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Added a way for user to define tune convergence checking in CMA-ES. 

git-svn-id: https://svn.apache.org/repos/asf/commons/proper/math/trunk@1198839 13f79535-47bb-0310-9956-ffa450edef68
This commit is contained in:
Luc Maisonobe 2011-11-07 17:47:43 +00:00
parent ae22813ab1
commit 70667484eb
1 changed files with 29 additions and 0 deletions
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29
src/main/java/org/apache/commons/math/optimization/direct/CMAESOptimizer.java
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@ -31,9 +31,11 @@ import org.apache.commons.math.linear.Array2DRowRealMatrix;
import org.apache.commons.math.linear.EigenDecomposition; import org.apache.commons.math.linear.EigenDecomposition;
import org.apache.commons.math.linear.MatrixUtils; import org.apache.commons.math.linear.MatrixUtils;
import org.apache.commons.math.linear.RealMatrix; import org.apache.commons.math.linear.RealMatrix;
import org.apache.commons.math.optimization.ConvergenceChecker;
import org.apache.commons.math.optimization.GoalType; import org.apache.commons.math.optimization.GoalType;
import org.apache.commons.math.optimization.MultivariateRealOptimizer; import org.apache.commons.math.optimization.MultivariateRealOptimizer;
import org.apache.commons.math.optimization.RealPointValuePair; import org.apache.commons.math.optimization.RealPointValuePair;
import org.apache.commons.math.optimization.SimpleScalarValueChecker;
import org.apache.commons.math.random.MersenneTwister; import org.apache.commons.math.random.MersenneTwister;
import org.apache.commons.math.random.RandomGenerator; import org.apache.commons.math.random.RandomGenerator;
import org.apache.commons.math.util.MathArrays; import org.apache.commons.math.util.MathArrays;
@ -276,6 +278,33 @@ public class CMAESOptimizer
double[][] boundaries, int maxIterations, double stopFitness, double[][] boundaries, int maxIterations, double stopFitness,
boolean isActiveCMA, int diagonalOnly, int checkFeasableCount, boolean isActiveCMA, int diagonalOnly, int checkFeasableCount,
RandomGenerator random, boolean generateStatistics) { RandomGenerator random, boolean generateStatistics) {
this(lambda, inputSigma, boundaries, maxIterations, stopFitness, isActiveCMA,
diagonalOnly, checkFeasableCount, random, generateStatistics,
new SimpleScalarValueChecker());
}
/**
* @param lambda Population size.
* @param inputSigma Initial search volume; sigma of offspring objective variables.
* @param boundaries Boundaries for objective variables.
* @param maxIterations Maximal number of iterations.
* @param stopFitness Whether to stop if objective function value is smaller than
* {@code stopFitness}.
* @param isActiveCMA Chooses the covariance matrix update method.
* @param diagonalOnly Number of initial iterations, where the covariance matrix
* remains diagonal.
* @param checkFeasableCount Determines how often new random objective variables are
* generated in case they are out of bounds.
* @param random Random generator.
* @param generateStatistics Whether statistic data is collected.
* @param checker Convergence checker.
*/
public CMAESOptimizer(int lambda, double[] inputSigma,
double[][] boundaries, int maxIterations, double stopFitness,
boolean isActiveCMA, int diagonalOnly, int checkFeasableCount,
RandomGenerator random, boolean generateStatistics,
ConvergenceChecker<RealPointValuePair> checker) {
super(checker);
this.lambda = lambda; this.lambda = lambda;
this.inputSigma = inputSigma == null ? null : (double[]) inputSigma.clone(); this.inputSigma = inputSigma == null ? null : (double[]) inputSigma.clone();
if (boundaries == null) { if (boundaries == null) {