| /* |
| * 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; |
| import org.apache.commons.math.MathRuntimeException; |
| import org.apache.commons.math.exception.util.LocalizedFormats; |
| import org.apache.commons.math.special.Gamma; |
| import org.apache.commons.math.util.FastMath; |
| |
| /** |
| * The default implementation of {@link GammaDistribution}. |
| * |
| * @version $Revision: 1054524 $ $Date: 2011-01-03 05:59:18 +0100 (lun. 03 janv. 2011) $ |
| */ |
| public class GammaDistributionImpl extends AbstractContinuousDistribution |
| implements GammaDistribution, 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 = -3239549463135430361L; |
| |
| /** The shape parameter. */ |
| private double alpha; |
| |
| /** The scale parameter. */ |
| private double beta; |
| |
| /** Inverse cumulative probability accuracy */ |
| private final double solverAbsoluteAccuracy; |
| |
| /** |
| * Create a new gamma distribution with the given alpha and beta values. |
| * @param alpha the shape parameter. |
| * @param beta the scale parameter. |
| */ |
| public GammaDistributionImpl(double alpha, double beta) { |
| this(alpha, beta, DEFAULT_INVERSE_ABSOLUTE_ACCURACY); |
| } |
| |
| /** |
| * Create a new gamma distribution with the given alpha and beta values. |
| * @param alpha the shape parameter. |
| * @param beta the scale parameter. |
| * @param inverseCumAccuracy the maximum absolute error in inverse cumulative probability estimates |
| * (defaults to {@link #DEFAULT_INVERSE_ABSOLUTE_ACCURACY}) |
| * @since 2.1 |
| */ |
| public GammaDistributionImpl(double alpha, double beta, double inverseCumAccuracy) { |
| super(); |
| setAlphaInternal(alpha); |
| setBetaInternal(beta); |
| solverAbsoluteAccuracy = inverseCumAccuracy; |
| } |
| |
| /** |
| * For this distribution, X, this method returns P(X < x). |
| * |
| * The implementation of this method is based on: |
| * <ul> |
| * <li> |
| * <a href="http://mathworld.wolfram.com/Chi-SquaredDistribution.html"> |
| * Chi-Squared Distribution</a>, equation (9).</li> |
| * <li>Casella, G., & Berger, R. (1990). <i>Statistical Inference</i>. |
| * Belmont, CA: Duxbury Press.</li> |
| * </ul> |
| * |
| * @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{ |
| double ret; |
| |
| if (x <= 0.0) { |
| ret = 0.0; |
| } else { |
| ret = Gamma.regularizedGammaP(alpha, x / beta); |
| } |
| |
| return ret; |
| } |
| |
| /** |
| * 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); |
| } |
| |
| /** |
| * Modify the shape parameter, alpha. |
| * @param alpha the new shape parameter. |
| * @throws IllegalArgumentException if <code>alpha</code> is not positive. |
| * @deprecated as of 2.1 (class will become immutable in 3.0) |
| */ |
| @Deprecated |
| public void setAlpha(double alpha) { |
| setAlphaInternal(alpha); |
| } |
| |
| /** |
| * Modify the shape parameter, alpha. |
| * @param newAlpha the new shape parameter. |
| * @throws IllegalArgumentException if <code>newAlpha</code> is not positive. |
| */ |
| private void setAlphaInternal(double newAlpha) { |
| if (newAlpha <= 0.0) { |
| throw MathRuntimeException.createIllegalArgumentException( |
| LocalizedFormats.NOT_POSITIVE_ALPHA, |
| newAlpha); |
| } |
| this.alpha = newAlpha; |
| } |
| |
| /** |
| * Access the shape parameter, alpha |
| * @return alpha. |
| */ |
| public double getAlpha() { |
| return alpha; |
| } |
| |
| /** |
| * Modify the scale parameter, beta. |
| * @param newBeta the new scale parameter. |
| * @throws IllegalArgumentException if <code>newBeta</code> is not positive. |
| * @deprecated as of 2.1 (class will become immutable in 3.0) |
| */ |
| @Deprecated |
| public void setBeta(double newBeta) { |
| setBetaInternal(newBeta); |
| } |
| |
| /** |
| * Modify the scale parameter, beta. |
| * @param newBeta the new scale parameter. |
| * @throws IllegalArgumentException if <code>newBeta</code> is not positive. |
| */ |
| private void setBetaInternal(double newBeta) { |
| if (newBeta <= 0.0) { |
| throw MathRuntimeException.createIllegalArgumentException( |
| LocalizedFormats.NOT_POSITIVE_BETA, |
| newBeta); |
| } |
| this.beta = newBeta; |
| } |
| |
| /** |
| * Access the scale parameter, beta |
| * @return beta. |
| */ |
| public double getBeta() { |
| return beta; |
| } |
| |
| /** |
| * Returns the probability density for a particular point. |
| * |
| * @param x The point at which the density should be computed. |
| * @return The pdf at point x. |
| */ |
| @Override |
| public double density(double x) { |
| if (x < 0) return 0; |
| return FastMath.pow(x / beta, alpha - 1) / beta * FastMath.exp(-x / beta) / FastMath.exp(Gamma.logGamma(alpha)); |
| } |
| |
| /** |
| * 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()); |
| } |
| |
| /** |
| * 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) { |
| // TODO: try to improve on this estimate |
| return Double.MIN_VALUE; |
| } |
| |
| /** |
| * 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) { |
| // TODO: try to improve on this estimate |
| // NOTE: gamma is skewed to the left |
| // NOTE: therefore, P(X < μ) > .5 |
| |
| double ret; |
| |
| if (p < .5) { |
| // use mean |
| ret = alpha * beta; |
| } else { |
| // use max value |
| 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) { |
| // TODO: try to improve on this estimate |
| // Gamma is skewed to the left, therefore, P(X < μ) > .5 |
| |
| double ret; |
| |
| if (p < .5) { |
| // use 1/2 mean |
| ret = alpha * beta * .5; |
| } else { |
| // use mean |
| ret = alpha * beta; |
| } |
| |
| return ret; |
| } |
| |
| /** |
| * 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 upper bound of the support for the distribution. |
| * |
| * The lower bound of the support is always 0, regardless of the parameters. |
| * |
| * @return lower bound of the support (always 0) |
| * @since 2.2 |
| */ |
| public double getSupportLowerBound() { |
| return 0; |
| } |
| |
| /** |
| * Returns the upper bound of the support for the distribution. |
| * |
| * The upper bound of the support is always positive infinity, |
| * regardless of the parameters. |
| * |
| * @return upper bound of the support (always Double.POSITIVE_INFINITY) |
| * @since 2.2 |
| */ |
| public double getSupportUpperBound() { |
| return Double.POSITIVE_INFINITY; |
| } |
| |
| /** |
| * Returns the mean. |
| * |
| * For shape parameter <code>alpha</code> and scale |
| * parameter <code>beta</code>, the mean is |
| * <code>alpha * beta</code> |
| * |
| * @return the mean |
| * @since 2.2 |
| */ |
| public double getNumericalMean() { |
| return getAlpha() * getBeta(); |
| } |
| |
| /** |
| * Returns the variance. |
| * |
| * For shape parameter <code>alpha</code> and scale |
| * parameter <code>beta</code>, the variance is |
| * <code>alpha * beta^2</code> |
| * |
| * @return the variance |
| * @since 2.2 |
| */ |
| public double getNumericalVariance() { |
| final double b = getBeta(); |
| return getAlpha() * b * b; |
| } |
| } |