147 lines
4.3 KiB
Java
147 lines
4.3 KiB
Java
package Presenter.Evaluation;
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import Model.LineModel;
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import Model.Interval;
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import Model.Line;
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import Presenter.Algorithms.*;
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import Presenter.Generator.DatasetGenerator;
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import java.util.ArrayList;
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import java.util.LinkedList;
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import java.util.List;
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import java.util.Observable;
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/**
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* Implementierung verschiedener Algorithmen zur Berechnung von Ausgleichsgeraden.
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*
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* @Author: Armin Wolf
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* @Email: a_wolf28@uni-muenster.de
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* @Date: 01.08.2017.
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*/
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public class EvaluateAlgorithms extends Observable{
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private LineModel arrangement;
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private Object[] lmsResult;
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private Object[] rmResult;
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private Object[] tsResult;
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private Thread lmsThread;
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private Thread rmThread;
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private Thread tsThread;
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public EvaluateAlgorithms(){}
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public void run() throws InterruptedException {
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this.arrangement = new LineModel();
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DatasetGenerator generator = new DatasetGenerator();
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arrangement.setLines(generator.generateDataset());
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IntersectionCounter counter = new IntersectionCounter();
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counter.run(arrangement.getLines(), new Interval(-99999,99999));
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counter.calculateIntersectionAbscissas(arrangement);
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lmsThread = new Thread(() -> {
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LeastMedianOfSquaresEstimator lmsAlg = new LeastMedianOfSquaresEstimator(arrangement.getLines()
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,arrangement.getNodes());
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lmsAlg.run();
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lmsAlg.getResult();
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List<Double> errors = sampsonError(arrangement.getLines(), lmsAlg.getSlope(), lmsAlg.getyInterception());
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lmsResult = getResults(errors, "Least Median of Squares");
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setChanged();
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notifyObservers(lmsResult);
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});
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rmThread = new Thread(() -> {
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RepeatedMedianEstimator rmAlg = new RepeatedMedianEstimator(arrangement.getLines());
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rmAlg.run();
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rmAlg.getResult();
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List<Double> errors = sampsonError(arrangement.getLines(), rmAlg.getSlope(), rmAlg.getyInterception());
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rmResult = getResults(errors, "Repeated-Median");
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setChanged();
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notifyObservers(rmResult);
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});
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tsThread = new Thread(() -> {
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TheilSenEstimator tsAlg = new TheilSenEstimator(arrangement.getLines(), arrangement.getNodes());
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tsAlg.run();
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tsAlg.getResult();
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List<Double> errors = sampsonError(arrangement.getLines(), tsAlg.getSlope(), tsAlg.getyInterception());
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tsResult = getResults(errors, "Theil-Sen");
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setChanged();
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notifyObservers(tsResult);
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});
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lmsThread.start();
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rmThread.start();
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tsThread.start();
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lmsThread.join();
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rmThread.join();
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tsThread.join();
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}
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public String[] getResults(List<Double> errorValues, String name){
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String[] ret = new String[6];
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ret[0] = name;
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ret[1] = mse(errorValues).toString();
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ret[2] = rmse(errorValues).toString();
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ret[3] = mae(errorValues).toString();
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ret[4] = mdae(errorValues).toString();
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ret[5] = "eval";
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return ret;
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}
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/* Skalierungs Abhängige Approximationsgüten */
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public Double mse(List<Double> errorValues){
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double error = 0;
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for (Double d : errorValues){
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error += Math.pow(d,2);
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}
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error /= errorValues.size();
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return error;
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}
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public Double rmse(List<Double> errorValues){
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return Math.sqrt(mse(errorValues));
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}
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public Double mae(List<Double> errorValues){
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double error = 0;
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for (Double d : errorValues){
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error += Math.abs(d);
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}
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error /= errorValues.size();
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return error;
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}
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public Double mdae(List<Double> errorValues){
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return FastElementSelector.randomizedSelect((ArrayList<Double>) errorValues, errorValues.size()*0.5);
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}
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public List<Double> sampsonError(final LinkedList<Line> lines, Double m, Double b){
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//Liste mit den Fehler zu jedem Punkt
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List<Double> sampsonerror = new ArrayList<>();
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for (Line line : lines){
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Double error = Math.pow(m * line.getM() - line.getB() + b, 2) / (Math.pow(m,2) + 1);
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sampsonerror.add(error);
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}
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return sampsonerror;
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}
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}
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