tensorflow_probability/python/internal/distribution_util.py
def process_quadrature_grid_and_probs(quadrature_grid_and_probs,
                                      dtype,
                                      validate_args,
                                      name=None):
  """Validates quadrature grid, probs or computes them as necessary.

  Args:
    quadrature_grid_and_probs: Python pair of `float`-like `Tensor`s
      representing the sample points and the corresponding (possibly
      normalized) weight.  When `None`, defaults to:
        `np.polynomial.hermite.hermgauss(deg=8)`.
    dtype: The expected `dtype` of `grid` and `probs`.
    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:
     quadrature_grid_and_probs: Python pair of `float`-like `Tensor`s
      representing the sample points and the corresponding (possibly
      normalized) weight.

  Raises:
    ValueError: if `quadrature_grid_and_probs is not None` and
      `len(quadrature_grid_and_probs[0]) != len(quadrature_grid_and_probs[1])`
  """
  with tf.name_scope(name or "process_quadrature_grid_and_probs"):
    if quadrature_grid_and_probs is None:
      grid, probs = np.polynomial.hermite.hermgauss(deg=8)
      grid = grid.astype(dtype_util.as_numpy_dtype(dtype))
      probs = probs.astype(dtype_util.as_numpy_dtype(dtype))
      probs /= np.linalg.norm(probs, ord=1, keepdims=True)
      grid = tf.convert_to_tensor(value=grid, name="grid", dtype=dtype)
      probs = tf.convert_to_tensor(value=probs, name="probs", dtype=dtype)
      return grid, probs

    grid, probs = tuple(quadrature_grid_and_probs)
    grid = tf.convert_to_tensor(value=grid, name="grid", dtype=dtype)
    probs = tf.convert_to_tensor(
        value=probs, name="unnormalized_probs", dtype=dtype)
    probs /= tf.norm(tensor=probs, ord=1, axis=-1, keepdims=True, name="probs")

    def _static_event_size(x):
      """Returns the static size of a specific dimension or `None`."""
      return tf.compat.dimension_value(
          tensorshape_util.with_rank_at_least(x.shape, 1)[-1])

    m, n = _static_event_size(probs), _static_event_size(grid)
    if m is not None and n is not None:
      if m != n:
        raise ValueError("`quadrature_grid_and_probs` must be a `tuple` of "
                         "same-length zero-th-dimension `Tensor`s "
                         "(saw lengths {}, {})".format(m, n))
    elif validate_args:
      assertions = [
          assert_util.assert_equal(
              dimension_size(probs, axis=-1),
              dimension_size(grid, axis=-1),
              message=("`quadrature_grid_and_probs` must be a `tuple` of "
                       "same-length zero-th-dimension `Tensor`s")),
      ]
      with tf.control_dependencies(assertions):
        grid = tf.identity(grid)
        probs = tf.identity(probs)
    return grid, probs
def process_quadrature_grid_and_probs(quadrature_grid_and_probs,
                                      dtype,
                                      validate_args,
                                      name=None):
  with tf.name_scope(name or "process_quadrature_grid_and_probs"):
    if quadrature_grid_and_probs is None:
      grid, probs = np.polynomial.hermite.hermgauss(deg=8)
      grid = grid.astype(dtype_util.as_numpy_dtype(dtype))
      probs = probs.astype(dtype_util.as_numpy_dtype(dtype))
      probs /= np.linalg.norm(probs, ord=1, keepdims=True)
      grid = tf.convert_to_tensor(value=grid, name="grid", dtype=dtype)
      probs = tf.convert_to_tensor(value=probs, name="probs", dtype=dtype)
      return grid, probs

    grid, probs = tuple(quadrature_grid_and_probs)
    grid = tf.convert_to_tensor(value=grid, name="grid", dtype=dtype)
    probs = tf.convert_to_tensor(
        value=probs, name="unnormalized_probs", dtype=dtype)
    probs /= tf.norm(tensor=probs, ord=1, axis=-1, keepdims=True, name="probs")

    def _static_event_size(x):
      """Returns the static size of a specific dimension or `None`."""
      return tf.compat.dimension_value(
          tensorshape_util.with_rank_at_least(x.shape, 1)[-1])

    m, n = _static_event_size(probs), _static_event_size(grid)
    if m is not None and n is not None:
      if m != n:
        raise ValueError("`quadrature_grid_and_probs` must be a `tuple` of "
                         "same-length zero-th-dimension `Tensor`s "
                         "(saw lengths {}, {})".format(m, n))
    elif validate_args:
      assertions = [
          assert_util.assert_equal(
              dimension_size(probs, axis=-1),
              dimension_size(grid, axis=-1),
              message=("`quadrature_grid_and_probs` must be a `tuple` of "
                       "same-length zero-th-dimension `Tensor`s")),
      ]
      with tf.control_dependencies(assertions):
        grid = tf.identity(grid)
        probs = tf.identity(probs)
    return grid, probs
def process_quadrature_grid_and_probs ( quadrature_grid_and_probs , dtype , validate_args , name = None ) : with tf . name_scope ( name or "process_quadrature_grid_and_probs" ) : if quadrature_grid_and_probs is None : grid , probs = np . polynomial . hermite . hermgauss ( deg = 8 ) grid = grid . astype ( dtype_util . as_numpy_dtype ( dtype ) ) probs = probs . astype ( dtype_util . as_numpy_dtype ( dtype ) ) probs /= np . linalg . norm ( probs , ord = 1 , keepdims = True ) grid = tf . convert_to_tensor ( value = grid , name = "grid" , dtype = dtype ) probs = tf . convert_to_tensor ( value = probs , name = "probs" , dtype = dtype ) return grid , probs grid , probs = tuple ( quadrature_grid_and_probs ) grid = tf . convert_to_tensor ( value = grid , name = "grid" , dtype = dtype ) probs = tf . convert_to_tensor ( value = probs , name = "unnormalized_probs" , dtype = dtype ) probs /= tf . norm ( tensor = probs , ord = 1 , axis = - 1 , keepdims = True , name = "probs" ) def _static_event_size ( x ) : """Returns the static size of a specific dimension or `None`.""" return tf . compat . dimension_value ( tensorshape_util . with_rank_at_least ( x . shape , 1 ) [ - 1 ] ) m , n = _static_event_size ( probs ) , _static_event_size ( grid ) if m is not None and n is not None : if m != n : raise ValueError ( "`quadrature_grid_and_probs` must be a `tuple` of " "same-length zero-th-dimension `Tensor`s " "(saw lengths {}, {})" . format ( m , n ) ) elif validate_args : assertions = [ assert_util . assert_equal ( dimension_size ( probs , axis = - 1 ) , dimension_size ( grid , axis = - 1 ) , message = ( "`quadrature_grid_and_probs` must be a `tuple` of " "same-length zero-th-dimension `Tensor`s" ) ) , ] with tf . control_dependencies ( assertions ) : grid = tf . identity ( grid ) probs = tf . identity ( probs ) return grid , probs
Validates quadrature grid, probs or computes them as necessary.

  Args:
    quadrature_grid_and_probs: Python pair of `float`-like `Tensor`s
      representing the sample points and the corresponding (possibly
      normalized) weight.  When `None`, defaults to:
        `np.polynomial.hermite.hermgauss(deg=8)`.
    dtype: The expected `dtype` of `grid` and `probs`.
    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:
     quadrature_grid_and_probs: Python pair of `float`-like `Tensor`s
      representing the sample points and the corresponding (possibly
      normalized) weight.

  Raises:
    ValueError: if `quadrature_grid_and_probs is not None` and
      `len(quadrature_grid_and_probs[0]) != len(quadrature_grid_and_probs[1])`
Validates quadrature grid probs or computes them as necessary .