sbss.common.distributions.FastComplexWishart

class sbss.common.distributions.FastComplexWishart(nu, covariance_matrix)

Fast approximation of Complex Wishart distribution.

This variant omits constant normalization terms for faster computation while preserving dependence on covariance matrices.

Parameters:

• nu (float or torch.Tensor) – Degrees of freedom of the distribution.

• covariance_matrix (torch.Tensor) – Positive-definite covariance matrix.

__init__(nu, covariance_matrix)

Methods

__init__(nu, covariance_matrix)
cdf(value)	Returns the cumulative density/mass function evaluated at value.
entropy()	Method to compute the entropy using Bregman divergence of the log normalizer.
enumerate_support([expand])	Returns tensor containing all values supported by a discrete distribution.
expand(batch_shape[, _instance])	Returns a new distribution instance (or populates an existing instance provided by a derived class) with batch dimensions expanded to batch_shape.
icdf(value)	Returns the inverse cumulative density/mass function evaluated at value.
log_prob(Sig)	Returns the log of the probability density/mass function evaluated at value.
perplexity()	Returns perplexity of distribution, batched over batch_shape.
rsample([sample_shape])	Generates a sample_shape shaped reparameterized sample or sample_shape shaped batch of reparameterized samples if the distribution parameters are batched.
sample([sample_shape])	Generates a sample_shape shaped sample or sample_shape shaped batch of samples if the distribution parameters are batched.
sample_n(n)	Generates n samples or n batches of samples if the distribution parameters are batched.
set_default_validate_args(value)	Sets whether validation is enabled or disabled.

Attributes

arg_constraints	Returns a dictionary from argument names to Constraint objects that should be satisfied by each argument of this distribution.
batch_shape	Returns the shape over which parameters are batched.
event_shape	Returns the shape of a single sample (without batching).
has_enumerate_support
has_rsample
mean	Returns the mean of the distribution.
mode	Returns the mode of the distribution.
stddev	Returns the standard deviation of the distribution.
support	Returns a Constraint object representing this distribution's support.
variance	Returns the variance of the distribution.