gmm_ops.py 文件源码

python
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项目:lsdc 作者: febert 项目源码 文件源码
def _define_distance_to_clusters(self, data):
    """Defines the Mahalanobis distance to the assigned Gaussian."""
    # TODO(xavigonzalvo): reuse (input - mean) * cov^-1 * (input -
    # mean) from log probability function.
    self._all_scores = []
    for shard in data:
      all_scores = []
      shard = tf.expand_dims(shard, 0)
      for c in xrange(self._num_classes):
        if self._covariance_type == FULL_COVARIANCE:
          cov = self._covs[c, :, :]
        elif self._covariance_type == DIAG_COVARIANCE:
          cov = tf.diag(self._covs[c, :])
        inverse = tf.matrix_inverse(cov + self._min_var)
        inv_cov = tf.tile(
            tf.expand_dims(inverse, 0),
            tf.pack([self._num_examples, 1, 1]))
        diff = tf.transpose(shard - self._means[c, :, :], perm=[1, 0, 2])
        m_left = tf.batch_matmul(diff, inv_cov)
        all_scores.append(tf.sqrt(tf.batch_matmul(
            m_left, tf.transpose(diff, perm=[0, 2, 1])
        )))
      self._all_scores.append(tf.reshape(
          tf.concat(1, all_scores),
          tf.pack([self._num_examples, self._num_classes])))

    # Distance to the associated class.
    self._all_scores = tf.concat(0, self._all_scores)
    assignments = tf.concat(0, self.assignments())
    rows = tf.to_int64(tf.range(0, self._num_examples))
    indices = tf.concat(1, [tf.expand_dims(rows, 1),
                            tf.expand_dims(assignments, 1)])
    self._scores = tf.gather_nd(self._all_scores, indices)
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