generalbgh/algorithms-for-computing-line-estimators
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algorithms-for-computing-line-estimators
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ScaledErrorBasedMeasure hinzugefügt mit den naiven algorithmen berechnet.

This commit is contained in:
Armin Wolf 2017-09-16 15:26:33 +02:00
parent 30ff17328e
commit b1c026e574
6 changed files with 87 additions and 44 deletions
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22
src/main/java/presenter/algorithms/naiv/NaivLeastMedianOfSquaresEstimator.java
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@ -20,7 +20,7 @@ public class NaivLeastMedianOfSquaresEstimator implements Algorithm {
private ArrayList<Point> set = new ArrayList<>();
private Integer n;
private Double ds,as,bs;
private Double ds, b, m;
public NaivLeastMedianOfSquaresEstimator(LinkedList<Line> lines) {
for (Line l :lines){
@ -31,8 +31,8 @@ public class NaivLeastMedianOfSquaresEstimator implements Algorithm {
private void crudeAlg(){
ds = Double.MAX_VALUE;
as = 0d;
bs = 0d;
b = 0d;
m = 0d;
ArrayList<Point> triple = new ArrayList<>();
Double beta;
Double alpha;
@ -49,15 +49,15 @@ public class NaivLeastMedianOfSquaresEstimator implements Algorithm {
dijk = f(alpha, beta);
if (dijk < ds){
ds = dijk;
as = alpha;
bs = beta;
// System.out.printf("Distanz: %6.2f\tAlpha: %6.2f\tBeta: %6.2f",ds,as,bs);
b = alpha;
m = beta;
// System.out.printf("Distanz: %6.2f\tAlpha: %6.2f\tBeta: %6.2f",ds,b,m);
}
triple.clear();
}
}
}
System.out.printf("Naiv LMS: %6.2f * x + %6.3f\n",bs, as);
System.out.printf("Naiv LMS: %6.2f * x + %6.3f\n", m, b);
}
@ -80,11 +80,11 @@ public class NaivLeastMedianOfSquaresEstimator implements Algorithm {
}
public Double getAs() {
return as;
public Double getB() {
return b * -1;
}
public Double getBs() {
return bs;
public Double getM() {
return m * -1;
}
}

8
src/main/java/presenter/algorithms/naiv/NaivRepeatedMedianEstimator.java
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@ -111,11 +111,11 @@ public class NaivRepeatedMedianEstimator implements Algorithm{
return new Point(m,b);
}
public Double getMedianX() {
return medianX;
public Double getM() {
return medianX * -1;
}
public Double getMedianY() {
return medianY;
public Double getB() {
return medianY * -1;
}
}

8
src/main/java/presenter/algorithms/naiv/NaivTheilSenEstimator.java
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@ -65,11 +65,11 @@ public class NaivTheilSenEstimator implements Algorithm {
}
public Double getSlope() {
return slope;
public Double getM() {
return slope * -1;
}
public Double getyInterception() {
return yInterception;
public Double getB() {
return yInterception * -1;
}
}

64
src/main/java/presenter/evaluation/EvaluateAlgorithms.java
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@ -45,7 +45,7 @@ public class EvaluateAlgorithms extends Observable {
private DatasetGenerator generator;
private String[][] names = {{"MSE", "RMSE", "MAE", "MDAE","Steigung","y-Achsenabschnitt"}, {"MAPE", "MDAPE", "RMSPE", "RMDSPE","Steigung","y-Achsenabschnitt"}};
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"}};
//übergebene Parameter
private int type;
@ -103,8 +103,7 @@ public class EvaluateAlgorithms extends Observable {
ArrayList<String> result;
ArrayList<ArrayList<String>> multipleResults = new ArrayList<>();
/* DEBUG */
/* DEBUG
NaivLeastMedianOfSquaresEstimator l = new NaivLeastMedianOfSquaresEstimator(arrangement.getLines());
NaivRepeatedMedianEstimator r = new NaivRepeatedMedianEstimator(arrangement.getLines());
NaivTheilSenEstimator t = new NaivTheilSenEstimator(arrangement.getLines());
@ -113,9 +112,8 @@ public class EvaluateAlgorithms extends Observable {
r.run();
t.run();
System.out.println();
DEBUG */
/* DEBUG */
startLMS();
startRM();
startTS();
@ -123,17 +121,52 @@ public class EvaluateAlgorithms extends Observable {
case 0:
//der alg der gewählt wurde
if (alg == 0) {
final double[] m = new double[1];
final double[] b = new double[1];
Thread t = new Thread(() -> {
NaivLeastMedianOfSquaresEstimator l = new NaivLeastMedianOfSquaresEstimator(arrangement.getLines());
l.run();
m[0] = l.getM();
b[0] = l.getB();
});
t.start();
startLMS();
t.join();
result = getScaleDependentMeasure(arrangement.getLines(), lmsRes[0], lmsRes[1]);
sendPlotLineResults(lmsRes, 0);
result.addAll(getScaledErrorBasedMeasure(arrangement.getLines(), lmsRes[0], lmsRes[1],m[0], b[0]));
Double[] tmp = {lmsRes[0], lmsRes[1],m[0], b[0]};
sendPlotLineResults(tmp, 0);
} else if (alg == 1) {
final double[] m = new double[1];
final double[] b = new double[1];
Thread t = new Thread(() -> {
NaivRepeatedMedianEstimator r = new NaivRepeatedMedianEstimator(arrangement.getLines());
r.run();
m[0] = r.getM();
b[0] = r.getB();
});
t.start();
startRM();
result = getScaleDependentMeasure(arrangement.getLines(), rmRes[0], rmRes[1]);
sendPlotLineResults(rmRes, 1);
result.addAll(getScaledErrorBasedMeasure(arrangement.getLines(), rmRes[0], rmRes[1],m[0], b[0]));
Double[] tmp = {rmRes[0], rmRes[1],m[0], b[0]};
sendPlotLineResults(tmp, 1);
} else {
final double[] m = new double[1];
final double[] b = new double[1];
Thread t = new Thread(() -> {
NaivTheilSenEstimator ts = new NaivTheilSenEstimator(arrangement.getLines());
ts.run();
m[0] = ts.getM();
b[0] = ts.getB();
});
t.start();
startTS();
result = getScaleDependentMeasure(arrangement.getLines(), tsRes[0], tsRes[1]);
sendPlotLineResults(tsRes, 2);
result.addAll(getScaledErrorBasedMeasure(arrangement.getLines(), tsRes[0], tsRes[1],m[0], b[0]));
Double[] tmp = {tsRes[0], tsRes[1],m[0], b[0]};
sendPlotLineResults(tmp, 2);
}
sendTableApproximationTypes();
sendTableApproximationData(result, alg);
@ -265,9 +298,9 @@ public class EvaluateAlgorithms extends Observable {
lines.add("lines-res");
lines.add("" + alg);
//lms res
lines.add(res[0] + "");
lines.add(res[1] + "");
for (int i=0;i<res.length;i++) {
lines.add(res[i] + "");
}
setChanged();
notifyObservers(lines.stream().toArray(String[]::new));
}
@ -325,6 +358,7 @@ public class EvaluateAlgorithms extends Observable {
}
public ArrayList<String> getScaleDependentMeasure(final LinkedList<Line> lines, final Double m, final Double b) {
ScaleDependentMeasure scaleDependentMeasure = new ScaleDependentMeasure(lines, m, b);
ArrayList<String> ret = new ArrayList<>();
@ -350,15 +384,15 @@ public class EvaluateAlgorithms extends Observable {
return ret;
}
public ArrayList<String> getScaledErrorBasedMeasure(final LinkedList<Line> lines, final Double m, final Double b) {
ScaledErrorBasedMeasure scaledErrorBasedMeasure = new ScaledErrorBasedMeasure(lines, m, b);
public ArrayList<String> getScaledErrorBasedMeasure(final LinkedList<Line> lines, final Double m, final Double b, final Double nM, final Double nB) {
ScaledErrorBasedMeasure scaledErrorBasedMeasure = new ScaledErrorBasedMeasure(lines, m, b, nM, nB);
ArrayList<String> ret = new ArrayList<>();
ret.add(scaledErrorBasedMeasure.mse().toString());
ret.add(scaledErrorBasedMeasure.rmse().toString());
ret.add(scaledErrorBasedMeasure.mae().toString());
ret.add(scaledErrorBasedMeasure.mdae().toString());
ret.add(m.toString());
ret.add(b.toString());
ret.add(nM.toString());
ret.add(nB.toString());
return ret;
}

24
src/main/java/presenter/evaluation/ScaledErrorBasedMeasure.java
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@ -16,25 +16,29 @@ import java.util.LinkedList;
public class ScaledErrorBasedMeasure {
private ArrayList<Double> sampsonError;
private ArrayList<Double> naivSampsonError;
private ArrayList<Double> scaledError;
public ScaledErrorBasedMeasure(final LinkedList<Line> lines, Double m, Double b) {
//Liste mit den Fehler zu jedem Punkt
ArrayList<Double> sampson = new ArrayList<>();
public ScaledErrorBasedMeasure(final LinkedList<Line> lines, Double m, Double b, Double naivSlope, Double naivInterception) {
this.sampsonError = new ArrayList<>();
this.naivSampsonError = new ArrayList<>();
this.scaledError = new ArrayList<>();
Double denum = 0d;
for (int i = 2; i < lines.size(); i++) {
denum += Math.abs(lines.get(i).getM() - lines.get(i - 1).getM());
for (Line line : lines) {
Double e = Math.pow(naivSlope * line.getM() - line.getB() + naivInterception, 2) / (Math.pow(naivSlope, 2) + 1);
naivSampsonError.add(e);
}
denum *= 1 / (lines.size() - 1);
for (Line line : lines) {
Double e = Math.pow(m * line.getM() - line.getB() + b, 2) / (Math.pow(m, 2) + 1);
sampson.add(e);
sampsonError.add(e);
}
Double val = e / (denum);
scaledError.add(val);
for (int i=0;i<sampsonError.size();i++){
scaledError.add(sampsonError.get(i) / naivSampsonError.get(i));
}

5
src/main/java/view/panels/EvaluationPanel.java
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@ -303,10 +303,15 @@ public class EvaluationPanel extends JPanel {
String[] castedResults = Arrays.copyOf(results, results.length, String[].class);
Paint[] color = {Color.ORANGE, Color.RED, Color.MAGENTA};
String[] name = {"LMS", "RM", "TS"};
String[] nName = {"Brute-force LMS", "Brute-force RM", "Brute-force TS"};
Double m = Double.parseDouble(castedResults[0]);
Double b = Double.parseDouble(castedResults[1]);
plotPanel.addLineToPlot(m, b, color[alg], name[alg]);
Double nM = Double.parseDouble(castedResults[2]);
Double nB = Double.parseDouble(castedResults[3]);
plotPanel.addLineToPlot(nM, nB, Color.BLACK, nName[alg]);
}
public void setDualPoints(LinkedList<Line> points) {