tensorflow_probability/python/bijectors/affine.py
def _create_scale_operator(self, identity_multiplier, diag, tril,
                             perturb_diag, perturb_factor, shift, validate_args,
                             dtype):
    """Construct `scale` from various components.

    Args:
      identity_multiplier: floating point rank 0 `Tensor` representing a scaling
        done to the identity matrix.
      diag: Floating-point `Tensor` representing the diagonal matrix.`diag` has
        shape `[N1, N2, ...  k]`, which represents a k x k diagonal matrix.
      tril: Floating-point `Tensor` representing the lower triangular matrix.
       `tril` has shape `[N1, N2, ...  k, k]`, which represents a k x k lower
       triangular matrix.
      perturb_diag: Floating-point `Tensor` representing the diagonal matrix of
        the low rank update.
      perturb_factor: Floating-point `Tensor` representing factor matrix.
      shift: Floating-point `Tensor` representing `shift in `scale @ X + shift`.
      validate_args: Python `bool` indicating whether arguments should be
        checked for correctness.
      dtype: `DType` for arg `Tensor` conversions.

    Returns:
      scale. In the case of scaling by a constant, scale is a
      floating point `Tensor`. Otherwise, scale is a `LinearOperator`.

    Raises:
      ValueError: if all of `tril`, `diag` and `identity_multiplier` are `None`.
    """
    identity_multiplier = _as_tensor(identity_multiplier, "identity_multiplier",
                                     dtype)
    diag = _as_tensor(diag, "diag", dtype)
    tril = _as_tensor(tril, "tril", dtype)
    perturb_diag = _as_tensor(perturb_diag, "perturb_diag", dtype)
    perturb_factor = _as_tensor(perturb_factor, "perturb_factor", dtype)

    # If possible, use the low rank update to infer the shape of
    # the identity matrix, when scale represents a scaled identity matrix
    # with a low rank update.
    shape_hint = None
    if perturb_factor is not None:
      shape_hint = distribution_util.dimension_size(perturb_factor, axis=-2)

    if self._is_only_identity_multiplier:
      if validate_args:
        return distribution_util.with_dependencies([
            assert_util.assert_none_equal(
                identity_multiplier, tf.zeros([], identity_multiplier.dtype),
                ["identity_multiplier should be non-zero."])
        ], identity_multiplier)
      return identity_multiplier

    scale = distribution_util.make_tril_scale(
        loc=shift,
        scale_tril=tril,
        scale_diag=diag,
        scale_identity_multiplier=identity_multiplier,
        validate_args=validate_args,
        assert_positive=False,
        shape_hint=shape_hint)

    if perturb_factor is not None:
      return tf.linalg.LinearOperatorLowRankUpdate(
          scale,
          u=perturb_factor,
          diag_update=perturb_diag,
          is_diag_update_positive=perturb_diag is None,
          is_non_singular=True,  # Implied by is_positive_definite=True.
          is_self_adjoint=True,
          is_positive_definite=True,
          is_square=True)

    return scale
def _create_scale_operator(self, identity_multiplier, diag, tril,
                             perturb_diag, perturb_factor, shift, validate_args,
                             dtype):
    """Construct `scale` from various components.

    Args:
      identity_multiplier: floating point rank 0 `Tensor` representing a scaling
        done to the identity matrix.
      diag: Floating-point `Tensor` representing the diagonal matrix.`diag` has
        shape `[N1, N2, ...  k]`, which represents a k x k diagonal matrix.
      tril: Floating-point `Tensor` representing the lower triangular matrix.
       `tril` has shape `[N1, N2, ...  k, k]`, which represents a k x k lower
       triangular matrix.
      perturb_diag: Floating-point `Tensor` representing the diagonal matrix of
        the low rank update.
      perturb_factor: Floating-point `Tensor` representing factor matrix.
      shift: Floating-point `Tensor` representing `shift in `scale @ X + shift`.
      validate_args: Python `bool` indicating whether arguments should be
        checked for correctness.
      dtype: `DType` for arg `Tensor` conversions.

    Returns:
      scale. In the case of scaling by a constant, scale is a
      floating point `Tensor`. Otherwise, scale is a `LinearOperator`.

    Raises:
      ValueError: if all of `tril`, `diag` and `identity_multiplier` are `None`.
    """
    identity_multiplier = _as_tensor(identity_multiplier, "identity_multiplier",
                                     dtype)
    diag = _as_tensor(diag, "diag", dtype)
    tril = _as_tensor(tril, "tril", dtype)
    perturb_diag = _as_tensor(perturb_diag, "perturb_diag", dtype)
    perturb_factor = _as_tensor(perturb_factor, "perturb_factor", dtype)

    # If possible, use the low rank update to infer the shape of
    # the identity matrix, when scale represents a scaled identity matrix
    # with a low rank update.
    shape_hint = None
    if perturb_factor is not None:
      shape_hint = distribution_util.dimension_size(perturb_factor, axis=-2)

    if self._is_only_identity_multiplier:
      if validate_args:
        return distribution_util.with_dependencies([
            assert_util.assert_none_equal(
                identity_multiplier, tf.zeros([], identity_multiplier.dtype),
                ["identity_multiplier should be non-zero."])
        ], identity_multiplier)
      return identity_multiplier

    scale = distribution_util.make_tril_scale(
        loc=shift,
        scale_tril=tril,
        scale_diag=diag,
        scale_identity_multiplier=identity_multiplier,
        validate_args=validate_args,
        assert_positive=False,
        shape_hint=shape_hint)

    if perturb_factor is not None:
      return tf.linalg.LinearOperatorLowRankUpdate(
          scale,
          u=perturb_factor,
          diag_update=perturb_diag,
          is_diag_update_positive=perturb_diag is None,
          is_non_singular=True,  # Implied by is_positive_definite=True.
          is_self_adjoint=True,
          is_positive_definite=True,
          is_square=True)

    return scale
Construct `scale` from various components.

    Args:
      identity_multiplier: floating point rank 0 `Tensor` representing a scaling
        done to the identity matrix.
      diag: Floating-point `Tensor` representing the diagonal matrix.`diag` has
        shape `[N1, N2, ...  k]`, which represents a k x k diagonal matrix.
      tril: Floating-point `Tensor` representing the lower triangular matrix.
       `tril` has shape `[N1, N2, ...  k, k]`, which represents a k x k lower
       triangular matrix.
      perturb_diag: Floating-point `Tensor` representing the diagonal matrix of
        the low rank update.
      perturb_factor: Floating-point `Tensor` representing factor matrix.
      shift: Floating-point `Tensor` representing `shift in `scale @ X + shift`.
      validate_args: Python `bool` indicating whether arguments should be
        checked for correctness.
      dtype: `DType` for arg `Tensor` conversions.

    Returns:
      scale. In the case of scaling by a constant, scale is a
      floating point `Tensor`. Otherwise, scale is a `LinearOperator`.

    Raises:
      ValueError: if all of `tril`, `diag` and `identity_multiplier` are `None`.