def buildXor(self):
self.params['dataset'] = 'XOR'
d = ClassificationDataSet(2)
d.addSample([0., 0.], [0.])
d.addSample([0., 1.], [1.])
d.addSample([1., 0.], [1.])
d.addSample([1., 1.], [0.])
d.setField('class', [[0.], [1.], [1.], [0.]])
self.trn_data = d
self.tst_data = d
global trn_data
trn_data = self.trn_data
nn = FeedForwardNetwork()
inLayer = TanhLayer(2, name='in')
hiddenLayer = TanhLayer(3, name='hidden0')
outLayer = ThresholdLayer(1, name='out')
nn.addInputModule(inLayer)
nn.addModule(hiddenLayer)
nn.addOutputModule(outLayer)
in_to_hidden = FullConnection(inLayer, hiddenLayer)
hidden_to_out = FullConnection(hiddenLayer, outLayer)
nn.addConnection(in_to_hidden)
nn.addConnection(hidden_to_out)
nn.sortModules()
nn.randomize()
self.net_settings = str(nn.connections)
self.nn = nn
def buildXor(self):
self.params['dataset'] = 'XOR'
d = ClassificationDataSet(2)
d.addSample([0., 0.], [0.])
d.addSample([0., 1.], [1.])
d.addSample([1., 0.], [1.])
d.addSample([1., 1.], [0.])
d.setField('class', [[0.], [1.], [1.], [0.]])
self.trn_data = d
self.tst_data = d
global trn_data
trn_data = self.trn_data
nn = FeedForwardNetwork()
inLayer = TanhLayer(2, name='in')
hiddenLayer = TanhLayer(3, name='hidden0')
outLayer = ThresholdLayer(1, name='out')
nn.addInputModule(inLayer)
nn.addModule(hiddenLayer)
nn.addOutputModule(outLayer)
in_to_hidden = FullConnection(inLayer, hiddenLayer)
hidden_to_out = FullConnection(hiddenLayer, outLayer)
nn.addConnection(in_to_hidden)
nn.addConnection(hidden_to_out)
nn.sortModules()
nn.randomize()
self.net_settings = str(nn.connections)
self.nn = nn
def xorDataSet():
d = ClassificationDataSet(2)
d.addSample([0., 0.], [0.])
d.addSample([0., 1.], [1.])
d.addSample([1., 0.], [1.])
d.addSample([1., 1.], [0.])
d.setField('class', [[0.], [1.], [1.], [0.]])
return d
def xorDataSet():
d = ClassificationDataSet(2)
d.addSample([0., 0.], [0.])
d.addSample([0., 1.], [1.])
d.addSample([1., 0.], [1.])
d.addSample([1., 1.], [0.])
d.setField('class', [[0.], [1.], [1.], [0.]])
return d
def neutral_net(train_data,test_data,n_est,maxd):
#ds = SupervisedDataSet(len(train_data[0,:])-1,1)
ds = ClassificationDataSet(len(train_data[0,:])-1,1,nb_classes=2,class_labels=['Lived','Died'])
X=[];y=[]; X1=[]; y1=[]
for row in range(0,len(train_data[:,0])):
X.append(train_data[row,1:].astype(int))
y.append([train_data[row,0].astype(int)])
#ds.addSample(train_data[row,1:].astype(int),train_data[row,0].astype(int))
#for row in range(0,len(test_data[:,0])):
# X.append(test_data[row,1:].astype(int))
# y.append([test_data[row,0].astype(int)])
X=np.array(X); y=np.array(y)
ds.setField('input',X)
ds.setField('target',y)
ds._convertToOneOfMany(bounds=[0,1]) # only for classification
#net = buildNetwork(len(train_data[0,:])-1,100, 1)
read = False
if read:
#net = NetworkReader.readFrom('10_200.xml') # hiddenclass=SigmoidLayer
pass
else:
net = buildNetwork(ds.indim,maxd,ds.outdim,bias=True,hiddenclass=SigmoidLayer,outclass=SoftmaxLayer)#SoftmaxLayer)
trainer = BackpropTrainer(net,dataset=ds,verbose=False,learningrate=0.01,momentum=0.1,weightdecay=0.01)
trainer.trainUntilConvergence(maxEpochs=n_est,continueEpochs=10,validationProportion=0.3)
#NetworkWriter.writeToFile(net, '10_200.xml')
tot = 0.
for a,b in zip(X,y):
val = net.activate(a)
tot+=int((val[0] > val[1] and b==0) or (val[0]<val[1] and b==1))
'''num = int((net.activate(a)<0.5 and b<0.5) or (net.activate(a)>0.5 and b>0.5))
tot+=num'''
for row in range(0,len(test_data[:,0])):
X1.append(test_data[row,1:].astype(int))
y1.append([test_data[row,0].astype(int)])
X1=np.array(X1); y1=np.array(y1)
tot1 = 0.
output = []
for a,b in zip(X1,y1):
val = net.activate(a)
tot1+=int((val[0] > val[1] and b==0) or (val[0]<val[1] and b==1))
output.append(int(val[0]<val[1]))
'''num = int((net.activate(a)<0.5 and b<0.5) or (net.activate(a)>0.5 and b>0.5))
tot1+=num
output.append(int(net.activate(a)>0.5))'''
pr.print_results(output)
return [tot/len(y),tot1/len(y1)]
def predict(self, x_test):
DS = ClassificationDataSet(x_test.shape[1], nb_classes=self.__class_num)
DS.setField('input', x_test)
DS.setField('target', np.zeros((x_test.shape[0], 1)))
DS._convertToOneOfMany()
out = self.__pybrain_bpnn.activateOnDataset(DS)
# this part converts an activation vector to a class number
# i'm saving this for a future purpose
#out = out.argmax(axis=1) # the highest output activation gives the class
#if not self.__class_zero_indexing: # indexing from 1 - add one to result
# out += 1
return out
def train(self, x, y, class_number=-1):
self.__class_num = max(np.unique(y).size, class_number)
if max(y) == self.__class_num:
self.__class_zero_indexing = False
y = np.array([i - 1 for i in y])
DS = ClassificationDataSet(x.shape[1], nb_classes=self.__class_num)
DS.setField('input', x)
DS.setField('target', y.reshape(y.size, 1))
DS._convertToOneOfMany()
hidden_num = (DS.indim + DS.outdim) / 2
self.__pybrain_bpnn = buildNetwork(DS.indim, hidden_num, DS.outdim, bias=True, hiddenclass=SigmoidLayer, outclass=SoftmaxLayer)
trainer = BackpropTrainer(self.__pybrain_bpnn, dataset=DS, learningrate=0.07, lrdecay=1.0, momentum=0.6)
trainer.trainUntilConvergence(DS, maxEpochs=30)
def test_ann(self):
from pybrain.datasets.classification import ClassificationDataSet
# below line can be replaced with the algorithm of choice e.g.
# from pybrain.optimization.hillclimber import HillClimber
from pybrain.optimization.populationbased.ga import GA
from pybrain.tools.shortcuts import buildNetwork
# create XOR dataset
d = ClassificationDataSet(2)
d.addSample([181, 80], [1])
d.addSample([177, 70], [1])
d.addSample([160, 60], [0])
d.addSample([154, 54], [0])
d.setField('class', [[0.], [1.], [1.], [0.]])
nn = buildNetwork(2, 3, 1)
# d.evaluateModuleMSE takes nn as its first and only argument
ga = GA(d.evaluateModuleMSE, nn, minimize=True)
for i in range(100):
nn = ga.learn(0)[0]
print nn.activate([181, 80])
def test_ann(self):
from pybrain.datasets.classification import ClassificationDataSet
# below line can be replaced with the algorithm of choice e.g.
# from pybrain.optimization.hillclimber import HillClimber
from pybrain.optimization.populationbased.ga import GA
from pybrain.tools.shortcuts import buildNetwork
# create XOR dataset
d = ClassificationDataSet(2)
d.addSample([181, 80], [1])
d.addSample([177, 70], [1])
d.addSample([160, 60], [0])
d.addSample([154, 54], [0])
d.setField('class', [ [0.],[1.],[1.],[0.]])
nn = buildNetwork(2, 3, 1)
# d.evaluateModuleMSE takes nn as its first and only argument
ga = GA(d.evaluateModuleMSE, nn, minimize=True)
for i in range(100):
nn = ga.learn(0)[0]
print nn.activate([181, 80])
from pybrain.datasets.classification import ClassificationDataSet
from pybrain.optimization.populationbased.ga import GA
from pybrain.tools.shortcuts import buildNetwork
# create XOR dataset
d = ClassificationDataSet(2)
d.addSample([0., 0.], [0.])
d.addSample([0., 1.], [1.])
d.addSample([1., 0.], [1.])
d.addSample([1., 1.], [0.])
d.setField('class', [ [0.],[1.],[1.],[0.]])
nn = buildNetwork(2, 3, 1)
ga = GA(d.evaluateModuleMSE, nn, minimize=True)
for i in range(100):
nn = ga.learn(0)[0]
# test results after the above script
In [68]: nn.activate([0,0])
Out[68]: array([-0.07944574])
In [69]: nn.activate([1,0])
Out[69]: array([ 0.97635635])
In [70]: nn.activate([0,1])
Out[70]: array([ 1.0216745])
In [71]: nn.activate([1,1])
Out[71]: array([ 0.03604205])
