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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[1] = { 0 };
Sample Acc(AccCNs, 1);
// Used to avoid clobbering the accumulator when moving the window
CalculatedNumber TmpCNs[1] = { 0 };
Sample Tmp(TmpCNs, 1);
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);
}
}
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);
}
}
void SamplesBuffer::preprocess(std::vector<DSample> &Samples) {
assert(Preprocessed == false);
size_t OutN = NumSamples;
// Diff
if (DiffN >= OutN)
return;
OutN -= DiffN;
diffSamples();
// Smooth
if (SmoothN == 0 || SmoothN > OutN)
return;
OutN -= (SmoothN - 1);
smoothSamples();
// Lag
if (LagN >= OutN)
return;
OutN -= LagN;
lagSamples();
Samples.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);
Samples.push_back(std::move(DS));
}
}
void SamplesBuffer::preprocess(DSample &Feature) {
assert(Preprocessed == false);
size_t OutN = NumSamples;
// Diff
if (DiffN >= OutN)
return;
OutN -= DiffN;
diffSamples();
// Smooth
if (SmoothN == 0 || SmoothN > OutN)
return;
OutN -= (SmoothN - 1);
smoothSamples();
// Lag
if (LagN >= OutN)
return;
OutN -= LagN;
lagSamples();
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;
Feature.set_size(NumDimsPerSample * (LagN + 1));
const Sample PS = getPreprocessedSample(Idx);
PS.initDSample(Feature);
}
}
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