using?System;
using?System.Collections;
using?System.Collections.Generic;
/**
*?
*?@author?aturbo
*?1、隨機選擇k個點作為中心點（centroid）
*?2、計算點到各個類中心的距離；
*?3、將點放入最近的中心點所在的類
*?4、重新計算中心點
*?5、判斷目標函數是否收斂，收斂停止，否則循環2-4步
*?
*/
public?class?MyKmeans
{

????public?static??double[][]?points?=?{?new?double[]{?1?1?}new?double[]?{?2?1?}new?double[]?{?1?2?}?new?double[]{?2?2?}?new?double[]{?3?3?}?new?double[]{?8?8?}?new?double[]{?9?8?}
????????????new?double[]{?8?9?}?new?double[]{?9?9?}?};

????/**
	?*?隨機選擇k個點作為中心點
	?*?@param?k?
	?*?@return?k個中心點
	?*/
????private?static?double[][]?chooseinitK（int?k）
????{
????????double[][]?cluster?=?new?double[k][];
????????List?set?=?new?List（）;
????????for?（int?i?=?0;?i?????????{
????????????set.Add（i）;
????????}
????????//在set中剩下的序列點就為隨機選擇的
????????for?（int?i?=?0;?i?????????{
????????????Random?random?=?new?Random（）;
????????????int?a?=?random.Next（points.Length）;
????????????if?（!set.Contains（a））
????????????????continue;
????????????set.Remove（a）;
????????????//System.out.println（“a“?+?a）;
????????????i++;
????????}
????????//Iterator?iterator?=?set.iterator（）;
????????int?j?=?0;
????????foreach?（int?iterator??in?set）
????????{
????????????cluster[j]?=?points[iterator];
????????????j++;
????????}
????????for?（int?i?=?0;?i?????????{
????????????//System.out.println（“隨機選擇的k個節點：“?+?cluster[i][0]?+?“\t“?+?cluster[i][1]）;
????????}
????????return?cluster;
????}
????/**
	?*?歐式距離計算公式
	?*?@param?center?（中心）點
	?*?@param?otherpoint?
	?*?@return?歐式距離
	?*/
????private?static?double?eurDistance（double[]?center?double[]?otherpoint）
????{
????????double?distance?=?0.0;
????????for?（int?i?=?0;?i?????????{
????????????distance?+=?（（center[i]?-?otherpoint[i]）?*?（center[i]?-?otherpoint[i]））;
????????}
????????distance?=?Math.Sqrt（distance）;
????????return?distance;
????}
????/**
	?*?目標函數——也就是每個聚類中的點到它中心點的距離和
	?*?@param?center?中心點
	?*?@param?cluster?劃分的（中間）聚類
	?*?@return?cost
	?*/
????private?static?double?cost（double[][]?center?List[]?cluster）
????{
????????double?cost?=?0.0;
????????for?（int?i?=?0;?i?????????{
????????????for?（int?j?=?0;?j?????????????{
????????????????double?tempCost?=?0.0;
????????????????for?（int?k?=?0;?k?????????????????{
????????????????????//System.out.println（cluster[i].get（j）[k]）;
????????????????????tempCost?+=?（cluster[i][j][k]?-?center[i][k]）?*?（cluster[i][j][k]?-?center[i][k]）;
????????????????}

????????????????cost?+=?Math.Sqrt（tempCost）;
????????????}

????????}
????????return?cost;
????}
????/**
	?*?聚類算法——將所有點和各中心點計算距離，將點放入最近距離點的類中
	?*?@param?points?所有點
	?*?@param?centers?中心點
	?*?@param?k
	?*?@return?聚類
	?*/
????private?static?List[]?returnCluster（double[][]?points?double[][]?centers?int?k）
???

?屬性????????????大小?????日期????時間???名稱
-----------?---------??----------?-----??----

?????文件???????5647??2019-04-25?15:50??kmeans\MyKmeans.cs

?????目錄??????????0??2019-04-25?16:07??kmeans

-----------?---------??----------?-----??----

?????????????????5647????????????????????2