This package contains all the source code to reproduce the numerical experiments described in the paper Discrete Sparse Coding.

• Python (>= 2.6)
• NumPy (reasonably recent)
• SciPy (reasonably recent)
• pytables (reasonably recent)
• mpi4py (>= 1.3)

• pulp/ - Python library/framework for MPI parallelized EM-based algorithms. The models' implementations can be found in pulp/em/camodels/.

• examples/ - Small examples for initializing and running the models

To run the barstest experiment:

  $ cd examples/barstest
  $ python dsc_run.py

To run the natural images experiment:

  $ cd ../natims
  $ python dsc_run.py

To run the spikes experiment:

  $ cd ../spikes
  $ python dsc_on_hc1_run.py

To run the audio experiment:

  $ cd ../audio
  $ python dsc_run_audio.py

Some of this experiments are too big to run in a single workstation and should be executed on a cluster. Running our experiments on the cluster largely depends on the configuration. Example batch files for our cluster (slurm based) configuration (GOLD cluster - Uni Oldenburg) are given in examples//batchscript.sh

The results produced by the code are stored in a 'results.h5' file under "./output/.../". The file stores the model parameters (e.g., W, pi etc.) for each EM iteration performed. To read the results file, you can use openFile function of the standard tables package in python. Moreover, the results files can also be easily read by other packages such as Matlab etc.

The code uses MPI based parallelization. If you have parallel resources (i.e., a multi-core system or a compute cluster), the provided code can make a use of parallel compute resources by evenly distributing the training data among multiple cores.

To run the same script as above, e.g.,

a) On a multi-core machine with 32 cores:

 $ mpirun -np 32 python dsc_run.py

b) On a cluster:

 $ mpirun --hostfile machines python dsc_run.py

where 'machines' contains a list of suitable machines.

See your MPI documentation for the details on how to start MPI parallelized programs.