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Cleanup.

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This commit is contained in:
Fabian Becker 2014-10-16 01:16:27 +02:00
parent 659a77c0dc
commit daef96899a
2 changed files with 1 additions and 215 deletions
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11
src/eva2/optimization/modules/ModuleAdapter.java
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@ -1,15 +1,4 @@
package eva2.optimization.modules;
/*
* Title: EvA2
* Description:
* Copyright: Copyright (c) 2003
* Company: University of Tuebingen, Computer Architecture
* @author Holger Ulmer, Felix Streichert, Hannes Planatscher
* @version: $Revision: 272 $
* $Date: 2007-11-21 18:06:36 +0100 (Wed, 21 Nov 2007) $
* $Author: mkron $
*/
import eva2.gui.EvATabbedFrameMaker;
import eva2.optimization.OptimizationStateListener;
import eva2.optimization.stat.OptimizationJob;

205
src/eva2/optimization/strategies/ParticleSwarmOptimization.java
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@ -155,25 +155,6 @@ public class ParticleSwarmOptimization implements InterfaceOptimizer, java.io.Se
topology = topo;
}
// /**
// * Constructor for most common parameters with constriction based approach and local search.
// *
// * @param popSize swarm size
// * @param p1 the value for phi1
// * @param p2 the value for phi1
// * @param topo type of the neighbourhood topology
// * @param topoRange range of the neighbourhood topology
// * @param lsEveryNGens interval of local search steps in generations
// * @param stepsPerInd number of local search steps per individual
// * @param candidateRatio ratio of population on which local search is performed
// */
// public ParticleSwarmOptimization(int popSize, double p1, double p2, PSOTopologyEnum topo, int topoRange, int lsEveryNGens, int stepsPerInd, double candidateRatio) {
// this(popSize, p1, p2, topo, topoRange);
// setDoLocalSearch(true);
// localSearchGens=lsEveryNGens;
// lsStepsPerInd=stepsPerInd;
// lsCandidateRatio = candidateRatio;
// }
@Override
public Object clone() {
return (Object) new ParticleSwarmOptimization(this);
@ -539,9 +520,6 @@ public class ParticleSwarmOptimization implements InterfaceOptimizer, java.io.Se
if (emaPeriods > 0) {
traceEMA(population);
}
// AbstractEAIndividual indy = population.getBestEAIndividual();
//System.out.println("best ind at " + indy.getStringRepresentation() + " , fit is " + indy.getFitness(0));
//try { Thread.sleep(10); } catch(Exception e) {}
}
public static void dumpPop(String prefix, Population pop) {
@ -681,30 +659,6 @@ public class ParticleSwarmOptimization implements InterfaceOptimizer, java.io.Se
this.plot.setConnectedPoint(curPosition[0], curPosition[1], index);
this.plot.setConnectedPoint(curPosition[0] + curVelocity[0], curPosition[1] + curVelocity[1], index);
}
// plot = null;
// show();
// if (pop.getGeneration()%15 == 0) {
// this.plot.clearAll();
// plot.setUnconnectedPoint(-10, -10, 0);
// plot.setUnconnectedPoint(10, 10, 0);
// }
// if (index != 0) return;
// double[] bestPosition = (double[])bestIndividual.getData(partBestPosKey);
// double[] localBestPos = findNeighbourhoodOptimum(index, population);
// this.plot.setConnectedPoint(curPosition[0], curPosition[1], index+1);
// this.plot.setConnectedPoint(curPosition[0] + curVelocity[0], curPosition[1] + curVelocity[1], index+1);
// this.plot.setConnectedPoint(curPosition[0], curPosition[1], index+2);
// this.plot.setConnectedPoint(bestPosition[0], bestPosition[1], index+2);
// this.plot.setConnectedPoint(curPosition[0], curPosition[1], index+3);
// this.plot.setConnectedPoint(localBestPos[0], localBestPos[1], index+3);
// this.plot.setConnectedPoint(curPosition[0], curPosition[1], index+1);
// this.plot.setConnectedPoint(localBestPosition[0], localBestPosition[1], index+1);
// this.plot.setConnectedPoint(curPosition[0], curPosition[1], index+1);
// this.plot.setConnectedPoint(bestPosition[0], bestPosition[1], index+1);
// this.plot.setUnconnectedPoint(curPosition[0], curPosition[1], 100*index+1);
}
}
@ -720,7 +674,6 @@ public class ParticleSwarmOptimization implements InterfaceOptimizer, java.io.Se
if (isIndividualToUpdate(indy)) {
updateIndProps(indy, indy);
indy.putData(lastSuccessKey, indy.getData(partVelKey));
// System.err.println("updated " + i + " - "+ getParticleInfo(indy));
} else {
indy.putData(lastSuccessKey, null);
}
@ -733,9 +686,6 @@ public class ParticleSwarmOptimization implements InterfaceOptimizer, java.io.Se
* @param indy
*/
protected void resetFitness(AbstractEAIndividual indy) {
// double[] fit = new double[1];
// fit[0] = 0;
// indy.SetFitness(fit);
indy.resetFitness(0);
indy.resetConstraintViolation();
}
@ -775,46 +725,6 @@ public class ParticleSwarmOptimization implements InterfaceOptimizer, java.io.Se
* @return
*/
protected double[] updateVelocity(int index, double[] lastVelocity, double[] personalBestPos, double[] curPosition, double[] neighbourBestPos, double[][] range) {
// for (int i = 0; i < lastVelocity.length; i++) {
// socCogn[i] = (personalBestPos[i]-curPosition[i]);
// neiDiff[i] = (neighbourBestPos[i]-curPosition[i]);
// }
//
// System.out.println("-- len bef " + vecLen(socCogn));
// rotateAllAxes(socCogn, .5, false);
// System.out.println("-- len aft " + vecLen(socCogn));
// rotateAllAxes(neiDiff, .5, false);
// // TODO!!!
// if (algType.getSelectedTag().getID()==1) chi=inertnessOrChi;
// else chi = 1.;
//
// double scaleCog = this.phi1*chi*RNG.randomDouble(0,1);
// double scaleNei = this.phi2*chi*RNG.randomDouble(0,1);
//
//
// for (int i=0; i<lastVelocity.length; i++) {
// curVelocity[i] = this.inertnessOrChi * lastVelocity[i];
// curVelocity[i] += scaleCog*socCogn[i];
// curVelocity[i] += scaleNei*neiDiff[i];
// }
// for (int i = 0; i < lastVelocity.length; i++) {
// // the component from the old velocity
// curVelocity[i] = this.inertnessOrChi * lastVelocity[i];
// if (algType.getSelectedTag().getID()==1) chi=inertnessOrChi;
// else chi = 1.;
// // the component from the cognition model
// //curVelocity[i] += this.phi1*chi*RNG.randomDouble(0,1)*(personalBestPos[i]-curPosition[i]);
// double dir,diff;
// dir = (personalBestPos[i] < curPosition[i]) ? -1 : 1;
// diff = Math.abs((personalBestPos[i]-curPosition[i]));
// curVelocity[i] += this.phi1*chi*.5*dir*Math.max(diff, .1);
// // the component from the social model
// //curVelocity[i] += this.phi2*chi*RNG.randomDouble(0,1)*(neighbourBestPos[i]-curPosition[i]);
// dir = (neighbourBestPos[i]< curPosition[i]) ? -1 : 1;
// diff = Math.abs((neighbourBestPos[i]-curPosition[i]));
// curVelocity[i] += this.phi2*chi*.5*dir*Math.max(diff, .1);
// }
double[] accel, curVelocity = new double[lastVelocity.length];
if (useAlternative) {
@ -823,8 +733,6 @@ public class ParticleSwarmOptimization implements InterfaceOptimizer, java.io.Se
accel = getAcceleration(personalBestPos, neighbourBestPos, curPosition, range);
}
//System.out.println("accel is " + getVecNorm(accel));
for (int i = 0; i < lastVelocity.length; i++) {
curVelocity[i] = this.inertnessOrChi * lastVelocity[i];
curVelocity[i] += accel[i];
@ -835,9 +743,7 @@ public class ParticleSwarmOptimization implements InterfaceOptimizer, java.io.Se
protected double[] getAcceleration(double[] personalBestPos, double[] neighbourBestPos, double[] curPosition, double[][] range) {
double[] accel = new double[curPosition.length];
double chi;
// double r1, r2;
// r1=RNG.randomDouble(0,1);
// r2=RNG.randomDouble(0,1);
for (int i = 0; i < personalBestPos.length; i++) {
// the component from the old velocity
accel[i] = 0;
@ -857,7 +763,6 @@ public class ParticleSwarmOptimization implements InterfaceOptimizer, java.io.Se
protected double[] getAccelerationAlternative(int index, double[] personalBestPos, double[] neighbourBestPos, double[] curPosition, double[][] range) {
double[] accel = getAcceleration(personalBestPos, neighbourBestPos, curPosition, range);
double[] successfulVel = getSuccessfulVel(index);
// double succW = 0.5;
if (successfulVel != null) {
Mathematics.vvAdd(accel, successfulVel, accel);
Mathematics.svMult(0.5, accel, accel);
@ -885,72 +790,6 @@ public class ParticleSwarmOptimization implements InterfaceOptimizer, java.io.Se
}
}
// protected double[] getAccelerationAlternative(double[] personalBestPos, double[] neighbourBestPos, double[] curPosition, double[][] range) {
// double[] accel = new double[curPosition.length];
// double chi;
//
// if (algType.getSelectedTag().getID()==1) chi=inertnessOrChi;
// else chi = 1.;
//
// boolean rotatedVect=false;
// if (rotatedVect) {
// Matrix cogVecB = new Matrix(curPosition.length, 1);
// Matrix socVecB = new Matrix(curPosition.length, 1);
// for (int i = 0; i < personalBestPos.length; i++) {
// cogVecB.set(i, 0, (personalBestPos[i]-curPosition[i]));
// socVecB.set(i, 0, (neighbourBestPos[i]-curPosition[i]));
// }
// Matrix cogRandB = getOrientedGaussianRandomVectorB(cogVecB, 5);
// Matrix socRandB = getOrientedGaussianRandomVectorB(socVecB, 5);
//
// for (int i = 0; i < curPosition.length; i++) {
// // the component from the cognition model
// // accel[i] = this.phi1*chi*/*RNG.randomDouble(0,1)**/cogRand.getElement(i);
// accel[i] = this.phi1*chi*/*RNG.randomDouble(0,1)**/cogRandB.get(i,0);
// // the component from the social model
// // accel[i] += this.phi2*chi*/*RNG.randomDouble(0,1)**/socRand.getElement(i);
// accel[i] += this.phi2*chi*/*RNG.randomDouble(0,1)**/socRandB.get(i,0);
// }
// return accel;
// } else {
// double sPB = RNG.randomDouble();
// double sNB = 1.-sPB;
// double[] mean= personalBestPos.clone();
// Mathematics.svMult(sPB, mean);
// Mathematics.svvAddScaled(sNB, neighbourBestPos, mean, mean); // middle position
// double stddev = chi*EuclideanMetric.euclideanDistance(personalBestPos, neighbourBestPos)/3.; // std.dev is one third of the distance
// double[] gausRnd = getGaussianVector(mean, stddev);
// for (int i=0; i<accel.length; i++) {
// accel[i] = (gausRnd[i] - curPosition[i]);
// }
// return accel;
// }
// }
// public static void main(String[] args) {
// ParticleSwarmOptimization pso = new ParticleSwarmOptimization();
// GVector tmp, vec = new GVector(5);
// GVector vecSum = new GVector(5);
// vec.setElement(1,3);
// vec.setElement(2,3);
//
// int max=10000;
// for (int i=0; i<max; i++) {
// tmp = pso.getOrientedGaussianRandomVector(vec, 5.);
// System.out.print(tmp.toString());
// System.out.println(" " + tmp.norm());
// //vecSum.add(tmp);
// }
//
// for (int i=0; i<max; i++) {
// tmp = pso.getOrientedGaussianRandomVector(vec, 5.);
// System.out.print(tmp.toString());
// System.out.println(" " + tmp.norm());
// //vecSum.add(tmp);
// }
// //vecSum.normalize();
// //System.out.println(vec.toString() + " -> " + vecSum.toString());
// }
/**
* Return a random vector after a gaussian distribution oriented along dir,
* meaning that variance is len(dir) along dir and len(dir)/scale in any
@ -1620,26 +1459,6 @@ public class ParticleSwarmOptimization implements InterfaceOptimizer, java.io.Se
return links;
}
// /**
// * Randomly assign groups of size groupSize.
// *
// * @param links
// * @param groupSize
// */
// private int[] regroupSwarm(Population pop, int groupSize) {
// int groupIndex, numGroups = pop.size() / groupSize; // truncated integer: last group is larger
//// int hangover = pop.size()-(numGroups*groupSize); // Ueberhangmandate ... woanders zuteilen um einergruppen zu vermeiden
//
// int[] perm = RNG.randomPerm(pop.size());
//
// for (int k=0; k<perm.length; k++) {
// groupIndex=k/groupSize;
// if (groupIndex>=numGroups) groupIndex--;
// pop.getEAIndividual(perm[k]).putData(dmsGroupIndexKey, groupIndex);
// }
// return perm;
// }
/**
* This method is simply for debugging.
*/
@ -2083,13 +1902,6 @@ public class ParticleSwarmOptimization implements InterfaceOptimizer, java.io.Se
return "if activated, the speed limit is enforced for the particles";
}
// public int getEMAPeriods() {
// return emaPeriods;
// }
// public void setEMAPeriods(int emaP) {
// this.emaPeriods = emaP;
// }
/**
* @return the sleepTime
*/
@ -2140,14 +1952,6 @@ public class ParticleSwarmOptimization implements InterfaceOptimizer, java.io.Se
this.treeStruct = treeStruct;
}
// This was for testing rotation operators
// public boolean isUseAlternative() {
// return useAlternative;
// }
//
// public void setUseAlternative(boolean useAlternative) {
// this.useAlternative = useAlternative;
// }
public boolean isWrapTopology() {
return wrapTopology;
}
@ -2239,13 +2043,6 @@ public class ParticleSwarmOptimization implements InterfaceOptimizer, java.io.Se
return "The number of generations after which new subswarms are randomly formed.";
}
// public boolean isDoLocalSearch() {
// return doLocalSearch;
// }
//
// public void setDoLocalSearch(boolean doLocalSearch) {
// this.doLocalSearch = doLocalSearch;
// }
@Override
public String[] getAdditionalDataHeader() {
if (emaPeriods > 0) {