Example usage for weka.classifiers.trees REPTree classifyInstance

Introduction

In this page you can find the example usage for weka.classifiers.trees REPTree classifyInstance.

Prototype

@Override
public double classifyInstance(Instance instance) throws Exception 

Source Link

Document

Classifies the given test instance.

Usage

From source file:de.ugoe.cs.cpdp.dataselection.DecisionTreeSelection.java

License:Apache License

@Override
public void apply(Instances testdata, SetUniqueList<Instances> traindataSet) {
    final Instances data = characteristicInstances(testdata, traindataSet);

    final ArrayList<String> attVals = new ArrayList<String>();
    attVals.add("same");
    attVals.add("more");
    attVals.add("less");
    final ArrayList<Attribute> atts = new ArrayList<Attribute>();
    for (int j = 0; j < data.numAttributes(); j++) {
        atts.add(new Attribute(data.attribute(j).name(), attVals));
    }//from  ww  w.jav a2 s  .c o  m
    atts.add(new Attribute("score"));
    Instances similarityData = new Instances("similarity", atts, 0);
    similarityData.setClassIndex(similarityData.numAttributes() - 1);

    try {
        Classifier classifier = new J48();
        for (int i = 0; i < traindataSet.size(); i++) {
            classifier.buildClassifier(traindataSet.get(i));
            for (int j = 0; j < traindataSet.size(); j++) {
                if (i != j) {
                    double[] similarity = new double[data.numAttributes() + 1];
                    for (int k = 0; k < data.numAttributes(); k++) {
                        if (0.9 * data.get(i + 1).value(k) > data.get(j + 1).value(k)) {
                            similarity[k] = 2.0;
                        } else if (1.1 * data.get(i + 1).value(k) < data.get(j + 1).value(k)) {
                            similarity[k] = 1.0;
                        } else {
                            similarity[k] = 0.0;
                        }
                    }

                    Evaluation eval = new Evaluation(traindataSet.get(j));
                    eval.evaluateModel(classifier, traindataSet.get(j));
                    similarity[data.numAttributes()] = eval.fMeasure(1);
                    similarityData.add(new DenseInstance(1.0, similarity));
                }
            }
        }
        REPTree repTree = new REPTree();
        if (repTree.getNumFolds() > similarityData.size()) {
            repTree.setNumFolds(similarityData.size());
        }
        repTree.setNumFolds(2);
        repTree.buildClassifier(similarityData);

        Instances testTrainSimilarity = new Instances(similarityData);
        testTrainSimilarity.clear();
        for (int i = 0; i < traindataSet.size(); i++) {
            double[] similarity = new double[data.numAttributes() + 1];
            for (int k = 0; k < data.numAttributes(); k++) {
                if (0.9 * data.get(0).value(k) > data.get(i + 1).value(k)) {
                    similarity[k] = 2.0;
                } else if (1.1 * data.get(0).value(k) < data.get(i + 1).value(k)) {
                    similarity[k] = 1.0;
                } else {
                    similarity[k] = 0.0;
                }
            }
            testTrainSimilarity.add(new DenseInstance(1.0, similarity));
        }

        int bestScoringProductIndex = -1;
        double maxScore = Double.MIN_VALUE;
        for (int i = 0; i < traindataSet.size(); i++) {
            double score = repTree.classifyInstance(testTrainSimilarity.get(i));
            if (score > maxScore) {
                maxScore = score;
                bestScoringProductIndex = i;
            }
        }
        Instances bestScoringProduct = traindataSet.get(bestScoringProductIndex);
        traindataSet.clear();
        traindataSet.add(bestScoringProduct);
    } catch (Exception e) {
        Console.printerr("failure during DecisionTreeSelection: " + e.getMessage());
        throw new RuntimeException(e);
    }
}

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