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/dirichlet.py (3584B)

# This helper is exposed for testing.
def _Dirichlet_backward(x, concentration, grad_output):
    total = concentration.sum(-1, True).expand_as(concentration)
    grad = torch._dirichlet_grad(x, concentration, total)
    return grad * (grad_output - (x * grad_output).sum(-1, True))

class _Dirichlet(Function):
    @staticmethod
    def forward(ctx, concentration):
        x = torch._sample_dirichlet(concentration)
        ctx.save_for_backward(x, concentration)
        return x

@staticmethod
    @once_differentiable
    def backward(ctx, grad_output):
        x, concentration = ctx.saved_tensors
        return _Dirichlet_backward(x, concentration, grad_output)

class Dirichlet(ExponentialFamily):
    r"""
    Creates a Dirichlet distribution parameterized by concentration :attr:`concentration`.

Example::

>>> m = Dirichlet(torch.tensor([0.5, 0.5]))
        >>> m.sample()  # Dirichlet distributed with concentrarion concentration
        tensor([ 0.1046,  0.8954])

Args:
        concentration (Tensor): concentration parameter of the distribution
            (often referred to as alpha)
    """
    arg_constraints = {'concentration': constraints.independent(constraints.positive, 1)}
    support = constraints.simplex
    has_rsample = True

def __init__(self, concentration, validate_args=None):
        if concentration.dim() < 1:
            raise ValueError("`concentration` parameter must be at least one-dimensional.")
        self.concentration = concentration
        batch_shape, event_shape = concentration.shape[:-1], concentration.shape[-1:]
        super(Dirichlet, self).__init__(batch_shape, event_shape, validate_args=validate_args)

def expand(self, batch_shape, _instance=None):
        new = self._get_checked_instance(Dirichlet, _instance)
        batch_shape = torch.Size(batch_shape)
        new.concentration = self.concentration.expand(batch_shape + self.event_shape)
        super(Dirichlet, new).__init__(batch_shape, self.event_shape, validate_args=False)
        new._validate_args = self._validate_args
        return new

def rsample(self, sample_shape=()):
        shape = self._extended_shape(sample_shape)
        concentration = self.concentration.expand(shape)
        return _Dirichlet.apply(concentration)

def log_prob(self, value):
        if self._validate_args:
            self._validate_sample(value)
        return ((torch.log(value) * (self.concentration - 1.0)).sum(-1) +
                torch.lgamma(self.concentration.sum(-1)) -
                torch.lgamma(self.concentration).sum(-1))

@property
    def mean(self):
        return self.concentration / self.concentration.sum(-1, True)

@property
    def variance(self):
        con0 = self.concentration.sum(-1, True)
        return self.concentration * (con0 - self.concentration) / (con0.pow(2) * (con0 + 1))

def entropy(self):
        k = self.concentration.size(-1)
        a0 = self.concentration.sum(-1)
        return (torch.lgamma(self.concentration).sum(-1) - torch.lgamma(a0) -
                (k - a0) * torch.digamma(a0) -
                ((self.concentration - 1.0) * torch.digamma(self.concentration)).sum(-1))

@property
    def _natural_params(self):
        return (self.concentration, )

def _log_normalizer(self, x):
        return x.lgamma().sum(-1) - torch.lgamma(x.sum(-1))

import torch from torch.autograd import Function from torch.autograd.function import once_differentiable from torch.distributions import constraints from torch.distributions.exp_family import ExponentialFamily # This helper is exposed for testing. def _Dirichlet_backward(x, concentration, grad_output): total = concentration.sum(-1, True).expand_as(concentration) grad = torch._dirichlet_grad(x, concentration, total) return grad * (grad_output - (x * grad_output).sum(-1, True)) class _Dirichlet(Function): @staticmethod def forward(ctx, concentration): x = torch._sample_dirichlet(concentration) ctx.save_for_backward(x, concentration) return x @staticmethod @once_differentiable def backward(ctx, grad_output): x, concentration = ctx.saved_tensors return _Dirichlet_backward(x, concentration, grad_output) class Dirichlet(ExponentialFamily): r""" Creates a Dirichlet distribution parameterized by concentration :attr:`concentration`. Example:: >>> m = Dirichlet(torch.tensor([0.5, 0.5])) >>> m.sample() # Dirichlet distributed with concentrarion concentration tensor([ 0.1046, 0.8954]) Args: concentration (Tensor): concentration parameter of the distribution (often referred to as alpha) """ arg_constraints = {'concentration': constraints.independent(constraints.positive, 1)} support = constraints.simplex has_rsample = True def __init__(self, concentration, validate_args=None): if concentration.dim() < 1: raise ValueError("`concentration` parameter must be at least one-dimensional.") self.concentration = concentration batch_shape, event_shape = concentration.shape[:-1], concentration.shape[-1:] super(Dirichlet, self).__init__(batch_shape, event_shape, validate_args=validate_args) def expand(self, batch_shape, _instance=None): new = self._get_checked_instance(Dirichlet, _instance) batch_shape = torch.Size(batch_shape) new.concentration = self.concentration.expand(batch_shape + self.event_shape) super(Dirichlet, new).__init__(batch_shape, self.event_shape, validate_args=False) new._validate_args = self._validate_args return new def rsample(self, sample_shape=()): shape = self._extended_shape(sample_shape) concentration = self.concentration.expand(shape) return _Dirichlet.apply(concentration) def log_prob(self, value): if self._validate_args: self._validate_sample(value) return ((torch.log(value) * (self.concentration - 1.0)).sum(-1) + torch.lgamma(self.concentration.sum(-1)) - torch.lgamma(self.concentration).sum(-1)) @property def mean(self): return self.concentration / self.concentration.sum(-1, True) @property def variance(self): con0 = self.concentration.sum(-1, True) return self.concentration * (con0 - self.concentration) / (con0.pow(2) * (con0 + 1)) def entropy(self): k = self.concentration.size(-1) a0 = self.concentration.sum(-1) return (torch.lgamma(self.concentration).sum(-1) - torch.lgamma(a0) - (k - a0) * torch.digamma(a0) - ((self.concentration - 1.0) * torch.digamma(self.concentration)).sum(-1)) @property def _natural_params(self): return (self.concentration, ) def _log_normalizer(self, x): return x.lgamma().sum(-1) - torch.lgamma(x.sum(-1))