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Tom Mitchell's Version Space learner that works on any data structure that implements 3 generality functions:

 - minimum_generalization(example, is_positive, hypotheses=[default_max_specific_hyp])
 - minimum_specialization(example, is_positive, hypotheses=[default_max_general_hyp])
 - covers(hypothesis, example)

It takes a set of positive/negative example Frames (dictionary) and then learns the generalization boundaries:  a most general and specific frames that include positive examples and exclude negative examples: G, S.

Together, these can be thought of as the 'Critics', with a new example, E, it matches the learned concept iff:  covers(G,E) or covers(S,E)
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src/i/s/IsisWorld-HEAD/agents/som/arch.py   IsisWorld(Download)
import xmlrpclib, sys, time
import random
from collections import defaultdict
from utils import dprint
import concept_learning