algorithms-for-computing-line-estimators/src/main/java/Presenter/Evaluation/EvaluateAlgorithms.java

package Presenter.Evaluation;

import Model.Interval;
import Model.Line;
import Model.LineModel;
import Presenter.Algorithms.*;
import Presenter.Generator.DatasetGenerator;

import java.util.ArrayList;
import java.util.LinkedList;
import java.util.List;
import java.util.Observable;

/**
 * Implementierung verschiedener Algorithmen zur Berechnung von Ausgleichsgeraden.
 *
 * @Author: Armin Wolf
 * @Email: a_wolf28@uni-muenster.de
 * @Date: 01.08.2017.
 */
public class EvaluateAlgorithms extends Observable {

    private Double m;
    private Double b;
    private Integer iterationCount;

    private LineModel arrangement;
    private Object[] lmsResult;
    private Object[] rmResult;
    private Object[] tsResult;

    private Thread lmsThread;
    private Thread rmThread;
    private Thread tsThread;

    public EvaluateAlgorithms(Double m, Double b, Integer iterationCount) {
        this.m = m;
        this.b = b;
        this.iterationCount = iterationCount;
    }

    public EvaluateAlgorithms(){
        this.m = null;
        this.b = null;
        this.iterationCount = 1;
    }

    public void run() throws InterruptedException {

        for (int i = 0; i < iterationCount; i++) {
            this.arrangement = new LineModel();

            DatasetGenerator generator;

            if (m != null && b != null) {
                generator = new DatasetGenerator(m, b);
            } else {
                generator = new DatasetGenerator();
            }

            arrangement.setLines(generator.generateDataset());

            IntersectionCounter counter = new IntersectionCounter();
            counter.run(arrangement.getLines(), new Interval(-99999, 99999));
            counter.calculateIntersectionAbscissas(arrangement);


            lmsThread = new Thread(() -> {
                LeastMedianOfSquaresEstimator lmsAlg = new LeastMedianOfSquaresEstimator(arrangement.getLines()
                        , arrangement.getNodes());
                lmsAlg.run();
                lmsAlg.getResult();
                List<Double> errors = sampsonError(arrangement.getLines(), lmsAlg.getSlope(), lmsAlg.getyInterception());
                lmsResult = getResults(errors, "Least Median of Squares");
                setChanged();
                notifyObservers(lmsResult);
            });
            rmThread = new Thread(() -> {
                RepeatedMedianEstimator rmAlg = new RepeatedMedianEstimator(arrangement.getLines());
                rmAlg.run();
                rmAlg.getResult();
                List<Double> errors = sampsonError(arrangement.getLines(), rmAlg.getSlope(), rmAlg.getyInterception());
                rmResult = getResults(errors, "Repeated-Median");
                setChanged();
                notifyObservers(rmResult);
            });
            tsThread = new Thread(() -> {
                TheilSenEstimator tsAlg = new TheilSenEstimator(arrangement.getLines(), arrangement.getNodes());
                tsAlg.run();
                tsAlg.getResult();
                List<Double> errors = sampsonError(arrangement.getLines(), tsAlg.getSlope(), tsAlg.getyInterception());
                tsResult = getResults(errors, "Theil-Sen");
                setChanged();
                notifyObservers(tsResult);
            });

            lmsThread.start();
            rmThread.start();
            tsThread.start();

            lmsThread.join();
            rmThread.join();
            tsThread.join();
        }
    }


    public String[] getResults(List<Double> errorValues, String name) {

        ArrayList<String> ret = new ArrayList<>();
        ret.add("eval");
        ret.add(name);
        ret.add(mse(errorValues).toString());
        ret.add(rmse(errorValues).toString());
        ret.add(mae(errorValues).toString());
        ret.add(mdae(errorValues).toString());


        return ret.stream().toArray(String[]::new);
    }

    /* Skalierungs Abhängige Approximationsgüten */
    public Double mse(List<Double> errorValues) {
        double error = 0;

        for (Double d : errorValues) {
            error += Math.pow(d, 2);
        }

        error /= errorValues.size();

        return error;
    }

    public Double rmse(List<Double> errorValues) {
        return Math.sqrt(mse(errorValues));
    }

    public Double mae(List<Double> errorValues) {
        double error = 0;

        for (Double d : errorValues) {
            error += Math.abs(d);
        }

        error /= errorValues.size();

        return error;
    }

    public Double mdae(List<Double> errorValues) {
        return FastElementSelector.randomizedSelect((ArrayList<Double>) errorValues, errorValues.size() * 0.5);
    }


    public List<Double> sampsonError(final LinkedList<Line> lines, Double m, Double b) {

        //Liste mit den Fehler zu jedem Punkt
        List<Double> sampsonerror = new ArrayList<>();

        for (Line line : lines) {
            Double error = Math.pow(m * line.getM() - line.getB() + b, 2) / (Math.pow(m, 2) + 1);
            sampsonerror.add(error);
        }

        return sampsonerror;
    }

}