45d0afb3145714a6f04697fd64670cc9b07c5a78,snips_nlu/intent_classifier/log_reg_classifier_utils.py,,build_training_data,#Any#Any#Any#Any#Any#,111

Before Change

    noise_class = intent_index

    // Computing dataset statistics
    nb_utterances = [len(intent[UTTERANCES]) for intent in itervalues(intents)]

    augmented_utterances = []
    utterance_classes = []
    for nb_utterance, intent_name in zip(nb_utterances, intents):

After Change

    augmented_utterances = []
    utterance_classes = []
    for intent_name, intent_data in sorted(iteritems(intents)):
        nb_utterances = len(intent_data[UTTERANCES])
        min_utterances_to_generate = max(
            data_augmentation_config.min_utterances, nb_utterances)

Instances