/*
 * 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.distribution;

/**
 * Test cases for ChiSquareDistribution. Extends
 * ContinuousDistributionAbstractTest. See class javadoc for
 * ContinuousDistributionAbstractTest for details.
 * 
 * @version $Revision$ $Date$
 */
public class ChiSquareDistributionTest extends ContinuousDistributionAbstractTest {

	/**
	 * Constructor for ChiSquareDistributionTest.
	 * 
	 * @param name
	 */
	public ChiSquareDistributionTest(String name) {
		super(name);
	}

	// -------------- Implementations for abstract methods
	// -----------------------

	/** Creates the default continuous distribution instance to use in tests. */
	@Override
	public ContinuousDistribution makeDistribution() {
		return new ChiSquaredDistributionImpl(5.0);
	}

	/**
	 * Creates the default cumulative probability distribution test input values
	 */
	@Override
	public double[] makeCumulativeTestPoints() {
		// quantiles computed using R version 1.8.1 (linux version)
		return new double[] { 0.210216d, 0.5542981d, 0.8312116d, 1.145476d, 1.610308d, 20.51501d, 15.08627d, 12.83250d,
				11.07050d, 9.236357d };
	}

	/**
	 * Creates the default cumulative probability density test expected values
	 */
	@Override
	public double[] makeCumulativeTestValues() {
		return new double[] { 0.001d, 0.01d, 0.025d, 0.05d, 0.1d, 0.999d, 0.990d, 0.975d, 0.950d, 0.900d };
	}

	/** Creates the default inverse cumulative probability test input values */
	@Override
	public double[] makeInverseCumulativeTestPoints() {
		return new double[] { 0, 0.001d, 0.01d, 0.025d, 0.05d, 0.1d, 0.999d, 0.990d, 0.975d, 0.950d, 0.900d, 1 };
	}

	/**
	 * Creates the default inverse cumulative probability density test expected
	 * values
	 */
	@Override
	public double[] makeInverseCumulativeTestValues() {
		return new double[] { 0, 0.210216d, 0.5542981d, 0.8312116d, 1.145476d, 1.610308d, 20.51501d, 15.08627d,
				12.83250d, 11.07050d, 9.236357d, Double.POSITIVE_INFINITY };
	}

	// --------------------- Override tolerance --------------
	@Override
	protected void setUp() throws Exception {
		super.setUp();
		setTolerance(5e-6);
	}

	// ---------------------------- Additional test cases
	// -------------------------

	public void testSmallDf() throws Exception {
		setDistribution(new ChiSquaredDistributionImpl(0.1d));
		setTolerance(1E-4);
		// quantiles computed using R version 1.8.1 (linux version)
		setCumulativeTestPoints(new double[] { 1.168926E-60, 1.168926E-40, 1.063132E-32, 1.144775E-26, 1.168926E-20,
				5.472917, 2.175255, 1.13438, 0.5318646, 0.1526342 });
		setInverseCumulativeTestValues(getCumulativeTestPoints());
		setInverseCumulativeTestPoints(getCumulativeTestValues());
		verifyCumulativeProbabilities();
		verifyInverseCumulativeProbabilities();
	}

	public void testDfAccessors() {
		ChiSquaredDistribution distribution = (ChiSquaredDistribution) getDistribution();
		assertEquals(5d, distribution.getDegreesOfFreedom(), Double.MIN_VALUE);
		distribution.setDegreesOfFreedom(4d);
		assertEquals(4d, distribution.getDegreesOfFreedom(), Double.MIN_VALUE);
		try {
			distribution.setDegreesOfFreedom(0d);
			fail("Expecting IllegalArgumentException for df = 0");
		} catch (IllegalArgumentException ex) {
			// expected
		}
	}

	public void testDensity() {
		double[] x = new double[] { -0.1, 1e-6, 0.5, 1, 2, 5 };
		// R 2.5: print(dchisq(x, df=1), digits=10)
		checkDensity(1, x, new double[] { 0.00000000000, 398.94208093034, 0.43939128947, 0.24197072452, 0.10377687436,
				0.01464498256 });
		// R 2.5: print(dchisq(x, df=0.1), digits=10)
		checkDensity(0.1, x, new double[] { 0.000000000e+00, 2.486453997e+04, 7.464238732e-02, 3.009077718e-02,
				9.447299159e-03, 8.827199396e-04 });
		// R 2.5: print(dchisq(x, df=2), digits=10)
		checkDensity(2, x, new double[] { 0.00000000000, 0.49999975000, 0.38940039154, 0.30326532986, 0.18393972059,
				0.04104249931 });
		// R 2.5: print(dchisq(x, df=10), digits=10)
		checkDensity(10, x, new double[] { 0.000000000e+00, 1.302082682e-27, 6.337896998e-05, 7.897534632e-04,
				7.664155024e-03, 6.680094289e-02 });
	}

	private void checkDensity(double df, double[] x, double[] expected) {
		ChiSquaredDistribution d = new ChiSquaredDistributionImpl(df);
		for (int i = 0; i < x.length; i++) {
			assertEquals(expected[i], d.density(x[i]), 1e-5);
		}
	}

}
