Class RaphstoBaumWelchTrainer

x.__init__(...) initializes x; see help(type(x)) for signature

Overrides: object.__init__

(inherited documentation)

Performs unsupervised training using Baum-Welch EM.

This is an instance method, because it is performed on a model that has already been initialized. You might, for example, create such a model using initialize_chord_types.

This is based on the training procedure in NLTK for HMMs: nltk.tag.hmm.HiddenMarkovModelTrainer.train_unsupervised.

Parameters:

• emissions (list of lists of emissions) - training data. Each element is a list of emissions representing a sequence in the training data. Each emission is an emission like those used for jazzparser.misc.raphsto.RaphstoHmm.emission_log_probability, i.e. a list of note observations
• max_iterations (int) - maximum number of iterations to allow for EM (default 100). Overrides the corresponding module option
• convergence_logprob (float) - maximum change in log probability to consider convergence to have been reached (default 1e-3). Overrides the corresponding module option
• logger (logging.Logger) - a logger to send progress logging to
• processes (int) - number processes to spawn. A pool of this many processes will be used to compute distribution updates for sequences in parallel during each iteration.
• save (bool) - save the model at the end of training
• save_intermediate (bool) - save the model after each iteration. Implies save

Replaces the distributions of the saved model with those of the given model and saves it.

Parameters:

• save (bool) - save the model. Otherwise just updates the distributions.

OPTIONS

Value:

[ModuleOption('max_iterations', filter= int, help_text= "Number of tra ining iterations to give up after " "if we don't reach convergence bef ore.", usage= "max_iterations=N, where N is an integer", default= 100) , ModuleOption('convergence_logprob', filter= float, help_text= "Diffe rence in overall log probability of the " "training data made by one i teration after which we " "consider the training to have converged.", usage= "convergence_logprob=X, where X is a small floating " "point nu mber (e.g. 1e-3)", default= 1e-3),]

MODEL_TYPES

Value:

[<class 'jazzparser.misc.raphsto.RaphstoHmm'>, <class 'jazzparser.misc.raphsto.RaphstoHmmThreeChord'>, <class 'jazzparser.misc.raphsto.RaphstoHmmFourChord'>]