diff options
| -rw-r--r-- | km-find.c | 49 | ||||
| -rw-r--r-- | km-solve.c | 1 | ||||
| -rw-r--r-- | km.h | 9 | ||||
| -rw-r--r-- | main.c | 44 | ||||
| -rw-r--r-- | tests/c6-1e4-0.dat | 11000 | ||||
| -rw-r--r-- | tests/c7-1e2-0.dat | 101 | ||||
| -rw-r--r-- | tests/c9-1e2-0.dat | 101 | ||||
| -rw-r--r-- | tests/c9-1e3-0.dat | 1001 |
8 files changed, 11267 insertions, 1039 deletions
@@ -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 @@ -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); @@ -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; @@ -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 -0.33853 0.18643 -0.25026 0.83486 -0.74893 0.30345 -0.78407 0.78539 -0.55052 0.44492 -0.26261 0.50200 -0.26305 0.24854 -0.24986 0.74707 -0.84555 0.24321 -0.75919 0.75905 -0.55926 0.51217 -0.32627 0.53381 -0.25923 0.32612 -0.18112 0.80118 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