| 1 | #ifndef MARS_MJTrainSeparation
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| 2 | #define MARS_MJTrainSeparation
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| 3 |
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| 4 | #ifndef MARS_MJTrainRanForest
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| 5 | #include "MJTrainRanForest.h"
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| 6 | #endif
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| 7 |
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| 8 | #ifndef MARS_MDataSet
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| 9 | #include "MDataSet.h"
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| 10 | #endif
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| 11 |
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| 12 | class MH3;
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| 13 |
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| 14 | class MJTrainSeparation : public MJTrainRanForest
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| 15 | {
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| 16 | private:
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| 17 | MDataSet fDataSetTest;
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| 18 | MDataSet fDataSetTrain;
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| 19 |
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| 20 | UInt_t fNumTrainOn;
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| 21 | UInt_t fNumTrainOff;
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| 22 |
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| 23 | UInt_t fNumTestOn;
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| 24 | UInt_t fNumTestOff;
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| 25 |
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| 26 | TList fPreTasksOn;
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| 27 | TList fPreTasksOff;
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| 28 | TList fPostTasksOn;
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| 29 | TList fPostTasksOff;
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| 30 |
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| 31 | Bool_t fAutoTrain;
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| 32 | Bool_t fUseRegression;
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| 33 | Bool_t fEnableWeightsOn;
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| 34 | Bool_t fEnableWeightsOff;
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| 35 |
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| 36 | Float_t fFlux;
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| 37 |
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| 38 | void DisplayResult(MH3 &h31, MH3 &h32);
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| 39 |
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| 40 | Bool_t GetEventsProduced(MDataSet &set, Double_t &num, Double_t &min, Double_t &max) const;
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| 41 | Double_t GetDataRate(MDataSet &set, Double_t &num) const;
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| 42 | Double_t GetNumMC(MDataSet &set) const;
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| 43 | Bool_t AutoTrain(MDataSet &set, UInt_t &on, UInt_t &off);
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| 44 |
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| 45 | public:
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| 46 | MJTrainSeparation() :
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| 47 | fNumTrainOn((UInt_t)-1), fNumTrainOff((UInt_t)-1),
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| 48 | fNumTestOn((UInt_t)-1), fNumTestOff((UInt_t)-1),
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| 49 | fAutoTrain(kFALSE), fUseRegression(kFALSE),
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| 50 | fEnableWeightsOn(kFALSE), fEnableWeightsOff(kFALSE), fFlux(2e-7)
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| 51 | { }
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| 52 |
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| 53 | void SetDataSetTrain(const MDataSet &ds, UInt_t non=(UInt_t)-1, UInt_t noff=(UInt_t)-1)
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| 54 | {
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| 55 | ds.Copy(fDataSetTrain);
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| 56 |
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| 57 | fDataSetTrain.SetNumAnalysis(1);
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| 58 |
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| 59 | fNumTrainOn = non;
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| 60 | fNumTrainOff = noff;
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| 61 | }
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| 62 | void SetDataSetTest(const MDataSet &ds, UInt_t non=(UInt_t)-1, UInt_t noff=(UInt_t)-1)
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| 63 | {
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| 64 | ds.Copy(fDataSetTest);
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| 65 |
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| 66 | fDataSetTest.SetNumAnalysis(1);
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| 67 |
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| 68 | fNumTestOn = non;
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| 69 | fNumTestOff = noff;
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| 70 | }
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| 71 |
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| 72 | void AddPreTaskOn(MTask *t) { Add(fPreTasksOn, t); }
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| 73 | void AddPreTaskOn(const char *rule,
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| 74 | const char *name="MWeight") { AddPar(fPreTasksOn, rule, name); }
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| 75 |
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| 76 | void AddPreTaskOff(MTask *t) { Add(fPreTasksOff, t); }
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| 77 | void AddPreTaskOff(const char *rule,
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| 78 | const char *name="MWeight") { AddPar(fPreTasksOff, rule, name); }
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| 79 |
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| 80 | void AddPostTaskOn(MTask *t) { Add(fPostTasksOn, t); }
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| 81 | void AddPostTaskOn(const char *rule,
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| 82 | const char *name="MWeight") { AddPar(fPostTasksOn, rule, name); }
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| 83 |
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| 84 | void AddPostTaskOff(MTask *t) { Add(fPostTasksOff, t); }
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| 85 | void AddPostTaskOff(const char *rule,
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| 86 | const char *name="MWeight") { AddPar(fPostTasksOff, rule, name); }
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| 87 |
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| 88 | void SetWeightsOn(const char *rule) { if (fEnableWeightsOn) return; fEnableWeightsOn=kTRUE; AddPostTaskOn(rule); }
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| 89 | void SetWeightsOn(MTask *t) { if (fEnableWeightsOn) return; fEnableWeightsOn=kTRUE; AddPostTaskOn(t); }
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| 90 |
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| 91 | void SetWeightsOff(const char *rule) { if (fEnableWeightsOff) return; fEnableWeightsOff=kTRUE; AddPostTaskOff(rule); }
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| 92 | void SetWeightsOff(MTask *t) { if (fEnableWeightsOff) return; fEnableWeightsOff=kTRUE; AddPostTaskOff(t); }
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| 93 |
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| 94 | void SetFlux(Float_t f) { fFlux = f; }
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| 95 |
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| 96 | void EnableAutoTrain(Bool_t b=kTRUE) { fAutoTrain = b; }
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| 97 | void EnableRegression(Bool_t b=kTRUE) { fUseRegression = b; }
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| 98 | void EnableClassification(Bool_t b=kTRUE) { fUseRegression = !b; }
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| 99 |
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| 100 | Bool_t Train(const char *out);
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| 101 |
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| 102 | ClassDef(MJTrainSeparation, 0)//Class to train Random Forest gamma-/background-separation
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| 103 | };
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| 104 |
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| 105 | #endif
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