def get_CRBM(how_long=4000):
print "Creating data...."
mean1 = np.array([-0.2, 0.3])
mean2 = np.array([0.5, -0.5])
cov1 = np.array([[0.02, 0.005], [0.001, 0.01]])
cov2 = np.array([[0.02, 0.0], [0.0, 0.02]])
dataset = np.random.multivariate_normal(mean1, cov1, 200).tolist()
dataset += np.random.multivariate_normal(mean2, cov2, 200).tolist()
r = CRBM(2, 4)
# Train at the following learning rates:
print "Training..."
for i in range(how_long):
print ' ' + str(i) + '...',
r.train_epoch(dataset, (0.9, 0.9), 20)
E = np.sum([r.energy(data) for data in dataset])
print 'Energy = ' + str(E) + '...',
print 'Done'
return r, dataset
def get_CRBM(how_long=4000):
print "Creating data...."
mean1 = np.array([-0.2, 0.3])
mean2 = np.array([0.5, -0.5])
cov1 = np.array([[0.02, 0.005], [0.001, 0.01]])
cov2 = np.array([[0.02, 0.0], [0.0, 0.02]])
dataset = np.random.multivariate_normal(mean1, cov1, 200).tolist()
dataset += np.random.multivariate_normal(mean2, cov2, 200).tolist()
r = CRBM(2, 4)
# Train at the following learning rates:
print "Training..."
for i in range(how_long):
print " " + str(i) + "...",
r.train_epoch(dataset, (0.9, 0.9), 20)
E = np.sum([r.energy(data) for data in dataset])
print "Energy = " + str(E) + "...",
print "Done"
return r, dataset
plt.draw()
f.show()
# Make a CRBM
print "Creating RBM"
rbm = CRBM(2,20)
rbm.lo = -2
rbm.hi = 2
# Train it
k=20
for i in range(10000):
#for i in range(0):
err = rbm.train_epoch(dataset, 0.1, k)
if i%10 == 0:
print "Epoch", i, ": Error =", err
# Now reconstruct some stuff
XY_rec = np.random.uniform(-2,2,(100,2))
for i in range(100):
v0 = np.array([XY_rec[i,:]]).transpose()
for j in range(20):
h0 = rbm.sample_hidden(v0)
v0 = rbm.sample_visible(h0)
XY_rec[i,0] = v0[0,0]
XY_rec[i,1] = v0[1,0]
# Finally, draw the sampled points