static void Main(string[] args)
{
WriteLine("Finding the files");
var backgroundEvents = Files.GetJZ(2).Select(e => e.Data);
//var signalHV125pi15Events = Files.Get125pi15();
//var signalHV125pi40Events = Files.Get125pi40();
var signalHV600pi100Events = Files.Get600pi150lt9m();
//var signalGet200pi25lt5mEvents = Files.Get200pi25lt5m();
//
// Do a simple cut training here
//
var t = TrainingIQueriable(signalHV600pi100Events, true)
.AsSignal("HV600pi100")
.Background(TrainingIQueriable(backgroundEvents, false), "J2Z")
.IgnoreVariables(p => p.lowestPtTrack);
var mCuts = t.AddMethod(ROOTNET.Interface.NTMVA.NTypes.EMVA.kCuts, "SimpleCuts")
.Option("!H:V:FitMethod=MC:EffSel:SampleSize=200000:VarTransform=Decorrelate")
.ParameterOption(p => p.logR, "VarProp", "FSmart")
.ParameterOption(p => p.nTracks, "VarProp", "FSmart")
.ParameterOption(p => p.lowestPtTrack, "VarProp", "FSmart");
var mMVA = t.AddMethod(ROOTNET.Interface.NTMVA.NTypes.EMVA.kBDT, "SimpleBDT")
.Option("MaxDepth=3");
t.Train("VerySimpleTraining");
// Lets make a measurement of the efficiency for the standard cut.
var effResults = new FutureTFile(Path.Combine(FileInfoUtilities.FindDirectoryWithFileMatching("*.sln").FullName,"trainingResults.root"));
var stdCutVale = CalcEff(effResults.mkdir("std"), td => (td.logR > 1.2 && td.nTracks == 0) ? 1.0 : 0.0, 0.5, TrainingIQueriable(backgroundEvents, false), TrainingIQueriable(signalHV600pi100Events, true));
// Next, do it with a reader
#if false
var r = new ROOTNET.NTMVA.NReader();
var s = new FileInfo("C:\\Users\\gordo\\Documents\\Code\\calratio2015\\JetCutStudies\\SimpleJetCutTraining\\bin\\x86\\Debug\\weights\\VerySimpleTraining_SimpleCuts.weights.xml");
float[] logR = new float[2];
float[] nTracks = new float[2];
r.AddVariable("logR".AsTS(), logR);
r.AddVariable("nTracks".AsTS(), nTracks);
r.BookMVA("SimpleCuts".AsTS(), s.FullName.AsTS());
Expression<Func<TrainingData, double>> simpleCutsReader = tv => TMVAReaders.TMVASelectorSimpleCutsTest(r, tv.logR, tv.lowestPtTrack, stdCutVale.Value);
#else
Expression<Func<TrainingData, double>> simpleCutsReader = tv => TMVAReaders.TMVASelectorSimpleCuts(tv.logR, tv.nTracks, 0.72);
#endif
var simpleCutValue = CalcEff(effResults.mkdir("SimpleCuts"), simpleCutsReader, 0.5, TrainingIQueriable(backgroundEvents, false), TrainingIQueriable(signalHV600pi100Events, true));
// Next, we need to get the MVA and figure out the efficiency.
Expression<Func<TrainingData, double>> simpleMVAReader = tv => TMVAReaders.TMVASelectorSimpleBDT(tv.logR, tv.nTracks);
var simpleBDTValue = CalcEff(effResults.mkdir("SimpleBDT"), simpleMVAReader, 0.99999, TrainingIQueriable(backgroundEvents, false), TrainingIQueriable(signalHV600pi100Events, true));
// Write out everything
effResults.Write();
effResults.Close();
//Emit();
}
static void Main(string[] args)
{
// Next, lets see if we can't createa reader.
var reader = new ROOTNET.NTMVA.NReader();
// Declare the variables
float[] nTracks = new float[1];
float[] logR = new float[1];
reader.AddVariable(new NTString("logR"), logR);
reader.AddVariable(new NTString("nTracks"), nTracks);
// Book it
var s = "C:\\Users\\gordo\\Documents\\Code\\calratio2015\\JetCutStudies\\SimpleJetCutTraining\\bin\\x86\\Debug\\weights\\VerySimpleTraining_SimpleCuts.weights.xml";
reader.BookMVA(new NTString("SimpleCuts"), new NTString(s));
var f = NTFile.Open("junk.root", "RECREATE");
reader.Write("myreader");
f.Close();
#if false
// Now get some values
logR = 1.0;
nTracks = 5;
cout << "LogR: " << logR << " nTracks: " << nTracks << ": result=" << reader->EvaluateMVA("SimpleCuts", 0.22) << endl;
logR = 2.0;
nTracks = 00;
cout << "LogR: " << logR << " nTracks: " << nTracks << ": result=" << reader->EvaluateMVA("SimpleCuts", 0.22) << endl;
vector<double> stuff;
stuff.push_back(1.0);
stuff.push_back(5);
cout << "result=" << reader->EvaluateMVA(stuff, "SimpleCuts", 0.22) << endl;
stuff.clear();
stuff.push_back(2.0);
stuff.push_back(0);
cout << "result=" << reader->EvaluateMVA(stuff, "SimpleCuts", 0.22) << endl;
return 0;
#endif
}
