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new 4ff3819 [MINOR] K-means seeded fix
4ff3819 is described below
commit 4ff3819c20eb5035749cfd88d32c92848e187207
Author: baunsgaard <baunsga...@tugraz.at>
AuthorDate: Thu Oct 29 21:42:44 2020 +0100
[MINOR] K-means seeded fix
This commit change the seeding of k-means to actually work.
Since the last change to add the seed, unfortunately, made each initial
cluster assign to the same location, making the entire selection and
sampling in the beginning invalid.
Now the seed are incremented and different for each of the initial values.
---
scripts/builtin/kmeans.dml | 12 ++++++------
1 file changed, 6 insertions(+), 6 deletions(-)
diff --git a/scripts/builtin/kmeans.dml b/scripts/builtin/kmeans.dml
index 4b407f0..646ec34 100644
--- a/scripts/builtin/kmeans.dml
+++ b/scripts/builtin/kmeans.dml
@@ -67,7 +67,7 @@ m_kmeans = function(Matrix[Double] X, Integer k = 10, Integer
runs = 10, Integer
print ("Taking data samples for initialization...");
[sample_maps, samples_vs_runs_map, sample_block_size] = get_sample_maps(
- num_records, num_runs, num_centroids * avg_sample_size_per_centroid);
+ num_records, num_runs, num_centroids * avg_sample_size_per_centroid, seed);
is_row_in_samples = rowSums (sample_maps);
X_samples = sample_maps %*% X;
@@ -84,6 +84,8 @@ m_kmeans = function(Matrix[Double] X, Integer k = 10, Integer
runs = 10, Integer
min_distances = is_row_in_samples; # Pick the 1-st centroids uniformly at
random
+ seed = ifelse(seed == -1, -1, seed + 1)
+ random_row = rand(rows = 1, cols = num_runs, seed = seed);
for (i in 1 : num_centroids)
{
# "Matricize" and prefix-sum to compute the cumulative distribution
function:
@@ -92,7 +94,7 @@ m_kmeans = function(Matrix[Double] X, Integer k = 10, Integer
runs = 10, Integer
# Select the i-th centroid in each sample as a random sample row id with
# probability ~ min_distances:
- random_row = rand(rows = 1, cols = num_runs, seed = seed);
+ random_row = random_row + rand(rows = 1, cols = 1, seed = seed + i)
threshold_matrix = random_row * cdf_min_distances [sample_block_size, ];
centroid_ids = t(colSums (cdf_min_distances < threshold_matrix)) + 1;
@@ -222,12 +224,10 @@ m_kmeans = function(Matrix[Double] X, Integer k = 10,
Integer runs = 10, Integer
C = matrix(0, num_centroids, num_records)
Y = matrix(-1, 1, num_records)
}
-
-
}
-get_sample_maps = function (int num_records, int num_samples, int
approx_sample_size)
+get_sample_maps = function (int num_records, int num_samples, int
approx_sample_size, int seed)
return (Matrix[double] sample_maps, Matrix[double]
sample_col_map, int sample_block_size)
{
if (approx_sample_size < num_records)
@@ -239,7 +239,7 @@ get_sample_maps = function (int num_records, int
num_samples, int approx_sample_
# Generate all samples in parallel by converting uniform random values
into random
# integer skip-ahead intervals and prefix-summing them:
- sample_rec_ids = Rand (rows = sample_block_size, cols = num_samples, min =
0.0, max = 1.0);
+ sample_rec_ids = Rand (rows = sample_block_size, cols = num_samples, min =
0.0, max = 1.0, seed = seed);
sample_rec_ids = round (log (sample_rec_ids) / log (1.0 -
approx_sample_size / num_records) + 0.5);
# Prob [k-1 < log(uniform)/log(1-p) < k] = p*(1-p)^(k-1) = Prob [k-1 zeros
before a one]
sample_rec_ids = cumsum (sample_rec_ids); # (skip to next one) --> (skip
to i-th one)
