InitialP = [0.5, 0.5]
EmissionP = {
'a': [0.25, 0.1],
't': [0.25, 0.1],
'c': [0.25, 0.4],
'g': [0.25, 0.4]
}
for seq in ["aactgcacatgcggcgcgcccgcgctaat", "gggcgcgggcgccccgcg"]:
# NB. Book and Lio's notes use integrated transition and initial
# distribution matrix (initial step is transition from dummy state 0)
# This is confusing, so I will separate them out.
# Wiki has non-integrated Viterbi algorithm implementation
# 1.1. Implement Forward algorithm
fwd = Forward(TransitionP, EmissionP, InitialP)
p = fwd.prob(seq)
print "**************************************"
print "Probability of", seq, ":", p
print "Log probability:", -log(p)
print "**************************************"
# 1.2. Implement Viterbi algorithm
vtb = Viterbi(TransitionP, EmissionP, InitialP)
(prob, path) = vtb.maxSeq(seq)
print "**************************************"
print "Viterbi path:"
print "P =", prob
print seq
print ''.join(str(i) for i in path)
print "**************************************"
