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Added kmeansiou.c

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AlexeyAB 8 years ago
parent c38ec62419
commit 25750d59cc
1 changed files with 392 additions and 0 deletions
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      scripts/kmeansiou.c

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scripts/kmeansiou.c
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//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(0);
}
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;
}
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