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Learn Finite State Automata that generalize over a set of training instances
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README ====== This is an (incomplete) implementation of GAL, the Genetic Automaton Learner as defined in Chapter 5 of Belz (2000). The point of this code is to learn a FSA that *generalizes* from a set of positive examples. (If you just want to cover exactly the input, you can use any existing package for doing FSA minimization.) The input file is in text format, one sequence per line, the alphabet will be induced by tokens separated by white-spaces. This project currently builds under Eclipse, feel free to contribute an ant script. To run an example, use the LearnFSA run configuration in run_config/ To create a graph from the output, install the package graphviz and use dot -Tpng best-instance.dot > best-instance.png Take a look at the PNG in the repository for an example of the learned automata. Edit the default.properties to get better goodness of fit. Read chapter 5 of Belz (2000) for more details and for further ideas about how to improve it. Patches are welcomed. Pablo Duboue, November 2010 References Belz, Anja (2000) "Computational Learning of Finite-State Models for Natural Language Processing", PhD Thesis, University of Sussex.