Class ScaledDPAlgorithms
AbstractDPAlgorithms
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ScaledDPAlgorithms
Implement forward and backward algorithms using a rescaling
approach.
This scales the f and b variables, so that they remain within a
manageable numerical interval during calculations. This approach is
described in Durbin et al. on p 78.
This approach is a little more straightfoward then log transformation
but may still give underflow errors for some types of models. In these
cases, the LogDPAlgorithms class should be used.
Method Summary |
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__init__ (self,
markov_model,
sequence)
Initialize the scaled approach to calculating probabilities. |
Inherited from AbstractDPAlgorithms |
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backward_algorithm (self)
Calculate sequence probability using the backward algorithm. |
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forward_algorithm (self)
Calculate sequence probability using the forward algorithm. |
__init__(self,
markov_model,
sequence)
(Constructor)
Initialize the scaled approach to calculating probabilities.
Arguments:
o markov_model -- The current Markov model we are working with.
o sequence -- A TrainingSequence object that must have a set of
emissions to work with.
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- Overrides:
Bio.HMM.DynamicProgramming.AbstractDPAlgorithms.__init__
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