== Introduction == This package contains a parallelized implementation of the BSC [1] generative model training algorithm. If you have problems running the code, please contact Marc Henniges == Overview == pulp/ - Python library/framework for MPI parallelized EM-based algorithms. The BSC implementation is in pulp/em/camodels/linca_et.py examples/ - Small example programs for the pulp library == Software dependencies == * Python (>= 2.6) * NumPy (reasonable recent) * SciPy (reasonable recent) * pytables (reasonable recent) * mpi4py (>= 1.2) == Running == $ cd examples $ python bsc-barstest.py This will run the BSC algorithm on artificaial bars data and visualize the result. The parameters for each iteration will be saved in 'output/result.h5'. Running the code on large datasets with high values of H, Hprime and gamma is computationally very expensive; use MPI to parallelize: a) On a multi-core machine with 32 cores $ mpirun -np 32 python b) On a cluster: $ mpirun --hostfile machines python where 'machines' contains a list of suitable machines. See your MPI documentation for the details how to start MPI parallelized. == References == [1] Binary Sparse Coding, M. Henniges, G. Puertas, J. Bornschein, J. Lücke, Proc. LVA/ICA 2010, LNCS 6365, 450-457