Adding upstream version 1.39.0.upstream/1.39.0

Signed-off-by: Daniel Baumann <daniel.baumann@progress-linux.org>

1 files changed, 0 insertions, 198 deletions

diff --git a/ml/Dimension.h b/ml/Dimension.h
deleted file mode 100644
index 2b1adfff..00000000
--- a/ml/Dimension.h
+++ /dev/null
@@ -1,198 +0,0 @@
-// SPDX-License-Identifier: GPL-3.0-or-later
-
-#ifndef ML_DIMENSION_H
-#define ML_DIMENSION_H
-
-#include "Mutex.h"
-#include "Stats.h"
-#include "Query.h"
-#include "Config.h"
-
-#include "ml-private.h"
-
-namespace ml {
-
-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();
-}
-
-enum class MachineLearningStatus {
-    // Enable training/prediction
-    Enabled,
-
-    // Disable due to update every being different from the host's
-    DisabledDueToUniqueUpdateEvery,
-
-    // Disable because configuration pattern matches the chart's id
-    DisabledDueToExcludedChart,
-};
-
-enum class TrainingStatus {
-    // We don't have a model for this dimension
-    Untrained,
-
-    // Request for training sent, but we don't have any models yet
-    PendingWithoutModel,
-
-    // Request to update existing models sent
-    PendingWithModel,
-
-    // Have a valid, up-to-date model
-    Trained,
-};
-
-enum class MetricType {
-    // The dimension has constant values, no need to train
-    Constant,
-
-    // The dimension's values fluctuate, we need to generate a model
-    Variable,
-};
-
-struct TrainingRequest {
-    // Chart/dimension we want to train
-    STRING *ChartId;
-    STRING *DimensionId;
-    
-    // Creation time of request
-    time_t RequestTime;
-    
-    // First/last entry of this dimension in DB
-    // at the point the request was made
-    time_t FirstEntryOnRequest;
-    time_t LastEntryOnRequest;
-};
-
-void dumpTrainingRequest(const TrainingRequest &TrainingReq, const char *Prefix);
-
-enum TrainingResult {
-    // We managed to create a KMeans model
-    Ok,
-    // Could not query DB with a correct time range
-    InvalidQueryTimeRange,
-    // Did not gather enough data from DB to run KMeans
-    NotEnoughCollectedValues,
-    // Acquired a null dimension
-    NullAcquiredDimension,
-    // Chart is under replication
-    ChartUnderReplication,
-};
-
-struct TrainingResponse {
-    // Time when the request for this response was made
-    time_t RequestTime;
-
-    // First/last entry of the dimension in DB when generating the request
-    time_t FirstEntryOnRequest;
-    time_t LastEntryOnRequest;
-    
-    // First/last entry of the dimension in DB when generating the response
-    time_t FirstEntryOnResponse;
-    time_t LastEntryOnResponse;
-    
-    // After/Before timestamps of our DB query
-    time_t QueryAfterT;
-    time_t QueryBeforeT;
-    
-    // Actual after/before returned by the DB query ops
-    time_t DbAfterT;
-    time_t DbBeforeT;
-    
-    // Number of doubles returned by the DB query
-    size_t CollectedValues;
-    
-    // Number of values we return to the caller
-    size_t TotalValues;
-
-    // Result of training response
-    TrainingResult Result;
-};
-
-void dumpTrainingResponse(const TrainingResponse &TrainingResp, const char *Prefix);
-
-class Dimension {
-public:
-    Dimension(RRDDIM *RD) :
-        RD(RD),
-        MT(MetricType::Constant),
-        TS(TrainingStatus::Untrained),
-        TR(),
-        LastTrainingTime(0)
-    {
-        if (simple_pattern_matches(Cfg.SP_ChartsToSkip, rrdset_name(RD->rrdset)))
-            MLS = MachineLearningStatus::DisabledDueToExcludedChart;
-        else if (RD->update_every != RD->rrdset->rrdhost->rrd_update_every)
-            MLS = MachineLearningStatus::DisabledDueToUniqueUpdateEvery;
-        else
-            MLS = MachineLearningStatus::Enabled;
-
-        Models.reserve(Cfg.NumModelsToUse);
-    }
-
-    RRDDIM *getRD() const {
-        return RD;
-    }
-
-    unsigned updateEvery() const {
-        return RD->update_every;
-    }
-
-    MetricType getMT() const {
-        return MT;
-    }
-
-    TrainingStatus getTS() const {
-        return TS;
-    }
-
-    MachineLearningStatus getMLS() const {
-        return MLS;
-    }
-
-    TrainingResult trainModel(const TrainingRequest &TR);
-
-    void scheduleForTraining(time_t CurrT);
-
-    bool predict(time_t CurrT, CalculatedNumber Value, bool Exists);
-
-    std::vector<KMeans> getModels();
-    
-    void dump() const;
-
-private:
-    TrainingRequest getTrainingRequest(time_t CurrT) const {
-        return TrainingRequest {
-                string_dup(RD->rrdset->id),
-                string_dup(RD->id),
-                CurrT,
-                rrddim_first_entry_s(RD),
-                rrddim_last_entry_s(RD)
-        };
-    }
-
-private:
-    std::pair<CalculatedNumber *, TrainingResponse> getCalculatedNumbers(const TrainingRequest &TrainingReq);
-
-public:
-    RRDDIM *RD;
-    MetricType MT;
-    TrainingStatus TS;
-    TrainingResponse TR;
-
-    time_t LastTrainingTime;
-
-    MachineLearningStatus MLS;
-
-    std::vector<CalculatedNumber> CNs;
-    DSample Feature;
-    std::vector<KMeans> Models;
-    Mutex M;
-};
-
-} // namespace ml
-
-#endif /* ML_DIMENSION_H */