Forward algorithm

forward.py

states = ("Sad","Happy")
observations = ("Crying","Laughing","Eating")

stateM = {'Sad': 0.3, 'Happy': 0.7}
transM = {'Sad': {'Sad': 0.3, 'Happy': 0.7}, 'Happy': {'Sad': 0.4, 'Happy': 0.6}}
observM = {'Sad': {'Crying': 0.7, 'Laughing': 0.1, 'Eating': 0.2}, 'Happy': {'Crying': 0.1, 'Laughing': 0.6, 'Eating': 0.3}}

observS = ["Laughing","Eating","Laughing"]

bayes = lambda a,b: ((a/(a+b)),(b/(a+b)))

def forward(states,observations,statesM,transM,observM,observSeq):

        stateLen = len(states)

        sM = statesM.copy() # don't change the original

        forward_p = []

        # for every observation in the sequence
        for o in observSeq:
                
                newstates = sM.copy() #temporary copy

                #state matrix * transition matrix
                for col in range(stateLen):
                        val = 0

                        for n in range(stateLen):
                                val += sM[states[n]] * transM[states[n]][states[col]]

                        newstates[states[col]] = val
                        
                sM = newstates.copy()

                #new state matrix * observation 
                for s in sM:
                        sM[s] *= observM[s][o]

                sM = {states[n]:i for n,i in enumerate(bayes(sM[states[0]],sM[states[1]]))}

                forward_p.append(sM)

        return forward_p

print(forward(states,observations,stateM,transM,observM,observS))