empiricalpar.java

一个用于排队系统仿真的开源软件,有非常形象的图象仿真过程!

/**    
  * Copyright (C) 2006, Laboratorio di Valutazione delle Prestazioni - Politecnico di Milano

  * This program is free software; you can redistribute it and/or modify
  * it under the terms of the GNU General Public License as published by
  * the Free Software Foundation; either version 2 of the License, or
  * (at your option) any later version.

  * This program 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.

  * You should have received a copy of the GNU General Public License
  * along with this program; if not, write to the Free Software
  * Foundation, Inc., 51 Franklin St, Fifth Floor, Boston, MA  02110-1301  USA
  */
  
package jmt.engine.random;

import jmt.common.exception.IncorrectDistributionParameterException;


/**
 *
 * This is the parameter that should be passed to the empirical
 * distribution.
 *
 * <br><br>Copyright (c) 2003
 * <br>Politecnico di Milano - dipartimento di Elettronica e Informazione
 * @author Fabrizio Frontera - [e-mail]
 *
 */

public class EmpiricalPar extends Parameter {

	/** cumulative distribution function*/
    protected double[] cdf;
    /** probability distribution function*/
	protected double[] pdf;

    /** values of the parameter*/
	protected Object[] values;


    /**
	 * This is the default constructor. It creates a new empty empirical parameter
	 * that must be set with the setPDF method before being used.
	 *
	 */
    public EmpiricalPar() {
	}

	/**
	 * It creates a new empirical parameter. It accepts an array of double greater or
	 * equal to zero and verifies that the sum of all these data is one.
	 *
	 * @param pdf array of <code>double</code> for the new empirical distribution.
	 * @throws IncorrectDistributionParameterException when any of the provided data
     *  is less than zero or the sum of all of them is not one.
	 *
	 */
    public EmpiricalPar(double[] pdf)
	        throws IncorrectDistributionParameterException {
		setPDF(pdf);
	}


    /**
	 * It creates a new empirical parameter. It accepts an array of Double greater or
	 * equal to zero and verifies that the sum of all these data is one.
	 *
	 * @param wpdf array of <code>Double</code> for the new empirical distribution.
	 * @throws IncorrectDistributionParameterException when any of the provided data
     * is less than zero or the sum of all of them is not one.
	 *
	 */
	public EmpiricalPar(Double[] wpdf)
	        throws IncorrectDistributionParameterException {
		double[] wpdf2pdf = new double[wpdf.length];
		for (int i = 0; i < wpdf.length; i++)
			wpdf2pdf[i] = wpdf[i].doubleValue();
		this.setPDF(wpdf2pdf);
	}

	/**
	 * Creates an empirical parameter with these entries (each entry contains
     * a value and a probability).
     * The Empirical can return not only an integer, but also directly
     * an object, given the right probability table.
	 * @param entries Empirical entries
	 * @throws IncorrectDistributionParameterException
	 */
	public EmpiricalPar(EmpiricalEntry[] entries)
	        throws IncorrectDistributionParameterException {
		values = new Object[entries.length];
		double prob[] = new double[entries.length];
		for (int i = 0; i < entries.length; i++) {
			EmpiricalEntry entry = entries[i];
			values[i] = entry.getValue();
			prob[i] = entry.getProbability();
		}
		setPDF(prob);
	}

	/**
	 * it returns the pdf of the distribution.
	 * It returns the value of the parameters for the empirical distribution, that is
	 * the vector of double representing the probabilities provided by the user.
	 *
	 * @return  array of double with the probability distribution function tabulated.
	 */

	public double[] getPDF() {
		return pdf;
	}

	/**
	 * It returns the cdf of the distribution.
	 * It returns the value of the parameters for the empirical distribution, that is
	 * the vector of double reppresenting the cumulative distribution function tabulated.
	 *
	 * @return  array of double with the cumulative distribution function tabulated.
	 */

	public double[] getCDF() {
		return cdf;
	}

	/**
	 * it changes the pdf of the distribution.
	 * It allows the user to change the value of the parameter of the empirical distribution.
	 * Takes an existent array of pdf and tries to convert it in a pdf for
	 * an empirical distribution, checking the conditions which must be respected.
	 *
	 * @param pdf array of double containing an existent pdf.
	 * @throws IncorrectDistributionParameterException if, among the provided data, there is
     * a value less than zero or the sum of all of them is not 1.
	 *
	 */
    public void setPDF(double[] pdf)
	        throws IncorrectDistributionParameterException {
		double sumProb = 0;
		this.pdf = new double[pdf.length];
		for (int i = 0; i < pdf.length; i++)
			//OLD
            //if (pdf[i] > 0) {
            //NEW
            //TODO:debug!!!! Vedere se il valore zero crea problemi...
            if (pdf[i] >= 0) {
				this.pdf[i] = pdf[i];
				sumProb += this.pdf[i];
			} else
                //negative probability not allowed
				throw new IncorrectDistributionParameterException("Found a probability less than zero. Only value gtz allowed.");

        if (Math.abs(sumProb - 1.0) > 1E-14)
		    //total probability must be near 1 (10exp(-14) error allowed)
            throw new IncorrectDistributionParameterException("The sum of all the given probability must be 1.0");

        //sets the cdf with the new probabilities
        setCDF();
	}


	/**
	 * Generate the CDF from the PDF. This is only a service method for setPdf.
	 * Each new cdf value is the sum of the previous cdf value and the current pdf value.
	 *
	 */
    private void setCDF() {
		int nBins = pdf.length;
		this.cdf = new double[nBins + 1];
		this.cdf[0] = 0;
		//TODO: cambia qualcosa per il ++ptn ??
        //OLD
        //for (int ptn = 0; ptn < nBins; ++ptn)
        //NEW
        for (int ptn = 0; ptn < nBins; ptn++)
			this.cdf[ptn + 1] = cdf[ptn] + pdf[ptn];
	}

	/**
	 * It controls whether the parameter is correct or not.
	 * For the empirical distribution, the parameter is correct if the
	 * values in the pdf array are greater than zero and they sum to 1.0.
 	 *
	 * @return boolean, indicating whether the parameter is correct or not.
	 *
	 */
	public boolean check() {
	   /*
       The gtz condition and the sum to 1 condition are actually controlled
       * by the setPDF, therefore it is sufficient to control that the CDF is not empty.
       */
        return (!(cdf == null));
	}



// 11/09/03 - Massimo Cattai //

    //TODO: