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// SPDX-License-Identifier: GPL-3.0-or-later
#ifndef ML_DIMENSION_H
#define ML_DIMENSION_H
#include "Query.h"
#include "Config.h"
#include "ml-private.h"
namespace ml {
enum class MLResult {
Success = 0,
MissingData,
NaN,
};
static inline std::string getMLDimensionID(RRDDIM *RD) {
RRDSET *RS = RD->rrdset;
std::stringstream SS;
SS << rrdset_context(RS) << "|" << rrdset_id(RS) << "|" << rrddim_name(RD);
return SS.str();
}
class Dimension {
public:
Dimension(RRDDIM *RD) :
RD(RD),
LastTrainedAt(Seconds(0)),
Trained(false),
ConstantModel(false),
AnomalyScore(0.0),
AnomalyBit(0)
{ }
RRDDIM *getRD() const {
return RD;
}
unsigned updateEvery() const {
return RD->update_every;
}
time_t latestTime() const {
return Query(RD).latestTime();
}
time_t oldestTime() const {
return Query(RD).oldestTime();
}
bool isTrained() const {
return Trained;
}
bool isAnomalous() const {
return AnomalyBit;
}
bool shouldTrain(const TimePoint &TP) const;
bool isActive() const;
MLResult trainModel();
bool predict(CalculatedNumber Value, bool Exists);
std::pair<bool, double> detect(size_t WindowLength, bool Reset);
std::array<KMeans, 1> getModels();
private:
std::pair<CalculatedNumber *, size_t> getCalculatedNumbers();
public:
RRDDIM *RD;
TimePoint LastTrainedAt;
std::atomic<bool> Trained;
std::atomic<bool> ConstantModel;
CalculatedNumber AnomalyScore;
std::atomic<bool> AnomalyBit;
std::vector<CalculatedNumber> CNs;
std::array<KMeans, 1> Models;
std::mutex Mutex;
};
} // namespace ml
#endif /* ML_DIMENSION_H */
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