tensorflow_probability/python/distributions/pareto.py
def _kl_pareto_pareto(a, b, name=None):
  """Calculate the batched KL divergence KL(a || b) with a and b Pareto.

  Args:
    a: instance of a Pareto distribution object.
    b: instance of a Pareto distribution object.
    name: (optional) Name to use for created operations.
      default is "kl_pareto_pareto".

  Returns:
    Batchwise KL(a || b)
  """
  with tf.name_scope(name or "kl_pareto_pareto"):
    # Consistent with
    # http://www.mast.queensu.ca/~communications/Papers/gil-msc11.pdf, page 55
    # Terminology is different from source to source for Pareto distributions.
    # The 'concentration' parameter corresponds to 'a' in that source, and the
    # 'scale' parameter corresponds to 'm'.
    final_batch_shape = distribution_util.get_broadcast_shape(
        a.concentration, b.concentration, a.scale, b.scale)
    common_type = dtype_util.common_dtype(
        [a.concentration, b.concentration, a.scale, b.scale], tf.float32)
    return tf.where(
        a.scale >= b.scale,
        b.concentration * (tf.math.log(a.scale) - tf.math.log(b.scale)) +
        tf.math.log(a.concentration) - tf.math.log(b.concentration) +
        b.concentration / a.concentration - 1.0,
        tf.broadcast_to(tf.cast(np.inf, common_type), final_batch_shape))
def _kl_pareto_pareto(a, b, name=None):
  """Calculate the batched KL divergence KL(a || b) with a and b Pareto.

  Args:
    a: instance of a Pareto distribution object.
    b: instance of a Pareto distribution object.
    name: (optional) Name to use for created operations.
      default is "kl_pareto_pareto".

  Returns:
    Batchwise KL(a || b)
  """
  with tf.name_scope(name or "kl_pareto_pareto"):
    # Consistent with
    # http://www.mast.queensu.ca/~communications/Papers/gil-msc11.pdf, page 55
    # Terminology is different from source to source for Pareto distributions.
    # The 'concentration' parameter corresponds to 'a' in that source, and the
    # 'scale' parameter corresponds to 'm'.
    final_batch_shape = distribution_util.get_broadcast_shape(
        a.concentration, b.concentration, a.scale, b.scale)
    common_type = dtype_util.common_dtype(
        [a.concentration, b.concentration, a.scale, b.scale], tf.float32)
    return tf.where(
        a.scale >= b.scale,
        b.concentration * (tf.math.log(a.scale) - tf.math.log(b.scale)) +
        tf.math.log(a.concentration) - tf.math.log(b.concentration) +
        b.concentration / a.concentration - 1.0,
        tf.broadcast_to(tf.cast(np.inf, common_type), final_batch_shape))
def _kl_pareto_pareto ( a , b , name = None ) : with tf . name_scope ( name or "kl_pareto_pareto" ) : # Consistent with # http://www.mast.queensu.ca/~communications/Papers/gil-msc11.pdf, page 55 # Terminology is different from source to source for Pareto distributions. # The 'concentration' parameter corresponds to 'a' in that source, and the # 'scale' parameter corresponds to 'm'. final_batch_shape = distribution_util . get_broadcast_shape ( a . concentration , b . concentration , a . scale , b . scale ) common_type = dtype_util . common_dtype ( [ a . concentration , b . concentration , a . scale , b . scale ] , tf . float32 ) return tf . where ( a . scale >= b . scale , b . concentration * ( tf . math . log ( a . scale ) - tf . math . log ( b . scale ) ) + tf . math . log ( a . concentration ) - tf . math . log ( b . concentration ) + b . concentration / a . concentration - 1.0 , tf . broadcast_to ( tf . cast ( np . inf , common_type ) , final_batch_shape ) )
Calculate the batched KL divergence KL(a || b) with a and b Pareto.

  Args:
    a: instance of a Pareto distribution object.
    b: instance of a Pareto distribution object.
    name: (optional) Name to use for created operations.
      default is "kl_pareto_pareto".

  Returns:
    Batchwise KL(a || b)
Calculate the batched KL divergence KL ( a || b ) with a and b Pareto .