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# Copyright 2016 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 Exponential distribution class."""
from __future__ import absolute_import
from __future__ import division
from __future__ import print_function
import numpy as np
from tensorflow.contrib.distributions.python.ops import gamma
from tensorflow.python.framework import dtypes
from tensorflow.python.framework import ops
from tensorflow.python.ops import array_ops
from tensorflow.python.ops import math_ops
from tensorflow.python.ops import nn
from tensorflow.python.ops import random_ops
class Exponential(gamma.Gamma):
"""The Exponential distribution with rate parameter lam.
The PDF of this distribution is:
```prob(x) = (lam * e^(-lam * x)), x > 0```
Note that the Exponential distribution is a special case of the Gamma
distribution, with Exponential(lam) = Gamma(1, lam).
"""
def __init__(self,
lam,
validate_args=False,
allow_nan_stats=True,
name="Exponential"):
"""Construct Exponential distribution with parameter `lam`.
Args:
lam: Floating point tensor, the rate of the distribution(s).
`lam` must contain only positive values.
validate_args: `Boolean`, default `False`. Whether to assert that
`lam > 0`, and that `x > 0` in the methods `prob(x)` and `log_prob(x)`.
If `validate_args` is `False` and the inputs are invalid, correct
behavior is not guaranteed.
allow_nan_stats: `Boolean`, default `True`. If `False`, raise an
exception if a statistic (e.g. mean/mode/etc...) is undefined for any
batch member. If `True`, batch members with valid parameters leading to
undefined statistics will return NaN for this statistic.
name: The name to prepend to all ops created by this distribution.
"""
parameters = locals()
parameters.pop("self")
# Even though all statistics of are defined for valid inputs, this is not
# true in the parent class "Gamma." Therefore, passing
# allow_nan_stats=True
# through to the parent class results in unnecessary asserts.
with ops.name_scope(name, values=[lam]) as ns:
self._lam = ops.convert_to_tensor(lam, name="lam")
super(Exponential, self).__init__(
alpha=array_ops.ones((), dtype=self._lam.dtype),
beta=self._lam,
allow_nan_stats=allow_nan_stats,
validate_args=validate_args,
name=ns)
# While the Gamma distribution is not re-parameterizable, the
# exponential distribution is.
self._is_reparameterized = True
self._parameters = parameters
self._graph_parents += [self._lam]
@staticmethod
def _param_shapes(sample_shape):
return {"lam": ops.convert_to_tensor(sample_shape, dtype=dtypes.int32)}
@property
def lam(self):
return self._lam
def _sample_n(self, n, seed=None):
shape = array_ops.concat(([n], array_ops.shape(self._lam)), 0)
# Sample uniformly-at-random from the open-interval (0, 1).
sampled = random_ops.random_uniform(
shape,
minval=np.nextafter(self.dtype.as_numpy_dtype(0.),
self.dtype.as_numpy_dtype(1.)),
maxval=array_ops.ones((), dtype=self.dtype),
seed=seed,
dtype=self.dtype)
return -math_ops.log(sampled) / self._lam
class ExponentialWithSoftplusLam(Exponential):
"""Exponential with softplus transform on `lam`."""
def __init__(self,
lam,
validate_args=False,
allow_nan_stats=True,
name="ExponentialWithSoftplusLam"):
parameters = locals()
parameters.pop("self")
with ops.name_scope(name, values=[lam]) as ns:
super(ExponentialWithSoftplusLam, self).__init__(
lam=nn.softplus(lam, name="softplus_lam"),
validate_args=validate_args,
allow_nan_stats=allow_nan_stats,
name=ns)
self._parameters = parameters
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