ktext/preprocess.py
def tokenized_texts_to_sequences_generator(self, tok_texts):
        """Transforms tokenized text to a sequence of integers.
        Only top "num_words" most frequent words will be taken into account.
        Only words known by the tokenizer will be taken into account.
        # Arguments
            tokenized texts:  List[List[str]]
        # Yields
            Yields individual sequences.
        """
        for seq in tok_texts:
            vect = []
            for w in seq:
                # if the word is missing you get oov_index
                i = self.word_index.get(w, 1)
                vect.append(i)
            yield vect
def tokenized_texts_to_sequences_generator(self, tok_texts):
        """Transforms tokenized text to a sequence of integers.
        Only top "num_words" most frequent words will be taken into account.
        Only words known by the tokenizer will be taken into account.
        # Arguments
            tokenized texts:  List[List[str]]
        # Yields
            Yields individual sequences.
        """
        for seq in tok_texts:
            vect = []
            for w in seq:
                # if the word is missing you get oov_index
                i = self.word_index.get(w, 1)
                vect.append(i)
            yield vect
Transforms tokenized text to a sequence of integers.
        Only top "num_words" most frequent words will be taken into account.
        Only words known by the tokenizer will be taken into account.
        # Arguments
            tokenized texts:  List[List[str]]
        # Yields
            Yields individual sequences.