wehub-resource-sync
124 lines
3.9 KiB
Python
124 lines
3.9 KiB
Python
# Copyright (c) 2022 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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from op_test import get_device, get_places
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import paddle
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def numpy_corr(np_arr, rowvar=True, dtype='float64'):
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# np.corrcoef support parameter 'dtype' since 1.20
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if np.lib.NumpyVersion(np.__version__) < "1.20.0":
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return np.corrcoef(np_arr, rowvar=rowvar)
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return np.corrcoef(np_arr, rowvar=rowvar, dtype=dtype)
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class Corr_Test(unittest.TestCase):
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def setUp(self):
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self.shape = [4, 5]
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def test_tensor_corr_default(self):
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typelist = ['float64', 'float32']
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for idx, p in enumerate(get_places()):
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if idx == 0:
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paddle.set_device('cpu')
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else:
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paddle.set_device(get_device())
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for dtype in typelist:
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np_arr = np.random.rand(*self.shape).astype(dtype)
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tensor = paddle.to_tensor(np_arr, place=p)
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corr = paddle.linalg.corrcoef(tensor)
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np_corr = numpy_corr(np_arr, rowvar=True, dtype=dtype)
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if dtype == 'float32':
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np.testing.assert_allclose(
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np_corr, corr.numpy(), rtol=1e-05, atol=1e-05
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)
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else:
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np.testing.assert_allclose(
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np_corr, corr.numpy(), rtol=1e-05
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)
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def test_tensor_corr_rowvar(self):
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typelist = ['float64', 'float32']
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for idx, p in enumerate(get_places()):
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if idx == 0:
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paddle.set_device('cpu')
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else:
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paddle.set_device(get_device())
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for dtype in typelist:
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np_arr = np.random.rand(*self.shape).astype(dtype)
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tensor = paddle.to_tensor(np_arr, place=p)
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corr = paddle.linalg.corrcoef(tensor, rowvar=False)
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np_corr = numpy_corr(np_arr, rowvar=False, dtype=dtype)
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if dtype == 'float32':
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np.testing.assert_allclose(
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np_corr, corr.numpy(), rtol=1e-05, atol=1e-05
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)
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else:
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np.testing.assert_allclose(
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np_corr, corr.numpy(), rtol=1e-05
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)
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# Input(x) only support N-D (1<=N<=2) tensor
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class Corr_Test2(Corr_Test):
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def setUp(self):
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self.shape = [10]
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class Corr_Test3(Corr_Test):
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def setUp(self):
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self.shape = [4, 5]
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# Input(x) only support N-D (1<=N<=2) tensor
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class Corr_Test4(unittest.TestCase):
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def setUp(self):
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self.shape = [2, 5, 2]
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def test_errors(self):
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def test_err():
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np_arr = np.random.rand(*self.shape).astype('float64')
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tensor = paddle.to_tensor(np_arr)
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covrr = paddle.linalg.corrcoef(tensor)
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self.assertRaises(ValueError, test_err)
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# test unsupported complex input
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class Corr_Comeplex_Test(unittest.TestCase):
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def setUp(self):
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self.dtype = 'complex128'
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def test_errors(self):
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paddle.enable_static()
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x1 = paddle.static.data(name=self.dtype, shape=[2], dtype=self.dtype)
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self.assertRaises(TypeError, paddle.linalg.corrcoef, x=x1)
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paddle.disable_static()
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class Corr_Test5(Corr_Comeplex_Test):
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def setUp(self):
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self.dtype = 'complex64'
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if __name__ == '__main__':
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unittest.main()
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