def train_all_words(training: WordsData, model_selector):
    sequences = training.get_all_sequences()
    Xlengths = training.get_all_Xlengths()
    model_dict = {}
    for word in training.words:
        model = model_selector(sequences, Xlengths, word,
                               n_constant=3).select()
        model_dict[word] = model
    return model_dict
Exemplo n.º 2
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def train_all_words(training: WordsData, model_selector):
    """ train all words given a training set and selector

    :param training: WordsData object (training set)
    :param model_selector: class (subclassed from ModelSelector)
    :return: dict of models keyed by word
    """
    sequences = training.get_all_sequences()
    Xlengths = training.get_all_Xlengths()
    model_dict = {}
    for word in training.words:
        model = model_selector(sequences, Xlengths, word, n_constant=3).select
        model_dict[word] = model
    return model_dict
Exemplo n.º 3
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def train_all_words(training: WordsData, model_selector):
    """ train all words given a training set and selector

    :param training: WordsData object (training set)
    :param model_selector: class (subclassed from ModelSelector)
    :return: dict of models keyed by word
    """
    sequences = training.get_all_sequences()
    Xlengths = training.get_all_Xlengths()
    model_dict = {}
    for word in training.words:
        model = model_selector(sequences, Xlengths, word,
                               n_constant=3).select()
        model_dict[word] = model
    return model_dict
Exemplo n.º 4
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def train_all_words(training_set: WordsData,
                    model_selector,
                    word_list=None,
                    verbose=False,
                    features=[]):
    """
    Train all words given a training set and selector
    :param training: WordsData object (training set)
    :param model_selector: class (subclassed from ModelSelector)
    :return: dict of models keyed by word
    """
    sequences = training_set.get_all_sequences()
    Xlengths = training_set.get_all_Xlengths()
    model_dict = {}
    if word_list == None: word_list = training_set.words
    for word in word_list:
        try:
            start = timeit.default_timer()
            model = model_selector(sequences,
                                   Xlengths,
                                   word,
                                   verbose=verbose,
                                   features=features).select()
            model_dict[word] = model
            end = timeit.default_timer() - start
            if model is not None:
                if verbose:
                    print(
                        "Training complete for {} with {} states with time {} seconds"
                        .format(word, model.n_components, end))
            else:
                if verbose: print("Training failed for {}".format(word))
        except Exception as e:
            if verbose:
                print("Training failed for {}, error: {}".format(word, e))
            model_dict[word] = None
    return model_dict