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
|
# 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.
# ==============================================================================
"""Tests for Bijector."""
from __future__ import absolute_import
from __future__ import division
from __future__ import print_function
import numpy as np
from tensorflow.contrib.distributions.python.ops.bijectors.bijector_test_util import assert_bijective_and_finite
from tensorflow.contrib.distributions.python.ops.bijectors.bijector_test_util import assert_scalar_congruency
from tensorflow.contrib.distributions.python.ops.bijectors.power_transform import PowerTransform
from tensorflow.python.platform import test
class PowerTransformBijectorTest(test.TestCase):
"""Tests correctness of the power transformation."""
def testBijector(self):
with self.test_session():
c = 0.2
bijector = PowerTransform(
power=c, event_ndims=1, validate_args=True)
self.assertEqual("power_transform", bijector.name)
x = np.array([[[-1.], [2.], [-5. + 1e-4]]])
y = (1. + x * c)**(1. / c)
self.assertAllClose(y, bijector.forward(x).eval())
self.assertAllClose(x, bijector.inverse(y).eval())
self.assertAllClose(
(c - 1.) * np.sum(np.log(y), axis=-1),
bijector.inverse_log_det_jacobian(y).eval())
self.assertAllClose(
-bijector.inverse_log_det_jacobian(y).eval(),
bijector.forward_log_det_jacobian(x).eval(),
rtol=1e-4,
atol=0.)
def testScalarCongruency(self):
with self.test_session():
bijector = PowerTransform(
power=0.2, validate_args=True)
assert_scalar_congruency(
bijector, lower_x=-2., upper_x=1.5, rtol=0.05)
def testBijectiveAndFinite(self):
with self.test_session():
bijector = PowerTransform(
power=0.2, event_ndims=0, validate_args=True)
x = np.linspace(-4.999, 10, num=10).astype(np.float32)
y = np.logspace(0.001, 10, num=10).astype(np.float32)
assert_bijective_and_finite(bijector, x, y, rtol=1e-3)
if __name__ == "__main__":
test.main()
|
