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):
  """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
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])`