Example usage for weka.classifiers.evaluation NominalPrediction actual

Introduction

In this page you can find the example usage for weka.classifiers.evaluation NominalPrediction actual.

Prototype

public double actual() 

Source Link

Document

Gets the actual class value.

Usage

From source file:CopiaSeg3.java

public static double calculateAccuracy(FastVector predictions) {
    double correct = 0;

    for (int i = 0; i < predictions.size(); i++) {
        NominalPrediction np = (NominalPrediction) predictions.elementAt(i);
        if (np.predicted() == np.actual()) {
            correct++;/*from w  ww. j av a  2  s.  c  o m*/
        }
    }

    return 100 * correct / predictions.size();
}

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From source file:org.uclab.mm.kcl.ddkat.modellearner.ModelLearner.java

License:Apache License

/**
* Method to compute the classification accuracy.
*
* @param algo the algorithm name/*from w  w w  .  j  a  v a2  s.  com*/
* @param data the data instances
* @param datanature the dataset nature (i.e. original or processed data)
* @throws Exception the exception
*/
protected String[] modelAccuracy(String algo, Instances data, String datanature) throws Exception {

    String modelResultSet[] = new String[4];
    String modelStr = "";
    Classifier classifier = null;

    // setting class attribute if the data format does not provide this information           
    if (data.classIndex() == -1)
        data.setClassIndex(data.numAttributes() - 1);

    String decisionAttribute = data.attribute(data.numAttributes() - 1).toString();
    String res[] = decisionAttribute.split("\\s+");
    decisionAttribute = res[1];

    if (algo.equals("BFTree")) {

        // Use BFTree classifiers
        BFTree BFTreeclassifier = new BFTree();
        BFTreeclassifier.buildClassifier(data);
        modelStr = BFTreeclassifier.toString();
        classifier = BFTreeclassifier;

    } else if (algo.equals("FT")) {

        // Use FT classifiers
        FT FTclassifier = new FT();
        FTclassifier.buildClassifier(data);
        modelStr = FTclassifier.toString();
        classifier = FTclassifier;

    } else if (algo.equals("J48")) {

        // Use J48 classifiers
        J48 J48classifier = new J48();
        J48classifier.buildClassifier(data);
        modelStr = J48classifier.toString();
        classifier = J48classifier;
        System.out.println("Model String: " + modelStr);

    } else if (algo.equals("J48graft")) {

        // Use J48graft classifiers
        J48graft J48graftclassifier = new J48graft();
        J48graftclassifier.buildClassifier(data);
        modelStr = J48graftclassifier.toString();
        classifier = J48graftclassifier;

    } else if (algo.equals("RandomTree")) {

        // Use RandomTree classifiers
        RandomTree RandomTreeclassifier = new RandomTree();
        RandomTreeclassifier.buildClassifier(data);
        modelStr = RandomTreeclassifier.toString();
        classifier = RandomTreeclassifier;

    } else if (algo.equals("REPTree")) {

        // Use REPTree classifiers
        REPTree REPTreeclassifier = new REPTree();
        REPTreeclassifier.buildClassifier(data);
        modelStr = REPTreeclassifier.toString();
        classifier = REPTreeclassifier;

    } else if (algo.equals("SimpleCart")) {

        // Use SimpleCart classifiers
        SimpleCart SimpleCartclassifier = new SimpleCart();
        SimpleCartclassifier.buildClassifier(data);
        modelStr = SimpleCartclassifier.toString();
        classifier = SimpleCartclassifier;

    }

    modelResultSet[0] = algo;
    modelResultSet[1] = decisionAttribute;
    modelResultSet[2] = modelStr;

    // Collect every group of predictions for J48 model in a FastVector
    FastVector predictions = new FastVector();

    Evaluation evaluation = new Evaluation(data);
    int folds = 10; // cross fold validation = 10
    evaluation.crossValidateModel(classifier, data, folds, new Random(1));
    // System.out.println("Evaluatuion"+evaluation.toSummaryString());
    System.out.println("\n\n" + datanature + " Evaluatuion " + evaluation.toMatrixString());

    // ArrayList<Prediction> predictions = evaluation.predictions();
    predictions.appendElements(evaluation.predictions());

    System.out.println("\n\n 11111");
    // Calculate overall accuracy of current classifier on all splits
    double correct = 0;

    for (int i = 0; i < predictions.size(); i++) {
        NominalPrediction np = (NominalPrediction) predictions.elementAt(i);
        if (np.predicted() == np.actual()) {
            correct++;
        }
    }

    System.out.println("\n\n 22222");
    double accuracy = 100 * correct / predictions.size();
    String accString = String.format("%.2f%%", accuracy);
    modelResultSet[3] = accString;
    System.out.println(datanature + " Accuracy " + accString);

    String modelFileName = algo + "-DDKA.model";

    System.out.println("\n\n 33333");

    ObjectOutputStream oos = new ObjectOutputStream(
            new FileOutputStream("D:\\DDKAResources\\" + modelFileName));
    oos.writeObject(classifier);
    oos.flush();
    oos.close();

    return modelResultSet;

}

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From source file:tubes1.Main.java

public static double calculateAccuracy(FastVector predictions) {
    double correct = 0;
    for (int i = 0; i < predictions.size(); i++) {
        NominalPrediction np = (NominalPrediction) predictions.elementAt(i);
        if (np.predicted() == np.actual()) {
            correct++;//from w  w  w .j a v a2s  .c o m
        }
    }
    return 100 * correct / predictions.size();
}

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