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From er...@apache.org
Subject svn commit: r1400108 - in /commons/proper/math/trunk/src: main/java/org/apache/commons/math3/optimization/direct/CMAESOptimizer.java test/java/org/apache/commons/math3/optimization/direct/CMAESOptimizerTest.java
Date Fri, 19 Oct 2012 14:20:16 GMT
Author: erans
Date: Fri Oct 19 14:20:16 2012
New Revision: 1400108

URL: http://svn.apache.org/viewvc?rev=1400108&view=rev
Log:
MATH-868
MATH-879
"lambda" and "inputSigma" should be passed to "optimize" as arguments of
type "OptimizationData". Deprecated constructors. Updated unit tests.

Modified:
    commons/proper/math/trunk/src/main/java/org/apache/commons/math3/optimization/direct/CMAESOptimizer.java
    commons/proper/math/trunk/src/test/java/org/apache/commons/math3/optimization/direct/CMAESOptimizerTest.java

Modified: commons/proper/math/trunk/src/main/java/org/apache/commons/math3/optimization/direct/CMAESOptimizer.java
URL: http://svn.apache.org/viewvc/commons/proper/math/trunk/src/main/java/org/apache/commons/math3/optimization/direct/CMAESOptimizer.java?rev=1400108&r1=1400107&r2=1400108&view=diff
==============================================================================
--- commons/proper/math/trunk/src/main/java/org/apache/commons/math3/optimization/direct/CMAESOptimizer.java
(original)
+++ commons/proper/math/trunk/src/main/java/org/apache/commons/math3/optimization/direct/CMAESOptimizer.java
Fri Oct 19 14:20:16 2012
@@ -24,6 +24,7 @@ import java.util.List;
 import org.apache.commons.math3.analysis.MultivariateFunction;
 import org.apache.commons.math3.exception.DimensionMismatchException;
 import org.apache.commons.math3.exception.NotPositiveException;
+import org.apache.commons.math3.exception.NotStrictlyPositiveException;
 import org.apache.commons.math3.exception.OutOfRangeException;
 import org.apache.commons.math3.exception.TooManyEvaluationsException;
 import org.apache.commons.math3.linear.Array2DRowRealMatrix;
@@ -225,6 +226,10 @@ public class CMAESOptimizer
 
     /**
      * Default constructor, uses default parameters
+     *
+     * @deprecated As of version 3.1: Parameter {@code lambda} must be
+     * passed with the call to {@link #optimize(int,MultivariateFunction,GoalType,OptimizationData[])
+     * optimize} (whereas in the current code it is set to an undocumented value).
      */
     public CMAESOptimizer() {
         this(0);
@@ -232,9 +237,12 @@ public class CMAESOptimizer
 
     /**
      * @param lambda Population size.
+     * @deprecated As of version 3.1: Parameter {@code lambda} must be
+     * passed with the call to {@link #optimize(int,MultivariateFunction,GoalType,OptimizationData[])
+     * optimize} (whereas in the current code it is set to an undocumented value)..
      */
     public CMAESOptimizer(int lambda) {
-        this(lambda, DEFAULT_MAXITERATIONS, DEFAULT_STOPFITNESS,
+        this(lambda, null, DEFAULT_MAXITERATIONS, DEFAULT_STOPFITNESS,
              DEFAULT_ISACTIVECMA, DEFAULT_DIAGONALONLY,
              DEFAULT_CHECKFEASABLECOUNT, DEFAULT_RANDOMGENERATOR,
              false, null);
@@ -244,7 +252,7 @@ public class CMAESOptimizer
      * @param lambda Population size.
      * @param inputSigma Initial standard deviations to sample new points
      * around the initial guess.
-     * @deprecated As of version 3.1: Parameter {@code inputSigma} must be
+     * @deprecated As of version 3.1: Parameters {@code lambda} and {@code inputSigma} must
be
      * passed with the call to {@link #optimize(int,MultivariateFunction,GoalType,OptimizationData[])
      * optimize}.
      */
@@ -296,7 +304,7 @@ public class CMAESOptimizer
      * @param random Random generator.
      * @param generateStatistics Whether statistic data is collected.
      * @param checker Convergence checker.
-     * @deprecated As of version 3.1: Parameter {@code inputSigma} must be
+     * @deprecated As of version 3.1: Parameters {@code lambda} and {@code inputSigma} must
be
      * passed with the call to {@link #optimize(int,MultivariateFunction,GoalType,OptimizationData[])
      * optimize}.
      */
@@ -319,7 +327,6 @@ public class CMAESOptimizer
     }
 
     /**
-     * @param lambda Population size.
      * @param maxIterations Maximal number of iterations.
      * @param stopFitness Whether to stop if objective function value is smaller than
      * {@code stopFitness}.
@@ -331,9 +338,10 @@ public class CMAESOptimizer
      * @param random Random generator.
      * @param generateStatistics Whether statistic data is collected.
      * @param checker Convergence checker.
+     *
+     * @since 3.1
      */
-    public CMAESOptimizer(int lambda,
-                          int maxIterations,
+    public CMAESOptimizer(int maxIterations,
                           double stopFitness,
                           boolean isActiveCMA,
                           int diagonalOnly,
@@ -342,7 +350,6 @@ public class CMAESOptimizer
                           boolean generateStatistics,
                           ConvergenceChecker<PointValuePair> checker) {
         super(checker);
-        this.lambda = lambda;
         this.maxIterations = maxIterations;
         this.stopFitness = stopFitness;
         this.isActiveCMA = isActiveCMA;
@@ -397,8 +404,17 @@ public class CMAESOptimizer
 
         /**
          * @param s Sigma values.
+         * @throws NotPositiveException if any of the array entries is smaller
+         * than zero.
          */
-        public Sigma(double[] s) {
+        public Sigma(double[] s)
+            throws NotPositiveException {
+            for (int i = 0; i < s.length; i++) {
+                if (s[i] < 0) {
+                    throw new NotPositiveException(s[i]);
+                }
+            }
+
             sigma = s.clone();
         }
 
@@ -411,6 +427,40 @@ public class CMAESOptimizer
     }
 
     /**
+     * Population size.
+     * The number of offspring is the primary strategy parameter.
+     * In the absence of better clues, a good default could be an
+     * integer close to {@code 4 + 3 ln(n)}, where {@code n} is the
+     * number of optimized parameters.
+     * Increasing the population size improves global search properties
+     * at the expense of speed (which in general decreases at most
+     * linearly with increasing population size).
+     */
+    public static class PopulationSize implements OptimizationData {
+        /** Population size. */
+        private final int lambda;
+
+        /**
+         * @param size Population size.
+         * @throws NotStrictlyPositiveException if {@code size <= 0}.
+         */
+        public PopulationSize(int size)
+            throws NotStrictlyPositiveException {
+            if (size <= 0) {
+                throw new NotStrictlyPositiveException(size);
+            }
+            lambda = size;
+        }
+
+        /**
+         * @return the population size.
+         */
+        public int getPopulationSize() {
+            return lambda;
+        }
+    }
+
+    /**
      * Optimize an objective function.
      *
      * @param maxEval Allowed number of evaluations of the objective function.
@@ -420,6 +470,7 @@ public class CMAESOptimizer
      * <ul>
      *  <li>{@link org.apache.commons.math3.optimization.InitialGuess InitialGuess}</li>
      *  <li>{@link Sigma}</li>
+     *  <li>{@link PopulationSize}</li>
      * </ul>
      * @return the point/value pair giving the optimal value for objective
      * function.
@@ -593,6 +644,7 @@ public class CMAESOptimizer
      * @param optData Optimization data. The following data will be looked for:
      * <ul>
      *  <li>{@link Sigma}</li>
+     *  <li>{@link PopulationSize}</li>
      * </ul>
      */
     private void parseOptimizationData(OptimizationData... optData) {
@@ -603,6 +655,10 @@ public class CMAESOptimizer
                 inputSigma = ((Sigma) data).getSigma();
                 continue;
             }
+            if (data instanceof PopulationSize) {
+                lambda = ((PopulationSize) data).getPopulationSize();
+                continue;
+            }
         }
     }
 
@@ -620,6 +676,7 @@ public class CMAESOptimizer
             }
             for (int i = 0; i < init.length; i++) {
                 if (inputSigma[i] < 0) {
+                    // XXX Remove this block in 4.0 (check performed in "Sigma" class).
                     throw new NotPositiveException(inputSigma[i]);
                 }
                 if (inputSigma[i] > uB[i] - lB[i]) {
@@ -636,11 +693,15 @@ public class CMAESOptimizer
      */
     private void initializeCMA(double[] guess) {
         if (lambda <= 0) {
+            // XXX Line below to replace the current one in 4.0 (MATH-879).
+            // throw new NotStrictlyPositiveException(lambda);
             lambda = 4 + (int) (3 * Math.log(dimension));
         }
         // initialize sigma
         final double[][] sigmaArray = new double[guess.length][1];
         for (int i = 0; i < guess.length; i++) {
+            // XXX Line below to replace the current one in 4.0 (MATH-868).
+            // sigmaArray[i][0] = inputSigma[i];
             sigmaArray[i][0] = inputSigma == null ? 0.3 : inputSigma[i];
         }
         final RealMatrix insigma = new Array2DRowRealMatrix(sigmaArray, false);

Modified: commons/proper/math/trunk/src/test/java/org/apache/commons/math3/optimization/direct/CMAESOptimizerTest.java
URL: http://svn.apache.org/viewvc/commons/proper/math/trunk/src/test/java/org/apache/commons/math3/optimization/direct/CMAESOptimizerTest.java?rev=1400108&r1=1400107&r2=1400108&view=diff
==============================================================================
--- commons/proper/math/trunk/src/test/java/org/apache/commons/math3/optimization/direct/CMAESOptimizerTest.java
(original)
+++ commons/proper/math/trunk/src/test/java/org/apache/commons/math3/optimization/direct/CMAESOptimizerTest.java
Fri Oct 19 14:20:16 2012
@@ -52,7 +52,7 @@ public class CMAESOptimizerTest {
     @Test(expected = NumberIsTooLargeException.class)
     public void testInitOutofbounds1() {
         double[] startPoint = point(DIM,3);
-        double[] insigma = null;
+        double[] insigma = point(DIM, 0.3);
         double[][] boundaries = boundaries(DIM,-1,2);
         PointValuePair expected =
             new PointValuePair(point(DIM,1.0),0.0);
@@ -63,7 +63,7 @@ public class CMAESOptimizerTest {
     @Test(expected = NumberIsTooSmallException.class)
     public void testInitOutofbounds2() {
         double[] startPoint = point(DIM, -2);
-        double[] insigma = null;
+        double[] insigma = point(DIM, 0.3);
         double[][] boundaries = boundaries(DIM,-1,2);
         PointValuePair expected =
             new PointValuePair(point(DIM,1.0),0.0);
@@ -75,7 +75,7 @@ public class CMAESOptimizerTest {
     @Test(expected = DimensionMismatchException.class)
     public void testBoundariesDimensionMismatch() {
         double[] startPoint = point(DIM,0.5);
-        double[] insigma = null;
+        double[] insigma = point(DIM, 0.3);
         double[][] boundaries = boundaries(DIM+1,-1,2);
         PointValuePair expected =
             new PointValuePair(point(DIM,1.0),0.0);
@@ -111,7 +111,7 @@ public class CMAESOptimizerTest {
     @Test(expected = DimensionMismatchException.class)
     public void testInputSigmaDimensionMismatch() {
         double[] startPoint = point(DIM,0.5);
-        double[] insigma = point(DIM+1,-0.5);
+        double[] insigma = point(DIM + 1, 0.5);
         double[][] boundaries = null;
         PointValuePair expected =
             new PointValuePair(point(DIM,1.0),0.0);
@@ -459,17 +459,20 @@ public class CMAESOptimizerTest {
             PointValuePair expected) {
         int dim = startPoint.length;
         // test diagonalOnly = 0 - slow but normally fewer feval#
-        CMAESOptimizer optim = new CMAESOptimizer(lambda, inSigma, 30000,
-                                                  stopValue, isActive, diagonalOnly,
-                                                  0, new MersenneTwister(), false);
+        CMAESOptimizer optim = new CMAESOptimizer(30000, stopValue, isActive, diagonalOnly,
+                                                  0, new MersenneTwister(), false, null);
         final double[] lB = boundaries == null ? null : boundaries[0];
         final double[] uB = boundaries == null ? null : boundaries[1];
         PointValuePair result = boundaries == null ?
             optim.optimize(maxEvaluations, func, goal,
-                           new InitialGuess(startPoint)) :
+                           new InitialGuess(startPoint),
+                           new CMAESOptimizer.Sigma(inSigma),
+                           new CMAESOptimizer.PopulationSize(lambda)) :
             optim.optimize(maxEvaluations, func, goal,
                            new InitialGuess(startPoint),
-                           new SimpleBounds(lB, uB));
+                           new SimpleBounds(lB, uB),
+                           new CMAESOptimizer.Sigma(inSigma),
+                           new CMAESOptimizer.PopulationSize(lambda));
         // System.out.println("sol=" + Arrays.toString(result.getPoint()));
         Assert.assertEquals(expected.getValue(), result.getValue(), fTol);
         for (int i = 0; i < dim; i++) {



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