/**
* 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 Empirical distribution, which is constructed with
* data provided by the user.
*
* <br><br>Copyright (c) 2003
* <br>Politecnico di Milano - dipartimento di Elettronica e Informazione
* @author Fabrizio Frontera - ffrontera@yahoo.it
*/
public class Empirical extends AbstractDistribution implements Distribution {
/**
* This is the constructor. It creates a new empirical distribution.
*/
public Empirical() {
}
/**
*
* This method is used to evaluate the probability
* distribution function (pdf) where required by the user.
*
* @param x double indicating where to evaluate the pdf.
* @param p parameter of the empirical distribution.
* @throws IncorrectDistributionParameterException
* @return double with the probability distribution function evaluated in x.
*/
public double pdf(double x, Parameter p) throws IncorrectDistributionParameterException {
if (p.check()) {
int k = (int) x;
double[] pdf = ((EmpiricalPar) p).getPDF();
//OLD
//return pdf[k];
//TODO: l'approssimazione del casting è per difetto o per eccesso??
//TODO: e se vado oltre??? return 0???
//NEW
//@author Stefano Omini
//
//if the given x exceeds the pdf array limits, there may be problems!!
if (k < 0 || k > pdf.length - 1) {
//pdf is defined only for K = 0, 1, 2 ... (length-1)
return 0.0;
} else {
return pdf[k];
}
//end NEW
} else {
throw new IncorrectDistributionParameterException(
"Remember: all the probability value given must be gtz and the sum of all the value must be 1.0");
}
}
/**
*
* This method is used to evaluate the cumulative distribution
* function (cdf) where required by the user.
*
* @param x double indicating where to evaluate the cdf.
* @param p parameter of the empirical distribution.
* @throws IncorrectDistributionParameterException
* @return double with the cumulative distribution function evaluated in x.
*/
public double cdf(double x, Parameter p) throws IncorrectDistributionParameterException {
if (p.check()) {
//TODO: x è la posizione???
int k = (int) x;
double[] cdf = ((EmpiricalPar) p).getCDF();
if (k < 0) {
return 0.0;
}
if (k >= cdf.length - 1) {
return 1.0;
}
return cdf[k];
} else {
throw new IncorrectDistributionParameterException("Remember: all the probability"
+ " value given must be greater than zero and the sum of all the values must be 1.0");
}
}
/**
* It returns the mean value of the distribution.
* This method is used to obtain from the distribution the value of his own
* theoretic mean, which is is calculated from the data provided by the user
* (contained in the parameter p) as the sum of all the data value, divided by
* the number of data.
*
* @param p parameter of the empirical distribution.
* @throws IncorrectDistributionParameterException
* @return double with the theoretic mean of the distribution.
*
*/
public double theorMean(Parameter p) throws IncorrectDistributionParameterException {
if (p.check()) {
double[] pdf = ((EmpiricalPar) p).getPDF();
double mean = 0;
//it's a simple weighted mean: the weight of each value is the pdf of the value
//itself
for (int ptn = 0; ptn < pdf.length; ptn++) {
mean += pdf[ptn] * ptn;
}
return mean;
} else {
throw new IncorrectDistributionParameterException(
"Remember: all the probability value given must be gtz and the sum of all the value must be 1.0");
}
}
/**
* it returns the variance of the distribution.
* This method is used to obtain from the distribution his own theoretical
* variance which is calculated from the data provided by the user (contained
* in the parameter p) as the sum of all the squares of the differences between each
* data value and the theoretic mean, divided by the number of data.
*
* @param p parameter of the empirical distribution.
* @throws IncorrectDistributionParameterException
* @return double with the theoretic varance of the distribution.
*
*/
public double theorVariance(Parameter p) throws IncorrectDistributionParameterException {
if (p.check()) {
double[] pdf = ((EmpiricalPar) p).getPDF();
double mean = 0;
double variance = 0;
mean = theorMean(p);
for (int ptn = 0; ptn < pdf.length; ptn++) {
// TODO: qual è la formula giusta??
//OLD
//variance += (((pdf[ptn] - mean) * (pdf[ptn] - mean)) * ptn);
//NEW
//@author Stefano Omini
variance += (((ptn - mean) * (ptn - mean)) * pdf[ptn]);
//end NEW
}
return variance;
} else {
throw new IncorrectDistributionParameterException("Remember: "
+ "all the probability value given must be greater than zero and the sum of all the values must be 1.0");
}
}
/**
* it returns the new random number.
* This method is used to obtain from the distribution the next number distributed
* according to the distribution parameter.
*
* @param p parameter of the empirical distribution.
* @throws IncorrectDistributionParameterException
* @return double with the next random number of this distribution.
*/
public double nextRand(Parameter p) throws IncorrectDistributionParameterException {
if (p.check()) {
double rand = engine.raw();
double[] cdf = ((EmpiricalPar) p).getCDF();
// binary search in cumulative distribution function:
int nBins = cdf.length - 1;
int nbelow = 0; // biggest k such that I[k] is known to be <= rand
int nabove = nBins; // biggest k such that I[k] is known to be > rand
while (nabove > nbelow + 1) {
int middle = (nabove + nbelow + 1) >> 1; // division by 2 obtained as a bit shifting
if (rand >= cdf[middle]) {
nbelow = middle;
} else {
nabove = middle;
}
}
// after this binary search, nabove is always nbelow+1 and they straddle rand:
return nbelow;
} else {
throw new IncorrectDistributionParameterException("Remember: "
+ "all the probability value given must be greater than zero and the sum of all the values must be 1.0");
}
}
/**
* Computes the next random value and returns the corresponding object
* @param p the parameter of the empirical distribution
* @return the object corresponding to the computed random value
* @throws IncorrectDistributionParameterException
*/
public Object nextRandObject(Parameter p) throws IncorrectDistributionParameterException {
int random = (int) nextRand(p);
if (p instanceof EmpiricalPar) {
EmpiricalPar empiricalPar = (EmpiricalPar) p;
return empiricalPar.getValue(random);
}
return null;
}
} // end Empirical