48 lines
1.7 KiB
Java
48 lines
1.7 KiB
Java
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package edu.nju.ics.frontier.learning;
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import edu.nju.ics.frontier.util.Assertion;
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import weka.classifiers.Classifier;
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import weka.classifiers.trees.RandomForest;
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import weka.core.Instance;
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import weka.core.Instances;
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import java.util.ArrayList;
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import java.util.HashMap;
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import java.util.List;
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import java.util.Map;
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public class RFModel extends Model {
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public RFModel(String name) {
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super(name);
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}
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@Override
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public Classifier fit(TimeDatabase trainDb) throws Exception {
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Instances instances = trainDb.getLabeledInstances();
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if (instances == null) {
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throw new NullPointerException("No labeled data in the training set!");
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}
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RandomForest metaClassifier = new RandomForest();
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metaClassifier.buildClassifier(instances);
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return metaClassifier;
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}
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@Override
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public Map<String, List<int[]>> predict(Classifier classifier, TimeDatabase trainDb, TimeDatabase testDb, ModelPerformanceSampler sampler) throws Exception {
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Map<String, List<int[]>> confusionMatrices = new HashMap<String, List<int[]>>();
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testDb.getLabeledAndUnlabeledInstances();
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for (TimeSeries ts : testDb.getTimeSeries()) {
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for (TimePoint tp : ts.getTimePoints()) {
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if (!tp.isEmpty()) {
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Instance instance = tp.getInstance();
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Assertion.assertNotNull(instance);
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double[] probDist = classifier.distributionForInstance(instance);
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tp.setFinalProbDist(probDist);
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}
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}
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}
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confusionMatrices.put("final", sampler.collectTrueAndPredLabels("final", testDb));
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return confusionMatrices;
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}
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}
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