knnclassifier.KNNClassifier.java Source code

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Here is the source code for knnclassifier.KNNClassifier.java

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package knnclassifier;

import static java.lang.Math.abs;
import static java.lang.Math.pow;
import java.util.TreeMap;
import weka.classifiers.Classifier;
import weka.core.Instances;
import weka.core.Instance;

/**
 *
 * @author Mark
 */

public class KNNClassifier extends Classifier {

    Integer k = 5;
    Instances data;
    TreeMap<Double, Integer> map;

    @Override
    public void buildClassifier(Instances i) throws Exception {

        data = i;
    }

    @Override
    public double classifyInstance(Instance instance) throws Exception {

        createMap(instance);

        return classify(instance);
    }

    double classify(Instance instance) {

        int tally[] = new int[data.numClasses()];
        int i = 0;

        for (Double key : map.keySet()) {

            tally[map.get(key)]++;

            if (i >= k) {

                break;
            }

            i++;
        }

        int highestValue = 0;
        int highestIndex = 0;
        i = 0;

        for (int value : tally) {

            if (highestValue < value) {

                highestValue = value;
                highestIndex = i;
            }

            i++;
        }

        System.out.println(highestIndex);
        return highestIndex;
    }

    double EuclideanDistance(Instance instanceLHS, Instance instanceRHS) {

        double distance = 0;

        for (int i = 0; i < instanceLHS.numAttributes() - 1 && i < instanceRHS.numAttributes() - 1; i++) {

            if (instanceLHS.attribute(i).isNumeric() && instanceRHS.attribute(i).isNumeric()) {

                distance += pow(instanceLHS.value(i) - instanceRHS.value(i), 2);
            } else {

                if (instanceLHS.stringValue(i).equals(instanceRHS.stringValue(i))) {

                    distance += 0;
                }

                distance += 5;
            }
        }

        return distance;
    }

    void createMap(Instance instance) {

        map = new TreeMap<>();

        for (int i = 0; i < data.numInstances(); i++) {

            map.put(EuclideanDistance(data.instance(i), instance), (int) (data.instance(i).classValue()));
        }
    }

    double ManhattenDistance(Instance instanceLHS, Instance instanceRHS) {

        double distance = 0;

        for (int i = 0; i < instanceLHS.numAttributes() - 1 && i < instanceRHS.numAttributes() - 1; i++) {

            if (instanceLHS.attribute(i).isNumeric() && instanceRHS.attribute(i).isNumeric()) {

                distance += abs(instanceLHS.value(i) - instanceRHS.value(i));
            } else {

                if (instanceLHS.stringValue(i).equals(instanceRHS.stringValue(i))) {

                    distance = 0;
                }

                distance += 5;
            }
        }

        return distance;
    }
}