//usr/bin/cc -Ofast -lm "${0}" -o "${0%.c}" && ./"${0%.c}" "$@"; s=$?; rm ./"${0%.c}"; exit $s #include #include #include #include #include typedef struct matrix{ int rows, cols; double **vals; } matrix; matrix csv_to_matrix(char *filename, int header); matrix make_matrix(int rows, int cols); void zero_matrix(matrix m); void copy(double *x, double *y, int n); double dist(double *x, double *y, int n); int *sample(int n); int find_int_arg(int argc, char **argv, char *arg, int def); int find_arg(int argc, char* argv[], char *arg); int closest_center(double *datum, matrix centers) { int j; int best = 0; double best_dist = dist(datum, centers.vals[best], centers.cols); for(j = 0; j < centers.rows; ++j){ double new_dist = dist(datum, centers.vals[j], centers.cols); if(new_dist < best_dist){ best_dist = new_dist; best = j; } } return best; } double dist_to_closest_center(double *datum, matrix centers) { int ci = closest_center(datum, centers); return dist(datum, centers.vals[ci], centers.cols); } int kmeans_expectation(matrix data, int *assignments, matrix centers) { int i; int converged = 1; for(i = 0; i < data.rows; ++i){ int closest = closest_center(data.vals[i], centers); if(closest != assignments[i]) converged = 0; assignments[i] = closest; } return converged; } void kmeans_maximization(matrix data, int *assignments, matrix centers) { int i,j; int *counts = calloc(centers.rows, sizeof(int)); zero_matrix(centers); for(i = 0; i < data.rows; ++i){ ++counts[assignments[i]]; for(j = 0; j < data.cols; ++j){ centers.vals[assignments[i]][j] += data.vals[i][j]; } } for(i = 0; i < centers.rows; ++i){ if(counts[i]){ for(j = 0; j < centers.cols; ++j){ centers.vals[i][j] /= counts[i]; } } } } double WCSS(matrix data, int *assignments, matrix centers) { int i, j; double sum = 0; for(i = 0; i < data.rows; ++i){ int ci = assignments[i]; sum += (1 - dist(data.vals[i], centers.vals[ci], data.cols)); } return sum / data.rows; } typedef struct{ int *assignments; matrix centers; } model; void smart_centers(matrix data, matrix centers) { int i,j; copy(data.vals[rand()%data.rows], centers.vals[0], data.cols); double *weights = calloc(data.rows, sizeof(double)); int clusters = centers.rows; for (i = 1; i < clusters; ++i) { double sum = 0; centers.rows = i; for (j = 0; j < data.rows; ++j) { weights[j] = dist_to_closest_center(data.vals[j], centers); sum += weights[j]; } double r = sum*((double)rand()/RAND_MAX); for (j = 0; j < data.rows; ++j) { r -= weights[j]; if(r <= 0){ copy(data.vals[j], centers.vals[i], data.cols); break; } } } free(weights); } void random_centers(matrix data, matrix centers){ int i; int *s = sample(data.rows); for(i = 0; i < centers.rows; ++i){ copy(data.vals[s[i]], centers.vals[i], data.cols); } free(s); } model do_kmeans(matrix data, int k) { matrix centers = make_matrix(k, data.cols); int *assignments = calloc(data.rows, sizeof(int)); smart_centers(data, centers); //random_centers(data, centers); if(k == 1) kmeans_maximization(data, assignments, centers); while(!kmeans_expectation(data, assignments, centers)){ kmeans_maximization(data, assignments, centers); } model m; m.assignments = assignments; m.centers = centers; return m; } int main(int argc, char *argv[]) { if(argc < 3){ fprintf(stderr, "usage: %s [points/centers/stats]\n", argv[0]); return 0; } int i,j; srand(time(0)); matrix data = csv_to_matrix(argv[1], 0); int k = find_int_arg(argc, argv, "-k", 2); int header = find_arg(argc, argv, "-h"); int count = find_arg(argc, argv, "-c"); if(strcmp(argv[2], "assignments")==0){ model m = do_kmeans(data, k); int *assignments = m.assignments; for(i = 0; i < k; ++i){ if(i != 0) printf("-\n"); for(j = 0; j < data.rows; ++j){ if(!(assignments[j] == i)) continue; printf("%f, %f\n", data.vals[j][0], data.vals[j][1]); } } }else if(strcmp(argv[2], "centers")==0){ model m = do_kmeans(data, k); printf("WCSS: %f\n", WCSS(data, m.assignments, m.centers)); int *counts = 0; if(count){ counts = calloc(k, sizeof(int)); for(j = 0; j < data.rows; ++j){ ++counts[m.assignments[j]]; } } for(j = 0; j < m.centers.rows; ++j){ if(count) printf("%d, ", counts[j]); printf("%f, %f\n", m.centers.vals[j][0], m.centers.vals[j][1]); } }else if(strcmp(argv[2], "scan")==0){ for(i = 1; i <= k; ++i){ model m = do_kmeans(data, i); printf("%f\n", WCSS(data, m.assignments, m.centers)); } } return 0; } // Utility functions int *sample(int n) { int i; int *s = calloc(n, sizeof(int)); for(i = 0; i < n; ++i) s[i] = i; for(i = n-1; i >= 0; --i){ int swap = s[i]; int index = rand()%(i+1); s[i] = s[index]; s[index] = swap; } return s; } double dist(double *x, double *y, int n) { int i; double mw = (x[0] < y[0]) ? x[0] : y[0]; double mh = (x[1] < y[1]) ? x[1] : y[1]; double inter = mw*mh; double sum = x[0]*x[1] + y[0]*y[1]; double un = sum - inter; double iou = inter/un; return 1-iou; } void copy(double *x, double *y, int n) { int i; for(i = 0; i < n; ++i) y[i] = x[i]; } void error(char *s){ fprintf(stderr, "Error: %s\n", s); exit(-1); } char *fgetl(FILE *fp) { if(feof(fp)) return 0; int size = 512; char *line = malloc(size*sizeof(char)); if(!fgets(line, size, fp)){ free(line); return 0; } int curr = strlen(line); while(line[curr-1]!='\n'){ size *= 2; line = realloc(line, size*sizeof(char)); if(!line) error("Malloc"); fgets(&line[curr], size-curr, fp); curr = strlen(line); } line[curr-1] = '\0'; return line; } // Matrix stuff int count_fields(char *line) { int count = 0; int done = 0; char *c; for(c = line; !done; ++c){ done = (*c == '\0'); if(*c == ',' || done) ++count; } return count; } double *parse_fields(char *l, int n) { int i; double *field = calloc(n, sizeof(double)); for(i = 0; i < n; ++i){ field[i] = atof(l); l = strchr(l, ',')+1; } return field; } matrix make_matrix(int rows, int cols) { matrix m; m.rows = rows; m.cols = cols; m.vals = calloc(m.rows, sizeof(double *)); int i; for(i = 0; i < m.rows; ++i) m.vals[i] = calloc(m.cols, sizeof(double)); return m; } void zero_matrix(matrix m) { int i, j; for(i = 0; i < m.rows; ++i){ for(j = 0; j < m.cols; ++j) m.vals[i][j] = 0; } } matrix csv_to_matrix(char *filename, int header) { FILE *fp = fopen(filename, "r"); if(!fp) error(filename); matrix m; m.cols = -1; char *line; int n = 0; int size = 1024; m.vals = calloc(size, sizeof(double*)); if(header) fgetl(fp); while((line = fgetl(fp))){ if(m.cols == -1) m.cols = count_fields(line); if(n == size){ size *= 2; m.vals = realloc(m.vals, size*sizeof(double*)); } m.vals[n] = parse_fields(line, m.cols); free(line); ++n; } m.vals = realloc(m.vals, n*sizeof(double*)); m.rows = n; return m; } // Arguement parsing void del_arg(int argc, char **argv, int index) { int i; for(i = index; i < argc-1; ++i) argv[i] = argv[i+1]; argv[i] = 0; } int find_arg(int argc, char* argv[], char *arg) { int i; for(i = 0; i < argc; ++i) { if(!argv[i]) continue; if(0==strcmp(argv[i], arg)) { del_arg(argc, argv, i); return 1; } } return 0; } int find_int_arg(int argc, char **argv, char *arg, int def) { int i; for(i = 0; i < argc-1; ++i){ if(!argv[i]) continue; if(0==strcmp(argv[i], arg)){ def = atoi(argv[i+1]); del_arg(argc, argv, i); del_arg(argc, argv, i); break; } } return def; } float find_float_arg(int argc, char **argv, char *arg, float def) { int i; for(i = 0; i < argc-1; ++i){ if(!argv[i]) continue; if(0==strcmp(argv[i], arg)){ def = atof(argv[i+1]); del_arg(argc, argv, i); del_arg(argc, argv, i); break; } } return def; } char *find_char_arg(int argc, char **argv, char *arg, char *def) { int i; for(i = 0; i < argc-1; ++i){ if(!argv[i]) continue; if(0==strcmp(argv[i], arg)){ def = argv[i+1]; del_arg(argc, argv, i); del_arg(argc, argv, i); break; } } return def; }