gaussian_process.py 文件源码

def _build_entropy(self, weights, means, covars):
        # First build half a square matrix of normals. This avoids re-computing symmetric normals.
        log_normal_probs = util.init_list(0.0, [self.num_components, self.num_components])
        for i in xrange(self.num_components):
            for j in xrange(i, self.num_components):
                for k in xrange(self.num_latent):
                    if self.diag_post:
                        normal = util.DiagNormal(means[i, k, :], covars[i, k, :] +
                                                                 covars[j, k, :])
                    else:
                        if i == j:
                            # Compute chol(2S) = sqrt(2)*chol(S).
                            covars_sum = tf.sqrt(2.0) * covars[i, k, :, :]
                        else:
                            # TODO(karl): Can we just stay in cholesky space somehow?
                            covars_sum = tf.cholesky(util.mat_square(covars[i, k, :, :]) +
                                                     util.mat_square(covars[j, k, :, :]))
                        normal = util.CholNormal(means[i, k, :], covars_sum)
                    log_normal_probs[i][j] += normal.log_prob(means[j, k, :])

        # Now compute the entropy.
        entropy = 0.0
        for i in xrange(self.num_components):
            weighted_log_probs = util.init_list(0.0, [self.num_components])
            for j in xrange(self.num_components):
                if i <= j:
                    weighted_log_probs[j] = tf.log(weights[j]) + log_normal_probs[i][j]
                else:
                    weighted_log_probs[j] = tf.log(weights[j]) + log_normal_probs[j][i]

            entropy -= weights[i] * util.logsumexp(tf.stack(weighted_log_probs))

        return entropy