lsmarkov
A Stochastic Local Search Approach for Learning Chordal Markov Networks
Developed by the Constraint Reasoning and Optimization Group at the Department of Computer Science, University of Helsinki.
The program finds a chordal Markov networks given a CSV file. Details on the algorithm can be found in [1].
Usage
Usage: ./lsmarkov [arguments...]
Arguments (defaults in parentheses):
--greedy Use greedy search instead of a stochastic one. (false)
--restart-rate <value> Maximum number of iterations without local improvement. (1000)
--ess <value> Sets the equivalent sample size when computing scores. (1)
--max-clique <value> Limits the maximum clique size (for CSV inputs). -1=limitless (-1)
--precompute <value> Precompute clique scores (for CSV inputs) 0=no, 1=some, 2=all (0)
--dynamictw Start with small clique limit and then increase (3, 4, 8, 16, ...) (false)
--fixed-iters Have a fixed number of iterations between restarts. (false)
--chow-liu Use Chow-Liu to find the optimal tree in the beginning. (false)
--operations <value> Operations to use; [C]lique, [E]dge, [V]ertex; e.g. CE ("CEV")
--seed <value> Set a custom RNG seed. 0=random (0)
Downloads
The source code of the C++ implementation can be downloaded here.
References
[1] Learning Chordal Markov Networks via Stochastic Local Search
Kari Rantanen, Antti Hyttinen and Matti Järvisalo.
In Proceedings of the
24th European Conference on Artificial Intelligence
(ECAI 2020), pages ???—???.
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