isodata_iris.rar_cluster of iris da_isodata_isodata on the iris_

#include<stdio.h> #include<math.h> typedef struct sample { float x; float y; float z; float p; } SAMPLE; float distance(SAMPLE a,SAMPLE b) { float d; float a1=a.x, a2=a.y,a3=a.z,a4=a.p; float b1=b.x, b2=b.y,b3=a.z,b4=b.p; d=sqrt((a1-b1)*(a1-b1)+(a2-b2)*(a2-b2)+(a3-b3)*(a3-b3)+(a4-b4)*(a4-b4)); return d; } int MIN(float D[],int n) { int i; int p=0; float fmin=D[0]; for(i=1;i<n;i++) { if(D[i]<fmin) { fmin=D[i], p=i; } } return p; } int main() { SAMPLE point[150]={(5.1,3.5,1.4,0.2), (4.9,3.0,1.4,0.2), (4.7,3.2,1.3,0.2), (4.6,3.1,1.5,0.2), (5.0,3.6,1.4,0.2), (5.4,3.9,1.7,0.4), (4.6,3.4,1.4,0.3), (5.0,3.4,1.5,0.2), (4.4,2.9,1.4,0.2), (4.9,3.1,1.5,0.1), (5.4,3.7,1.5,0.2), (4.8,3.4,1.6,0.2), (4.8,3.0,1.4,0.1), (4.3,3.0,1.1,0.1), (5.8,4.0,1.2,0.2), (5.7,4.4,1.5,0.4), (5.4,3.9,1.3,0.4), (5.1,3.5,1.4,0.3), (5.7,3.8,1.7,0.3), (5.1,3.8,1.5,0.3), (5.4,3.4,1.7,0.2), (5.1,3.7,1.5,0.4), (4.6,3.6,1.0,0.2), (5.1,3.3,1.7,0.5), (4.8,3.4,1.9,0.2), (5.0,3.0,1.6,0.2), (5.0,3.4,1.6,0.4), (5.2,3.5,1.5,0.2), (5.2,3.4,1.4,0.2), (4.7,3.2,1.6,0.2), (4.8,3.1,1.6,0.2), (5.4,3.4,1.5,0.4), (5.2,4.1,1.5,0.1), (5.5,4.2,1.4,0.2), (4.9,3.1,1.5,0.1), (5.0,3.2,1.2,0.2), (5.5,3.5,1.3,0.2), (4.9,3.1,1.5,0.1), (4.4,3.0,1.3,0.2), (5.1,3.4,1.5,0.2), (5.0,3.5,1.3,0.3), (4.5,2.3,1.3,0.3), (4.4,3.2,1.3,0.2), (5.0,3.5,1.6,0.6), (5.1,3.8,1.9,0.4), (4.8,3.0,1.4,0.3), (5.1,3.8,1.6,0.2), (4.6,3.2,1.4,0.2), (5.3,3.7,1.5,0.2), (5.0,3.3,1.4,0.2), (7.0,3.2,4.7,1.4), (6.4,3.2,4.5,1.5), (6.9,3.1,4.9,1.5), (5.5,2.3,4.0,1.3), (6.5,2.8,4.6,1.5), (5.7,2.8,4.5,1.3), (6.3,3.3,4.7,1.6), (4.9,2.4,3.3,1.0), (6.6,2.9,4.6,1.3), (5.2,2.7,3.9,1.4), (5.0,2.0,3.5,1.0), (5.9,3.0,4.2,1.5), (6.0,2.2,4.0,1.0), (6.1,2.9,4.7,1.4), (5.6,2.9,3.6,1.3), (6.7,3.1,4.4,1.4), (5.6,3.0,4.5,1.5), (5.8,2.7,4.1,1.0), (6.2,2.2,4.5,1.5), (5.6,2.5,3.9,1.1), (5.9,3.2,4.8,1.8), (6.1,2.8,4.0,1.3), (6.3,2.5,4.9,1.5), (6.1,2.8,4.7,1.2), (6.4,2.9,4.3,1.3), (6.6,3.0,4.4,1.4), (6.8,2.8,4.8,1.4), (6.7,3.0,5.0,1.7), (6.0,2.9,4.5,1.5), (5.7,2.6,3.5,1.0), (5.5,2.4,3.8,1.1), (5.5,2.4,3.7,1.0), (5.8,2.7,3.9,1.2), (6.0,2.7,5.1,1.6), (5.4,3.0,4.5,1.5), (6.0,3.4,4.5,1.6), (6.7,3.1,4.7,1.5), (6.3,2.3,4.4,1.3), (5.6,3.0,4.1,1.3), (5.5,2.5,4.0,1.3), (5.5,2.6,4.4,1.2), (6.1,3.0,4.6,1.4), (5.8,2.6,4.0,1.2), (5.0,2.3,3.3,1.0), (5.6,2.7,4.2,1.3), (5.7,3.0,4.2,1.2), (5.7,2.9,4.2,1.3), (6.2,2.9,4.3,1.3), (5.1,2.5,3.0,1.1), (5.7,2.8,4.1,1.3), (6.3,3.3,6.0,2.5), (5.8,2.7,5.1,1.9), (7.1,3.0,5.9,2.1), (6.3,2.9,5.6,1.8), (6.5,3.0,5.8,2.2), (7.6,3.0,6.6,2.1), (4.9,2.5,4.5,1.7), (7.3,2.9,6.3,1.8), (6.7,2.5,5.8,1.8), (7.2,3.6,6.1,2.5), (6.5,3.2,5.1,2.0), (6.4,2.7,5.3,1.9), (6.8,3.0,5.5,2.1), (5.7,2.5,5.0,2.0), (5.8,2.8,5.1,2.4), (6.4,3.2,5.3,2.3), (6.5,3.0,5.5,1.8), (7.7,3.8,6.7,2.2), (7.7,2.6,6.9,2.3), (6.0,2.2,5.0,1.5), (6.9,3.2,5.7,2.3), (5.6,2.8,4.9,2.0), (7.7,2.8,6.7,2.0), (6.3,2.7,4.9,1.8), (6.7,3.3,5.7,2.1), (7.2,3.2,6.0,1.8), (6.2,2.8,4.8,1.8), (6.1,3.0,4.9,1.8), (6.4,2.8,5.6,2.1), (7.2,3.0,5.8,1.6), (7.4,2.8,6.1,1.9), (7.9,3.8,6.4,2.0), (6.4,2.8,5.6,2.2), (6.3,2.8,5.1,1.5), (6.1,2.6,5.6,1.4), (7.7,3.0,6.1,2.3), (6.3,3.4,5.6,2.4), (6.4,3.1,5.5,1.8), (6.0,3.0,4.8,1.8), (6.9,3.1,5.4,2.1), (6.7,3.1,5.6,2.4), (6.9,3.1,5.1,2.3), (5.8,2.7,5.1,1.9), (6.8,3.2,5.9,2.3), (6.7,3.3,5.7,2.5), (6.7,3.0,5.2,2.3), (6.3,2.5,5.0,1.9), (6.5,3.0,5.2,2.0), (6.2,3.4,5.4,2.3), (5.9,3.0,5.1,1.8)}; //模式样本 SAMPLE center[3]; //聚类中心 SAMPLE sd[3];//standard deviation标准差向量 float sdm[3];//标准差向量中的最大分量（特征） int flag[150];//每个样本所属类别 int count[3];//各类别样本数 float D[3]; //样本到每个聚类中心的距离 float Da[3]; //各聚类域中诸样本与聚类中心间的平均距离 float Dl; //全部模式样本对其相应聚类中心的总平均距离 float Dc[3]; //全部聚类中心的距离 float a=0.5;//系数 int t,t1,t2; int i,j; int n=0; //迭代次数 int K=3,Nl=1,S=1,C=4,L=1,I=6; //预设参数 int N=150; int Nc=1;//分类次数 center[0]=point[0]; for(i=1;i<3;i++) { center[i].x=-1; center[i].y=-1; center[i].z=-1; center[i].p=-1; } while(1) { n++; //按近邻原则聚类 for(i=0;i<N;i++) { for(j=0;j<Nc;j++) D[j]=distance(point[i],center[j]); flag[i]=MIN(D,Nc);//注意 } //赋初值 for(j=0;j<Nc;j++) { count[j]=0; center[j].x=0; center[j].y=0; center[j].z=0; center[j].p=0; Da[j]=0; Dl=0; } //修正聚类中心 for(i=0;i<N;i++) { j=flag[i]; center[j].x=center[j].x+point[i].x; center[j].y=center[j].y+point[i].y; center[j].z=center[j].z+point[i].z; center[j].p=center[j].p+point[i].p; count[j]++; } for(j=0;j<Nc;j++) { center[j].x=center[j].x/count[j]; center[j].y=center[j].y/count[j]; center[j].z=center[j].z/count[j]; center[j].p=center[j].p/count[j]; } //计算各聚类域中诸样本与聚类中心间的平均距离 //和全部模式样本对其相应聚类中心的总平均距离 for(i=0;i<N;i++) { j=flag[i]; Da[j]+=distance(point[i],center[j]); } for(j=0;j<Nc;j++) { Dl+=Da[j]; Da[j]=Da[j]/count[j]; } Dl=Dl/N; //进行分裂处理 if(n!=I&&(Nc<=K/2||(Nc>K/2&&((fmod(n,2)!=0)&&Nc<2*K)))) { for(j=0;j<Nc;j++) { sd[j].x=0; sd[j].y=0; sd[j].z=0; sd[j].p=0; } for(i=0;i<N;i++) { j=flag[i]), sd[j].x+=(point[i].x-center[j].x)*(point[i].x-center[j].x); sd[j].y+=(point[i].y-center[j].y)*(point[i].y-center[j].y); sd[j].z+=(point[i].z-center[j].z)*(point[i].z-center[j].z); sd[j].p+=(point[i].p-center[j].p)*(point[i].p-center[j].p); } t=Nc; for(j=0;j<t;j++) { float u; sd[j].x=sqrt(sd[j].x/count[j]); sd[j].y=sqrt(sd[j].y/count[j]); sd[j].z=sqrt(sd[j].z/count[j]); sd[j].p=sqrt(sd[j].p/count[j]); if(sd[j].x>=sd[j].y) u=sa[j].x; else u=sa[j].y; if(u<sd[j].z) u=sa[j].z; if(u<sd[j].p) u=sd[j].p; if(u==sd[j].x) { sdm[j]=sd[j].x; if(sdm[j]>S&&((Da[j]>Dl&&count[j]>2*(Nl+1))||Nc<=K/2)) { center[Nc].x=center[j].x-a*sdm[j]; center[Nc].y=center[j].y; center[Nc].z=center[j].z; center[Nc].p=center[j].p; center[j].x=center[j].x+a*sdm[j]; Nc++; } } if(u==sd[j].y) { sdm[j]=sd[j].y; if(sdm[j]>S&&((Da[j]>Dl&&count[j]>2*(Nl+1))||Nc<=K/2)) { center[Nc].x=center[j].x; center[Nc].z=center[j].z; center[Nc].p=center[j].p; center[Nc].y=center[j].y-a*sdm[j]; center[j].y=center[j].y+a*sdm[j]; Nc++; } } if(u==sd[j].z) { sdm[j]=sd[j].z; if(sdm[j]>S&&((Da[j]>Dl&&count[j]>2*(Nl+1))||Nc<=K/2)) { center[Nc].x=center[j].x; center[Nc].y=center[j].y; center[Nc].p=center[j].p; center[Nc].z=center[j].z-a*sdm[j]; center[j].z=center[j].z+a*sdm[j]; Nc++; } } if(u==sd[j].p) { sdm[j]=sd[j].p; if(sdm[j]>S&&((Da[j]>Dl&&count[j]>2*(Nl+1))||Nc<=K/2)) { center[Nc].x=center[j].x; center[Nc].z=center[j].z; center[Nc].y=center[j].y; center[Nc].p=center[j].p-a*sdm[j]; center[j].p=center[j].p+a*sdm[j]; Nc++; } } } if(Nc!=t) //进行了分裂处理 continue; } if(n==I) C=0; //最后一次迭代 //进行合并处理 t=0; t1=-1; t2=-1; for(i=1;i<Nc;i++) { for(j=0;j<i;j++) { Dc[t]=distance(center[i],center[j]); if(t>0&&Dc[t]<C&&Dc[t-1]>Dc[t]) Dc[t]=Dc[t-1]; else if(Dc[t]<C) { t1=j; t2=i; }//标记需要合并的 t++; } } if(t1!=-1&&t2!=-1) { center[t1].x=(count[t1]*center[t1].x+count[t2]*center[t2].x)/(count[t1]+count[t2]); center[t1].y=(count[t1]*center[t1].y+count[t2]*center[t2].y)/(count[t1]+count[t2]); center[t1].z=(count[t1]*center[t1].z+count[t2]*center[t2].z)/(