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 }