316 lines
11 KiB
Java
316 lines
11 KiB
Java
package presenter;
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import model.LineModel;
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import presenter.algorithms.advanced.LeastMedianOfSquaresEstimator;
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import presenter.algorithms.advanced.RepeatedMedianEstimator;
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import presenter.algorithms.advanced.TheilSenEstimator;
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import presenter.evaluation.EvaluateAlgorithms;
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import presenter.evaluation.PictureProcessor;
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import presenter.generator.DatasetGenerator;
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import presenter.io.DataExporter;
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import presenter.io.DataImporter;
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import presenter.io.EvalResultLatexExport;
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import view.MainFrame;
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import javax.swing.table.DefaultTableModel;
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import java.io.File;
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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: 28.05.2017.
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*/
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public class Presenter extends AbstractPresenter {
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/* Threads */
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private Thread tsThread;
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private Thread rmThread;
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private Thread lmsThread;
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private Thread importThread;
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private Thread importPictureThread;
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private Thread exportThread;
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private Thread exportResultThread;
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private Thread generatorThread;
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private Thread evalThread;
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public Presenter(LineModel model, MainFrame view) {
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super(model, view);
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}
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public void visualizeDualLines() {
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getView().createDualityDialog();
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}
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/***************************************************************************************************************************
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* Ausführung der Algorithmen
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***************************************************************************************************************************/
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/**
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* Die Berechnung durch des Algorithmus zum LMS-Schätzer wird gestartet.
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* @param input Parameter für den Algorithmus
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*/
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public void calculateLMS(String[] input) {
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if (input[0] != null && input[1] != null) {
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if (lmsThread == null || !lmsThread.isAlive()) {
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lmsThread = new Thread(() -> {
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Double constant = Double.parseDouble(input[0]);
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Double error = Double.parseDouble(input[1]);
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LeastMedianOfSquaresEstimator lms = new LeastMedianOfSquaresEstimator(getModel().getLines(), getModel().getNodes(), this);
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lms.setConstant(constant);
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lms.setQuantileError(error);
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lms.addObserver(this);
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lms.run();
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lms.getResult();
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});
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lmsThread.start();
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try {
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lmsThread.join();
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} catch (InterruptedException e) {
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e.printStackTrace();
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}
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}
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}
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}
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/**
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* Die Berechnung durch des Algorithmus zum RM-Schätzer wird gestartet.
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* @param input Parameter für den Algorithmus
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*/
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public void calculateRM(String input) {
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if (input != null) {
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if (rmThread == null || !rmThread.isAlive()) {
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rmThread = new Thread(() -> {
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RepeatedMedianEstimator rm = new RepeatedMedianEstimator(getModel().getLines(), this);
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Double parameter = Double.parseDouble(input);
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rm.setBeta(parameter);
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rm.addObserver(this);
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rm.run();
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rm.getResult();
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});
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rmThread.start();
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try {
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rmThread.join();
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} catch (InterruptedException e) {
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e.printStackTrace();
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}
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}
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}
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}
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/**
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* Die Berechnung durch des Algorithmus zum TS-Schätzer wird gestartet.
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* @param input Parameter für den Algorithmus
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*/
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public void calculateTS(String input) {
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if (input != null) {
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if (tsThread == null || !tsThread.isAlive()) {
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tsThread = new Thread(() -> {
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TheilSenEstimator ts = new TheilSenEstimator(getModel().getLines(), getModel().getNodes(), this);
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ts.addObserver(this);
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ts.run();
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ts.getResult();
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});
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tsThread.start();
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try {
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tsThread.join();
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} catch (InterruptedException e) {
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e.printStackTrace();
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}
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}
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}
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}
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/***************************************************************************************************************************
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* Hilfsmethoden
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***************************************************************************************************************************/
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/**
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* Startet den Import des Datensatzes.
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* @param file importierender Datensatz
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*/
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public void startImport(File file) {
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if (importThread == null || !importThread.isAlive()) {
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importThread = new Thread(() -> {
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DataImporter importer = new DataImporter(file);
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importer.addObserver(this);
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getModel().setLines(importer.run());
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computeIntersections();
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});
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importThread.start();
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try {
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importThread.join();
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} catch (InterruptedException e) {
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e.printStackTrace();
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}
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}
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}
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/**
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* Startet den Import eines Bildes.
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* @param file importierendes Bild
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*/
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public void startPictureDataImport(File file) {
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if (importPictureThread == null || !importPictureThread.isAlive()) {
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importPictureThread = new Thread(() -> {
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PictureProcessor pictureProcessor = new PictureProcessor(this, file);
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pictureProcessor.addObserver(this);
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pictureProcessor.run();
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computeIntersections();
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});
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importPictureThread.start();
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try {
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importPictureThread.join();
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} catch (InterruptedException e) {
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e.printStackTrace();
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}
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}
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}
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/**
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* Startet den Export des akteullen Datensatzes (nur der Geraden)
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* @param file Datei in der die Informationen gespeichert werden sollen
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*/
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public void startExport(File file) {
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if (exportThread == null || !exportThread.isAlive()) {
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exportThread = new Thread(() -> {
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DataExporter exporter = new DataExporter(getModel().getLines(), file);
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exporter.addObserver(this);
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exporter.export();
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});
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exportThread.start();
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try {
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exportThread.join();
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} catch (InterruptedException e) {
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e.printStackTrace();
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}
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}
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}
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/**
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* Startet den Export des akteullen Datensatzes im Rahmen der Evaluation (nur der Geraden)
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* @param file Datei in der die Informationen gespeichert werden sollen
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*/
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public void startDatasetExportEvaluation(File file) {
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if (exportThread == null || !exportThread.isAlive()) {
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exportThread = new Thread(() -> {
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DataExporter exporter = new DataExporter(getEval().getData(), file);
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exporter.addObserver(this);
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exporter.export();
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});
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exportThread.start();
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try {
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exportThread.join();
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} catch (InterruptedException e) {
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e.printStackTrace();
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}
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}
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}
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/**
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* Startet den Export der Ergebnisse der Evaluation in Form einer LaTeX Datei
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* @param model TableModel das die Berechnenten Ergebnisse enthält
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* @param file Datei in die exportiert werden soll
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*/
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public void startResultExport(DefaultTableModel model, File file) {
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if (exportResultThread == null || !exportResultThread.isAlive()) {
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exportResultThread = new Thread(() -> {
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EvalResultLatexExport exporter = new EvalResultLatexExport(model, file);
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exporter.writeFile();
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});
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exportResultThread.start();
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try {
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exportResultThread.join();
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} catch (InterruptedException e) {
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e.printStackTrace();
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}
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}
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}
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/**
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* Startet das generieren der Datensätze der Größe <code>n</code>
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* @param n Größe des Datensatzes
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* @param type Art der Datensatzes
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*/
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public void generateDataset(int n, int type) {
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if (generatorThread == null || !generatorThread.isAlive()) {
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generatorThread = new Thread(() -> {
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DatasetGenerator generator = new DatasetGenerator();
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generator.addObserver(this);
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switch (type){
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case 0:
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getModel().setLines(generator.generateDataCloud(n));
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break;
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case 1:
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getModel().setLines(generator.generateDataLines(n));
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break;
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case 2:
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getModel().setLines(generator.generateCircle(n));
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break;
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default:
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getModel().setLines(generator.generateDataCloud(n));
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break;
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}
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computeIntersections();
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getView().enableFunctionality();
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});
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generatorThread.start();
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try {
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generatorThread.join();
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} catch (InterruptedException e) {
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e.printStackTrace();
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}
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}
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}
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/**
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*
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* @param typ
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* @param n
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* @param alg
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* @param datasettyp
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*/
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public void startEvaluation(int typ, int n, int alg, String datasettyp) {
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if (evalThread == null || !evalThread.isAlive()) {
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evalThread = new Thread(() -> {
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try {
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setEval(new EvaluateAlgorithms(typ, n, alg, datasettyp));
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getEval().addObserver(this);
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getEval().run();
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} catch (InterruptedException e) {
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e.printStackTrace();
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}
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});
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evalThread.start();
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}
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}
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/**
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*
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* @param typ
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* @param n
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* @param alg
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* @param file
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*/
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public void startEvaluation(int typ, int n, int alg, File file) {
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if (evalThread == null || !evalThread.isAlive()) {
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evalThread = new Thread(() -> {
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try {
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setEval(new EvaluateAlgorithms(typ, n, alg, file));
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getEval().addObserver(this);
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getEval().run();
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} catch (InterruptedException e) {
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e.printStackTrace();
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}
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});
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evalThread.start();
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}
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}
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}
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