414 lines
16 KiB
Java
414 lines
16 KiB
Java
package presenter.evaluation;
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import model.Interval;
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import model.Line;
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import model.LineModel;
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import model.Point;
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import presenter.algorithms.naiv.NaivLeastMedianOfSquaresEstimator;
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import presenter.algorithms.naiv.NaivRepeatedMedianEstimator;
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import presenter.algorithms.naiv.NaivTheilSenEstimator;
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import presenter.algorithms.util.IntersectionCounter;
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import presenter.algorithms.advanced.LeastMedianOfSquaresEstimator;
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import presenter.algorithms.advanced.RepeatedMedianEstimator;
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import presenter.algorithms.advanced.TheilSenEstimator;
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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.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 LinkedList<Line> lmsL;
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private LinkedList<Line> rmL;
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private LinkedList<Line> tsL;
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private LinkedList<Point> lmsP;
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private LinkedList<Point> tsP;
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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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private Double[] tsRes = new Double[2];
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private Double[] rmRes = new Double[2];
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private Double[] lmsRes = new Double[2];
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private DatasetGenerator generator;
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private String[][] names = {{"MSE", "RMSE", "MAE", "MDAE","Steigung","y-Achsenabschnitt", "S-MSE", "S-RMSE", "S-MAE", "S-MDAE", "Brute-force Steigung", "Brute-force y-Achsenabschnitt"}, {"MAPE", "MDAPE", "RMSPE", "RMDSPE","Steigung","y-Achsenabschnitt"}};
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//übergebene Parameter
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private int type;
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private int iterations;
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private int alg;
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/**
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* Konstruktor zur evaluation
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*
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* @param type Typ der evaluation
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* @param n Größe des Datensatzes
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* @param alg 0 = lms,
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* 1 = rm,
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* 2 = ts,
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* 3 = lms, rm,
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* 4 = lms, ts,
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* 5 = rm, ts,
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* 6 = lms, rm, ts,
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*/
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public EvaluateAlgorithms(int type, int n, int alg, String datasettyp) {
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this.arrangement = new LineModel();
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generator = new DatasetGenerator();
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switch (datasettyp) {
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case "Punktwolke":
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arrangement.setLines(generator.generateDataCloud(n));
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break;
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case "Gerade":
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arrangement.setLines(generator.generateDataLines(n));
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break;
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case "Kreis und Gerade":
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arrangement.setLines(generator.generateCircle(n));
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break;
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}
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this.type = type;
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this.iterations = n;
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this.alg = alg;
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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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lmsL = new LinkedList<>(arrangement.getLines());
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rmL = new LinkedList<>(arrangement.getLines());
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tsL = new LinkedList<>(arrangement.getLines());
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lmsP = new LinkedList<>(arrangement.getNodes());
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tsP = new LinkedList<>(arrangement.getNodes());
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}
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public void run() throws InterruptedException {
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setChanged();
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String[] msg = {"eval-dataset-generated"};
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notifyObservers(msg);
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ArrayList<String> result;
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ArrayList<ArrayList<String>> multipleResults = new ArrayList<>();
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/* DEBUG
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NaivLeastMedianOfSquaresEstimator l = new NaivLeastMedianOfSquaresEstimator(arrangement.getLines());
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NaivRepeatedMedianEstimator r = new NaivRepeatedMedianEstimator(arrangement.getLines());
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NaivTheilSenEstimator t = new NaivTheilSenEstimator(arrangement.getLines());
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l.run();
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r.run();
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t.run();
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System.out.println();
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DEBUG */
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startLMS();
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startRM();
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startTS();
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switch (type) {
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case 0:
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//der alg der gewählt wurde
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if (alg == 0) {
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final double[] m = new double[1];
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final double[] b = new double[1];
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Thread t = new Thread(() -> {
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NaivLeastMedianOfSquaresEstimator l = new NaivLeastMedianOfSquaresEstimator(arrangement.getLines());
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l.run();
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m[0] = l.getM();
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b[0] = l.getB();
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});
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t.start();
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startLMS();
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t.join();
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result = getScaleDependentMeasure(arrangement.getLines(), lmsRes[0], lmsRes[1]);
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result.addAll(getScaledErrorBasedMeasure(arrangement.getLines(), lmsRes[0], lmsRes[1],m[0], b[0]));
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Double[] tmp = {lmsRes[0], lmsRes[1],m[0], b[0]};
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sendPlotLineResults(tmp, 0);
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} else if (alg == 1) {
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final double[] m = new double[1];
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final double[] b = new double[1];
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Thread t = new Thread(() -> {
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NaivRepeatedMedianEstimator r = new NaivRepeatedMedianEstimator(arrangement.getLines());
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r.run();
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m[0] = r.getM();
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b[0] = r.getB();
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});
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t.start();
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startRM();
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result = getScaleDependentMeasure(arrangement.getLines(), rmRes[0], rmRes[1]);
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result.addAll(getScaledErrorBasedMeasure(arrangement.getLines(), rmRes[0], rmRes[1],m[0], b[0]));
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Double[] tmp = {rmRes[0], rmRes[1],m[0], b[0]};
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sendPlotLineResults(tmp, 1);
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} else {
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final double[] m = new double[1];
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final double[] b = new double[1];
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Thread t = new Thread(() -> {
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NaivTheilSenEstimator ts = new NaivTheilSenEstimator(arrangement.getLines());
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ts.run();
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m[0] = ts.getM();
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b[0] = ts.getB();
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});
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t.start();
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startTS();
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result = getScaleDependentMeasure(arrangement.getLines(), tsRes[0], tsRes[1]);
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result.addAll(getScaledErrorBasedMeasure(arrangement.getLines(), tsRes[0], tsRes[1],m[0], b[0]));
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Double[] tmp = {tsRes[0], tsRes[1],m[0], b[0]};
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sendPlotLineResults(tmp, 2);
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}
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sendTableApproximationTypes();
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sendTableApproximationData(result, alg);
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break;
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case 1:
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ArrayList<Double[]> lineRes;
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switch (alg) {
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case 3:
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result = getPercentigeErrorBasedMeasure(arrangement.getLines(), lmsRes[0], lmsRes[1]);
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multipleResults.add(result);
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result = getPercentigeErrorBasedMeasure(arrangement.getLines(), rmRes[0], rmRes[1]);
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multipleResults.add(result);
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result = fillPseudoResults();
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multipleResults.add(result);
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lineRes = new ArrayList<>();
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lineRes.add(lmsRes);
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lineRes.add(rmRes);
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sendPloteLineResults(lineRes, new Integer[]{0,1});
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break;
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case 4:
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result = getPercentigeErrorBasedMeasure(arrangement.getLines(), lmsRes[0], lmsRes[1]);
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multipleResults.add(result);
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result = fillPseudoResults();
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multipleResults.add(result);
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result = getPercentigeErrorBasedMeasure(arrangement.getLines(), tsRes[0], tsRes[1]);
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multipleResults.add(result);
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lineRes = new ArrayList<>();
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lineRes.add(lmsRes);
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lineRes.add(tsRes);
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sendPloteLineResults(lineRes, new Integer[]{0, 2});
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break;
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case 5:
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result = fillPseudoResults();
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multipleResults.add(result);
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result = getPercentigeErrorBasedMeasure(arrangement.getLines(), rmRes[0], rmRes[1]);
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multipleResults.add(result);
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result = getPercentigeErrorBasedMeasure(arrangement.getLines(), tsRes[0], tsRes[1]);
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multipleResults.add(result);
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lineRes = new ArrayList<>();
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lineRes.add(rmRes);
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lineRes.add(tsRes);
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sendPloteLineResults(lineRes, new Integer[]{1, 2});
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break;
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case 6:
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result = getPercentigeErrorBasedMeasure(arrangement.getLines(), lmsRes[0], lmsRes[1]);
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multipleResults.add(result);
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result = getPercentigeErrorBasedMeasure(arrangement.getLines(), rmRes[0], rmRes[1]);
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multipleResults.add(result);
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result = getPercentigeErrorBasedMeasure(arrangement.getLines(), tsRes[0], tsRes[1]);
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multipleResults.add(result);
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lineRes = new ArrayList<>();
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lineRes.add(lmsRes);
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lineRes.add(rmRes);
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lineRes.add(tsRes);
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sendPloteLineResults(lineRes, new Integer[]{0, 1, 2});
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break;
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}
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sendTableApproximationData(multipleResults);
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break;
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}
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}
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public void sendTableApproximationData(ArrayList<String> result, int col) {
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ArrayList<String> tableInput = new ArrayList<>();
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tableInput.add("eval-d");
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tableInput.add("" + col);
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for (int i = 0; i < names[type].length; i++) {
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tableInput.add(result.get(i));
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}
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tableInput.add("");
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setChanged();
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notifyObservers(tableInput.stream().toArray(String[]::new));
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tableInput.clear();
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}
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public void sendTableApproximationData(ArrayList<ArrayList<String>> result) {
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ArrayList<String> tableInput = new ArrayList<>();
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//iteration über die ApproximationsGüten -- Zeilen
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for (int j = 0; j <= result.get(0).size(); j++) {
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tableInput.add("eval-ds");
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if (j != result.get(0).size()) {
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tableInput.add(names[type][j]);
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//iteration über die alg. -- Spalten
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for (int i = 0; i < 3; i++) {
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tableInput.add(result.get(i).get(j));
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}
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} else {
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tableInput.add("");
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tableInput.add("");
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tableInput.add("");
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tableInput.add("");
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}
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setChanged();
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notifyObservers(tableInput.stream().toArray(String[]::new));
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tableInput.clear();
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}
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}
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public void sendTableApproximationTypes() {
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ArrayList<String> tableInput = new ArrayList<>();
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tableInput.add("eval-t");
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tableInput.add("" + 0);
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for (int i = 0; i < names[type].length; i++) {
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tableInput.add(names[type][i]);
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}
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tableInput.add("");
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setChanged();
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notifyObservers(tableInput.stream().toArray(String[]::new));
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tableInput.clear();
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}
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public void sendPlotLineResults(Double[] res, int alg) {
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//visualisiere m,b
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ArrayList<String> lines = new ArrayList<>();
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lines.add("lines-res");
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lines.add("" + alg);
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//lms res
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for (int i=0;i<res.length;i++) {
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lines.add(res[i] + "");
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}
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setChanged();
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notifyObservers(lines.stream().toArray(String[]::new));
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}
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public void sendPloteLineResults(ArrayList<Double[]> res, Integer[] algs) {
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ArrayList<String> lines = new ArrayList<>();
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lines.add("lines-res-mult");
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for (int i = 0; i < algs.length; i++) {
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lines.add("" + algs[i]);
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//lms res
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Double[] tmp = res.get(i);
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lines.add(tmp[0] + "");
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lines.add(tmp[1] + "");
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}
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setChanged();
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notifyObservers(lines.stream().toArray(String[]::new));
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}
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public void startLMS() throws InterruptedException {
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lmsThread = new Thread(() -> {
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LeastMedianOfSquaresEstimator lmsAlg = new LeastMedianOfSquaresEstimator(lmsL,lmsP);
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lmsAlg.run();
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lmsAlg.getResult();
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lmsRes[0] = lmsAlg.getSlope();
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lmsRes[1] = lmsAlg.getyInterception();
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});
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lmsThread.start();
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lmsThread.join();
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}
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public void startRM() throws InterruptedException {
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rmThread = new Thread(() -> {
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RepeatedMedianEstimator rmAlg = new RepeatedMedianEstimator(rmL);
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rmAlg.run();
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rmAlg.getResult();
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rmRes[0] = rmAlg.getSlope();
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rmRes[1] = rmAlg.getyInterception();
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});
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rmThread.start();
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rmThread.join();
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}
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public void startTS() throws InterruptedException {
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tsThread = new Thread(() -> {
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TheilSenEstimator tsAlg = new TheilSenEstimator(tsL,tsP);
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tsAlg.run();
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tsAlg.getResult();
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tsRes[0] = tsAlg.getSlope();
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tsRes[1] = tsAlg.getyInterception();
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});
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tsThread.start();
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tsThread.join();
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}
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public ArrayList<String> getScaleDependentMeasure(final LinkedList<Line> lines, final Double m, final Double b) {
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ScaleDependentMeasure scaleDependentMeasure = new ScaleDependentMeasure(lines, m, b);
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ArrayList<String> ret = new ArrayList<>();
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ret.add(scaleDependentMeasure.mse().toString());
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ret.add(scaleDependentMeasure.rmse().toString());
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ret.add(scaleDependentMeasure.mae().toString());
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ret.add(scaleDependentMeasure.mdae().toString());
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ret.add(m.toString());
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ret.add(b.toString());
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return ret;
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}
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public ArrayList<String> getPercentigeErrorBasedMeasure(final LinkedList<Line> lines, final Double m, final Double b) {
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PercentageErrorBasedMeasure percentageErrorBasedMeasure = new PercentageErrorBasedMeasure(lines, m, b);
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ArrayList<String> ret = new ArrayList<>();
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ret.add(percentageErrorBasedMeasure.mape().toString());
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ret.add(percentageErrorBasedMeasure.mdape().toString());
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ret.add(percentageErrorBasedMeasure.rmspe().toString());
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ret.add(percentageErrorBasedMeasure.rmdspe().toString());
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ret.add(m.toString());
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ret.add(b.toString());
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return ret;
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}
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public ArrayList<String> getScaledErrorBasedMeasure(final LinkedList<Line> lines, final Double m, final Double b, final Double nM, final Double nB) {
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ScaledErrorBasedMeasure scaledErrorBasedMeasure = new ScaledErrorBasedMeasure(lines, m, b, nM, nB);
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ArrayList<String> ret = new ArrayList<>();
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ret.add(scaledErrorBasedMeasure.mse().toString());
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ret.add(scaledErrorBasedMeasure.rmse().toString());
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ret.add(scaledErrorBasedMeasure.mae().toString());
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ret.add(scaledErrorBasedMeasure.mdae().toString());
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ret.add(nM.toString());
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ret.add(nB.toString());
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return ret;
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}
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private ArrayList<String> fillPseudoResults() {
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ArrayList<String> result = new ArrayList<>();
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result.add(" ");
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result.add(" ");
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result.add(" ");
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result.add(" ");
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result.add(" ");
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result.add(" ");
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return result;
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}
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public LinkedList<Line> getData() {
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return arrangement.getLines();
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}
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}
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