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.InferenceMode; import de.fub.maps.project.detector.model.inference.InferenceModelInputDataSet; import de.fub.maps.project.detector.model.inference.features.FeatureProcess; import de.fub.maps.project.detector.model.inference.ui.InferenceResultPanel; import de.fub.maps.project.detector.model.xmls.ProcessHandlerDescriptor; import java.text.MessageFormat; import java.util.ArrayList; import java.util.Collection; import java.util.Collections; import java.util.HashMap; import java.util.HashSet; import java.util.List; import java.util.Map; import java.util.Map.Entry; import java.util.Set; import javax.swing.JComponent; import javax.swing.SwingUtilities; import org.openide.util.Exceptions; import org.openide.util.NbBundle; import org.openide.util.lookup.ServiceProvider; import weka.classifiers.Classifier; import weka.core.Attribute; import weka.core.DenseInstance; import weka.core.Instance; import weka.core.Instances; /** * This is a special implementation of the clustering processhander. Instead of * a specified inference set it will use the trainingsset and labels each * tracksegment with its transportation mode. * * This processhandler is only for the use of computing the the accuracy for * each transport mode * * @author Serdar */ @NbBundle.Messages({ "LBL_Detector_special_clustering_Title=Clustering", "CLT_Special_InferenceDataProcessHandler_Name=Inference ProcessHandler", "CLT_Special_InferenceDataProcessHandler_Description=No description available" }) @ServiceProvider(service = InferenceModelProcessHandler.class) public class SpecialInferenceDataProcessHandler extends InferenceModelProcessHandler { private InferenceResultPanel inferenceResultPanel = null; // key = class/transport mode name, value = list of instance whose label is the key; // don't acces this member directly. use the respective methods private final HashMap<String, List<Instance>> resultMap = new HashMap<String, List<Instance>>(); // helper map to map instances to the original dataset private final HashMap<Instance, TrackSegment> instanceToTrackSegmentMap = new HashMap<Instance, TrackSegment>(); public SpecialInferenceDataProcessHandler() { super(null); } public SpecialInferenceDataProcessHandler(AbstractInferenceModel inferenceModel) { super(inferenceModel); } private void setClassesToView(Collection<String> classes) { ArrayList<String> arrayList = new ArrayList<String>(classes); Collections.sort(arrayList); for (String string : arrayList) { resultMap.put(string, new ArrayList<Instance>()); } updateVisualRepresentation(); } @Override protected void handle() { clearResults(); Classifier classifier = getInferenceModel().getClassifier(); Collection<Attribute> attributeList = getInferenceModel().getAttributes(); if (!attributeList.isEmpty()) { Set<String> keySet = getInferenceModel().getInput().getTrainingsSet().keySet(); setClassesToView(keySet); Instances unlabeledInstances = new Instances("Unlabeld Tracks", new ArrayList<Attribute>(attributeList), 0); //NO18N unlabeledInstances.setClassIndex(0); ArrayList<TrackSegment> segmentList = new ArrayList<TrackSegment>(); for (Entry<String, HashSet<TrackSegment>> entry : getInferenceModel().getInput().getTrainingsSet() .entrySet()) { for (TrackSegment segment : entry.getValue()) { segment.setLabel(entry.getKey()); Instance instance = getInstance(segment); unlabeledInstances.add(instance); segmentList.add(segment); } } // create copy Instances labeledInstances = new Instances(unlabeledInstances); for (int index = 0; index < labeledInstances.numInstances(); index++) { try { Instance instance = labeledInstances.instance(index); // classify instance double classifyed = classifier.classifyInstance(instance); instance.setClassValue(classifyed); // get class label String value = unlabeledInstances.classAttribute().value((int) classifyed); if (index < segmentList.size()) { instanceToTrackSegmentMap.put(instance, segmentList.get(index)); } // put label and instance to result map put(value, instance); } catch (Exception ex) { Exceptions.printStackTrace(ex); } } // update visw updateVisualRepresentation(); // update result set of the inferenceModel for (Map.Entry<String, List<Instance>> entry : resultMap.entrySet()) { HashSet<TrackSegment> trackSegmentList = new HashSet<TrackSegment>(); for (Instance instance : entry.getValue()) { TrackSegment trackSegment = instanceToTrackSegmentMap.get(instance); if (trackSegment != null) { trackSegmentList.add(trackSegment); } } // only those classes are put into the result data set, which are not empty if (!trackSegmentList.isEmpty()) { getInferenceModel().getResult().put(entry.getKey(), trackSegmentList); } } } else { throw new InferenceModelClassifyException(MessageFormat .format("No attributes available. Attribute list lengeth == {0}", attributeList.size())); } resultMap.clear(); instanceToTrackSegmentMap.clear(); } protected void updateVisualRepresentation() { final HashMap<String, List<Instance>> transportModeToInstanceMap = new HashMap<String, List<Instance>>( resultMap); final HashMap<Instance, TrackSegment> instanceTrackSegmentMap = new HashMap<Instance, TrackSegment>( instanceToTrackSegmentMap); SwingUtilities.invokeLater(new Runnable() { @Override public void run() { getInferenceResultPanel().updateView(new InferenceDataProcessHandler.ClassificationResult( transportModeToInstanceMap, instanceTrackSegmentMap)); // TODO } }); } private void put(String className, Instance instance) { if (!resultMap.containsKey(className)) { resultMap.put(className, new ArrayList<Instance>()); } resultMap.get(className).add(instance); } private void clearResults() { getInferenceModel().getResult().clear(); resultMap.clear(); instanceToTrackSegmentMap.clear(); } public HashSet<TrackSegment> getInferenceDataSet() { InferenceModelInputDataSet input = getInferenceModel().getInput(); HashSet<TrackSegment> dataset = input.getInferenceSet(); return dataset; } @Override public JComponent getVisualRepresentation() { return getInferenceResultPanel(); } private InferenceResultPanel getInferenceResultPanel() { if (inferenceResultPanel == null) { inferenceResultPanel = new InferenceResultPanel(); inferenceResultPanel.getTitle().setText(Bundle.LBL_Detector_clustering_Title()); } return inferenceResultPanel; } private Instance getInstance(TrackSegment segment) { Instance instance = new DenseInstance(getInferenceModel().getAttributes().size()); for (FeatureProcess feature : getInferenceModel().getFeatureList()) { feature.setInput(segment); feature.run(); String featureName = feature.getName(); Attribute attribute = getInferenceModel().getAttributeMap().get(featureName); Double result = feature.getResult(); instance.setValue(attribute, result); } return instance; } @Override protected ProcessHandlerDescriptor createDefaultDescriptor() { ProcessHandlerDescriptor descriptor = new ProcessHandlerDescriptor(); descriptor.setJavaType(InferenceDataProcessHandler.class.getName()); descriptor.setInferenceMode(InferenceMode.INFERENCE_MODE); descriptor.setName(Bundle.CLT_InferenceDataProcessHandler_Name()); descriptor.setDescription(Bundle.CLT_InferenceDataProcessHandler_Description()); return descriptor; } public static class ClassificationResult { private final HashMap<String, List<Instance>> resultMap; private final HashMap<Instance, TrackSegment> instanceToTrackSegmentMap; public ClassificationResult(HashMap<String, List<Instance>> resultMap, HashMap<Instance, TrackSegment> instanceToTrackSegmentMap) { this.instanceToTrackSegmentMap = instanceToTrackSegmentMap; this.resultMap = resultMap; } public Map<String, List<Instance>> getResultMap() { return Collections.unmodifiableMap(resultMap); } public HashMap<Instance, TrackSegment> getInstanceToTrackSegmentMap() { return instanceToTrackSegmentMap; } } }