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#include <stdbool.h> // bool
#include <string.h> // memset()
#include <float.h> // FLT_MAX
#include "util.h"
#include "km.h"
// sum the squared distance of every row in set from this point
static inline float
sum_distance_squared(
const float * const floats,
const km_set_t * const set
) {
float r = 0;
// sum squared distances
for (size_t i = 0; i < set->num_rows; i++) {
const float * const vals = km_set_get_row(set, i);
r += distance_squared(set->shape.num_floats, floats, vals);
}
// return result
return r;
}
static const float *
get_random_row(
const km_set_t * const set,
km_rand_t * const rs
) {
// get random offset
size_t ofs = 0;
if (!km_rand_get_sizes(rs, 1, &ofs)) {
die("km_rand_fill_sizes()");
}
return km_set_get_row(set, ofs % set->num_rows);
}
// init a cluster set of num_clusters by picking the first cluster from
// the set of points at random
// random initial points from the set
bool
km_init_kmeans(
km_set_t * const cs,
const size_t num_clusters,
const km_set_t * const set,
km_rand_t * const rs
) {
const size_t num_floats = set->shape.num_floats,
stride = sizeof(float) * num_floats;
// row values (filled below)
float sums[num_clusters];
float floats[num_floats * num_clusters];
// pick first row randomly
{
// get random row
const float * const vals = get_random_row(set, rs);
// copy row values to floats buffer
memcpy(floats, vals, stride);
// copy row values to floats buffer
sums[0] = sum_distance_squared(floats, set);
}
for (size_t i = 1; i < num_clusters;) {
// get random row
const float * const vals = get_random_row(set, rs);
// calculate squared distance to nearest cluster
size_t ofs = 0;
float min_d2 = FLT_MAX;
for (size_t j = 0; j < i; j++) {
const float d2 = distance_squared(num_floats, vals, floats + j * stride);
if (d2 < min_d2) {
ofs = j;
min_d2 = d2;
}
}
// get a random floating point value
float rand_val = 0;
if (!km_rand_get_floats(rs, 1, &rand_val)) {
// return failure
return false;
}
// check random value
if (rand_val / sums[ofs] < min_d2) {
// get destination
float * const dst = floats + i * num_floats;
// copy row values
memcpy(dst, vals, stride);
// calculate total cluster distance
sums[i] = sum_distance_squared(dst, set);
// increment cluster count
i++;
}
}
// FIXME: should probably be heap-allocated
int ints[num_clusters];
memset(ints, 0, sizeof(ints));
// init cluster shape
const km_shape_t shape = {
.num_floats = num_floats,
.num_ints = 1,
};
// init cluster set
if (!km_set_init(cs, &shape, num_clusters)) {
// return failure
return false;
}
// add data, return result
return km_set_push(cs, num_clusters, floats, ints);
}
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