System requirements:
--------------------

  TMVA has been tested to run on Linux, MAC/OSX and Windows platforms.

  Running TMVA requires the availability of ROOT shared libraries with ROOT_VERSION >= 5.14
  (type "root-config --version" to see the version installed)

========================================================================================

Getting Started: 
----------------

  How to compile the code:
  ------------------------
  /home> cd TMVA
  /home/TMVA> make                   # compile and build the library lib/libTMVA.1.so

  How to run the code as ROOT macro: # training/testing of an academic example
  ----------------------------------
  /home/TMVA> cd test
  /home/TMVA/test> source setup.sh   # setup script must be exectuted before running macros (use setup.csh for c-shell)

  --- For classification:
  /home/TMVA/test> root -l TMVAClassification.C                       # run all standard classifiers (takes a while)
  /home/TMVA/test> root -l TMVAClassification.C\(\"LD,Likelihood\"\)  # run LD and Likelihood classifiers

  --- For regression:
  /home/TMVA/test> root -l TMVARegression.C                           # run all regression algorithms n(takes a while)
  /home/TMVA/test> root -l TMVARegression.C\(\"LD,KNN\"\)             # run LD and k-NN regression algorithms

  --> at the end of the jobs, a GUI will pop up: try to click through all the buttons;
      some of the lower buttons are method-specific, and will only work when the
      corresponding classifiers/regression algorithms have been trained/tested before
      (unless they are greyed out)

  How to run the code as an executable: 
  -------------------------------------
  /home/TMVA/test> make
  /home/TMVA/test> ./TMVAClassification                              # run all standard classifiers 
  /home/TMVA/test> ./TMVAClassification LD Likelihood                # run LD and Likelihood classifiers 

  ... and similarly for regression

  /home/TMVA/test> root -l TMVAGui.C   # start the GUI

  How to run the code as an python script using PyROOT: 
  -------------------------------------
  /home/TMVA/test> make
  /home/TMVA/test> python ./TMVAClassification.py --method LD,Likelihood

  How to apply the TMVA methods:
  -------------------------------------
  /home/TMVA> cd test

  --- For classification:
  /home/TMVA/test> root -l TMVAClassificationApplication.C                
  /home/TMVA/test> root -l TMVAClassificationApplication.C\(\"LD,Likelihood\"\) 

  ... and similar for regression.
  ... and similar for executables.

  The directory structure:
  ------------------------
  inc/          : the TMVA class headers
  src/          : the TMVA class source
  lib/          : here you'll find the TMVA library (libTMVA.1.so) after compilation
  test/         : example code for analysis macros and executables, the GUI and analysis scripts.