__init__ (
        self,
        markov_model,
        sequence,
        )

Initialize the scaled approach to calculating probabilities. Arguments:

• markov_model -- The current Markov model we are working with.

• sequence -- A TrainingSequence object that must have a set of emissions to work with.

_backward_recursion (
        self,
        cur_state,
        sequence_pos,
        backward_vars,
        )

Calculate the value of the backward recursion

Arguments:

• cur_state -- The letter of the state we are calculating the forward variable for.

• sequence_pos -- The position we are at in the training seq.

• backward_vars -- The current set of backward variables

_calculate_s_value (
        self,
        seq_pos,
        previous_vars,
        )

Calculate the next scaling variable for a sequence position.

This utilizes the approach of choosing s values such that the sum of all of the scaled f values is equal to 1.

Arguments:

• seq_pos -- The current position we are at in the sequence.

• previous_vars -- All of the forward or backward variables calculated so far.

Returns:

• The calculated scaling variable for the sequence item.

_forward_recursion (
        self,
        cur_state,
        sequence_pos,
        forward_vars,
        )

Calculate the value of the forward recursion.

Arguments:

• cur_state -- The letter of the state we are calculating the forward variable for.

• sequence_pos -- The position we are at in the training seq.

• forward_vars -- The current set of forward variables