file1.cpp

此程序采用K均值的方法对样本数据进行模式分类

//------------------------------------------------------------------------------
#include "unit1.h"
#include <math.h>

 //聚类初始化，指定前三个样本分别为三类中心
void TForm1::InitClusters()
{
  /*ClusterMember[0][0]=0;
    ClusterMember[0][0]=50;
    ClusterMember[0][0]=100;
    for(int j=0;j<4;j++)
    {
      ClusterCenter[0][j]=Pattern[0][j];
      ClusterCenter[1][j]=Pattern[50][j];
      ClusterCenter[2][j]=Pattern[100][j];
     }  */

  int i,j;
  for(i=0;i<3;i++)
  {
    ClusterMember[i][0]=i;
    for(j=0;j<4;j++)
      ClusterCenter[i][j]=Pattern[i][j];
  }
}

  //欧式范数计算
double TForm1::EucNorm(int p,int c)
{
   double dist;
   int i;
   dist=0;
   for(i=0;i<4;i++)
       dist+=(Pattern[p][i]-ClusterCenter[c][i])*(Pattern[p][i]-ClusterCenter[c][i]);
   return dist;
}

  //求离样本最近的聚类中心
 int TForm1::FindClosestCluster(int pat)
{
   int i,ClustID;
   double MinDist,d;
   MinDist=9.9e+99;
   ClustID=-1;
   for(i=0;i<3;i++)
   {
     d=EucNorm(pat,i);
     if(d<MinDist)
     {
       MinDist=d;
       ClustID=i;
     }
   }
   return ClustID;
}

 //样本分类
 void TForm1::DistributeSamples()
 {
    int i,pat,Clustid,MemberIndex;
    for(i=0;i<3;i++)
    ClusterNumMembers[i]=0;
    for(pat=0;pat<150;pat++)
    {
      Clustid=FindClosestCluster(pat);
      MemberIndex=ClusterNumMembers[Clustid];
      ClusterMember[Clustid][MemberIndex]=pat;
      ClusterNumMembers[Clustid]++;
    }
  }

  //重新计算各分类中心
  int TForm1::CalcNewClustCenters()
  {
     int ConvFlag,PatternID,i,j,k;
     double Tmp[4];
     ConvFlag=1;
     for(i=0;i<3;i++)
     {
       for(j=0;j<4;j++)
         Tmp[j]=0.0;

       for(j=0;j<ClusterNumMembers[i];j++)
       {
         PatternID=ClusterMember[i][j];
         for(k=0;k<4;k++)
           Tmp[k]+=Pattern[PatternID][k];
        }

        for(j=0;j<4;j++)
        {
          Tmp[j]=Tmp[j]/ClusterNumMembers[i];
          if(Tmp[j]!=ClusterCenter[i][j])
          {
            ConvFlag=0;
            ClusterCenter[i][j]=Tmp[j];
          }
        }
     }
      return ConvFlag;
  }