adjusted gui, adjusted line and point model classes

.gitattributes

@@ -0,0 +1,6 @@
+#
+# https://help.github.com/articles/dealing-with-line-endings/
+#
+# These are explicitly windows files and should use crlf
+*.bat           text eol=crlf
+

LinearRegressionTool/build.gradle

@@ -8,6 +8,7 @@ plugins {
 
 repositories {
     mavenLocal()
+    mavenCentral()
     maven {
         url = uri('http://repo.maven.apache.org/maven2')
     }
@@ -21,6 +22,7 @@ dependencies {
     implementation 'com.opencsv:opencsv:5.1'
     implementation 'com.google.guava:guava:28.2-jre'
     implementation 'org.powermock:powermock-core:2.0.6'
+    compile group: 'org.apache.commons', name: 'commons-math3', version: '3.6.1'
 }
 
 group = 'de.wwu.awolf'

LinearRegressionTool/settings.gradle

@@ -1,5 +1,4 @@
 /*
  * This file was generated by the Gradle 'init' task.
  */
-
 rootProject.name = 'masterarbeit'

LinearRegressionTool/src/main/java/de/wwwu/awolf/App.java

@@ -51,9 +51,13 @@ public class App extends Application {
                 //register at presenter
                 new GuiRegisterService(fxmlLoader.getController()).start();
                 primaryStage.setTitle(TITLE);
-                Scene scene = new Scene(pane, 1000, 800);
+                Scene scene = new Scene(pane, 1200, 900);
-                scene.getStylesheets().add(ViewController.class.getResource("/style/console.css").toExternalForm());
+                scene.getStylesheets().add(ViewController.class.getResource("/style/style.css").toExternalForm());
                 primaryStage.setScene(scene);
+                primaryStage.setOnCloseRequest(e -> {
+                    Platform.exit();
+                    System.exit(0);
+                });
                 primaryStage.show();
             } catch (IOException e) {
                 Logging.logError("Error reading FXML file. ", e);

LinearRegressionTool/src/main/java/de/wwwu/awolf/model/Line.java

@@ -1,5 +1,9 @@
 package de.wwwu.awolf.model;
 
+import org.apache.commons.math3.util.Precision;
+
+import java.util.Objects;
+
 /**
  * Implementierung verschiedener Algorithmen zur Berechnung von Ausgleichsgeraden.
  *
@@ -7,7 +11,9 @@ package de.wwwu.awolf.model;
  * @Email: a_wolf28@uni-muenster.de
  * @Date: 12.06.2017.
  */
-public class Line implements Comparable<Line>{
+public class Line implements Comparable<Line> {
+
+    private final double epsilon = 0.00001;
 
     private final Double MAX = 9999d;
     private final Double MIN = -9999d;
@@ -76,20 +82,20 @@ public class Line implements Comparable<Line>{
 
     }
 
-    private Double calculateX1(Double min) {
+    public Double calculateX1(Double min) {
         return min;
     }
 
-    private Double calculateY1(Double min) {
+    public Double calculateY1(Double min) {
-        return (min * m) + b;
+        return (min * (m * -1)) + b;
     }
 
-    private Double calculateX2(Double max) {
+    public Double calculateX2(Double max) {
         return max;
     }
 
-    private Double calculateY2(Double max) {
+    public Double calculateY2(Double max) {
-        return (max * m) + b;
+        return (max * (m * -1)) + b;
     }
 
     /**
@@ -189,7 +195,7 @@ public class Line implements Comparable<Line>{
     public boolean equals(Object obj) {
         if (obj instanceof Line) {
             Line other = (Line) obj;
-            return other.getM().equals(this.getM()) && other.getB().equals(this.getB());
+            return Precision.equals(other.getM(), this.getM(), epsilon) && Precision.equals(other.getB(), this.getB(), epsilon);
         } else {
             return super.equals(obj);
         }
@@ -197,7 +203,7 @@ public class Line implements Comparable<Line>{
 
     @Override
     public int hashCode() {
-        return super.hashCode() + this.getM().hashCode() + this.getB().hashCode();
+        return Objects.hash(Precision.round(m, 5), Precision.round(b, 5));
     }
 
     @Override
@@ -272,16 +278,10 @@ public class Line implements Comparable<Line>{
 
     @Override
     public int compareTo(Line line) {
-        if (this.getM().equals(line.getM())) {
+        if (Precision.compareTo(this.getM(), line.getM(), epsilon) == 0) {
-            if (this.getB() <= line.getB()) {
+            return this.getB().compareTo(line.getB());
-                return -1;
-            } else {
-                return 1;
-            }
-        } else if (this.getM() < line.getM()) {
-            return -1;
         } else {
-            return 1;
+            return this.getM().compareTo(line.getM());
         }
     }
 }

LinearRegressionTool/src/main/java/de/wwwu/awolf/model/LineModel.java

@@ -17,6 +17,8 @@ public class LineModel {
     private Double xMaximum;
     private Double yMinimum;
     private Double yMaximum;
+    private Double MIN;
+    private Double MAX;
     private int size;
 
     /**
@@ -31,6 +33,8 @@ public class LineModel {
         yMinimum = Double.MAX_VALUE;
         yMaximum = Double.MIN_VALUE;
 
+        MIN = 0d;
+        MAX = 0d;
     }
 
 
@@ -41,8 +45,17 @@ public class LineModel {
      */
     public void addLine(Line line) {
         this.lines.add(line);
+        MIN = line.getM() <= MIN ? line.getM() : MIN;
+        MAX = line.getM() >= MAX ? line.getM() : MAX;
     }
 
+    public Double getMIN() {
+        return MIN;
+    }
+
+    public Double getMAX() {
+        return MAX;
+    }
 
     /**
      * @return Liste der Geraden
@@ -55,7 +68,7 @@ public class LineModel {
      * @param lines Liste der Geraden
      */
     public void setLines(Set<Line> lines) {
-        this.lines = lines;
+        lines.forEach(this::addLine);
         this.size = lines.size();
     }
 

LinearRegressionTool/src/main/java/de/wwwu/awolf/model/Point.java

@@ -1,5 +1,9 @@
 package de.wwwu.awolf.model;
 
+import org.apache.commons.math3.util.Precision;
+
+import java.util.Objects;
+
 /**
  * Implementierung verschiedener Algorithmen zur Berechnung von Ausgleichsgeraden.
  *
@@ -9,6 +13,8 @@ package de.wwwu.awolf.model;
  */
 public class Point implements Comparable<Point> {
 
+    private final double epsilon = 0.00001;
+
     private Double x;
     private Double y;
     private String id;
@@ -67,16 +73,10 @@ public class Point implements Comparable<Point> {
 
     @Override
     public int compareTo(Point o) {
-        if (this.getX().equals(o.getX())) {
+        if (Precision.compareTo(this.getX(), o.getX(), epsilon) == 0) {
-            if (this.getY() <= o.getY()) {
+            return this.getY().compareTo(o.getY());
-                return -1;
-            } else {
-                return 1;
-            }
-        } else if (this.getX() < o.getX()) {
-            return -1;
         } else {
-            return 1;
+            return this.getX().compareTo(o.getX());
         }
     }
 
@@ -90,7 +90,7 @@ public class Point implements Comparable<Point> {
     public boolean equals(Object obj) {
         if (obj instanceof Point) {
             Point other = (Point) obj;
-            return other.getX().equals(this.getX()) && other.getY().equals(this.getY());
+            return Precision.equals(other.getX(), this.getX(), epsilon) && Precision.equals(other.getY(), this.getY(), epsilon);
         } else {
             return super.equals(obj);
         }
@@ -98,7 +98,7 @@ public class Point implements Comparable<Point> {
 
     @Override
     public int hashCode() {
-        return super.hashCode() + this.getX().hashCode() + this.getY().hashCode();
+        return Objects.hash(Precision.round(x, 5), Precision.round(y, 5));
     }
 
     /**

LinearRegressionTool/src/main/java/de/wwwu/awolf/model/communication/AlgorithmData.java

@@ -1,12 +1,22 @@
 package de.wwwu.awolf.model.communication;
 
 import de.wwwu.awolf.model.Line;
+import de.wwwu.awolf.presenter.algorithms.Algorithm;
 
 public class AlgorithmData implements Data {
 
     private SubscriberType type;
+    private Algorithm.Type algorithmType;
     private Line lineData;
 
+    public Algorithm.Type getAlgorithmType() {
+        return algorithmType;
+    }
+
+    public void setAlgorithmType(Algorithm.Type algorithmType) {
+        this.algorithmType = algorithmType;
+    }
+
     @Override
     public SubscriberType getType() {
         return type;

LinearRegressionTool/src/main/java/de/wwwu/awolf/model/communication/SubscriberType.java

@@ -7,9 +7,7 @@ public enum SubscriberType {
     EVAL_T,
     LINES_RES,
     LINES_RES_MULT,
-    LMS,
+    ALGORITHM,
-    RM,
-    TS,
     PICTURE,
     GENERATOR
 }

LinearRegressionTool/src/main/java/de/wwwu/awolf/presenter/AbstractPresenter.java

@@ -10,6 +10,7 @@ import de.wwwu.awolf.presenter.evaluation.EvaluatationHandler;
 import de.wwwu.awolf.presenter.util.Logging;
 import de.wwwu.awolf.view.ViewController;
 import javafx.application.Platform;
+import javafx.beans.property.BooleanProperty;
 
 import java.util.Objects;
 import java.util.Set;
@@ -57,32 +58,20 @@ public abstract class AbstractPresenter implements Flow.Subscriber<Data> {
 
     @Override
     public void onNext(Data data) {
+        Logging.logDebug("Presenter received message. Type: " + data.getType());
         switch (data.getType()) {
             case EVALUATION_TABLE_DATA:
                 evaluatedDatas(data);
                 break;
-            case LMS:
+            case ALGORITHM:
-                visualizeLmsAlgorithm(data);
+                visualizeAlgorithm(data);
-                break;
-            case RM:
-                visualizeRmAlgorithm(data);
-                break;
-            case TS:
-                visualizeTsAlgorithm(data);
-                break;
-            case GENERATOR:
-                Logging.logInfo("Generierung war Erfolgreich");
                 break;
             default:
                 break;
         }
     }
 
-    protected abstract void visualizeTsAlgorithm(Data data);
+    protected abstract void visualizeAlgorithm(Data data);
-
-    protected abstract void visualizeRmAlgorithm(Data data);
-
-    protected abstract void visualizeLmsAlgorithm(Data data);
 
     protected abstract void evaluatedDatas(Data data);
 
@@ -107,18 +96,29 @@ public abstract class AbstractPresenter implements Flow.Subscriber<Data> {
         this.algorithmHandler.runAlgorithmByType(type, lines);
     }
 
+    /**
+     * Execute an algorithm specified by a type
+     *
+     * @param type  algorithm type
+     * @param lines set of lines
+     */
+    public void executeAlgorithmByType(Algorithm.Type type, Set<Line> lines, BooleanProperty guiFlag) {
+        this.algorithmHandler.runAlgorithmByType(type, lines, guiFlag);
+    }
+
     /**
      * Execute an algorithm specified by a type
      * (use the Lines from the LineModel)
      *
      * @param type  algorithm type
      */
-    public void executeAlgorithmByType(Algorithm.Type type) {
+    public Line executeAlgorithmByType(Algorithm.Type type, BooleanProperty guiFlag) {
         if (getModel().getSize() == 0) {
             Logging.logDebug("No lines in the Model. Nothing to calculate.");
+            throw new IllegalArgumentException();
         } else {
             Logging.logDebug("AlgorithmHandler will start " + type.getName() + ", with " + getModel().getSize());
-            this.algorithmHandler.runAlgorithmByType(type, getModel().getLines());
+            return this.algorithmHandler.runAlgorithmByType(type, getModel().getLines(), guiFlag);
         }
     }
 
@@ -141,7 +141,6 @@ public abstract class AbstractPresenter implements Flow.Subscriber<Data> {
      */
     public void registerView(ViewController view) {
         this.view = view;
-        Logging.enableGuiLogging();
         Logging.logDebug("View has been set.");
 
 

LinearRegressionTool/src/main/java/de/wwwu/awolf/presenter/Presenter.java

@@ -7,6 +7,7 @@ import de.wwwu.awolf.model.communication.EvaluationData;
 import de.wwwu.awolf.presenter.data.DataHandler;
 import de.wwwu.awolf.presenter.evaluation.EvaluatationHandler;
 import de.wwwu.awolf.presenter.util.Logging;
+import javafx.application.Platform;
 
 import javax.swing.*;
 import java.io.File;
@@ -37,27 +38,13 @@ public class Presenter extends AbstractPresenter {
     }
 
     @Override
-    protected void visualizeTsAlgorithm(Data data) {
+    protected void visualizeAlgorithm(Data data) {
         AlgorithmData algorithmData = (AlgorithmData) data;
-        SwingUtilities.invokeLater(() -> getView().visualizeTS(algorithmData.getLineData()));
-        Logging.logInfo("Theil-Sen Estimator");
-        Logging.logInfo(algorithmData.getLineData().toString());
-    }
 
-    @Override
+        Platform.runLater(() -> {
-    protected void visualizeRmAlgorithm(Data data) {
+            getView().getAlgorithmTabControllers().get(algorithmData.getAlgorithmType()).updatePlot(getModel(), algorithmData.getLineData());
-        AlgorithmData algorithmData = (AlgorithmData) data;
+        });
-        SwingUtilities.invokeLater(() -> getView().visualizeRM(algorithmData.getLineData()));
+        Logging.logInfo("Type: " + algorithmData.getType() + ". Result: " + algorithmData.getLineData().toString());
-        Logging.logInfo("Repeated Median Estimator");
-        Logging.logInfo(algorithmData.getLineData().toString());
-    }
-
-    @Override
-    protected void visualizeLmsAlgorithm(Data data) {
-        AlgorithmData algorithmData = (AlgorithmData) data;
-        SwingUtilities.invokeLater(() -> getView().visualizeLMS(algorithmData.getLineData()));
-        Logging.logInfo("Least Median of Squares");
-        Logging.logInfo(algorithmData.getLineData().toString());
     }
 
     @Override
@@ -100,6 +87,9 @@ public class Presenter extends AbstractPresenter {
         getModel().setLines(data);
         //Berechnung der Schnittpunkte und vis. der Ergebnisse (anz. Geraden, anz. Schnittpunkte)
         Logging.logInfo("Import was successful! ");
+        Platform.runLater(() -> {
+            getView().getAlgorithmTabControllers().values().forEach(e -> e.updatePlot(getModel(), null));
+        });
         return data;
     }
 
@@ -113,6 +103,9 @@ public class Presenter extends AbstractPresenter {
         Set<Line> data = getDataHandler().getData(type, n);
         getModel().setLines(data);
         Logging.logInfo("Generate was successful!");
+        Platform.runLater(() -> {
+            getView().getAlgorithmTabControllers().values().forEach(e -> e.updatePlot(getModel(), null));
+        });
         return data;
     }
 

LinearRegressionTool/src/main/java/de/wwwu/awolf/presenter/algorithms/AlgorithmHandler.java

@@ -3,6 +3,7 @@ package de.wwwu.awolf.presenter.algorithms;
 import de.wwwu.awolf.model.Line;
 import de.wwwu.awolf.presenter.Presenter;
 import de.wwwu.awolf.presenter.util.Logging;
+import javafx.beans.property.BooleanProperty;
 
 import javax.annotation.Nullable;
 import java.util.EnumMap;
@@ -38,7 +39,11 @@ public class AlgorithmHandler {
     }
 
     @Nullable
-    public Line runAlgorithmByType(final Algorithm.Type type, final Set<Line> setOfLines) {
+    public Line runAlgorithmByType(final Algorithm.Type type, final Set<Line> setOfLines, final BooleanProperty guiFlag) {
+
+        if (guiFlag != null)
+            guiFlag.setValue(true);
+
         //get the instance
         Algorithm algorithm = algorithmMapping.get(type);
         algorithm.setPresenter(Presenter.getInstance());
@@ -51,16 +56,25 @@ public class AlgorithmHandler {
 
         completionService.submit(algorithm);
         try {
+
             return completionService.take().get();
         } catch (InterruptedException e) {
             Logging.logError("Interrupt Exception while waiting for result of Algorithm: " + algorithm.getType(), e);
             Thread.currentThread().interrupt();
         } catch (ExecutionException e) {
             Logging.logError("Execution Exception while computing the result of Algorithm: " + algorithm.getType(), e);
+        } finally {
+            if (guiFlag != null)
+                guiFlag.setValue(false);
         }
         return null;
     }
 
+    @Nullable
+    public Line runAlgorithmByType(final Algorithm.Type type, final Set<Line> setOfLines) {
+        return runAlgorithmByType(type, setOfLines, null);
+    }
+
     private void lookUp() {
         Logging.logDebug("Lookup for Algorithm Classes.");
         ServiceLoader<Algorithm> load = ServiceLoader.load(Algorithm.class);

LinearRegressionTool/src/main/java/de/wwwu/awolf/presenter/algorithms/advanced/LeastMedianOfSquaresEstimator.java

@@ -362,14 +362,15 @@ public class LeastMedianOfSquaresEstimator implements Algorithm {
 
     private Line pepareResult() {
         if (this.subscriber != null) {
-            double m = (getSigmaMin().getX2() + getSigmaMin().getX1()) * 0.5;
+            double m = ((getSigmaMin().getX2() + getSigmaMin().getX1()) * 0.5);
             double b = (getSigmaMin().getY2() + getSigmaMin().getY1()) * -0.5;
 
             slope = m;
             yInterception = b;
 
             AlgorithmData data = new AlgorithmData();
-            data.setType(SubscriberType.LMS);
+            data.setAlgorithmType(getType());
+            data.setType(SubscriberType.ALGORITHM);
             data.setLineData(new Line(m, b));
             this.subscriber.onNext(data);
         } else {

LinearRegressionTool/src/main/java/de/wwwu/awolf/presenter/algorithms/advanced/RepeatedMedianEstimator.java

@@ -237,7 +237,8 @@ public class RepeatedMedianEstimator implements Algorithm {
 
         if (this.subscriber != null) {
             AlgorithmData data = new AlgorithmData();
-            data.setType(SubscriberType.RM);
+            data.setAlgorithmType(getType());
+            data.setType(SubscriberType.ALGORITHM);
             data.setLineData(new Line(getSlope(), getyInterception()));
             this.subscriber.onNext(data);
         }

LinearRegressionTool/src/main/java/de/wwwu/awolf/presenter/algorithms/advanced/TheilSenEstimator.java

@@ -206,7 +206,8 @@ public class TheilSenEstimator implements Algorithm {
 
         if (this.subscriber != null) {
             AlgorithmData data = new AlgorithmData();
-            data.setType(SubscriberType.TS);
+            data.setAlgorithmType(getType());
+            data.setType(SubscriberType.ALGORITHM);
             data.setLineData(new Line(m, b));
             this.subscriber.onNext(data);
         }

LinearRegressionTool/src/main/java/de/wwwu/awolf/presenter/algorithms/naiv/NaivLeastMedianOfSquaresEstimator.java

@@ -1,7 +1,9 @@
 package de.wwwu.awolf.presenter.algorithms.naiv;
 
 import de.wwwu.awolf.model.Line;
+import de.wwwu.awolf.model.communication.AlgorithmData;
 import de.wwwu.awolf.model.communication.Data;
+import de.wwwu.awolf.model.communication.SubscriberType;
 import de.wwwu.awolf.presenter.AbstractPresenter;
 import de.wwwu.awolf.presenter.algorithms.Algorithm;
 import de.wwwu.awolf.presenter.util.FastElementSelector;
@@ -66,6 +68,12 @@ public class NaivLeastMedianOfSquaresEstimator implements Algorithm {
         Logging.logInfo("=== E N D - naiv L M S ===");
         Logging.logInfo("Slope: "  + getSlope() + ", y-Interception: " + getYInterception());
 
+        AlgorithmData data = new AlgorithmData();
+        data.setAlgorithmType(getType());
+        data.setType(SubscriberType.ALGORITHM);
+        data.setLineData(new Line(getSlope(), getYInterception()));
+        this.subscriber.onNext(data);
+
         return new Line(getSlope(), getYInterception());
     }
 

LinearRegressionTool/src/main/java/de/wwwu/awolf/presenter/algorithms/naiv/NaivRepeatedMedianEstimator.java

@@ -2,7 +2,9 @@ package de.wwwu.awolf.presenter.algorithms.naiv;
 
 import de.wwwu.awolf.model.Line;
 import de.wwwu.awolf.model.Point;
+import de.wwwu.awolf.model.communication.AlgorithmData;
 import de.wwwu.awolf.model.communication.Data;
+import de.wwwu.awolf.model.communication.SubscriberType;
 import de.wwwu.awolf.presenter.AbstractPresenter;
 import de.wwwu.awolf.presenter.algorithms.Algorithm;
 import de.wwwu.awolf.presenter.util.FastElementSelector;
@@ -85,7 +87,11 @@ public class NaivRepeatedMedianEstimator implements Algorithm {
         end = System.currentTimeMillis();
         Logging.logInfo("=== E N D - naiv R M ===");
         Logging.logInfo("Slope: "  + getSlope() + ", y-Interception: " + getYInterception());
-
+        AlgorithmData data = new AlgorithmData();
+        data.setAlgorithmType(getType());
+        data.setType(SubscriberType.ALGORITHM);
+        data.setLineData(new Line(getSlope(), getYInterception()));
+        this.subscriber.onNext(data);
         return new Line(getSlope(), getYInterception());
     }
 

LinearRegressionTool/src/main/java/de/wwwu/awolf/presenter/algorithms/naiv/NaivTheilSenEstimator.java

@@ -1,7 +1,9 @@
 package de.wwwu.awolf.presenter.algorithms.naiv;
 
 import de.wwwu.awolf.model.Line;
+import de.wwwu.awolf.model.communication.AlgorithmData;
 import de.wwwu.awolf.model.communication.Data;
+import de.wwwu.awolf.model.communication.SubscriberType;
 import de.wwwu.awolf.presenter.AbstractPresenter;
 import de.wwwu.awolf.presenter.algorithms.Algorithm;
 import de.wwwu.awolf.presenter.util.FastElementSelector;
@@ -66,7 +68,11 @@ public class NaivTheilSenEstimator implements Algorithm {
         end = System.currentTimeMillis();
         Logging.logInfo("=== E N D - naiv T S ===");
         Logging.logInfo("Slope: "  + getSlope() + ", y-Interception: " + getYInterception());
-
+        AlgorithmData data = new AlgorithmData();
+        data.setAlgorithmType(getType());
+        data.setType(SubscriberType.ALGORITHM);
+        data.setLineData(new Line(getSlope(), getYInterception()));
+        this.subscriber.onNext(data);
         return new Line(getSlope(), getYInterception());
     }
 

LinearRegressionTool/src/main/java/de/wwwu/awolf/presenter/data/DataHandler.java

@@ -78,5 +78,11 @@ public class DataHandler {
         CIRCLE
     }
 
+    public enum ActionType {
+        IMPORT,
+        EXPORT,
+        GENERATE
+    }
+
 
 }

LinearRegressionTool/src/main/java/de/wwwu/awolf/presenter/util/IntersectionComputer.java

@@ -8,6 +8,7 @@ import java.util.*;
 import java.util.concurrent.ForkJoinPool;
 import java.util.concurrent.ForkJoinTask;
 import java.util.concurrent.RecursiveTask;
+import java.util.concurrent.atomic.AtomicInteger;
 import java.util.stream.Collectors;
 
 /**
@@ -45,15 +46,21 @@ public class IntersectionComputer {
      * @param higher obere Schranke
      * @return Liste der Schnittpunkte
      */
-    public Collection<Point> compute(final List<Line> lines, final double lower, final double higher) {
+    public Set<Point> compute(final Collection<Line> lines, final double lower, final double higher) {
+
+        final Set<Line> fullInput = new HashSet<>(lines);
+        final Set<Line> copyInput = new HashSet<>(lines);
+
+
         if (lower == higher) {
-            return Collections.emptyList();
+            return Collections.emptySet();
         } else {
             Logging.logDebug("Open ForkJoinPool: lines: " + lines.size() + " I(" + lower + ", " + higher + "]");
             ForkJoinPool pool = ForkJoinPool.commonPool();
-            RecursiveComputationTask recursiveComputationTask = new RecursiveComputationTask(lines, lines, lower, higher);
+            RecursiveComputationTask recursiveComputationTask = new RecursiveComputationTask(fullInput, copyInput, lower, higher);
             pool.execute(recursiveComputationTask);
-            return recursiveComputationTask.join();
+            Set<Point> join = recursiveComputationTask.join();
+            return join;
         }
     }
 
@@ -64,7 +71,7 @@ public class IntersectionComputer {
      * @param sampledLine eine spezielle Gerade
      * @return Liste mit x Koordinaten der Schnittpunkte
      */
-    public List<Double> calculateIntersectionAbscissas(List<Line> set, Line sampledLine, double lower, double upper) {
+    public List<Double> calculateIntersectionAbscissas(Collection<Line> set, Line sampledLine, double lower, double upper) {
         List<Line> lines = new LinkedList<>(set);
         Set<Double> intersections = new HashSet<>();
 
@@ -79,16 +86,16 @@ public class IntersectionComputer {
         return new ArrayList<>(intersections);
     }
 
-    private class RecursiveComputationTask extends RecursiveTask<Collection<Point>> {
+    private class RecursiveComputationTask extends RecursiveTask<Set<Point>> {
 
-        private static final int THRESHOLD = 20;
+        private static final int THRESHOLD = 200;
 
-        private final List<Line> lines;
+        private final Set<Line> lines;
-        private final List<Line> fullList;
+        private final Set<Line> fullList;
         private final double lower;
         private final double upper;
 
-        public RecursiveComputationTask(final List<Line> fullList, final List<Line> lines, final double lower, final double upper) {
+        public RecursiveComputationTask(final Set<Line> fullList, final Set<Line> lines, final double lower, final double upper) {
             this.lines = lines;
             this.fullList = fullList;
             this.lower = lower;
@@ -96,12 +103,11 @@ public class IntersectionComputer {
         }
 
         @Override
-        protected Collection<Point> compute() {
+        protected Set<Point> compute() {
             if (this.lines.isEmpty()) {
-                return Collections.emptyList();
+                return Collections.emptySet();
             } else if (this.lines.size() > THRESHOLD) {
-                Logging.logDebug("Bigger than threshold, split into subtask.");
+                return ForkJoinTask.invokeAll(createSubTask()).stream().map(ForkJoinTask::join).flatMap(Collection::stream).collect(Collectors.toSet());
-                return ForkJoinTask.invokeAll(createSubTask()).stream().map(ForkJoinTask::join).flatMap(Collection::stream).collect(Collectors.toList());
             } else {
                 return work(this.fullList, this.lines, this.lower, this.upper);
             }
@@ -109,12 +115,27 @@ public class IntersectionComputer {
 
         private Collection<RecursiveComputationTask> createSubTask() {
             List<RecursiveComputationTask> dividedTasks = new ArrayList<>();
-            dividedTasks.add(new RecursiveComputationTask(this.fullList, this.lines.subList(0, this.lines.size() / 2), this.lower, this.upper));
+
-            dividedTasks.add(new RecursiveComputationTask(this.fullList, this.lines.subList(this.lines.size() / 2, this.lines.size()), this.lower, this.upper));
+            long midpoint = Math.round(this.lines.size() * 0.5);
+
+            Set<Line> firstSubSet = new HashSet<>();
+            Set<Line> secondSubSet = new HashSet<>();
+            AtomicInteger count = new AtomicInteger();
+            this.lines.forEach(next -> {
+                int index = count.getAndIncrement();
+                if (index < midpoint) {
+                    firstSubSet.add(next);
+                } else {
+                    secondSubSet.add(next);
+                }
+            });
+
+            dividedTasks.add(new RecursiveComputationTask(this.fullList, firstSubSet, this.lower, this.upper));
+            dividedTasks.add(new RecursiveComputationTask(this.fullList, secondSubSet, this.lower, this.upper));
             return dividedTasks;
         }
 
-        private Collection<Point> work(List<Line> fullList, List<Line> lines, double lower, double higher) {
+        private Set<Point> work(Set<Line> fullList, Set<Line> lines, double lower, double higher) {
             Set<Point> points = new HashSet<>();
             List<List<Line>> lists = Lists.cartesianProduct(new ArrayList<>(fullList), new ArrayList<>(lines));
             lists.forEach(entry -> {

LinearRegressionTool/src/main/java/de/wwwu/awolf/presenter/util/Logging.java

@@ -10,9 +10,7 @@ public class Logging {
 
     }
 
-    public static void enableGuiLogging() {
+
-        logger.addAppender(new GuiAppender());
-    }
 
     private static Logger getLogger() {
         return logger;

LinearRegressionTool/src/main/java/de/wwwu/awolf/view/ViewController.java

@@ -2,23 +2,21 @@ package de.wwwu.awolf.view;
 
 
 import de.wwwu.awolf.model.Line;
+import de.wwwu.awolf.presenter.Presenter;
 import de.wwwu.awolf.presenter.algorithms.Algorithm;
 import de.wwwu.awolf.presenter.data.DataHandler;
 import de.wwwu.awolf.presenter.util.Logging;
 import de.wwwu.awolf.view.controller.AlgorithmTabController;
 import de.wwwu.awolf.view.services.DataService;
 import javafx.application.Platform;
-import javafx.event.ActionEvent;
-import javafx.event.EventHandler;
 import javafx.fxml.FXMLLoader;
-import javafx.scene.Node;
 import javafx.scene.Parent;
 import javafx.scene.control.*;
+import javafx.stage.FileChooser;
 
+import java.io.File;
 import java.io.IOException;
-import java.util.Collection;
+import java.util.*;
-import java.util.HashMap;
-import java.util.Map;
 
 /**
  * Implementierung verschiedener Algorithmen zur Berechnung von Ausgleichsgeraden.
@@ -34,10 +32,15 @@ public class ViewController {
 
     public TabPane tabPane;
     public MenuBar menuBar;
-    public TextArea logArea;
+    private Map<Algorithm.Type, AlgorithmTabController> algorithmTabControllers;
+
+    public Map<Algorithm.Type, AlgorithmTabController> getAlgorithmTabControllers() {
+        return algorithmTabControllers;
+    }
 
     public ViewController() {
         super();
+        this.algorithmTabControllers = new EnumMap<>(Algorithm.Type.class);
     }
 
     public void initGui() {
@@ -54,7 +57,6 @@ public class ViewController {
         props.put("dsfsdfsdf", "dsadasd");
 
 
-
         try {
             for (Algorithm.Type value : Algorithm.Type.values()) {
                 FXMLLoader loader = new FXMLLoader();
@@ -63,7 +65,9 @@ public class ViewController {
                 AlgorithmTabController controller = loader.getController();
                 controller.setAlgorithmType(value);
                 controller.setProperties(props);
+                controller.setModel(Presenter.getInstance().getModel());
                 controller.init();
+                this.algorithmTabControllers.put(value, controller);
                 Tab tab = new Tab(value.getName(), parent);
                 tab.setId(value.name());
                 tabPane.getTabs().add(tab);
@@ -74,7 +78,6 @@ public class ViewController {
         }
 
 
-
     }
 
     private void initMenuBar() {
@@ -84,18 +87,39 @@ public class ViewController {
         Menu helpMenu = new Menu("Help");
 
         // Create MenuItems
+        //export the data
         MenuItem exportItem = new MenuItem("Export");
+        exportItem.setOnAction(actionEvent -> {
+            FileChooser fileChooser = new FileChooser();
+            File file = fileChooser.showOpenDialog(null);
+            if (file != null && file.canWrite() && file.canRead()) {
+                new DataService(DataHandler.ActionType.EXPORT, file).start();
+            }
+        });
+
+        //start an import of data
         MenuItem importItem = new MenuItem("Import");
+        importItem.setOnAction(actionEvent -> {
+            FileChooser fileChooser = new FileChooser();
+            File file = fileChooser.showOpenDialog(null);
+            if (file != null && file.canWrite() && file.canRead()) {
+                new DataService(DataHandler.ActionType.IMPORT, file).start();
+            }
+        });
+
+        //exit menu item should stop the application
         MenuItem exitItem = new MenuItem("Exit");
+        exitItem.setOnAction(actionEvent -> {
+            Platform.exit();
+            System.exit(0);
+        });
+
+        // about the application
         MenuItem aboutItem = new MenuItem("About");
 
         MenuItem generationItem = new MenuItem("Generate");
-        generationItem.setOnAction(new EventHandler<ActionEvent>() {
+        generationItem.setOnAction(actionEvent -> {
-            @Override
+            new DataService(DataHandler.ActionType.GENERATE, DataHandler.DataType.LINE, 1000).start();
-            public void handle(ActionEvent actionEvent) {
-                //TODO
-                new DataService(DataHandler.DataType.LINE, 100).start();
-            }
         });
         MenuItem evaluationItem = new MenuItem("Evaluate");
 
@@ -156,11 +180,6 @@ public class ViewController {
      */
     public void appendEvalResult(Map<Algorithm.Type, Map<String, String>> tableEntries) {
 
-//        SwingUtilities.invokeLater(() -> {
-//            evaluationPanel.addRow(tableEntries);
-//            evaluationPanel.repaint();
-//            evaluationPanel.revalidate();
-//        });
     }
 
     /**
@@ -172,154 +191,16 @@ public class ViewController {
         appendEvalResult(res);
     }
 
-
-    /*******************************************************************************************************************
-     *                                          init GUI
-     ******************************************************************************************************************/
-    /**
-     * Komponenten werden in Container gesetzt
-     */
-    private void addComponents() {
-//        toolBar.add(importButton);
-//        toolBar.add(exportButton);
-//        toolBar.add(generateButton);
-//        toolBar.setFloatable(false);
-//
-//        setJMenuBar(menu.getMenuBar());
-//        add(toolBar);
-//
-//        setupSplitPane();
-//        setupTabbedPane();
-//        ((JPanel) progressDialog.getContentPane()).setBorder(BorderFactory.createLineBorder(Color.DARK_GRAY));
-//
-//        this.add(toolBar, BorderLayout.NORTH);
-//        this.add(splitpane, BorderLayout.CENTER);
-    }
-
-
-    /**
-     * Initialisierung der Komponenten
-     */
-    private void initializeComponents(Collection<Node> list) {
-
-//        //panels
-//        toolBar = new JToolBar();
-//        lmsPanel = new LMSPanel();
-//        rmPanel = new RMPanel();
-//        tsPanel = new TSPanel();
-//        menu = new MenuBar();
-//
-//        //Dialogs
-//        progressDialog = new JDialog();
-//        progressDialog.setLocationRelativeTo(null);
-//        progressContent = progressDialog.getContentPane();
-//        progressBar = new JProgressBar();
-//
-//        //Panes
-//        tabbedPane = new JTabbedPane();
-//        output = new InfoPanel();
-//        splitpane = new JSplitPane();
-//
-//        //Buttons
-//        importButton = new JButton();
-//        generateButton = new JButton();
-//        exportButton = new JButton();
-
-    }
-
-    /**
-     * Icons werden passend gesetzt
-     */
-    private void setIcons() {
-//        try {
-//            ClassLoader classLoader = getClass().getClassLoader();
-//            Image imgImport = ImageIO.read(classLoader.getResource("import.png")).getScaledInstance(16, 16, Image.SCALE_SMOOTH);
-//            Image imgStart = ImageIO.read(classLoader.getResource("start.png")).getScaledInstance(32, 32, Image.SCALE_SMOOTH);
-//            Image imgGenerate = ImageIO.read(classLoader.getResource("generate.png")).getScaledInstance(16, 16, Image.SCALE_SMOOTH);
-//            Image imgExport = ImageIO.read(classLoader.getResource("export.png")).getScaledInstance(16, 16, Image.SCALE_SMOOTH);
-//            Image imgFrame = ImageIO.read(classLoader.getResource("frame.png")).getScaledInstance(32, 23, Image.SCALE_SMOOTH);
-//
-//            importButton.setIcon(new ImageIcon(imgImport));
-//            exportButton.setIcon(new ImageIcon(imgExport));
-//            generateButton.setIcon(new ImageIcon(imgGenerate));
-//            lmsPanel.getStartButton().setIcon(new ImageIcon(imgStart));
-//            rmPanel.getStartButton().setIcon(new ImageIcon(imgStart));
-//            tsPanel.getStartButton().setIcon(new ImageIcon(imgStart));
-//            this.setIconImage(imgFrame);
-//        } catch (IOException e) {
-//            Logging.logError(e.getMessage(), e);
-//        }
-    }
-
-    /**
-     * Funktionalitäten werden hinzugefügt
-     */
-    public void setActionListeners() {
-//        //action listener für MenuItems
-//        menu.addActionListeners();
-//
-//        //action listener für diese Klasse
-//        lmsPanel.getStartButton().addActionListener(new StartAlgorithmListener(((Presenter) getPresenter()), lmsPanel));
-//        rmPanel.getStartButton().addActionListener(new StartAlgorithmListener(((Presenter) getPresenter()), rmPanel));
-//        tsPanel.getStartButton().addActionListener(new StartAlgorithmListener(((Presenter) getPresenter()), tsPanel));
-//        importButton.addActionListener(new ImportDataListener((Presenter) this.getPresenter()));
-//        exportButton.addActionListener(new ExportDataListener((Presenter) this.getPresenter()));
-//        generateButton.addActionListener(new GenerateDataListener((Presenter) this.getPresenter()));
-
-    }
-
     /**
      * Funktionalitäten werden aktiviert.
      */
     public void enableFunctionality() {
-//        this.getLmsPanel().getStartButton().setEnabled(true);
-//        this.getRmPanel().getStartButton().setEnabled(true);
-//        this.getTsPanel().getStartButton().setEnabled(true);
-//        this.getExportButton().setEnabled(true);
     }
 
     /**
      * Funktionalitäten werden deaktiviert.
      */
     public void disableFunctionality() {
-//        this.getLmsPanel().getStartButton().setEnabled(false);
-//        this.getRmPanel().getStartButton().setEnabled(false);
-//        this.getTsPanel().getStartButton().setEnabled(false);
-//        this.getExportButton().setEnabled(false);
     }
 
-    /*******************************************************************************************************************
-     *                                          log Methode
-     ******************************************************************************************************************/
-    /**
-     * @param s Text der ausgegeben wird
-     */
-    public void logInfo(String s) {
-        Platform.runLater(() -> {
-            logArea.appendText(s + "\n");
-        });
-    }
-
-    /**
-     * @param s Fehlertext der ausgegeben wird
-     */
-    public void logError(String s) {
-        Platform.runLater(() -> {
-            logArea.appendText(s + "\n");
-        });
-    }
-
-    /**
-     * @param s Text der in grüner Farbe ausgegeben wird
-     */
-    public void logWarning(String s) {
-        Platform.runLater(() -> {
-            logArea.appendText(s + "\n");
-        });
-    }
-
-
-    /*******************************************************************************************************************
-     *                                          Getter und Setter Methoden
-     ******************************************************************************************************************/
 }

LinearRegressionTool/src/main/java/de/wwwu/awolf/view/controller/AlgorithmTabController.java

@@ -1,45 +1,59 @@
 package de.wwwu.awolf.view.controller;
 
+import de.wwwu.awolf.model.Line;
+import de.wwwu.awolf.model.LineModel;
 import de.wwwu.awolf.presenter.algorithms.Algorithm;
 import de.wwwu.awolf.view.services.ButtonClickService;
+import javafx.beans.property.BooleanProperty;
 import javafx.fxml.FXML;
 import javafx.geometry.Insets;
 import javafx.geometry.Pos;
-import javafx.scene.canvas.Canvas;
+import javafx.scene.chart.LineChart;
+import javafx.scene.chart.XYChart;
 import javafx.scene.control.Button;
 import javafx.scene.control.Label;
 import javafx.scene.control.TextField;
 import javafx.scene.layout.BorderPane;
 import javafx.scene.layout.VBox;
-import javafx.scene.text.Font;
 
 import java.util.Map;
 
 public class AlgorithmTabController {
 
+    private final XYChart.Series<Double, Double> dataSerie;
+    private final XYChart.Series<Double, Double> lineSerie;
     private Algorithm.Type algorithmType;
     private Map<String, String> properties;
+    private LineModel model;
 
     @FXML
-    public Canvas canvas;
+    public LineChart<Double, Double> chart;
     @FXML
     public VBox vBox;
     @FXML
     public Button startButton;
 
-
-
     public AlgorithmTabController() {
-
+        dataSerie = new XYChart.Series<>();
+        dataSerie.setName("Datapoints");
+        lineSerie = new XYChart.Series<>();
+        lineSerie.setName("Estimated Line");
     }
 
+    public LineModel getModel() {
+        return model;
+    }
+
+    public void setModel(LineModel model) {
+        this.model = model;
+    }
 
     public void init() {
         // add GUI elements to maintain the parameters
         properties.forEach((key, value) -> {
             //Pane component for the layout
             BorderPane borderPane = new BorderPane();
-            borderPane.setPadding(new Insets(10,0,10,0));
+            borderPane.setPadding(new Insets(10, 0, 10, 0));
 
             //text field for the input
             TextField textField = new TextField(value);
@@ -47,9 +61,8 @@ public class AlgorithmTabController {
             //label
             Label label = new Label(key);
             label.setAlignment(Pos.BASELINE_LEFT);
-            label.setFont(new Font("Arial", 17));
             label.setLabelFor(textField);
-            label.setPadding(new Insets(2,10,0,0));
+            label.setPadding(new Insets(2, 10, 0, 0));
 
             //add components
             borderPane.setLeft(label);
@@ -57,9 +70,32 @@ public class AlgorithmTabController {
             vBox.getChildren().add(borderPane);
         });
 
+        BooleanProperty booleanProperty = startButton.disableProperty();
         startButton.setOnAction(event -> {
-            new ButtonClickService(algorithmType).start();
+                    ButtonClickService buttonClickService = new ButtonClickService(algorithmType, booleanProperty);
-        });
+                    buttonClickService.start();
+                }
+        );
+
+        updatePlot(this.model, null);
+        chart.getData().add(dataSerie);
+        chart.getData().add(lineSerie);
+    }
+
+    public void updatePlot(final LineModel model, final Line pLine) {
+        dataSerie.getData().clear();
+        model.getLines().forEach(line -> dataSerie.getData().add(new XYChart.Data<>(line.getM(), line.getB())));
+
+        if (pLine != null) {
+            lineSerie.getData().clear();
+            Double x1 = pLine.calculateX1(model.getMIN());
+            Double y1 = pLine.calculateY1(model.getMIN());
+            Double x2 = pLine.calculateX2(model.getMAX());
+            Double y2 = pLine.calculateY2(model.getMAX());
+
+            lineSerie.getData().add(new XYChart.Data<>(x1, y1));
+            lineSerie.getData().add(new XYChart.Data<>(x2, y2));
+        }
     }
 
     public Algorithm.Type getAlgorithmType() {
@@ -77,4 +113,6 @@ public class AlgorithmTabController {
     public void setProperties(Map<String, String> properties) {
         this.properties = properties;
     }
+
+
 }

LinearRegressionTool/src/main/java/de/wwwu/awolf/view/services/ButtonClickService.java

@@ -1,26 +1,37 @@
 package de.wwwu.awolf.view.services;
 
+import de.wwwu.awolf.model.Line;
 import de.wwwu.awolf.presenter.Presenter;
 import de.wwwu.awolf.presenter.algorithms.Algorithm;
+import javafx.beans.property.BooleanProperty;
 import javafx.concurrent.Service;
 import javafx.concurrent.Task;
 
-public class ButtonClickService extends Service<String> {
+public class ButtonClickService extends Service<Boolean> {
 
+    private final BooleanProperty startButtonDisabled;
     private Algorithm.Type type;
 
-    public ButtonClickService(Algorithm.Type type) {
+    public ButtonClickService(Algorithm.Type algorithmType, BooleanProperty startButtonDisabled) {
-        this.type = type;
+        this.startButtonDisabled = startButtonDisabled;
+        this.type = algorithmType;
     }
 
     @Override
-    protected Task<String> createTask() {
+    protected Task<Boolean> createTask() {
 
-        return new Task<String>() {
+        return new Task<Boolean>() {
             @Override
-            protected String call() throws Exception {
+            protected Boolean call() throws Exception {
-                Presenter.getInstance().executeAlgorithmByType(type);
+                try {
-                return "done";
+                    Line line = Presenter.getInstance().executeAlgorithmByType(type, startButtonDisabled);
+                    if (line != null) {
+                        return true;
+                    }
+                } catch (IllegalArgumentException e) {
+                    return true;
+                }
+                return false;
             }
         };
     }

LinearRegressionTool/src/main/java/de/wwwu/awolf/view/services/DataService.java

@@ -5,23 +5,46 @@ import de.wwwu.awolf.presenter.data.DataHandler;
 import javafx.concurrent.Service;
 import javafx.concurrent.Task;
 
-public class DataService extends Service<String> {
+import java.io.File;
 
+public class DataService extends Service<Boolean> {
+
+    private File file;
     private DataHandler.DataType type;
+    private DataHandler.ActionType actionType;
     private int n;
 
-    public DataService(DataHandler.DataType type, int n) {
+    public DataService(final DataHandler.ActionType actionType, final DataHandler.DataType type, final int n) {
         this.type = type;
+        this.actionType = actionType;
         this.n = n;
     }
 
+    public DataService(final DataHandler.ActionType actionType, final File file) {
+        this.actionType = actionType;
+        this.file = file;
+        this.type = null;
+        this.n = -1;
+    }
+
     @Override
-    protected Task<String> createTask() {
+    protected Task<Boolean> createTask() {
         return new Task<>() {
             @Override
-            protected String call() throws Exception {
+            protected Boolean call() throws Exception {
-                Presenter.getInstance().generateDataset(n, type);
+                switch (actionType) {
-                return "done";
+                    case EXPORT:
+                        Presenter.getInstance().exportDataset(file);
+                        break;
+                    case IMPORT:
+                        Presenter.getInstance().importDataset(file);
+                        break;
+                    case GENERATE:
+                        Presenter.getInstance().generateDataset(n, type);
+                        break;
+                }
+
+                return true;
             }
         };
     }

LinearRegressionTool/src/main/java/de/wwwu/awolf/view/services/GuiRegisterService.java

@@ -5,7 +5,7 @@ import de.wwwu.awolf.view.ViewController;
 import javafx.concurrent.Service;
 import javafx.concurrent.Task;
 
-public class GuiRegisterService extends Service<String> {
+public class GuiRegisterService extends Service<Boolean> {
 
     private final ViewController view;
 
@@ -15,12 +15,12 @@ public class GuiRegisterService extends Service<String> {
     }
 
     @Override
-    protected Task<String> createTask() {
+    protected Task<Boolean> createTask() {
         return new Task<>() {
             @Override
-            protected String call() throws Exception {
+            protected Boolean call() throws Exception {
                 Presenter.getInstance().registerView(view);
-                return "Done";
+                return true;
             }
         };
     }

LinearRegressionTool/src/main/resources/views/AlgorithmTab.fxml

@@ -2,11 +2,11 @@
 
 <?import javafx.geometry.*?>
 <?import javafx.scene.*?>
-<?import javafx.scene.canvas.*?>
+<?import javafx.scene.chart.*?>
 <?import javafx.scene.control.*?>
 <?import javafx.scene.layout.*?>
 
-<BorderPane prefHeight="400.0" prefWidth="600.0" xmlns="http://javafx.com/javafx/10.0.2-internal" xmlns:fx="http://javafx.com/fxml/1" fx:controller="de.wwwu.awolf.view.controller.AlgorithmTabController">
+<BorderPane prefHeight="400.0" prefWidth="600.0" styleClass="algoPane" xmlns="http://javafx.com/javafx/10.0.2-internal" xmlns:fx="http://javafx.com/fxml/1" fx:controller="de.wwwu.awolf.view.controller.AlgorithmTabController">
     <left>
         <VBox fx:id="vBox" alignment="TOP_CENTER" prefHeight="200.0" prefWidth="200.0" BorderPane.alignment="CENTER">
             <children>
@@ -22,16 +22,6 @@
          </padding>
         </VBox>
     </left>
-    <center>
-        <Canvas fx:id="canvas" height="334.0" width="391.0" BorderPane.alignment="CENTER" />
-    </center>
-    <bottom>
-        <FlowPane alignment="CENTER" minWidth="-Infinity">
-         <BorderPane.margin>
-            <Insets bottom="10.0" left="10.0" right="10.0" top="10.0" />
-         </BorderPane.margin>
-        </FlowPane>
-    </bottom>
    <top>
       <FlowPane hgap="5.0" prefHeight="25.0" prefWidth="200.0" BorderPane.alignment="CENTER">
          <children>
@@ -46,4 +36,20 @@
          </BorderPane.margin>
       </FlowPane>
    </top>
+   <center>
+      <LineChart fx:id="chart" prefHeight="398.0" prefWidth="390.0" title="Plot" BorderPane.alignment="CENTER">
+        <xAxis>
+          <NumberAxis label="X" side="BOTTOM">
+               <padding>
+                  <Insets bottom="10.0" left="10.0" right="10.0" top="10.0" />
+               </padding></NumberAxis>
+        </xAxis>
+        <yAxis>
+          <NumberAxis label="Y" side="LEFT">
+               <padding>
+                  <Insets bottom="10.0" left="10.0" right="10.0" top="10.0" />
+               </padding></NumberAxis>
+        </yAxis>
+      </LineChart>
+   </center>
 </BorderPane>

LinearRegressionTool/src/main/resources/views/MainFrame.fxml

@@ -12,12 +12,6 @@
    <center>
       <TabPane fx:id="tabPane" prefHeight="200.0" prefWidth="200.0" tabClosingPolicy="UNAVAILABLE" BorderPane.alignment="CENTER">
         <tabs>
-          <Tab text="Logging">
-              <BorderPane xmlns="http://javafx.com/javafx/10.0.2-internal" xmlns:fx="http://javafx.com/fxml/1">
-                  <center>
-                     <TextArea fx:id="logArea" editable="false" prefHeight="200.0" prefWidth="200.0" BorderPane.alignment="CENTER" />
-                  </center></BorderPane>
-          </Tab>
         </tabs>
       </TabPane>
    </center>

LinearRegressionTool/src/test/java/de/wwwu/awolf/presenter/algorithms/advanced/LeastMedianOfSquaresEstimatorTest.java

@@ -1,5 +1,18 @@
 package de.wwwu.awolf.presenter.algorithms.advanced;
 
+import de.wwwu.awolf.model.Line;
+import de.wwwu.awolf.presenter.algorithms.Algorithm;
+import de.wwwu.awolf.presenter.algorithms.AlgorithmHandler;
+import org.junit.Before;
+import org.junit.Test;
+
+import java.util.HashSet;
+import java.util.List;
+import java.util.Set;
+
+import static org.junit.Assert.assertArrayEquals;
+import static org.junit.Assert.assertEquals;
+
 /**
  * Implementierung verschiedener Algorithmen zur Berechnung von Ausgleichsgeraden.
  *
@@ -9,68 +22,44 @@ package de.wwwu.awolf.presenter.algorithms.advanced;
  */
 public class LeastMedianOfSquaresEstimatorTest {
 
-//    private LeastMedianOfSquaresEstimator lms;
+    private LeastMedianOfSquaresEstimator lms;
-//
+    private Set<Line> lines;
-//    @Before
+    private Line line;
-//    public void setUp(){
+
-//        Double[] x = {18d, 24d, 30d, 34d, 38d};
+    @Before
-//        Double[] y = {18d, 26d, 30d, 40d, 70d};
+    public void setUp(){
-//
+        Double[] x = {18d, 24d, 30d, 34d, 38d};
-//
+        Double[] y = {18d, 26d, 30d, 40d, 70d};
-//        LinkedList<Line> lines = new LinkedList<>();
+
-//
+
-//        for (int i = 0; i < 5; i++) {
+        lines = new HashSet<>();
-//            lines.add(new Line(x[i], y[i]));
+
-//        }
+        for (int i = 0; i < 5; i++) {
-//
+            lines.add(new Line(x[i], y[i]));
-//        lms = new LeastMedianOfSquaresEstimator(lines);
+        }
-//    }
+        line = AlgorithmHandler.getInstance().runAlgorithmByType(Algorithm.Type.LMS, lines);
-//
+    }
-//
+
-//    @Test
+
-//    public void geEjValues() throws Exception {
+    @Test
-//
+    public void geEjValues() throws Exception {
-//        Double[] expected = {36d, 50d, 60d, 74d, 108d};
+
-//        List<Double> actual = lms.getEjValues(1d);
+        Double[] expected = {36d, 50d, 60d, 74d, 108d};
-//        assertArrayEquals(expected, actual.toArray());
+        List<Double> actual = lms.getEjValues(1d);
-//    }
+        assertArrayEquals(expected, actual.toArray());
-//
+    }
-//    @Test
+
-//    public void calcKMinusBracelet() throws Exception {
+
-//
+    @Test
-//        Point point = new Point(1d, 1d);
+    public void upperBound() throws Exception {
-//        Double[] expected = {24d, 36d, 60d};
+
-//        Double[] actual = lms.calcKMinusBracelet(point, 3);
+        Line expected = new Line(5, 5, 146, 210);
-//
+
-//        assertArrayEquals(expected, actual);
+        assertEquals(expected.getX1(), line.getX1(), 0.01);
-//
+        assertEquals(expected.getX2(), line.getX2(), 0.01);
-//    }
+        assertEquals(expected.getY1(), line.getY1(), 0.01);
-//
+        assertEquals(expected.getY2(), line.getY2(), 0.01);
-//    @Test
+    }
-//    public void upperBound() throws Exception {
+
-//        lms.setkMinus(3);
-//        lms.setHeightsigmaMin(500);
-//        lms.setSigmaMin(new Line(0, 0, 0, 0));
-//        lms.setN(5);
-//        Line expected = new Line(5, 5, 146, 210);
-//        lms.upperBound(5d);
-//
-//        assertEquals(expected.getX1(), lms.getSigmaMin().getX1(), 0.01);
-//        assertEquals(expected.getX2(), lms.getSigmaMin().getX2(), 0.01);
-//        assertEquals(expected.getY1(), lms.getSigmaMin().getY1(), 0.01);
-//        assertEquals(expected.getY2(), lms.getSigmaMin().getY2(), 0.01);
-//    }
-//
-//    @Test
-//    public void lowerBound() throws Exception {
-//        //kann nur über sout geprüft werden test passt aber
-//        Double[] expectedAlpha = {0d, 0d, 0d, 2d, 4d};
-//        Double[] expectedBeta = {2d, 4d, 4d, 2d, 1d};
-//        lms.setHeightsigmaMin(500);
-//
-//        Interval interval = new Interval(-2, 0);
-//        lms.lowerBound(interval);
-//        assertTrue(interval.getActivity());
-//    }
 
 }

LinearRegressionTool/src/test/java/de/wwwu/awolf/presenter/algorithms/advanced/TheilSenEstimatorTest.java

@@ -1,5 +1,11 @@
 package de.wwwu.awolf.presenter.algorithms.advanced;
 
+import de.wwwu.awolf.model.Line;
+import org.junit.Before;
+
+import java.util.LinkedList;
+import java.util.List;
+
 /**
  * Implementierung verschiedener Algorithmen zur Berechnung von Ausgleichsgeraden.
  *
@@ -8,47 +14,20 @@ package de.wwwu.awolf.presenter.algorithms.advanced;
  * @Date: 23.10.2017.
  */
 public class TheilSenEstimatorTest {
-//
+
-//    TheilSenEstimator estimator;
+
-//
+    @Before
-//    @Before
+    public void setUp(){
-//    public void setUp(){
+        Double[] x = {18d, 24d, 30d, 34d, 38d};
-//        Double[] x = {18d, 24d, 30d, 34d, 38d};
+        Double[] y = {18d, 26d, 30d, 40d, 70d};
-//        Double[] y = {18d, 26d, 30d, 40d, 70d};
+
-//
+
-//
+        List<Line> lines = new LinkedList<>();
-//        List<Line> lines = new LinkedList<>();
+
-//
+        for (int i = 0; i < 5; i++) {
-//        for (int i = 0; i < 5; i++) {
+            lines.add(new Line(x[i], y[i]));
-//            lines.add(new Line(x[i], y[i]));
+        }
-//        }
+    }
-//
+
-//        estimator = new TheilSenEstimator(lines);
-//    }
-//
-//
-//    @Test
-//    public void getIntervalSize() throws Exception {
-//      //  assertEquals(estimator.getIntervalSize(-2d, 0d), 5, 0.001);
-//    }
-//
-//    @Test
-//    public void getOpenIntervalSize() throws Exception {
-//       // assertEquals(estimator.getIntervalSize(-1.4d, 0.6666d), 4, 0.001);
-//    }
-//
-//    @Test
-//    public void getOpenIntervalElements() throws Exception {
-//        List<Point> intersectionInInterval = estimator.getOpenIntervalElements(-1.4d, -0.67d);
-//        double[] expected = {-1.375, -1.333, -1.0};
-//        double[] actual   = new double[3];
-//        for (int i=0;i<intersectionInInterval.size();i++) {
-//            actual[i] = intersectionInInterval.get(i).getX();
-//        }
-//
-//        Arrays.sort(expected);
-//        Arrays.sort(actual);
-//        assertArrayEquals(expected, actual, 0.001);
-//    }
 
 }

LinearRegressionTool/src/test/java/de/wwwu/awolf/presenter/util/IntersectionCounterTest.java

@@ -5,6 +5,10 @@ import de.wwwu.awolf.model.LineModel;
 import org.junit.Before;
 import org.junit.Test;
 
+import java.util.ArrayList;
+
+import static org.junit.Assert.assertEquals;
+
 /**
  * Implementierung verschiedener Algorithmen zur Berechnung von Ausgleichsgeraden.
  *
@@ -29,8 +33,8 @@ public class IntersectionCounterTest {
 
     @Test
     public void run() throws Exception {
-//        IntersectionComputer instance = IntersectionComputer.getInstance();
+        IntersectionComputer instance = IntersectionComputer.getInstance();
-//        assertEquals(3,  IntersectionComputer.getInstance().compute(lineModel.getLines(), -9999,9999).size());
+        assertEquals(3,  IntersectionComputer.getInstance().compute(new ArrayList<>(lineModel.getLines()), -9999,9999).size());
     }
 
 }