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# -*- coding: utf-8 -*-

import sys

import numpy

from RBM import RBM

from utils import *

class CRBM(RBM):

def propdown(self, h):

pre_activation = numpy.dot(h, self.W.T) + self.vbias

return pre_activation

def sample_v_given_h(self, h0_sample):

a_h = self.propdown(h0_sample)

en = numpy.exp(-a_h)

ep = numpy.exp(a_h)

v1_mean = 1 / (1 - en) - 1 / a_h

U = numpy.array(self.rng.uniform(

low=0,

high=1,

size=v1_mean.shape))

v1_sample = numpy.log((1 - U * (1 - ep))) / a_h

return [v1_mean, v1_sample]

def test_crbm(learning_rate=0.1, k=1, training_epochs=1000):

data = numpy.array([[0.4, 0.5, 0.5, 0., 0., 0.],

[0.5, 0.3, 0.5, 0., 0., 0.],

[0.4, 0.5, 0.5, 0., 0., 0.],

[0., 0., 0.5, 0.3, 0.5, 0.],

[0., 0., 0.5, 0.4, 0.5, 0.],

[0., 0., 0.5, 0.5, 0.5, 0.]])

rng = numpy.random.RandomState(123)

# construct CRBM

rbm = CRBM(input=data, n_visible=6, n_hidden=5, rng=rng)

# train

for epoch in xrange(training_epochs):

rbm.contrastive_divergence(lr=learning_rate, k=k)

# cost = rbm.get_reconstruction_cross_entropy()

# print >> sys.stderr, 'Training epoch %d, cost is ' % epoch, cost

# test

v = numpy.array([[0.5, 0.5, 0., 0., 0., 0.],

[0., 0., 0., 0.5, 0.5, 0.]])

print rbm.reconstruct(v)

if __name__ == "__main__":

test_crbm()