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-rw-r--r--km-find.c49
-rw-r--r--km-solve.c1
-rw-r--r--km.h9
-rw-r--r--main.c44
-rw-r--r--tests/c6-1e4-0.dat11000
-rw-r--r--tests/c7-1e2-0.dat101
-rw-r--r--tests/c9-1e2-0.dat101
-rw-r--r--tests/c9-1e3-0.dat1001
8 files changed, 11267 insertions, 1039 deletions
diff --git a/km-find.c b/km-find.c
index 3716123..657c645 100644
--- a/km-find.c
+++ b/km-find.c
@@ -5,11 +5,6 @@
#define MIN_CLUSTER_DISTANCE 0.0001
-typedef struct {
- float sum,
- silouette;
-} find_solve_data_t;
-
static float
get_mean_cluster_size(
const km_set_t * const set
@@ -24,7 +19,7 @@ get_mean_cluster_size(
}
}
- return (num_filled > 0) ? sum / num_filled : 0;
+ return (num_filled > 0) ? (sum / num_filled) : 0;
}
static void
@@ -33,12 +28,12 @@ find_solve_on_stats(
const km_solve_stats_t * const stats,
void * const cb_data
) {
- find_solve_data_t * const solve_data = cb_data;
+ km_find_data_t * const find_data = cb_data;
UNUSED(set);
// save total sum and silouette
- solve_data->sum = stats->sum;
- solve_data->silouette = stats->silouette;
+ find_data->distance_sum = stats->sum;
+ find_data->silouette = stats->silouette;
}
static const km_solve_cbs_t
@@ -67,7 +62,7 @@ km_find(
return false;
}
- float best_score = 0.0;
+ float best_silouette = -2.0;
for (size_t i = 2; i < cbs->max_clusters; i++) {
for (size_t j = 0; j < cbs->num_tests; j++) {
// init cluster set
@@ -77,42 +72,34 @@ km_find(
return false;
}
- // init solve data
- find_solve_data_t solve_data = {
- .sum = 0,
- .silouette = 0,
+ // init find data
+ km_find_data_t find_data = {
+ .cluster_set = &cs,
+ .num_clusters = i,
};
// solve test
- if (!km_solve(&cs, set, &FIND_SOLVE_CBS, &solve_data)) {
+ // (populates sum and silouette)
+ if (!km_solve(&cs, set, &FIND_SOLVE_CBS, &find_data)) {
// return failure
return false;
}
- // init result data
- const km_find_data_t result = {
- .cluster_set = &cs,
- .num_clusters = i,
- .distance_sum = solve_data.sum,
- .silouette = solve_data.silouette,
- .mean_cluster_size = get_mean_cluster_size(&cs),
- };
+ // populate mean cluster size
+ find_data.mean_cluster_size = get_mean_cluster_size(&cs);
// emit result
- cbs->on_data(&result, cb_data);
-
- // score result
- const float score = solve_data.silouette;
+ cbs->on_data(&find_data, cb_data);
- if (score > best_score) {
+ if (find_data.silouette > best_silouette) {
// emit new best result
- if (cbs->on_best && !cbs->on_best(score, &cs, cb_data)) {
+ if (cbs->on_best && !cbs->on_best(find_data.silouette, &cs, cb_data)) {
// return failure
return false;
}
- // update best score
- best_score = score;
+ // update best silouette
+ best_silouette = find_data.silouette;
}
// finalize cluster set
diff --git a/km-solve.c b/km-solve.c
index 8a70a48..f579a41 100644
--- a/km-solve.c
+++ b/km-solve.c
@@ -163,6 +163,7 @@ km_solve(
mean_nears[row_map[i].cluster] += row_map[i].d2_near;
// calculate silouette denominator
+ // (https://en.wikipedia.org/wiki/Silhouette_%28clustering%29)
const float delta = row_map[i].d2_near - row_map[i].d2;
if (fabsf(delta) > MIN_CLUSTER_DISTANCE) {
silouette += delta / MAX(row_map[i].d2, row_map[i].d2_near);
diff --git a/km.h b/km.h
index bd3fe41..5a34261 100644
--- a/km.h
+++ b/km.h
@@ -84,7 +84,8 @@ _Bool km_set_normalize(km_set_t * const);
float *km_set_get_row(const km_set_t *, const size_t);
typedef struct {
- float d2, d2_near;
+ float d2,
+ d2_near;
size_t cluster;
} km_row_map_t;
@@ -117,9 +118,9 @@ km_solve(
typedef struct {
const km_set_t * const cluster_set;
- const float distance_sum,
- silouette,
- mean_cluster_size;
+ float distance_sum,
+ silouette,
+ mean_cluster_size;
const size_t num_clusters,
num_empty_clusters;
diff --git a/main.c b/main.c
index ed46af7..55056b7 100644
--- a/main.c
+++ b/main.c
@@ -69,7 +69,7 @@ ctx_best_sort(
}
static void
-ctx_best_each(
+ctx_best_walk(
const ctx_t * const ctx,
void (*on_best)(const km_set_t * const, const size_t, const float, void *),
void * const cb_data
@@ -176,11 +176,13 @@ find_on_best(
) {
ctx_t * const ctx = cb_data;
- D("best score = %0.3f, num_clusters = %zu", score, cs->num_rows);
+ D("new best: score = %0.3f, num_clusters = %zu", score, cs->num_rows);
// get pointer to destination set
// (note: data->best is a ring buffer)
- km_set_t * const dst = &(ctx->best[ctx->num_best % MAX_BEST].set);
+ const size_t ofs = ctx->num_best % MAX_BEST;
+ ctx->best[ofs].score = score;
+ km_set_t * const dst = &(ctx->best[ofs].set);
if (ctx->num_best >= MAX_BEST) {
// finalize old best data set
@@ -287,7 +289,7 @@ ctx_save_png(
}
// draw best cluster points
- ctx_best_each(ctx, save_on_best, (void*) ctx);
+ ctx_best_walk(ctx, save_on_best, (void*) ctx);
// save png
if (!stbi_write_png(png_path, IM_WIDTH, IM_HEIGHT, 3, im_data, IM_STRIDE)) {
@@ -295,6 +297,37 @@ ctx_save_png(
}
}
+static void
+ctx_best_print_on_best(
+ const km_set_t * const set,
+ const size_t rank,
+ const float score,
+ void * const cb_data
+) {
+ FILE * const fh = cb_data;
+
+ fprintf(fh, "rank = %zu, score = %0.3f, num_clusters = %zu: [\n", rank, score, set->num_rows);
+ for (size_t i = 0; i < set->num_rows; i++) {
+ const float * const vals = km_set_get_row(set, i);
+ fprintf(fh, " [");
+
+ for (size_t j = 0; j < set->shape.num_floats; j++) {
+ fprintf(fh, "%s%0.3f", (j > 0) ? ", " : "", vals[j]);
+ }
+
+ fprintf(fh, "], (%d rows)\n", set->ints[i]);
+ }
+ fprintf(fh, "]\n");
+}
+
+static void
+ctx_best_print(
+ const ctx_t * const ctx,
+ FILE * const fh
+) {
+ ctx_best_walk(ctx, ctx_best_print_on_best, fh);
+}
+
static const char USAGE_FORMAT[] =
"Usage: %s [init] [data_path] <png_path>\n"
"\n"
@@ -349,6 +382,9 @@ int main(int argc, char *argv[]) {
// sort best results from lowest to highest
ctx_best_sort(&ctx);
+ // print best
+ ctx_best_print(&ctx, stdout);
+
if (png_path) {
// save png of normalized data set and best clusters
ctx_save_png(&ctx, png_path, &set);
diff --git a/tests/c6-1e4-0.dat b/tests/c6-1e4-0.dat
index 05963b6..4c9a62d 100644
--- a/tests/c6-1e4-0.dat
+++ b/tests/c6-1e4-0.dat
@@ -1,1001 +1,10001 @@
2 0
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-0.25923 0.32612
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-0.82930 0.34452
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diff --git a/tests/c7-1e2-0.dat b/tests/c7-1e2-0.dat
new file mode 100644
index 0000000..8792570
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new file mode 100644
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new file mode 100644
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