From cb7823eaa631c43ed2f9620c30e9fbbbe574bd41 Mon Sep 17 00:00:00 2001
From: Paul Duncan <pabs@pablotron.org>
Date: Mon, 4 Feb 2019 21:31:19 -0500
Subject: add distance sum

---
 km-find.c  | 16 +++++++++++-----
 km-solve.c | 20 ++++++++++++++------
 km.h       |  4 +++-
 3 files changed, 28 insertions(+), 12 deletions(-)

diff --git a/km-find.c b/km-find.c
index 8e7f9ac..7cd24f6 100644
--- a/km-find.c
+++ b/km-find.c
@@ -6,7 +6,8 @@
 #define MIN_CLUSTER_DISTANCE 0.0001
 
 typedef struct {
-  float distance_sum,
+  float sum,
+        mean_sum,
         variance_sum;
   size_t num_empty_clusters;
 } find_solve_data_t;
@@ -17,7 +18,7 @@ find_get_mean_distance(
   const size_t num_clusters
 ) {
   const bool num_filled = num_clusters - d->num_empty_clusters;
-  return num_filled ? (d->distance_sum / num_filled) : 0;
+  return num_filled ? (d->mean_sum / num_filled) : 0;
 }
 
 static float
@@ -55,12 +56,15 @@ find_solve_on_stats(
   find_solve_data_t * const solve_data = cb_data;
   UNUSED(set);
 
+  // save total sum
+  solve_data->sum = stats->sum;
+
   // 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->mean_sum += stats->means[i];
       solve_data->variance_sum += stats->variances[i];
     } else {
       // increment empty cluster count
@@ -107,7 +111,8 @@ km_find(
 
       // init solve data
       find_solve_data_t solve_data = {
-        .distance_sum       = 0,
+        .sum                = 0,
+        .mean_sum           = 0,
         .variance_sum       = 0,
         .num_empty_clusters = 0,
       };
@@ -122,6 +127,7 @@ km_find(
       const km_find_data_t result = {
         .cluster_set        = &cs,
         .num_clusters       = i,
+        .distance_sum       = solve_data.sum,
         .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),
@@ -132,7 +138,7 @@ km_find(
       cbs->on_data(&result, cb_data);
 
       // score result
-      float score = km_score(result.mean_distance, result.num_empty_clusters);
+      float score = km_score(result.distance_sum, result.num_empty_clusters);
 
       if (score > best_score) {
         // emit new best result
diff --git a/km-solve.c b/km-solve.c
index 9eac13c..2a4de16 100644
--- a/km-solve.c
+++ b/km-solve.c
@@ -70,13 +70,14 @@ km_solve(
       const float * const floats = km_set_get_row(cs, i);
 
       for (size_t j = 0; j < set->num_rows; j++) {
-        const float * const row_floats = km_set_get_row(set, j);
+        // get row values
+        const float * const vals = km_set_get_row(set, j);
 
-        // calculate the distance squared between these clusters
-        const float d2 = distance_squared(num_floats, floats, row_floats);
+        // calculate the distance squared between row and cluster
+        const float d2 = distance_squared(num_floats, floats, vals);
 
         if (d2 < row_map[j].d2) {
-          // row is closer to this cluster, update distance and cluster
+          // row is closer to this cluster, update row map
           row_map[j].d2 = d2;
           row_map[j].cluster = i;
 
@@ -129,16 +130,22 @@ km_solve(
   } while (changed);
 
   if (cbs && cbs->on_stats) {
-    float means[num_clusters],
+    float sum = 0,
+          means[num_clusters],
           variances[num_clusters];
+
     memset(means, 0, sizeof(float) * num_clusters);
     memset(variances, 0, sizeof(float) * num_clusters);
 
+    // calculate sum of distances across all clusters
+    for (size_t i = 0; i < set->num_rows; i++) {
+      sum += row_map[i].d2;
+    }
+
     // calculate mean distances
     for (size_t i = 0; i < set->num_rows; i++) {
       means[row_map[i].cluster] += row_map[i].d2;
     }
-
     // finalize means
     for (size_t i = 0; i < num_clusters; i++) {
       means[i] = (cs->ints[i]) ? (sqrt(means[i]) / cs->ints[i]) : 0;
@@ -159,6 +166,7 @@ km_solve(
 
     // build stats
     const km_solve_stats_t stats = {
+      .sum          = sum,
       .means        = means,
       .variances    = variances,
       .num_clusters = num_clusters,
diff --git a/km.h b/km.h
index 097026e..bc2e3a6 100644
--- a/km.h
+++ b/km.h
@@ -89,6 +89,7 @@ typedef struct {
 } km_row_map_t;
 
 typedef struct {
+  const float sum;
   const float *means;
   const float *variances;
   const size_t num_clusters;
@@ -112,7 +113,8 @@ km_solve(
 typedef struct {
   const km_set_t * const cluster_set;
 
-  const float mean_distance,
+  const float distance_sum,
+              mean_distance,
               mean_variance,
               mean_cluster_size;
 
-- 
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