/*
* 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.stat.descriptive.rank;
import static org.apache.commons.math4.stat.descriptive.rank.Percentile.EstimationType.LEGACY;
import static org.apache.commons.math4.stat.descriptive.rank.Percentile.EstimationType.R_1;
import static org.apache.commons.math4.stat.descriptive.rank.Percentile.EstimationType.R_2;
import static org.apache.commons.math4.stat.descriptive.rank.Percentile.EstimationType.R_3;
import static org.apache.commons.math4.stat.descriptive.rank.Percentile.EstimationType.R_4;
import static org.apache.commons.math4.stat.descriptive.rank.Percentile.EstimationType.R_5;
import static org.apache.commons.math4.stat.descriptive.rank.Percentile.EstimationType.R_6;
import static org.apache.commons.math4.stat.descriptive.rank.Percentile.EstimationType.R_7;
import static org.apache.commons.math4.stat.descriptive.rank.Percentile.EstimationType.R_8;
import static org.apache.commons.math4.stat.descriptive.rank.Percentile.EstimationType.R_9;
import org.apache.commons.math4.stat.descriptive.UnivariateStatistic;
import org.apache.commons.math4.stat.descriptive.UnivariateStatisticAbstractTest;
import org.apache.commons.math4.stat.descriptive.rank.Median;
import org.apache.commons.math4.stat.descriptive.rank.Percentile.EstimationType;
import org.apache.commons.math4.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.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);
}
}
}