def learn_wfsa(wfsa, corpus, distfp=None, checkpoint=None):
wfsa_ = copy(wfsa)
wfsa_.round_and_normalize()
if not checkpoint:
checkpoint = lambda x: wfsa_.dump(open('{0}.wfsa'.format(x), 'w'))
if distfp is not None:
if wfsa_.quantizer is not None:
bits = int(round(math.log(wfsa_.quantizer.levels, 2)))
f = [2 ** i for i in xrange(max(0, bits-5), -1, -1)]
t = [1e-5/2**i for i in xrange(0, max(1, bits-4))]
learner = Learner(wfsa_, corpus, checkpoint, pref_prob=0.0,
distfp=distfp, turns_for_each=300, factors=f,
temperatures=t)
else:
# continuous case, not implemented
pass
learner.main()
logging.debug("WFSA learnt")
return wfsa_
