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diff --git a/2017/free-will/scripts/python-learning.py b/2017/free-will/scripts/python-learning.py
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+++ b/2017/free-will/scripts/python-learning.py
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+import numpy as np
+import pandas
+from math import ceil, floor
+from sklearn.model_selection import train_test_split
+from sklearn import svm
+from sklearn.model_selection import KFold, StratifiedKFold, permutation_test_score
+from sklearn import linear_model
+from sklearn.svm import SVC
+import pickle
+import matplotlib.pyplot as plt
+import numpy.fft as fft
+from sklearn import datasets
+
+class MyOVBox(OVBox):
+	def __init__(self):
+		OVBox.__init__(self)
+		# Names of CSV files
+		self.signalFileName = ""
+		self.stimFileName = ""
+		# Name of Save File
+		self.saveFileName = ""
+		# other variables
+		self.windowSize = 0  # window size in ms
+		self.numOfPreviousWindowsAsOne = 0  # number of windows before actual stimulation to be marked as 1
+		self.numOfWindowsBefore = 0  # number of windows before those marked as 1, to be marked as 0
+		self.numOfWindowsAfter = 0  # number of windows after those marked as 1, to be marked as 0
+		
+		
+	def filter_signal(self, sampleRate, numberOfSamplesWindow, stimulationTimes, splittedSignal):
+		""" returns tuple of filtered (signal chunks, classes) """
+		splittedSignal_filtrd = []
+		classes_filtrd = []
+		
+		temp_classes = np.zeros(len(splittedSignal))
+		
+		for stim in stimulationTimes:
+			index = int(floor(stim*sampleRate/numberOfSamplesWindow))
+			temp_classes[index] = 1
+			for i in range(1, self.numOfPreviousWindowsAsOne):
+				temp_classes[index-i] = 1
+			
+			tmp_cls_winds = temp_classes[(index - self.numOfPreviousWindowsAsOne - self.numOfWindowsBefore):index+self.numOfWindowsAfter]
+			tmp_sig_winds = np.concatenate(splittedSignal[(index - self.numOfPreviousWindowsAsOne - self.numOfWindowsBefore):index+self.numOfWindowsAfter])
+			
+			if len(tmp_sig_winds)/len(tmp_cls_winds)!=numberOfSamplesWindow:  # if np.array_split does not split in equal windows
+				tmp_sig_winds = np.lib.pad(tmp_sig_winds, ((0, int(len(tmp_cls_winds)*numberOfSamplesWindow-len(tmp_sig_winds))),(0, 0)), 'edge')  # pad with same values on end of array
+			
+			classes_filtrd.extend(tmp_cls_winds)
+			splittedSignal_filtrd.extend(np.array_split(tmp_sig_winds, len(tmp_cls_winds)))		
+		
+		return (splittedSignal_filtrd, classes_filtrd)
+
+		
+	def avg_k_fold(self, data, classes, k=4):
+		""" return average CA of k-fold cross validation """
+		avg_val = 0
+	
+		kf = KFold(n_splits=k)
+		for train, test in kf.split(data):
+			clf = svm.SVC(kernel='linear', C=1).fit(data[train], classes[train])
+			cur_score = clf.score(data[test], classes[test])
+			avg_val += cur_score
+			# print cur_score
+		return avg_val/k
+
+		
+	def permutation_significance_classification_score(self, X, y, k_folds=4):
+		n_classes = np.unique(y).size
+		
+		svm = SVC(kernel='linear')
+		cv = StratifiedKFold(k_folds)
+
+		score, permutation_scores, pvalue = permutation_test_score(svm, X, y, scoring="accuracy", cv=cv, n_permutations=200, n_jobs=1)
+		print("Classification score %s (pvalue : %s)" % (score, pvalue))
+		
+		plt.hist(permutation_scores, 20, label='Permutation scores')
+		ylim = plt.ylim()
+		plt.plot(2 * [score], ylim, '--g', linewidth=3,
+				 label='Classification Score'
+				 ' (pvalue %s)' % pvalue)
+		plt.plot(2 * [1. / n_classes], ylim, '--k', linewidth=3, label='Luck')
+
+		plt.ylim(ylim)
+		plt.legend()
+		plt.xlabel('Score')
+		plt.show()
+		
+			
+	def initialize(self):
+		# Names of CSV files
+		self.signalFileName = self.setting['InputCSVSignal']
+		self.stimFileName = self.setting['InputCSVStimulations']
+		# Name of Save File
+		self.saveFileName = self.setting['SaveFile']
+		# other variables
+		self.windowSize = int(self.setting['WindowSize (ms)'])  # in ms
+		self.numOfPreviousWindowsAsOne = int(self.setting['NumOfPrevWindows'])
+		self.numOfWindowsBefore = int(self.setting['NumOfWindowsBefore'])-1
+		self.numOfWindowsAfter = int(self.setting['NumOfWindowsAfter'])+1
+		self.k_folds = int(self.setting['K-folds'])
+		
+		print "Reading files..."
+		# read CSV files
+		signalArray = pandas.read_csv(self.signalFileName, delimiter=";", encoding="utf-8-sig")
+		stimsArray = pandas.read_csv(self.stimFileName, delimiter=";", encoding="utf-8-sig")
+		print "Files read!"
+		
+		# sort information from tables (pandas dataframe)
+		time = signalArray['Time (s)']
+		electrodes = signalArray.iloc[:, 1:signalArray.shape[1]-1]
+		sampleRate = signalArray['Sampling Rate'][0]
+		stimulationTimes = stimsArray['Time (s)']
+		
+		numberOfSamplesWindow = floor(self.windowSize / ((1/sampleRate)*1000))  # number of samples for approximately self.windowSize ms
+		
+		splittedSignal = np.array_split(electrodes, ceil(len(time)/numberOfSamplesWindow))  # split signal into chunks of specified length
+		
+		s_c = self.filter_signal(sampleRate, numberOfSamplesWindow, stimulationTimes, splittedSignal)  # filter signal and return sparsed version of chunks and assign classes
+		splittedSignal_filtrd = s_c[0]
+		classes_filtrd = s_c[1]
+
+		splittedSignal_filtrd_means = np.array(np.mean(splittedSignal_filtrd, axis=1))  # for each window calculate mean value
+		# additional attributes could be added besides splittedSignal_filtrd_means
+		classes_filtrd = np.array(classes_filtrd)
+		
+		# print average k-fold CA
+		print "Average " + str(self.k_folds) + "-folds value: " + str(self.avg_k_fold(splittedSignal_filtrd_means, classes_filtrd, k=self.k_folds))
+		#self.permutation_significance_classification_score(splittedSignal_filtrd_means, classes_filtrd, k_folds=2)  # k_folds=2, last long if k is bigger
+		
+		if self.saveFileName:
+			clf = svm.SVC(kernel='linear', C=1).fit(splittedSignal_filtrd_means, classes_filtrd)
+			clf.fit(splittedSignal_filtrd_means, classes_filtrd)
+			
+			print "Saving to pickle file: " + self.saveFileName
+			pickle.dump(clf, open(self.saveFileName, 'wb'))
+			print "Learned score: " + str(clf.score(splittedSignal_filtrd_means, classes_filtrd))
+		
+		# send finish stimulation output (for OpenViBE)
+		self.finishBySendingStimulation(32774)  # OVTK_StimulationId_TrialStop code
+		
+		
+	def finishBySendingStimulation(self, stimulationCode):
+		stimSetFinish = OVStimulationSet(self.getCurrentTime(), self.getCurrentTime()+1./self.getClock())
+		stimSetFinish.append(OVStimulation(stimulationCode, self.getCurrentTime(), 0.))
+		self.output[0].append(stimSetFinish)
+
+		
+	def process(self):
+		
+		return
+		
+	def uninitialize(self):
+		# nop
+		
+		return
+
+box = MyOVBox()