文章目录

• day56-57kMeans 聚类
• 代码
• 运行截图

day56-57kMeans 聚类

1.kMeans 聚类需要中心点收敛时结束.
2.数据集为 iris, 所以最后一个属性没使用. 如果对于没有决策属性的数据集, 需要进行相应修改.
3.数据没有归一化.
4.getRandomIndices() 和 knn 的完全相同, 拷贝过来. 本来应该写在 SimpleTools.java 里面的, 代码不多, 为保证独立性就放这里了.
5.distance() 和 knn 的相似, 注意不要用决策属性, 而且参数不同. 第 2 个参数为实数向量, 这是类为中心可能为虚拟的, 而中心点那里并没有对象.
6.获得虚拟中心后, 换成与其最近的点作为实际中心, 再聚类.(写了两个clustering方法，注释掉一个，第二个实现此功能)

代码

package machineLearning.kmeans;import weka.core.Instances;import java.io.FileReader;
import java.util.Arrays;
import java.util.Random;/*** ClassName: KMeans* Package: machineLearning.kmeans* Description: kMeans clustering.** @Author: luv_x_c* @Create: 2023/7/31 12:29*/
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 pratLength The length of the sequence.* @return AN array of indices, e.g.,{4,3,1,5,0,2} with length of 6.*/public static int[] getRandomIndices(int pratLength) {int[] resultIndices = new int[pratLength];// Step1. Initialize.for (int i = 0; i < pratLength; i++) {resultIndices[i] = i;}// Of for i// Step2. Randomly swap.for (int i = 0; i < pratLength; i++) {// Generate two random indices.int tempFirst = RANDOM.nextInt(pratLength);int tempSecond = RANDOM.nextInt(pratLength);//Swap.int tempValue = resultIndices[tempFirst];resultIndices[tempFirst] = resultIndices[tempSecond];resultIndices[tempSecond] = tempValue;}// OF for ireturn resultIndices;}// OF getRandomIndices/*** The distance between two instances.** @param paraI     The index of first instance.* @param paraArray The array representing a point in the space .* @return The distance.*/public double distance(int paraI, double[] paraArray) {double 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 ibreak;case EUCLIDEAN:for (int i = 0; i < dataset.numAttributes() - 1; i++) {tempDifference = dataset.instance(paraI).value(i) - paraArray[i];resultDistance += tempDifference * tempDifference;}// OF for ibreak;default:System.out.println("Unsupported distance measure :" + distanceMeasure);}// Of switchreturn 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];
//
//        // Step1. 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()];
//
//            // Step2.1 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
//
//            // Step2.2. Mean. Find new centers.
//            tempClusterLengths = new int[numClusters];
//            Arrays.fill(tempClusterLengths, 0);
//            double[][] tempNewCenters = new double[numClusters][dataset.numAttributes() - 1];
//            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
//
//            // Step2.3. 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 centers are: " + Arrays.deepToString(tempNewCenters));
//            tempCenters = tempNewCenters;
//        }// Of while
//
//        //Step3. 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 fot i
//
//        System.out.println("The clusters are:   " + Arrays.deepToString(clusters));
//    }// Of clusteringpublic 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];// Step1. 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 iint[] tempClusterLengths = null;while (!Arrays.equals(tempOldClusterArray, tempClusterArray)) {System.out.println("New loop ...");tempOldClusterArray = tempClusterArray;tempClusterArray = new int[dataset.numInstances()];// Step2.1 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 jtempClusterArray[i] = tempNearestCenter;}// of for i// Step2.2. Mean. Find new centers.tempClusterLengths = new int[numClusters];Arrays.fill(tempClusterLengths, 0);double[][] tempNewCenters = new double[numClusters][dataset.numAttributes() - 1];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 jtempClusterLengths[tempClusterArray[i]]++;}// Of for i// Step2.3. 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 centers are: " + Arrays.deepToString(tempNewCenters));tempCenters = tempNewCenters;// 新增部分：虚拟中心替换为实际中心int[] nearestCenters = new int[numClusters]; // 用于存储每个虚拟中心最近的实际中心的索引for (int i = 0; i < numClusters; i++) {double minDistance = Double.MAX_VALUE;int nearestCenterIndex = -1;for (int j = 0; j < dataset.numInstances(); j++) {double distanceToCenter = distance(j, tempCenters[i]);if (distanceToCenter < minDistance) {minDistance = distanceToCenter;nearestCenterIndex = j;}}nearestCenters[i] = nearestCenterIndex;}// 替换虚拟中心为实际中心for (int i = 0; i < numClusters; i++) {for (int j = 0; j < tempCenters[0].length; j++) {tempCenters[i][j] = dataset.instance(nearestCenters[i]).value(j);}}System.out.println("Now the centers are: " + Arrays.deepToString(tempCenters));}// Of while//Step3. 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 ifor (int i = 0; i < tempClusterArray.length; i++) {clusters[tempClusterArray[i]][tempCounters[tempClusterArray[i]]] = i;tempCounters[tempClusterArray[i]]++;}//  of fot iSystem.out.println("The clusters are:   " + Arrays.deepToString(clusters));}/*** A test unit.*/public static void testClustering() {KMeans tempKmeans = new KMeans("E:\\java_code\\data\\sampledata\\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

运行截图

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