{

  gSystem->Load("libMLP");
  
  TFile input("exampleTree.root");
  TTree *inTree = input.Get("smallTree");
  
  TMultiLayerPerceptron *mlp = new TMultiLayerPerceptron("@trkPHperPlane, @eventPlanes, @shwPHperStrip:5:type!", "1+(type==0)", inTree, "Entry$%5","!(Entry$%5)");
//  mlp->SetLearningMethod(TMultiLayerPerceptron::kStochastic);
  mlp->Train(200,"text,graph,update=10");
  mlp->DrawResult(0,"test");
  cout << "Test set distribution.  Mean : " << MLP_test0.GetMean() << " RMS " << MLP_test0.GetRMS() << endl;

}