network.reset()
network.loadWeights('XORWeight.p')
network.test(samples)
# Example 4 : Learning sin(x)
# -------------------------------------------------------------------------
print "Learning the sin function"
network = MLP(1,10,1)
samples = np.zeros(500, dtype=[('x', float, 1), ('y', float, 1)])
samples['x'] = np.linspace(0,1,500)
samples['y'] = np.sin(samples['x']*np.pi)
for i in range(10000):
n = np.random.randint(samples.size)
network.propagate_forward(samples['x'][n])
network.propagate_backward(samples['y'][n])
#plt.figure(figsize=(10,5))
# Draw real function
x,y = samples['x'],samples['y']
#plt.plot(x,y,color='b',lw=1)
# Draw network approximated function
for i in range(samples.shape[0]):
y[i] = network.propagate_forward(x[i])
#plt.plot(x,y,color='r',lw=3)
#plt.axis([0,1,0,1])
#plt.show()
# Example 5: Learning game 3 spike training data