#!/usr/bin/env python
# -*- coding: utf-8 -*-

"""
 RBM  w/ continuous-valued inputs (Linear Energy)

 References :
   - Y. Bengio, P. Lamblin, D. Popovici, H. Larochelle: Greedy Layer-Wise
   Training of Deep Networks, Advances in Neural Information Processing
   Systems 19, 2007

"""

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.numpy_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, numpy_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()