Day 56 kMeans 聚类

代码：

package dl;

import java.io.FileReader;
import java.util.Arrays;
import java.util.Random;
import weka.core.Instances;

/**
 * kMeans clustering.
 */
public class kMeans {

    /**
     * Manhattan distance.
     */
    public static final int MANHATTAN = 0;

    /**
     * Euclidean distance.
     */
    public static final int EUCLIDEAN = 1;

    /**
     * The distance measure.
     */
    public int distanceMeasure = EUCLIDEAN;

    /**
     * A random instance;
     */
    public static final Random random = new Random();

    /**
     * The data.
     */
    Instances dataset;

    /**
     * The number of clusters.
     */
    int numClusters = 2;

    /**
     * The clusters.
     */
    int[][] clusters;


    /**
     *******************************
     * The first constructor.
     *
     * @param paraFilename
     *            The data filename.
     *******************************
     */
    public kMeans(String paraFilename) {
        dataset = null;
        try {
            FileReader fileReader = new FileReader(paraFilename);
            dataset = new Instances(fileReader);
            fileReader.close();
        } catch (Exception ee) {
            System.out.println("Cannot read the file: " + paraFilename + "\r\n" + ee);
            System.exit(0);
        } // Of try
    }// Of the first constructor

    /**
     *******************************
     * A setter.
     *******************************
     */
    public void setNumClusters(int paraNumClusters) {
        numClusters = paraNumClusters;
    }// Of the setter

    /**
     *********************
     * Get a random indices for data randomization.
     *
     * @param paraLength
     *            The length of the sequence.
     * @return An array of indices, e.g., {4, 3, 1, 5, 0, 2} with length 6.
     *********************
     */
    public static int[] getRandomIndices(int paraLength) {
        int[] resultIndices = new int[paraLength];

        // Step 1. Initialize.
        for (int i = 0; i < paraLength; i++) {
            resultIndices[i] = i;
        } // Of for i

        // Step 2. Randomly swap.
        int tempFirst, tempSecond, tempValue;
        for (int i = 0; i < paraLength; i++) {
            // Generate two random indices.
            tempFirst = random.nextInt(paraLength);
            tempSecond = random.nextInt(paraLength);

            // Swap.
            tempValue = resultIndices[tempFirst];
            resultIndices[tempFirst] = resultIndices[tempSecond];
            resultIndices[tempSecond] = tempValue;
        } // Of for i

        return resultIndices;
    }// Of getRandomIndices

    /**
     *********************
     * The distance between two instances.
     *
     * @param paraI
     *            The index of the first instance.
     * @param paraArray
     *            The array representing a point in the space.
     * @return The distance.
     *********************
     */
    public double distance(int paraI, double[] paraArray) {
        int resultDistance = 0;
        double tempDifference;
        switch (distanceMeasure) {
            case MANHATTAN:
                for (int i = 0; i < dataset.numAttributes() - 1; i++) {
                    tempDifference = dataset.instance(paraI).value(i) - paraArray[i];
                    if (tempDifference < 0) {
                        resultDistance -= tempDifference;
                    } else {
                        resultDistance += tempDifference;
                    } // Of if
                } // Of for i
                break;

            case EUCLIDEAN:
                for (int i = 0; i < dataset.numAttributes() - 1; i++) {
                    tempDifference = dataset.instance(paraI).value(i) - paraArray[i];
                    resultDistance += tempDifference * tempDifference;
                } // Of for i
                break;
            default:
                System.out.println("Unsupported distance measure: " + distanceMeasure);
        }// Of switch

        return resultDistance;
    }// Of distance

    /**
     *******************************
     * Clustering.
     *******************************
     */
    public void clustering() {
        int[] tempOldClusterArray = new int[dataset.numInstances()];
        tempOldClusterArray[0] = -1;
        int[] tempClusterArray = new int[dataset.numInstances()];
        Arrays.fill(tempClusterArray, 0);
        double[][] tempCenters = new double[numClusters][dataset.numAttributes() - 1];

        // Step 1. Initialize centers.
        int[] tempRandomOrders = getRandomIndices(dataset.numInstances());
        for (int i = 0; i < numClusters; i++) {
            for (int j = 0; j < tempCenters[0].length; j++) {
                tempCenters[i][j] = dataset.instance(tempRandomOrders[i]).value(j);
            } // Of for j
        } // Of for i

        int[] tempClusterLengths = null;
        while (!Arrays.equals(tempOldClusterArray, tempClusterArray)) {
            System.out.println("New loop ...");
            tempOldClusterArray = tempClusterArray;
            tempClusterArray = new int[dataset.numInstances()];

            // Step 2.1 Minimization. Assign cluster to each instance.
            int tempNearestCenter;
            double tempNearestDistance;
            double tempDistance;

            for (int i = 0; i < dataset.numInstances(); i++) {
                tempNearestCenter = -1;
                tempNearestDistance = Double.MAX_VALUE;

                for (int j = 0; j < numClusters; j++) {
                    tempDistance = distance(i, tempCenters[j]);
                    if (tempNearestDistance > tempDistance) {
                        tempNearestDistance = tempDistance;
                        tempNearestCenter = j;
                    } // Of if
                } // Of for j
                tempClusterArray[i] = tempNearestCenter;
            } // Of for i

            // Step 2.2 Mean. Find new centers.
            tempClusterLengths = new int[numClusters];
            Arrays.fill(tempClusterLengths, 0);
            double[][] tempNewCenters = new double[numClusters][dataset.numAttributes() - 1];
            // Arrays.fill(tempNewCenters, 0);
            for (int i = 0; i < dataset.numInstances(); i++) {
                for (int j = 0; j < tempNewCenters[0].length; j++) {
                    tempNewCenters[tempClusterArray[i]][j] += dataset.instance(i).value(j);
                } // Of for j
                tempClusterLengths[tempClusterArray[i]]++;
            } // Of for i

            // Step 2.3 Now average
            for (int i = 0; i < tempNewCenters.length; i++) {
                for (int j = 0; j < tempNewCenters[0].length; j++) {
                    tempNewCenters[i][j] /= tempClusterLengths[i];
                } // Of for j
            } // Of for i

            System.out.println("Now the new centers are: " + Arrays.deepToString(tempNewCenters));
            tempCenters = tempNewCenters;
        } // Of while

        // Step 3. Form clusters.
        clusters = new int[numClusters][];
        int[] tempCounters = new int[numClusters];
        for (int i = 0; i < numClusters; i++) {
            clusters[i] = new int[tempClusterLengths[i]];
        } // Of for i

        for (int i = 0; i < tempClusterArray.length; i++) {
            clusters[tempClusterArray[i]][tempCounters[tempClusterArray[i]]] = i;
            tempCounters[tempClusterArray[i]]++;
        } // Of for i

        System.out.println("The clusters are: " + Arrays.deepToString(clusters));
    }// Of clustering

    /**
     ********************
     * A testing method.
     ********************
     */
    public static void testClustering() {
        kMeans tempKMeans = new kMeans("C:\\Users\\86183\\IdeaProjects\\deepLearning\\src\\main\\java\\resources\\iris.arff");
        tempKMeans.setNumClusters(3);
        tempKMeans.clustering();
    }// Of testClustering

    /**
     ****************
     * The entrance of the program.
     *
     * @param args
     *            Not used now.
     ****************
     */
    public static void main(String args[]) {
        testClustering();
    }// Of main

}// Of class kMeans

结果：