Example usage for weka.attributeSelection ChiSquaredAttributeEval ChiSquaredAttributeEval

Introduction

In this page you can find the example usage for weka.attributeSelection ChiSquaredAttributeEval ChiSquaredAttributeEval.

Prototype

public ChiSquaredAttributeEval() 

Source Link

Document

Constructor

Usage

From source file:trabfs.machineLeaningFrameWork.core.Problema.java

public double[] getAttributeQuality() {
    try {/*  w w w  . j av a2s.  c om*/

        ASEvaluation[] filters = { new InfoGainAttributeEval(), new ChiSquaredAttributeEval(),
                new ReliefFAttributeEval() };
        R = new double[data.numAttributes() - 1][filters.length];
        Ranker rk = new Ranker();
        AttributeSelection selec = new AttributeSelection();
        selec.setSearch(rk);

        for (int j = 0; j < filters.length; j++) {
            selec.setEvaluator(filters[j]);
            selec.SelectAttributes(data);
            double[][] full = selec.rankedAttributes();
            //double[] r = new double[full.length];

            Arrays.sort(full, new Comparator() {
                @Override
                public int compare(Object t, Object t1) {
                    double[] a1 = (double[]) t;
                    double[] a2 = (double[]) t1;
                    if (a1[0] > a2[0])
                        return 1;
                    else if (a1[0] < a2[0])
                        return -1;
                    else
                        return 0;
                }
            });

            double max = Double.NEGATIVE_INFINITY, min = Double.POSITIVE_INFINITY;
            for (int i = 0; i < full.length; i++) {
                if (full[i][1] < min)
                    min = full[i][1];
                if (full[i][1] > max)
                    max = full[i][1];
            }

            // armazena
            for (int i = 0; i < full.length; i++) {
                R[i][j] = (full[i][1] - min) / (max - min);
            }
        }

        double[] Rfinal = new double[data.numAttributes() - 1];
        double SW = 1.0f;
        for (int i = 0; i < Rfinal.length; i++) {
            Rfinal[i] = somaWK(i) / 3.0f;
        }

        return Rfinal;
    } catch (Exception ex) {
        Logger.getLogger(Problema.class.getName()).log(Level.SEVERE, null, ex);
    }
    return null;
}

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