mlda.attributes.MeanKurtosis.java Source code

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Here is the source code for mlda.attributes.MeanKurtosis.java

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/*
 * This file is part of the MLDA.
 *
 * (c)  Jose Maria Moyano Murillo
 *      Eva Lucrecia Gibaja Galindo
 *      Sebastian Ventura Soto <sventura@uco.es>
 *
 * For the full copyright and license information, please view the LICENSE
 * file that was distributed with this source code.
 */

package mlda.attributes;

import java.util.Set;

import mlda.base.MLDataMetric;
import mulan.data.MultiLabelInstances;
import weka.core.Attribute;
import weka.core.Instance;
import weka.core.Instances;

/**
* Class implementing the Mean of kurtosis
*
* @author Jose Maria Moyano Murillo
*/
public class MeanKurtosis extends MLDataMetric {

    /**
     * Constructor
     */
    public MeanKurtosis() {
        super("Mean of kurtosis");
    }

    /**
     * Calculate metric value
     * 
     * @param mlData Multi-label dataset to which calculate the metric
     * @return Value of the metric
     */
    public double calculate(MultiLabelInstances mlData) {
        Instances instances = mlData.getDataSet();
        int nInstances = mlData.getNumInstances();

        double avg;
        double var2;
        double var4;
        double val;
        int nNumeric = 0;
        double mean = 0;

        Set<Attribute> attributesSet = mlData.getFeatureAttributes();

        for (Attribute att : attributesSet) {
            if (att.isNumeric()) {
                nNumeric++;
                avg = instances.meanOrMode(att);
                var2 = 0;
                var4 = 0;

                for (Instance inst : instances) {
                    val = inst.value(att);
                    var2 += Math.pow(val - avg, 2);
                    var4 += Math.pow(val - avg, 4);
                }

                double kurtosis = (nInstances * var4 / Math.pow(var2, 2)) - 3;
                double sampleKurtosis = (kurtosis * (nInstances + 1) + 6) * (nInstances - 1)
                        / ((nInstances - 2) * (nInstances - 3));
                mean += sampleKurtosis;
            }
        }
        if (nNumeric > 0) {
            mean = mean / nNumeric;
        } else {
            mean = Double.NaN;
        }

        this.value = mean;
        return value;
    }

}