Computes Y = g(X) = exp(-((X - loc) / scale)**(-concentration)), the Frechet
CDF.
This bijector maps inputs from [loc, inf] to [0, 1]. The inverse of the
bijector applied to a uniform random variable X ~ U(0, 1) gives back a
random variable with the
Frechet distribution:
Float-like Tensor that is the same dtype and is
broadcastable with scale. This is loc in
Y = g(X) = exp(-((X - loc) / scale)**(-concentration)).
scale
Positive Float-like Tensor that is the same dtype and is
broadcastable with loc. This is scale in
Y = g(X) = exp(-((X - loc) / scale)**(-concentration)).
concentration
Positive Float-like Tensor that is the same dtype and is
broadcastable with loc. This is concentration in
Y = g(X) = exp(-((X - loc) / scale)**(-concentration)).
validate_args
Python bool indicating whether arguments should be
checked for correctness.
name
Python str name given to ops managed by this object.
Attributes
concentration
The concentration of the Frechet distribution.
dtype
dtype of Tensors transformable by this distribution.
forward_min_event_ndims
Returns the minimal number of dimensions bijector.forward operates on.
graph_parents
Returns this Bijector's graph_parents as a Python list.
inverse_min_event_ndims
Returns the minimal number of dimensions bijector.inverse operates on.
is_constant_jacobian
Returns true iff the Jacobian matrix is not a function of x.
loc
The lower bound of the Frechet distribution.
name
Returns the string name of this Bijector.
parameters
Dictionary of parameters used to instantiate this Bijector.
scale
The scale of the Frechet distribution.
trainable_variables
validate_args
Returns True if Tensor arguments will be validated.
Tensor. The input to the 'forward' Jacobian determinant evaluation.
event_ndims
Number of dimensions in the probabilistic events being
transformed. Must be greater than or equal to
self.forward_min_event_ndims. The result is summed over the final
dimensions to produce a scalar Jacobian determinant for each event, i.e.
it has shape rank(x) - event_ndims dimensions.
name
The name to give this op.
**kwargs
Named arguments forwarded to subclass implementation.
Returns
Tensor, if this bijector is injective.
If not injective this is not implemented.
Raises
TypeError
if self.dtype is specified and y.dtype is not
self.dtype.
NotImplementedError
if neither _forward_log_det_jacobian
nor {_inverse, _inverse_log_det_jacobian} are implemented, or
this is a non-injective bijector.
Note that forward_log_det_jacobian is the negative of this function,
evaluated at g^{-1}(y).
Args
y
Tensor. The input to the 'inverse' Jacobian determinant evaluation.
event_ndims
Number of dimensions in the probabilistic events being
transformed. Must be greater than or equal to
self.inverse_min_event_ndims. The result is summed over the final
dimensions to produce a scalar Jacobian determinant for each event, i.e.
it has shape rank(y) - event_ndims dimensions.
name
The name to give this op.
**kwargs
Named arguments forwarded to subclass implementation.
Returns
ildj
Tensor, if this bijector is injective.
If not injective, returns the tuple of local log det
Jacobians, log(det(Dg_i^{-1}(y))), where g_i is the restriction
of g to the ith partition Di.
Raises
TypeError
if self.dtype is specified and y.dtype is not
self.dtype.