| /* |
| * 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. |
| */ |
| package org.apache.opennlp.utils.regression; |
| |
| import org.apache.opennlp.utils.TrainingExample; |
| import org.apache.opennlp.utils.TrainingSet; |
| import org.junit.Test; |
| |
| import static org.junit.Assert.assertNotNull; |
| import static org.junit.Assert.assertTrue; |
| |
| /** |
| * Testcase for {@link org.apache.opennlp.utils.regression.RegressionModelUtils} |
| */ |
| public class RegressionModelUtilsTest { |
| |
| @Test |
| public void testLMS() throws Exception { |
| TrainingSet trainingSet = new TrainingSet(); |
| trainingSet.add(new TrainingExample(new double[]{10, 10}, 1)); |
| LinearCombinationHypothesis hypothesis = new LinearCombinationHypothesis(1, 1); |
| double[] updatedParameters = RegressionModelUtils.batchLeastMeanSquareUpdate(new double[]{1, 1}, 0.1, trainingSet, hypothesis); |
| assertNotNull(updatedParameters); |
| assertTrue(updatedParameters.length == 2); |
| assertTrue(updatedParameters[0] == -18d); |
| assertTrue(updatedParameters[1] == -18d); |
| } |
| } |