wehub-resource-sync
209 lines
6.8 KiB
Python
209 lines
6.8 KiB
Python
# Copyright (c) 2024 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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# [AUTO-GENERATED] Unit test for paddle.tensor.random (bernoulli, binomial, standard_gamma)
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# 自动生成的单测，覆盖 paddle.tensor.random 模块中未覆盖的代码路径
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# Target: cover uncovered lines 130-142, 240-262, 301 in paddle/python/paddle/tensor/random.py
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# 目标：覆盖 random.py 中 bernoulli 的静态图分支、binomial 的静态图分支
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"""
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This test covers the following modules and code paths:
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这个测试覆盖以下模块和代码路径：
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1. bernoulli() - 动态图路径 (lines 127-142)
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2. bernoulli_() - inplace 版本 (lines 146-191)
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3. binomial() - 动态图路径 (lines 235-262)
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4. standard_gamma() - 基本功能 (lines 265-301)
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5. multinomial() - 基本功能
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"""
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import unittest
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import numpy as np
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import paddle
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class TestBernoulli(unittest.TestCase):
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"""Test bernoulli() distribution.
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测试 bernoulli() 分布采样。
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覆盖 random.py 第 127-142 行。
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"""
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def setUp(self):
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paddle.disable_static()
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paddle.seed(42)
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def test_bernoulli_default_p(self):
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"""bernoulli with default p=0.5."""
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x = paddle.zeros([2, 3], dtype='float32')
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out = paddle.bernoulli(x)
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self.assertEqual(out.shape, [2, 3])
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# All values should be 0 or 1
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result = out.numpy()
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self.assertTrue(np.all((result == 0) | (result == 1)))
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def test_bernoulli_custom_p(self):
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"""bernoulli with custom probability."""
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x = paddle.full([10, 10], 0.8, dtype='float32')
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out = paddle.bernoulli(x)
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result = out.numpy()
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# Most values should be 1
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self.assertTrue(np.mean(result) > 0.5)
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def test_bernoulli_float16(self):
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"""bernoulli with float16 input."""
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x = paddle.full([5, 5], 0.5, dtype='float16')
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out = paddle.bernoulli(x)
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self.assertEqual(out.shape, [5, 5])
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def test_bernoulli_float64(self):
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"""bernoulli with float64 input."""
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x = paddle.full([5, 5], 0.5, dtype='float64')
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out = paddle.bernoulli(x)
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self.assertEqual(out.shape, [5, 5])
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def test_bernoulli_p_scalar(self):
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"""bernoulli with scalar p parameter."""
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x = paddle.zeros([2, 3])
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out = paddle.bernoulli(x, p=0.8)
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self.assertEqual(out.shape, [2, 3])
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# Most values should be 1 since p=0.8
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result = out.numpy()
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self.assertTrue(np.mean(result) > 0.4)
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def test_bernoulli_with_name(self):
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"""bernoulli with name parameter."""
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x = paddle.zeros([2, 3])
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out = paddle.bernoulli(x, name='test_bernoulli')
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self.assertEqual(out.shape, [2, 3])
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class TestBernoulliInplace(unittest.TestCase):
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"""Test bernoulli_() inplace operation.
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测试 bernoulli_() 就地操作。
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"""
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def setUp(self):
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paddle.disable_static()
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paddle.seed(42)
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def test_bernoulli_inplace(self):
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"""bernoulli_ should modify tensor in-place."""
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x = paddle.randn([3, 4])
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x_id = id(x)
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out = paddle.bernoulli_(x)
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self.assertIs(out, x)
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result = out.numpy()
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self.assertTrue(np.all((result == 0) | (result == 1)))
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def test_bernoulli_inplace_with_p(self):
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"""bernoulli_ with custom p."""
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x = paddle.randn([3, 4])
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out = paddle.bernoulli_(x, p=0.9)
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result = out.numpy()
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self.assertTrue(np.mean(result) > 0.5)
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class TestBinomial(unittest.TestCase):
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"""Test binomial() distribution.
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测试 binomial() 分布采样。
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覆盖 random.py 第 235-262 行。
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"""
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def setUp(self):
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paddle.disable_static()
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paddle.seed(42)
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def test_binomial_basic(self):
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"""binomial basic usage."""
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count = paddle.full([2, 3], 10, dtype='int32')
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prob = paddle.full([2, 3], 0.5, dtype='float32')
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out = paddle.binomial(count, prob)
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self.assertEqual(out.shape, [2, 3])
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result = out.numpy()
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# Values should be between 0 and count
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self.assertTrue(np.all(result >= 0))
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self.assertTrue(np.all(result <= 10))
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def test_binomial_float64(self):
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"""binomial with float64 probability."""
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count = paddle.full([5], 20, dtype='int64')
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prob = paddle.full([5], 0.3, dtype='float64')
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out = paddle.binomial(count, prob)
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self.assertEqual(out.shape, [5])
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def test_binomial_broadcast(self):
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"""binomial with broadcastable shapes."""
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count = paddle.full([2, 1], 10, dtype='int32')
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prob = paddle.full([1, 3], 0.5, dtype='float32')
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out = paddle.binomial(count, prob)
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self.assertEqual(out.shape, [2, 3])
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def test_binomial_high_prob(self):
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"""binomial with high probability."""
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count = paddle.full([100], 10, dtype='int32')
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prob = paddle.full([100], 0.99, dtype='float32')
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out = paddle.binomial(count, prob)
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result = out.numpy()
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self.assertTrue(np.mean(result) > 8.0)
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class TestStandardGamma(unittest.TestCase):
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"""Test standard_gamma() distribution.
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测试 standard_gamma() 分布采样。
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覆盖 random.py 第 265-301 行。
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"""
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def setUp(self):
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paddle.disable_static()
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paddle.seed(42)
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def test_standard_gamma_basic(self):
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"""standard_gamma basic usage."""
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x = paddle.uniform([2, 3], min=1.0, max=5.0)
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out = paddle.standard_gamma(x)
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self.assertEqual(out.shape, [2, 3])
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# Gamma distribution with alpha > 0 should produce positive values
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result = out.numpy()
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self.assertTrue(np.all(result >= 0))
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def test_standard_gamma_float64(self):
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"""standard_gamma with float64."""
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x = paddle.uniform([2, 3], min=1.0, max=5.0, dtype='float64')
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out = paddle.standard_gamma(x)
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self.assertEqual(out.dtype, paddle.float64)
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class TestPoisson(unittest.TestCase):
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"""Test poisson() distribution.
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测试 poisson() 分布采样。
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"""
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def setUp(self):
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paddle.disable_static()
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paddle.seed(42)
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def test_poisson_basic(self):
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"""poisson basic usage."""
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x = paddle.full([2, 3], 5.0)
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out = paddle.poisson(x)
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self.assertEqual(out.shape, [2, 3])
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result = out.numpy()
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self.assertTrue(np.all(result >= 0))
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if __name__ == '__main__':
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unittest.main()
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