def exec_algo(xml_file, output_location):
rootObj=ml.parse(xml_file)
file_name=rootObj.MachineLearning.prediction.datafile
file=open(file_name)
var_input=rootObj.MachineLearning.prediction.input
var_output=rootObj.MachineLearning.prediction.output
var_classes=rootObj.MachineLearning.prediction.classes
DS=ClassificationDataSet(var_input,var_output,nb_classes=var_classes)
#DS1=ClassificationDataSet(13,1,nb_classes=10)
for line in file.readlines():
data=[float(x) for x in line.strip().split(',') if x != '']
inp=tuple(data[:var_input])
output=tuple(data[var_input:])
DS.addSample(inp,output)
tstdata,trndata=DS.splitWithProportion(0)
#trndatatest,tstdatatest=DS1.splitWithProportion(0)
trdata=ClassificationDataSet(trndata.indim,1,nb_classes=10)
#tsdata=ClassificationDataSet(DS1.indim,1,nb_classes=10)
#tsdata1=ClassificationDataSet(DS1.indim,1,nb_classes=10)
for i in xrange(trndata.getLength()):
if (trndata.getSample(i)[1][0]!=100):
trdata.addSample(trndata.getSample(i)[0],trndata.getSample(i)[1])
trdata._convertToOneOfMany()
#tsdata._convertToOneOfMany()
#tsdata1._convertToOneOfMany()
print "%d" % (trdata.getLength())
rnn=RecurrentNetwork()
inputLayer=LinearLayer(trdata.indim)
hiddenLayer=rootObj.MachineLearning.prediction.algorithm.RecurrentNeuralNetwork.hiddenLayerActivation
hiddenNeurons=rootObj.MachineLearning.prediction.algorithm.RecurrentNeuralNetwork.hiddenNeurons
if hiddenLayer=='Sigmoid':
hiddenLayer=SigmoidLayer(hiddenNeurons)
elif hiddenLayer=='Softmax':
hiddenLayer=SoftmaxLayer(hiddenNeurons)
else:
hiddenLayer=LinearLayer(hiddenNeurons)
outputLayer=rootObj.MachineLearning.prediction.algorithm.RecurrentNeuralNetwork.outputLayerActivation
if outputLayer=='Sigmoid':
outputLayer=SigmoidLayer(trdata.outdim)
elif outputLayer=='Softmax':
outputLayer=SoftmaxLayer(trdata.outdim)
else:
outputLayer=LinearLayer(trdata.outdim)
rnn.addInputModule(inputLayer)
rnn.addModule(hiddenLayer)
rnn.addOutputModule(outputLayer)
rnn_type=rootObj.MachineLearning.prediction.algorithm.RecurrentNeuralNetwork.RNN_Type
in_to_hidden=FullConnection(inputLayer,hiddenLayer)
hidden_to_outputLayer=FullConnection(hiddenLayer,outputLayer)
rnn.addConnection(in_to_hidden)
rnn.addConnection(hidden_to_outputLayer)
if rnn_type=='Elman':
hidden_to_hidden=FullConnection(hiddenLayer,hiddenLayer, name='c3')
rnn.addRecurrentConnection(hidden_to_hidden)
#hidden_to_hidden=FullConnection(hiddenLayer,hiddenLayer, name='c3')
if rnn_type=='Jordan':
output_to_hidden=FullConnection(outputLayer,hiddenLayer, name='c3')
rnn.addRecurrentConnection(output_to_hidden)
#rnn.addRecurrentConnection(hidden_to_hidden)
momentum=rootObj.MachineLearning.prediction.algorithm.RecurrentNeuralNetwork.momentum
weightdecay=rootObj.MachineLearning.prediction.algorithm.RecurrentNeuralNetwork.learningRate
rnn.sortModules()
trainer=BackpropTrainer(rnn,dataset=trdata,momentum=0.1,verbose=True,weightdecay=0.01)
trainer.train();
result=(percentError(trainer.testOnClassData(dataset=trdata),trdata['class']))
#result1=percentError(trainer.testOnClassData(dataset=tsdata1),tsdata1['class'])
print ('%f \n') % (100-result)
#print ('%f \n') % (100-result1)
ts=time.time()
directory = output_location + sep + str(int(ts))
makedirs(directory)
fileObject=open(output_location + sep + str(int(ts)) + sep + 'pybrain_RNN','w')
pickle.dump(trainer,fileObject)
pickle.dump(rnn,fileObject)
fileObject.close()
def exec_algo(xml_file, output_location):
rootObj=ml.parse(xml_file)
#Getting the root element so that we get the subclasses and its members and member function
file=open(rootObj.MachineLearning.classification.datafile)
var_inp=rootObj.MachineLearning.classification.input
var_out=rootObj.MachineLearning.classification.output
classes=rootObj.MachineLearning.classification.classes
DS=ClassificationDataSet(var_inp,var_out,nb_classes=classes)
for line in file.readlines():
data=[float(x) for x in line.strip().split(',') if x != '']
inp=tuple(data[:var_inp])
output=tuple(data[var_inp:])
DS.addSample(inp,output)
split=rootObj.MachineLearning.classification.split
tstdata,trndata=DS.splitWithProportion(split)
trdata=ClassificationDataSet(trndata.indim,var_out,nb_classes=classes)
tsdata=ClassificationDataSet(tstdata.indim,var_out,nb_classes=classes)
for i in xrange(trndata.getLength()):
trdata.addSample(trndata.getSample(i)[0],trndata.getSample(i)[1])
for i in xrange(tstdata.getLength()):
tsdata.addSample(tstdata.getSample(i)[0],tstdata.getSample(i)[1])
trdata._convertToOneOfMany()
tsdata._convertToOneOfMany()
hiddenNeurons=rootObj.MachineLearning.classification.algorithm.RadialBasisFunctionNetwork.hiddenNeurons
fnn=FeedForwardNetwork()
inputLayer=LinearLayer(trdata.indim)
hiddenLayer=GaussianLayer(hiddenNeurons)
outputLayer=LinearLayer(trdata.outdim)
fnn.addInputModule(inputLayer)
fnn.addModule(hiddenLayer)
fnn.addOutputModule(outputLayer)
in_to_hidden=FullConnection(inputLayer,hiddenLayer)
hidden_to_outputLayer=FullConnection(hiddenLayer,outputLayer)
fnn.addConnection(in_to_hidden)
fnn.addConnection(hidden_to_outputLayer)
fnn.sortModules()
learningrate=rootObj.MachineLearning.classification.algorithm.RadialBasisFunctionNetwork.learningRate
momentum=rootObj.MachineLearning.classification.algorithm.RadialBasisFunctionNetwork.momentum
epochs=rootObj.MachineLearning.classification.algorithm.RadialBasisFunctionNetwork.epochs
trainer=BackpropTrainer(fnn,dataset=trdata, verbose=True, learningrate=learningrate, momentum=momentum)
trainer.trainEpochs(epochs=epochs)
#trainer.train()
#trainer.trainUntilConvergence(dataset=trdata, maxEpochs=500, verbose=True, continueEpochs=10, validationProportion=0.25)
trresult=percentError(trainer.testOnClassData(),trdata['class'])
#testingResult=percentError(trainer.testOnClassData(dataset=tsdata),tsdata['class'])
#print "Training accuracy : %f , Testing Accuracy: %f" % (100-trresult,100-testingResult)
print "Training accuracy : %f " % (100-trresult)
ts=time.time()
directory = output_location + sep + str(int(ts)) ;
makedirs(directory)
fileObject=open(output_location + sep + str(int(ts)) + sep + 'pybrain_RBF','w')
pickle.dump(trainer,fileObject)
pickle.dump(fnn,fileObject)
fileObject.close()
def exec_algo(xml_file, output_location):
rootObj = ml.parse(xml_file)
#Getting the root element so that we get the subclasses and its members and member function
xmlParamDetails = rootObj.MachineLearning.classification
#Gather param values from the XML parsed object
file = open(xmlParamDetails.datafile)
var_inp = xmlParamDetails.input
var_out = xmlParamDetails.output
classes = xmlParamDetails.classes
split = xmlParamDetails.split
learningrate = xmlParamDetails.algorithm.MultiLayerPerceptron.learningRate
momentum = xmlParamDetails.algorithm.MultiLayerPerceptron.momentum
epochs = xmlParamDetails.algorithm.MultiLayerPerceptron.epochs
hiddenNeurons = int(
xmlParamDetails.algorithm.MultiLayerPerceptron.hiddenLayers)
hiddenLayer = xmlParamDetails.algorithm.MultiLayerPerceptron.hiddenLayerActivation
outputLayer = xmlParamDetails.algorithm.MultiLayerPerceptron.outputLayerActivation
delimiter = xmlParamDetails.delimiter
DS = ClassificationDataSet(var_inp, var_out, nb_classes=classes)
for line in file.readlines():
data = [float(x) for x in line.strip().split(',') if x != '']
inp = tuple(data[:var_inp])
output = tuple(data[var_inp:])
DS.addSample(inp, output)
tstdata, trndata = DS.splitWithProportion(split)
trdata = ClassificationDataSet(trndata.indim, var_out, nb_classes=classes)
tsdata = ClassificationDataSet(tstdata.indim, var_out, nb_classes=classes)
for i in xrange(trndata.getLength()):
trdata.addSample(trndata.getSample(i)[0], trndata.getSample(i)[1])
for i in xrange(tstdata.getLength()):
tsdata.addSample(tstdata.getSample(i)[0], tstdata.getSample(i)[1])
trdata._convertToOneOfMany()
tsdata._convertToOneOfMany()
fnn = FeedForwardNetwork()
inputLayer = LinearLayer(trdata.indim)
if hiddenLayer == 'Sigmoid':
hiddenLayer = SigmoidLayer(hiddenNeurons)
elif hiddenLayer == 'Softmax':
hiddenLayer = SoftmaxLayer(hiddenNeurons)
else:
hiddenLayer = LinearLayer(hiddenNeurons)
if outputLayer == 'Sigmoid':
outputLayer = SigmoidLayer(trdata.outdim)
elif outputLayer == 'Softmax':
outputLayer = SoftmaxLayer(trdata.outdim)
else:
outputLayer = LinearLayer(trdata.outdim)
fnn.addInputModule(inputLayer)
fnn.addModule(hiddenLayer)
fnn.addOutputModule(outputLayer)
in_to_hidden = FullConnection(inputLayer, hiddenLayer)
hidden_to_outputLayer = FullConnection(hiddenLayer, outputLayer)
fnn.addConnection(in_to_hidden)
fnn.addConnection(hidden_to_outputLayer)
fnn.sortModules()
trainer = BackpropTrainer(fnn,
dataset=trdata,
verbose=True,
learningrate=learningrate,
momentum=momentum)
trainer.trainEpochs(epochs=epochs)
trresult = percentError(trainer.testOnClassData(), trdata['class'])
print("Training accuracy : %f " % (100 - trresult))
ts = time.time()
directory = output_location + sep + str(int(ts))
makedirs(directory)
fileObject = open(
output_location + sep + str(int(ts)) + sep + 'pybrain_MLP', 'w')
pickle.dump(trainer, fileObject)
pickle.dump(fnn, fileObject)
fileObject.close()
def exec_algo(xml_file, output_location):
rootObj = ml.parse(xml_file)
file_name = rootObj.MachineLearning.prediction.datafile
file = open(file_name)
var_input = rootObj.MachineLearning.prediction.input
var_output = rootObj.MachineLearning.prediction.output
var_classes = rootObj.MachineLearning.prediction.classes
DS = ClassificationDataSet(var_input, var_output, nb_classes=var_classes)
#DS1=ClassificationDataSet(13,1,nb_classes=10)
for line in file.readlines():
data = [float(x) for x in line.strip().split(',') if x != '']
inp = tuple(data[:var_input])
output = tuple(data[var_input:])
DS.addSample(inp, output)
tstdata, trndata = DS.splitWithProportion(0)
#trndatatest,tstdatatest=DS1.splitWithProportion(0)
trdata = ClassificationDataSet(trndata.indim, 1, nb_classes=10)
#tsdata=ClassificationDataSet(DS1.indim,1,nb_classes=10)
#tsdata1=ClassificationDataSet(DS1.indim,1,nb_classes=10)
for i in xrange(trndata.getLength()):
if (trndata.getSample(i)[1][0] != 100):
trdata.addSample(trndata.getSample(i)[0], trndata.getSample(i)[1])
trdata._convertToOneOfMany()
#tsdata._convertToOneOfMany()
#tsdata1._convertToOneOfMany()
print "%d" % (trdata.getLength())
rnn = RecurrentNetwork()
inputLayer = LinearLayer(trdata.indim)
hiddenLayer = rootObj.MachineLearning.prediction.algorithm.RecurrentNeuralNetwork.hiddenLayerActivation
hiddenNeurons = rootObj.MachineLearning.prediction.algorithm.RecurrentNeuralNetwork.hiddenNeurons
if hiddenLayer == 'Sigmoid':
hiddenLayer = SigmoidLayer(hiddenNeurons)
elif hiddenLayer == 'Softmax':
hiddenLayer = SoftmaxLayer(hiddenNeurons)
else:
hiddenLayer = LinearLayer(hiddenNeurons)
outputLayer = rootObj.MachineLearning.prediction.algorithm.RecurrentNeuralNetwork.outputLayerActivation
if outputLayer == 'Sigmoid':
outputLayer = SigmoidLayer(trdata.outdim)
elif outputLayer == 'Softmax':
outputLayer = SoftmaxLayer(trdata.outdim)
else:
outputLayer = LinearLayer(trdata.outdim)
rnn.addInputModule(inputLayer)
rnn.addModule(hiddenLayer)
rnn.addOutputModule(outputLayer)
rnn_type = rootObj.MachineLearning.prediction.algorithm.RecurrentNeuralNetwork.RNN_Type
in_to_hidden = FullConnection(inputLayer, hiddenLayer)
hidden_to_outputLayer = FullConnection(hiddenLayer, outputLayer)
rnn.addConnection(in_to_hidden)
rnn.addConnection(hidden_to_outputLayer)
if rnn_type == 'Elman':
hidden_to_hidden = FullConnection(hiddenLayer, hiddenLayer, name='c3')
rnn.addRecurrentConnection(hidden_to_hidden)
#hidden_to_hidden=FullConnection(hiddenLayer,hiddenLayer, name='c3')
if rnn_type == 'Jordan':
output_to_hidden = FullConnection(outputLayer, hiddenLayer, name='c3')
rnn.addRecurrentConnection(output_to_hidden)
#rnn.addRecurrentConnection(hidden_to_hidden)
momentum = rootObj.MachineLearning.prediction.algorithm.RecurrentNeuralNetwork.momentum
weightdecay = rootObj.MachineLearning.prediction.algorithm.RecurrentNeuralNetwork.learningRate
rnn.sortModules()
trainer = BackpropTrainer(rnn,
dataset=trdata,
momentum=0.1,
verbose=True,
weightdecay=0.01)
trainer.train()
result = (percentError(trainer.testOnClassData(dataset=trdata),
trdata['class']))
#result1=percentError(trainer.testOnClassData(dataset=tsdata1),tsdata1['class'])
print('%f \n') % (100 - result)
#print ('%f \n') % (100-result1)
ts = time.time()
directory = output_location + sep + str(int(ts))
makedirs(directory)
fileObject = open(
output_location + sep + str(int(ts)) + sep + 'pybrain_RNN', 'w')
pickle.dump(trainer, fileObject)
pickle.dump(rnn, fileObject)
fileObject.close()
def exec_algo(xml_file, output_location):
rootObj=ml.parse(xml_file)
#Getting the root element so that we get the subclasses and its members and member function
xmlParamDetails = rootObj.MachineLearning.classification
#Gather param values from the XML parsed object
file=open(xmlParamDetails.datafile)
var_inp=xmlParamDetails.input
var_out=xmlParamDetails.output
classes=xmlParamDetails.classes
split=xmlParamDetails.split
learningrate=xmlParamDetails.algorithm.MultiLayerPerceptron.learningRate
momentum=xmlParamDetails.algorithm.MultiLayerPerceptron.momentum
epochs=xmlParamDetails.algorithm.MultiLayerPerceptron.epochs
hiddenNeurons=int(xmlParamDetails.algorithm.MultiLayerPerceptron.hiddenLayers)
hiddenLayer=xmlParamDetails.algorithm.MultiLayerPerceptron.hiddenLayerActivation
outputLayer=xmlParamDetails.algorithm.MultiLayerPerceptron.outputLayerActivation
delimiter=xmlParamDetails.delimiter
DS=ClassificationDataSet(var_inp,var_out,nb_classes=classes)
for line in file.readlines():
data=[float(x) for x in line.strip().split(',') if x != '']
inp=tuple(data[:var_inp])
output=tuple(data[var_inp:])
DS.addSample(inp,output)
tstdata,trndata=DS.splitWithProportion(split)
trdata=ClassificationDataSet(trndata.indim,var_out,nb_classes=classes)
tsdata=ClassificationDataSet(tstdata.indim,var_out,nb_classes=classes)
for i in xrange(trndata.getLength()):
trdata.addSample(trndata.getSample(i)[0],trndata.getSample(i)[1])
for i in xrange(tstdata.getLength()):
tsdata.addSample(tstdata.getSample(i)[0],tstdata.getSample(i)[1])
trdata._convertToOneOfMany()
tsdata._convertToOneOfMany()
fnn=FeedForwardNetwork()
inputLayer=LinearLayer(trdata.indim)
if hiddenLayer=='Sigmoid':
hiddenLayer=SigmoidLayer(hiddenNeurons)
elif hiddenLayer=='Softmax':
hiddenLayer=SoftmaxLayer(hiddenNeurons)
else:
hiddenLayer=LinearLayer(hiddenNeurons)
if outputLayer=='Sigmoid':
outputLayer=SigmoidLayer(trdata.outdim)
elif outputLayer=='Softmax':
outputLayer=SoftmaxLayer(trdata.outdim)
else:
outputLayer=LinearLayer(trdata.outdim)
fnn.addInputModule(inputLayer)
fnn.addModule(hiddenLayer)
fnn.addOutputModule(outputLayer)
in_to_hidden=FullConnection(inputLayer,hiddenLayer)
hidden_to_outputLayer=FullConnection(hiddenLayer,outputLayer)
fnn.addConnection(in_to_hidden)
fnn.addConnection(hidden_to_outputLayer)
fnn.sortModules()
trainer=BackpropTrainer(fnn,dataset=trdata, verbose=True, learningrate=learningrate, momentum=momentum)
trainer.trainEpochs(epochs=epochs)
trresult=percentError(trainer.testOnClassData(),trdata['class'])
print("Training accuracy : %f " % (100-trresult))
ts=time.time()
directory = output_location + sep + str(int(ts))
makedirs(directory)
fileObject=open(output_location + sep + str(int(ts)) + sep + 'pybrain_MLP','w')
pickle.dump(trainer,fileObject)
pickle.dump(fnn,fileObject)
fileObject.close()