FM / distributions

/usr/local/lib64/python3.6/site-packages/torch/distributions/

/usr/local/lib64/python3.6/site-packages/torch/distributions

Name	Size	Mode	Actions
__pycache__/	-	0755	rm
bernoulli.py	3904	0644	edit dl rm
beta.py	3406	0644	edit dl rm
binomial.py	5179	0644	edit dl rm
categorical.py	5488	0644	edit dl rm
cauchy.py	2714	0644	edit dl rm
chi2.py	909	0644	edit dl rm
constraints.py	17288	0644	edit dl rm
constraint_registry.py	10234	0644	edit dl rm
continuous_bernoulli.py	8532	0644	edit dl rm
dirichlet.py	3584	0644	edit dl rm
distribution.py	11735	0644	edit dl rm
exponential.py	2525	0644	edit dl rm
exp_family.py	2275	0644	edit dl rm
fishersnedecor.py	3152	0644	edit dl rm
gamma.py	3121	0644	edit dl rm
geometric.py	4266	0644	edit dl rm
gumbel.py	2528	0644	edit dl rm
half_cauchy.py	2257	0644	edit dl rm
half_normal.py	2058	0644	edit dl rm
independent.py	4361	0644	edit dl rm
kl.py	29998	0644	edit dl rm
kumaraswamy.py	2927	0644	edit dl rm
laplace.py	3054	0644	edit dl rm
lkj_cholesky.py	6124	0644	edit dl rm
logistic_normal.py	1983	0644	edit dl rm
log_normal.py	1772	0644	edit dl rm
lowrank_multivariate_normal.py	9930	0644	edit dl rm
mixture_same_family.py	8636	0644	edit dl rm
multinomial.py	4776	0644	edit dl rm
multivariate_normal.py	10548	0644	edit dl rm
negative_binomial.py	4091	0644	edit dl rm
normal.py	3351	0644	edit dl rm
one_hot_categorical.py	4375	0644	edit dl rm
pareto.py	2057	0644	edit dl rm
poisson.py	2066	0644	edit dl rm
relaxed_bernoulli.py	5360	0644	edit dl rm
relaxed_categorical.py	5202	0644	edit dl rm
studentT.py	3550	0644	edit dl rm
transformed_distribution.py	8270	0644	edit dl rm
transforms.py	38408	0644	edit dl rm
uniform.py	3112	0644	edit dl rm
utils.py	6196	0644	edit dl rm
von_mises.py	5091	0644	edit dl rm
weibull.py	2854	0644	edit dl rm
__init__.py	5884	0644	edit dl rm

Edit: /usr/local/lib64/python3.6/site-packages/torch/distributions/exp_family.py (2275B)

import torch
from torch.distributions.distribution import Distribution

class ExponentialFamily(Distribution):
    r"""
    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).
    """

@property
    def _natural_params(self):
        """
        Abstract method for natural parameters. Returns a tuple of Tensors based
        on the distribution
        """
        raise NotImplementedError

def _log_normalizer(self, *natural_params):
        """
        Abstract method for log normalizer function. Returns a log normalizer based on
        the distribution and input
        """
        raise NotImplementedError

@property
    def _mean_carrier_measure(self):
        """
        Abstract method for expected carrier measure, which is required for computing
        entropy.
        """
        raise NotImplementedError

def entropy(self):
        """
        Method to compute the entropy using Bregman divergence of the log normalizer.
        """
        result = -self._mean_carrier_measure
        nparams = [p.detach().requires_grad_() for p in self._natural_params]
        lg_normal = self._log_normalizer(*nparams)
        gradients = torch.autograd.grad(lg_normal.sum(), nparams, create_graph=True)
        result += lg_normal
        for np, g in zip(nparams, gradients):
            result -= np * g
        return result

import torch from torch.distributions.distribution import Distribution class ExponentialFamily(Distribution): r""" 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). """ @property def _natural_params(self): """ Abstract method for natural parameters. Returns a tuple of Tensors based on the distribution """ raise NotImplementedError def _log_normalizer(self, *natural_params): """ Abstract method for log normalizer function. Returns a log normalizer based on the distribution and input """ raise NotImplementedError @property def _mean_carrier_measure(self): """ Abstract method for expected carrier measure, which is required for computing entropy. """ raise NotImplementedError def entropy(self): """ Method to compute the entropy using Bregman divergence of the log normalizer. """ result = -self._mean_carrier_measure nparams = [p.detach().requires_grad_() for p in self._natural_params] lg_normal = self._log_normalizer(*nparams) gradients = torch.autograd.grad(lg_normal.sum(), nparams, create_graph=True) result += lg_normal for np, g in zip(nparams, gradients): result -= np * g return result