ROOT
6.06/09
Reference Guide
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Definition at line 208 of file DataSetFactory.h.
Classes | |
class | EventStats |
Public Member Functions | |
DataSet * | CreateDataSet (DataSetInfo &, DataInputHandler &) |
steering the creation of a new dataset More... | |
Static Public Member Functions | |
static DataSetFactory & | Instance () |
static void | destroyInstance () |
static DataSetFactory * | NewInstance () |
static void | destroyNewInstance (DataSetFactory *iOther) |
Protected Member Functions | |
~DataSetFactory () | |
destructor More... | |
DataSetFactory () | |
constructor More... | |
DataSet * | BuildInitialDataSet (DataSetInfo &, TMVA::DataInputHandler &) |
if no entries, than create a DataSet with one Event which uses dynamic variables (pointers to variables) More... | |
DataSet * | BuildDynamicDataSet (DataSetInfo &) |
void | BuildEventVector (DataSetInfo &dsi, DataInputHandler &dataInput, EventVectorOfClassesOfTreeType &eventsmap, EvtStatsPerClass &eventCounts) |
build empty event vectors distributes events between kTraining/kTesting/kMaxTreeType More... | |
DataSet * | MixEvents (DataSetInfo &dsi, EventVectorOfClassesOfTreeType &eventsmap, EvtStatsPerClass &eventCounts, const TString &splitMode, const TString &mixMode, const TString &normMode, UInt_t splitSeed) |
Select and distribute unassigned events to kTraining and kTesting Bool_t emptyUndefined = kTRUE;. More... | |
void | RenormEvents (DataSetInfo &dsi, EventVectorOfClassesOfTreeType &eventsmap, const EvtStatsPerClass &eventCounts, const TString &normMode) |
renormalisation of the TRAINING event weights -none (kind of obvious) . More... | |
void | InitOptions (DataSetInfo &dsi, EvtStatsPerClass &eventsmap, TString &normMode, UInt_t &splitSeed, TString &splitMode, TString &mixMode) |
the dataset splitting More... | |
TMatrixD * | CalcCorrelationMatrix (DataSet *, const UInt_t classNumber) |
computes correlation matrix for variables "theVars" in tree; "theType" defines the required event "type" ("type" variable must be present in tree) More... | |
TMatrixD * | CalcCovarianceMatrix (DataSet *, const UInt_t classNumber) |
compute covariance matrix More... | |
void | CalcMinMax (DataSet *, DataSetInfo &dsi) |
compute covariance matrix More... | |
void | ResetBranchAndEventAddresses (TTree *) |
void | ResetCurrentTree () |
void | ChangeToNewTree (TreeInfo &, const DataSetInfo &) |
While the data gets copied into the local training and testing trees, the input tree can change (for intance when changing from signal to background tree, or using TChains as input) The TTreeFormulas, that hold the input expressions need to be reassociated with the new tree, which is done here. More... | |
Bool_t | CheckTTreeFormula (TTreeFormula *ttf, const TString &expression, Bool_t &hasDollar) |
checks a TTreeFormula for problems More... | |
Bool_t | Verbose () |
MsgLogger & | Log () const |
message logger More... | |
Protected Attributes | |
Bool_t | fVerbose |
TString | fVerboseLevel |
Verbosity. More... | |
Bool_t | fScaleWithPreselEff |
VerboseLevel. More... | |
TTree * | fCurrentTree |
how to deal with requested #events in connection with preselection cuts More... | |
UInt_t | fCurrentEvtIdx |
the tree, events are currently read from More... | |
std::vector< TTreeFormula * > | fInputFormulas |
the current event (to avoid reading of the same event) More... | |
std::vector< TTreeFormula * > | fTargetFormulas |
input variables More... | |
std::vector< TTreeFormula * > | fCutFormulas |
targets More... | |
std::vector< TTreeFormula * > | fWeightFormula |
cuts More... | |
std::vector< TTreeFormula * > | fSpectatorFormulas |
weights More... | |
MsgLogger * | fLogger |
spectators More... | |
Static Protected Attributes | |
static DataSetFactory * | fgInstance = 0 |
Private Types | |
typedef std::vector< Event * > | EventVector |
typedef std::vector< EventVector > | EventVectorOfClasses |
typedef std::map< Types::ETreeType, EventVectorOfClasses > | EventVectorOfClassesOfTreeType |
typedef std::map< Types::ETreeType, EventVector > | EventVectorOfTreeType |
typedef std::vector< Double_t > | ValuePerClass |
typedef std::map< Types::ETreeType, ValuePerClass > | ValuePerClassOfTreeType |
typedef std::vector< int > | NumberPerClass |
typedef std::vector< EventStats > | EvtStatsPerClass |
#include <TMVA/DataSetFactory.h>
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Definition at line 210 of file DataSetFactory.h.
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Definition at line 211 of file DataSetFactory.h.
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Definition at line 212 of file DataSetFactory.h.
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Definition at line 213 of file DataSetFactory.h.
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Definition at line 245 of file DataSetFactory.h.
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Definition at line 244 of file DataSetFactory.h.
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Definition at line 215 of file DataSetFactory.h.
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Definition at line 216 of file DataSetFactory.h.
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destructor
Definition at line 114 of file DataSetFactory.cxx.
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constructor
Definition at line 100 of file DataSetFactory.cxx.
Referenced by Instance(), and NewInstance().
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Definition at line 153 of file DataSetFactory.cxx.
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build empty event vectors distributes events between kTraining/kTesting/kMaxTreeType
Definition at line 681 of file DataSetFactory.cxx.
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if no entries, than create a DataSet with one Event which uses dynamic variables (pointers to variables)
Definition at line 200 of file DataSetFactory.cxx.
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computes correlation matrix for variables "theVars" in tree; "theType" defines the required event "type" ("type" variable must be present in tree)
Definition at line 508 of file DataSetFactory.cxx.
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compute covariance matrix
Definition at line 539 of file DataSetFactory.cxx.
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compute covariance matrix
Definition at line 439 of file DataSetFactory.cxx.
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While the data gets copied into the local training and testing trees, the input tree can change (for intance when changing from signal to background tree, or using TChains as input) The TTreeFormulas, that hold the input expressions need to be reassociated with the new tree, which is done here.
Definition at line 291 of file DataSetFactory.cxx.
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checks a TTreeFormula for problems
Definition at line 249 of file DataSetFactory.cxx.
TMVA::DataSet * TMVA::DataSetFactory::CreateDataSet | ( | TMVA::DataSetInfo & | dsi, |
TMVA::DataInputHandler & | dataInput | ||
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steering the creation of a new dataset
Definition at line 130 of file DataSetFactory.cxx.
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inlinestatic |
Definition at line 253 of file DataSetFactory.h.
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inlinestatic |
Definition at line 258 of file DataSetFactory.h.
Referenced by TMVA::DataSetManager::~DataSetManager().
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the dataset splitting
Definition at line 598 of file DataSetFactory.cxx.
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inlinestatic |
Definition at line 252 of file DataSetFactory.h.
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message logger
Definition at line 330 of file DataSetFactory.h.
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Select and distribute unassigned events to kTraining and kTesting Bool_t emptyUndefined = kTRUE;.
Definition at line 948 of file DataSetFactory.cxx.
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inlinestatic |
Definition at line 257 of file DataSetFactory.h.
Referenced by TMVA::DataSetManager::CreateDataSet().
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renormalisation of the TRAINING event weights -none (kind of obvious) .
. use the weights as supplied by the user.. (we store however the relative weight for later use) -numEvents -equalNumEvents reweight the training events such that the sum of all
Definition at line 1389 of file DataSetFactory.cxx.
Referenced by MixEvents().
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Definition at line 303 of file DataSetFactory.h.
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Definition at line 308 of file DataSetFactory.h.
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the tree, events are currently read from
Definition at line 320 of file DataSetFactory.h.
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how to deal with requested #events in connection with preselection cuts
Definition at line 319 of file DataSetFactory.h.
Referenced by ResetCurrentTree().
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targets
Definition at line 325 of file DataSetFactory.h.
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Definition at line 264 of file DataSetFactory.h.
Referenced by destroyInstance(), and Instance().
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the current event (to avoid reading of the same event)
Definition at line 323 of file DataSetFactory.h.
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VerboseLevel.
Definition at line 316 of file DataSetFactory.h.
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weights
Definition at line 327 of file DataSetFactory.h.
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input variables
Definition at line 324 of file DataSetFactory.h.
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Definition at line 313 of file DataSetFactory.h.
Referenced by Verbose().
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Verbosity.
Definition at line 314 of file DataSetFactory.h.
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cuts
Definition at line 326 of file DataSetFactory.h.