net.finmath.randomnumbers.AcceptanceRejectionRandomNumberGenerator Maven / Gradle / Ivy
Go to download
Show more of this group Show more artifacts with this name
Show all versions of finmath-lib Show documentation
Show all versions of finmath-lib Show documentation
finmath lib is a Mathematical Finance Library in Java.
It provides algorithms and methodologies related to mathematical finance.
/*
* (c) Copyright Christian P. Fries, Germany. Contact: [email protected].
*
* Created on 21 May 2018
*/
package net.finmath.randomnumbers;
import java.util.function.DoubleUnaryOperator;
import org.apache.commons.lang3.Validate;
/**
* Class implementing RandomNumberGenerator by the acceptance rejection method.
*
* Note that the acceptance rejection methods requires a two dimensional uniform random number sequence with independent components.
*
* @author Christian Fries
* @version 1.0
*/
public class AcceptanceRejectionRandomNumberGenerator implements RandomNumberGenerator {
private static final long serialVersionUID = -9060003224133337426L;
private final RandomNumberGenerator uniformRandomNumberGenerator;
private final DoubleUnaryOperator targetDensity;
private final DoubleUnaryOperator referenceDensity;
private final DoubleUnaryOperator referenceDistributionICDF;
private final double acceptanceLevel;
public AcceptanceRejectionRandomNumberGenerator(final RandomNumberGenerator uniformRandomNumberGenerator,
final DoubleUnaryOperator targetDensity,
final DoubleUnaryOperator referenceDensity,
final DoubleUnaryOperator referenceDistributionICDF,
final double acceptanceLevel) {
Validate.inclusiveBetween(2, Integer.MAX_VALUE, uniformRandomNumberGenerator.getDimension(), "The acceptance rejection method requires a uniform distributed random number generator with at least dimension 2.");
this.uniformRandomNumberGenerator = uniformRandomNumberGenerator;
this.targetDensity = targetDensity;
this.referenceDensity = referenceDensity;
this.referenceDistributionICDF = referenceDistributionICDF;
this.acceptanceLevel = acceptanceLevel;
}
@Override
public double[] getNext() {
boolean rejected = true;
double y = Double.NaN;
while(rejected) {
final double[] uniform = uniformRandomNumberGenerator.getNext();
final double u = uniform[0];
y = referenceDistributionICDF.applyAsDouble(uniform[1]);
rejected = targetDensity.applyAsDouble(y) < u * acceptanceLevel * referenceDensity.applyAsDouble(y);
}
return new double[] { y };
}
@Override
public int getDimension() {
return 1;
}
}