[skip-ci] Fix TMVA tutorials typos (root-project#8460)

- remove trailing spaces
- remove tabs

tutorials/tmva/TMVAClassification.C

@@ -449,7 +449,7 @@ int TMVAClassification( TString myMethodList = "" )
       // General layout.
       TString layoutString ("Layout=TANH|128,TANH|128,TANH|128,LINEAR");
 
-      // Define Training strategy. One could define multiple stratgey string separated by the "|" delimiter 
+      // Define Training strategy. One could define multiple strategy string separated by the "|" delimiter
 
       TString trainingStrategyString = ("TrainingStrategy=LearningRate=1e-2,Momentum=0.9,"
                                         "ConvergenceSteps=20,BatchSize=100,TestRepetitions=1,"

tutorials/tmva/TMVAClassificationApplication.C

@@ -5,7 +5,7 @@
 /// within an analysis module
 /// - Project   : TMVA - a Root-integrated toolkit for multivariate data analysis
 /// - Package   : TMVA
-/// - Exectuable: TMVAClassificationApplication
+/// - Executable: TMVAClassificationApplication
 ///
 /// \macro_output
 /// \macro_code
@@ -315,7 +315,7 @@ void TMVAClassificationApplication( TString myMethodList = "" )
       // Return the MVA outputs and fill into histograms
 
       if (Use["CutsGA"]) {
-         // Cuts is a special case: give the desired signal efficienciy
+         // Cuts is a special case: give the desired signal efficiency
          Bool_t passed = reader->EvaluateMVA( "CutsGA method", effS );
          if (passed) nSelCutsGA++;
       }
@@ -382,7 +382,7 @@ void TMVAClassificationApplication( TString myMethodList = "" )
    if (Use["CutsGA"]) {
 
       // test: retrieve cuts for particular signal efficiency
-      // CINT ignores dynamic_casts so we have to use a cuts-secific Reader function to acces the pointer
+      // CINT ignores dynamic_casts so we have to use a cuts-specific Reader function to acces the pointer
       TMVA::MethodCuts* mcuts = reader->FindCutsMVA( "CutsGA method" ) ;
 
       if (mcuts) {

tutorials/tmva/TMVAClassificationCategoryApplication.C

@@ -5,7 +5,7 @@
 /// (with categories) within an analysis module
 /// - Project   : TMVA - a Root-integrated toolkit for multivariate data analysis
 /// - Package   : TMVA
-/// - Exectuable: TMVAClassificationCategoryApplication
+/// - Executable: TMVAClassificationCategoryApplication
 ///
 /// \macro_output
 /// \macro_code
@@ -104,7 +104,7 @@ void TMVAClassificationCategoryApplication()
    //   but of course you can use different ones and copy the values inside the event loop
    //
    TTree* theTree = (TTree*)input->Get("TreeS");
-   std::cout << "--- Use signal sample for evalution" << std::endl;
+   std::cout << "--- Use signal sample for evaluation" << std::endl;
    theTree->SetBranchAddress( "var1", &var1 );
    theTree->SetBranchAddress( "var2", &var2 );
    theTree->SetBranchAddress( "var3", &var3 );

tutorials/tmva/TMVACrossValidation.C

@@ -4,7 +4,7 @@
 /// This macro provides an example of how to use TMVA for k-folds cross
 /// evaluation.
 ///
-/// As input data is used a toy-MC sample consisting of two guassian
+/// As input data is used a toy-MC sample consisting of two gaussian
 /// distributions.
 ///
 /// The output file "TMVA.root" can be analysed with the use of dedicated
@@ -174,7 +174,7 @@ int TMVACrossValidation(bool useRandomSplitting = false)
    //
    UInt_t numFolds = 2;
    TString analysisType = "Classification";
-   
+
    TString splitType = (useRandomSplitting) ? "Random" : "Deterministic";
 
    //
@@ -185,14 +185,14 @@ int TMVACrossValidation(bool useRandomSplitting = false)
    // random and independent of the data, generated only once. This last
    // property ensures that if a calibration is changed the same event will
    // still be assigned the same fold.
-   // 
+   //
    // This can be used to use the cross validated classifiers in application,
    // a technique that can simplify statistical analysis.
-   // 
-   // If you want to run TMVACrossValidationApplication, make sure you have 
+   //
+   // If you want to run TMVACrossValidationApplication, make sure you have
    // run this tutorial with Deterministic splitting type, i.e.
    // with the option useRandomSPlitting = false
-   // 
+   //
 
    TString splitExpr = (!useRandomSplitting) ? "int(fabs([eventID]))%int([NumFolds])" : "";
 

tutorials/tmva/TMVACrossValidationApplication.C

@@ -92,12 +92,12 @@ int TMVACrossValidationApplication()
                       " splitExpr?" << std::endl;
          exit(0);
       }
-      
+
    }
    {
       TString methodName = "Fisher";
       TString weightfile = TString("dataset/weights/") + jobname + "_" + methodName + TString(".weights.xml");
-      
+
       Bool_t weightfileExists = (gSystem->AccessPathName(weightfile) == kFALSE);
       if (weightfileExists) {
          reader->BookMVA(methodName, weightfile);
@@ -145,10 +145,10 @@ int TMVACrossValidationApplication()
       c->Divide(2,1);
       c->cd(1);
       histBDTG.DrawClone();
-      c->cd(2); 
+      c->cd(2);
       histFisher.DrawClone();
    }
-   else 
+   else
    { // Write histograms to output file
       TFile *target = new TFile("TMVACrossEvaluationApp.root", "RECREATE");
       histBDTG.Write();

tutorials/tmva/TMVACrossValidationRegression.C

@@ -4,7 +4,7 @@
 /// This macro provides an example of how to use TMVA for k-folds cross
 /// evaluation.
 ///
-/// As input data is used a toy-MC sample consisting of two guassian
+/// As input data is used a toy-MC sample consisting of two gaussian
 /// distributions.
 ///
 /// The output file "TMVA.root" can be analysed with the use of dedicated
@@ -108,7 +108,7 @@ int TMVACrossValidationRegression()
    TFile * inputFile = getDataFile(infileName);
 
    TMVA::DataLoader *dataloader=new TMVA::DataLoader("dataset");
-   
+
    dataloader->AddVariable("var1", "Variable 1", "units", 'F');
    dataloader->AddVariable("var2", "Variable 2", "units", 'F');
 
@@ -142,7 +142,7 @@ int TMVACrossValidationRegression()
    // This sets up a CrossValidation class (which wraps a TMVA::Factory
    // internally) for 2-fold cross validation. The data will be split into the
    // two folds randomly if `splitExpr` is `""`.
-   // 
+   //
    // One can also give a deterministic split using spectator variables. An
    // example would be e.g. `"int(fabs([spec1]))%int([NumFolds])"`.
    //

tutorials/tmva/TMVAGAexample.C

@@ -1,11 +1,11 @@
 /// \file
 /// \ingroup tutorial_tmva
 /// \notebook -nodraw
-/// This exectutable gives an example of a very simple use of the genetic algorithm
+/// This executable gives an example of a very simple use of the genetic algorithm
 /// of TMVA
 /// - Project   : TMVA - a Root-integrated toolkit for multivariate data analysis
 /// - Package   : TMVA
-/// - Exectuable: TMVAGAexample
+/// - Executable: TMVAGAexample
 ///
 /// \macro_output
 /// \macro_code
@@ -36,7 +36,7 @@ class MyFitness : public IFitterTarget {
        // to (int). In this case the variable-range has to be chosen +1 ( to get 0..5, take Interval(0,6) )
        // since the introduction of "Interval" ranges can be defined with a third parameter
        // which gives the number of bins within the interval. With that technique discrete values
-       // can be achieved easier. The random selection out of this discrete numbers is completly uniform.
+       // can be achieved easier. The random selection out of this discrete numbers is completely uniform.
        //
        Double_t EstimatorFunction( std::vector<Double_t> & factors ){
            //return (10.- (int)factors.at(0) *factors.at(1) + (int)factors.at(2));

tutorials/tmva/TMVAGAexample2.C

@@ -1,11 +1,11 @@
 /// \file
 /// \ingroup tutorial_tmva
 /// \notebook -nodraw
-/// This exectutable gives an example of a very simple use of the genetic algorithm
+/// This executable gives an example of a very simple use of the genetic algorithm
 /// of TMVA.
 /// - Project   : TMVA - a Root-integrated toolkit for multivariate data analysis
 /// - Package   : TMVA
-/// - Exectuable: TMVAGAexample
+/// - Executable: TMVAGAexample
 ///
 /// \macro_output
 /// \macro_code
@@ -38,7 +38,7 @@ class MyFitness : public IFitterTarget {
        // to (int). In this case the variable-range has to be chosen +1 ( to get 0..5, take Interval(0,6) )
        // since the introduction of "Interval" ranges can be defined with a third parameter
        // which gives the number of bins within the interval. With that technique discrete values
-       // can be achieved easier. The random selection out of this discrete numbers is completly uniform.
+       // can be achieved easier. The random selection out of this discrete numbers is completely uniform.
        //
        Double_t EstimatorFunction( std::vector<Double_t> & factors ){
            //return (10.- (int)factors.at(0) *factors.at(1) + (int)factors.at(2));

tutorials/tmva/TMVAMinimalClassification.C

@@ -6,11 +6,11 @@
 ///
 /// This is intended as a simple foundation to build on. It assumes you are
 /// familiar with TMVA already. As such concepts like the Factory, the DataLoader
-/// and others are not explained. For descriptions and tutuorials use the TMVA
+/// and others are not explained. For descriptions and tutorials use the TMVA
 /// User's Guide (https://root.cern.ch/root-user-guides-and-manuals under TMVA)
 /// or the more detailed examples provided with TMVA e.g. TMVAClassification.C.
 ///
-/// Sets up a minimal binary classification example with two slighly overlapping
+/// Sets up a minimal binary classification example with two slightly overlapping
 /// 2-D gaussian distributions and trains a BDT classifier to discriminate the
 /// data.
 ///
@@ -63,7 +63,7 @@ TTree *genTree(Int_t nPoints, Double_t offset, Double_t scale, UInt_t seed = 100
 }
 
 //
-// Minimal setup for perfroming binary classification in TMVA.
+// Minimal setup for performing binary classification in TMVA.
 //
 // Modify the setup to your liking and run with
 //    `root -l -b -q TMVAMinimalClassification.C`.
@@ -75,7 +75,7 @@ void TMVAMinimalClassification()
    TString outputFilename = "out.root";
    TFile *outFile = new TFile(outputFilename, "RECREATE");
 
-   // Data generatration
+   // Data generation
    TTree *signalTree = genTree(1000, 0.0, 2.0, 100);
    TTree *backgroundTree = genTree(1000, 1.0, 2.0, 101);
 

tutorials/tmva/TMVAMulticlassApplication.C

@@ -101,7 +101,7 @@ void TMVAMulticlassApplication( TString myMethodList = "" )
    // book output histograms
    UInt_t nbin = 100;
    TH1F *histMLP_signal(0), *histBDTG_signal(0), *histFDAGA_signal(0), *histPDEFoam_signal(0);
-   TH1F *histDLCPU_signal(0), *histDLGPU_signal(0); 
+   TH1F *histDLCPU_signal(0), *histDLGPU_signal(0);
    if (Use["MLP"])
       histMLP_signal    = new TH1F( "MVA_MLP_signal",    "MVA_MLP_signal",    nbin, 0., 1.1 );
    if (Use["BDTG"])