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creating_vector.py

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+import csv
+from collections import defaultdict
+import numpy as np
+from scipy.signal import *
+from numpy.fft import * 
+from scipy import *
+from pylab import *
+import pywt
+fout_data = open("train.csv",'a')
+vec = []
+chan = ['Fp1','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']
+columns = defaultdict(list) # each value in each column is appended to a list
+
+with open("features_raw.csv") as f:
+    reader = csv.DictReader(f) # read rows into a dictionary format
+    for row in reader: # read a row as {column1: value1, column2: value2,...}
+        for (k,v) in row.items(): # go over each column name and value 
+            columns[k].append(v) # append the value into the appropriate list
+                                 # based on column name k
+for i in chan:
+	x = np.array(columns[i]).astype(np.float)
+	coeffs = pywt.wavedec(x, 'db4', level=6)
+	cA2, cD1, cD2,cD3,cD4,cD5,cD6 = coeffs
+	cD5 = np.std(cD5)
+	cD4 = np.std(cD4)
+	cD3 = np.std(cD3)
+	cD2 = np.std(cD2)
+	cD1 = np.std(cD1)
+	if i =="O2":
+		fout_data.write(str(cD5)+",")
+		fout_data.write(str(cD4)+",")
+		fout_data.write(str(cD3)+",")
+		fout_data.write(str(cD2)+",")
+		fout_data.write(str(cD1))
+	else:
+		fout_data.write(str(cD5)+",")
+		fout_data.write(str(cD4)+",")
+		fout_data.write(str(cD3)+",")
+		fout_data.write(str(cD2)+",")
+		fout_data.write(str(cD1)+",")
+fout_data.write("\n")
+fout_data.close()

load_deap.py

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+import cPickle
+import os
+from multiprocessing import Pool
+import sys
+import numpy as np
+chan = ['Fp1','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']
+nLabel, nTrial, nUser, nChannel, nTime  = 4, 40, 32, 32, 8064
+print "Program started \n"
+fout_labels0 = open("labels_0.dat",'w')
+fout_labels1 = open("labels_1.dat",'w')
+fout_labels2 = open("labels_2.dat",'w')
+fout_labels3 = open("labels_3.dat",'w')
+for i in range(nUser):#4, 40, 32, 32, 8064
+	if i < 10:
+		name = '%0*d' % (2,i+1)
+	else:
+		name = i+1
+	fname = "/data_preprocessed_python/data_preprocessed_python/s"+str(name)+".dat"
+	x = cPickle.load(open(fname, 'rb'))
+	print fname
+	for tr in range(nTrial):
+		fout_data = open("features_raw.csv",'w')
+		for ch in chan:
+			fout_data.write(ch+",")
+		fout_data.write("\n")
+		for dat in range(nTime):
+			for ch in range(nChannel):
+				if ch <32:
+					if ch == 31:
+						fout_data.write(str(x['data'][tr][ch][dat]));	
+					else:					
+						fout_data.write(str(x['data'][tr][ch][dat])+",");
+			fout_data.write("\n");
+		fout_labels0.write(str(x['labels'][tr][0]) + "\n");
+		fout_labels1.write(str(x['labels'][tr][1]) + "\n");
+		fout_labels2.write(str(x['labels'][tr][2]) + "\n");
+		fout_labels3.write(str(x['labels'][tr][3]) + "\n");
+		fout_data.close()
+		os.system('python creating_vector.py')
+		print "user "+ str(i) +" trail"+ str(tr)
+fout_labels0.close()
+fout_labels1.close()
+fout_labels2.close()
+fout_labels3.close()
+print "\n"+"Print Successful"

svm_clas.py

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+from sklearn import svm
+import numpy as np
+train_y = []
+train_a = []
+train_x = np.genfromtxt('train.csv',delimiter=',')
+f = open("labels_0.dat","r")
+for i in f:
+	train_y.append(i)
+train_y = np.array(train_y).astype(np.float)
+train_y = train_y.astype(np.int)
+train_x = np.array(train_x)
+#print "valence",train_y
+#print train_x
+#print "train_x",train_x
+clf = svm.SVC()
+clf.fit(train_x, train_y)
+
+
+f = open("labels_1.dat","r")
+for i in f:
+	train_a.append(i)
+train_a = np.array(train_a).astype(np.float)
+train_a = train_a.astype(np.int)
+#print "arousal",train_a[1040:1280]
+#print "train_x",len(train_x[0:26])
+clf1 = svm.SVC()
+clf1.fit(train_x, train_a)
+#print test_a
+predict_al = clf1.predict(train_x)
+#print "alrosal",predict_al
+predict_val = clf.predict(train_x) 
+#print "valence",predict_val 
+val_count = al_count = 0
+for i in range(len(train_y)):
+	if train_y[i] == predict_val[i]:
+		val_count = val_count+1
+	if train_a[i] == predict_al[i]:
+		al_count = al_count+1
+print "predicted valence",(float(val_count)/len(train_y))*100
+print "predicted arousal",(float(al_count)/len(train_y))*100
+# classifier efficiency
+'''
+predicted valence 98.046875 percentage
+predicted arousal 97.890625 percentage
+
+predicted valence 95.0
+predicted arousal 96.09375 
+'''
+# output
+'''
+predicted valence 17.9166666667
+predicted arousal 13.3333333333
+'''
+#chan = ['Fp1','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']
+