def _ulogprob_hid(self, Y, num_is_samples=100): """ Estimates the unnormalized marginal log-probabilities of hidden states. Use this method only if you know what you are doing. """ # approximate this SRBM with an RBM rbm = RBM(self.X.shape[0], self.Y.shape[0]) rbm.W = self.W rbm.b = self.b rbm.c = self.c # allocate memory Q = np.asmatrix(np.zeros([num_is_samples, Y.shape[1]])) for k in range(num_is_samples): # draw importance samples X = rbm.backward(Y) # store importance weights Q[k, :] = self._ulogprob(X, Y) - rbm._clogprob_vis_hid(X, Y) # average importance weights to get estimates return utils.logmeanexp(Q, 0)
def _ulogprob_hid(self, Y, num_is_samples=100):
"""
Estimates the unnormalized marginal log-probabilities of hidden states.
Use this method only if you know what you are doing.
"""
# approximate this SRBM with an RBM
rbm = RBM(self.X.shape[0], self.Y.shape[0])
rbm.W = self.W
rbm.b = self.b
rbm.c = self.c
# allocate memory
Q = np.asmatrix(np.zeros([num_is_samples, Y.shape[1]]))
for k in range(num_is_samples):
# draw importance samples
X = rbm.backward(Y)
# store importance weights
Q[k, :] = self._ulogprob(X, Y) - rbm._clogprob_vis_hid(X, Y)
# average importance weights to get estimates
return utils.logmeanexp(Q, 0)
