Merging mk branch rev 152:153

src/eva2/server/go/populations/Population.java

@@ -436,8 +436,7 @@ public class Population extends ArrayList implements PopulationInterface, Clonea
     /** 
      * This method returns the n current best individuals from the population, where
      * the sorting criterion is delivered by an AbstractEAIndividualComparator.
-     * There are less than n individuals returned if the population is smaller than n.
+     * @see getSortedNIndividuals(int n, boolean bBestOrWorst, Population res)
-     * This does not check constraints!
      * 
      * @param n	number of individuals to look out for
      * @param bBestOrWorst if true, the best n are returned, else the worst n individuals
@@ -445,6 +444,24 @@ public class Population extends ArrayList implements PopulationInterface, Clonea
      * 
      */
     public Population getSortedNIndividuals(int n, boolean bBestOrWorst) {
+    	Population result = new Population(n);
+    	getSortedNIndividuals(n, bBestOrWorst, result);
+    	return result;
+    }
+    
+    /** 
+     * This method returns the n current best individuals from the population, where
+     * the sorting criterion is delivered by an AbstractEAIndividualComparator.
+     * There are less than n individuals returned if the population is smaller than n.
+     * This does not check constraints!
+     * 
+     * @param n	number of individuals to look out for
+     * @param bBestOrWorst if true, the best n are returned, else the worst n individuals
+     * @param res	sorted result population, will be cleared
+     * @return The m sorted best or worst individuals, where m <= n
+     * 
+     */
+    public void getSortedNIndividuals(int n, boolean bBestOrWorst, Population res) {
     	if ((n < 0) || (n>super.size())) {
     		System.err.println("invalid request to getSortedNIndividuals: n="+n + ", size is " + super.size());
     		n = super.size();
@@ -452,14 +469,12 @@ public class Population extends ArrayList implements PopulationInterface, Clonea
     	int skip = 0;
     	if (!bBestOrWorst) skip = super.size()-n;
     	
-    	Population result = new Population(n);
     	ArrayList<AbstractEAIndividual> sorted = getSorted();
-
+    	res.clear();
         for (int i = skip; i < skip+n; i++) {
-        	result.add(sorted.get(i));
+        	res.add(sorted.get(i));
         }
-        result.setPopulationSize(result.size());
+        res.setPopulationSize(res.size());
-    	return result;
     }
     
     /**

src/eva2/server/go/problems/AbstractProblemDouble.java

@@ -46,7 +46,12 @@ public abstract class AbstractProblemDouble extends AbstractOptimizationProblem
         return x;
 	}
 	
-	@Override
+	/**
+	 * When implementing a double problem, inheriting classes should not override this method (or only
+	 * extend it) and do the fitness calculations in the method eval(double[]).
+	 * 
+	 * @see eval(double[] x)
+	 */
 	public void evaluate(AbstractEAIndividual individual) {
         double[]        x;
         double[]        fitness;

src/eva2/server/go/strategies/ParticleSwarmOptimization.java

@@ -36,6 +36,9 @@ import wsi.ra.math.Jama.Matrix;
  * whether the new individual would violate range constraints, if so
  * the velocity vector is reduced.
  * 
+ * Possible topologies are: "Linear", "Grid", "Star", "Multi-Swarm", "Tree", "HPSO", "Random"
+ * in that order starting by 0.
+ * 
  * Created by IntelliJ IDEA.
  * User: streiche
  * Date: 28.10.2004
@@ -589,9 +592,6 @@ public class ParticleSwarmOptimization implements InterfaceOptimizer, java.io.Se
 	 */
 	protected boolean isIndividualToUpdate(AbstractEAIndividual indy) {
 		double[] bestFitness    = (double[]) indy.getData(partBestFitKey);
-		// TODO Multi-obj.?
-//		if (bestFitness.length>1) System.err.println("warning, multi-objective fitness disregarded here (PSO)");
-//		return (indy.getFitness()[0] <= bestFitness[0]);
 		return (AbstractEAIndividual.isDominatingFitnessNotEqual(indy.getFitness(), bestFitness));
 	}
 
@@ -611,14 +611,6 @@ public class ParticleSwarmOptimization implements InterfaceOptimizer, java.io.Se
 		}
 	}
 	
-	private double vecLen(double[] vec) {
-		double sum = 0.;
-		for (int i=0; i<vec.length; i++) {
-			sum += vec[i]*vec[i];
-		}
-		return Math.sqrt(sum);
-	}
-	
 	/**
 	 * Main velocity update step.
 	 *
@@ -688,12 +680,6 @@ public class ParticleSwarmOptimization implements InterfaceOptimizer, java.io.Se
 		return curVelocity;
 	}
 
-	private double getVecNorm(double[] vect) {
-		double res = 0.;
-		for (int i=0; i<vect.length; i++) res += vect[i]*vect[i];
-		return Math.sqrt(res);
-	}
-
 	protected double[] getAcceleration(double[] personalBestPos, double[] neighbourBestPos, double[] curPosition, double[][] range) {
 		double[] accel = new double[curPosition.length];
 		double chi;
@@ -719,21 +705,13 @@ public class ParticleSwarmOptimization implements InterfaceOptimizer, java.io.Se
 		
 		Matrix cogVecB = new Matrix(curPosition.length, 1);
 		Matrix socVecB = new Matrix(curPosition.length, 1);
-//		GVector cogVec = new GVector(curPosition.length);
-//		GVector socVec = new GVector(curPosition.length);
 		
 		for (int i = 0; i < personalBestPos.length; i++) {
 			cogVecB.set(i, 0, (personalBestPos[i]-curPosition[i]));
 			socVecB.set(i, 0, (neighbourBestPos[i]-curPosition[i]));
-//			cogVec.setElement(i, (personalBestPos[i]-curPosition[i]));
-//			socVec.setElement(i, (neighbourBestPos[i]-curPosition[i]));
 		}
 		Matrix cogRandB = getOrientedGaussianRandomVectorB(cogVecB, 5);
-//		GVector cogRand = getOrientedGaussianRandomVector(cogVec, 5);
 		Matrix socRandB = getOrientedGaussianRandomVectorB(socVecB, 5);
-//		GVector socRand = getOrientedGaussianRandomVector(socVec, 5);
-		
-		//System.out.println(cogVec.norm() + " " + cogRand.norm() + " / " + socVec.norm() + " " + socRand.norm());
 
 		for (int i = 0; i < curPosition.length; i++) {
 			if (algType.getSelectedTag().getID()==1) chi=m_InertnessOrChi;
@@ -812,29 +790,6 @@ public class ParticleSwarmOptimization implements InterfaceOptimizer, java.io.Se
 		else return val;
 	}
 
-//	protected GVector getOrientedGaussianRandomVector(GVector dir, double scale) {
-//		double len = dir.norm();
-//		int dim = dir.getSize();
-//
-//		GVector resVec = new GVector(dim);
-//		GVector randVec = new GVector(dim);
-//
-//		if (len > 0) {
-//			// initialize random vector
-//			randVec.setElement(0, len/2.+RNG.gaussianDouble(len/2));
-//			//randVec.setElement(0, RNG.randomDouble(0, len));
-//			for (int i=1; i<dim; i++) {
-//				randVec.setElement(i, RNG.gaussianDouble(len/(scale*2)));
-//			}
-//
-//			GMatrix rotation = nrotate(dir);
-//			rotation.transpose();
-//			printGMatrix(rotation);
-//			resVec.mul(rotation, randVec);
-//		}
-//		return resVec;
-//	}
-
 	protected void printMatrix(Matrix M) {
 		for (int i=0; i<M.getRowDimension(); i++) {
 			for (int j=0; j<M.getColumnDimension(); j++) System.out.print(" " + M.get(i, j));
@@ -842,25 +797,6 @@ public class ParticleSwarmOptimization implements InterfaceOptimizer, java.io.Se
 		}
 	}
 	
-//	private void printGMatrix(GMatrix M) {
-//		for (int i=0; i<M.getNumRow(); i++) {
-//			for (int j=0; j<M.getNumCol(); j++) System.out.print(" " + M.getElement(i, j));
-//			System.out.println("");
-//		}
-//	}
-//	
-//	protected GVector getUnitVec(int dim, int k) {
-//		GVector v = new GVector(dim);
-//		v.setElement(k, 1.);
-//		return v;
-//	}
-	
-//	protected void switchElements(GVector vec, int i, int j) {
-//		double val1 = vec.getElement(i);
-//		vec.setElement(i, vec.getElement(j));
-//		vec.setElement(j, val1);
-//	}
-	
 	/**
 	 * In the topology range for the given index, find the best stored individual and return its position.
 	 *

src/eva2/tools/Mathematics.java

@@ -337,13 +337,25 @@ public class Mathematics {
 	 */
 	public static double[] vvSub(double[] a, double[] b) {
 		double[] result = new double[a.length];
-		for (int i = 0; i < a.length; i++) {
+		vvSub(a, b, result);
-			result[i] = a[i] - b[i];
-		}
 		return result;
 	}
 
-	/** This method return a vector from a to b
+	/**
+	 * Subtract vectors returning a new vector c = a - b.
+	 * 
+	 * @param a
+	 * @param b
+	 * @return a new vector c = a - b
+	 */
+	public static void vvSub(double[] a, double[] b, double[] res) {
+		for (int i = 0; i < a.length; i++) {
+			res[i] = a[i] - b[i];
+		}
+	}
+	
+	/** 
+	 * This method return a vector from a to b
 	 * @param a     first vector
 	 * @param b     second vectors
 	 * @return the vector from a to b
@@ -393,6 +405,20 @@ public class Mathematics {
 		return res;
 	}
 	
+	/**
+	 * Add vectors scaled: res[i] = s*v[i] + w[i]
+	 * 
+	 * @param s
+	 * @param v
+	 * @param w
+	 * @return 
+	 */
+	public static void svvAddScaled(double s, double[] v, double[] w, double[] res) {
+		for (int i = 0; i < v.length; i++) {
+			res[i] = s*v[i] + w[i];
+		}
+	}
+
 	/**
 	 * Scalar product of two vectors returning sum_i (a_i * b_i).
 	 * 

src/wsi/ra/math/Jama/Matrix.java

@@ -9,6 +9,8 @@ import java.text.DecimalFormatSymbols;
 import java.text.NumberFormat;
 import java.util.Locale;
 
+import eva2.gui.BeanInspector;
+
 import wsi.ra.math.Jama.util.Maths;
 
 
@@ -762,6 +764,15 @@ public class Matrix implements Cloneable, java.io.Serializable {
       return X;
    }
    
+   public String toString() {
+	   StringBuffer sb = new StringBuffer();
+	   for (int i=0; i<m; i++) {
+		   sb.append(BeanInspector.toString(A[i]));
+		   sb.append("\n");
+	   }
+	   return sb.toString();
+   }
+
    /** Multiply a matrix by a scalar in place, A = s*A
    @param s    scalar
    @return     replace A by s*A