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KmeansCluster.cs
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using System;
using System.Collections.Generic;
using System.IO;
using System.Linq;
using System.Text;
namespace WinFormWithEcharts {
class KmeansCluster {
int _N; //
int _K; //
private float[][] _centroids;
private float[][] _ctsCopy;
private bool cv = false;
public KmeansCluster () {
_N = 2;
_K = 3;
}
public KmeansCluster (int n, int k) {
_N = n;
_K = k;
}
private List<float[]> allDlst = new List<float[]>();//为省内存不用double
public void loadData (string filenm) {
//目前只针对二维的,想改进,之后再说吧
StreamReader csvRder = new StreamReader(filenm);
string csvall = csvRder.ReadToEnd();
string[] csvlst = csvall.Split('\n');
foreach (string c in csvlst) {
string[] curLine = c.Split(',');
if (curLine.Length == 2) {
float[] cline = new float[] {0f,0f,0f };
cline[0] =Convert.ToSingle( curLine[0]);
cline[1] = Convert.ToSingle(curLine[1]);
allDlst.Add(cline);
}
else {
Console.WriteLine(curLine.Length);
}
}
}
public float distEclud (float[] va, float[] vb) {
//要求va vb里面元素个数相等
if (va.Length != vb.Length) {
throw new FormatException("va.Length 需要等于 vb.Length");
}
double sc = 0;
for(int i = 0; i < va.Length; i++) {
sc = sc + Math.Pow((va[i] - vb[i]), 2);
}
return (float)Math.Sqrt(sc);
}
public float[][] rand_center (List<float[]> data, int k) {
//输出为k*n的矩阵;也可以用List<float[]>装,n指原先维数,默认为2
int n = data[0].Length;
float[][] centroids = new float[k][]; //这里填k而不是填n没问题
for (int q = 0; q < k; q++) {
centroids[q] = new float[] { 0, 0, 0 };
}
for (int i = 0; i < n; i++) {
float _max = data[0][i];//or float.MinValue;//
float _min= data[0][i];//循环一次可以获得最大最小值
for(int j = 1; j < data.Count; j++) {
if (data[j][i] > _max) {
_max = data[j][i];
}
if (data[j][i] < _min) {
_min = data[j][i];
}
}
for (int q = 0; q < k; q++) {
var seed = Guid.NewGuid().GetHashCode();
Random ran = new Random(seed);//必须设置随机数种子,否则centroids相同
float rfloat = (float)ran.NextDouble();
centroids[q][i] =_min+(_max - _min)*rfloat;
}
}
return centroids;
}
public bool converged (float[][] c1, float[][] c2) {
//centroids not changed -> true
for (int j = 0; j < c1.Length; j++) { //k
for (int i = 0; i < c1[0].Length; i++) {// n
double a2 = Math.Abs(c1[j][i] - c2[j][i]);//这一句之后去优化
if (a2>0.0001) {
return false;
}
}
}
return true;
}
/// <summary>
/// 更新质心
/// </summary>
/// <returns></returns>
private void updateCentroids () {
for (int i = 0; i < _K; i++) {
}
}
private float meanByAxis2 (int axis,float typec) {
double msun = 0;
int mc = 0;
foreach(float[] a in allDlst) {
if (a[_N - 1] == typec) {
msun = msun + a[axis];//这里特别怕溢出
mc += 1;
}
}
return (float) msun /mc;
}
private float meanByAxis (int axis, float typec) {
double mavg = 0;
int mc = 0;
foreach (float[] a in allDlst) {
if (a[_N - 1] == typec) {
mc += 1;
mavg = mavg + (a[axis]-mavg)/mc;//
}
}
return (float) mavg;
}
private float[] meanAll (float typec) {
float[] newCts = new float[_N];
for (int j = 0; j < _N; j++) {
newCts[j] = meanByAxis(j, typec);
}
return newCts;
}
public List<float[]> cluster () {
string fn = "D:/python_works/testSet3.csv";
loadData(fn);
_centroids = rand_center(allDlst, _K);
int ncount = allDlst.Count;//注意和维度 _N 区分
//目前用不到 assement
while (!cv) {
_ctsCopy = _centroids;
for (int i = 0; i < ncount; i++) {
float minDist = float.MaxValue;
int minIndex = -1;
for (int j = 0; j < _K; j++) {
float dist = distEclud(allDlst[i], _centroids[j]);//注意j索引是否对
if (dist < minDist) {
minDist = dist;
minIndex = j;
allDlst[i][_N] = j;
}
}
}
for (int q = 0; q < _K; q++) { // update centroid
_centroids[q] = meanAll(q);
}
cv = converged(_centroids, _ctsCopy);
}
return allDlst;
}
private string FloatLstToStr (float[] fl) {
string otxt = "";
foreach (float f in fl) {
otxt = otxt + Convert.ToString(f) + ",";
}
return otxt;
}
public void toCluster () {
List<float[]> allTwo = cluster();
listFloatToCsv(allTwo);
}
//[lng,lat,flag] 写入csv
public void listFloatToCsv (List<float[]> allT) {
string spath = @"D:/python_works/testSet4_out.csv";
StreamWriter swt = new StreamWriter(spath);
foreach (float[] a in allT) {
string outxt = FloatLstToStr(a);
swt.WriteLine(outxt);
}
swt.Close();
}
}
}