Merging upstream version 1.37.0.

Signed-off-by: Daniel Baumann <daniel.baumann@progress-linux.org>

6 files changed, 0 insertions, 532 deletions

diff --git a/ml/kmeans/KMeans.cc b/ml/kmeans/KMeans.cc
deleted file mode 100644
index e66c66c1..00000000
--- 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 4ea3b6a8..00000000
--- 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 babdcf0d..00000000
--- 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 d276c6e0..00000000
--- 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 1c7215cc..00000000
--- 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 0cb59594..00000000
--- 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;
-}