K-means算法

K-means算法

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#include<iostream> #include<fstream> #include<stdlib.h> #include<math.h>   using namespace std;   #define K 4 /*数据维度*/ #define C 8 /*聚类数*/ #define N 150 /*样本数*/ #define IterMax 5/*最大迭代数*/ #define IterTherhold  0.0000001 /*结束条件*/   /*样本数据*/ typedef struct{     double p[K];     int Lable;     double dis[K]; }Data;   Data dat[N]; double cluster[C][K] = {0.0}; double oldfitness = 0.0; double fitness = 0.0;   bool is_equal(int rand_num[], int n, int index) {     for(int i = 0; i < n; i++) {         if(rand_num[i] == index) {             return true;         }     }     return false; }   void input_data() {     ifstream in("test.data", ios::in);       int i = 0;     while(i < N) {         for(int k = 0; k < K; k++){             in >> dat[i].p[k];         }           i++;     } } //初始化质心 void Init_center() {     int rand_num[C] ={0} ;     int i = 0;     while(i < C) {         int index = rand()%N;         if(!is_equal(rand_num, i, index)) {             rand_num[i++] = index;         }     }     for(int i = 0; i < K ; i++) {         for(int j = 0; j < C; j++) {             cluster[j][i] = dat[rand_num[j]].p[i];         }     } } double Eulid_dis(int x, int y) {     double distance = 0.0;     for(int i = 0 ; i < K ; i++) {         distance += pow(dat[x].p[i] - cluster[y][i], 2);     }     distance = sqrt(distance);     return distance; } void Make_new_cluster() {     double bias = 0.0;     for(int i = 0; i < N; i++) {         double mindis = dat[i].dis[0];         dat[i].Lable = 0;         for(int j = 1; j < C; j++) {             if(mindis > dat[i].dis[j]) {                 mindis = dat[i].dis[j];                 dat[i].Lable = j;             }         }     }     for(int i = 0; i < N; i++) {         bias += dat[i].dis[dat[i].Lable];     }           oldfitness = fitness;     fitness = bias; } void calculate_distance() {     for(int i = 0; i < N; i++) {         for(int j = 0; j < C; j++) {             dat[i].dis[j] = Eulid_dis(i, j);         }     } } void Make_new_center() {     for(int i = 0; i < C; i++) {         for(int k = 0; k < K; k++) {             double tmp = 0.0;             int total = 0;             for(int j = 0; j < N; j++) {                 if(dat[j].Lable == i) {                     tmp += dat[j].p[k];                     total++;                 }             }             if(total > 0) {                 cluster[i][k] = tmp/total;             }         }     } } /************************************ *              主函数               * ************************************/ int main() {     input_data();     Init_center();     int i = 0;     double differ = 1.0;     while(i < IterMax && differ > IterTherhold) {         calculate_distance();         Make_new_cluster();         Make_new_center();         differ = abs(oldfitness - fitness);         cout << fitness << endl;         i++;     }     for (int i = 0; i < C; ++i) {         for(int j = 0; j < K ; j++) {             cout << cluster[i][j] << "\t";         }         cout << endl;     }     return 0; }