Deduplicate norm() method and use the one provided by Mathematics

src/main/java/eva2/optimization/operator/distancemetric/DoubleIntegralMetric.java

@@ -4,6 +4,7 @@ import eva2.optimization.individuals.AbstractEAIndividual;
 import eva2.tools.EVAERROR;
 import eva2.tools.math.Mathematics;
 import eva2.util.annotation.Description;
+import eva2.util.annotation.Parameter;
 
 import java.io.Serializable;
 
@@ -19,8 +20,7 @@ import java.io.Serializable;
 public class DoubleIntegralMetric implements InterfaceDistanceMetric, Serializable {
     boolean oneNormed = true; // if true, the vectors are normed to unity sum before comparison.
 
-    public DoubleIntegralMetric() {
+    public DoubleIntegralMetric() {}
-    }
 
     public DoubleIntegralMetric(boolean normed) {
         oneNormed = normed;
@@ -35,10 +35,8 @@ public class DoubleIntegralMetric implements InterfaceDistanceMetric, Serializab
     public double distance(AbstractEAIndividual indy1, AbstractEAIndividual indy2) {
         double[] dIndy1 = null, dIndy2 = null;
 
-        if (dIndy1 == null || dIndy2 == null) {
         dIndy1 = AbstractEAIndividual.getDoublePositionShallow(indy1);
         dIndy2 = AbstractEAIndividual.getDoublePositionShallow(indy2);
-        }
 
         if (oneNormed) {
             double l1 = Mathematics.sum(dIndy1);
@@ -53,9 +51,8 @@ public class DoubleIntegralMetric implements InterfaceDistanceMetric, Serializab
         }
 
         double sum1 = 0, sum2 = 0;
-        double tmpDiff = 0, sqrDiffSum = 0;
+        double tmpDiff, sqrDiffSum = 0;
-//		System.out.println(Mathematics.sum(dIndy1));
+
-//		System.out.println(Mathematics.sum(dIndy2));
         for (int i = 0; (i < dIndy1.length) && (i < dIndy2.length); i++) {
             sum1 += dIndy1[i];
             sum2 += dIndy2[i];
@@ -73,12 +70,8 @@ public class DoubleIntegralMetric implements InterfaceDistanceMetric, Serializab
         return oneNormed;
     }
 
+    @Parameter(description = "If true, both vectors are normed to unity sum before comparison.")
     public void setOneNormed(boolean normedByLength) {
         this.oneNormed = normedByLength;
     }
-
-    public String oneNormedTipText() {
-        return "If true, both vectors are normed to unity sum before comparison.";
-    }
-
 }

src/main/java/eva2/optimization/operator/distancemetric/PhenotypeMetric.java

@@ -3,6 +3,7 @@ package eva2.optimization.operator.distancemetric;
 
 import eva2.gui.BeanInspector;
 import eva2.optimization.individuals.*;
+import eva2.tools.math.Mathematics;
 import eva2.util.annotation.Description;
 
 import java.util.BitSet;
@@ -130,7 +131,6 @@ public class PhenotypeMetric implements InterfaceDistanceMetric, java.io.Seriali
         if ((indy1 instanceof InterfaceDataTypePermutation) && (indy2 instanceof InterfaceDataTypePermutation)) {
             int[] dIndy1, dIndy2;
             String s1 = "", s2 = "";
-//            double  tmpResult = 0;
             for (int p = 0; p < ((InterfaceDataTypePermutation) indy1).getPermutationData().length; p++) {
                 dIndy1 = ((InterfaceDataTypePermutation) indy1).getPermutationData()[p];
                 dIndy2 = ((InterfaceDataTypePermutation) indy2).getPermutationData()[p];
@@ -192,7 +192,7 @@ public class PhenotypeMetric implements InterfaceDistanceMetric, java.io.Seriali
             return result / d1.length;
         }
         if (indy instanceof InterfaceDataTypeDouble) {
-            result = norm(((InterfaceDataTypeDouble) indy).getDoubleData());
+            result = Mathematics.norm(((InterfaceDataTypeDouble) indy).getDoubleData());
             return result;
         }
         if (indy instanceof InterfaceDataTypePermutation) {
@@ -208,20 +208,6 @@ public class PhenotypeMetric implements InterfaceDistanceMetric, java.io.Seriali
         return 0;
     }
 
-    /**
-     * Calculates the 2 norm of a given vector.
-     *
-     * @param v1
-     * @return
-     */
-    public static double norm(double[] v1) {
-        double result = 0;
-        for (int i = 0; i < v1.length; i++) {
-            result += Math.pow(v1[i], 2);
-        }
-        return Math.sqrt(result);
-    }
-
     /**
      * This method will return a naming String
      *

src/main/java/eva2/optimization/operator/distancemetric/SigmaSingleMetricGlobalMutation.java

@@ -15,15 +15,11 @@ import eva2.util.annotation.Description;
 @Description("This is an experimental method for individuals using global ES mutation.")
 public class SigmaSingleMetricGlobalMutation implements InterfaceDistanceMetric, java.io.Serializable {
 
-    public SigmaSingleMetricGlobalMutation() {
+    public SigmaSingleMetricGlobalMutation() {}
-    }
-
-    public SigmaSingleMetricGlobalMutation(SigmaSingleMetricGlobalMutation a) {
-    }
 
     @Override
     public Object clone() {
-        return new SigmaSingleMetricGlobalMutation(this);
+        return new SigmaSingleMetricGlobalMutation();
     }
 
     /**

src/main/java/eva2/optimization/operator/postprocess/PostProcess.java

@@ -248,9 +248,9 @@ public class PostProcess {
         Population result = new Population();
         for (int i = 0; i < pop.size(); i++) {
             indy = pop.getEAIndividual(i);
-            if (bSmallerEq && (PhenotypeMetric.norm(indy.getFitness()) <= fitNorm)) {
+            if (bSmallerEq && (Mathematics.norm(indy.getFitness()) <= fitNorm)) {
                 result.add(indy);
-            } else if (!bSmallerEq && (PhenotypeMetric.norm(indy.getFitness()) > fitNorm)) {
+            } else if (!bSmallerEq && (Mathematics.norm(indy.getFitness()) > fitNorm)) {
                 result.add(indy);
             }
         }
@@ -279,7 +279,7 @@ public class PostProcess {
             if ((crit >= 0) && (crit < indy.getFitness().length)) {
                 curFit = indy.getFitness(crit);
             } else {
-                curFit = PhenotypeMetric.norm(indy.getFitness());
+                curFit = Mathematics.norm(indy.getFitness());
             }
 
             if (bSmallerEq && (curFit <= fitThresh)) {
@@ -1058,7 +1058,7 @@ public class PostProcess {
                 plot = draw("After " + stepsDone + " steps (" + params.getPPMethod() + ")" + ((params.getPostProcessClusterSigma() > 0) ? " and second clustering" : ""), null, outputPop, null, problem);
             }
             // ##### some statistics
-            double upBnd = PhenotypeMetric.norm(outputPop.getWorstEAIndividual().getFitness()) * 1.1;
+            double upBnd = Mathematics.norm(outputPop.getWorstEAIndividual().getFitness()) * 1.1;
             upBnd = Math.pow(10, Math.floor(Math.log10(upBnd) + 1));
             double lowBnd = 0;
             int fitCrit = 0; // use first fitness criterion