用matlab语言实现K-MEANS聚类算法

/**************************************************************************** * KMEANS *****************************************************************************/ #include <stdio.h> #include <stdlib.h> #include <string.h> #include <conio.h> #include <math.h> // FUNCTION PROTOTYPES // DEFINES #define SUCCESS 1 #define FAILURE 0 #define TRUE 1 #define FALSE 0 #define MAXVECTDIM 20 #define MAXPATTERN 20 #define MAXCLUSTER 10 char *f2a(double x, int width){ char cbuf[255]; char *cp; int i,k; int d,s; cp=fcvt(x,width,&d,&s); if (s) { strcpy(cbuf,"-"); } else { strcpy(cbuf," "); } /* endif */ if (d>0) { for (i=0; i<d; i++) { cbuf[i+1]=cp[i]; } /* endfor */ cbuf[d+1]=0; cp+=d; strcat(cbuf,"."); strcat(cbuf,cp); } else { if (d==0) { strcat(cbuf,"."); strcat(cbuf,cp); } else { k=-d; strcat(cbuf,"."); for (i=0; i<k; i++) { strcat(cbuf,"0"); } /* endfor */ strcat(cbuf,cp); } /* endif */ } /* endif */ cp=&cbuf[0]; return cp; } // ***** Defined structures & classes ***** struct aCluster { double Center[MAXVECTDIM]; int Member[MAXPATTERN]; //Index of Vectors belonging to this cluster int NumMembers; }; struct aVector { double Center[MAXVECTDIM]; int Size; }; class System { private: double Pattern[MAXPATTERN][MAXVECTDIM+1]; aCluster Cluster[MAXCLUSTER]; int NumPatterns; // Number of patterns int SizeVector; // Number of dimensions in vector int NumClusters; // Number of clusters void DistributeSamples(); // Step 2 of K-means algorithm int CalcNewClustCenters();// Step 3 of K-means algorithm double EucNorm(int, int); // Calc Euclidean norm vector int FindClosestCluster(int); //ret indx of clust closest to pattern //whose index is arg public: system(); int LoadPatterns(char *fname); // Get pattern data to be clustered void InitClusters(); // Step 1 of K-means algorithm void RunKMeans(); // Overall control K-means process void ShowClusters(); // Show results on screen void SaveClusters(char *fname); // Save results to file void ShowCenters(); }; void System::ShowCenters(){ int i,j; printf("Cluster centers:\n"); for (i=0; i<NumClusters; i++) { Cluster[i].Member[0]=i; printf("ClusterCenter[%d]=(%f,%f)\n",i,Cluster[i].Center[0],Cluster[i].Center[1]); } /* endfor */ printf("\n"); } int System::LoadPatterns(char *fname){ FILE *InFilePtr; int i,j; double x; if((InFilePtr = fopen(fname, "r")) == NULL) return FAILURE; fscanf(InFilePtr, "%d", &NumPatterns); // Read # of patterns fscanf(InFilePtr, "%d", &SizeVector); // Read dimension of vector fscanf(InFilePtr, "%d", &NumClusters); // Read # of clusters for K-Means for (i=0; i<NumPatterns; i++) { // For each vector for (j=0; j<SizeVector; j++) { // create a pattern fscanf(InFilePtr,"%lg",&x); // consisting of all elements Pattern[i][j]=x; } /* endfor */ } /* endfor */ printf("Input patterns:\n"); for (i=0; i<NumPatterns; i++) { printf("Pattern[%d]=(%2.3f,%2.3f)\n",i,Pattern[i][0],Pattern[i][1]); } /* endfor */ printf("\n--------------------\n"); return SUCCESS; } //*************************************************************************** // InitClusters // Arbitrarily assign a vector to each of the K clusters * // We choose the first K vectors to do this //*************************************************************************** void System::InitClusters(){ int i,j; printf("Initial cluster centers:\n"); for (i=0; i<NumClusters; i++) { Cluster[i].Member[0]=i; for (j=0; j<SizeVector; j++) { Cluster[i].Center[j]=Pattern[i][j]; } /* endfor */ } /* endfor */ for (i=0; i<NumClusters; i++) { printf("ClusterCenter[%d]=(%f,%f)\n",i,Cluster[i].Center[0],Cluster[i].Center[1]); } /* endfor */ printf("\n"); } void System::RunKMeans(){ int converged; int pass; pass=1; converged=FALSE; while (converged==FALSE) { printf("PASS=%d\n",pass++); DistributeSamples(); converged=CalcNewClustCenters(); ShowCenters(); } /* endwhile */ } double System::EucNorm(int p, int c){ // Calc Euclidean norm of vector difference double dist,x; // between pattern vector, p, and cluster int i; // cen