nltk.tag.HiddenMarkovModelTrainer
¶
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class
nltk.tag.
HiddenMarkovModelTrainer
(states=None, symbols=None)[source]¶ Algorithms for learning HMM parameters from training data. These include both supervised learning (MLE) and unsupervised learning (Baum-Welch).
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
Methods¶
__init__ ([states, symbols]) |
|
train ([labeled_sequences, unlabeled_sequences]) |
Trains the HMM using both (or either of) supervised and unsupervised techniques. |
train_supervised (labelled_sequences[, estimator]) |
Supervised training maximising the joint probability of the symbol and state sequences. |
train_unsupervised (unlabeled_sequences[, ...]) |
Trains the HMM using the Baum-Welch algorithm to maximise the probability of the data sequence. |