implementation of the particle swarm algorithm to optimize the paramters and Input Variables of TMVA Classificators.
The program will create a swarm of Classifiers in the paramter space.
Each classifier will try to improve its input variable combination.
Classifiers move in the paramter space using the information of the whole swarm.
Utilizes batch system to parallelize classifier training.

1. clone the repository

2. The python interface manages the communication between the particles and is run on a portal (use screen because of long runtimes).
   The Training of the BDTs is done on the batch system and is implemented in Particle.C. This file is also recompiled when you start the PSO now.

3. change Example_PSOConfig to suit your needs
   should work on ekp and NAF batch system, for other batch system change PSO/QueHelper.py
   play around with the swarm parameters (15 to 25 particles recommended)
   choose the Figure of Merit to optimize (at the moment ROC or experimentally the chi of a b-only fit)
   specify TMVA factory and method options
   declare coordinate space you want to search
   specify Signal and Background trees
   Variables the swarm starts with
   pool of additional Variables the swarm will try

4. Start the Optimization with
   python RunPSO.py Example_PSOConfig.txt

5. After each iteration the ten best classifiers are writen to PSOResult.txt
   The best classifier and all necessary information is written to a file starting with "FinalMVAConfig_"