# Copyright 2020 Huawei Technologies Co., Ltd
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
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# ============================================================================
"""Bernoulli Distribution"""
from mindspore.ops import operations as P
from mindspore.ops import composite as C
from .distribution import Distribution
from ._utils.utils import cast_to_tensor, check_prob
from ...common import dtype as mstype
[docs]class Bernoulli(Distribution):
"""
Example class: Bernoulli Distribution.
Args:
probs (int, float, list, numpy.ndarray, Tensor, Parameter): probability of 1 as outcome.
seed (int): seed to use in sampling. Default: 0.
dtype (mindspore.dtype): type of the distribution. Default: mstype.int32.
name (str): name of the distribution. Default: Bernoulli.
Note:
probs should be proper probabilities (0 <= p <= 1).
Examples:
>>> # To initialize a Bernoulli distribution which has equal probability of getting 1 and 0
>>> b = nn.Bernoulli(0.5, dtype = mstype.int32)
>>> # The following create two independent Bernoulli distributions
>>> b = nn.Bernoulli([0.7, 0.2], dtype = mstype.int32)
"""
def __init__(self,
probs=None,
seed=0,
dtype=mstype.int32,
name="Bernoulli"):
"""
Constructor of Bernoulli distribution.
"""
param = dict(locals())
super(Bernoulli, self).__init__(dtype, name, param)
if probs is not None:
self._probs = cast_to_tensor(probs)
check_prob(self._probs)
else:
self._probs = probs
self.seed = seed
# ops needed for the class
self.log = P.Log()
self.add = P.TensorAdd()
self.mul = P.Mul()
self.sqrt = P.Sqrt()
self.realdiv = P.RealDiv()
self.shape = P.Shape()
self.const = P.ScalarToArray()
self.less = P.Less()
self.cast = P.Cast()
self.erf = P.Erf()
self.sqrt = P.Sqrt()
def extend_repr(self):
str_info = f'probs = {self._probs}'
return str_info
[docs] def probs(self):
"""
Returns the probability for the outcome is 1.
"""
return self._probs
def _mean(self, name='mean', probs1=None):
r"""
.. math::
MEAN(B) = probs1
"""
if name == 'mean':
return self._probs if probs1 is None else probs1
return None
def _var(self, name='var', probs1=None):
r"""
.. math::
VAR(B) = probs1 * probs0
"""
if name in ('sd', 'var'):
probs1 = self._probs if probs1 is None else probs1
probs0 = self.add(1, -1 * probs1)
return self.mul(probs0, probs1)
return None
def _prob(self, name, value, probs=None):
r"""
pmf of Bernoulli distribution.
Args:
name (str): name of the function. Should be "prob" when passed in from construct.
value (Tensor): a Tensor composed of only zeros and ones.
probs (Tensor): probability of outcome is 1. Default: self._probs.
.. math::
pmf(k) = probs1 if k = 1;
pmf(k) = probs0 if k = 0;
"""
if name in ('prob', 'log_prob'):
probs1 = self._probs if probs is None else probs
probs0 = self.add(1, -1 * probs1)
return self.add(self.mul(probs1, value),
self.mul(probs0, self.add(1, -1 * value)))
return None
def _kl_loss(self, name, dist, probs1_b, probs1_a=None):
r"""
Evaluate bernoulli-bernoulli kl divergence, i.e. KL(a||b).
Args:
name (str): name of the funtion. Should always be "kl_loss" when passed in from construct.
dist (str): type of the distributions. Should be "Bernoulli" in this case.
probs1_b (Tensor): probs1 of distribution b.
probs1_a (Tensor): probs1 of distribution a. Default: self._probs.
.. math::
KL(a||b) = probs1_a * \log(\fract{probs1_a}{probs1_b}) +
probs0_a * \log(\fract{probs0_a}{probs0_b})
"""
if name == 'kl_loss' and dist == 'Bernoulli':
probs1_a = self._probs if probs1_a is None else probs1_a
probs0_a = self.add(1, -1 * probs1_a)
probs0_b = self.add(1, -1 * probs1_b)
return self.add(probs1_a * self.log(self.realdiv(probs1_a, probs1_b)),
probs0_a * self.log(self.realdiv(probs0_a, probs0_b)))
return None
def _sample(self, name, shape=(), probs=None):
"""
Sampling.
Args:
name (str): name of the function. Should always be 'sample' when passed in from construct.
shape (tuple): shape of the sample. Default: ().
probs (Tensor): probs1 of the samples. Default: self._probs.
Returns:
Tensor, shape is shape + batch_shape.
"""
if name == 'sample':
probs1 = self._probs if probs is None else probs
batch_shape = self.shape(probs1)
sample_shape = shape + batch_shape
mean_zero = self.const(0.0)
sd_one = self.const(1.0)
sqrt_two = self.sqrt(self.const(2.0))
sample_norm = C.normal(sample_shape, mean_zero, sd_one, self.seed)
sample_uniform = 0.5 * (1 + self.erf(self.realdiv(sample_norm, sqrt_two)))
sample = self.less(sample_uniform, probs1)
sample = self.cast(sample, self._dtype)
return sample
return None