generalbgh/algorithms-for-computing-line-estimators
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Evaluation Fix. 

Terminnierung des Alg. Theil-Sen ist jetzt garantiert. Parameter werden vom der Evaluations Klasse eingelesen und berücksichtigt. Zellen sind nicht mehr Editable. Weitere Approximationsgüten fehlen noch. Die Row und Cols könnten getauscht werden.. Blt wäre das Sinnvoller..
This commit is contained in:
Armin Wolf 2017-08-03 08:27:38 +02:00
parent 7f015eaa0c
commit 2eb1768052
6 changed files with 144 additions and 81 deletions
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9
src/main/java/Presenter/Algorithms/TheilSenEstimator.java
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@ -127,8 +127,13 @@ public class TheilSenEstimator extends Observable implements Algorithm {
* @return Boolscher Wert ob die Bedingung erfüllt ist
*/
private Boolean checkCondition() {
Double kthElement = FastElementSelector.randomizedSelect(xCoordinates, k);
Boolean cond1 = (kthElement > aVariant) && (kthElement <= bVariant);
//Double kthElement = FastElementSelector.randomizedSelect(xCoordinates, k);
//Boolean cond1 = (kthElement > aVariant) && (kthElement <= bVariant);
Boolean conda = k > getIntervalSize(NEGATIV_INF, aVariant, setOfIntersections);
Boolean condb = k <= getIntervalSize(NEGATIV_INF, bVariant, setOfIntersections);
Boolean cond1 = conda && condb;
Boolean cond2 = (getIntervalSize(aVariant+1, bVariant+1, intervalIntersections) <= ((11 * N) / Math.sqrt(r)));
return cond1 && cond2;

151
src/main/java/Presenter/Evaluation/EvaluateAlgorithms.java
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@ -1,8 +1,8 @@
package Presenter.Evaluation;
import Model.LineModel;
import Model.Interval;
import Model.Line;
import Model.LineModel;
import Presenter.Algorithms.*;
import Presenter.Generator.DatasetGenerator;
@ -18,7 +18,11 @@ import java.util.Observable;
* @Email: a_wolf28@uni-muenster.de
* @Date: 01.08.2017.
*/
public class EvaluateAlgorithms extends Observable{
public class EvaluateAlgorithms extends Observable {
private Double m;
private Double b;
private Integer iterationCount;
private LineModel arrangement;
private Object[] lmsResult;
@ -29,79 +33,98 @@ public class EvaluateAlgorithms extends Observable{
private Thread rmThread;
private Thread tsThread;
public EvaluateAlgorithms(){}
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 {
this.arrangement = new LineModel();
for (int i = 0; i < iterationCount; i++) {
this.arrangement = new LineModel();
DatasetGenerator generator = new DatasetGenerator();
arrangement.setLines(generator.generateDataset());
DatasetGenerator generator;
IntersectionCounter counter = new IntersectionCounter();
counter.run(arrangement.getLines(), new Interval(-99999,99999));
counter.calculateIntersectionAbscissas(arrangement);
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 = 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();
lmsThread.start();
rmThread.start();
tsThread.start();
lmsThread.join();
rmThread.join();
tsThread.join();
}
}
public String[] getResults(List<Double> errorValues, String name){
public String[] getResults(List<Double> errorValues, String name) {
String[] ret = new String[6];
ret[0] = name;
ret[1] = mse(errorValues).toString();
ret[2] = rmse(errorValues).toString();
ret[3] = mae(errorValues).toString();
ret[4] = mdae(errorValues).toString();
ret[5] = "eval";
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;
return ret.stream().toArray(String[]::new);
}
/* Skalierungs Abhängige Approximationsgüten */
public Double mse(List<Double> errorValues){
public Double mse(List<Double> errorValues) {
double error = 0;
for (Double d : errorValues){
error += Math.pow(d,2);
for (Double d : errorValues) {
error += Math.pow(d, 2);
}
error /= errorValues.size();
@ -109,14 +132,14 @@ public class EvaluateAlgorithms extends Observable{
return error;
}
public Double rmse(List<Double> errorValues){
public Double rmse(List<Double> errorValues) {
return Math.sqrt(mse(errorValues));
}
public Double mae(List<Double> errorValues){
public Double mae(List<Double> errorValues) {
double error = 0;
for (Double d : errorValues){
for (Double d : errorValues) {
error += Math.abs(d);
}
@ -125,18 +148,18 @@ public class EvaluateAlgorithms extends Observable{
return error;
}
public Double mdae(List<Double> errorValues){
return FastElementSelector.randomizedSelect((ArrayList<Double>) errorValues, errorValues.size()*0.5);
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){
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);
for (Line line : lines) {
Double error = Math.pow(m * line.getM() - line.getB() + b, 2) / (Math.pow(m, 2) + 1);
sampsonerror.add(error);
}

22
src/main/java/Presenter/Generator/DatasetGenerator.java
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@ -14,17 +14,29 @@ import java.util.Random;
*/
public class DatasetGenerator {
private Double m;
private Double b;
private Random random;
public DatasetGenerator(){}
public DatasetGenerator(Double m, Double b){
this.m = m;
this.b = b;
random = new Random();
random.setSeed(9999);
}
public DatasetGenerator(){
random = new Random();
random.setSeed(9999);
m = 1 + random.nextDouble();
b = random.nextDouble();
}
public LinkedList<Line> generateDataset(){
LinkedList<Line> lines = new LinkedList<>();
Random random = new Random();
random.setSeed(9999);
double m = 1 + random.nextDouble();
double b = random.nextDouble();
for (int i=1;i<101;i++){
double y = (random.nextGaussian() * 100) % 100;

23
src/main/java/Presenter/Presenter.java
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@ -44,9 +44,6 @@ public class Presenter implements Observer {
public Presenter(LineModel model, MainFrame view) {
this.model = model;
this.view = view;
eval = new EvaluateAlgorithms();
eval.addObserver(this);
}
@ -54,7 +51,7 @@ public class Presenter implements Observer {
public void update(Observable o, Object arg) {
String[] result = ((String[]) arg);
if (result.length > 3){
if (result[0] == "eval"){
SwingUtilities.invokeLater(() -> {
getView().appendEvalResult(result);
});
@ -273,11 +270,27 @@ public class Presenter implements Observer {
}
}
public void startEvaluation(){
public void startEvaluation(String[] args){
if (evalThread == null || !evalThread.isAlive()){
evalThread = new Thread(() ->{
Double m = null;
Double b = null;
Integer i = null;
if (!(args[0].isEmpty() && args[1].isEmpty() && args[2].isEmpty())) {
m = Double.parseDouble(args[0]);
b = Double.parseDouble(args[1]);
i = Integer.parseInt(args[2]);
}
try {
if (m != null && b!= null && i != null) {
eval = new EvaluateAlgorithms(m, b, i);
}else {
eval = new EvaluateAlgorithms();
}
eval.addObserver(this);
eval.run();
} catch (InterruptedException e) {
e.printStackTrace();

5
src/main/java/View/MainFrame.java
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@ -11,6 +11,7 @@ import java.awt.*;
import java.awt.event.ActionEvent;
import java.io.File;
import java.io.IOException;
import java.util.Arrays;
import java.util.List;
/**
@ -150,8 +151,8 @@ public class MainFrame extends JFrame {
public void appendEvalResult(Object[] res){
SwingUtilities.invokeLater(() -> {
Object[] tmp = res;
evaluationPanel.appendData(res);
Object[] tmp = Arrays.asList(res).subList(1,res.length).toArray();
evaluationPanel.appendData(tmp);
evaluationPanel.repaint();
evaluationPanel.revalidate();
});

15
src/main/java/View/Panels/EvaluationPanel.java
View File

@ -49,7 +49,7 @@ public class EvaluationPanel extends JPanel{
buttonPanel = new JPanel(new FlowLayout());
buttonPanel.add(start);
buttonPanel.add(stop);
//buttonPanel.add(stop);
comp = new JPanel();
comp.setLayout(new GridLayout(0,2));
@ -61,7 +61,8 @@ public class EvaluationPanel extends JPanel{
comp.add(iYinterception);
start.addActionListener(e -> {
view.getPresenter().startEvaluation();
String[] params = {iSlope.getText(), iYinterception.getText(), iIteration.getText() };
view.getPresenter().startEvaluation(params);
});
stop.addActionListener(e -> {
@ -69,10 +70,18 @@ public class EvaluationPanel extends JPanel{
});
String[] selections = { "Schätzer","MSE", "RMSE", "MAE", "MdAE"};
model = new DefaultTableModel();
model = new DefaultTableModel(){
@Override
public boolean isCellEditable(int row, int column) {
//all cells false
return false;
}
};
model.setColumnIdentifiers(selections);
table = new JTable(model);
table.setDragEnabled(true);
JScrollPane scrollPane = new JScrollPane(table);
scrollPane.setWheelScrollingEnabled(true);
this.add(scrollPane, BorderLayout.CENTER);