untitled

    def forward_algorithm(self, observation):
        """given an observation as a list of symbols,
        run the forward algorithm"""
        
        # initialize forward algorithm table
        fwd_table={}
        fwd_table['scaling factor']=[]
        for i in range(len(observation)):
            fwd_table['scaling factor'].append(0)

        for state in self.states:
            fwd_table[state]=[]
            for i in range(len(observation)):
                fwd_table[state].append(0)

        # initialize first col of fwd algorithm table
        for state in self.states:
            # logs will be taken at the end 
            fwd_table[state][0] = (self.transitions[self.start][state] * self.emissions[state][observation[0]] ) 

        # fill in the rest of the forward table
        for output in range(1,len(observation)):
            for state in self.states:
                fwd=0
                for prev_state in self.states:
                    fwd+=fwd_table[prev_state][output-1] * self.transitions[prev_state][state] * self.emissions[state][observation[output]]
                fwd_table[state][output] = fwd

        return fwd_table