#include <TTree.h>
#include <TMultiLayerPerceptron.h>
#include <TCanvas.h>
#include <TMLPAnalyzer.h>
#include "TChain.h"
#include "TSystem.h"
#include <iostream>

void NN()
{
    if(!gROOT->GetClass("TMultiLayerPerceptron"))
    {
        gSystem->Load("libMLP");
    }

    TChain *ch = new TChain("Tree","");
    ch->Add("Signal.root/Tree");
    ch->Add("Bkground.root/Tree");
    ch->Merge("all.root");

    TFile input("all.root");
    TTree *simu = (TTree *) input.Get("Tree");
    std::cout<<" Entries : "<<simu->GetEntries()<<std::endl;

    
    //NMdt - number of MDT hits per candidate
    //NTgcPhi number of TGC phi hits per candidate
    //NTgcEta number of TGC eta hits
    //NRpcPhi number of RPC phi hits per candidate
    //NRpcEta number of RPC eta hits
    //NMdtSeg - number of best(best Probapility) MDT segments
    //NTgcSeg - number of best TGC segments
    //NMdtHitsSeg - number of MDT hits in best segment
    //NTgcHitsSeg - number of TGC hits in best segment
    
    //end - cap
    TMultiLayerPerceptron *mlp = new TMultiLayerPerceptron("NMdt,NTgcPhi,NTgcEta,NMdtSeg,NTgcSeg,NMdtHitsSeg,NTgcHitsSeg:6:3:type",simu,"Entry$%2","Entry$/2");

    //barrel
    //TMultiLayerPerceptron *mlp = new TMultiLayerPerceptron("NMdt,NRpcPhi,NRpcEta,NMdtSeg,NMdtHitsSeg:4:2:type",simu,"Entry$%2","Entry$/2");

    mlp->Train(150,"text,graph,update=10");
  
    // Use TMLPAnalyzer to see what it looks for
    TCanvas* mlpa_canvas = new TCanvas("mlpa_canvas","Network analysis");
  
    mlpa_canvas->Divide(2,2);
    mlpa_canvas->SetFillColor(0);
    mlpa_canvas->SetBorderMode(0);
    mlpa_canvas->SetBorderSize(1);
    mlpa_canvas->SetFrameFillColor(1);
    mlpa_canvas->SetFrameFillStyle(0);
    
    TMLPAnalyzer ana(mlp);
  
    // Initialisation
    ana.GatherInformations();
  
    // output to the console
    ana.CheckNetwork();
    mlpa_canvas->cd(1);
  
    // shows how each variable influences the network
    ana.DrawDInputs();
    mlpa_canvas->cd(2);
  
    // shows the network structure
    mlp->Draw();
    mlpa_canvas->cd(3);
  
    // draws the resulting network
    ana.DrawNetwork(0,"type==1","type==0");
    mlpa_canvas->cd(4);
    
    // draws the resulting network
    ana.DrawNetwork(0,"type==1","type==0");
    mlp->Export("NameFile");

}