From f557d1f49a2914c6084dd18efc783395228d8ce0 Mon Sep 17 00:00:00 2001
From: Paul Duncan <pabs@pablotron.org>
Date: Tue, 5 Feb 2019 00:22:15 -0500
Subject: mv *.[hc] src/

---
 km-init-kmeans.c | 124 -------------------------------------------------------
 1 file changed, 124 deletions(-)
 delete mode 100644 km-init-kmeans.c

(limited to 'km-init-kmeans.c')

diff --git a/km-init-kmeans.c b/km-init-kmeans.c
deleted file mode 100644
index f0c1b5f..0000000
--- a/km-init-kmeans.c
+++ /dev/null
@@ -1,124 +0,0 @@
-#include <stdbool.h> // bool
-#include <string.h> // memset()
-#include <float.h> // FLT_MAX
-#include "util.h"
-#include "km.h"
-
-// sum the squared distance of every row in set from this point
-static inline float
-sum_distance_squared(
-  const float * const floats,
-  const km_set_t * const set
-) {
-  float r = 0;
-
-  // sum squared distances
-  for (size_t i = 0; i < set->num_rows; i++) {
-    const float * const vals = km_set_get_row(set, i);
-    r += distance_squared(set->shape.num_floats, floats, vals);
-  }
-
-  // return result
-  return r;
-}
-
-static const float *
-get_random_row(
-  const km_set_t * const set,
-  km_rand_t * const rs
-) {
-  // get random offset
-  size_t ofs = 0;
-  if (!km_rand_get_sizes(rs, 1, &ofs)) {
-    die("km_rand_fill_sizes()");
-  }
-
-  return km_set_get_row(set, ofs % set->num_rows);
-}
-
-// init a cluster set of num_clusters by picking the first cluster from
-// the set of points at random
-// random initial points from the set
-bool
-km_init_kmeans(
-  km_set_t * const cs,
-  const size_t num_clusters,
-  const km_set_t * const set,
-  km_rand_t * const rs
-) {
-  const size_t num_floats = set->shape.num_floats,
-               stride = sizeof(float) * num_floats;
-  
-  // row values (filled below)
-  float sums[num_clusters];
-  float floats[num_floats * num_clusters];
-
-  // pick first row randomly
-  {
-    // get random row
-    const float * const vals = get_random_row(set, rs);
-
-    // copy row values to floats buffer
-    memcpy(floats, vals, stride);
-
-    // copy row values to floats buffer
-    sums[0] = sum_distance_squared(floats, set);
-  }
-
-  for (size_t i = 1; i < num_clusters;) {
-    // get random row
-    const float * const vals = get_random_row(set, rs);
-
-    // calculate squared distance to nearest cluster
-    size_t ofs = 0;
-    float min_d2 = FLT_MAX;
-    for (size_t j = 0; j < i; j++) {
-      const float d2 = distance_squared(num_floats, vals, floats + j * stride);
-      if (d2 < min_d2) {
-        ofs = j;
-        min_d2 = d2;
-      }
-    }
-
-    // get a random floating point value
-    float rand_val = 0;
-    if (!km_rand_get_floats(rs, 1, &rand_val)) {
-      // return failure
-      return false;
-    }
-
-    // check random value
-    if (rand_val / sums[ofs] < min_d2) {
-      // get destination
-      float * const dst = floats + i * num_floats;
-
-      // copy row values
-      memcpy(dst, vals, stride);
-
-      // calculate total cluster distance
-      sums[i] = sum_distance_squared(dst, set);
-
-      // increment cluster count
-      i++;
-    }
-  }
-
-  // FIXME: should probably be heap-allocated
-  int ints[num_clusters];
-  memset(ints, 0, sizeof(ints));
-
-  // init cluster shape
-  const km_shape_t shape = {
-    .num_floats = num_floats,
-    .num_ints = 1,
-  };
-
-  // init cluster set
-  if (!km_set_init(cs, &shape, num_clusters)) {
-    // return failure
-    return false;
-  }
-
-  // add data, return result
-  return km_set_push(cs, num_clusters, floats, ints);
-}
-- 
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