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// SPDX-License-Identifier: GPL-3.0-or-later
#ifndef ML_DIMENSION_H
#define ML_DIMENSION_H
#include "BitBufferCounter.h"
#include "Config.h"
#include "ml-private.h"
namespace ml {
class RrdDimension {
public:
RrdDimension(RRDDIM *RD) : RD(RD), Ops(&RD->state->query_ops) {
std::stringstream SS;
SS << RD->rrdset->id << "|" << RD->name;
ID = SS.str();
}
RRDDIM *getRD() const { return RD; }
time_t latestTime() { return Ops->latest_time(RD); }
time_t oldestTime() { return Ops->oldest_time(RD); }
unsigned updateEvery() const { return RD->update_every; }
const std::string getID() const { return ID; }
virtual ~RrdDimension() {}
private:
RRDDIM *RD;
struct rrddim_volatile::rrddim_query_ops *Ops;
std::string ID;
};
enum class MLResult {
Success = 0,
MissingData,
NaN,
};
class TrainableDimension : public RrdDimension {
public:
TrainableDimension(RRDDIM *RD) :
RrdDimension(RD), TrainEvery(Cfg.TrainEvery * updateEvery()) {}
MLResult trainModel();
CalculatedNumber computeAnomalyScore(SamplesBuffer &SB) {
return Trained ? KM.anomalyScore(SB) : 0.0;
}
bool shouldTrain(const TimePoint &TP) const {
return (LastTrainedAt + TrainEvery) < TP;
}
bool isTrained() const { return Trained; }
double updateTrainingDuration(double Duration) {
return TrainingDuration.exchange(Duration);
}
private:
std::pair<CalculatedNumber *, size_t> getCalculatedNumbers();
public:
TimePoint LastTrainedAt{Seconds{0}};
private:
Seconds TrainEvery;
KMeans KM;
std::atomic<bool> Trained{false};
std::atomic<double> TrainingDuration{0.0};
};
class PredictableDimension : public TrainableDimension {
public:
PredictableDimension(RRDDIM *RD) : TrainableDimension(RD) {}
std::pair<MLResult, bool> predict();
void addValue(CalculatedNumber Value, bool Exists);
bool isAnomalous() { return AnomalyBit; }
private:
CalculatedNumber AnomalyScore{0.0};
std::atomic<bool> AnomalyBit{false};
std::vector<CalculatedNumber> CNs;
};
class DetectableDimension : public PredictableDimension {
public:
DetectableDimension(RRDDIM *RD) : PredictableDimension(RD) {}
std::pair<bool, double> detect(size_t WindowLength, bool Reset) {
bool AnomalyBit = isAnomalous();
if (Reset)
NumSetBits = BBC.numSetBits();
NumSetBits += AnomalyBit;
BBC.insert(AnomalyBit);
double AnomalyRate = static_cast<double>(NumSetBits) / WindowLength;
return { AnomalyBit, AnomalyRate };
}
private:
BitBufferCounter BBC{static_cast<size_t>(Cfg.ADMinWindowSize)};
size_t NumSetBits{0};
};
using Dimension = DetectableDimension;
} // namespace ml
#endif /* ML_DIMENSION_H */
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