Java tutorial
/* * Copyright (C) 2013 Serdar * * This program is free software: you can redistribute it and/or modify * it under the terms of the GNU General Public License as published by * the Free Software Foundation, either version 3 of the License, or * (at your option) any later version. * * This program is distributed in the hope that it will be useful, * but WITHOUT ANY WARRANTY; without even the implied warranty of * MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the * GNU General Public License for more details. * * You should have received a copy of the GNU General Public License * along with this program. If not, see <http://www.gnu.org/licenses/>. */ package de.fub.maps.project.detector.model.inference.processhandler; import de.fub.maps.project.detector.model.gpx.TrackSegment; import de.fub.maps.project.detector.model.inference.AbstractInferenceModel; import de.fub.maps.project.detector.model.inference.ui.EvaluationPanel; import de.fub.maps.project.detector.model.xmls.ProcessHandlerDescriptor; import de.fub.maps.project.detector.model.xmls.Property; import java.util.ArrayList; import java.util.Collection; import java.util.HashMap; import java.util.HashSet; import java.util.Map.Entry; import javax.swing.JComponent; import javax.swing.SwingUtilities; import org.netbeans.api.progress.ProgressHandle; import org.netbeans.api.progress.ProgressHandleFactory; import org.openide.util.NbBundle; import org.openide.util.lookup.ServiceProvider; import weka.classifiers.Classifier; import weka.classifiers.Evaluation; import weka.core.Attribute; import weka.core.Instance; import weka.core.Instances; /** * * @author Serdar */ @NbBundle.Messages({ "LBL_Detector_trainingsPanel_Title=Training", "CLT_TrainingsDataProcessHandler_Name=Trainings ProcessHandler", "CLT_TrainingsDataProcessHandler_Description=No description available", "CLT_TrainingsDataProcessHandler_Property_Ratio_Name=Training set size", "CLT_TrainingsDataProcessHandler_Property_Ratio_Description=This property indicates the ratio (between 0.01 and 1) the trainings set will be divides for the actual training and for successive the test." }) @ServiceProvider(service = InferenceModelProcessHandler.class) public class TrainingsDataProcessHandler extends EvaluationProcessHandler { private static final String TRAININGS_SET_RATIO = "trainings.set.ratio"; private EvaluationPanel evaluationPanel = null; public TrainingsDataProcessHandler() { super(null); } public TrainingsDataProcessHandler(AbstractInferenceModel inferenceModel) { super(inferenceModel); } @Override protected void handle() { final ProgressHandle handle = ProgressHandleFactory.createHandle("Trainings"); try { handle.start(); Collection<Attribute> attributeCollection = getInferenceModel().getAttributes(); ArrayList<Attribute> arrayList = new ArrayList<Attribute>(attributeCollection); Instances trainingSet = new Instances("Classes", arrayList, 0); trainingSet.setClassIndex(0); Instances testingSet = new Instances("Classes", arrayList, 0); testingSet.setClassIndex(0); HashMap<String, HashSet<TrackSegment>> dataset = getInferenceModel().getInput().getTrainingsSet(); int datasetCount = 0; for (HashSet<TrackSegment> list : dataset.values()) { for (TrackSegment trackSegment : list) { datasetCount += trackSegment.getWayPointList().size(); } } handle.switchToDeterminate(datasetCount); int trackCount = 0; for (Entry<String, HashSet<TrackSegment>> entry : dataset.entrySet()) { int trainingsSetSize = (int) Math.ceil(entry.getValue().size() * getTrainingsSetRatioParameter()); int index = 0; for (TrackSegment trackSegment : entry.getValue()) { Instance instance = getInstance(entry.getKey(), trackSegment); if (index < trainingsSetSize) { trainingSet.add(instance); } else { testingSet.add(instance); } handle.progress(trackCount++); index++; } } assert trainingSet.numInstances() > 0 : "Training set is empty and has no instances"; //NO18N assert testingSet.numInstances() > 0 : "Testing set is empty and has no instances"; //NO18N handle.switchToIndeterminate(); evaluate(trainingSet, testingSet); } finally { handle.finish(); } } private void evaluate(Instances trainingSet, Instances testingSet) { Classifier classifier = getInferenceModel().getClassifier(); try { classifier.buildClassifier(trainingSet); Evaluation evaluation = new Evaluation(testingSet); evaluation.evaluateModel(classifier, testingSet); updateVisualRepresentation(evaluation); } catch (Exception ex) { throw new InferenceModelClassifyException(ex.getMessage(), ex); } } @Override protected void updateVisualRepresentation(final Evaluation evaluation) { SwingUtilities.invokeLater(new Runnable() { @Override public void run() { getEvaluationPanel().updatePanel(evaluation); } }); } private double getTrainingsSetRatioParameter() { for (Property property : getDescriptor().getProperties().getPropertyList()) { if (TRAININGS_SET_RATIO.equals(property.getId())) { return Double.parseDouble(property.getValue()); } } return .75; } @Override public JComponent getVisualRepresentation() { return getEvaluationPanel(); } private EvaluationPanel getEvaluationPanel() { if (evaluationPanel == null) { evaluationPanel = new EvaluationPanel(); evaluationPanel.getTitle().setText(Bundle.LBL_Detector_trainingsPanel_Title()); } return evaluationPanel; } @Override protected ProcessHandlerDescriptor createDefaultDescriptor() { ProcessHandlerDescriptor descriptor = new ProcessHandlerDescriptor(); descriptor.setJavaType(TrainingsDataProcessHandler.class.getName()); descriptor.setName(Bundle.CLT_TrainingsDataProcessHandler_Name()); descriptor.setDescription(Bundle.CLT_TrainingsDataProcessHandler_Description()); Property property = new Property(); property.setId(TRAININGS_SET_RATIO); property.setJavaType(Double.class.getName()); property.setValue("0.75"); property.setName(Bundle.CLT_TrainingsDataProcessHandler_Property_Ratio_Name()); property.setDescription(Bundle.CLT_TrainingsDataProcessHandler_Property_Ratio_Description()); descriptor.getProperties().getPropertyList().add(property); return descriptor; } }