/*
 * 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.math.stat.descriptive.moment;

import junit.framework.Test;
import junit.framework.TestSuite;

import org.apache.commons.math.stat.descriptive.StorelessUnivariateStatisticAbstractTest;
import org.apache.commons.math.stat.descriptive.UnivariateStatistic;

/**
 * Test cases for the {@link UnivariateStatistic} class.
 * 
 * @version $Revision$ $Date$
 */
public class VarianceTest extends StorelessUnivariateStatisticAbstractTest {

	protected Variance stat;

	/**
	 * @param name
	 */
	public VarianceTest(String name) {
		super(name);
	}

	/**
	 * {@inheritDoc}
	 */
	@Override
	public UnivariateStatistic getUnivariateStatistic() {
		return new Variance();
	}

	public static Test suite() {
		TestSuite suite = new TestSuite(VarianceTest.class);
		suite.setName("Variance Tests");
		return suite;
	}

	/**
	 * {@inheritDoc}
	 */
	@Override
	public double expectedValue() {
		return this.var;
	}

	/**
	 * Make sure Double.NaN is returned iff n = 0
	 *
	 */
	public void testNaN() {
		StandardDeviation std = new StandardDeviation();
		assertTrue(Double.isNaN(std.getResult()));
		std.increment(1d);
		assertEquals(0d, std.getResult(), 0);
	}

	/**
	 * Test population version of variance
	 */
	public void testPopulation() {
		double[] values = { -1.0d, 3.1d, 4.0d, -2.1d, 22d, 11.7d, 3d, 14d };
		SecondMoment m = new SecondMoment();
		m.evaluate(values); // side effect is to add values
		Variance v1 = new Variance();
		v1.setBiasCorrected(false);
		assertEquals(populationVariance(values), v1.evaluate(values), 1E-14);
		v1.incrementAll(values);
		assertEquals(populationVariance(values), v1.getResult(), 1E-14);
		v1 = new Variance(false, m);
		assertEquals(populationVariance(values), v1.getResult(), 1E-14);
		v1 = new Variance(false);
		assertEquals(populationVariance(values), v1.evaluate(values), 1E-14);
		v1.incrementAll(values);
		assertEquals(populationVariance(values), v1.getResult(), 1E-14);
	}

	/**
	 * Definitional formula for population variance
	 */
	protected double populationVariance(double[] v) {
		double mean = new Mean().evaluate(v);
		double sum = 0;
		for (int i = 0; i < v.length; i++) {
			sum += (v[i] - mean) * (v[i] - mean);
		}
		return sum / v.length;
	}

}
