X = X[idx]
y = y[idx]
# split the data
Xtrain = X[:nTrain, :]
ytrain = y[:nTrain]
Xtest = X[nTrain:, :]
ytest = y[nTrain:]
# train the decision tree
modelDT = DecisionTreeClassifier()
modelDT.fit(Xtrain, ytrain)
# train the naive Bayes
layers = np.array(([25]))
modelNN = NeuralNet(layers=layers, learningRate =2, numEpochs=500, epsilon=.62)
modelNN.fit(Xtrain, ytrain)
ypred_NNtrain = modelNN.predict(Xtrain)
# output predictions on the remaining data
ypred_NN = modelNN.predict(Xtest)
# compute the training accuracy of the model
accuracyNT = accuracy_score(ytrain, ypred_NNtrain)
accuracyNN = accuracy_score(ytest, ypred_NN)
print "Training = "+str(accuracyNT)
print "Neural Net accuracy = "+str(accuracyNN)
modelNN.visualizeHiddenNodes("visualizeHiddenNodes.bmp")
