tensorflow_probability/python/distributions/poisson_lognormal.py
def quadrature_scheme_lognormal_gauss_hermite(
    loc, scale, quadrature_size,
    validate_args=False, name=None):  # pylint: disable=unused-argument
  """Use Gauss-Hermite quadrature to form quadrature on positive-reals.

  Note: for a given `quadrature_size`, this method is generally less accurate
  than `quadrature_scheme_lognormal_quantiles`.

  Args:
    loc: `float`-like (batch of) scalar `Tensor`; the location parameter of
      the LogNormal prior.
    scale: `float`-like (batch of) scalar `Tensor`; the scale parameter of
      the LogNormal prior.
    quadrature_size: Python `int` scalar representing the number of quadrature
      points.
    validate_args: Python `bool`, default `False`. When `True` distribution
      parameters are checked for validity despite possibly degrading runtime
      performance. When `False` invalid inputs may silently render incorrect
      outputs.
    name: Python `str` name prefixed to Ops created by this class.

  Returns:
    grid: (Batch of) length-`quadrature_size` vectors representing the
      `log_rate` parameters of a `Poisson`.
    probs: (Batch of) length-`quadrature_size` vectors representing the
      weight associate with each `grid` value.
  """
  with tf.name_scope(
      name or "vector_diffeomixture_quadrature_gauss_hermite"):
    grid, probs = np.polynomial.hermite.hermgauss(deg=quadrature_size)
    npdt = dtype_util.as_numpy_dtype(loc.dtype)
    grid = grid.astype(npdt)
    probs = probs.astype(npdt)
    probs /= np.linalg.norm(probs, ord=1, keepdims=True)
    probs = tf.convert_to_tensor(value=probs, name="probs", dtype=loc.dtype)
    # The following maps the broadcast of `loc` and `scale` to each grid
    # point, i.e., we are creating several log-rates that correspond to the
    # different Gauss-Hermite quadrature points and (possible) batches of
    # `loc` and `scale`.
    grid = (loc[..., tf.newaxis] + np.sqrt(2.) * scale[..., tf.newaxis] * grid)
    return grid, probs
def quadrature_scheme_lognormal_gauss_hermite(
    loc, scale, quadrature_size,
    validate_args=False, name=None):  # pylint: disable=unused-argument
  """Use Gauss-Hermite quadrature to form quadrature on positive-reals.

  Note: for a given `quadrature_size`, this method is generally less accurate
  than `quadrature_scheme_lognormal_quantiles`.

  Args:
    loc: `float`-like (batch of) scalar `Tensor`; the location parameter of
      the LogNormal prior.
    scale: `float`-like (batch of) scalar `Tensor`; the scale parameter of
      the LogNormal prior.
    quadrature_size: Python `int` scalar representing the number of quadrature
      points.
    validate_args: Python `bool`, default `False`. When `True` distribution
      parameters are checked for validity despite possibly degrading runtime
      performance. When `False` invalid inputs may silently render incorrect
      outputs.
    name: Python `str` name prefixed to Ops created by this class.

  Returns:
    grid: (Batch of) length-`quadrature_size` vectors representing the
      `log_rate` parameters of a `Poisson`.
    probs: (Batch of) length-`quadrature_size` vectors representing the
      weight associate with each `grid` value.
  """
  with tf.name_scope(
      name or "vector_diffeomixture_quadrature_gauss_hermite"):
    grid, probs = np.polynomial.hermite.hermgauss(deg=quadrature_size)
    npdt = dtype_util.as_numpy_dtype(loc.dtype)
    grid = grid.astype(npdt)
    probs = probs.astype(npdt)
    probs /= np.linalg.norm(probs, ord=1, keepdims=True)
    probs = tf.convert_to_tensor(value=probs, name="probs", dtype=loc.dtype)
    # The following maps the broadcast of `loc` and `scale` to each grid
    # point, i.e., we are creating several log-rates that correspond to the
    # different Gauss-Hermite quadrature points and (possible) batches of
    # `loc` and `scale`.
    grid = (loc[..., tf.newaxis] + np.sqrt(2.) * scale[..., tf.newaxis] * grid)
    return grid, probs
Use Gauss-Hermite quadrature to form quadrature on positive-reals.

  Note: for a given `quadrature_size`, this method is generally less accurate
  than `quadrature_scheme_lognormal_quantiles`.

  Args:
    loc: `float`-like (batch of) scalar `Tensor`; the location parameter of
      the LogNormal prior.
    scale: `float`-like (batch of) scalar `Tensor`; the scale parameter of
      the LogNormal prior.
    quadrature_size: Python `int` scalar representing the number of quadrature
      points.
    validate_args: Python `bool`, default `False`. When `True` distribution
      parameters are checked for validity despite possibly degrading runtime
      performance. When `False` invalid inputs may silently render incorrect
      outputs.
    name: Python `str` name prefixed to Ops created by this class.

  Returns:
    grid: (Batch of) length-`quadrature_size` vectors representing the
      `log_rate` parameters of a `Poisson`.
    probs: (Batch of) length-`quadrature_size` vectors representing the
      weight associate with each `grid` value.