/*
 *  
 *  S.Vanniarajan  V.Suyam.Jothi@kvi.nl
 *
 *  This is the Mutivariate Event classification tool
 *  designed for PANDAROOT Analysis package for 
 *  PANDA Experiments.
 *       TMVA(Toolkit for MultiVariate Analysis) is a 
 *  two class classifier(signal and background). 
 *  available with ROOT package. mainly used for 
 *  Event Classification in High Energy Physics Experiments.
 *         
 *       This tool here is Designed from TMVA, for Multiclass 
 * Classification purpose. 
 *
 *                  S.Vanniarajan  01-08-08
 */


#include "PndMultiClassMVA.h"
using namespace std;
PndMultiClassMVA::PndMultiClassMVA()
{
 fNCLASS = 0;
 fNVAR   = 0;
 fNSigTrain = "100";
 fNSigTest = "100";
 fNSigTrain = "100";
 fNSigTest = "100";
 fPruneStrengthBDT = "4.0";
 fBoostTypeBDT = "AdaBoost";
 fNCutsBDT = "10";
 fNTreeBDT = "100";
 fNKNN     = "40";
}
/*
PndMultiClassMVA::PndMultiClassMVA( Int_t NoClass, Int_t NoPar, TString fname,TString anaName )
{
  fNCLASS   = NoClass;
  fNVAR     = NoPar;
  fFILENAME = fname;
  fAPPNAME  = anaName;
}
*/
PndMultiClassMVA::~PndMultiClassMVA()
{
}


void PndMultiClassMVA::AddVar(string varName)
{
  fVarNameArray.push_back(varName);
  fNVAR = fNVAR +1;
}

void PndMultiClassMVA::AddClass(string className)
{
  vector<pair<string,string> > stringVec;
  fClassNameArray.push_back(className);
  fInFileNameArray.insert(pair<string,vector<pair<string,string> > >(className,stringVec));
  fNCLASS = fNCLASS +1;
}
/*
void PndMultiClassMVA::AddInFile(string className,string simFileName,string recoFileName)
{
 pair<string,string> stringPair;
 stringPair.first = simFileName;
 stringPair.second = recoFileName;
 cout<<"  -I-  Adding Input files for the class"<<className<<endl;
 if (fInFileNameArray.find(className) == fInFileNameArray.end()) 
  {
   cout<<"  -E- This class named "<<className<<" is not a part of your problem"<<endl;
   return; 
  }
  else
  {
   (fInFileNameArray.find(className)->second).push_back(stringPair); 
  }

}

void PndMultiClassMVA::GenerateTree()
{
 cout<<"  -I-  Generating the Input Tree"<<endl;
 system("sleep 2");
 for(int i=0; i <fNCLASS; i++)
 {
  string className;
  vector < pair<string,string> > fileNameVec;
  className = fClassNameArray[i];
  fileNameVec = fInFileNameArray.find(className)->second;
  for(int j = 0; j < fileNameVec.size();j++ )
   {
    string simFile,recoFile;
    pair<string,string> filePair;
    filePair = fileNameVec.at(j);
    cout<<filePair.first<<"  "<<filePair.second<<endl;   
   }  
  
 }
}
*/

void PndMultiClassMVA::WriteConfigFile()
{
  TString fileName = fAPPNAME + ".dat";
  TString varString = ":";
  TString classString = ":";
  fstream config;
  config.open(fileName,fstream::out);
  config<<fNVAR<<endl;
  config<<fNCLASS<<endl;

  for (int i = 0; i < fNVAR; i++ ) {
           varString = varString + fVarNameArray.at(i) + ":";
     }

  for (int i = 0; i < fNCLASS; i++ ) {
           classString = classString + fClassNameArray.at(i) + ":";
     }

  config<<varString<<endl;
  config<<classString<<endl;

  config.close(); 
}

void PndMultiClassMVA::TrainTest()
{
 TFile *input(0);
  if (fNCLASS < 2 ) {
        std::cout<< " you need atleast two classes for the classification "<<endl;
        exit(0);
      }

  else if (fNVAR < 2 ) {
        std::cout<< " you need atleast two variables for the Multivariate Analysis "<<endl;
        exit(0);
      }

  else if (!gSystem->AccessPathName( fINFILENAME )) {
         std::cout << "--- BDTAnalysis  : accessing " << fINFILENAME << std::endl;
         input = TFile::Open( fINFILENAME );
      } 
  else if (!input) {
         std::cout << "ERROR: could not open data file" << std::endl;
        exit(0);
      }
   {

 TTree    *TreeArray[50]; // = new TTree[10];
	 for (int i = 0; i < fNCLASS ; i++)
 	{
  	  TString s,treeName;
 	  treeName = fClassNameArray.at(i);
   	  cout<< (TTree*)input->Get(treeName)<<endl;  
   	  TreeArray[i] = (TTree*)input->Get(treeName);  
        }

 	for (int i = 0 ; i < fNCLASS ; i++ )
  	{
	  TString s,OutFileName,anaName;
	  anaName = fAPPNAME +fClassNameArray.at(i);
	  s = s +".root";
	  OutFileName = fClassNameArray.at(i) + ".root";

	  TFile* outputFile = TFile::Open( OutFileName, "RECREATE" );
 	  cout<<"hai"<<endl;
	  TMVA::Factory *factory = new TMVA::Factory( anaName, outputFile, Form("!V:%sColor", 0?"!":"") );
	   Double_t signalWeight     = 1.0;
	   Double_t backgroundWeight = 1.0;

	    for (int j = 0 ; j < fNCLASS ; j++ )
	      {
	       if (i == j ) 
        	  {
      		    factory->AddSignalTree(TreeArray[j],signalWeight);  
          	  }
  	     else
        	  {
         	    factory->AddBackgroundTree(TreeArray[j],backgroundWeight); 
          	  }
     	       }
  
   	for (int k = 0 ; k < fNVAR ; k++ )
   	{
	   TString varName;
	   varName = fVarNameArray.at(k);
	   factory->AddVariable(varName,'F');
       	}
	  TCut mycuts = ""; // for example: TCut mycuts = "abs(var1)<0.5 && abs(var2-0.5)<1";
	  TCut mycutb = ""; // for example: TCut mycutb = "abs(var1)<0.5";
          TString trainConfig = "NSigTrain=" + fNSigTrain + ":NBkgTrain=" + fNBkgTrain + ":NSigTest=" + fNSigTest + ":NBkgTest=" +fNBkgTest
                                + "SplitMode=Random:!V";
  cout<<trainConfig<<endl; 
	  factory->PrepareTrainingAndTestTree( mycuts, mycutb, trainConfig ); 

          TString BdtConfig = "!H:!V:NTrees=" + fNTreeBDT + ":BoostType=" + fBoostTypeBDT + ":SeparationType=GiniIndex:nCuts=" + fNCutsBDT +
                                "PruneMethod=CostComplexity:PruneStrength=" + fPruneStrengthBDT; 
	  factory->BookMethod( TMVA::Types::kBDT, "BDT", BdtConfig); 
          TString kNNConfig = "nkNN=" + fNKNN + ":TreeOptDepth=6:ScaleFrac=0.8:!UseKernel:!Trim";
          factory->BookMethod( TMVA::Types::kKNN, "KNN", kNNConfig);

          factory->BookMethod( TMVA::Types::kMLP, "MLP", "Normalise:H:!V:NeuronType=tanh:NCycles=200:HiddenLayers=N+1,N:TestRate=5"); 

	  factory->TrainAllMethods();
	  factory->TestAllMethods();
	  factory->EvaluateAllMethods();    
	  outputFile->Close();
	  delete factory;
        }
   }
    WriteConfigFile();
}

ClassImp(PndMultiClassMVA)