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-rw-r--r--ml/KMeans.cc43
1 files changed, 0 insertions, 43 deletions
diff --git a/ml/KMeans.cc b/ml/KMeans.cc
deleted file mode 100644
index edc2ef49e..000000000
--- a/ml/KMeans.cc
+++ /dev/null
@@ -1,43 +0,0 @@
-// SPDX-License-Identifier: GPL-3.0-or-later
-
-#include "KMeans.h"
-#include <dlib/clustering.h>
-
-void KMeans::train(const std::vector<DSample> &Samples, size_t MaxIterations) {
- MinDist = std::numeric_limits<CalculatedNumber>::max();
- MaxDist = std::numeric_limits<CalculatedNumber>::min();
-
- ClusterCenters.clear();
-
- dlib::pick_initial_centers(NumClusters, ClusterCenters, Samples);
- dlib::find_clusters_using_kmeans(Samples, ClusterCenters, MaxIterations);
-
- for (const auto &S : Samples) {
- CalculatedNumber MeanDist = 0.0;
-
- for (const auto &KMCenter : ClusterCenters)
- MeanDist += dlib::length(KMCenter - S);
-
- MeanDist /= NumClusters;
-
- if (MeanDist < MinDist)
- MinDist = MeanDist;
-
- if (MeanDist > MaxDist)
- MaxDist = MeanDist;
- }
-}
-
-CalculatedNumber KMeans::anomalyScore(const DSample &Sample) const {
- CalculatedNumber MeanDist = 0.0;
- for (const auto &CC: ClusterCenters)
- MeanDist += dlib::length(CC - Sample);
-
- MeanDist /= NumClusters;
-
- if (MaxDist == MinDist)
- return 0.0;
-
- CalculatedNumber AnomalyScore = 100.0 * std::abs((MeanDist - MinDist) / (MaxDist - MinDist));
- return (AnomalyScore > 100.0) ? 100.0 : AnomalyScore;
-}