/*
    * 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.descriptive.rank;
  
  import static org.apache.commons.math4.legacy.stat.descriptive.rank.Percentile.EstimationType.LEGACY;
  import static org.apache.commons.math4.legacy.stat.descriptive.rank.Percentile.EstimationType.R_1;
  import static org.apache.commons.math4.legacy.stat.descriptive.rank.Percentile.EstimationType.R_2;
  import static org.apache.commons.math4.legacy.stat.descriptive.rank.Percentile.EstimationType.R_3;
  import static org.apache.commons.math4.legacy.stat.descriptive.rank.Percentile.EstimationType.R_4;
  import static org.apache.commons.math4.legacy.stat.descriptive.rank.Percentile.EstimationType.R_5;
  import static org.apache.commons.math4.legacy.stat.descriptive.rank.Percentile.EstimationType.R_6;
  import static org.apache.commons.math4.legacy.stat.descriptive.rank.Percentile.EstimationType.R_7;
  import static org.apache.commons.math4.legacy.stat.descriptive.rank.Percentile.EstimationType.R_8;
  import static org.apache.commons.math4.legacy.stat.descriptive.rank.Percentile.EstimationType.R_9;
  
  import org.apache.commons.math4.legacy.stat.descriptive.UnivariateStatistic;
  import org.apache.commons.math4.legacy.stat.descriptive.UnivariateStatisticAbstractTest;
  import org.apache.commons.math4.legacy.stat.descriptive.rank.Percentile.EstimationType;
  import org.apache.commons.math4.legacy.stat.ranking.NaNStrategy;
  import org.junit.Assert;
  import org.junit.Before;
  import org.junit.Test;
  
  /**
   * Test cases for the {@link UnivariateStatistic} class.
   */
  public class MedianTest extends UnivariateStatisticAbstractTest{
  
      protected Median stat;
  
      /**
       * {@link  org.apache.commons.math4.legacy.stat.descriptive.rank.Percentile.EstimationType type}
       *  to be used while calling
       * {@link #getUnivariateStatistic()}
       */
      protected EstimationType estimationType = LEGACY;
  
      /**
       * {@inheritDoc}
       */
      @Override
      public UnivariateStatistic getUnivariateStatistic() {
          return new Median();
      }
  
      private Median getTestMedian(EstimationType type) {
          NaNStrategy strategy = (type == LEGACY) ? NaNStrategy.FIXED : NaNStrategy.REMOVED;
          return new Median().withEstimationType(type).withNaNStrategy(strategy);
      }
  
      /**
       * {@inheritDoc}
       */
      @Override
      public double expectedValue() {
          return this.median;
      }
  
      @Before
      public void before() {
          estimationType=LEGACY;
      }
  
      @Test
      public void testAllTechniquesSingleton() {
          double[] singletonArray = new double[] { 1d };
          for (EstimationType e : EstimationType.values()) {
              UnivariateStatistic percentile = getTestMedian(e);
              Assert.assertEquals(1d, percentile.evaluate(singletonArray), 0);
              Assert.assertEquals(1d, percentile.evaluate(singletonArray, 0, 1), 0);
              Assert.assertEquals(1d, new Median().evaluate(singletonArray, 0, 1, 5), 0);
              Assert.assertEquals(1d, new Median().evaluate(singletonArray, 0, 1, 100), 0);
              Assert.assertTrue(Double.isNaN(percentile.evaluate(singletonArray, 0, 0)));
          }
      }
  
      @Test
      public void testAllTechniquesMedian() {
          double[] d = new double[] { 1, 3, 2, 4 };
          testAssertMappedValues(d, new Object[][] { { LEGACY, 2.5d },
              { R_1, 2d }, { R_2, 2.5d }, { R_3, 2d }, { R_4, 2d }, { R_5, 2.5 },
              { R_6, 2.5 },{ R_7, 2.5 },{ R_8, 2.5 }, { R_9 , 2.5 } },  1.0e-05);
      }
  
      /**
      * Simple test assertion utility method
      *
      * @param d input data
      * @param map of expected result against a {@link EstimationType}
      * @param tolerance the tolerance of difference allowed
      */
     protected void testAssertMappedValues(double[] d, Object[][] map, Double tolerance) {
         for (Object[] o : map) {
             EstimationType e = (EstimationType) o[0];
             double expected = (Double) o[1];
             double result = getTestMedian(e).evaluate(d);
             Assert.assertEquals("expected[" + e + "] = " + expected +
                     " but was = " + result, expected, result, tolerance);
         }
     }
 }