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85 changes: 85 additions & 0 deletions
85
tensorflow/contrib/distributions/python/kernel_tests/chi2_test.py
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| Original file line number | Diff line number | Diff line change |
|---|---|---|
| @@ -0,0 +1,85 @@ | ||
| # Copyright 2016 Google Inc. 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 initializers.""" | ||
|
|
||
| from __future__ import absolute_import | ||
| from __future__ import division | ||
| from __future__ import print_function | ||
|
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| import numpy as np | ||
| from scipy import stats | ||
| import tensorflow as tf | ||
|
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| class Chi2Test(tf.test.TestCase): | ||
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| def testChi2LogPDF(self): | ||
| with tf.Session(): | ||
| batch_size = 6 | ||
| df = tf.constant([2.0] * batch_size, dtype=np.float64) | ||
| df_v = 2.0 | ||
| x = np.array([2.5, 2.5, 4.0, 0.1, 1.0, 2.0], dtype=np.float64) | ||
| chi2 = tf.contrib.distributions.Chi2(df=df) | ||
| expected_log_pdf = stats.chi2.logpdf(x, df_v) | ||
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| log_pdf = chi2.log_pdf(x) | ||
| self.assertEqual(log_pdf.get_shape(), (6,)) | ||
| self.assertAllClose(log_pdf.eval(), expected_log_pdf) | ||
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| pdf = chi2.pdf(x) | ||
| self.assertEqual(pdf.get_shape(), (6,)) | ||
| self.assertAllClose(pdf.eval(), np.exp(expected_log_pdf)) | ||
|
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| def testChi2CDF(self): | ||
| with tf.Session(): | ||
| batch_size = 6 | ||
| df = tf.constant([2.0] * batch_size, dtype=np.float64) | ||
| df_v = 2.0 | ||
| x = np.array([2.5, 2.5, 4.0, 0.1, 1.0, 2.0], dtype=np.float64) | ||
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| chi2 = tf.contrib.distributions.Chi2(df=df) | ||
| expected_cdf = stats.chi2.cdf(x, df_v) | ||
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| cdf = chi2.cdf(x) | ||
| self.assertEqual(cdf.get_shape(), (6,)) | ||
| self.assertAllClose(cdf.eval(), expected_cdf) | ||
|
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| def testChi2Mean(self): | ||
| with tf.Session(): | ||
| df_v = np.array([1., 3, 5], dtype=np.float64) | ||
| expected_mean = stats.chi2.mean(df_v) | ||
| chi2 = tf.contrib.distributions.Chi2(df=df_v) | ||
| self.assertEqual(chi2.mean.get_shape(), (3,)) | ||
| self.assertAllClose(chi2.mean.eval(), expected_mean) | ||
|
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| def testChi2Variance(self): | ||
| with tf.Session(): | ||
| df_v = np.array([1., 3, 5], np.float64) | ||
| expected_variances = stats.chi2.var(df_v) | ||
| chi2 = tf.contrib.distributions.Chi2(df=df_v) | ||
| self.assertEqual(chi2.variance.get_shape(), (3,)) | ||
| self.assertAllClose(chi2.variance.eval(), expected_variances) | ||
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| def testChi2Entropy(self): | ||
| with tf.Session(): | ||
| df_v = np.array([1., 3, 5], dtype=np.float64) | ||
| expected_entropy = stats.chi2.entropy(df_v) | ||
| chi2 = tf.contrib.distributions.Chi2(df=df_v) | ||
| self.assertEqual(chi2.entropy().get_shape(), (3,)) | ||
| self.assertAllClose(chi2.entropy().eval(), expected_entropy) | ||
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| if __name__ == '__main__': | ||
| tf.test.main() |
85 changes: 85 additions & 0 deletions
85
tensorflow/contrib/distributions/python/kernel_tests/exponential_test.py
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| Original file line number | Diff line number | Diff line change |
|---|---|---|
| @@ -0,0 +1,85 @@ | ||
| # Copyright 2016 Google Inc. 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 initializers.""" | ||
|
|
||
| from __future__ import absolute_import | ||
| from __future__ import division | ||
| from __future__ import print_function | ||
|
|
||
| import numpy as np | ||
| from scipy import stats | ||
| import tensorflow as tf | ||
|
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|
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| class ExponentialTest(tf.test.TestCase): | ||
|
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| def testExponentialLogPDF(self): | ||
| with tf.Session(): | ||
| batch_size = 6 | ||
| lam = tf.constant([2.0] * batch_size) | ||
| lam_v = 2.0 | ||
| x = np.array([2.5, 2.5, 4.0, 0.1, 1.0, 2.0], dtype=np.float32) | ||
| exponential = tf.contrib.distributions.Exponential(lam=lam) | ||
| expected_log_pdf = stats.expon.logpdf(x, scale=1 / lam_v) | ||
|
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| log_pdf = exponential.log_pdf(x) | ||
| self.assertEqual(log_pdf.get_shape(), (6,)) | ||
| self.assertAllClose(log_pdf.eval(), expected_log_pdf) | ||
|
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| pdf = exponential.pdf(x) | ||
| self.assertEqual(pdf.get_shape(), (6,)) | ||
| self.assertAllClose(pdf.eval(), np.exp(expected_log_pdf)) | ||
|
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| def testExponentialCDF(self): | ||
| with tf.Session(): | ||
| batch_size = 6 | ||
| lam = tf.constant([2.0] * batch_size) | ||
| lam_v = 2.0 | ||
| x = np.array([2.5, 2.5, 4.0, 0.1, 1.0, 2.0], dtype=np.float32) | ||
|
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| exponential = tf.contrib.distributions.Exponential(lam=lam) | ||
| expected_cdf = stats.expon.cdf(x, scale=1 / lam_v) | ||
|
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| cdf = exponential.cdf(x) | ||
| self.assertEqual(cdf.get_shape(), (6,)) | ||
| self.assertAllClose(cdf.eval(), expected_cdf) | ||
|
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| def testExponentialMean(self): | ||
| with tf.Session(): | ||
| lam_v = np.array([1.0, 4.0, 2.5]) | ||
| expected_mean = stats.expon.mean(scale=1 / lam_v) | ||
| exponential = tf.contrib.distributions.Exponential(lam=lam_v) | ||
| self.assertEqual(exponential.mean.get_shape(), (3,)) | ||
| self.assertAllClose(exponential.mean.eval(), expected_mean) | ||
|
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| def testExponentialVariance(self): | ||
| with tf.Session(): | ||
| lam_v = np.array([1.0, 4.0, 2.5]) | ||
| expected_variance = stats.expon.var(scale=1 / lam_v) | ||
| exponential = tf.contrib.distributions.Exponential(lam=lam_v) | ||
| self.assertEqual(exponential.variance.get_shape(), (3,)) | ||
| self.assertAllClose(exponential.variance.eval(), expected_variance) | ||
|
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| def testExponentialEntropy(self): | ||
| with tf.Session(): | ||
| lam_v = np.array([1.0, 4.0, 2.5]) | ||
| expected_entropy = stats.expon.entropy(scale=1 / lam_v) | ||
| exponential = tf.contrib.distributions.Exponential(lam=lam_v) | ||
| self.assertEqual(exponential.entropy().get_shape(), (3,)) | ||
| self.assertAllClose(exponential.entropy().eval(), expected_entropy) | ||
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| if __name__ == '__main__': | ||
| tf.test.main() |
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