wehub-resource-sync
123 lines
4.6 KiB
Python
123 lines
4.6 KiB
Python
# Copyright (c) 2021 PaddlePaddle 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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import unittest
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import numpy as np
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import scipy.stats
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from distribution.config import ATOL, DEVICES, RTOL
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from parameterize import TEST_CASE_NAME, parameterize_cls, place
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import paddle
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np.random.seed(2022)
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paddle.enable_static()
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@place(DEVICES)
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@parameterize_cls(
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(TEST_CASE_NAME, 'concentration'),
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[('test-one-dim', np.random.rand(89) + 5.0)],
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)
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class TestDirichlet(unittest.TestCase):
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def setUp(self):
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self.program = paddle.static.Program()
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self.executor = paddle.static.Executor()
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with paddle.static.program_guard(self.program):
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conc = paddle.static.data(
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'conc', self.concentration.shape, self.concentration.dtype
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)
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self._paddle_diric = paddle.distribution.Dirichlet(conc)
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self.feeds = {'conc': self.concentration}
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def test_mean(self):
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with paddle.static.program_guard(self.program):
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[out] = self.executor.run(
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self.program,
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feed=self.feeds,
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fetch_list=[self._paddle_diric.mean],
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)
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np.testing.assert_allclose(
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out,
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scipy.stats.dirichlet.mean(self.concentration),
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rtol=RTOL.get(str(self.concentration.dtype)),
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atol=ATOL.get(str(self.concentration.dtype)),
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)
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def test_variance(self):
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with paddle.static.program_guard(self.program):
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[out] = self.executor.run(
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self.program,
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feed=self.feeds,
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fetch_list=[self._paddle_diric.variance],
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)
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np.testing.assert_allclose(
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out,
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scipy.stats.dirichlet.var(self.concentration),
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rtol=RTOL.get(str(self.concentration.dtype)),
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atol=ATOL.get(str(self.concentration.dtype)),
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)
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def test_prob(self):
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with paddle.static.program_guard(self.program):
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random_number = np.random.rand(*self.concentration.shape)
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random_number = random_number / random_number.sum()
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feeds = dict(self.feeds, value=random_number)
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value = paddle.static.data(
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'value', random_number.shape, random_number.dtype
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)
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out = self._paddle_diric.prob(value)
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[out] = self.executor.run(
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self.program, feed=feeds, fetch_list=[out]
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)
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np.testing.assert_allclose(
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out,
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scipy.stats.dirichlet.pdf(random_number, self.concentration),
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rtol=RTOL.get(str(self.concentration.dtype)),
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atol=ATOL.get(str(self.concentration.dtype)),
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)
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def test_log_prob(self):
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with paddle.static.program_guard(self.program):
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random_number = np.random.rand(*self.concentration.shape)
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random_number = random_number / random_number.sum()
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feeds = dict(self.feeds, value=random_number)
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value = paddle.static.data(
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'value', random_number.shape, random_number.dtype
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)
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out = self._paddle_diric.log_prob(value)
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[out] = self.executor.run(
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self.program, feed=feeds, fetch_list=[out]
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)
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np.testing.assert_allclose(
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out,
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scipy.stats.dirichlet.logpdf(random_number, self.concentration),
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rtol=RTOL.get(str(self.concentration.dtype)),
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atol=ATOL.get(str(self.concentration.dtype)),
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)
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def test_entropy(self):
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with paddle.static.program_guard(self.program):
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[out] = self.executor.run(
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self.program,
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feed=self.feeds,
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fetch_list=[self._paddle_diric.entropy()],
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)
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np.testing.assert_allclose(
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out,
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scipy.stats.dirichlet.entropy(self.concentration),
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rtol=RTOL.get(str(self.concentration.dtype)),
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atol=ATOL.get(str(self.concentration.dtype)),
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)
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