Commit b2c0ae94 authored by Fabian Becker's avatar Fabian Becker

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

parent 6ea7dc46
......@@ -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);
}
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;
// System.out.println(Mathematics.sum(dIndy1));
// System.out.println(Mathematics.sum(dIndy2));
double tmpDiff, sqrDiffSum = 0;
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.";
}
}
......@@ -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
*
......
......@@ -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(SigmaSingleMetricGlobalMutation a) {
}
public SigmaSingleMetricGlobalMutation() {}
@Override
public Object clone() {
return new SigmaSingleMetricGlobalMutation(this);
return new SigmaSingleMetricGlobalMutation();
}
/**
......
......@@ -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
......
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