Example usage for weka.filters.unsupervised.attribute Discretize Discretize

Introduction

In this page you can find the example usage for weka.filters.unsupervised.attribute Discretize Discretize.

Prototype

public Discretize() 

Source Link

Document

Constructor - initialises the filter

Usage

From source file:org.uclab.mm.kcl.ddkat.datapreprocessor.DataDiscretizer.java

License:Apache License

/**
 * Method to discretize the input data using equal-width binning approach.
 *
 * @throws Exception the exception//from   w  w  w  .j a  v a 2  s.  c  o  m
 */
public void discretizeData() throws Exception {

    this.confirmationMessage = new ArrayList<String>();

    Instances inputData, outputData;
    String inputFile = BASE_DIR + "OriginalDataSet.csv";

    // load CSV file
    CSVLoader fileLoader = new CSVLoader();
    fileLoader.setSource(new File(inputFile));
    inputData = fileLoader.getDataSet();

    Discretize discrete = new Discretize();
    discrete.setInputFormat(inputData);
    outputData = Filter.useFilter(inputData, discrete);

    saveDiscretizedData(inputFile, outputData);

}

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From source file:tubes2ai.DriverNB.java

public static void run(String data) throws Exception {
    //System.out.println("tes driver");

    ConverterUtils.DataSource source = new ConverterUtils.DataSource(data);
    Instances dataTrain = source.getDataSet();
    //if (dataTrain.classIndex() == -1)
    dataTrain.setClassIndex(0);//from w  ww .  j a v  a2 s . co  m
    ArffSaver saver = new ArffSaver();

    //        dataTrain.setClassIndex();
    Discretize discretize = new Discretize();
    discretize.setInputFormat(dataTrain);
    Instances dataTrainDisc = Filter.useFilter(dataTrain, discretize);

    //NaiveBayes NB = new NaiveBayes();
    AIJKNaiveBayes NB = new AIJKNaiveBayes();
    NB.buildClassifier(dataTrainDisc);

    Evaluation eval = new Evaluation(dataTrainDisc);
    eval.evaluateModel(NB, dataTrainDisc);

    System.out.println(eval.toSummaryString());
    System.out.println(eval.toClassDetailsString());
    System.out.println(eval.toMatrixString());
    /*Instance inst = new DenseInstance(5);
            
    inst.setDataset(dataTrain);
    inst.setValue(0, "sunny");
    inst.setValue(1, "hot");
    inst.setValue(2, "high");
    inst.setValue(3, "FALSE");
    inst.setValue(4, "yes");
    double a = NB.classifyInstance(inst);
    String hasil="";
    if(a==0.0){
    hasil="YES";
    } else{
    hasil="NO";
    }
    //double[] b = NB.distributionForInstance(inst);
    System.out.println("Hasil klasifikasi: "+hasil);
    //System.out.println(b);*/
}

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