我是Java的初学者。这是我创建KMeans算法的大学工作。一个人在SAMPLES中写入数字，在NUM_CLUSTERS中写入数量。算法给出了集群中心Points
我需要从这个方法返回一个数组，但我不能：
public static int[][] getCentroid_arr(int SAMPLES[],int NUM_CLUSTERS,int centroid_arr[][])。
究竟是什么阵列？ - 它显示在控制台中：
Points
1, 2, 7, 22, 44,
返回数组是必要的，因为我想用它来检查JUnit测试。
而不是return centroid_arr必须是这个排序的数组：
printArray(arr_sort_asc(sorted_distTocentroid_arr))
在这个问题上花了大约3天后，我仍然不知道如何解决问题。我只能打印出来。
我要感谢任何帮助。
package kmeans;
import java.util.*;
import java.util.Map.Entry;
public class KM_don_touch {
private static final int NUM_CLUSTERS = 5; // Total clusters.
private static final int SAMPLES[] = new int[]{1, 2, 3, 6, 7, 9, 11, 22, 44, 50};
public static void main(String[] args) {
System.out.println("data size is: " + SAMPLES.length);
System.out.println("number of clusters is: " + NUM_CLUSTERS);
System.out.print("dataset is: ");
printArray(SAMPLES);
System.out.println();
int maxDiffData_arr[]=new int[NUM_CLUSTERS];
Kmeans_test3 get_prepare_data = new Kmeans_test3();
maxDiffData_arr=get_prepare_data.prepare_data(SAMPLES);
int centroid_arr[][]=new int[2][NUM_CLUSTERS];
for (int i=0; i<NUM_CLUSTERS; i++) {
centroid_arr[0][i] = 0;
centroid_arr[1][i] = SAMPLES[maxDiffData_arr[i]];
}
System.out.println();
System.out.print("centroid_arr 0 is: ");
printArray(centroid_arr[0]);
System.out.println();
System.out.print("centroid_arr 1 is: ");
printArray(centroid_arr[1]);
System.out.println();
getCentroid_arr(SAMPLES, NUM_CLUSTERS, centroid_arr);
}
public static void firstDiffSort(int[] difference){
int tmp[]=new int[difference.length];
tmp = arr_sort_desc(difference);
for (int i=0; i<difference.length; i++){
}
}
public static int[] arr_sort_desc(int[] arr) {
int tmp;
for (int k = 0; k < arr.length; k++) {
for (int j = k + 1; j < arr.length; j++) {
if (arr[k] < arr[j]) {
tmp = arr[k];
arr[k] = arr[j];
arr[j] = tmp;
}
}
}
return arr;
}
public static int[] arr_sort_asc(int[] arr) {
int tmp;
for (int k = 0; k < arr.length; k++) {
for (int j = k + 1; j < arr.length; j++) {
if (arr[k] > arr[j]) {
tmp = arr[k];
arr[k] = arr[j];
arr[j] = tmp;
}
}
}
return arr;
}
public static int getMinValue(int[] numbers){
int minValue = numbers[0];
for(int i=1; i<numbers.length; i++){
if(numbers[i] < minValue){
minValue = numbers[i];
}
}
return minValue;
}
public static int FindSmallest (int [] arr1){
int index = 0;
int min = arr1[index];
for (int i=1; i<arr1.length; i++){
if (arr1[i] < min ){
min = arr1[i];
index = i;
}
}
return index;
}
public static int[][] getCentroid_arr(int SAMPLES[],int NUM_CLUSTERS,int centroid_arr[][]){
int distance[][]=new int[NUM_CLUSTERS][SAMPLES.length];
int cluster[]=new int[SAMPLES.length];
int clusternodecount[]=new int[NUM_CLUSTERS];
int storedData[][]=new int[1][NUM_CLUSTERS];
int clusterelements[][]=new int[NUM_CLUSTERS][SAMPLES.length];
centroid_arr[0] = centroid_arr[1];
centroid_arr[1]=new int[NUM_CLUSTERS];
for (int i=0; i<NUM_CLUSTERS; i++) {
centroid_arr[1][i] = 0;
}
System.out.println("========== Starting to get new centroid_arr =========");
//сложность алгоритма С2
for(int i=0;i<NUM_CLUSTERS;i++){
for(int j=0;j<SAMPLES.length;j++){
distance[i][j]=Math.abs(SAMPLES[j]-centroid_arr[0][i]);
System.out.print(distance[i][j]+" ,");
}
System.out.println();
}
System.out.println("========== Just ranspose =========");
int smallerDistance=0;
int[][] numbers = new int[SAMPLES.length][NUM_CLUSTERS];
for(int i=0;i<SAMPLES.length;i++){
for(int j=0; j<NUM_CLUSTERS; j++){
numbers[i][j]=distance[j][i];
System.out.print(numbers[i][j]+", ");
}
System.out.println();
smallerDistance = FindSmallest(numbers[i]);
centroid_arr[1][smallerDistance] = centroid_arr[1][smallerDistance]+SAMPLES[i];
clusternodecount[smallerDistance]=clusternodecount[smallerDistance]+1;
cluster[i]=smallerDistance;
}
System.out.println("New clusters are ");
for (int i = 0; i < NUM_CLUSTERS; i++) {
System.out.print("C" + (i + 1) + ": ");
for (int j = 0; j < SAMPLES.length; j++) {
if (cluster[j] == i)
System.out.print(SAMPLES[j] + ", ");
}
System.out.println();
}
System.out.println("======================================== ");
System.out.println("New centroid_arr is ");
for (int k = 0; k < NUM_CLUSTERS; k++) {
centroid_arr[1][k] = centroid_arr[1][k] / clusternodecount[k];
System.out.print(centroid_arr[1][k] + ", ");
}
System.out.println();
boolean isAchived = true;
for (int j = 0; j < NUM_CLUSTERS; j++) {
if (isAchived && centroid_arr[0][j] == centroid_arr[1][j]) {
isAchived = true;
continue;
}
isAchived = false;
}
if (!isAchived) {
getCentroid_arr(SAMPLES, NUM_CLUSTERS, centroid_arr);
}
if (isAchived) {
System.out.println("======================================== ");
System.out.println(" Final Cluster is ");
for (int i = 0; i < NUM_CLUSTERS; i++) {
System.out.print("C" + (i + 1) + ": ");
for (int j = 0; j < SAMPLES.length; j++) {
if (cluster[j] == i)
System.out.print(SAMPLES[j] + ", ");
}
System.out.println();
}
for (int i=0; i<SAMPLES.length; i++){
for (int j=0; j<NUM_CLUSTERS; j++){
if (j==cluster[i])
clusterelements[j][i]=SAMPLES[i];
}
}
int distTocentroid_arr[][]=new int[NUM_CLUSTERS][SAMPLES.length];
for (int i=0; i<NUM_CLUSTERS; i++){
for (int j=0; j<SAMPLES.length; j++){
distTocentroid_arr[i][j]=Math.abs(centroid_arr[1][i]-clusterelements[i][j]);
}
}
System.out.println("Points:");
int sorted_distTocentroid_arr[] = new int[NUM_CLUSTERS];
for (int i=0; i<NUM_CLUSTERS; i++){
smallerDistance = FindSmallest(distTocentroid_arr[i]);
sorted_distTocentroid_arr[i]=clusterelements[i][smallerDistance];
}
printArray(arr_sort_asc(sorted_distTocentroid_arr));
}
return centroid_arr;
}
public int[] prepare_data(int[] arr) {
int sorted_arr[] = arr_sort_desc(SAMPLES);
int maxDiff_arr[] = new int[SAMPLES.length];
int sorted_diff[] = new int[SAMPLES.length];
maxDiff_arr[0] = Math.abs(sorted_arr[0] - sorted_arr[1]);
for (int i = 1; i < SAMPLES.length; i++) {
maxDiff_arr[i] = Math.abs(sorted_arr[i] - sorted_arr[i - 1]);
}
Map<Integer, Integer> m1= new HashMap<Integer, Integer>();
int k=0;
for(int i:maxDiff_arr){
m1.put(k++, i);
}
m1= sortByValues(m1);
int i=0;
for (int key: m1.keySet()){
sorted_diff[i++]=key;
}
return sorted_diff;
}
public static <K extends Comparable,V extends Comparable> Map<K,V> sortByValues(Map<K,V> map){
List<Map.Entry<K,V>> entries = new LinkedList<Map.Entry<K,V>>(map.entrySet());
Collections.sort(entries, new Comparator<Map.Entry<K,V>>() {
@Override
public int compare(Entry<K, V> o1, Entry<K, V> o2) {
return o2.getValue().compareTo(o1.getValue());
}
});
Map<K,V> sortedMap = new LinkedHashMap<K,V>();
for(Map.Entry<K,V> entry: entries){
sortedMap.put(entry.getKey(), entry.getValue());
}
return sortedMap;
}
public static void printArray(int[] array) {
for (int i = 0; i < array.length; i++) {
System.out.print(array[i] + ", ");
}
}
public static void printArray2(int[][] array) {
for (int i = 0; i < array.length; i++) {
System.out.print(array[1][i] + ", ");
}
}
}
