U ʹj1 @s.ddlZddlmZdgZGdddeZdS)N) DistributionExponentialFamilyc@s8eZdZdZeddZddZeddZdd Zd S) ra ExponentialFamily is the abstract base class for probability distributions belonging to an exponential family, whose probability mass/density function has the form is defined below .. math:: p_{F}(x; \theta) = \exp(\langle t(x), \theta\rangle - F(\theta) + k(x)) where :math:`\theta` denotes the natural parameters, :math:`t(x)` denotes the sufficient statistic, :math:`F(\theta)` is the log normalizer function for a given family and :math:`k(x)` is the carrier measure. Note: This class is an intermediary between the `Distribution` class and distributions which belong to an exponential family mainly to check the correctness of the `.entropy()` and analytic KL divergence methods. We use this class to compute the entropy and KL divergence using the AD framework and Bregman divergences (courtesy of: Frank Nielsen and Richard Nock, Entropies and Cross-entropies of Exponential Families). cCstdS)zv Abstract method for natural parameters. Returns a tuple of Tensors based on the distribution NNotImplementedErrorselfr]/var/www/html/bwave/python/venv/lib/python3.8/site-packages/torch/distributions/exp_family.py_natural_paramssz!ExponentialFamily._natural_paramscGstdS)z Abstract method for log normalizer function. Returns a log normalizer based on the distribution and input Nr)rZnatural_paramsrrr _log_normalizer$sz!ExponentialFamily._log_normalizercCstdS)zp Abstract method for expected carrier measure, which is required for computing entropy. Nrrrrr _mean_carrier_measure+sz'ExponentialFamily._mean_carrier_measurecCsv|j }dd|jD}|j|}tjj||dd}||7}t||D]&\}}||||j dd8}qJ|S)z_ Method to compute the entropy using Bregman divergence of the log normalizer. cSsg|]}|qSr)detachZrequires_grad_).0prrr 8sz-ExponentialFamily.entropy..T)Z create_graph)r) r r r torchZautogradZgradsumzipZreshapeZ _batch_shape)rresultnparamsZ lg_normalZ gradientsnpgrrr entropy3s  zExponentialFamily.entropyN) __name__ __module__ __qualname____doc__propertyr r r rrrrr rs  )rZ torch.distributions.distributionr__all__rrrrr s