAfNkYo3xlntNfoIhIAdbaOBNXIbyxGrVIbUrN9/PvObt/pxq1SG1IzYWyNWNXHHW/yjSl5qu7EvgkMDLF4XHgE8Af4IT8mU6UvDgRkTnAWvtj4M6MXWcBr69SOVIiCshERCZhrX0OeDpj1wVabVBK7Ec52y+tShVSK97FeEcPwA+stT3VKkbmL2tt0lr7CeBM4K+A+4EjOKu3nQL2AP8EnG2t/aS1NgF05Vxmd+UqFhGpuNyu2UurUoWUjFbLEhGZ2j7gnNRjA6xm4iqEIjN1IGe7sxpFSM3IHV6pyfllVqy1x3CCsH8q4vDMgD9M9i+YRERqzb6c7bVVqUJKRh1kIiJTi+VsazVLKaXRnO2GqlQh854x5jzg3Ixdp4D/rVI5UmeMMcuA5Rm7HkpNVSAiUqv0GaHGKCATEZla7pARDVeSUsrtGOurShVSC3K7x+5IzXMnUglvz9n+cjWKEBGpIH1GqDEKyEREJmGMaQZenrErjDMHi0ipvDxn+2hVqpB5zRjjwZl/LJOGV0pFGGN8wAcydg0C/1OlckREKuV1Odu/qUoVUjIKyEREJvcXgC9j+8fW2ki1ipGalNt18dOqVCHz3euBRRnbz1lrH6lWMVJ3PgKsy9j+J2vtVKtfiojMW8aYDuCPcnZ/txq1SOkoIBORumCM+ZAxpmma51wF/E3O7v8oXVVS74wxFzIxINMPVzITmpxfSibVkVjssX8A/H3GrmeBm0telIhIGRhjLjXGvGqa57QA3wYyV7Z/LHWTeUwBmYjUi78FXjTGfMYYc9FkP/wbY843xnwF+Abgznjqu9ba75S7UJmfjDHvTw3JLfb4jcC3yP63+GFr7Y9LXpzUNGNMO/CmjF1J4PYqlSO14d+MMV83xmwzxgTyHWCMOccYcyfwXzgrPIOz6MjvW2tzJ64WEZkVY8zqfDegLefQzgLHLilw6bOBXcaYnxhj3muMWTxJDc3GmPcBzwAXZzyVAK631tqZf4UyFxT92yGRSkh9k8sn7ze+PMeFrbXHS1iS1JZO4M9St7Ax5hngOHAaZxjlApwV4BbmOfcR4OoK1Snz00eBfzTGfBW4A3gk3wpuqTDjj3G6EzO7GiM4702R6Xo72Stn/chaq7kSZTZ8wDtSt2jq38uDwAjQAawH1uScEwbeZq19tJKFytxQ7p/hjTFtea6Vj2eSWo5ba8NFXEPmpheLPO5m8nexPgi8ZpLzLkndMMYcBp7H+YwQBpqB1cBGJmYoSeDd1tqHi6xP5jCjkFPmEmPMbN+QD1prX1OKWqS2GGNOA60zONUC/w/4iLV2uLRVSS0xxhwAVmXsCgNP44SwA0Bj6vnzyO5MBOc3j++01t5Z/kql1hhjHgIuytj1Tmvt16tVj8x/xpgvAu+dxim/wXnfad67OlXun+GNMZ8APj7L17jEWvvALK8hVVKu95gx5gbg/8zwmvuBP1T3f+1QB5mI1Iu3Am/GWW3mLKYeYt4L3An8p7X2iTLXJrUpAFxQxHHdOB8sf1bmeqQGGWPWkx2ODQL3VKkcqR3fwgn0t5DdnZjrBeBW4FYtYCMi89TXgRjwBuCVTN2pmAQeBr4EfM1aO1re8qSSFJCJSF1I/Wbnx5CeWPMcnOEhi3A6e+I4XT4ngT3WWi3TLNP1KZx5oC7GGc47GQs8idOduEOrvcks5E7Of6d+WJfZstZ+F/iuMcYPbMJZoXIJ0IAzz9gR4DFr7d7qVSki9cRaa6Y+akbXPQF8DvicMcbgfL9bB6wAWnCGnA/hfE74DfC4tTZUjlqk+jTEUkREpMSMMSuADTg/XHXgdJOFgX6cD5a/tNb2V69CERERERHJpIBMRERERERERETq2lRz8IiIiIiIiIiIiNQ0BWQiIiIiIiIiIlLXFJCJiIiIiIiIiEhdU0AmIiIiIiIiIiJ1TQGZiIiIiIiIiIjUNQVkIiIiIiIiIiJS1xSQiYiIiIiIiIhIXVNAJiIiIiIiIiIidU0BmYiIiIiIiIiI1DUFZCIiIiIiIiIiUtcUkImIiIiIiIiISF1TQCYiIiIiRTPG2Izbl6tdj1SWMeZAxt//A9WuR0REpFQUkImIiNQYY8zqnBCjHLdPVPvrrBRjzAN5vv5fzuA6n8i5xrYS11nKv/fLS1mbiIiIyFyngExERERk+i40xry52kVIeRhjXpMTGP5+tWsSERGR8lJAJiIiIjIzf2+MMdUuQkRERERmz1PtAkRERKTkDgNrijz2DuAVGdtXAw8Xcd7p6RZVg84D3gbcWe1CCjgCvGqG5/aUshARERGRuU4BmYiISI2x1saBA8Uca4wJ5+w6bq0t6lwB4JPGmLustYlqF5JHXH+XIiIiIsXREEsRERGR6flOxuOXANf8/+3debyk053H8c83GkHbadoyeuxCDNraBm2JyTBGE1skNEIyEZkgMcYysYVgEoYkJPYwdDeR4JXXEElIW4ZuYgkdW1uaYNq+ttDNb/44T+mnnn6q6ql761Z1u9/363VfrzqnznPOqTr13L7167P0qiNmZmZm1hkOkJmZmZm15wRgVj4taf5edcbMzMzM+s9LLM3MzKxPsqDQKGBVYFlS0Ogl4OGIeKDDbQ0FtgJWBpYCpgN/BiZHRHSyrQqeAC4HDsrSfwt8Bfhpl/sxz5K0FrABMAwYCrwCTANuj4j3OlD/fMBIYA3SZ3MR4G3S0uMHI2Jaf9voD0krApsAw0mf51eBcRHxZpNrFiPdAysCS5Nez3RgUkQ824E+LQ2MBlYC5iftZTglIh7qb90lbQ3I+OfqXT6rdxbwDvAc6b59tAe/L8zMbB7hAJmZmZm1RdIKwEnAXsBiDcq8APwM+EFEzKhQ52jg1lzWgRFxmaTlgFNIhwcMLbl0mqSTIuLS9l5Fv51MWlq5QJY+TtKlEfF+l/sxz5C0EHA4cAiND5H4q6RfAsf1Zf80SasDxwNjgMWblJsKXAOcHxHP5fKfAVYpueRSSY0+YxMjYnSh/suAsbV0RCjL35L02RnNnCs57gbmCCxL2ph0D2xPClyVvZ6HgNNJQba2AkCSVgbOBnal5LuBpPuBMyJiQjv1ltQzIOOfBer/Ffg6sFqL4m9K+j3w04j4baWOm5nZoOEllmZmZlaZpD1JMzEOpkFwLFMLoj0qaf0+trU+KWBwCOXBMUjBjEskXSdpgQZlOi6bgXRRLmsl0hd0KyFpFDAVOI3mJ6x+GtiX9Ln5Yhv1S9IpwCOkwFTD4FhmdeAY4NtV2+gvSUcBE4HtqPA3ePaazgQmA5+nQXAs81ngSuBWSUu20afPAVOAL9D4P843BMZL+knVekvaGZDxl7QsMAn4Aa2DY5A+F7sD36hQ1szMBhnPIDMzM7NKJI0FLmHOL/f3A0+SvsCvR/0X1ZWB2yTtEBH3ttHcssCNpKVSkJZJ3UVaijUM2AJYOFd+V2CCpN27uITqe8CBwEJZ+hhJF0bEu11qf54gaRfgalLwI+9R4HHS2C4HbMbsQOiCwJWShkTEFS3qnw8YD+xR8vRjpMDMm6SA7hrAmoD69GL6SNLewJm5rCdJS4RnkILJm5ZcdiFp6W7eB6SZZi8ASwAbA8vknt+GdL9tHRGvt+jTKOB6Zn9+a6aQ3jeRDqFYJ8s/VNLzzeps0M6AjL8kAb8kBfDypgMPkX5XfEQKiq1GCor6u4+ZmTXkfyTMzMysJUlrA+dTHxz7HXBoRDxRKLsNcAEpEAHpC+o4SRu0ETw6lhQAmAmcCJyd35tI0iLAvwHHAfNl2WOAr5KWdg64iHhR0nnMnoU0jLTU6/vdaH9eIGkN4CrqgyOXAN+LiKcLZRcEDiPNMlqAFKA5X9KkiHi8STMnUx8cC+DSsjaydpYCdgO+VlLX35P+Pt4cGJfLPwr4RYP2/9qkbzW12YaTgW9GxORCn5YmBb9q6bHUB8cCOBc4MSLeyJUbQpoxdxazZ3SuB5xHWpZcStLCwBXUB8fuAw6OiPsLZTcmBes2IN2LH1DRAI//TqTxqpkK/AtwS1mQPPudsQPpfZmv+LyZmZkDZGZmZlbFj6n/Mv0rYM+I+LBYMCImZnst3QGslWWvTgp6HVexvSVIQYEvR8TVJW28Szo98hnSF+6aMySNi4i3KrbTX6eTAi21mS/fkXRes83Wu2iIpBF9uG5GRLzUoT5cTv3y2IMj4uKygtn+bT+U9CBwEymIsQhwKrBn2TWSNiEtlayZBYyNiKsadSgiXgMuBi7O9rjLP/eXrN4Rhcte6cueaDlDSXvs7Vy2CX1EvFp7nB1IcU6hyLcj4uyS62aRXsfDwC3MnlW5j6RLmuyzdRTpcI2aycB2ZQHsiLhX0tZZ/0fSfKln0UCO/z/lHs8CdiwLiObqf5c0Y+56ScXZbGZmZt6DzMzMzJqTtC5pg/Ca6aRN9OcIjtVExCvAfqQlTjVfbfOL6eVlwbFCO5cC1+ayFidtvTGhWAAAC0lJREFUnt8V2ev8r1zWUsCR3Wq/hRWBp/vwc0EnGpe0LWkmVs35jYIjeRHxO9Km8TW7SfqbBsWPpX655KnNgmMlbU2vWrafZpACd1VOaCzuoXZTWXAsLyImAd8tZH+rrGw26yw/e+4DYL9mszsj4m3S/TyzWT8K7Qz0+OfzHmgWHCtpo8qsPzMzG2QcIDMzM7NWvlRIn1VlhlRE3APckMtahrTZeFWnVCx3ciFd7O9A+yHwRi59RLZkbrDLH1owi3RoQ1U/yj2ej5LPjaRhpL3naqaTlufNja7On5bZQvHze0LF684FXs6ld8qWkxZtBwwv9K3ZElYAIuIRGi8zLTOg41+wbBt1m5mZlXKAzMzMzFoZVUiPKy1Vrjibp1hXI/dFxJNVCkbEn0gbfteM7PKJlm+QTtGrWRQ4ulvtz8VG5x7f0c5srYh4FpiWy9qypNg21M8euzwiKu+P1WU3tC7y8T5cI3NZTxX3K2skImYC1+Sro34GV80WhXTTWZoFE9ooOzr3eCDG/7Hc41Uk+WRKMzPrF+9BZmZmZq3kv7C/2MZMGEgn7jWqq5l72mijVn7t7PGCwLqk0zW75RzSkrbaTJZvSDorIv6vi30omhYRI3rRsKQ1qZ/VM60P+6G9DqySPS67drNC+vY26++mByqWW4+0QX3NpDbbuRs4NJceCfxPocxGhXQ791qlsl0a//HAEbn0jyWNIR3QcGOrUzzNzMyKHCAzMzOzhrI9w/KbbD/RqGyZiHhO0nvM3uC/6lKoSrPHcqYW0sPavL5fIuIdSaeTlltC2iz9OOCb3ezHXGSlQnps9tNXZUsFly+kH+lH/QPt5dZFgDnvj7buN+pnVZXVB5A/mGBGO0HciHihcD83MuDjHxGTs1Nk8wHBHbKfjyQ9BNwJ3Abc2sGDJ8zM7BPKSyzNzMysmSUK6b6cDpnfr2zJite0205xT7Riv7vhPOCFXPoQSSv3oB9zg7KAVn8MLckr7vP2RkmZuUJEvFOxaH/vt+J9UHa/5dvo7/3cSDfGH+AwUiB6RiH/U8DfkYJn44EXJd0qaQ9JwszMrIQDZGZmZmYdkJ2Md2oua0HmPFlwsJi/w/VVCWpEh9u0vuvK+EdyGrAqad+/u0gHAhR9irQn2jXAREnDS8qYmdkg5wCZmZmZNVOclbNYH+pYPPe46r5A7bazeCHdq9lEFwHP5NIHSFq9R33ppdcK6TMjQv34GVGhjV7MGuy0/t5vxfug7H7Lt9Hf+7mRboz/xyJiekScGRGjSLPmtgdOBCYyZ8BsK+Cm7EAEMzOzjzlAZmZmZg1ls6Lyy8PaCvZIWon6/Yqq7sW0WjvtMGe/erLfUHaK4sm5rCGkL+qDTfH9X2YA2ijunbXOALTRbcX7o93g6pot6gPInya5sKTiXm4NSVqB1vuPQXfGv1REvBMRt0TESRExGhgOHA+8lyu2PnBQt/pkZmbzBgfIzMzMrJU/5h6vkAW9qtq8SV3NbNJGG8Xy7wNT2ry+ky4HHs+lvyjpM73qTI88DLybSxdPnOyEuwrprTpYd6+Waz4MfJBLb9rm9VXut/sK6XbutapluzH+lUTEKxFxKnBI4aldetEfMzObezlAZmZmZq38byG9dxvX7ltIF4MajWxUdWmipPWBtXNZf8xmcvVERHwInJDL+hT1s8o+8SJiJml5W826ktbtcDMTgY9y6f0ldWrvq/cL6QU6VG9TEfE+9UGt1SWNrHKtpCHAnvnqgEklRYv34F5tdLHSvd+l8W/XBOrHdUSP+mFmZnMpB8jMzMyslasK6SMkNTpV7mOSNgJ2zWW9CtzYRrvHVyxX3Aj/yjbaGCgTgIdy6d2BjXrUl175WSF9RidPEIyIl4HrclnLA//eoeqLJzVWXobYAcX7repBD4cBw3LpGyPi1ZJytwAv5tJ7SSouzZyDpLWBPSr2BQZ4/NsVEbOon9XWsyC6mZnNnRwgMzMzs6Yi4mHg1lzWisCFkhr+HSFpaeC/qf9b48JsT7Oq9pe0Z7MCkg4AvpDLejNrt6ciIqgPbIhBtqQrIm6gfjnfzsDZkuarWoekIZL2zWZHlfk+9cshvyup8gxHScs1eOop6jd337ZqnR3wc+oDdP8s6dBmF0jaBDilkH1uWdksUHRBLmsB4ApJizSpfyjpvqo8Q2+gx1/S1yUt20Zd/wgslct6rOq1ZmY2ODhAZmZmZlUcBuSDW/sAv5a0arGgpK2BO6nfNP0p4NQ22nuDFFS6UtKxkuo2Bpe0iKQTSadG5h0dEW+10c6AiYjrgHt62IUhkkb08WdY6+or+RKQH49vAbdL+nyjQEkWFNlc0hnA06QZgaUBsoi4FzgtlzUEGCfpAkkjGtS/lKSDJE0GjmlQ7/vUL08cLekiSdtLWqPwXnV0dllEvA0cWcj+kaT/lFR3gmT2Xh0I/BbIz+q8OiJ+06SZM0n3ZM2mwERJGxYLStoYuA0YCcykfhZWKwM5/kcDz0q6UtIYSYs2qW9/YFzhqZ4H0s3MbO6i9B+cZmZmNhhJ+gOwTS5r24j4Q4OyB5ECUvllUkGaJfIUaXbJesx58t5bwOciYnKTfoymfpba0cDhpBPoAN4m7Z30KrAssAVQnPFyHbB7dPiPm5L3aNGIeKdB8eK1/wDc1ODpXSLi1/3sXr6tEaSAQidcHxFjGrSTf39/HhEHtOjXjsC11AdwII3pfaQTD2cCiwMrAJ8BFiyUXajR7MMs0DKB+pmENY8AU0mfwcWANUgnPdb+k/iciDi8Qb17A+ObvbbMxOy0xPy1lwFja+mIaHtpYbGOzPuk++BFYAnSpvnFEyL/DGwVEa+1qH9LUmCteCrlw6TZVSLt7Zc/YOI/gIOBVbL0HK+9pJ0BGX9Jz+T6Ael30eOke+D1LG84sGFWd951EbFbs36bmdng02i6upmZmVmdiLhE0gzgEmZ/qRZpZkmjjcSfJwWC7m+zuZeAnYCbSQGxRYEdm5S/Adi708Gx/oqI30i6nc6esDhPiYibJW0OXEP9rMJFqQ88NvIW9ZvxF+v/UNJepOWW36F+hcQ6hTYri4gJkjYDjujL9R1wIPAaKVBcC7AtCIxucs0dwK6tgmMAEXGnpDHAL0hjUbNe9lN0AWkW6MEte17fzoCOf46AtbKfZiYwZ+DRzMzMSyzNzMysuogYT5qFczFpBkgjLwInAWv1IThWa+sBYAPgUmBGg2LTgK9ExK69PLmyhaqHDXxiRcQU4LPAfqRlp60CHm8AvwL2B4a3GtuI+Cgijs7amEDrZYCPkT6fZ7So90hgM+AnWb9fpUubu0dyJLA5KVA8q0nxKaT3ausqwbFcGzeTgmHXNqn/T8CXI+JrfQ1AD9D470oaw7tJM9Ca+Qj4PbBzROyTLaE1MzOr4yWWZmZm1ieS5ge2BFYlzfKaRZr5NQW4v50v0yVLLA+MiMtyzy8KbA2sDCyZa2fS3DZrzFqTtCQwirQEbmnSf9q+BbxAWhb5RER82I/6F8jqH0H6bM6f1f808GBE/KU//e+FbP+xrUmHZCwFvANMJ90Dz3Sg/mVIhxGsRFpl8jwwJSIe7G/dJW11dPwlfZrZy7uXJy3nnEk67GAqcF87gUMzMxucHCAzMzOznmsVIDMzMzMzG0heYmlmZmZmZmZmZoOaA2RmZmZmZmZmZjaoOUBmZmZmZmZmZmaDmgNkZmZmZmZmZmY2qDlAZmZmZmZmZmZmg5oDZGZmZmZmZmZmNqg5QGZmZmZmZmZmZoOaIqLXfTAzMzMzMzMzM+sZzyAzMzMzMzMzM7NBzQEyMzMzMzMzMzMb1BwgMzMzMzMzMzOzQc0BMjMzMzMzMzMzG9QcIDMzMzMzMzMzs0Ht/wHHp2PxsO+d4wAAAABJRU5ErkJggg==\n","text/plain":["
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\n","text/plain":["
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\n","text/plain":["
"]},"metadata":{"tags":[],"needs_background":"light"}},{"output_type":"display_data","data":{"text/plain":["
"]},"metadata":{"tags":[]}}]},{"cell_type":"code","metadata":{"id":"BVssYoX6nnQ8"},"source":[""],"execution_count":null,"outputs":[]}]} \ No newline at end of file