121 lines
4.5 KiB
Python
121 lines
4.5 KiB
Python
# Copyright 2016 The TensorFlow Authors. All Rights Reserved.
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#
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# Licensed under the Apache License, Version 2.0 (the "License");
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# you may not use this file except in compliance with the License.
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# You may obtain a copy of the License at
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#
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# http://www.apache.org/licenses/LICENSE-2.0
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#
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# Unless required by applicable law or agreed to in writing, software
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# distributed under the License is distributed on an "AS IS" BASIS,
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# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
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# See the License for the specific language governing permissions and
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# limitations under the License.
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# ==============================================================================
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"""The Exponential distribution class."""
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from __future__ import absolute_import
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from __future__ import division
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from __future__ import print_function
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import numpy as np
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from tensorflow.contrib.distributions.python.ops import gamma
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from tensorflow.python.framework import dtypes
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from tensorflow.python.framework import ops
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from tensorflow.python.ops import array_ops
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from tensorflow.python.ops import math_ops
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from tensorflow.python.ops import nn
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from tensorflow.python.ops import random_ops
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class Exponential(gamma.Gamma):
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"""The Exponential distribution with rate parameter lam.
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The PDF of this distribution is:
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```prob(x) = (lam * e^(-lam * x)), x > 0```
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Note that the Exponential distribution is a special case of the Gamma
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distribution, with Exponential(lam) = Gamma(1, lam).
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"""
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def __init__(self,
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lam,
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validate_args=False,
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allow_nan_stats=True,
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name="Exponential"):
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"""Construct Exponential distribution with parameter `lam`.
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Args:
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lam: Floating point tensor, the rate of the distribution(s).
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`lam` must contain only positive values.
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validate_args: `Boolean`, default `False`. Whether to assert that
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`lam > 0`, and that `x > 0` in the methods `prob(x)` and `log_prob(x)`.
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If `validate_args` is `False` and the inputs are invalid, correct
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behavior is not guaranteed.
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allow_nan_stats: `Boolean`, default `True`. If `False`, raise an
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exception if a statistic (e.g. mean/mode/etc...) is undefined for any
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batch member. If `True`, batch members with valid parameters leading to
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undefined statistics will return NaN for this statistic.
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name: The name to prepend to all ops created by this distribution.
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"""
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parameters = locals()
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parameters.pop("self")
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# Even though all statistics of are defined for valid inputs, this is not
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# true in the parent class "Gamma." Therefore, passing
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# allow_nan_stats=True
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# through to the parent class results in unnecessary asserts.
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with ops.name_scope(name, values=[lam]) as ns:
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self._lam = ops.convert_to_tensor(lam, name="lam")
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super(Exponential, self).__init__(
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alpha=array_ops.ones((), dtype=self._lam.dtype),
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beta=self._lam,
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allow_nan_stats=allow_nan_stats,
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validate_args=validate_args,
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name=ns)
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# While the Gamma distribution is not re-parameterizable, the
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# exponential distribution is.
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self._is_reparameterized = True
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self._parameters = parameters
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self._graph_parents += [self._lam]
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@staticmethod
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def _param_shapes(sample_shape):
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return {"lam": ops.convert_to_tensor(sample_shape, dtype=dtypes.int32)}
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@property
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def lam(self):
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return self._lam
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def _sample_n(self, n, seed=None):
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shape = array_ops.concat(([n], array_ops.shape(self._lam)), 0)
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# Sample uniformly-at-random from the open-interval (0, 1).
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sampled = random_ops.random_uniform(
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shape,
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minval=np.nextafter(self.dtype.as_numpy_dtype(0.),
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self.dtype.as_numpy_dtype(1.)),
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maxval=array_ops.ones((), dtype=self.dtype),
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seed=seed,
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dtype=self.dtype)
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return -math_ops.log(sampled) / self._lam
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class ExponentialWithSoftplusLam(Exponential):
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"""Exponential with softplus transform on `lam`."""
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def __init__(self,
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lam,
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validate_args=False,
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allow_nan_stats=True,
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name="ExponentialWithSoftplusLam"):
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parameters = locals()
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parameters.pop("self")
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with ops.name_scope(name, values=[lam]) as ns:
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super(ExponentialWithSoftplusLam, self).__init__(
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lam=nn.softplus(lam, name="softplus_lam"),
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validate_args=validate_args,
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allow_nan_stats=allow_nan_stats,
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name=ns)
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self._parameters = parameters
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