moa.classifiers.lazy.kNN.java Source code

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Here is the source code for moa.classifiers.lazy.kNN.java

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/*
 *    kNN.java
 *
 *    This program is free software; you can redistribute it and/or modify
 *    it under the terms of the GNU General Public License as published by
 *    the Free Software Foundation; either version 3 of the License, or
 *    (at your option) any later version.
 *
 *    This program is distributed in the hope that it will be useful,
 *    but WITHOUT ANY WARRANTY; without even the implied warranty of
 *    MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE.  See the
 *    GNU General Public License for more details.
 *
 *    You should have received a copy of the GNU General Public License
 *    along with this program. If not, see <http://www.gnu.org/licenses/>.
 *    
 */
package moa.classifiers.lazy;

import java.io.StringReader;
import moa.classifiers.AbstractClassifier;
import moa.classifiers.lazy.neighboursearch.KDTree;
import moa.classifiers.lazy.neighboursearch.LinearNNSearch;
import moa.classifiers.lazy.neighboursearch.NearestNeighbourSearch;
import moa.core.InstancesHeader;
import moa.core.Measurement;
import moa.options.IntOption;
import moa.options.MultiChoiceOption;
import weka.core.Instance;
import weka.core.Instances;

/**
 * k Nearest Neighbor.<p>
 *
 * Valid options are:<p>
 *
 * -k number of neighbours <br> -m max instances <br> 
 *
 * @author Jesse Read (jesse@tsc.uc3m.es)
 * @version 03.2012
 */
public class kNN extends AbstractClassifier {

    private static final long serialVersionUID = 1L;

    public IntOption kOption = new IntOption("k", 'k', "The number of neighbors", 10, 1, Integer.MAX_VALUE);

    public IntOption limitOption = new IntOption("limit", 'w', "The maximum number of instances to store", 1000, 1,
            Integer.MAX_VALUE);

    public MultiChoiceOption nearestNeighbourSearchOption = new MultiChoiceOption("nearestNeighbourSearch", 'n',
            "Nearest Neighbour Search to use", new String[] { "LinearNN", "KDTree" },
            new String[] { "Brute force search algorithm for nearest neighbour search. ",
                    "KDTree search algorithm for nearest neighbour search" },
            0);

    int C = 0;

    @Override
    public String getPurposeString() {
        return "kNN: special.";
    }

    protected Instances window;

    @Override
    public void setModelContext(InstancesHeader context) {
        try {
            this.window = new Instances(new StringReader(context.toString()), 0);
            this.window.setClassIndex(context.classIndex());
        } catch (Exception e) {
            System.err.println("Error: no Model Context available.");
            e.printStackTrace();
            System.exit(1);
        }
    }

    @Override
    public void resetLearningImpl() {
        this.window = null;
    }

    @Override
    public void trainOnInstanceImpl(Instance inst) {
        if (inst.classValue() > C)
            C = (int) inst.classValue();
        if (this.window == null) {
            this.window = new Instances(inst.dataset());
        }
        if (this.limitOption.getValue() <= this.window.numInstances()) {
            this.window.delete(0);
        }
        this.window.add(inst);
    }

    @Override
    public double[] getVotesForInstance(Instance inst) {
        double v[] = new double[C + 1];
        try {
            NearestNeighbourSearch search;
            if (this.nearestNeighbourSearchOption.getChosenIndex() == 0) {
                search = new LinearNNSearch(this.window);
            } else {
                search = new KDTree();
                search.setInstances(this.window);
            }
            if (this.window.numInstances() > 0) {
                Instances neighbours = search.kNearestNeighbours(inst,
                        Math.min(kOption.getValue(), this.window.numInstances()));
                for (int i = 0; i < neighbours.numInstances(); i++) {
                    v[(int) neighbours.instance(i).classValue()]++;
                }
            }
        } catch (Exception e) {
            //System.err.println("Error: kNN search failed.");
            //e.printStackTrace();
            //System.exit(1);
            return new double[inst.numClasses()];
        }
        return v;
    }

    @Override
    protected Measurement[] getModelMeasurementsImpl() {
        return null;
    }

    @Override
    public void getModelDescription(StringBuilder out, int indent) {
    }

    public boolean isRandomizable() {
        return false;
    }
}