mirror of https://github.com/AlexeyAB/darknet.git
Added kmeansiou.c
This commit is contained in:
AlexeyAB
parent
c38ec62419
commit
25750d59cc
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@ -0,0 +1,392 @@
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//usr/bin/cc -Ofast -lm "${0}" -o "${0%.c}" && ./"${0%.c}" "$@"; s=$?; rm ./"${0%.c}"; exit $s
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#include <math.h>
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#include <stdio.h>
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#include <stdlib.h>
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#include <string.h>
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#include <time.h>
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typedef struct matrix{
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int rows, cols;
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double **vals;
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} matrix;
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matrix csv_to_matrix(char *filename, int header);
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matrix make_matrix(int rows, int cols);
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void zero_matrix(matrix m);
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void copy(double *x, double *y, int n);
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double dist(double *x, double *y, int n);
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int *sample(int n);
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int find_int_arg(int argc, char **argv, char *arg, int def);
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int find_arg(int argc, char* argv[], char *arg);
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int closest_center(double *datum, matrix centers)
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{
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int j;
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int best = 0;
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double best_dist = dist(datum, centers.vals[best], centers.cols);
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for(j = 0; j < centers.rows; ++j){
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double new_dist = dist(datum, centers.vals[j], centers.cols);
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if(new_dist < best_dist){
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best_dist = new_dist;
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best = j;
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}
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}
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return best;
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}
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double dist_to_closest_center(double *datum, matrix centers)
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{
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int ci = closest_center(datum, centers);
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return dist(datum, centers.vals[ci], centers.cols);
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}
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int kmeans_expectation(matrix data, int *assignments, matrix centers)
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{
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int i;
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int converged = 1;
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for(i = 0; i < data.rows; ++i){
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int closest = closest_center(data.vals[i], centers);
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if(closest != assignments[i]) converged = 0;
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assignments[i] = closest;
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}
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return converged;
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}
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void kmeans_maximization(matrix data, int *assignments, matrix centers)
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{
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int i,j;
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int *counts = calloc(centers.rows, sizeof(int));
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zero_matrix(centers);
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for(i = 0; i < data.rows; ++i){
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++counts[assignments[i]];
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for(j = 0; j < data.cols; ++j){
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centers.vals[assignments[i]][j] += data.vals[i][j];
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}
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}
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for(i = 0; i < centers.rows; ++i){
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if(counts[i]){
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for(j = 0; j < centers.cols; ++j){
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centers.vals[i][j] /= counts[i];
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}
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}
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}
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}
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double WCSS(matrix data, int *assignments, matrix centers)
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{
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int i, j;
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double sum = 0;
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for(i = 0; i < data.rows; ++i){
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int ci = assignments[i];
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sum += (1 - dist(data.vals[i], centers.vals[ci], data.cols));
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}
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return sum / data.rows;
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}
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typedef struct{
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int *assignments;
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matrix centers;
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} model;
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void smart_centers(matrix data, matrix centers) {
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int i,j;
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copy(data.vals[rand()%data.rows], centers.vals[0], data.cols);
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double *weights = calloc(data.rows, sizeof(double));
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int clusters = centers.rows;
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for (i = 1; i < clusters; ++i) {
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double sum = 0;
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centers.rows = i;
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for (j = 0; j < data.rows; ++j) {
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weights[j] = dist_to_closest_center(data.vals[j], centers);
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sum += weights[j];
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}
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double r = sum*((double)rand()/RAND_MAX);
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for (j = 0; j < data.rows; ++j) {
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r -= weights[j];
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if(r <= 0){
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copy(data.vals[j], centers.vals[i], data.cols);
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break;
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}
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}
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}
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free(weights);
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}
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void random_centers(matrix data, matrix centers){
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int i;
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int *s = sample(data.rows);
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for(i = 0; i < centers.rows; ++i){
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copy(data.vals[s[i]], centers.vals[i], data.cols);
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}
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free(s);
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}
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model do_kmeans(matrix data, int k)
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{
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matrix centers = make_matrix(k, data.cols);
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int *assignments = calloc(data.rows, sizeof(int));
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smart_centers(data, centers);
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//random_centers(data, centers);
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if(k == 1) kmeans_maximization(data, assignments, centers);
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while(!kmeans_expectation(data, assignments, centers)){
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kmeans_maximization(data, assignments, centers);
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}
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model m;
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m.assignments = assignments;
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m.centers = centers;
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return m;
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}
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int main(int argc, char *argv[])
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{
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if(argc < 3){
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fprintf(stderr, "usage: %s <csv-file> [points/centers/stats]\n", argv[0]);
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return 0;
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}
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int i,j;
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srand(time(0));
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matrix data = csv_to_matrix(argv[1], 0);
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int k = find_int_arg(argc, argv, "-k", 2);
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int header = find_arg(argc, argv, "-h");
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int count = find_arg(argc, argv, "-c");
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if(strcmp(argv[2], "assignments")==0){
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model m = do_kmeans(data, k);
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int *assignments = m.assignments;
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for(i = 0; i < k; ++i){
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if(i != 0) printf("-\n");
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for(j = 0; j < data.rows; ++j){
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if(!(assignments[j] == i)) continue;
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printf("%f, %f\n", data.vals[j][0], data.vals[j][1]);
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}
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}
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}else if(strcmp(argv[2], "centers")==0){
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model m = do_kmeans(data, k);
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printf("WCSS: %f\n", WCSS(data, m.assignments, m.centers));
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int *counts = 0;
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if(count){
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counts = calloc(k, sizeof(int));
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for(j = 0; j < data.rows; ++j){
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++counts[m.assignments[j]];
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}
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}
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for(j = 0; j < m.centers.rows; ++j){
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if(count) printf("%d, ", counts[j]);
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printf("%f, %f\n", m.centers.vals[j][0], m.centers.vals[j][1]);
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}
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}else if(strcmp(argv[2], "scan")==0){
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for(i = 1; i <= k; ++i){
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model m = do_kmeans(data, i);
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printf("%f\n", WCSS(data, m.assignments, m.centers));
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}
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}
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return 0;
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}
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// Utility functions
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int *sample(int n)
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{
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int i;
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int *s = calloc(n, sizeof(int));
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for(i = 0; i < n; ++i) s[i] = i;
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for(i = n-1; i >= 0; --i){
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int swap = s[i];
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int index = rand()%(i+1);
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s[i] = s[index];
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s[index] = swap;
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}
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return s;
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}
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double dist(double *x, double *y, int n)
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{
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int i;
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double mw = (x[0] < y[0]) ? x[0] : y[0];
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double mh = (x[1] < y[1]) ? x[1] : y[1];
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double inter = mw*mh;
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double sum = x[0]*x[1] + y[0]*y[1];
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double un = sum - inter;
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double iou = inter/un;
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return 1-iou;
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}
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void copy(double *x, double *y, int n)
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{
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int i;
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for(i = 0; i < n; ++i) y[i] = x[i];
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}
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void error(char *s){
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fprintf(stderr, "Error: %s\n", s);
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exit(0);
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}
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char *fgetl(FILE *fp)
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{
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if(feof(fp)) return 0;
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int size = 512;
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char *line = malloc(size*sizeof(char));
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if(!fgets(line, size, fp)){
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free(line);
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return 0;
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}
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int curr = strlen(line);
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while(line[curr-1]!='\n'){
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size *= 2;
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line = realloc(line, size*sizeof(char));
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if(!line) error("Malloc");
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fgets(&line[curr], size-curr, fp);
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curr = strlen(line);
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}
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line[curr-1] = '\0';
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return line;
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}
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// Matrix stuff
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int count_fields(char *line)
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{
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int count = 0;
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int done = 0;
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char *c;
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for(c = line; !done; ++c){
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done = (*c == '\0');
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if(*c == ',' || done) ++count;
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}
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return count;
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}
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double *parse_fields(char *l, int n)
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{
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int i;
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double *field = calloc(n, sizeof(double));
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for(i = 0; i < n; ++i){
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field[i] = atof(l);
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l = strchr(l, ',')+1;
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}
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return field;
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}
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matrix make_matrix(int rows, int cols)
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{
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matrix m;
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m.rows = rows;
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m.cols = cols;
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m.vals = calloc(m.rows, sizeof(double *));
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int i;
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for(i = 0; i < m.rows; ++i) m.vals[i] = calloc(m.cols, sizeof(double));
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return m;
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}
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void zero_matrix(matrix m)
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{
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int i, j;
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for(i = 0; i < m.rows; ++i){
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for(j = 0; j < m.cols; ++j) m.vals[i][j] = 0;
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}
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}
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matrix csv_to_matrix(char *filename, int header)
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{
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FILE *fp = fopen(filename, "r");
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if(!fp) error(filename);
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matrix m;
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m.cols = -1;
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char *line;
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int n = 0;
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int size = 1024;
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m.vals = calloc(size, sizeof(double*));
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if(header) fgetl(fp);
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while((line = fgetl(fp))){
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if(m.cols == -1) m.cols = count_fields(line);
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if(n == size){
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size *= 2;
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m.vals = realloc(m.vals, size*sizeof(double*));
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}
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m.vals[n] = parse_fields(line, m.cols);
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free(line);
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++n;
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}
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m.vals = realloc(m.vals, n*sizeof(double*));
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m.rows = n;
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return m;
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}
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// Arguement parsing
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void del_arg(int argc, char **argv, int index)
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{
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int i;
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for(i = index; i < argc-1; ++i) argv[i] = argv[i+1];
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argv[i] = 0;
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}
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int find_arg(int argc, char* argv[], char *arg)
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{
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int i;
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for(i = 0; i < argc; ++i) {
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if(!argv[i]) continue;
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if(0==strcmp(argv[i], arg)) {
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del_arg(argc, argv, i);
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return 1;
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}
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}
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return 0;
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}
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int find_int_arg(int argc, char **argv, char *arg, int def)
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{
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int i;
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for(i = 0; i < argc-1; ++i){
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if(!argv[i]) continue;
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if(0==strcmp(argv[i], arg)){
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def = atoi(argv[i+1]);
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del_arg(argc, argv, i);
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del_arg(argc, argv, i);
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break;
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}
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}
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return def;
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}
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float find_float_arg(int argc, char **argv, char *arg, float def)
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{
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int i;
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for(i = 0; i < argc-1; ++i){
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if(!argv[i]) continue;
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if(0==strcmp(argv[i], arg)){
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def = atof(argv[i+1]);
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del_arg(argc, argv, i);
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del_arg(argc, argv, i);
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break;
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}
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}
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return def;
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}
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char *find_char_arg(int argc, char **argv, char *arg, char *def)
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{
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int i;
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for(i = 0; i < argc-1; ++i){
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if(!argv[i]) continue;
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if(0==strcmp(argv[i], arg)){
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def = argv[i+1];
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del_arg(argc, argv, i);
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del_arg(argc, argv, i);
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break;
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}
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}
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return def;
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}
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