Example usage for weka.classifiers.trees Id3 classifyInstance

Introduction

In this page you can find the example usage for weka.classifiers.trees Id3 classifyInstance.

Prototype

@Override
public double classifyInstance(Instance instance) throws NoSupportForMissingValuesException 

Source Link

Document

Classifies a given test instance using the decision tree.

Usage

From source file:tubes1.Main.java

/**
 * @param args the command line arguments
 *//*ww  w .j  a  v a2s  .  c  om*/
public static void main(String[] args) throws IOException, Exception {
    // TODO code application logic here
    String filename = "weather";

    //Masih belum mengerti tipe .csv yang dapat dibaca seperti apa
    //CsvToArff convert = new CsvToArff(filename+".csv");

    //LOAD FILE
    BufferedReader datafile = readDataFile("src/" + filename + ".arff");
    Instances data = new Instances(datafile);
    data.setClassIndex(data.numAttributes() - 1);
    //END OF LOAD FILE

    CustomFilter fil = new CustomFilter();

    //REMOVE USELESS ATTRIBUTE
    data = fil.removeAttribute(data);
    System.out.println(data);

    Instances[] allData = new Instances[4];
    //data for Id3
    allData[0] = fil.resampling(fil.convertNumericToNominal(data));
    //data for J48
    allData[1] = fil.convertNumericToNominal(fil.resampling(data));
    //data for myId3
    allData[2] = allData[0];
    //data for myC4.5
    allData[3] = fil.resampling(fil.convertNumericToNominal(fil.convertNumericRange(data)));

    data = fil.convertNumericToNominal(data);
    // BUILD CLASSIFIERS
    Classifier[] models = { new Id3(), //C4.5
            new J48(), new myID3(), new myC45() };

    for (int j = 0; j < models.length; j++) {
        FastVector predictions = new FastVector();
        //FOR TEN-FOLD CROSS VALIDATION
        Instances[][] split = crossValidationSplit(allData[j], 10);
        // Separate split into training and testing arrays
        Instances[] trainingSplits = split[0];
        Instances[] testingSplits = split[1];
        System.out.println("\n---------------------------------");
        for (int i = 0; i < trainingSplits.length; i++) {
            try {
                //                    System.out.println("Building for training Split : " + i);
                Evaluation validation = classify(models[j], trainingSplits[i], testingSplits[i]);

                predictions.appendElements(validation.predictions());

                // Uncomment to see the summary for each training-testing pair.
                //                    System.out.println(models[j].toString());
            } catch (Exception ex) {
                Logger.getLogger(Main.class.getName()).log(Level.SEVERE, null, ex);
            }
            // Calculate overall accuracy of current classifier on all splits
            double accuracy = calculateAccuracy(predictions);

            // Print current classifier's name and accuracy in a complicated,
            // but nice-looking way.
            System.out.println(String.format("%.2f%%", accuracy));
        }
        models[j].buildClassifier(allData[j]);
        Model.save(models[j], models[j].getClass().getSimpleName());
    }

    //test instance
    Instances trainingSet = new Instances("Rel", getFvWekaAttributes(data), 10);
    trainingSet.setClassIndex(data.numAttributes() - 1);

    Instance testInstance = new Instance(data.numAttributes());
    for (int i = 0; i < data.numAttributes() - 1; i++) {
        System.out.print("Masukkan " + data.attribute(i).name() + " : ");
        Scanner in = new Scanner(System.in);
        String att = in.nextLine();
        if (isNumeric(att)) {
            att = fil.convertToFit(att, data, i);
        }
        testInstance.setValue(data.attribute(i), att);
    }

    //        System.out.println(testInstance);
    //        System.out.println(testInstance.classAttribute().index());

    trainingSet.add(testInstance);

    Classifier Id3 = Model.load("Id3");
    Classifier J48 = Model.load("J48");
    Classifier myID3 = Model.load("myID3");
    Classifier MyC45 = Model.load("myC45");
    //        Classifier MyId3 = Model.load("myID3");

    Instance A = trainingSet.instance(0);
    Instance B = trainingSet.instance(0);
    Instance C = trainingSet.instance(0);
    Instance D = trainingSet.instance(0);

    //test with ID3 WEKA
    A.setClassValue(Id3.classifyInstance(trainingSet.instance(0)));
    System.out.println("Id3 Weka : " + A);

    //test with C4.5 WEKA
    B.setClassValue(J48.classifyInstance(trainingSet.instance(0)));
    System.out.println("C4.5 Weka : " + B);

    //test with my C4.5
    C.setClassValue(MyC45.classifyInstance(trainingSet.instance(0)));
    System.out.println("My C4.5 : " + C);

    //test with my ID3
    D.setClassValue(myID3.classifyInstance(trainingSet.instance(0)));
    System.out.println("My ID3 : " + D);
}

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