7e3032b51b197e833915bc353e9e063205c0d4f9,snips_nlu/intent_classifier/data_augmentation.py,,get_regularization_factor,#Any#,13

Before Change

def get_regularization_factor(dataset):
    intent_list = get_non_empty_intents(dataset)
    avg_utterances = np.mean([len(dataset[INTENTS][intent][UTTERANCES]) for
                             intent in intent_list])
    total_utterances = sum(len(dataset[INTENTS][intent][UTTERANCES]) for
                           intent in intent_list)
    alpha = 1.0 / (4 * (total_utterances + 5 * avg_utterances))
    return alpha

After Change

def get_regularization_factor(dataset):
    intents = dataset[INTENTS]
    nb_utterances = [len(intent[UTTERANCES]) for intent in intents.values()]
    avg_utterances = np.mean(nb_utterances)
    total_utterances = sum(nb_utterances)
    alpha = 1.0 / (4 * (total_utterances + 5 * avg_utterances))
    return alpha

Instances