| # ------------------------------------------------------------- |
| # |
| # Licensed to the Apache Software Foundation (ASF) under one |
| # or more contributor license agreements. See the NOTICE file |
| # distributed with this work for additional information |
| # regarding copyright ownership. The ASF licenses this file |
| # to you 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. |
| # |
| # ------------------------------------------------------------- |
| |
| import unittest |
| |
| import numpy as np |
| from systemds.context import SystemDSContext |
| from systemds.matrix import Matrix |
| |
| np.random.seed(7) |
| shape = np.random.randint(1, 100) |
| A = np.random.rand(shape, shape) |
| # set A = MM^T and A is a positive definite matrix |
| A = np.matmul(A, A.transpose()) |
| |
| m1 = -np.random.rand(shape, shape) |
| m2 = np.asarray([[4, 9], [1, 4]]) |
| m3 = np.random.rand(shape, shape + 1) |
| |
| class TestCholesky(unittest.TestCase): |
| |
| sds: SystemDSContext = None |
| |
| @classmethod |
| def setUpClass(cls): |
| cls.sds = SystemDSContext() |
| |
| @classmethod |
| def tearDownClass(cls): |
| cls.sds.close() |
| |
| def test_basic1(self): |
| L = Matrix(self.sds, A).cholesky().compute() |
| self.assertTrue(np.allclose(L, np.linalg.cholesky(A))) |
| |
| def test_basic2(self): |
| L = Matrix(self.sds, A).cholesky().compute() |
| # L * L.H = A |
| self.assertTrue(np.allclose(A, np.dot(L, L.T.conj()))) |
| |
| def test_pos_def(self): |
| with self.assertRaises(ValueError) as context: |
| Matrix(self.sds, m1).cholesky(safe=True).compute() |
| |
| def test_symmetric_matrix(self): |
| np.linalg.cholesky(m2) |
| with self.assertRaises(ValueError) as context: |
| Matrix(self.sds, m2).cholesky(safe=True).compute() |
| |
| def test_asymetric_dim(self): |
| with self.assertRaises(ValueError) as context: |
| Matrix(self.sds, m3).cholesky().compute() |
| |
| |
| if __name__ == "__main__": |
| unittest.main(exit=False) |