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-rw-r--r--ml/kmeans/KMeans.cc55
-rw-r--r--ml/kmeans/KMeans.h34
-rw-r--r--ml/kmeans/Makefile.am4
-rw-r--r--ml/kmeans/SamplesBuffer.cc150
-rw-r--r--ml/kmeans/SamplesBuffer.h146
-rw-r--r--ml/kmeans/Tests.cc143
6 files changed, 0 insertions, 532 deletions
diff --git a/ml/kmeans/KMeans.cc b/ml/kmeans/KMeans.cc
deleted file mode 100644
index e66c66c16..000000000
--- a/ml/kmeans/KMeans.cc
+++ /dev/null
@@ -1,55 +0,0 @@
-// SPDX-License-Identifier: GPL-3.0-or-later
-
-#include "KMeans.h"
-#include <dlib/clustering.h>
-
-void KMeans::train(SamplesBuffer &SB, size_t MaxIterations) {
- std::vector<DSample> Samples = SB.preprocess();
-
- MinDist = std::numeric_limits<CalculatedNumber>::max();
- MaxDist = std::numeric_limits<CalculatedNumber>::min();
-
- {
- std::lock_guard<std::mutex> Lock(Mutex);
-
- 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(SamplesBuffer &SB) {
- std::vector<DSample> DSamples = SB.preprocess();
-
- std::unique_lock<std::mutex> Lock(Mutex, std::defer_lock);
- if (!Lock.try_lock())
- return std::numeric_limits<CalculatedNumber>::quiet_NaN();
-
- CalculatedNumber MeanDist = 0.0;
- for (const auto &CC: ClusterCenters)
- MeanDist += dlib::length(CC - DSamples.back());
-
- 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;
-}
diff --git a/ml/kmeans/KMeans.h b/ml/kmeans/KMeans.h
deleted file mode 100644
index 4ea3b6a89..000000000
--- a/ml/kmeans/KMeans.h
+++ /dev/null
@@ -1,34 +0,0 @@
-// SPDX-License-Identifier: GPL-3.0-or-later
-
-#ifndef KMEANS_H
-#define KMEANS_H
-
-#include <atomic>
-#include <vector>
-#include <limits>
-#include <mutex>
-
-#include "SamplesBuffer.h"
-
-class KMeans {
-public:
- KMeans(size_t NumClusters = 2) : NumClusters(NumClusters) {
- MinDist = std::numeric_limits<CalculatedNumber>::max();
- MaxDist = std::numeric_limits<CalculatedNumber>::min();
- };
-
- void train(SamplesBuffer &SB, size_t MaxIterations);
- CalculatedNumber anomalyScore(SamplesBuffer &SB);
-
-private:
- size_t NumClusters;
-
- std::vector<DSample> ClusterCenters;
-
- CalculatedNumber MinDist;
- CalculatedNumber MaxDist;
-
- std::mutex Mutex;
-};
-
-#endif /* KMEANS_H */
diff --git a/ml/kmeans/Makefile.am b/ml/kmeans/Makefile.am
deleted file mode 100644
index babdcf0df..000000000
--- a/ml/kmeans/Makefile.am
+++ /dev/null
@@ -1,4 +0,0 @@
-# SPDX-License-Identifier: GPL-3.0-or-later
-
-AUTOMAKE_OPTIONS = subdir-objects
-MAINTAINERCLEANFILES = $(srcdir)/Makefile.in
diff --git a/ml/kmeans/SamplesBuffer.cc b/ml/kmeans/SamplesBuffer.cc
deleted file mode 100644
index d276c6e09..000000000
--- a/ml/kmeans/SamplesBuffer.cc
+++ /dev/null
@@ -1,150 +0,0 @@
-// SPDX-License-Identifier: GPL-3.0-or-later
-//
-#include "SamplesBuffer.h"
-
-#include <fstream>
-#include <sstream>
-#include <string>
-
-void Sample::print(std::ostream &OS) const {
- for (size_t Idx = 0; Idx != NumDims - 1; Idx++)
- OS << CNs[Idx] << ", ";
-
- OS << CNs[NumDims - 1];
-}
-
-void SamplesBuffer::print(std::ostream &OS) const {
- for (size_t Idx = Preprocessed ? (DiffN + (SmoothN - 1) + (LagN)) : 0;
- Idx != NumSamples; Idx++) {
- Sample S = Preprocessed ? getPreprocessedSample(Idx) : getSample(Idx);
- OS << S << std::endl;
- }
-}
-
-std::vector<Sample> SamplesBuffer::getPreprocessedSamples() const {
- std::vector<Sample> V;
-
- for (size_t Idx = Preprocessed ? (DiffN + (SmoothN - 1) + (LagN)) : 0;
- Idx != NumSamples; Idx++) {
- Sample S = Preprocessed ? getPreprocessedSample(Idx) : getSample(Idx);
- V.push_back(S);
- }
-
- return V;
-}
-
-void SamplesBuffer::diffSamples() {
- // Panda's DataFrame default behaviour is to subtract each element from
- // itself. For us `DiffN = 0` means "disable diff-ing" when preprocessing
- // the samples buffer. This deviation will make it easier for us to test
- // the KMeans implementation.
- if (DiffN == 0)
- return;
-
- for (size_t Idx = 0; Idx != (NumSamples - DiffN); Idx++) {
- size_t High = (NumSamples - 1) - Idx;
- size_t Low = High - DiffN;
-
- Sample LHS = getSample(High);
- Sample RHS = getSample(Low);
-
- LHS.diff(RHS);
- }
-}
-
-void SamplesBuffer::smoothSamples() {
- // Holds the mean value of each window
- CalculatedNumber *AccCNs = new CalculatedNumber[NumDimsPerSample]();
- Sample Acc(AccCNs, NumDimsPerSample);
-
- // Used to avoid clobbering the accumulator when moving the window
- CalculatedNumber *TmpCNs = new CalculatedNumber[NumDimsPerSample]();
- Sample Tmp(TmpCNs, NumDimsPerSample);
-
- CalculatedNumber Factor = (CalculatedNumber) 1 / SmoothN;
-
- // Calculate the value of the 1st window
- for (size_t Idx = 0; Idx != std::min(SmoothN, NumSamples); Idx++) {
- Tmp.add(getSample(NumSamples - (Idx + 1)));
- }
-
- Acc.add(Tmp);
- Acc.scale(Factor);
-
- // Move the window and update the samples
- for (size_t Idx = NumSamples; Idx != (DiffN + SmoothN - 1); Idx--) {
- Sample S = getSample(Idx - 1);
-
- // Tmp <- Next window (if any)
- if (Idx >= (SmoothN + 1)) {
- Tmp.diff(S);
- Tmp.add(getSample(Idx - (SmoothN + 1)));
- }
-
- // S <- Acc
- S.copy(Acc);
-
- // Acc <- Tmp
- Acc.copy(Tmp);
- Acc.scale(Factor);
- }
-
- delete[] AccCNs;
- delete[] TmpCNs;
-}
-
-void SamplesBuffer::lagSamples() {
- if (LagN == 0)
- return;
-
- for (size_t Idx = NumSamples; Idx != LagN; Idx--) {
- Sample PS = getPreprocessedSample(Idx - 1);
- PS.lag(getSample(Idx - 1), LagN);
- }
-}
-
-std::vector<DSample> SamplesBuffer::preprocess() {
- assert(Preprocessed == false);
-
- std::vector<DSample> DSamples;
- size_t OutN = NumSamples;
-
- // Diff
- if (DiffN >= OutN)
- return DSamples;
- OutN -= DiffN;
- diffSamples();
-
- // Smooth
- if (SmoothN == 0 || SmoothN > OutN)
- return DSamples;
- OutN -= (SmoothN - 1);
- smoothSamples();
-
- // Lag
- if (LagN >= OutN)
- return DSamples;
- OutN -= LagN;
- lagSamples();
-
- DSamples.reserve(OutN);
- Preprocessed = true;
-
- uint32_t MaxMT = std::numeric_limits<uint32_t>::max();
- uint32_t CutOff = static_cast<double>(MaxMT) * SamplingRatio;
-
- for (size_t Idx = NumSamples - OutN; Idx != NumSamples; Idx++) {
- if (RandNums[Idx] > CutOff)
- continue;
-
- DSample DS;
- DS.set_size(NumDimsPerSample * (LagN + 1));
-
- const Sample PS = getPreprocessedSample(Idx);
- PS.initDSample(DS);
-
- DSamples.push_back(DS);
- }
-
- return DSamples;
-}
diff --git a/ml/kmeans/SamplesBuffer.h b/ml/kmeans/SamplesBuffer.h
deleted file mode 100644
index 1c7215cca..000000000
--- a/ml/kmeans/SamplesBuffer.h
+++ /dev/null
@@ -1,146 +0,0 @@
-// SPDX-License-Identifier: GPL-3.0-or-later
-
-#ifndef SAMPLES_BUFFER_H
-#define SAMPLES_BUFFER_H
-
-#include <iostream>
-#include <vector>
-
-#include <cassert>
-#include <cstdlib>
-#include <cstring>
-
-#include <dlib/matrix.h>
-
-typedef double CalculatedNumber;
-typedef dlib::matrix<CalculatedNumber, 0, 1> DSample;
-
-class Sample {
-public:
- Sample(CalculatedNumber *Buf, size_t N) : CNs(Buf), NumDims(N) {}
-
- void initDSample(DSample &DS) const {
- for (size_t Idx = 0; Idx != NumDims; Idx++) {
- DS(Idx) = std::abs(CNs[Idx]);
- }
- }
-
- void add(const Sample &RHS) const {
- assert(NumDims == RHS.NumDims);
-
- for (size_t Idx = 0; Idx != NumDims; Idx++)
- CNs[Idx] += RHS.CNs[Idx];
- };
-
- void diff(const Sample &RHS) const {
- assert(NumDims == RHS.NumDims);
-
- for (size_t Idx = 0; Idx != NumDims; Idx++)
- CNs[Idx] -= RHS.CNs[Idx];
- };
-
- void copy(const Sample &RHS) const {
- assert(NumDims == RHS.NumDims);
-
- std::memcpy(CNs, RHS.CNs, NumDims * sizeof(CalculatedNumber));
- }
-
- void scale(CalculatedNumber Factor) {
- for (size_t Idx = 0; Idx != NumDims; Idx++)
- CNs[Idx] *= Factor;
- }
-
- void lag(const Sample &S, size_t LagN) {
- size_t N = S.NumDims;
-
- for (size_t Idx = 0; Idx != (LagN + 1); Idx++) {
- Sample Src(S.CNs - (Idx * N), N);
- Sample Dst(CNs + (Idx * N), N);
- Dst.copy(Src);
- }
- }
-
- const CalculatedNumber *getCalculatedNumbers() const {
- return CNs;
- };
-
- void print(std::ostream &OS) const;
-
-private:
- CalculatedNumber *CNs;
- size_t NumDims;
-};
-
-inline std::ostream& operator<<(std::ostream &OS, const Sample &S) {
- S.print(OS);
- return OS;
-}
-
-class SamplesBuffer {
-public:
- SamplesBuffer(CalculatedNumber *CNs,
- size_t NumSamples, size_t NumDimsPerSample,
- size_t DiffN, size_t SmoothN, size_t LagN,
- double SamplingRatio, std::vector<uint32_t> &RandNums) :
- CNs(CNs), NumSamples(NumSamples), NumDimsPerSample(NumDimsPerSample),
- DiffN(DiffN), SmoothN(SmoothN), LagN(LagN),
- SamplingRatio(SamplingRatio), RandNums(RandNums),
- BytesPerSample(NumDimsPerSample * sizeof(CalculatedNumber)),
- Preprocessed(false) {};
-
- std::vector<DSample> preprocess();
- std::vector<Sample> getPreprocessedSamples() const;
-
- size_t capacity() const { return NumSamples; }
- void print(std::ostream &OS) const;
-
-private:
- size_t getSampleOffset(size_t Index) const {
- assert(Index < NumSamples);
- return Index * NumDimsPerSample;
- }
-
- size_t getPreprocessedSampleOffset(size_t Index) const {
- assert(Index < NumSamples);
- return getSampleOffset(Index) * (LagN + 1);
- }
-
- void setSample(size_t Index, const Sample &S) const {
- size_t Offset = getSampleOffset(Index);
- std::memcpy(&CNs[Offset], S.getCalculatedNumbers(), BytesPerSample);
- }
-
- const Sample getSample(size_t Index) const {
- size_t Offset = getSampleOffset(Index);
- return Sample(&CNs[Offset], NumDimsPerSample);
- };
-
- const Sample getPreprocessedSample(size_t Index) const {
- size_t Offset = getPreprocessedSampleOffset(Index);
- return Sample(&CNs[Offset], NumDimsPerSample * (LagN + 1));
- };
-
- void diffSamples();
- void smoothSamples();
- void lagSamples();
-
-private:
- CalculatedNumber *CNs;
- size_t NumSamples;
- size_t NumDimsPerSample;
- size_t DiffN;
- size_t SmoothN;
- size_t LagN;
- double SamplingRatio;
- std::vector<uint32_t> &RandNums;
-
- size_t BytesPerSample;
- bool Preprocessed;
-};
-
-inline std::ostream& operator<<(std::ostream& OS, const SamplesBuffer &SB) {
- SB.print(OS);
- return OS;
-}
-
-#endif /* SAMPLES_BUFFER_H */
diff --git a/ml/kmeans/Tests.cc b/ml/kmeans/Tests.cc
deleted file mode 100644
index 0cb595945..000000000
--- a/ml/kmeans/Tests.cc
+++ /dev/null
@@ -1,143 +0,0 @@
-// SPDX-License-Identifier: GPL-3.0-or-later
-
-#include "ml/ml-private.h"
-#include <gtest/gtest.h>
-
-/*
- * The SamplesBuffer class implements the functionality of the following python
- * code:
- * >> df = pd.DataFrame(data=samples)
- * >> df = df.diff(diff_n).dropna()
- * >> df = df.rolling(smooth_n).mean().dropna()
- * >> df = pd.concat([df.shift(n) for n in range(lag_n + 1)], axis=1).dropna()
- *
- * Its correctness has been verified by automatically generating random
- * data frames in Python and comparing them with the correspondent preprocessed
- * SampleBuffers.
- *
- * The following tests are meant to catch unintended changes in the SamplesBuffer
- * implementation. For development purposes, one should compare changes against
- * the aforementioned python code.
-*/
-
-TEST(SamplesBufferTest, NS_8_NDPS_1_DN_1_SN_3_LN_1) {
- size_t NumSamples = 8, NumDimsPerSample = 1;
- size_t DiffN = 1, SmoothN = 3, LagN = 3;
-
- size_t N = NumSamples * NumDimsPerSample * (LagN + 1);
- CalculatedNumber *CNs = new CalculatedNumber[N]();
-
- CNs[0] = 0.7568336679490107;
- CNs[1] = 0.4814406581763254;
- CNs[2] = 0.40073555156221874;
- CNs[3] = 0.5973257298194408;
- CNs[4] = 0.5334727814345868;
- CNs[5] = 0.2632477193454843;
- CNs[6] = 0.2684839023122384;
- CNs[7] = 0.851332948637479;
-
- SamplesBuffer SB(CNs, NumSamples, NumDimsPerSample, DiffN, SmoothN, LagN);
- SB.preprocess();
-
- std::vector<Sample> Samples = SB.getPreprocessedSamples();
- EXPECT_EQ(Samples.size(), 2);
-
- Sample S0 = Samples[0];
- const CalculatedNumber *S0_CNs = S0.getCalculatedNumbers();
- Sample S1 = Samples[1];
- const CalculatedNumber *S1_CNs = S1.getCalculatedNumbers();
-
- EXPECT_NEAR(S0_CNs[0], -0.109614, 0.001);
- EXPECT_NEAR(S0_CNs[1], -0.0458293, 0.001);
- EXPECT_NEAR(S0_CNs[2], 0.017344, 0.001);
- EXPECT_NEAR(S0_CNs[3], -0.0531693, 0.001);
-
- EXPECT_NEAR(S1_CNs[0], 0.105953, 0.001);
- EXPECT_NEAR(S1_CNs[1], -0.109614, 0.001);
- EXPECT_NEAR(S1_CNs[2], -0.0458293, 0.001);
- EXPECT_NEAR(S1_CNs[3], 0.017344, 0.001);
-
- delete[] CNs;
-}
-
-TEST(SamplesBufferTest, NS_8_NDPS_1_DN_2_SN_3_LN_2) {
- size_t NumSamples = 8, NumDimsPerSample = 1;
- size_t DiffN = 2, SmoothN = 3, LagN = 2;
-
- size_t N = NumSamples * NumDimsPerSample * (LagN + 1);
- CalculatedNumber *CNs = new CalculatedNumber[N]();
-
- CNs[0] = 0.20511885291342846;
- CNs[1] = 0.13151717360306558;
- CNs[2] = 0.6017085062423134;
- CNs[3] = 0.46256882933941545;
- CNs[4] = 0.7887758447877941;
- CNs[5] = 0.9237989080034406;
- CNs[6] = 0.15552559051428083;
- CNs[7] = 0.6309750314597955;
-
- SamplesBuffer SB(CNs, NumSamples, NumDimsPerSample, DiffN, SmoothN, LagN);
- SB.preprocess();
-
- std::vector<Sample> Samples = SB.getPreprocessedSamples();
- EXPECT_EQ(Samples.size(), 2);
-
- Sample S0 = Samples[0];
- const CalculatedNumber *S0_CNs = S0.getCalculatedNumbers();
- Sample S1 = Samples[1];
- const CalculatedNumber *S1_CNs = S1.getCalculatedNumbers();
-
- EXPECT_NEAR(S0_CNs[0], 0.005016, 0.001);
- EXPECT_NEAR(S0_CNs[1], 0.326450, 0.001);
- EXPECT_NEAR(S0_CNs[2], 0.304903, 0.001);
-
- EXPECT_NEAR(S1_CNs[0], -0.154948, 0.001);
- EXPECT_NEAR(S1_CNs[1], 0.005016, 0.001);
- EXPECT_NEAR(S1_CNs[2], 0.326450, 0.001);
-
- delete[] CNs;
-}
-
-TEST(SamplesBufferTest, NS_8_NDPS_3_DN_2_SN_4_LN_1) {
- size_t NumSamples = 8, NumDimsPerSample = 3;
- size_t DiffN = 2, SmoothN = 4, LagN = 1;
-
- size_t N = NumSamples * NumDimsPerSample * (LagN + 1);
- CalculatedNumber *CNs = new CalculatedNumber[N]();
-
- CNs[0] = 0.34310900399667765; CNs[1] = 0.14694315994488194; CNs[2] = 0.8246677800938796;
- CNs[3] = 0.48249504592307835; CNs[4] = 0.23241087965531182; CNs[5] = 0.9595348555892567;
- CNs[6] = 0.44281094035598334; CNs[7] = 0.5143142171362715; CNs[8] = 0.06391303014242555;
- CNs[9] = 0.7460491027783901; CNs[10] = 0.43887217459032923; CNs[11] = 0.2814395025355999;
- CNs[12] = 0.9231114281214198; CNs[13] = 0.326882401786898; CNs[14] = 0.26747939220376216;
- CNs[15] = 0.7787571209969636; CNs[16] =0.5851700001235088; CNs[17] = 0.34410728945321567;
- CNs[18] = 0.9394494507088997; CNs[19] =0.17567223681734334; CNs[20] = 0.42732886195446984;
- CNs[21] = 0.9460522396152958; CNs[22] =0.23462747016780894; CNs[23] = 0.35983249900892145;
-
- SamplesBuffer SB(CNs, NumSamples, NumDimsPerSample, DiffN, SmoothN, LagN);
- SB.preprocess();
-
- std::vector<Sample> Samples = SB.getPreprocessedSamples();
- EXPECT_EQ(Samples.size(), 2);
-
- Sample S0 = Samples[0];
- const CalculatedNumber *S0_CNs = S0.getCalculatedNumbers();
- Sample S1 = Samples[1];
- const CalculatedNumber *S1_CNs = S1.getCalculatedNumbers();
-
- EXPECT_NEAR(S0_CNs[0], 0.198225, 0.001);
- EXPECT_NEAR(S0_CNs[1], 0.003529, 0.001);
- EXPECT_NEAR(S0_CNs[2], -0.063003, 0.001);
- EXPECT_NEAR(S0_CNs[3], 0.219066, 0.001);
- EXPECT_NEAR(S0_CNs[4], 0.133175, 0.001);
- EXPECT_NEAR(S0_CNs[5], -0.293154, 0.001);
-
- EXPECT_NEAR(S1_CNs[0], 0.174160, 0.001);
- EXPECT_NEAR(S1_CNs[1], -0.135722, 0.001);
- EXPECT_NEAR(S1_CNs[2], 0.110452, 0.001);
- EXPECT_NEAR(S1_CNs[3], 0.198225, 0.001);
- EXPECT_NEAR(S1_CNs[4], 0.003529, 0.001);
- EXPECT_NEAR(S1_CNs[5], -0.063003, 0.001);
-
- delete[] CNs;
-}
generated by cgit v1.2.3 (git 2.39.1) at 2024-05-23 14:51:57 +0000