def make_batch(self, batch_size):
        contexts = self.build_contexts()

        data = np.array([context for word, context in contexts if len(context) == 2 * self.window_size and word != 0])
        label = np.array([word for word, context in contexts if len(context) == 2 * self.window_size and word != 0])

        batch_count = int(math.ceil(len(data) / batch_size))
    idx = 0
    begin, end = idx * batch_size, min((idx + 1) * batch_size, len(contexts))
    indices = np.arange(len(data))
        while (idx < batch_count):
        batch_indices = indices[begin: end]
        batch_data, batch_labels = [], []
            np.random.shuffle(data)
        for data_ind in batch_indices:
            central_word, context = label[data_ind], data[data_ind]
            batch_labels.extend([central_word])
            batch_data.extend(context.reshape(-1, 2 * self.window_size))

                yield batch_data, batch_labels