1 /*
2 * Licensed to the Apache Software Foundation (ASF) under one or more
3 * contributor license agreements. See the NOTICE file distributed with
4 * this work for additional information regarding copyright ownership.
5 * The ASF licenses this file to You under the Apache License, Version 2.0
6 * (the "License"); you may not use this file except in compliance with
7 * the License. You may obtain a copy of the License at
8 *
9 * https://www.apache.org/licenses/LICENSE-2.0
10 *
11 * Unless required by applicable law or agreed to in writing, software
12 * distributed under the License is distributed on an "AS IS" BASIS,
13 * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
14 * See the License for the specific language governing permissions and
15 * limitations under the License.
16 */
17
18 /*
19 * This is not the original file distributed by the Apache Software Foundation
20 * It has been modified by the Hipparchus project
21 */
22 package org.hipparchus.stat.descriptive.moment;
23
24 import static org.junit.Assert.assertEquals;
25 import static org.junit.Assert.assertTrue;
26
27 import org.hipparchus.stat.descriptive.StorelessUnivariateStatisticAbstractTest;
28 import org.junit.Test;
29
30 /**
31 * Test cases for the {@link Mean} class.
32 */
33 public class MeanTest extends StorelessUnivariateStatisticAbstractTest{
34
35 @Override
36 public Mean getUnivariateStatistic() {
37 return new Mean();
38 }
39
40 @Override
41 public double expectedValue() {
42 return this.mean;
43 }
44
45 /** Expected value for the testArray defined in UnivariateStatisticAbstractTest */
46 public double expectedWeightedValue() {
47 return this.weightedMean;
48 }
49
50 @Test
51 public void testSmallSamples() {
52 Mean mean = getUnivariateStatistic();
53 assertTrue(Double.isNaN(mean.getResult()));
54 mean.increment(1d);
55 assertEquals(1d, mean.getResult(), 0);
56 }
57
58 @Test
59 public void testWeightedMean() {
60 Mean mean = getUnivariateStatistic();
61 assertEquals(expectedWeightedValue(),
62 mean.evaluate(testArray, testWeightsArray, 0, testArray.length),
63 getTolerance());
64 assertEquals(expectedValue(),
65 mean.evaluate(testArray, identicalWeightsArray, 0, testArray.length),
66 getTolerance());
67 }
68
69 }