Muhammad Nadzeri Munawar
34 linhas
1.6 KiB
Matlab
34 linhas
1.6 KiB
Matlab
function [valence,arousal] = getPrediction(file_path,preprocessing_method,classification_method)
|
|
%%%%%%%%%%%%%%
|
|
%file_path = 'D:\5112100136 - Muhammad Nadzeri Munawar\TUGAS AKHIR\Datasets\EEG\S04\S04-T13-10.11.15.11.40.03.csv';
|
|
project_path = strcat(pwd,'\..\..');
|
|
%preprocessing_method='fft';%'ica_fft','swt','ica_swt';
|
|
%classification_method='knn';
|
|
channel_v = [10,3,14,5,1,8,6,4,7,11];
|
|
fb_v = [3,4,5];
|
|
features_v = [2,3];
|
|
|
|
channel_a = [3,13,10,2,1,12,4,7,8,11];
|
|
fb_a = [3,4,5];
|
|
features_a = [2,3];
|
|
%%%%%%%%%%%%%%%
|
|
|
|
addpath(genpath('..\Process Data'));
|
|
addpath(genpath('..\Preprocessing'));
|
|
|
|
raw_data = cutDataPredict(file_path);
|
|
preprocessing_result_v = preProcessingPredict(raw_data,preprocessing_method,channel_v,fb_v,features_v);
|
|
preprocessing_result_a = preProcessingPredict(raw_data,preprocessing_method,channel_a,fb_a,features_a);
|
|
|
|
% Find Valence
|
|
datasets = getDatasetsPrediction(project_path,preprocessing_method,'valence');
|
|
label = separateClassPrediction(project_path,preprocessing_method,'valence');
|
|
[datasets,label] = getTwoClass(datasets,label);
|
|
valence = predictClassification(preprocessing_result_v,datasets,label,classification_method);
|
|
|
|
% Find Arousal
|
|
datasets = getDatasetsPrediction(project_path,preprocessing_method,'arousal');
|
|
label = separateClassPrediction(project_path,preprocessing_method,'arousal');
|
|
[datasets,label] = getTwoClass(datasets,label);
|
|
arousal = predictClassification(preprocessing_result_a,datasets,label,classification_method);
|
|
end |