rbmdownx.py

import numpy as np


# RBMDOWNX calculates p(v = 1 | h) for non label units
#
# INPUTS
#   rbm           : A rbm struct
#   hid_act       : the activation of the hidden layer
#   act_func      : the activation function, @sigm | @sigmrnd
#
# OUTPUTS
#   act_vis_x     : The activation of the x visible units
#
# see "A practical guide to training restricted Boltzmann machines" eqn 8
# act is the activation function. currently either sigm or sigmrnd
#
# NOTATION
# data  : all data given as      [n_samples   x #vis]
#    v  : all data given as      [n_samples   x #vis]
#   ey  : all data given as      [n_samples   x #n_classes]
#    W  : vis - hid weights      [ #hid       x #vis ]
#    U  : label - hid weights    [ #hid       x #n_classes ]
#    b  : bias of visible layer  [ #vis       x 1]
#    c  : bias of hidden layer   [ #hid       x 1]
#    d  : bias of label layer    [ #n_classes x 1]

def rbmdownx(rbm, hid_act, act_func):
    hid_act_add_rbm_W = np.matmul(hid_act, rbm.W)
    act_vis_x = act_func(np.add(np.transpose(rbm.b), hid_act_add_rbm_W))
    return act_vis_x