/
half_normal.py
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/
half_normal.py
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# Copyright 2017 The TensorFlow Authors. All Rights Reserved.
#
# 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
# limitations under the License.
# ==============================================================================
"""The Half Normal distribution class."""
from __future__ import absolute_import
from __future__ import division
from __future__ import print_function
import numpy as np
from tensorflow.python.framework import constant_op
from tensorflow.python.framework import dtypes
from tensorflow.python.framework import ops
from tensorflow.python.framework import tensor_shape
from tensorflow.python.ops import array_ops
from tensorflow.python.ops import check_ops
from tensorflow.python.ops import math_ops
from tensorflow.python.ops import nn
from tensorflow.python.ops import random_ops
from tensorflow.python.ops.distributions import distribution
from tensorflow.python.ops.distributions import special_math
from tensorflow.python.util import deprecation
__all__ = [
"HalfNormal",
]
class HalfNormal(distribution.Distribution):
"""The Half Normal distribution with scale `scale`.
#### Mathematical details
The half normal is a transformation of a centered normal distribution.
If some random variable `X` has normal distribution,
```none
X ~ Normal(0.0, scale)
Y = |X|
```
Then `Y` will have half normal distribution. The probability density
function (pdf) is:
```none
pdf(x; scale, x > 0) = sqrt(2) / (scale * sqrt(pi)) *
exp(- 1/2 * (x / scale) ** 2)
)
```
Where `scale = sigma` is the standard deviation of the underlying normal
distribution.
#### Examples
Examples of initialization of one or a batch of distributions.
```python
# Define a single scalar HalfNormal distribution.
dist = tf.contrib.distributions.HalfNormal(scale=3.0)
# Evaluate the cdf at 1, returning a scalar.
dist.cdf(1.)
# Define a batch of two scalar valued HalfNormals.
# The first has scale 11.0, the second 22.0
dist = tf.contrib.distributions.HalfNormal(scale=[11.0, 22.0])
# Evaluate the pdf of the first distribution on 1.0, and the second on 1.5,
# returning a length two tensor.
dist.prob([1.0, 1.5])
# Get 3 samples, returning a 3 x 2 tensor.
dist.sample([3])
```
"""
@deprecation.deprecated(
"2018-10-01",
"The TensorFlow Distributions library has moved to "
"TensorFlow Probability "
"(https://github.com/tensorflow/probability). You "
"should update all references to use `tfp.distributions` "
"instead of `tf.contrib.distributions`.",
warn_once=True)
def __init__(self,
scale,
validate_args=False,
allow_nan_stats=True,
name="HalfNormal"):
"""Construct HalfNormals with scale `scale`.
Args:
scale: Floating point tensor; the scales of the distribution(s).
Must contain only positive values.
validate_args: Python `bool`, default `False`. When `True` distribution
parameters are checked for validity despite possibly degrading runtime
performance. When `False` invalid inputs may silently render incorrect
outputs.
allow_nan_stats: Python `bool`, default `True`. When `True`,
statistics (e.g., mean, mode, variance) use the value "`NaN`" to
indicate the result is undefined. When `False`, an exception is raised
if one or more of the statistic's batch members are undefined.
name: Python `str` name prefixed to Ops created by this class.
"""
parameters = dict(locals())
with ops.name_scope(name, values=[scale]) as name:
with ops.control_dependencies([check_ops.assert_positive(scale)] if
validate_args else []):
self._scale = array_ops.identity(scale, name="scale")
super(HalfNormal, self).__init__(
dtype=self._scale.dtype,
reparameterization_type=distribution.FULLY_REPARAMETERIZED,
validate_args=validate_args,
allow_nan_stats=allow_nan_stats,
parameters=parameters,
graph_parents=[self._scale],
name=name)
@staticmethod
def _param_shapes(sample_shape):
return {"scale": ops.convert_to_tensor(sample_shape, dtype=dtypes.int32)}
@property
def scale(self):
"""Distribution parameter for the scale."""
return self._scale
def _batch_shape_tensor(self):
return array_ops.shape(self.scale)
def _batch_shape(self):
return self.scale.shape
def _event_shape_tensor(self):
return constant_op.constant([], dtype=dtypes.int32)
def _event_shape(self):
return tensor_shape.scalar()
def _sample_n(self, n, seed=None):
shape = array_ops.concat([[n], self.batch_shape_tensor()], 0)
sampled = random_ops.random_normal(
shape=shape, mean=0., stddev=1., dtype=self.dtype, seed=seed)
return math_ops.abs(sampled * self.scale)
def _prob(self, x):
coeff = np.sqrt(2) / self.scale / np.sqrt(np.pi)
pdf = coeff * math_ops.exp(- 0.5 * (x / self.scale) ** 2)
return pdf * math_ops.cast(x >= 0, self.dtype)
def _cdf(self, x):
truncated_x = nn.relu(x)
return math_ops.erf(truncated_x / self.scale / np.sqrt(2.0))
def _entropy(self):
return 0.5 * math_ops.log(np.pi * self.scale ** 2.0 / 2.0) + 0.5
def _mean(self):
return self.scale * np.sqrt(2.0) / np.sqrt(np.pi)
def _quantile(self, p):
return np.sqrt(2.0) * self.scale * special_math.erfinv(p)
def _mode(self):
return array_ops.zeros(self.batch_shape_tensor())
def _variance(self):
return self.scale ** 2.0 * (1.0 - 2.0 / np.pi)