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Kmeans.c
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Kmeans.c
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#include <stdio.h>
#include <stdlib.h>
#include <math.h>
#include <float.h>
typedef struct {
double x;
double y;
} Point;
double euclidean_distance(Point a, Point b) {
return sqrt(pow(a.x - b.x, 2) + pow(a.y - b.y, 2));
}
void kmeans(int k, Point *data, int data_size, int *assignments, Point *centroids) {
for (int i = 0; i < k; i++) {
centroids[i] = data[i];
}
int changed;
do {
changed = 0;
for (int i = 0; i < data_size; i++) {
int closest_centroid = -1;
double closest_distance = DBL_MAX;
for (int j = 0; j < k; j++) {
double distance = euclidean_distance(data[i], centroids[j]);
if (distance < closest_distance) {
closest_distance = distance;
closest_centroid = j;
}
}
if (assignments[i] != closest_centroid) {
assignments[i] = closest_centroid;
changed = 1;
}
}
for (int i = 0; i < k; i++) {
int count = 0;
Point sum = {0, 0};
for (int j = 0; j < data_size; j++) {
if (assignments[j] == i) {
count++;
sum.x += data[j].x;
sum.y += data[j].y;
}
}
if (count > 0) {
centroids[i].x = sum.x / count;
centroids[i].y = sum.y / count;
}
}
} while (changed);
}
int main() {
int k = 3;
Point data[] = {
{1, 1},
{1, 2},
{2, 1},
{2, 2},
{8, 8},
{8, 9},
{9, 8},
{9, 9},
};
int data_size = sizeof(data) / sizeof(data[0]);
int assignments[data_size];
Point centroids[k];
kmeans(k, data, data_size, assignments, centroids);
for (int i = 0; i < data_size; i++) {
printf("Point (%f, %f) belongs to cluster %d\n", data[i].x, data[i].y, assignments[i]);
}
for (int i = 0; i < k; i++) {
printf("Centroid %d: (%f, %f)\n", i, centroids[i].x, centroids[i].y);
}
return 0;
}