Metadata-Version: 1.0
Name: dictionary-model
Version: 0.2.4
Summary: Model for tracking context of utterance and predicting future characters.
Home-page: UNKNOWN
Author: Michał Kosturek
Author-email: eemkos@gmail.com
License: Michał Kosturek
Description: dictionary-model
        ----------------
        
        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)::
        
            >>> predictor.transform()
        
        Letters order::
        
             ' abcdefghijklmnopqrstuvwxyz' # space character comes at index 0, then alphabetical order for indices from 1 to 26
        
        
Platform: UNKNOWN
