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-rw-r--r--ml/SamplesBuffer.cc150
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diff --git a/ml/SamplesBuffer.cc b/ml/SamplesBuffer.cc
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+// 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;
+}