Source code for pyro.distributions.zero_inflated_poisson

import torch
from torch.distributions import constraints
from torch.distributions.utils import broadcast_all, lazy_property

from pyro.distributions import TorchDistribution

[docs]class ZeroInflatedPoisson(TorchDistribution): """ A Zero Inflated Poisson distribution. :param torch.Tensor gate: probability of extra zeros. :param torch.Tensor rate: rate of poisson distribution. """ arg_constraints = {'gate': constraints.unit_interval, 'rate': constraints.positive} support = constraints.nonnegative_integer def __init__(self, gate, rate, validate_args=None): self.gate, self.rate = broadcast_all(gate, rate) batch_shape = self.gate.shape event_shape = torch.Size() super(ZeroInflatedPoisson, self).__init__(batch_shape, event_shape, validate_args)
[docs] def log_prob(self, value): if self._validate_args: self._validate_sample(value) gate, rate, value = broadcast_all(self.gate, self.rate, value) log_prob = (-gate).log1p() + (rate.log() * value) - rate - (value + 1).lgamma() zeros = value == 0 log_prob = torch.where(zeros, (gate + log_prob.exp()).log(), log_prob) return log_prob
[docs] def sample(self, sample_shape=torch.Size()): shape = self._extended_shape(sample_shape) with torch.no_grad(): mask = torch.bernoulli(self.gate.expand(shape)).bool() samples = torch.poisson(self.rate.expand(shape)) samples.masked_fill_(mask, 0.) return samples
[docs] @lazy_property def mean(self): return (1 - self.gate) * self.rate
[docs] @lazy_property def variance(self): return self.rate * (1 - self.gate) * (1 + self.rate * self.gate)
[docs] def expand(self, batch_shape): try: return super(ZeroInflatedPoisson, self).expand(batch_shape) except NotImplementedError: validate_args = self.__dict__.get('_validate_args') gate = self.gate.expand(batch_shape) rate = self.rate.expand(batch_shape) return type(self)(gate, rate, validate_args=validate_args)