def test_baum_welch_multiple(number_obs, len_obs, initialisation="random"):
print "Baum-Welch from multiple observations."
print number_obs, "observations of", len_obs, "length. Init mode:", initialisation
h = HMM(['sunny', 'rainy', 'storm', 'cloudy', 'foggy'],
['dry', 'damp', 'wet', 'soaked'])
h.random_initialisation()
chains = []
for i in xrange(number_obs):
chains.append(h.simulate(len_obs))
h2 = deepcopy(h)
if initialisation.lower() == "equiprobable":
h.equiprobable_initialisation()
elif initialisation.lower() in ["no", "none"]:
pass
else:
h.random_initialisation()
h.baum_welch_multiple(chains)
print "---- generated ----"
print h
print "---- original ----"
print h2
h.integrity_check()
h2.integrity_check()
def multiple_baum_welch_test(nStates, nObs, nSims, simLength):
print "\n\n\n\n\n##### multiple_baum_welch_test",
print "(nStates = %d, nObs = %d, nSims = %d, simLength = %d): " % \
(nStates, nObs, nSims, simLength)
test = HMM(['s' + str(i) for i in range(1, nStates + 1)],
['o' + str(i) for i in range(1, nObs + 1)])
test.random_initialisation()
print "simple baum-welch.", nStates, "states", nObs, "symbols"
print "Original"
print test
print
print "sample simulations.", nSims, "simulations *", simLength, "length"
sample = test.simulate(simLength)
l = []
for i in range(nSims):
obs = test.simulate(simLength)
l.append(obs)
print "randomising model..."
test.random_initialisation()
print "baum-welch on the generated simulations..."
test.baum_welch_multiple(l)
print "result:"
print test
