/******************************************************************************
*  $Id: CbmRichRingSelectNeuralNet.cxx,v 1.1 2006/09/13 14:53:31 hoehne Exp $
*
*  Class  : CbmRichRingSelect2DCuts
*  Description : Implementation for concrete RICH ring selection algorithm:
*                reject rings using a trained neural net (input file with weights needed!)
*                store resulting value (0-1) in "SelectionNN":
*                0 = good rings
*                1 = rings to be rejected
*                --> choose a value in between depending on required purity/ efficiency
*
*  Author : Simeon Lebedev
*  E-mail : salebedev@jinr.ru
*
*******************************************************************************
*  $Log: CbmRichRingSelectNeuralNet.cxx,v $
*  Revision 1.1  2006/09/13 14:53:31  hoehne
*  initial version
*
*  
*
*******************************************************************************/


#include "CbmRichRingSelectNeuralNet.h"
#include "CbmRichHit.h"
#include "CbmRichRing.h"
#include "TMultiLayerPerceptron.h"
#include "TTree.h"
using namespace std;

//--------------------------------------------------------------------------
// -----   Default constructor   -------------------------------------------
CbmRichRingSelectNeuralNet::CbmRichRingSelectNeuralNet()
{
	fVerbose = 1;
}

// -----   Standard constructor   ------------------------------------------
CbmRichRingSelectNeuralNet::CbmRichRingSelectNeuralNet ( Int_t verbose, const char* NNFile )
{
	fVerbose = verbose;
	fNeuralNetWeights = NNFile;
}

// -----   Destructor   ----------------------------------------------------
CbmRichRingSelectNeuralNet::~CbmRichRingSelectNeuralNet()
{}

// -----   Initialization   ----------------------------------------------------
void CbmRichRingSelectNeuralNet::Init ()
{
    TTree *simu = new TTree ("MonteCarlo","MontecarloData");
	Float_t x1,x2,x3,x5,x7,x8;
	Int_t x4,x6,x9;

	simu->Branch("x1", &x1,"x1/F");
	simu->Branch("x2", &x2,"x2/F");
	simu->Branch("x3", &x3,"x3/F");
	simu->Branch("x4", &x4,"x4/I");
	simu->Branch("x5", &x5,"x5/F");
	simu->Branch("x6", &x6,"x6/I");
	simu->Branch("x7", &x7,"x7/F");
	simu->Branch("x8", &x8,"x8/F");
	simu->Branch("x9", &x9,"x9/I");

    fNN = new TMultiLayerPerceptron("x1,x2,x3,x4,x5,x6,x7,x8:30:x9",simu);

    //char* NeuralNetWeights = "NeuralNet_RingSelection_Weights.txt";
    cout << "-I- CbmRichRingSelectNeuralNet: get NeuralNet weight parameters from: " << fNeuralNetWeights << endl;
    fNN->LoadWeights(fNeuralNetWeights);
}

// -----   Exec   ----------------------------------------------------
void CbmRichRingSelectNeuralNet::DoSelect(CbmRichRing* ring, TClonesArray* rProjArray) 
{

	Double_t lChi2 = GetChi2(ring);
	if (fVerbose > 1) 
	 cout << " RingSelectNN: GetChi2 = " << lChi2 << endl;
	UInt_t lTBSum = GetTBSum(ring);
	if (fVerbose > 1) 
	 cout << " RingSelectNN: GetTBSum = " << lTBSum << endl;
	Double_t lAngle = GetAngle(ring);
	if (fVerbose > 1) 
	 cout << " RingSelectNN: GetAngle = " << lAngle << endl;
	Double_t lRadPos = GetRadPos(ring);
	if (fVerbose > 1) 
	 cout << " RingSelectNN: GetRadPos = " << lRadPos << endl;
	Double_t lTrackD = GetTrackDist(ring,rProjArray);
	if (fVerbose > 1) 
	 cout << " RingSelectNN: GetTrackDist = " << lTrackD << endl;
	Int_t lNofHits = ring->GetNoOfHits();
	if (fVerbose > 1) 
	 cout << " RingSelectNN: GetNoOfHits = " << lNofHits << endl;
	Double_t lRadius = ring->GetRadius();
	if (fVerbose > 1) 
	 cout << " RingSelectNN: GetRadius = " << lRadius << endl;
	 

        Double_t nnPar[8];
	    nnPar[0] =  lAngle;
	    nnPar[1] =  lChi2;
        nnPar[2] =  lRadPos;
        nnPar[3] =  lTBSum;
	    nnPar[4] =  lTrackD;
	    nnPar[5] =  lNofHits;
	    nnPar[6] =  (Double_t)lTBSum/(Double_t)lNofHits;
	    nnPar[7] =  lRadius;
        Double_t nnEval = fNN->Evaluate(0,nnPar);
        if (fVerbose > 1) cout <<"nnEval = "<<nnEval<<endl;
	
	if (TMath::IsNaN(nnEval) == 1) {
	  cout << " -W- RingSelectNN: nnEval nan " << endl;
	  nnEval = 1.;
	  }
		
	ring->SetSelectionNN(nnEval);
    
	
}

// -------------------------------------------------------------------------
ClassImp(CbmRichRingSelectNeuralNet)