/*
* 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 3 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, see <http://www.gnu.org/licenses/>.
*/
/*
* RandomVariates.java
* Copyright (C) 2002-2012 University of Waikato, Hamilton, New Zealand
*
*/
package weka.core;
import java.util.Random;
/**
* Class implementing some simple random variates generator.
*
* @author Xin Xu (xx5@cs.waikato.ac.nz)
* @version $Revision: 8034 $
*/
public final class RandomVariates extends Random implements RevisionHandler {
/** for serialization */
private static final long serialVersionUID = -4763742718209460354L;
/**
* Simply the constructor of super class
*/
public RandomVariates(){ super(); }
/**
* Simply the constructor of super class
*
* @param seed the seed in this random object
*/
public RandomVariates(long seed){ super(seed); }
/**
* Simply use the method of the super class
*
* @param bits - random bits
* @return the next pseudorandom value from this random number
* generator's sequence.
*/
protected int next(int bits) {return super.next(bits);}
/**
* Generate a value of a variate following standard exponential
* distribution using simple inverse method.<p>
*
* Variates related to standard Exponential can be generated using simple
* transformations.
* @return a value of the variate
*/
public double nextExponential(){
return -Math.log(1.0-super.nextDouble());
}
/**
* Generate a value of a variate following standard Erlang distribution.
* It can be used when the shape parameter is an integer and not too large,
* say, <100. When the parameter is not an integer (which is often named
* Gamma distribution) or is large, use <code>nextGamma(double a)</code>
* instead.
*
* @param a the shape parameter, must be no less than 1
* @return a value of the variate
* @exception Exception if parameter less than 1
*/
public double nextErlang(int a) throws Exception{
if(a<1)
throw new Exception("Shape parameter of Erlang distribution must be greater than 1!");
double product = 1.0;
for(int i=1; i<=a; i++)
product *= super.nextDouble();
return -Math.log(product);
}
/**
* Generate a value of a variate following standard Gamma distribution
* with shape parameter a.<p>
* If a>1, it uses a rejection method developed by Minh(1988)"Generating
* Gamma Variates", ACM Trans. on Math. Software, Vol.14, No.3, pp261-266.
* <br>
* If a<1, it uses the algorithm "GS" developed by Ahrens and Dieter(1974)
* "COMPUTER METHODS FOR SAMPLING FROM GAMMA, BETA, POISSON AND BINOMIAL
* DISTRIBUTIONS", COMPUTING, 12 (1974), pp223-246, and further implemented
* in Fortran by Ahrens, Kohrt and Dieter(1983) "Algorithm 599: sampling
* from Gamma and Poisson distributions", ACM Trans. on Math. Software,
* Vol.9 No.2, pp255-257.<p>
*
* Variates related to standard Gamma can be generated using simple
* transformations.
*
* @param a the shape parameter, must be greater than 1
* @return a value of the variate
* @exception Exception if parameter not greater than 1
*/
public double nextGamma(double a) throws Exception{
if(a<=0.0)
throw new Exception("Shape parameter of Gamma distribution"+
"must be greater than 0!");
else if (a==1.0)
return nextExponential();
else if (a<1.0){
double b=1.0+Math.exp(-1.0)*a, p,x, condition;
do{
p=b*super.nextDouble();
if(p<1.0){
x = Math.exp(Math.log(p)/a);
condition = x;
}
else{
x = -Math.log((b-p)/a);
condition = (1.0-a)*Math.log(x);
}
}
while(nextExponential() < condition);
return x;
}
else{ // a>1
double b=a-1.0, D=Math.sqrt(b), D1,x1,x2,xl,f1,f2,x4,x5,xr,f4,f5,
p1,p2,p3,p4;
// Initialization
if(a<=2.0){
D1 = b/2.0;
x1 = 0.0;
x2 = D1;
xl = -1.0;
f1 = 0.0;
}
else{
D1 = D-0.5;
x2 = b-D1;
x1 = x2-D1;
xl = 1.0-b/x1;
f1 = Math.exp(b*Math.log(x1/b)+2.0*D1);
}
f2=Math.exp(b*Math.log(x2/b)+D1);
x4 = b+D;
x5 = x4+D;
xr = 1.0-b/x5;
f4 = Math.exp(b*Math.log(x4/b)-D);
f5 = Math.exp(b*Math.log(x5/b)-2.0*D);
p1 = 2.0*f4*D;
p2 = 2.0*f2*D1+p1;
p3 = f5/xr+p2;
p4 = -f1/xl+p3;
// Generation
double u, w=Double.MAX_VALUE, x=b, v, xp;
while(Math.log(w) > (b*Math.log(x/b)+b-x) || x < 0.0){
u=super.nextDouble()*p4;
if(u<=p1){ // step 5-6
w = u/D-f4;
if(w<=0.0) return (b+u/f4);
if(w<=f5) return (x4+(w*D)/f5);
v = super.nextDouble();
x=x4+v*D;
xp=2.0*x4-x;
if(w >= f4+(f4-1)*(x-x4)/(x4-b))
return xp;
if(w <= f4+(b/x4-1)*f4*(x-x4))
return x;
if((w < 2.0*f4-1.0) ||
(w < 2.0*f4-Math.exp(b*Math.log(xp/b)+b-xp)))
continue;
return xp;
}
else if(u<=p2){ // step 7-8
w = (u-p1)/D1-f2;
if(w<=0.0) return (b-(u-p1)/f2);
if(w<=f1) return (x1+w*D1/f1);
v = super.nextDouble();
x=x1+v*D1;
xp=2.0*x2-x;
if(w >= f2+(f2-1)*(x-x2)/(x2-b))
return xp;
if(w <= f2*(x-x1)/D1)
return x;
if((w < 2.0*f2-1.0) ||
(w < 2.0*f2-Math.exp(b*Math.log(xp/b)+b-xp)))
continue;
return xp;
}
else if(u<p3){ // step 9
w = super.nextDouble();
u = (p3-u)/(p3-p2);
x = x5-Math.log(u)/xr;
if(w <= (xr*(x5-x)+1.0)/u) return x;
w = w*f5*u;
}
else{ // step 10
w = super.nextDouble();
u = (p4-u)/(p4-p3);
x = x1-Math.log(u)/xl;
if(x<0.0) continue;
if(w <= (xl*(x1-x)+1.0)/u) return x;
w = w*f1*u;
}
}
return x;
}
}
/**
* Returns the revision string.
*
* @return the revision
*/
public String getRevision() {
return RevisionUtils.extract("$Revision: 8034 $");
}
/**
* Main method for testing this class.
*
* @param ops # of variates/seed, default is 10/45
*/
public static void main(String[] ops) {
int n = Integer.parseInt(ops[0]);
if(n<=0)
n=10;
long seed = Long.parseLong(ops[1]);
if(seed <= 0)
seed = 45;
RandomVariates var = new RandomVariates(seed);
double varb[] = new double[n];
try {
System.out.println("Generate "+n+" values with std. exp dist:");
for(int i=0; i<n; i++){
varb[i] = var.nextExponential();
System.out.print("["+i+"] "+varb[i]+", ");
}
System.out.println("\nMean is "+Utils.mean(varb)+
", Variance is "+Utils.variance(varb)+
"\n\nGenerate "+n+" values with"+
" std. Erlang-5 dist:");
for(int i=0; i<n; i++){
varb[i] = var.nextErlang(5);
System.out.print("["+i+"] "+varb[i]+", ");
}
System.out.println("\nMean is "+Utils.mean(varb)+
", Variance is "+Utils.variance(varb)+
"\n\nGenerate "+n+" values with"+
" std. Gamma(4.5) dist:");
for(int i=0; i<n; i++){
varb[i] = var.nextGamma(4.5);
System.out.print("["+i+"] "+varb[i]+", ");
}
System.out.println("\nMean is "+Utils.mean(varb)+
", Variance is "+Utils.variance(varb)+
"\n\nGenerate "+n+" values with"+
" std. Gamma(0.5) dist:");
for(int i=0; i<n; i++){
varb[i] = var.nextGamma(0.5);
System.out.print("["+i+"] "+varb[i]+", ");
}
System.out.println("\nMean is "+Utils.mean(varb)+
", Variance is "+Utils.variance(varb)+
"\n\nGenerate "+n+" values with"+
" std. Gaussian(5, 2) dist:");
for(int i=0; i<n; i++){
varb[i] = var.nextGaussian()*2.0+5.0;
System.out.print("["+i+"] "+varb[i]+", ");
}
System.out.println("\nMean is "+Utils.mean(varb)+
", Variance is "+Utils.variance(varb)+"\n");
} catch (Exception e) {
e.printStackTrace();
}
}
}