Source code for torch.distributions.beta

from numbers import Number, Real

import torch
from torch.distributions import constraints
from torch.distributions.dirichlet import Dirichlet
from torch.distributions.exp_family import ExponentialFamily
from torch.distributions.utils import broadcast_all

__all__ = ["Beta"]

[docs]class Beta(ExponentialFamily): r""" Beta distribution parameterized by :attr:`concentration1` and :attr:`concentration0`. Example:: >>> # xdoctest: +IGNORE_WANT("non-deterinistic") >>> m = Beta(torch.tensor([0.5]), torch.tensor([0.5])) >>> m.sample() # Beta distributed with concentration concentration1 and concentration0 tensor([ 0.1046]) Args: concentration1 (float or Tensor): 1st concentration parameter of the distribution (often referred to as alpha) concentration0 (float or Tensor): 2nd concentration parameter of the distribution (often referred to as beta) """ arg_constraints = { "concentration1": constraints.positive, "concentration0": constraints.positive, } support = constraints.unit_interval has_rsample = True def __init__(self, concentration1, concentration0, validate_args=None): if isinstance(concentration1, Real) and isinstance(concentration0, Real): concentration1_concentration0 = torch.tensor( [float(concentration1), float(concentration0)] ) else: concentration1, concentration0 = broadcast_all( concentration1, concentration0 ) concentration1_concentration0 = torch.stack( [concentration1, concentration0], -1 ) self._dirichlet = Dirichlet( concentration1_concentration0, validate_args=validate_args ) super().__init__(self._dirichlet._batch_shape, validate_args=validate_args)
[docs] def expand(self, batch_shape, _instance=None): new = self._get_checked_instance(Beta, _instance) batch_shape = torch.Size(batch_shape) new._dirichlet = self._dirichlet.expand(batch_shape) super(Beta, new).__init__(batch_shape, validate_args=False) new._validate_args = self._validate_args return new
@property def mean(self): return self.concentration1 / (self.concentration1 + self.concentration0) @property def mode(self): return self._dirichlet.mode[..., 0] @property def variance(self): total = self.concentration1 + self.concentration0 return self.concentration1 * self.concentration0 / (total.pow(2) * (total + 1))
[docs] def rsample(self, sample_shape=()): return self._dirichlet.rsample(sample_shape).select(-1, 0)
[docs] def log_prob(self, value): if self._validate_args: self._validate_sample(value) heads_tails = torch.stack([value, 1.0 - value], -1) return self._dirichlet.log_prob(heads_tails)
[docs] def entropy(self): return self._dirichlet.entropy()
@property def concentration1(self): result = self._dirichlet.concentration[..., 0] if isinstance(result, Number): return torch.tensor([result]) else: return result @property def concentration0(self): result = self._dirichlet.concentration[..., 1] if isinstance(result, Number): return torch.tensor([result]) else: return result @property def _natural_params(self): return (self.concentration1, self.concentration0) def _log_normalizer(self, x, y): return torch.lgamma(x) + torch.lgamma(y) - torch.lgamma(x + y)


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