The scripts in this repository implement association graph matching to solve the combinatorial problem necessary when building heteroclinic networks classifying discrete inputs.
The compiler is written to be run on a computing cluster using a queueing system (only SGE). The individual steps of the solution pipeline are summarized at the end of this document.
The classification problem is to be specified in a problem file. See *.problem for examples.
To place the individual tasks in a processing queue run the following command:
bash queue_compile.sh <output_directory> <problem_file> <max_len> <compile_id>Parameters:
- output_directory: Intermediate and output files will be placed in the <problem_file> directory under this directory. This must be read/writable from all elements of the processing cluster
- problem_file: Specification of the classification problem. Leave out the .problem extension
- max_len: maximum length of cycles to use for solving the problem
- compile_id: (optional) when running multiple concurrent compiles this parameter is necessary for deconflicting the individual tasks
- Python with NetworkX (http://networkx.lanl.gov/) library
- Sage (http://www.sagemath.org)
- Build association graph (build_graph.sage) -> neighbours
- Induce node centered sub graphs (induce_subgraphs.py) -> graphs
- Prune sub graphs (prune_subgraphs.py) -> pruned_graphs
- Extract valid and consistent cliques (extract_cliques.sage) -> cliques
- Deduplicate cliques (deduplicate_cliques.py) (serial) -> unique_cliques
- Filter cliques (filter_cliques.sage) -> filtered_cliques