G. Cowan RHUL Physics 21 October, 2014 TMVA Examples ------------- The programs in this directory contain simple examples of multivariate analyses using routines in the TMVA package. The examples are standalone C++ programs that must be linked with the ROOT and TMVA libraries. Normally TMVA should be included automatically with ROOT. To build the programs, first download the files copying the directory structure. Alternatively you can copy the tarball tmvaExamples.tar to your area and type tar -xvf tmvaExamples.tar This will automatically place everything in its correct subdirectory. To build the programs, go into the corresponding subdirectory and type gmake. The ROOT environment needs to be set up first -- consult your local expert. Note that the subdirectory inc needs to contain the header file Event.h. This declares the class Event, which corresponds to a single data event. To allow one to store the data easily in a file, this class contains only data members, no member functions. For more information on the TMVA methods, see the website http://tmva.sourceforge.net/ The examples in the individual subdirectories are: generateData -- generates data according to a 3-dimensional distribution for two different hypotheses (signal and background) and stores the result in a root file (as two TTree objects). readData -- a test program to read the data. You can ignore this one. tmvaTrain -- reads in the root file and trains two simple multivariate discriminators: a Fisher discriminant and a multilayer perceptron (neural network). The coefficients for the discriminant functions are stored in a subdirectory ./train/weights/. analyzeData -- reads in n-tuples of signal and background data and computes the discriminating variable. The user can select events, compute efficiency, purity, etc. analyzeDataCon -- same as analyzeData but contains some additional code to plot the decision boundary (ignore for now).