Dear TMVA User,

this README contains three sections

  + Overview 
  + How to run the example
  + The TMVA GUI

Overview
========

  This directory contains two files that illustrate the usage of the
  TMVA package to perform a MultiVariate Analysis. It also contains a
  GUI for easy access to the training result.

  An MV analysis is executed in two steps, the training of the MVA
  methods, and the application of the best suited MVA method to your
  dataset. These two steps are shown in the files TMVAnalysis.C and
  TMVApplication.C, respectively.

  The example uses some toy data (tmva_example.root) that comes with
  the sourceforge distribution or is available on the ROOT web. The
  example files contains a signal and a background tree with four
  variables.


  TMVAnalysis.C
  -------------

  TMVAnalysis.C explains the usage of the TMVA::Factory class, which
  is the framework for the whole training process. The following steps
  are taken:

  1st: The factory is created (a name for the project is specified,
  which will help with the bookkeeping of different training cycles)

  2nd: The input data trees are made available to the factory ( one
  can also use text files as input, also shown in this example ). The
  sizes of the training and the testing data samples are also specified.

  3rd: The variables that are selected to be used in the MVA are
  declared to the factory

  4th: The factory needs to transfer the input data into the local
  data structure used for the training and testing

  5th: Methods are than booked with the factory. For each method
  number of options can be set that steer the training behavior. The
  options are described in greater detail in the method classes and
  can be looked at in the CVS web-viewer

  6th: The training of all methods on the training data sample is run
  by the factory. The training results for each method are stored in
  the directory weights/

  7th: The testing of all methods on the testing data sample is run by
  the factory. The test results are stored in a root file (filename
  specified in the Factory constructor)

  8th: The performance of all methods is analyzed by the factory. The
  performance results are stored in the root file (see last step)


  TMVApplication.C 
  ----------------

  TMVApplication.C explains the usage of the TMVA::Reader class, which
  is to be used to evaluate your data with the trained MVA
  methods. The following steps are taken:

  1st: The reader is created.

  2nd: A set of local variables is created and declared to the reader.

  3rd: The method(s) - which means the name of the weight files
  created in the training process - are booked with the reader.

  4th: Your event loop:
     - The values of the local variables are set (either by assignment
     of, as in the example, by setting the BranchAddresses of your
     datatree)
     - the reader is asked to evaluate the MVA, the result of which is
     used to separate signal from background


How to run the example
======================

  First, TMVAnalysis.C needs to be run to perform the training. You
  can edit TMVAnalysis.C (boolean flags at the head of the file) to
  enable (disable) the methods you would (not) like to use. Then run

  > root TMVAnalysis.C

  Add the end a file TMVA.root is written, and a GUI opens. Once
  TMVA.root exists, the GUI can also be started via

  > root TMVAGui.C

  You can also run

  > root TMVApplication.C 

  which evaluates the methods on the signal data from the toy
  tmva_example.root and writes the result to TMVApp.root. However,
  TMVApplication is more of a pedagogical example for the usage of
  TMVA inside your own analysis framework.


The TMVA GUI
============

  The GUI provides easy access to a large number of macros that plot
  various distributions and correlations of the input data, the MVA
  method output distributions and the performance comparison plot
  (background rejection versus efficiency). It can be used once the
  training is run, and is started with

  > root TMVAGui.C

  or

  > root TMVA\(\"MyTMVA.root\"\)

  in case you had directed your training output to a different root
  file (2nd argument of the TMVA::Factory constructor)