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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) { }
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 {
RRDSET *RS = RD->rrdset;
std::stringstream SS;
SS << RS->context << "|" << RS->id << "|" << RD->name;
return SS.str();
}
bool isActive() const {
if (rrdset_flag_check(RD->rrdset, RRDSET_FLAG_OBSOLETE))
return false;
if (rrddim_flag_check(RD, RRDDIM_FLAG_OBSOLETE))
return false;
return true;
}
void setAnomalyRateRD(RRDDIM *ARRD) { AnomalyRateRD = ARRD; }
RRDDIM *getAnomalyRateRD() const { return AnomalyRateRD; }
void setAnomalyRateRDName(const char *Name) const {
rrddim_set_name(AnomalyRateRD->rrdset, AnomalyRateRD, Name);
}
virtual ~RrdDimension() {
rrddim_free(AnomalyRateRD->rrdset, AnomalyRateRD);
}
private:
RRDDIM *RD;
RRDDIM *AnomalyRateRD;
struct 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 {
if (ConstantModel)
return false;
return (LastTrainedAt + TrainEvery) < TP;
}
bool isTrained() const { return Trained; }
private:
std::pair<CalculatedNumber *, size_t> getCalculatedNumbers();
public:
TimePoint LastTrainedAt{Seconds{0}};
protected:
std::atomic<bool> ConstantModel{false};
private:
Seconds TrainEvery;
KMeans KM;
std::atomic<bool> Trained{false};
};
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; }
void updateAnomalyBitCounter(RRDSET *RS, unsigned Elapsed, bool IsAnomalous) {
AnomalyBitCounter += IsAnomalous;
if (Elapsed == Cfg.DBEngineAnomalyRateEvery) {
double AR = static_cast<double>(AnomalyBitCounter) / Cfg.DBEngineAnomalyRateEvery;
rrddim_set_by_pointer(RS, getAnomalyRateRD(), AR * 1000);
AnomalyBitCounter = 0;
}
}
private:
CalculatedNumber AnomalyScore{0.0};
std::atomic<bool> AnomalyBit{false};
unsigned AnomalyBitCounter{0};
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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