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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  }