| /* |
| * 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.util.FastMath; |
| |
| /** |
| * The default implementation of {@link ExponentialDistribution}. |
| * |
| * @version $Revision: 1055914 $ $Date: 2011-01-06 16:34:34 +0100 (jeu. 06 janv. 2011) $ |
| */ |
| public class ExponentialDistributionImpl extends AbstractContinuousDistribution |
| implements ExponentialDistribution, 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 = 2401296428283614780L; |
| |
| /** The mean of this distribution. */ |
| private double mean; |
| |
| /** Inverse cumulative probability accuracy */ |
| private final double solverAbsoluteAccuracy; |
| |
| /** |
| * Create a exponential distribution with the given mean. |
| * @param mean mean of this distribution. |
| */ |
| public ExponentialDistributionImpl(double mean) { |
| this(mean, DEFAULT_INVERSE_ABSOLUTE_ACCURACY); |
| } |
| |
| /** |
| * Create a exponential distribution with the given mean. |
| * @param mean mean of this distribution. |
| * @param inverseCumAccuracy the maximum absolute error in inverse cumulative probability estimates |
| * (defaults to {@link #DEFAULT_INVERSE_ABSOLUTE_ACCURACY}) |
| * @since 2.1 |
| */ |
| public ExponentialDistributionImpl(double mean, double inverseCumAccuracy) { |
| super(); |
| setMeanInternal(mean); |
| solverAbsoluteAccuracy = inverseCumAccuracy; |
| } |
| |
| /** |
| * Modify the mean. |
| * @param mean the new mean. |
| * @throws IllegalArgumentException if <code>mean</code> is not positive. |
| * @deprecated as of 2.1 (class will become immutable in 3.0) |
| */ |
| @Deprecated |
| public void setMean(double mean) { |
| setMeanInternal(mean); |
| } |
| /** |
| * Modify the mean. |
| * @param newMean the new mean. |
| * @throws IllegalArgumentException if <code>newMean</code> is not positive. |
| */ |
| private void setMeanInternal(double newMean) { |
| if (newMean <= 0.0) { |
| throw MathRuntimeException.createIllegalArgumentException( |
| LocalizedFormats.NOT_POSITIVE_MEAN, newMean); |
| } |
| this.mean = newMean; |
| } |
| |
| /** |
| * Access the mean. |
| * @return the mean. |
| */ |
| public double getMean() { |
| return mean; |
| } |
| |
| /** |
| * 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 - use density(double) |
| */ |
| @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) { |
| if (x < 0) { |
| return 0; |
| } |
| return FastMath.exp(-x / mean) / mean; |
| } |
| |
| /** |
| * 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/ExponentialDistribution.html"> |
| * Exponential Distribution</a>, equation (1).</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 = 1.0 - FastMath.exp(-x / mean); |
| } |
| 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 p < 0 or p > 1. |
| */ |
| @Override |
| public double inverseCumulativeProbability(double p) throws MathException { |
| double ret; |
| |
| if (p < 0.0 || p > 1.0) { |
| throw MathRuntimeException.createIllegalArgumentException( |
| LocalizedFormats.OUT_OF_RANGE_SIMPLE, p, 0.0, 1.0); |
| } else if (p == 1.0) { |
| ret = Double.POSITIVE_INFINITY; |
| } else { |
| ret = -mean * FastMath.log(1.0 - p); |
| } |
| |
| return ret; |
| } |
| |
| /** |
| * Generates a random value sampled from this distribution. |
| * |
| * <p><strong>Algorithm Description</strong>: Uses the <a |
| * href="http://www.jesus.ox.ac.uk/~clifford/a5/chap1/node5.html"> Inversion |
| * Method</a> to generate exponentially distributed random values from |
| * uniform deviates. </p> |
| * |
| * @return random value |
| * @since 2.2 |
| * @throws MathException if an error occurs generating the random value |
| */ |
| @Override |
| public double sample() throws MathException { |
| return randomData.nextExponential(mean); |
| } |
| |
| /** |
| * Access the domain value lower bound, based on <code>p</code>, used to |
| * bracket a CDF root. |
| * |
| * @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 0; |
| } |
| |
| /** |
| * Access the domain value upper bound, based on <code>p</code>, used to |
| * bracket a CDF root. |
| * |
| * @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: exponential is skewed to the left |
| // NOTE: therefore, P(X < μ) > .5 |
| |
| if (p < .5) { |
| // use mean |
| return mean; |
| } else { |
| // use max |
| return Double.MAX_VALUE; |
| } |
| } |
| |
| /** |
| * Access the initial domain value, based on <code>p</code>, used to |
| * bracket a CDF root. |
| * |
| * @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 |
| // TODO: what should really happen here is not derive from AbstractContinuousDistribution |
| // TODO: because the inverse cumulative distribution is simple. |
| // Exponential is skewed to the left, therefore, P(X < μ) > .5 |
| if (p < .5) { |
| // use 1/2 mean |
| return mean * .5; |
| } else { |
| // use mean |
| return mean; |
| } |
| } |
| |
| /** |
| * 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, regardless of the mean. |
| * |
| * @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 mean. |
| * |
| * @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 mean parameter <code>k</code>, the mean is |
| * <code>k</code> |
| * |
| * @return the mean |
| * @since 2.2 |
| */ |
| public double getNumericalMean() { |
| return getMean(); |
| } |
| |
| /** |
| * Returns the variance of the distribution. |
| * |
| * For mean parameter <code>k</code>, the variance is |
| * <code>k^2</code> |
| * |
| * @return the variance |
| * @since 2.2 |
| */ |
| public double getNumericalVariance() { |
| final double m = getMean(); |
| return m * m; |
| } |
| |
| } |