Class HiddenMarkovModelTrainer

Creates an HMM trainer to induce an HMM with the given states and output symbol alphabet. A supervised and unsupervised training method may be used. If either of the states or symbols are not given, these may be derived from supervised training.

Parameters:

• states (sequence of any) - the set of state labels
• symbols (sequence of any) - the set of observation symbols

Overrides: object.__init__

Trains the HMM using both (or either of) supervised and unsupervised techniques.

Parameters:

• labelled_sequences (list) - the supervised training data, a set of labelled sequences of observations
• unlabeled_sequences (list) - the unsupervised training data, a set of sequences of observations
• kwargs - additional arguments to pass to the training methods

Returns: HiddenMarkovModelTagger

the trained model

Trains the HMM using the Baum-Welch algorithm to maximise the probability of the data sequence. This is a variant of the EM algorithm, and is unsupervised in that it doesn't need the state sequences for the symbols. The code is based on 'A Tutorial on Hidden Markov Models and Selected Applications in Speech Recognition', Lawrence Rabiner, IEEE, 1989.

Parameters:

• unlabeled_sequences (list) - the training data, a set of sequences of observations
• kwargs - may include the following parameters:

     model - a HiddenMarkovModelTagger instance used to begin
         the Baum-Welch algorithm
     max_iterations - the maximum number of EM iterations
     convergence_logprob - the maximum change in log probability to
         allow convergence
  

Returns: HiddenMarkovModelTagger

the trained model

Supervised training maximising the joint probability of the symbol and state sequences. This is done via collecting frequencies of transitions between states, symbol observations while within each state and which states start a sentence. These frequency distributions are then normalised into probability estimates, which can be smoothed if desired.

Parameters:

• labelled_sequences (list) - the training data, a set of labelled sequences of observations
• kwargs - may include an 'estimator' parameter, a function taking a FreqDist and a number of bins and returning a ProbDistI; otherwise a MLE estimate is used

Returns: HiddenMarkovModelTagger

the trained model