return ret vecSize = 100 subjects = [2, 5, 6, 7, 8, 12, 16, 35 ,39] ds = None for s in subjects: for cycleNum in range(1, 13): fileName = '../inputs/Vicon from CMU/subjects/'+str(s)+'/'+str(cycleNum)+'.amc' try: data = getData(fileName, vecSize) except IOError: continue if ds is None:#initialization ds = ClassificationDataSet( len(data), 1 ) ds.appendLinked(data , subjects.index(s)) ds.nClasses = len(subjects) decay= 0.99995 myWeightdecay = 0.8 initialLearningrate= 0.005 hidden_size = 1000 epochs=1000 splitProportion = 0.5 print 'dataset size', len(ds) print 'input layer size', len(ds.getSample(0)[0]) tstdata, trndata = ds.splitWithProportion( splitProportion ) trndata._convertToOneOfMany( ) tstdata._convertToOneOfMany( ) print "Number of training patterns: ", len(trndata)
try: data = ge.getFeatureVec(fileName) except IOError: continue if ds is None:#initialization ds = ClassificationDataSet( len(data), 1 ) excpectedLens[m]+=1 ds.appendLinked(data , moods.index(mood)) splitProportion = 0.2 decay= 0.99993 myWeightdecay = 0.5 initialLearningrate= 0.01 hidden_size = 200 epochs=1000 momentum=0.15 ds.nClasses = len(moods) tstdata, trndata = ds.splitWithProportion( splitProportion ) trndata._convertToOneOfMany( ) tstdata._convertToOneOfMany( ) inLayer = LinearLayer(len(trndata.getSample(0)[0])) hiddenLayer = SigmoidLayer(hidden_size) outLayer = LinearLayer(len(trndata.getSample(0)[1])) n = FeedForwardNetwork() n.addInputModule(inLayer) n.addModule(hiddenLayer) b = BiasUnit() n.addModule(b) n.addOutputModule(outLayer) in_to_hidden = FullConnection(inLayer, hiddenLayer) hidden_to_out = FullConnection(hiddenLayer, outLayer) b_to_hidden = FullConnection(b, hiddenLayer)
str(typeNum)+'_'+str(take)+'.skl' try: data = ge.getFeatureVec(fileName) except IOError: continue if ds is None:#initialization ds = ClassificationDataSet( len(data), 1 ) ds.appendLinked(data , couple.index(mood)) splitProportion = 0.2 decay= 0.9999 myWeightdecay = 1#0.75 initialLearningrate= 0.002 hidden_size = 75 epochs=1000 momentum=0.25 ds.nClasses = len(couple) tstdata, trndata = ds.splitWithProportion( splitProportion ) trndata._convertToOneOfMany( ) tstdata._convertToOneOfMany( ) inLayer = LinearLayer(len(trndata.getSample(0)[0])) hiddenLayer = SigmoidLayer(hidden_size) outLayer = LinearLayer(len(trndata.getSample(0)[1])) n = FeedForwardNetwork() n.addInputModule(inLayer) n.addModule(hiddenLayer) b = BiasUnit() n.addModule(b) n.addOutputModule(outLayer) in_to_hidden = FullConnection(inLayer, hiddenLayer) hidden_to_out = FullConnection(hiddenLayer, outLayer) b_to_hidden = FullConnection(b, hiddenLayer)