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SSJ is a Java library for stochastic simulation, developed under the direction of Pierre L'Ecuyer, in the Département d'Informatique et de Recherche Opérationnelle (DIRO), at the Université de Montréal. It provides facilities for generating uniform and nonuniform random variates, computing different measures related to probability distributions, performing goodness-of-fit tests, applying quasi-Monte Carlo methods, collecting (elementary) statistics, and programming discrete-event simulations with both events and processes.

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/*
 * Class:        ExponentialDistFromMean
 * Description:  exponential distribution
 * Environment:  Java
 * Software:     SSJ 
 * Copyright (C) 2001  Pierre L'Ecuyer and Université de Montréal
 * Organization: DIRO, Université de Montréal
 * @author       
 * @since

 * SSJ is free software: you can redistribute it and/or modify it under
 * the terms of the GNU General Public License (GPL) as published by the
 * Free Software Foundation, either version 3 of the License, or
 * any later version.

 * SSJ is distributed in the hope that it will be useful,
 * but WITHOUT ANY WARRANTY; without even the implied warranty of
 * MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE.  See the
 * GNU General Public License for more details.

 * A copy of the GNU General Public License is available at
   GPL licence site.
 */

package umontreal.iro.lecuyer.probdist;

/**
 * Extends the {@link ExponentialDist} class with a constructor accepting as
 * argument the mean 1/λ instead of the rate λ.
 * 
 */
public class ExponentialDistFromMean extends ExponentialDist {



   /**
    * Constructs a new exponential distribution with mean mean.
    * 
    * @param mean the required mean.
    * 
    */
   public ExponentialDistFromMean (double mean) {
      super (1.0 / mean);
   }


   /**
    * Calls 
    * {@link umontreal.iro.lecuyer.probdist.ExponentialDist#setLambda(double) setLambda}
    *  with argument 1/mean to change the mean of this distribution.
    * 
    * @param mean the new mean.
    * 
    */
   public void setMean (double mean) {
      setLambda (1.0 / mean);
   }
}