| /* |
| * 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.anomalydetection; |
| |
| import org.junit.Test; |
| |
| import org.apache.opennlp.utils.TestUtils; |
| import org.apache.opennlp.utils.TrainingExample; |
| import org.apache.opennlp.utils.TrainingSet; |
| |
| import static org.junit.Assert.assertNotNull; |
| import static org.junit.Assert.assertTrue; |
| |
| /** |
| * Testcase for {@link org.apache.opennlp.utils.anomalydetection.AnomalyDetectionUtils} |
| */ |
| public class AnomalyDetectionUtilsTest { |
| @Test |
| public void testGaussianDistributionProbability() throws Exception { |
| TrainingSet trainingSet = new TrainingSet(); |
| TestUtils.fillTrainingSet(trainingSet, 100, 5); |
| double[] mus = AnomalyDetectionUtils.fitMus(trainingSet); |
| assertNotNull(mus); |
| double[] sigmas = AnomalyDetectionUtils.fitSigmas(mus, trainingSet); |
| assertNotNull(sigmas); |
| TrainingExample newInput = new TrainingExample(new double[]{1d, 2d, 1000d, 123d, 0.1d}, 0d); |
| double probability = AnomalyDetectionUtils.getGaussianProbability(newInput, mus, sigmas); |
| assertTrue("negative probability " + probability, 0 <= probability); |
| assertTrue("probability bigger than 1 " + probability, 1 >= probability); |
| } |
| |
| @Test |
| public void testGaussianDistributionProbability2() throws Exception { |
| TrainingSet trainingSet = new TrainingSet(); |
| TestUtils.fillTrainingSet(trainingSet, 100, 5); |
| TrainingExample newInput = new TrainingExample(new double[]{1d, 2d, 1000d, 123d, 0.1d}, 0d); |
| double probability = AnomalyDetectionUtils.getGaussianProbability(newInput, trainingSet); |
| assertTrue("negative probability " + probability, 0 <= probability); |
| assertTrue("probability bigger than 1 " + probability, 1 >= probability); |
| } |
| } |