Model for tracking context of utterance and predicting future characters.
To use, first create CharPredictor object:
>>> predictor = CharPredictor()
This may take a while as model is being downloaded and loaded.
Then, to track utterance context, you can add letter index to context:
>>> letter_index = 1 # 1 -> a, letters should be indexed in order: ' abcdefghijklmnopqrstuvwxyz' (0 -> space) >>> predictor.add_to_context(letter_index)
or you can add letter as string of length 1 (make sure it is one of AsciiEncoder.AVAILABLE_CHARS):
>>> letter = 'a' >>> predictor.add_to_context(letter)
or you can add probability distribution for all AsciiEncoder.AVAILABLE_CHARS letters:
>>> import numpy as np >>> import AsciiEncoder as AE >>> >>> num_chars = len(AE.AVAILABLE_CHARS) >>> letter_distr = np.random.random((1, num_chars)) # random proba distribution >>> predictor.add_to_context(letter_distr)
And finally - you can predict probabilities of each letter coming next after text stored in context. (Letters are indexed in order shown below):
' abcdefghijklmnopqrstuvwxyz' # space character comes at index 0, then alphabetical order for indices from 1 to 26
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