yt.pop(popi)
# # / crossvalidation data construction
X = np.array(Xt)
y = np.array(yt)
Xp = np.array(Xp)
yp = np.array(yp)
# norm = normalizer(X)
# ffnn = FeedForwardNN([len(X[0]), len(X[0]), len(X[0]), len(X[0]), len(y[0])], hidden_layer="sigmoid", output_layer="softmax", input_layer="sigmoid")
ffnn = FeedForwardNN([len(X[0]), len(X[0]) * 2, len(y[0])], hidden_layer="sigmoid", output_layer="softmax", input_layer="sigmoid")
# ffnn.backpropagation_training(normalize(X, norm), y, alpha=0.00001, epoch=100, momentum=0.99, plot_error=True)
# ffnn.backpropagation_training(X, y, alpha=1e-7, epoch=100, momentum=0.99, plot_error=True)
# ffnn.SGD_training(X, y, alpha=1e-7, epoch=5000, momentum=0.99, mini_batch_size=10, plot_error=True)
# ffnn.adadelta_training(normalize(X, norm), y, epoch=100, plot_error=True)
ffnn.adadelta_training(X, y, epoch=100, l_regularization=0.1, plot_error=True)
# prediction = ffnn.run(normalize(X, norm))
prediction = ffnn.run(X)
print prediction
total = len(y)
count = 0
for i in xrange(total):
indexp = max(enumerate(prediction[i]), key=operator.itemgetter(1))[0]
indexe = max(enumerate(y[i]), key=operator.itemgetter(1))[0]
if indexp == indexe:
count = count + 1
print "Final rate on training set:", count / float(total)
prediction = ffnn.run(Xp)
