tensorflow_probability/python/internal/distribution_util.py
def mixture_stddev(mixture_weight_vector, mean_vector, stddev_vector):
  """Computes the standard deviation of a mixture distribution.

  This function works regardless of the component distribution, so long as
  each component's mean and standard deviation can be provided.

  Args:
    mixture_weight_vector: A 2D tensor with shape [batch_size, num_components]
    mean_vector: A 2D tensor of mixture component means. Has shape `[batch_size,
      num_components]`.
    stddev_vector: A 2D tensor of mixture component standard deviations. Has
      shape `[batch_size, num_components]`.

  Returns:
    A 1D tensor of shape `[batch_size]` representing the standard deviation of
    the mixture distribution with given weights and component means and standard
    deviations.
  Raises:
    ValueError: If the shapes of the input tensors are not as expected.
  """
  tensorshape_util.assert_has_rank(mixture_weight_vector.shape, 2)
  if not tensorshape_util.is_compatible_with(mean_vector.shape,
                                             mixture_weight_vector.shape):
    raise ValueError("Expecting means to have same shape as mixture weights.")
  if not tensorshape_util.is_compatible_with(stddev_vector.shape,
                                             mixture_weight_vector.shape):
    raise ValueError("Expecting stddevs to have same shape as mixture weights.")

  # Reshape the distribution parameters for batched vectorized dot products.
  pi_for_dot_prod = tf.expand_dims(mixture_weight_vector, axis=1)
  mu_for_dot_prod = tf.expand_dims(mean_vector, axis=2)
  sigma_for_dot_prod = tf.expand_dims(stddev_vector, axis=2)

  # weighted average of component means under mixture distribution.
  mean_wa = tf.matmul(pi_for_dot_prod, mu_for_dot_prod)
  mean_wa = tf.reshape(mean_wa, (-1,))
  # weighted average of component variances under mixture distribution.
  var_wa = tf.matmul(pi_for_dot_prod, tf.square(sigma_for_dot_prod))
  var_wa = tf.reshape(var_wa, (-1,))
  # weighted average of component squared means under mixture distribution.
  sq_mean_wa = tf.matmul(pi_for_dot_prod, tf.square(mu_for_dot_prod))
  sq_mean_wa = tf.reshape(sq_mean_wa, (-1,))
  mixture_variance = var_wa + sq_mean_wa - tf.square(mean_wa)
  return tf.sqrt(mixture_variance)
def mixture_stddev(mixture_weight_vector, mean_vector, stddev_vector):
  tensorshape_util.assert_has_rank(mixture_weight_vector.shape, 2)
  if not tensorshape_util.is_compatible_with(mean_vector.shape,
                                             mixture_weight_vector.shape):
    raise ValueError("Expecting means to have same shape as mixture weights.")
  if not tensorshape_util.is_compatible_with(stddev_vector.shape,
                                             mixture_weight_vector.shape):
    raise ValueError("Expecting stddevs to have same shape as mixture weights.")

  # Reshape the distribution parameters for batched vectorized dot products.
  pi_for_dot_prod = tf.expand_dims(mixture_weight_vector, axis=1)
  mu_for_dot_prod = tf.expand_dims(mean_vector, axis=2)
  sigma_for_dot_prod = tf.expand_dims(stddev_vector, axis=2)

  # weighted average of component means under mixture distribution.
  mean_wa = tf.matmul(pi_for_dot_prod, mu_for_dot_prod)
  mean_wa = tf.reshape(mean_wa, (-1,))
  # weighted average of component variances under mixture distribution.
  var_wa = tf.matmul(pi_for_dot_prod, tf.square(sigma_for_dot_prod))
  var_wa = tf.reshape(var_wa, (-1,))
  # weighted average of component squared means under mixture distribution.
  sq_mean_wa = tf.matmul(pi_for_dot_prod, tf.square(mu_for_dot_prod))
  sq_mean_wa = tf.reshape(sq_mean_wa, (-1,))
  mixture_variance = var_wa + sq_mean_wa - tf.square(mean_wa)
  return tf.sqrt(mixture_variance)
def mixture_stddev ( mixture_weight_vector , mean_vector , stddev_vector ) : tensorshape_util . assert_has_rank ( mixture_weight_vector . shape , 2 ) if not tensorshape_util . is_compatible_with ( mean_vector . shape , mixture_weight_vector . shape ) : raise ValueError ( "Expecting means to have same shape as mixture weights." ) if not tensorshape_util . is_compatible_with ( stddev_vector . shape , mixture_weight_vector . shape ) : raise ValueError ( "Expecting stddevs to have same shape as mixture weights." ) # Reshape the distribution parameters for batched vectorized dot products. pi_for_dot_prod = tf . expand_dims ( mixture_weight_vector , axis = 1 ) mu_for_dot_prod = tf . expand_dims ( mean_vector , axis = 2 ) sigma_for_dot_prod = tf . expand_dims ( stddev_vector , axis = 2 ) # weighted average of component means under mixture distribution. mean_wa = tf . matmul ( pi_for_dot_prod , mu_for_dot_prod ) mean_wa = tf . reshape ( mean_wa , ( - 1 , ) ) # weighted average of component variances under mixture distribution. var_wa = tf . matmul ( pi_for_dot_prod , tf . square ( sigma_for_dot_prod ) ) var_wa = tf . reshape ( var_wa , ( - 1 , ) ) # weighted average of component squared means under mixture distribution. sq_mean_wa = tf . matmul ( pi_for_dot_prod , tf . square ( mu_for_dot_prod ) ) sq_mean_wa = tf . reshape ( sq_mean_wa , ( - 1 , ) ) mixture_variance = var_wa + sq_mean_wa - tf . square ( mean_wa ) return tf . sqrt ( mixture_variance )
Computes the standard deviation of a mixture distribution.

  This function works regardless of the component distribution, so long as
  each component's mean and standard deviation can be provided.

  Args:
    mixture_weight_vector: A 2D tensor with shape [batch_size, num_components]
    mean_vector: A 2D tensor of mixture component means. Has shape `[batch_size,
      num_components]`.
    stddev_vector: A 2D tensor of mixture component standard deviations. Has
      shape `[batch_size, num_components]`.

  Returns:
    A 1D tensor of shape `[batch_size]` representing the standard deviation of
    the mixture distribution with given weights and component means and standard
    deviations.
  Raises:
    ValueError: If the shapes of the input tensors are not as expected.
Computes the standard deviation of a mixture distribution .