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-rw-r--r--ml/KMeans.cc43
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diff --git a/ml/KMeans.cc b/ml/KMeans.cc
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+// 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;
+}