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#include <stdbool.h> // bool
#include <math.h> // fabsf()
#include "util.h"
#include "km.h"
#define MIN_CLUSTER_DISTANCE 0.0001
static float
get_score(
const float mean_distance,
const size_t num_empty
) {
return 1.0 / (mean_distance + num_empty);
}
typedef struct {
float distance_sum,
variance_sum;
size_t num_empty_clusters;
} find_solve_data_t;
static float
find_get_mean_distance(
const find_solve_data_t * const d,
const size_t num_clusters
) {
const bool num_filled = num_clusters - d->num_empty_clusters;
return num_filled ? (d->distance_sum / num_filled) : 0;
}
static float
find_get_mean_variance(
const find_solve_data_t * const d,
const size_t num_clusters
) {
const bool num_filled = num_clusters - d->num_empty_clusters;
return num_filled ? (d->variance_sum / num_filled) : 0;
}
static float
find_get_mean_cluster_size(
const km_set_t * const set
) {
float sum = 0;
size_t num_filled = 0;
for (size_t i = 0; i < set->num_rows; i++) {
if (set->ints[i] > 0) {
sum += set->ints[i];
num_filled++;
}
}
return (num_filled > 0) ? sum / num_filled : 0;
}
static void
find_solve_on_stats(
const km_set_t * const set,
const km_solve_stats_t * const stats,
void * const cb_data
) {
find_solve_data_t * const solve_data = cb_data;
UNUSED(set);
// calculate numerator for the average distance across all clusters in
// this test
for (size_t i = 0; i < stats->num_clusters; i++) {
if (fabsf(stats->means[i]) > MIN_CLUSTER_DISTANCE) {
// increment mean count
solve_data->distance_sum += stats->means[i];
solve_data->variance_sum += stats->variances[i];
} else {
// increment empty cluster count
solve_data->num_empty_clusters++;
}
}
}
static const km_solve_cbs_t
FIND_SOLVE_CBS = {
.on_stats = find_solve_on_stats,
};
bool
km_find(
const km_set_t * const set,
const km_find_cbs_t * const cbs,
void * const cb_data
) {
// check init callback
if (!cbs->on_init) {
return false;
}
// check fini callback
if (!cbs->on_fini) {
return false;
}
// check data callback
if (!cbs->on_data) {
return false;
}
float best_score = 0.0;
for (size_t i = 2; i < cbs->max_clusters; i++) {
for (size_t j = 0; j < cbs->num_tests; j++) {
// init cluster set
km_set_t cs;
if (!cbs->on_init(&cs, set->shape.num_floats, i, cb_data)) {
// return failure
return false;
}
// init solve data
find_solve_data_t solve_data = {
.distance_sum = 0,
.variance_sum = 0,
.num_empty_clusters = 0,
};
// solve test
if (!km_solve(&cs, set, &FIND_SOLVE_CBS, &solve_data)) {
// return failure
return false;
}
// init result data
const km_find_data_t result = {
.cluster_set = &cs,
.num_clusters = i,
.mean_distance = find_get_mean_distance(&solve_data, i),
.mean_variance = find_get_mean_variance(&solve_data, i),
.mean_cluster_size = find_get_mean_cluster_size(&cs),
.num_empty_clusters = solve_data.num_empty_clusters,
};
// emit result
cbs->on_data(&result, cb_data);
// score result
float score = get_score(result.mean_distance, result.num_empty_clusters);
if (score > best_score) {
// emit new best result
if (cbs->on_best && !cbs->on_best(score, &cs, cb_data)) {
// return failure
return false;
}
// update best score
best_score = score;
}
// finalize cluster set
if (!cbs->on_fini(&cs, cb_data)) {
// return failure
return false;
}
}
}
// return success
return true;
}
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