1
2
3
4
5
6
7
8
9
10
11
12
13
14
15
16
17
18
19
20
21
22
23
24
25
26
27
28
29
30
31
32
33
34
35
36
37
38
39
40
41
42
43
44
45
46
47
48
49
50
51
52
53
54
55
56
57
58
59
60
61
62
63
64
65
66
67
68
69
70
71
72
73
74
75
|
# 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.
# ==============================================================================
"""Conditional distribution base class."""
from __future__ import absolute_import
from __future__ import division
from __future__ import print_function
from tensorflow.python.ops.distributions import distribution
from tensorflow.python.ops.distributions import util as distribution_util
class ConditionalDistribution(distribution.Distribution):
"""Distribution that supports intrinsic parameters (local latents).
Subclasses of this distribution may have additional keyword arguments passed
to their sample-based methods (i.e. `sample`, `log_prob`, etc.).
"""
@distribution_util.AppendDocstring(kwargs_dict={
"**condition_kwargs":
"Named arguments forwarded to subclass implementation."})
def sample(self, sample_shape=(), seed=None, name="sample",
**condition_kwargs):
return self._call_sample_n(sample_shape, seed, name, **condition_kwargs)
@distribution_util.AppendDocstring(kwargs_dict={
"**condition_kwargs":
"Named arguments forwarded to subclass implementation."})
def log_prob(self, value, name="log_prob", **condition_kwargs):
return self._call_log_prob(value, name, **condition_kwargs)
@distribution_util.AppendDocstring(kwargs_dict={
"**condition_kwargs":
"Named arguments forwarded to subclass implementation."})
def prob(self, value, name="prob", **condition_kwargs):
return self._call_prob(value, name, **condition_kwargs)
@distribution_util.AppendDocstring(kwargs_dict={
"**condition_kwargs":
"Named arguments forwarded to subclass implementation."})
def log_cdf(self, value, name="log_cdf", **condition_kwargs):
return self._call_log_cdf(value, name, **condition_kwargs)
@distribution_util.AppendDocstring(kwargs_dict={
"**condition_kwargs":
"Named arguments forwarded to subclass implementation."})
def cdf(self, value, name="cdf", **condition_kwargs):
return self._call_cdf(value, name, **condition_kwargs)
@distribution_util.AppendDocstring(kwargs_dict={
"**condition_kwargs":
"Named arguments forwarded to subclass implementation."})
def log_survival_function(self, value, name="log_survival_function",
**condition_kwargs):
return self._call_log_survival_function(value, name, **condition_kwargs)
@distribution_util.AppendDocstring(kwargs_dict={
"**condition_kwargs":
"Named arguments forwarded to subclass implementation."})
def survival_function(self, value, name="survival_function",
**condition_kwargs):
return self._call_survival_function(value, name, **condition_kwargs)
|
