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):  
  
  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)
    
    
    
    
    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 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.
Use Gauss - Hermite quadrature to form quadrature on positive - reals .