# Example 3 : XOR logical function
# -------------------------------------------------------------------------
print "Learning the XOR logical function"
network.reset()
samples[0] = (0,0), 0
samples[1] = (1,0), 1
samples[2] = (0,1), 1
samples[3] = (1,1), 0
network.learn(samples)
network.storeWeights('XORWeight.p')
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
