135 lines
3.9 KiB
Java
135 lines
3.9 KiB
Java
package de.wwwu.awolf.presenter.algorithms.naiv;
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import de.wwwu.awolf.model.Line;
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import de.wwwu.awolf.model.communication.AlgorithmData;
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import de.wwwu.awolf.model.communication.Data;
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import de.wwwu.awolf.model.communication.SubscriberType;
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import de.wwwu.awolf.presenter.AbstractPresenter;
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import de.wwwu.awolf.presenter.algorithms.Algorithm;
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import de.wwwu.awolf.presenter.util.FastElementSelector;
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import de.wwwu.awolf.presenter.util.Logging;
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import java.util.*;
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import java.util.concurrent.Flow;
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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: 15.09.2017.
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*/
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public class NaivLeastMedianOfSquaresEstimator implements Algorithm {
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private static final Algorithm.Type type = Type.NAIV_LMS;
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private List<Line> setOfLines = new ArrayList<>();
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private int n;
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private double ds, b, m;
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private Flow.Subscriber<? super Data> subscriber;
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private AbstractPresenter presenter;
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/**
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* Crude Algorithmus zum berechnen des LSM-Schätzers.
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*/
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private Line crudeAlg() {
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ds = Double.MAX_VALUE;
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b = 0d;
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m = 0d;
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List<Line> triple = new ArrayList<>();
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double beta;
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double alpha;
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double dijk;
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Logging.logInfo("=== S T A R T - naiv L M S ===");
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long start;
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long end;
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start = System.currentTimeMillis();
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for (Line i : setOfLines) {
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for (Line j : setOfLines) {
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for (Line k : setOfLines) {
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triple.add(i);
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triple.add(j);
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triple.add(k);
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Collections.sort(triple);
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beta = (triple.get(0).getB() - triple.get(2).getB()) / (triple.get(0).getM() - triple.get(2).getM());
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alpha = (triple.get(1).getB() + triple.get(2).getB() - (beta * (triple.get(1).getM() + triple.get(2).getM()))) * 0.5;
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dijk = f(alpha, beta);
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if (dijk < ds) {
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ds = dijk;
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b = alpha;
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m = beta;
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}
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triple.clear();
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}
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}
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}
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end = System.currentTimeMillis();
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Logging.logInfo("=== E N D - naiv L M S ===");
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Logging.logInfo("Slope: " + getSlope() + ", y-Interception: " + getYInterception());
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AlgorithmData data = new AlgorithmData();
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data.setAlgorithmType(getType());
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data.setType(SubscriberType.ALGORITHM);
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data.setLineData(new Line(getSlope(), getYInterception()));
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this.subscriber.onNext(data);
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return new Line(getSlope(), getYInterception());
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}
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/**
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* Berechnet die Gerade mit dem medianen Fehler
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*
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* @param a y-Achsenabschnitt
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* @param b Steigung
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* @return medianer Fehler
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*/
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private Double f(double a, double b) {
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List<Double> res = new ArrayList<>();
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for (Line p : setOfLines) {
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res.add(Math.abs(p.getB() - (a + b * p.getM())));
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}
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return FastElementSelector.randomizedSelect(res, res.size() * 0.5);
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}
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@Override
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public Line call() {
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return crudeAlg();
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}
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@Override
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public void setInput(Set<Line> lines) {
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this.setOfLines = new LinkedList<>(lines);
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}
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@Override
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public Type getType() {
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return type;
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}
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@Override
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public void setPresenter(AbstractPresenter presenter) {
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this.presenter = presenter;
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subscribe(presenter);
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}
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/**
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* @return y-Achsenabschnitt
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*/
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public Double getYInterception() {
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return b * -1;
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}
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/**
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* @return Steigung
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*/
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public Double getSlope() {
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return m * -1;
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}
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@Override
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public void subscribe(Flow.Subscriber<? super Data> subscriber) {
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this.subscriber = subscriber;
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}
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}
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