1 files changed, 144 insertions, 0 deletions
diff --git a/ml/kmeans/SamplesBuffer.cc b/ml/kmeans/SamplesBuffer.cc
new file mode 100644
index 000000000..f8211fb54
--- /dev/null
+++ b/ml/kmeans/SamplesBuffer.cc
@@ -0,0 +1,144 @@
+// 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;
+
+    for (size_t Idx = NumSamples - OutN; Idx != NumSamples; Idx++) {
+        DSample DS;
+        DS.set_size(NumDimsPerSample * (LagN + 1));
+
+        const Sample PS = getPreprocessedSample(Idx);
+        PS.initDSample(DS);
+
+        DSamples.push_back(DS);
+    }
+
+    return DSamples;
+}