working silouette scoring, add more tests

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 @@
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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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