| /* |
| * 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(); |
| } |
| } |