/*
    * 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.commons.math4.legacy.stat.inference;
  
  import org.apache.commons.math4.legacy.exception.NoDataException;
  import org.apache.commons.math4.legacy.exception.NullArgumentException;
  import org.junit.Assert;
  import org.junit.Test;
  
  
  /**
   * Test cases for the MannWhitneyUTestImpl class.
   *
   */
  
  public class MannWhitneyUTestTest {
  
      protected MannWhitneyUTest testStatistic = new MannWhitneyUTest();
  
      @Test
      public void testMannWhitneyUSimple() {
          /* Target values computed using R version 2.11.1
           * x <- c(19, 22, 16, 29, 24)
           * y <- c(20, 11, 17, 12)
           * wilcox.test(x, y, alternative = "two.sided", mu = 0, paired = FALSE, exact = FALSE, correct = FALSE)
           * W = 17, p-value = 0.08641
           */
          final double x[] = {19, 22, 16, 29, 24};
          final double y[] = {20, 11, 17, 12};
  
          Assert.assertEquals(3, testStatistic.mannWhitneyU(x, y), 1e-10);
          Assert.assertEquals(0.08641, testStatistic.mannWhitneyUTest(x, y), 1e-5);
      }
  
  
      @Test
      public void testMannWhitneyUInputValidation() {
          /* Samples must be present, i.e. length > 0
           */
          try {
              testStatistic.mannWhitneyUTest(new double[] { }, new double[] { 1.0 });
              Assert.fail("x does not contain samples (exact), NoDataException expected");
          } catch (NoDataException ex) {
              // expected
          }
  
          try {
              testStatistic.mannWhitneyUTest(new double[] { 1.0 }, new double[] { });
              Assert.fail("y does not contain samples (exact), NoDataException expected");
          } catch (NoDataException ex) {
              // expected
          }
  
          /*
           * x and y is null
           */
          try {
              testStatistic.mannWhitneyUTest(null, null);
              Assert.fail("x and y is null (exact), NullArgumentException expected");
          } catch (NullArgumentException ex) {
              // expected
          }
  
          try {
              testStatistic.mannWhitneyUTest(null, null);
              Assert.fail("x and y is null (asymptotic), NullArgumentException expected");
          } catch (NullArgumentException ex) {
              // expected
          }
  
          /*
           * x or y is null
           */
          try {
              testStatistic.mannWhitneyUTest(null, new double[] { 1.0 });
              Assert.fail("x is null (exact), NullArgumentException expected");
          } catch (NullArgumentException ex) {
              // expected
          }
  
          try {
              testStatistic.mannWhitneyUTest(new double[] { 1.0 }, null);
              Assert.fail("y is null (exact), NullArgumentException expected");
          } catch (NullArgumentException ex) {
              // expected
         }
     }
 
     @Test
     public void testBigDataSet() {
         double[] d1 = new double[1500];
         double[] d2 = new double[1500];
         for (int i = 0; i < 1500; i++) {
             d1[i] = 2 * i;
             d2[i] = 2 * i + 1;
         }
         double result = testStatistic.mannWhitneyUTest(d1, d2);
         Assert.assertTrue(result > 0.1);
     }
 
     @Test
     public void testBigDataSetOverflow() {
         // MATH-1145
         double[] d1 = new double[110000];
         double[] d2 = new double[110000];
         for (int i = 0; i < 110000; i++) {
             d1[i] = i;
             d2[i] = i;
         }
         double result = testStatistic.mannWhitneyUTest(d1, d2);
         Assert.assertEquals(1.0, result, 0.0);
     }
 }