| # ------------------------------------------------------------- |
| # |
| # 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 |
| from systemds.operator.algorithm import l2svm |
| |
| |
| class TestL2svm(unittest.TestCase): |
| |
| sds: SystemDSContext = None |
| |
| @classmethod |
| def setUpClass(cls): |
| cls.sds = SystemDSContext() |
| |
| @classmethod |
| def tearDownClass(cls): |
| cls.sds.close() |
| |
| def test_10x10(self): |
| features, labels = self.generate_matrices_for_l2svm(10, seed=1304) |
| model = l2svm(features, labels).compute() |
| # TODO make better verification. |
| self.assertTrue(np.allclose( |
| model, |
| np.array([[-0.03277166], [-0.00820981], [0.00657115], |
| [0.03228764], [-0.01685067], [0.00892918], |
| [0.00945636], [0.01514383], [0.0713272], |
| [-0.05113976]]))) |
| |
| def generate_matrices_for_l2svm(self, dims: int, seed: int = 1234): |
| np.random.seed(seed) |
| m1 = np.array(np.random.randint( |
| 100, size=dims * dims) + 1.01, dtype=np.double) |
| m1.shape = (dims, dims) |
| m2 = np.zeros((dims, 1)) |
| for i in range(dims): |
| if np.random.random() > 0.5: |
| m2[i][0] = 1 |
| return Matrix(self.sds, m1), Matrix(self.sds, m2) |
| |
| |
| if __name__ == "__main__": |
| unittest.main(exit=False) |