Java tutorial
/* * 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; import java.io.Serializable; import org.apache.commons.math.MathException; /** * The default implementation of {@link ChiSquaredDistribution} * * @version $Revision: 1054524 $ $Date: 2011-01-03 05:59:18 +0100 (lun. 03 janv. 2011) $ */ public class ChiSquaredDistributionImpl extends AbstractContinuousDistribution implements ChiSquaredDistribution, Serializable { /** * Default inverse cumulative probability accuracy * @since 2.1 */ public static final double DEFAULT_INVERSE_ABSOLUTE_ACCURACY = 1e-9; /** Serializable version identifier */ private static final long serialVersionUID = -8352658048349159782L; /** Internal Gamma distribution. */ private GammaDistribution gamma; /** Inverse cumulative probability accuracy */ private final double solverAbsoluteAccuracy; /** * Create a Chi-Squared distribution with the given degrees of freedom. * @param df degrees of freedom. */ public ChiSquaredDistributionImpl(double df) { this(df, new GammaDistributionImpl(df / 2.0, 2.0)); } /** * Create a Chi-Squared distribution with the given degrees of freedom. * @param df degrees of freedom. * @param g the underlying gamma distribution used to compute probabilities. * @since 1.2 * @deprecated as of 2.1 (to avoid possibly inconsistent state, the * "GammaDistribution" will be instantiated internally) */ @Deprecated public ChiSquaredDistributionImpl(double df, GammaDistribution g) { super(); setGammaInternal(g); setDegreesOfFreedomInternal(df); solverAbsoluteAccuracy = DEFAULT_INVERSE_ABSOLUTE_ACCURACY; } /** * Create a Chi-Squared distribution with the given degrees of freedom and * inverse cumulative probability accuracy. * @param df degrees of freedom. * @param inverseCumAccuracy the maximum absolute error in inverse cumulative probability estimates * (defaults to {@link #DEFAULT_INVERSE_ABSOLUTE_ACCURACY}) * @since 2.1 */ public ChiSquaredDistributionImpl(double df, double inverseCumAccuracy) { super(); gamma = new GammaDistributionImpl(df / 2.0, 2.0); setDegreesOfFreedomInternal(df); solverAbsoluteAccuracy = inverseCumAccuracy; } /** * Modify the degrees of freedom. * @param degreesOfFreedom the new degrees of freedom. * @deprecated as of 2.1 (class will become immutable in 3.0) */ @Deprecated public void setDegreesOfFreedom(double degreesOfFreedom) { setDegreesOfFreedomInternal(degreesOfFreedom); } /** * Modify the degrees of freedom. * @param degreesOfFreedom the new degrees of freedom. */ private void setDegreesOfFreedomInternal(double degreesOfFreedom) { gamma.setAlpha(degreesOfFreedom / 2.0); } /** * Access the degrees of freedom. * @return the degrees of freedom. */ public double getDegreesOfFreedom() { return gamma.getAlpha() * 2.0; } /** * Return the probability density for a particular point. * * @param x The point at which the density should be computed. * @return The pdf at point x. * @deprecated */ @Deprecated public double density(Double x) { return density(x.doubleValue()); } /** * Return the probability density for a particular point. * * @param x The point at which the density should be computed. * @return The pdf at point x. * @since 2.1 */ @Override public double density(double x) { return gamma.density(x); } /** * For this distribution, X, this method returns P(X < x). * @param x the value at which the CDF is evaluated. * @return CDF for this distribution. * @throws MathException if the cumulative probability can not be * computed due to convergence or other numerical errors. */ public double cumulativeProbability(double x) throws MathException { return gamma.cumulativeProbability(x); } /** * For this distribution, X, this method returns the critical point x, such * that P(X < x) = <code>p</code>. * <p> * Returns 0 for p=0 and <code>Double.POSITIVE_INFINITY</code> for p=1.</p> * * @param p the desired probability * @return x, such that P(X < x) = <code>p</code> * @throws MathException if the inverse cumulative probability can not be * computed due to convergence or other numerical errors. * @throws IllegalArgumentException if <code>p</code> is not a valid * probability. */ @Override public double inverseCumulativeProbability(final double p) throws MathException { if (p == 0) { return 0d; } if (p == 1) { return Double.POSITIVE_INFINITY; } return super.inverseCumulativeProbability(p); } /** * Access the domain value lower bound, based on <code>p</code>, used to * bracket a CDF root. This method is used by * {@link #inverseCumulativeProbability(double)} to find critical values. * * @param p the desired probability for the critical value * @return domain value lower bound, i.e. * P(X < <i>lower bound</i>) < <code>p</code> */ @Override protected double getDomainLowerBound(double p) { return Double.MIN_VALUE * gamma.getBeta(); } /** * Access the domain value upper bound, based on <code>p</code>, used to * bracket a CDF root. This method is used by * {@link #inverseCumulativeProbability(double)} to find critical values. * * @param p the desired probability for the critical value * @return domain value upper bound, i.e. * P(X < <i>upper bound</i>) > <code>p</code> */ @Override protected double getDomainUpperBound(double p) { // NOTE: chi squared is skewed to the left // NOTE: therefore, P(X < μ) > .5 double ret; if (p < .5) { // use mean ret = getDegreesOfFreedom(); } else { // use max ret = Double.MAX_VALUE; } return ret; } /** * Access the initial domain value, based on <code>p</code>, used to * bracket a CDF root. This method is used by * {@link #inverseCumulativeProbability(double)} to find critical values. * * @param p the desired probability for the critical value * @return initial domain value */ @Override protected double getInitialDomain(double p) { // NOTE: chi squared is skewed to the left // NOTE: therefore, P(X < μ) > .5 double ret; if (p < .5) { // use 1/2 mean ret = getDegreesOfFreedom() * .5; } else { // use mean ret = getDegreesOfFreedom(); } return ret; } /** * Modify the underlying gamma distribution. The caller is responsible for * insuring the gamma distribution has the proper parameter settings. * @param g the new distribution. * @since 1.2 made public * @deprecated as of 2.1 (class will become immutable in 3.0) */ @Deprecated public void setGamma(GammaDistribution g) { setGammaInternal(g); } /** * Modify the underlying gamma distribution. The caller is responsible for * insuring the gamma distribution has the proper parameter settings. * @param g the new distribution. * @since 1.2 made public */ private void setGammaInternal(GammaDistribution g) { this.gamma = g; } /** * Return the absolute accuracy setting of the solver used to estimate * inverse cumulative probabilities. * * @return the solver absolute accuracy * @since 2.1 */ @Override protected double getSolverAbsoluteAccuracy() { return solverAbsoluteAccuracy; } /** * Returns the lower bound of the support for the distribution. * * The lower bound of the support is always 0 no matter the * degrees of freedom. * * @return lower bound of the support (always 0) * @since 2.2 */ public double getSupportLowerBound() { return 0; } /** * Returns the upper bound for the support for the distribution. * * The upper bound of the support is always positive infinity no matter the * degrees of freedom. * * @return upper bound of the support (always Double.POSITIVE_INFINITY) * @since 2.2 */ public double getSupportUpperBound() { return Double.POSITIVE_INFINITY; } /** * Returns the mean of the distribution. * * For <code>k</code> degrees of freedom, the mean is * <code>k</code> * * @return the mean * @since 2.2 */ public double getNumericalMean() { return getDegreesOfFreedom(); } /** * Returns the variance of the distribution. * * For <code>k</code> degrees of freedom, the variance is * <code>2 * k</code> * * @return the variance * @since 2.2 */ public double getNumericalVariance() { return 2 * getDegreesOfFreedom(); } }