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tfp.substrates.jax.bijectors.SinhArcsinh

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Y = g(X) = Sinh( (Arcsinh(X) + skewness) * tailweight ) * multiplier.

Inherits From: AutoCompositeTensorBijector, Bijector

For skewness in (-inf, inf) and tailweight in (0, inf), this transformation is a diffeomorphism of the real line (-inf, inf). The inverse transform is X = g^{-1}(Y) = Sinh( ArcSinh(Y) / tailweight - skewness ).

The SinhArcsinh transformation of the Normal is described in Sinh-arcsinh distributions This Bijector allows a similar transformation of any distribution supported on (-inf, inf).

Meaning of the parameters

  • If skewness = 0 and tailweight = 1, this transform is the identity.
  • Positive (negative) skewness leads to positive (negative) skew.
    • positive skew means, for unimodal X centered at zero, the mode of Y is "tilted" to the right.
    • positive skew means positive values of Y become more likely, and negative values become less likely.
  • Larger (smaller) tailweight leads to fatter (thinner) tails.
    • Fatter tails mean larger values of |Y| become more likely.
    • If X is a unit Normal, tailweight < 1 leads to a distribution that is "flat" around Y = 0, and a very steep drop-off in the tails.
    • If X is a unit Normal, tailweight > 1 leads to a distribution more peaked at the mode with heavier tails.
  • The multiplier term is equal to 2 / F_0(2) where F_0 is the bijector with skewness = 0. This is important for CDF values of distributions.SinhArcsinh.

To see the argument about the tails, note that for |X| >> 1 and |X| >> (|skewness| * tailweight)**tailweight, we have Y approx 0.5 X**tailweight e**(sign(X) skewness * tailweight).

skewness Skewness parameter. Float-type Tensor. Default is 0 of type float32.
tailweight Tailweight parameter. Positive Tensor of same dtype as skewness and broadcastable shape. Default is 1 of type float32.
validate_args Python bool indicating whether arguments should be checked for correctness.
name Python str name given to ops managed by this object.

dtype

forward_min_event_ndims Returns the minimal number of dimensions bijector.forward operates on.

Multipart bijectors return structured ndims, which indicates the expected structure of their inputs. Some multipart bijectors, notably Composites, may return structures of None.

graph_parents Returns this Bijector's graph_parents as a Python list.
has_static_min_event_ndims Returns True if the bijector has statically-known min_event_ndims.
inverse_min_event_ndims Returns the minimal number of dimensions bijector.inverse operates on.

Multipart bijectors return structured event_ndims, which indicates the expected structure of their outputs. Some multipart bijectors, notably Composites, may return structures of None.

is_constant_jacobian Returns true iff the Jacobian matrix is not a function of x.

name Returns the string name of this Bijector.
parameters Dictionary of parameters used to instantiate this Bijector.
skewness The skewness in: Y = Sinh((Arcsinh(X) + skewness) * tailweight).
tailweight The tailweight in: Y = Sinh((Arcsinh(X) + skewness) * tailweight).
trainable_variables

validate_args Returns True if Tensor arguments will be validated.
variables

Methods

copy

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Creates a copy of the bijector.

Args
**override_parameters_kwargs String/value dictionary of initialization arguments to override with new values.

Returns
bijector A new instance of type(self) initialized from the union of self.parameters and override_parameters_kwargs, i.e., dict(self.parameters, **override_parameters_kwargs).

experimental_batch_shape

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Returns the batch shape of this bijector for inputs of the given rank.

The batch shape of a bijector decribes the set of distinct transformations it represents on events of a given size. For example: the bijector tfb.Scale([1., 2.]) has batch shape [2] for scalar events (event_ndims = 0), because applying it to a scalar event produces two scalar outputs, the result of two different scaling transformations. The same bijector has batch shape [] for vector events, because applying it to a vector produces (via elementwise multiplication) a single vector output.

Bijectors that operate independently on multiple state parts, such as tfb.JointMap, must broadcast to a coherent batch shape. Some events may not be valid: for example, the bijector tfd.JointMap([tfb.Scale([1., 2.]), tfb.Scale([1., 2., 3.])]) does not produce a valid batch shape when event_ndims = [0, 0], since the batch shapes of the two parts are inconsistent. The same bijector does define valid batch shapes of [], [2], and [3] if event_ndims is [1, 1], [0, 1], or [1, 0], respectively.

Since transforming a single event produces a scalar log-det-Jacobian, the batch shape of a bijector with non-constant Jacobian is expected to equal the shape of forward_log_det_jacobian(x, event_ndims=x_event_ndims) or inverse_log_det_jacobian(y, event_ndims=y_event_ndims), for x or y of the specified ndims.

Args
x_event_ndims Optional Python int (structure) number of dimensions in a probabilistic event passed to forward; this must be greater than or equal to self.forward_min_event_ndims. If None, defaults to self.forward_min_event_ndims. Mutually exclusive with y_event_ndims. Default value: None.
y_event_ndims Optional Python int (structure) number of dimensions in a probabilistic event passed to inverse; this must be greater than or equal to self.inverse_min_event_ndims. Mutually exclusive with x_event_ndims. Default value: None.

Returns
batch_shape TensorShape batch shape of this bijector for a value with the given event rank. May be unknown or partially defined.

experimental_batch_shape_tensor

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Returns the batch shape of this bijector for inputs of the given rank.

The batch shape of a bijector decribes the set of distinct transformations it represents on events of a given size. For example: the bijector tfb.Scale([1., 2.]) has batch shape [2] for scalar events (event_ndims = 0), because applying it to a scalar event produces two scalar outputs, the result of two different scaling transformations. The same bijector has batch shape [] for vector events, because applying it to a vector produces (via elementwise multiplication) a single vector output.

Bijectors that operate independently on multiple state parts, such as tfb.JointMap, must broadcast to a coherent batch shape. Some events may not be valid: for example, the bijector tfd.JointMap([tfb.Scale([1., 2.]), tfb.Scale([1., 2., 3.])]) does not produce a valid batch shape when event_ndims = [0, 0], since the batch shapes of the two parts are inconsistent. The same bijector does define valid batch shapes of [], [2], and [3] if event_ndims is [1, 1], [0, 1], or [1, 0], respectively.

Since transforming a single event produces a scalar log-det-Jacobian, the batch shape of a bijector with non-constant Jacobian is expected to equal the shape of forward_log_det_jacobian(x, event_ndims=x_event_ndims) or inverse_log_det_jacobian(y, event_ndims=y_event_ndims), for x or y of the specified ndims.

Args
x_event_ndims Optional Python int (structure) number of dimensions in a probabilistic event passed to forward; this must be greater than or equal to self.forward_min_event_ndims. If None, defaults to self.forward_min_event_ndims. Mutually exclusive with y_event_ndims. Default value: None.
y_event_ndims Optional Python int (structure) number of dimensions in a probabilistic event passed to inverse; this must be greater than or equal to self.inverse_min_event_ndims. Mutually exclusive with x_event_ndims. Default value: None.

Returns
batch_shape_tensor integer Tensor batch shape of this bijector for a value with the given event rank.

experimental_compute_density_correction

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Density correction for this transformation wrt the tangent space, at x.

Subclasses of Bijector may call the most specific applicable method of TangentSpace, based on whether the transformation is dimension-preserving, coordinate-wise, a projection, or something more general. The backward-compatible assumption is that the transformation is dimension-preserving (goes from R^n to R^n).

Args
x Tensor (structure). The point at which to calculate the density.
tangent_space TangentSpace or one of its subclasses. The tangent to the support manifold at x.
backward_compat bool specifying whether to assume that the Bijector is dimension-preserving.
**kwargs Optional keyword arguments forwarded to tangent space methods.

Returns
density_correction Tensor representing the density correction---in log space---under the transformation that this Bijector denotes.