Learn Finite State Automata that generalize over a set of training instances.
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.
These details are provided for information only. No information here is legal advice and should not be used as such.