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Package Bio :: Package HMM :: Module Trainer :: Class AbstractTrainer |
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BaumWelchTrainer
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KnownStateTrainer
Method Summary | |
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__init__(self,
markov_model)
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Get a maximum likelihood estimation of transition and emmission. | |
Calculate the log likelihood of the training seqs. | |
Calculate the maximum likelihood estimator. |
Method Details |
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estimate_params(self, transition_counts, emission_counts)Get a maximum likelihood estimation of transition and emmission. Arguments: o transition_counts -- A dictionary with the total number of counts of transitions between two states. o emissions_counts -- A dictionary with the total number of counts of emmissions of a particular emission letter by a state letter. This then returns the maximum likelihood estimators for the transitions and emissions, estimated by formulas 3.18 in Durbin et al: a_{kl} = A_{kl} / sum(A_{kl'}) e_{k}(b) = E_{k}(b) / sum(E_{k}(b')) Returns: Transition and emission dictionaries containing the maximum likelihood estimators. |
log_likelihood(self, probabilities)Calculate the log likelihood of the training seqs. Arguments: o probabilities -- A list of the probabilities of each training sequence under the current paramters, calculated using the forward algorithm. |
ml_estimator(self, counts)Calculate the maximum likelihood estimator. This can calculate maximum likelihoods for both transitions and emissions. Arguments: o counts -- A dictionary of the counts for each item. See estimate_params for a description of the formula used for calculation. |
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