weka Discretize Attribute - Java Machine Learning AI

Java examples for Machine Learning AI:weka

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Description

weka Discretize Attribute

Demo Code

/*// ww w.  j a v a  2  s.  co m
 *  How to use WEKA API in Java 
 *  Copyright (C) 2014 
 *  @author Dr Noureddin M. Sadawi (noureddin.sadawi@gmail.com)
 *  
 *  This program is free software: you can redistribute it and/or modify
 *  it as you wish ... 
 *  I ask you only, as a professional courtesy, to cite my name, web page 
 *  and my YouTube Channel!
 *  
 */

package weka.api;

//import required classes
import weka.core.Instances;
import weka.core.converters.ArffSaver;
import java.io.File;
import weka.core.converters.ConverterUtils.DataSource;
import weka.filters.Filter;
import weka.filters.unsupervised.attribute.Discretize;

public class DiscretizeAttribute {
    public static void main(String args[]) throws Exception {
        //load dataset
        DataSource source = new DataSource(
                "/home/likewise-open/ACADEMIC/csstnns/Desktop/qdb1.arff");
        Instances dataset = source.getDataSet();
        //set options
        String[] options = new String[5];
        //choose the number of intervals, e.g. 2 :
        options[0] = "-B";
        options[1] = "4";
        //choose the range of attributes on which to apply the filter:
        options[2] = "-R";
        options[3] = "1-3";
        options[4] = "-V";
        //Apply discretization:
        Discretize discretize = new Discretize();
        discretize.setOptions(options);
        discretize.setInputFormat(dataset);
        Instances newData = Filter.useFilter(dataset, discretize);

        ArffSaver saver = new ArffSaver();
        saver.setInstances(newData);
        saver.setFile(new File(
                "/home/likewise-open/ACADEMIC/csstnns/Desktop/qdb2.arff"));
        saver.writeBatch();
    }
}

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