source: trunk/CrossPare/src/de/ugoe/cs/cpdp/dataselection/SetWiseKNNSelection.java @ 10

package de.ugoe.cs.cpdp.dataselection;

import java.util.HashSet;
import java.util.Set;

import org.apache.commons.collections4.list.SetUniqueList;
import org.apache.commons.math3.util.MathArrays;

import weka.core.Instances;

/**
 * Filter based on the k-nearest neighbor (KNN) algorithm S. Herbold: Training data selection for cross-project defect prediction
 * @author Steffen Herbold
 */
public class SetWiseKNNSelection extends AbstractCharacteristicSelection {
        
        /**
         * number of neighbors selected
         */
        private int k = 1;
        
        /**
         * @see de.ugoe.cs.cpdp.dataselection.SetWiseDataselectionStrategy#apply(weka.core.Instances, org.apache.commons.collections4.list.SetUniqueList)
         */
        @Override
        public void apply(Instances testdata, SetUniqueList<Instances> traindataSet) {
                final Instances data = normalizedCharacteristicInstances(testdata, traindataSet);
                
                final Set<Integer> selected = new HashSet<Integer>();           
                for( int i=0 ; i<k ; i++ ) {
                        int closestIndex = getClosest(data);
                        
                        selected.add(closestIndex);
                        data.delete(closestIndex);
                }
                
                for( int i=traindataSet.size()-1; i>=0 ; i-- ) {
                        if( selected.contains(i) ) {
                                traindataSet.remove(i);
                        }
                }
        }
        
        /**
         * Helper method that determines the index of the instance with the smallest distance to the first instance (index 0).
         * @param data data set
         * @return index of the closest instance
         */
        private int getClosest(Instances data) {
                double closestDistance = Double.MAX_VALUE;
                int closestIndex = 1;
                for( int i=1 ; i<data.numInstances() ; i++ ) {
                        double distance = MathArrays.distance(data.instance(0).toDoubleArray(), data.instance(i).toDoubleArray());
                        if( distance < closestDistance) {
                                closestDistance = distance;
                                closestIndex = i;
                        }
                }
                return closestIndex;
        }

        /**
         * Sets the number of neighbors followed by the distributional characteristics, the values are separated by blanks.
         * @see AbstractCharacteristicSelection#setParameter(String) 
         */
        @Override
        public void setParameter(String parameters) {
                if( !"".equals(parameters) ) {
                        final String[] split = parameters.split(" ");
                        k = Integer.parseInt(split[0]);
                        String str = "";
                        for( int i=1 ; i<split.length; i++ ) {
                                str += split[i];
                                if( i<split.length-1 )  {
                                        str += " ";
                                }
                        }
                        super.setParameter(str);
                }
        }
}