Markov Models: Understanding Markov Models and Unsupervised Machine Learning in Python with Real-World Applications by Robert Tier

Author:Robert Tier
Language: eng
Format: azw3
Published: 2017-03-04T08:00:00+00:00

def iterateEM((I,F,S,T),sett):

backward = dict()

probs = []

for sequence in sett:

probs.append(computeprobability((I,F,S,T),sequence,backward))

# backward = P(s|start(q))

forward = dict()

for sequence in sett:

computeprobabilityreverse((I,F,S,T),sequence,forward)

# forward = P(s,end(q))

(Inew,Fnew,Snew,Tnew) = emptymodel(numstates,alphabet)

# P(I(q)|s) = P(I(q),s)/P(s)

# P(I(q)|s) = P(I(q))*P(s|start(q))/P(s)

for state in range(len(I)):

for seq in range(len(sett)):

sequence = sett[seq]

prob = probs[seq]

key = tuple([state] + sequence)

if backward.has_key(key):

Inew[state] = Inew[state] + ((I[state] * backward[key]) / prob)

normalize(Inew)

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