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# Copyright 2018 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 Kumaraswamy 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.kumaraswamy import Kumaraswamy
from tensorflow.python.ops.distributions.bijector_test_util import assert_bijective_and_finite
from tensorflow.python.ops.distributions.bijector_test_util import assert_scalar_congruency
from tensorflow.python.platform import test
class KumaraswamyBijectorTest(test.TestCase):
"""Tests correctness of the Kumaraswamy bijector."""
def testBijector(self):
with self.test_session():
a = 2.
b = 0.3
bijector = Kumaraswamy(
concentration1=a, concentration0=b,
event_ndims=0, validate_args=True)
self.assertEqual("kumaraswamy", bijector.name)
x = np.array([[[0.1], [0.2], [0.3], [0.4], [0.5]]], dtype=np.float32)
# Kumaraswamy cdf. This is the same as inverse(x).
y = 1. - (1. - x ** a) ** b
self.assertAllClose(y, bijector.inverse(x).eval())
self.assertAllClose(x, bijector.forward(y).eval())
kumaraswamy_log_pdf = (np.log(a) + np.log(b) + (a - 1) * np.log(x) +
(b - 1) * np.log1p(-x ** a))
self.assertAllClose(
# We should lose a dimension from calculating the determinant of the
# jacobian.
kumaraswamy_log_pdf,
bijector.inverse_log_det_jacobian(x).eval())
self.assertAllClose(
-bijector.inverse_log_det_jacobian(x).eval(),
bijector.forward_log_det_jacobian(y).eval(),
rtol=1e-4,
atol=0.)
def testScalarCongruency(self):
with self.test_session():
assert_scalar_congruency(
Kumaraswamy(concentration1=0.5, concentration0=1.1),
lower_x=0., upper_x=1., n=int(10e3), rtol=0.02)
def testBijectiveAndFinite(self):
with self.test_session():
concentration1 = 1.2
concentration0 = 2.
bijector = Kumaraswamy(
concentration1=concentration1,
concentration0=concentration0, validate_args=True)
# Omitting the endpoints 0 and 1, since idlj will be infinity at these
# endpoints.
y = np.linspace(.01, 0.99, num=10).astype(np.float32)
x = 1 - (1 - y ** concentration1) ** concentration0
assert_bijective_and_finite(bijector, x, y, rtol=1e-3)
if __name__ == "__main__":
test.main()
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