cocorobo-ai-training-darknet-linux/scripts/kmeansiou.c

//usr/bin/cc -Ofast -lm "${0}" -o "${0%.c}" && ./"${0%.c}" "$@"; s=$?; rm ./"${0%.c}"; exit $s

#include <math.h>
#include <stdio.h>
#include <stdlib.h>
#include <string.h>
#include <time.h>

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 <csv-file> [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;
}