Python distribution_util.shapes_from_loc_and_scale函数代码示例

  def __init__(self,
               loc=None,
               scale=None,
               validate_args=False,
               allow_nan_stats=True,
               name="VectorExponentialLinearOperator"):
    """Construct Vector Exponential distribution supported on a subset of `R^k`.

    The `batch_shape` is the broadcast shape between `loc` and `scale`
    arguments.

    The `event_shape` is given by last dimension of the matrix implied by
    `scale`. The last dimension of `loc` (if provided) must broadcast with this.

    Recall that `covariance = scale @ scale.T`.

    Additional leading dimensions (if any) will index batches.

    Args:
      loc: Floating-point `Tensor`. If this is set to `None`, `loc` is
        implicitly `0`. When specified, may have shape `[B1, ..., Bb, k]` where
        `b >= 0` and `k` is the event size.
      scale: Instance of `LinearOperator` with same `dtype` as `loc` and shape
        `[B1, ..., Bb, k, k]`.
      validate_args: Python `bool`, default `False`. Whether to validate input
        with asserts. If `validate_args` is `False`, and the inputs are
        invalid, correct behavior is not guaranteed.
      allow_nan_stats: Python `bool`, default `True`. If `False`, raise an
        exception if a statistic (e.g. mean/mode/etc...) is undefined for any
        batch member If `True`, batch members with valid parameters leading to
        undefined statistics will return NaN for this statistic.
      name: The name to give Ops created by the initializer.

    Raises:
      ValueError: if `scale` is unspecified.
      TypeError: if not `scale.dtype.is_floating`
    """
    parameters = locals()
    if scale is None:
      raise ValueError("Missing required `scale` parameter.")
    if not scale.dtype.is_floating:
      raise TypeError("`scale` parameter must have floating-point dtype.")

    with ops.name_scope(name, values=[loc] + scale.graph_parents):
      # Since expand_dims doesn't preserve constant-ness, we obtain the
      # non-dynamic value if possible.
      loc = ops.convert_to_tensor(loc, name="loc") if loc is not None else loc
      batch_shape, event_shape = distribution_util.shapes_from_loc_and_scale(
          loc, scale)

      super(VectorExponentialLinearOperator, self).__init__(
          distribution=exponential.Exponential(rate=array_ops.ones(
              [], dtype=scale.dtype), allow_nan_stats=allow_nan_stats),
          bijector=bijectors.AffineLinearOperator(
              shift=loc, scale=scale, validate_args=validate_args),
          batch_shape=batch_shape,
          event_shape=event_shape,
          validate_args=validate_args,
          name=name)
      self._parameters = parameters

  def test_none_loc_static_scale(self):
    loc = None
    scale = linear_operator_diag.LinearOperatorDiag(np.ones((5, 1, 3)))
    batch_shape, event_shape = distribution_util.shapes_from_loc_and_scale(
        loc, scale)

    self.assertEqual(tensor_shape.TensorShape([5, 1]), batch_shape)
    self.assertEqual(tensor_shape.TensorShape([3]), event_shape)

 def test_none_loc_dynamic_scale(self):
   loc = None
   diag = array_ops.placeholder(dtypes.float64)
   scale = linear_operator_diag.LinearOperatorDiag(diag)
   with self.test_session() as sess:
     batch_shape, event_shape = sess.run(
         distribution_util.shapes_from_loc_and_scale(loc, scale),
         feed_dict={diag: np.ones((5, 1, 3))})
     self.assertAllEqual([5, 1], batch_shape)
     self.assertAllEqual([3], event_shape)

 def test_dynamic_loc_static_scale(self):
   loc = array_ops.placeholder(dtypes.float64)
   diag = constant_op.constant(np.ones((5, 2, 3)))
   scale = linear_operator_diag.LinearOperatorDiag(diag)
   with self.test_session():
     batch_shape, event_shape = distribution_util.shapes_from_loc_and_scale(
         loc, scale)
     # batch_shape depends on both args, and so is dynamic.  Since loc did not
     # have static shape, we inferred event shape entirely from scale, and this
     # is available statically.
     self.assertAllEqual(
         [5, 2], batch_shape.eval(feed_dict={loc: np.zeros((2, 3))}))
     self.assertAllEqual([3], event_shape)

def determine_batch_event_shapes(mix_loc, mix_scale, endpoint_affine):
  """Helper to infer batch_shape and event_shape."""
  with ops.name_scope(name="determine_batch_event_shapes"):
    mix_batch_shape = distribution_util.prefer_static_broadcast_shape(
        array_ops.shape(mix_loc, name="mix_loc_shape"),
        array_ops.shape(mix_scale, name="mix_scale_shape"))
    if isinstance(mix_batch_shape, tensor_shape.TensorShape):
      mix_batch_shape = mix_batch_shape.with_rank_at_least(1)[:-1]
    else:
      s = static_value(mix_batch_shape)
      if s is not None:
        mix_batch_shape = ops.convert_to_tensor(
            s[:-1], dtype=dtypes.int32, name="mix_batch_shape")
      else:
        mix_batch_shape = mix_batch_shape[:-1]

    # We broadcast with a 1D constant to automatically make the result a
    # TensorShape if possible.
    batch_shape = distribution_util.prefer_static_broadcast_shape(
        mix_batch_shape,
        constant_op.constant([], dtype=dtypes.int32, name="batch_shape"))
    event_shape = constant_op.constant(
        [], dtype=dtypes.int32, name="event_shape")
    for aff in endpoint_affine:
      b, e = distribution_util.shapes_from_loc_and_scale(aff.shift, aff.scale)
      if batch_shape is None:
        batch_shape = distribution_util.prefer_static_broadcast_shape(
            mix_batch_shape, b)
      else:
        batch_shape = distribution_util.prefer_static_broadcast_shape(
            batch_shape, b)
      event_shape = distribution_util.prefer_static_broadcast_shape(
          event_shape, e)
    if isinstance(batch_shape, tensor_shape.TensorShape):
      batch_shape = ops.convert_to_tensor(
          batch_shape.as_list(), dtype=dtypes.int32, name="batch_shape")
    if isinstance(event_shape, tensor_shape.TensorShape):
      event_shape = ops.convert_to_tensor(
          event_shape.as_list(), dtype=dtypes.int32, name="event_shape")
    return batch_shape, event_shape

  def __init__(self,
               loc=None,
               scale_diag=None,
               scale_identity_multiplier=None,
               skewness=None,
               tailweight=None,
               distribution=None,
               validate_args=False,
               allow_nan_stats=True,
               name="MultivariateNormalLinearOperator"):
    """Construct VectorSinhArcsinhDiag distribution on `R^k`.

    The arguments `scale_diag` and `scale_identity_multiplier` combine to
    define the diagonal `scale` referred to in this class docstring:

    ```none
    scale = diag(scale_diag + scale_identity_multiplier * ones(k))
    ```

    The `batch_shape` is the broadcast shape between `loc` and `scale`
    arguments.

    The `event_shape` is given by last dimension of the matrix implied by
    `scale`. The last dimension of `loc` (if provided) must broadcast with this

    Additional leading dimensions (if any) will index batches.

    Args:
      loc: Floating-point `Tensor`. If this is set to `None`, `loc` is
        implicitly `0`. When specified, may have shape `[B1, ..., Bb, k]` where
        `b >= 0` and `k` is the event size.
      scale_diag: Non-zero, floating-point `Tensor` representing a diagonal
        matrix added to `scale`. May have shape `[B1, ..., Bb, k]`, `b >= 0`,
        and characterizes `b`-batches of `k x k` diagonal matrices added to
        `scale`. When both `scale_identity_multiplier` and `scale_diag` are
        `None` then `scale` is the `Identity`.
      scale_identity_multiplier: Non-zero, floating-point `Tensor` representing
        a scale-identity-matrix added to `scale`. May have shape
        `[B1, ..., Bb]`, `b >= 0`, and characterizes `b`-batches of scale
        `k x k` identity matrices added to `scale`. When both
        `scale_identity_multiplier` and `scale_diag` are `None` then `scale`
        is the `Identity`.
      skewness:  Skewness parameter.  floating-point `Tensor` with shape
        broadcastable with `event_shape`.
      tailweight:  Tailweight parameter.  floating-point `Tensor` with shape
        broadcastable with `event_shape`.
      distribution: `tf.Distribution`-like instance. Distribution from which `k`
        iid samples are used as input to transformation `F`.  Default is
        `tf.distributions.Normal(loc=0., scale=1.)`.
        Must be a scalar-batch, scalar-event distribution.  Typically
        `distribution.reparameterization_type = FULLY_REPARAMETERIZED` or it is
        a function of non-trainable parameters. WARNING: If you backprop through
        a VectorSinhArcsinhDiag sample and `distribution` is not
        `FULLY_REPARAMETERIZED` yet is a function of trainable variables, then
        the gradient will be incorrect!
      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.
      allow_nan_stats: Python `bool`, default `True`. When `True`,
        statistics (e.g., mean, mode, variance) use the value "`NaN`" to
        indicate the result is undefined. When `False`, an exception is raised
        if one or more of the statistic's batch members are undefined.
      name: Python `str` name prefixed to Ops created by this class.

    Raises:
      ValueError: if at most `scale_identity_multiplier` is specified.
    """
    parameters = dict(locals())

    with ops.name_scope(
        name,
        values=[
            loc, scale_diag, scale_identity_multiplier, skewness, tailweight
        ]) as name:
      loc = ops.convert_to_tensor(loc, name="loc") if loc is not None else loc
      tailweight = 1. if tailweight is None else tailweight
      has_default_skewness = skewness is None
      skewness = 0. if skewness is None else skewness

      # Recall, with Z a random variable,
      #   Y := loc + C * F(Z),
      #   F(Z) := Sinh( (Arcsinh(Z) + skewness) * tailweight )
      #   F_0(Z) := Sinh( Arcsinh(Z) * tailweight )
      #   C := 2 * scale / F_0(2)

      # Construct shapes and 'scale' out of the scale_* and loc kwargs.
      # scale_linop is only an intermediary to:
      #  1. get shapes from looking at loc and the two scale args.
      #  2. combine scale_diag with scale_identity_multiplier, which gives us
      #     'scale', which in turn gives us 'C'.
      scale_linop = distribution_util.make_diag_scale(
          loc=loc,
          scale_diag=scale_diag,
          scale_identity_multiplier=scale_identity_multiplier,
          validate_args=False,
          assert_positive=False)
      batch_shape, event_shape = distribution_util.shapes_from_loc_and_scale(
          loc, scale_linop)
      # scale_linop.diag_part() is efficient since it is a diag type linop.
#.........这里部分代码省略.........

 def test_static_loc_static_scale_non_matching_event_size_raises(self):
   loc = constant_op.constant(np.zeros((2, 4)))
   scale = linear_operator_diag.LinearOperatorDiag(np.ones((5, 1, 3)))
   with self.assertRaisesRegexp(ValueError, "could not be broadcast"):
     distribution_util.shapes_from_loc_and_scale(loc, scale)

  def __init__(self,
               df,
               loc=None,
               scale_identity_multiplier=None,
               scale_diag=None,
               scale_tril=None,
               scale_perturb_factor=None,
               scale_perturb_diag=None,
               validate_args=False,
               allow_nan_stats=True,
               name="VectorStudentT"):
    """Instantiates the vector Student's t-distributions on `R^k`.

    The `batch_shape` is the broadcast between `df.batch_shape` and
    `Affine.batch_shape` where `Affine` is constructed from `loc` and
    `scale_*` arguments.

    The `event_shape` is the event shape of `Affine.event_shape`.

    Args:
      df: Floating-point `Tensor`. The degrees of freedom of the
        distribution(s). `df` must contain only positive values. Must be
        scalar if `loc`, `scale_*` imply non-scalar batch_shape or must have the
        same `batch_shape` implied by `loc`, `scale_*`.
      loc: Floating-point `Tensor`. If this is set to `None`, no `loc` is
        applied.
      scale_identity_multiplier: floating point rank 0 `Tensor` representing a
        scaling done to the identity matrix. When `scale_identity_multiplier =
        scale_diag=scale_tril = None` then `scale += IdentityMatrix`. Otherwise
        no scaled-identity-matrix is added to `scale`.
      scale_diag: Floating-point `Tensor` representing the diagonal matrix.
        `scale_diag` has shape [N1, N2, ..., k], which represents a k x k
        diagonal matrix. When `None` no diagonal term is added to `scale`.
      scale_tril: Floating-point `Tensor` representing the diagonal matrix.
        `scale_diag` has shape [N1, N2, ..., k, k], which represents a k x k
        lower triangular matrix. When `None` no `scale_tril` term is added to
        `scale`. The upper triangular elements above the diagonal are ignored.
      scale_perturb_factor: Floating-point `Tensor` representing factor matrix
        with last two dimensions of shape `(k, r)`. When `None`, no rank-r
        update is added to `scale`.
      scale_perturb_diag: Floating-point `Tensor` representing the diagonal
        matrix. `scale_perturb_diag` has shape [N1, N2, ..., r], which
        represents an r x r Diagonal matrix. When `None` low rank updates will
        take the form `scale_perturb_factor * scale_perturb_factor.T`.
      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.
      allow_nan_stats: Python `bool`, default `True`. When `True`,
        statistics (e.g., mean, mode, variance) use the value "`NaN`" to
        indicate the result is undefined. When `False`, an exception is raised
        if one or more of the statistic's batch members are undefined.
      name: Python `str` name prefixed to Ops created by this class.
    """
    parameters = locals()
    graph_parents = [df, loc, scale_identity_multiplier, scale_diag,
                     scale_tril, scale_perturb_factor, scale_perturb_diag]
    with ops.name_scope(name) as name:
      with ops.name_scope("init", values=graph_parents):
        # The shape of the _VectorStudentT distribution is governed by the
        # relationship between df.batch_shape and affine.batch_shape. In
        # pseudocode the basic procedure is:
        #   if df.batch_shape is scalar:
        #     if affine.batch_shape is not scalar:
        #       # broadcast distribution.sample so
        #       # it has affine.batch_shape.
        #     self.batch_shape = affine.batch_shape
        #   else:
        #     if affine.batch_shape is scalar:
        #       # let affine broadcasting do its thing.
        #     self.batch_shape = df.batch_shape
        # All of the above magic is actually handled by TransformedDistribution.
        # Here we really only need to collect the affine.batch_shape and decide
        # what we're going to pass in to TransformedDistribution's
        # (override) batch_shape arg.
        affine = bijectors.Affine(
            shift=loc,
            scale_identity_multiplier=scale_identity_multiplier,
            scale_diag=scale_diag,
            scale_tril=scale_tril,
            scale_perturb_factor=scale_perturb_factor,
            scale_perturb_diag=scale_perturb_diag,
            validate_args=validate_args)
        distribution = student_t.StudentT(
            df=df,
            loc=array_ops.zeros([], dtype=affine.dtype),
            scale=array_ops.ones([], dtype=affine.dtype))
        batch_shape, override_event_shape = (
            distribution_util.shapes_from_loc_and_scale(
                affine.shift, affine.scale))
        override_batch_shape = distribution_util.pick_vector(
            distribution.is_scalar_batch(),
            batch_shape,
            constant_op.constant([], dtype=dtypes.int32))
        super(_VectorStudentT, self).__init__(
            distribution=distribution,
            bijector=affine,
            batch_shape=override_batch_shape,
            event_shape=override_event_shape,
            validate_args=validate_args,
#.........这里部分代码省略.........