【Machine Learning】K-means算法及C语言实现
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#include <math.h>
#include <stdlib.h>
#include <stdio.h>#define sqr(x) ((x)*(x))#define MAX_CLUSTERS 16#define MAX_ITERATIONS 100#define BIG_double (INFINITY)void fail(char *str){printf(str);exit(-1);}double calc_distance(int dim, double *p1, double *p2){double distance_sq_sum = 0;for (int ii = 0; ii < dim; ii++)distance_sq_sum += sqr(p1[ii] - p2[ii]);return distance_sq_sum;}void calc_all_distances(int dim, int n, int k, double *X, double *centroid, double *distance_output){for (int ii = 0; ii < n; ii++) // for each pointfor (int jj = 0; jj < k; jj++) // for each cluster{// calculate distance between point and cluster centroiddistance_output[ii*k + jj] = calc_distance(dim, &X[ii*dim], ¢roid[jj*dim]);}}double calc_total_distance(int dim, int n, int k, double *X, double *centroids, int *cluster_assignment_index)// NOTE: a point with cluster assignment -1 is ignored{double tot_D = 0;// for every pointfor (int ii = 0; ii < n; ii++){// which cluster is it in?int active_cluster = cluster_assignment_index[ii];// sum distanceif (active_cluster != -1)tot_D += calc_distance(dim, &X[ii*dim], ¢roids[active_cluster*dim]);}return tot_D;}void choose_all_clusters_from_distances(int dim, int n, int k, double *distance_array, int *cluster_assignment_index){// for each pointfor (int ii = 0; ii < n; ii++){int best_index = -1;double closest_distance = BIG_double;// for each clusterfor (int jj = 0; jj < k; jj++){// distance between point and cluster centroiddouble cur_distance = distance_array[ii*k + jj];if (cur_distance < closest_distance){best_index = jj;closest_distance = cur_distance;}}// record in arraycluster_assignment_index[ii] = best_index;}}void calc_cluster_centroids(int dim, int n, int k, double *X, int *cluster_assignment_index, double *new_cluster_centroid){int cluster_member_count[MAX_CLUSTERS];// initialize cluster centroid coordinate sums to zerofor (int ii = 0; ii < k; ii++) {cluster_member_count[ii] = 0;for (int jj = 0; jj < dim; jj++)new_cluster_centroid[ii*dim + jj] = 0;}// sum all points// for every pointfor (int ii = 0; ii < n; ii++){// which cluster is it in?int active_cluster = cluster_assignment_index[ii];// update count of members in that clustercluster_member_count[active_cluster]++;// sum point coordinates for finding centroidfor (int jj = 0; jj < dim; jj++)new_cluster_centroid[active_cluster*dim + jj] += X[ii*dim + jj];}// now divide each coordinate sum by number of members to find mean/centroid// for each clusterfor (int ii = 0; ii < k; ii++) {if (cluster_member_count[ii] == 0)printf("WARNING: Empty cluster %d! \n", ii);// for each dimensionfor (int jj = 0; jj < dim; jj++)new_cluster_centroid[ii*dim + jj] /= cluster_member_count[ii]; /// XXXX will divide by zero here for any empty clusters!}}void get_cluster_member_count(int n, int k, int *cluster_assignment_index, int *cluster_member_count){// initialize cluster member countsfor (int ii = 0; ii < k; ii++) cluster_member_count[ii] = 0;// count members of each cluster for (int ii = 0; ii < n; ii++)cluster_member_count[cluster_assignment_index[ii]]++;}void update_delta_score_table(int dim, int n, int k, double *X, int *cluster_assignment_cur, double *cluster_centroid, int *cluster_member_count, double *point_move_score_table, int cc){// for every point (both in and not in the cluster)for (int ii = 0; ii < n; ii++){double dist_sum = 0;for (int kk = 0; kk < dim; kk++){double axis_dist = X[ii*dim + kk] - cluster_centroid[cc*dim + kk]; dist_sum += sqr(axis_dist);}double mult = ((double)cluster_member_count[cc] / (cluster_member_count[cc] + ((cluster_assignment_cur[ii]==cc) ? -1 : +1)));point_move_score_table[ii*dim + cc] = dist_sum * mult;}}void perform_move(int dim, int n, int k, double *X, int *cluster_assignment, double *cluster_centroid, int *cluster_member_count, int move_point, int move_target_cluster){int cluster_old = cluster_assignment[move_point];int cluster_new = move_target_cluster;// update cluster assignment arraycluster_assignment[move_point] = cluster_new;// update cluster count arraycluster_member_count[cluster_old]--;cluster_member_count[cluster_new]++;if (cluster_member_count[cluster_old] <= 1)printf("WARNING: Can't handle single-member clusters! \n");// update centroid arrayfor (int ii = 0; ii < dim; ii++){cluster_centroid[cluster_old*dim + ii] -= (X[move_point*dim + ii] - cluster_centroid[cluster_old*dim + ii]) / cluster_member_count[cluster_old];cluster_centroid[cluster_new*dim + ii] += (X[move_point*dim + ii] - cluster_centroid[cluster_new*dim + ii]) / cluster_member_count[cluster_new];}} void cluster_diag(int dim, int n, int k, double *X, int *cluster_assignment_index, double *cluster_centroid){int cluster_member_count[MAX_CLUSTERS];get_cluster_member_count(n, k, cluster_assignment_index, cluster_member_count);printf(" Final clusters \n");for (int ii = 0; ii < k; ii++) printf(" cluster %d: members: %8d, centroid (%.1f %.1f) \n", ii, cluster_member_count[ii], cluster_centroid[ii*dim + 0], cluster_centroid[ii*dim + 1]);}void copy_assignment_array(int n, int *src, int *tgt){for (int ii = 0; ii < n; ii++)tgt[ii] = src[ii];}int assignment_change_count(int n, int a[], int b[]){int change_count = 0;for (int ii = 0; ii < n; ii++)if (a[ii] != b[ii])change_count++;return change_count;}void kmeans(int dim, // dimension of data double *X, // pointer to dataint n, // number of elementsint k, // number of clustersdouble *cluster_centroid, // initial cluster centroidsint *cluster_assignment_final // output){double *dist = (double *)malloc(sizeof(double) * n * k);int *cluster_assignment_cur = (int *)malloc(sizeof(int) * n);int *cluster_assignment_prev = (int *)malloc(sizeof(int) * n);double *point_move_score = (double *)malloc(sizeof(double) * n * k);if (!dist || !cluster_assignment_cur || !cluster_assignment_prev || !point_move_score)fail("Error allocating dist arrays");// initial setup calc_all_distances(dim, n, k, X, cluster_centroid, dist);choose_all_clusters_from_distances(dim, n, k, dist, cluster_assignment_cur);copy_assignment_array(n, cluster_assignment_cur, cluster_assignment_prev);// BATCH UPDATEdouble prev_totD = BIG_double;int batch_iteration = 0;while (batch_iteration < MAX_ITERATIONS){
// printf("batch iteration %d \n", batch_iteration);
// cluster_diag(dim, n, k, X, cluster_assignment_cur, cluster_centroid);// update cluster centroidscalc_cluster_centroids(dim, n, k, X, cluster_assignment_cur, cluster_centroid);// deal with empty clusters// XXXXXXXXXXXXXX// see if we've failed to improvedouble totD = calc_total_distance(dim, n, k, X, cluster_centroid, cluster_assignment_cur);if (totD > prev_totD)// failed to improve - currently solution worse than previous{// restore old assignmentscopy_assignment_array(n, cluster_assignment_prev, cluster_assignment_cur);// recalc centroidscalc_cluster_centroids(dim, n, k, X, cluster_assignment_cur, cluster_centroid);printf(" negative progress made on this step - iteration completed (%.2f) \n", totD - prev_totD);// done with this phasebreak;}// save previous stepcopy_assignment_array(n, cluster_assignment_cur, cluster_assignment_prev);// move all points to nearest clustercalc_all_distances(dim, n, k, X, cluster_centroid, dist);choose_all_clusters_from_distances(dim, n, k, dist, cluster_assignment_cur);int change_count = assignment_change_count(n, cluster_assignment_cur, cluster_assignment_prev);printf("%3d %u %9d %16.2f %17.2f\n", batch_iteration, 1, change_count, totD, totD - prev_totD);fflush(stdout);// done with this phase if nothing has changedif (change_count == 0){printf(" no change made on this step - iteration completed \n");break;}prev_totD = totD;batch_iteration++;}// write to output arraycopy_assignment_array(n, cluster_assignment_cur, cluster_assignment_final); free(dist);free(cluster_assignment_cur);free(cluster_assignment_prev);free(point_move_score);}
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