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Commons Math example source code file (ExponentialDistributionImpl.java)

This example Commons Math source code file (ExponentialDistributionImpl.java) is included in the DevDaily.com "Java Source Code Warehouse" project. The intent of this project is to help you "Learn Java by Example" TM.

Java - Commons Math tags/keywords

abstractcontinuousdistribution, default_inverse_absolute_accuracy, default_inverse_absolute_accuracy, deprecated, exponentialdistribution, exponentialdistributionimpl, exponentialdistributionimpl, io, mathexception, mathexception, override, override, serializable

The Commons Math ExponentialDistributionImpl.java source code

/*
 * 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;

/**
 * The default implementation of {@link ExponentialDistribution}.
 *
 * @version $Revision: 925900 $ $Date: 2010-03-21 17:10:07 -0400 (Sun, 21 Mar 2010) $
 */
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 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 is not positive.
     */
    private void setMeanInternal(double newMean) {
        if (newMean <= 0.0) {
            throw MathRuntimeException.createIllegalArgumentException(
                  "mean must be positive ({0})", 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)
     */
    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 Math.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).
     * </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 - Math.exp(-x / mean);
        }
        return ret;
    }

    /**
     * For this distribution, X, this method returns the critical point x, such
     * that P(X < x) = <code>p.
     * <p>
     * Returns 0 for p=0 and <code>Double.POSITIVE_INFINITY for p=1.

* * @param p the desired probability * @return x, such that P(X < x) = <code>p * @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( "{0} out of [{1}, {2}] range", p, 0.0, 1.0); } else if (p == 1.0) { ret = Double.POSITIVE_INFINITY; } else { ret = -mean * Math.log(1.0 - p); } return ret; } /** * Access the domain value lower bound, based on <code>p, 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) < p */ @Override protected double getDomainLowerBound(double p) { return 0; } /** * Access the domain value upper bound, based on <code>p, 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) > p */ @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, 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; } }

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