org.wkwk.classifier.Access.java Source code

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Here is the source code for org.wkwk.classifier.Access.java

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package org.wkwk.classifier;

import java.util.Random;
import weka.classifiers.Classifier;
import weka.classifiers.Evaluation;
import weka.classifiers.trees.*;
import weka.core.Instances;
import weka.core.converters.ConverterUtils.*;
import weka.filters.Filter;
import weka.filters.supervised.instance.*;
import weka.filters.unsupervised.attribute.*;

/**
 *
 * @author adarwawan
 */
public class Access {

    /**
     * @param args the command line arguments
     * args[0] = filename train set
     * args[1] = filename test set
     * args[2] = remove attribute
     * args[3] = bias resample
     * @throws java.lang.Exception
     */
    public static void main(String[] args) throws Exception {

        // Read Dataset (arff, csv)
        DataSource source = new DataSource("../data/weather.nominal.arff");
        //DataSource testSource = new DataSource(args[1]);
        Instances data = source.getDataSet();

        if (data.classIndex() == -1) {
            data.setClassIndex(data.numAttributes() - 1);
        }

        // Remove attr
        //        String[] rangeOps = new String[2];
        //        rangeOps[0] = "-R";                                    // "range"
        //        rangeOps[1] = args[2];                                 // first attribute
        //        Remove remove = new Remove();                         // new instance of filter
        //        remove.setOptions(rangeOps);                           // set options
        //        remove.setInputFormat(data);                          // inform filter about dataset **AFTER** setting options
        //        Instances newData = Filter.useFilter(data, remove);   // apply filter
        //        
        //        // Filter Resample
        //        String[] biasOps = new String[2];
        //        biasOps[0] = "-B";                                    // "range"
        //        biasOps[1] = args[3];                                 // first attribute
        //        Resample resample = new Resample();
        //        resample.setOptions(biasOps);
        //        resample.setInputFormat(data);
        //        newData = Filter.useFilter(data, resample);
        //        
        // Build Classifier
        MyC45 tree = new MyC45(); // new instance of tree
        tree.buildClassifier(data); // build classifier

        // Evaluation with test set
        //Instances testSet = testSource.getDataSet();
        // train classifier
        //Classifier cls = new MyId3();
        //cls.buildClassifier(data);
        // evaluate classifier and print some statistics
        //Evaluation eval = new Evaluation(data);
        //eval.evaluateModel(cls, testSet);
        //System.out.println(eval.toSummaryString("\nResults\n======\n", false));

        // Evaluation with 10 Fold-CV
        Evaluation evalCV = new Evaluation(data);
        evalCV.crossValidateModel(tree, data, 10, new Random(1));
        System.out.println(evalCV.toSummaryString("\nResults\n======\n", false));
    }

}