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lab1_sequential.c
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#include "lab1_io.h"
#include "lab1_omp.h"
#include <stdlib.h>
#include <time.h>
#include <limits.h>
#include <float.h>
#include <math.h>
#include <assert.h>
#define MAX_ITER 100
#define THRESHOLD 1e-6
#define min(a, b) \
({ __typeof__ (a) _a = (a); \
__typeof__ (b) _b = (b); \
_a < _b ? _a : _b; })
int num_points_global;
int num_iterations_global;
double delta_global = THRESHOLD + 1;
int K_global;
int *data_points_global;
float *centroids_global;
int *data_point_cluster_global;
void kmeans_sequential_execution()
{
printf("Sequential k-means start\n");
int i = 0, j = 0;
double min_dist, current_dist;
int *point_to_cluster = (int *)malloc(num_points_global * sizeof(int));
float *cluster_location = (float *)malloc(K_global * 3 * sizeof(float));
int *cluster_count = (int *)malloc(K_global * sizeof(int));
int iter_counter = 0;
double temp_delta = 0.0;
while ((delta_global > THRESHOLD) && (iter_counter < MAX_ITER)) //+1 is for the last assignment to cluster centroids (from previous iter)
{
for (i = 0; i < K_global * 3; i++)
cluster_location[i] = 0.0;
for (i = 0; i < K_global; i++)
cluster_count[i] = 0;
for (i = 0; i < num_points_global; i++)
{
//assign these points to their nearest cluster
min_dist = DBL_MAX;
for (j = 0; j < K_global; j++)
{
current_dist = pow((double)(centroids_global[(iter_counter * K_global + j) * 3] - (float)data_points_global[i * 3]), 2.0) +
pow((double)(centroids_global[(iter_counter * K_global + j) * 3 + 1] - (float)data_points_global[i * 3 + 1]), 2.0) +
pow((double)(centroids_global[(iter_counter * K_global + j) * 3 + 2] - (float)data_points_global[i * 3 + 2]), 2.0);
if (current_dist < min_dist)
{
min_dist = current_dist;
point_to_cluster[i] = j;
}
}
//add to local cluster_loc coordinates
cluster_count[point_to_cluster[i]] += 1;
cluster_location[point_to_cluster[i] * 3] += (float)data_points_global[i * 3];
cluster_location[point_to_cluster[i] * 3 + 1] += (float)data_points_global[i * 3 + 1];
cluster_location[point_to_cluster[i] * 3 + 2] += (float)data_points_global[i * 3 + 2];
}
//write cluster_location to centroids_global
for (i = 0; i < K_global; i++)
{
assert(cluster_count[i] != 0);
centroids_global[((iter_counter + 1) * K_global + i) * 3] = cluster_location[i * 3] / cluster_count[i];
centroids_global[((iter_counter + 1) * K_global + i) * 3 + 1] = cluster_location[i * 3 + 1] / cluster_count[i];
centroids_global[((iter_counter + 1) * K_global + i) * 3 + 2] = cluster_location[i * 3 + 2] / cluster_count[i];
}
// for (i = 0; i < K_global; i++)
// {
// printf("Sequential print of centroid # \033[1;31m%d: %f,%f,%f\n\033[0m", i + 1, centroids_global[((iter_counter + 1) * K_global + i) * 3], centroids_global[((iter_counter + 1) * K_global + i) * 3 + 1], centroids_global[((iter_counter + 1) * K_global + i) * 3 + 2]);
// }
/*Convergence check: Sum of L2-norms over every cluster*/
temp_delta = 0.0;
for (i = 0; i < K_global; i++)
{
temp_delta += (centroids_global[((iter_counter + 1) * K_global + i) * 3] - centroids_global[((iter_counter)*K_global + i) * 3]) * (centroids_global[((iter_counter + 1) * K_global + i) * 3] - centroids_global[((iter_counter)*K_global + i) * 3]) + (centroids_global[((iter_counter + 1) * K_global + i) * 3 + 1] - centroids_global[((iter_counter)*K_global + i) * 3 + 1]) * (centroids_global[((iter_counter + 1) * K_global + i) * 3 + 1] - centroids_global[((iter_counter)*K_global + i) * 3 + 1]) + (centroids_global[((iter_counter + 1) * K_global + i) * 3 + 2] - centroids_global[((iter_counter)*K_global + i) * 3 + 2]) * (centroids_global[((iter_counter + 1) * K_global + i) * 3 + 2] - centroids_global[((iter_counter)*K_global + i) * 3 + 2]);
}
delta_global = temp_delta;
//printf("Sequential delta_global:%f\n", delta_global);
iter_counter++;
}
num_iterations_global = iter_counter;
/*Assign points to final choice for cluster centroids:*/
for (i = 0; i < num_points_global; i++)
{
//assign points to clusters
data_point_cluster_global[i * 4] = data_points_global[i * 3];
data_point_cluster_global[i * 4 + 1] = data_points_global[i * 3 + 1];
data_point_cluster_global[i * 4 + 2] = data_points_global[i * 3 + 2];
data_point_cluster_global[i * 4 + 3] = point_to_cluster[i];
assert(point_to_cluster[i] >= 0 && point_to_cluster[i] < K_global);
}
}
void kmeans_sequential(int N,
int K,
int* data_points,
int** data_point_cluster,
float** centroids,
int* num_iterations
)
{
printf("in kmeans_openmp function number of iters:%d\n", *num_iterations);
int i = 0;
num_points_global = N;
num_iterations_global = *num_iterations;
K_global = K;
data_points_global = data_points;
/*Allocating space for data_points_cluster:*/
*data_point_cluster = (int *)malloc(N * 4 * sizeof(int));
data_point_cluster_global = *data_point_cluster;
/*Allocating space for centroids:*/
centroids_global = (float *)calloc((MAX_ITER + 1) * K * 3, sizeof(float));
/*Assigning first K points to be initial centroids:*/
for (i = 0; i < K; i++)
{
centroids_global[i * 3] = data_points[i * 3];
centroids_global[i * 3 + 1] = data_points[i * 3 + 1];
centroids_global[i * 3 + 2] = data_points[i * 3 + 2];
}
/*Printing initial centroids:*/
for (i = 0; i < K; i++)
{
printf("initial centroid #%d: %f,%f,%f\n", i + 1, centroids_global[i * 3], centroids_global[i * 3 + 1], centroids_global[i * 3 + 2]);
}
/*Executing k-means sequential:*/
kmeans_sequential_execution();
/*Record *num_iterations & write values to centroids from centroids_global:*/
*num_iterations = num_iterations_global;
int centroids_size = (*num_iterations + 1) * K * 3;
printf("number of iterations:%d\n", num_iterations_global);
printf("centroids_size:%d\n", centroids_size);
*centroids = (float *)calloc(centroids_size, sizeof(float));
for (i = 0; i < centroids_size; i++)
{
(*centroids)[i] = centroids_global[i];
}
/*Printing final centroids:*/
for (i = 0; i < K; i++)
{
printf("centroid #%d: %f,%f,%f\n", i + 1, (*centroids)[((*num_iterations) * K + i) * 3], (*centroids)[((*num_iterations) * K + i) * 3 + 1], (*centroids)[((*num_iterations) * K + i) * 3 + 2]);
}
};