def load_examples(self, examples):
        """Sets the internal state of the classifier, assigns random initial
        labels to the docs, and runs Gibbs sampling for _training_iterations
        number of iterations.
        """
        docs, labels_list = self._parse_examples(examples)

        label_set = set(
            [self.DEFAULT_LABEL] +
            [label for labels in labels_list for label in labels])

        self._num_labels = len(label_set)
        self._label_to_id = dict(zip(label_set, xrange(self._num_labels)))

        self._num_words = 0
        self._word_to_id = {}

        self._num_docs = len(docs)

        self._b_dl = numpy.array(
            map(self._get_label_vector, labels_list), dtype=int)
        self._w_dp = [map(self._get_word_id, doc) for doc in docs]
        self._c_dl = numpy.zeros(
            (self._num_docs, self._num_labels), dtype=int)
        self._c_lw = numpy.zeros(
            (self._num_labels, self._num_words), dtype=int)
        self._c_l = numpy.zeros(self._num_labels, dtype=int)
        self._l_dp = []

        for d in xrange(self._num_docs):
            doc, _ = self._get_doc_with_label_vector(d)
            l_p = numpy.random.random_integers(
                0, 1 - self._num_labels, size=len(doc)).tolist()
            self._l_dp.append(l_p)
            for w, l in zip(doc, l_p):
                self._increment_counting_matrices(d, w, l)
        self._iterate_gibbs_sampling(
            self._training_iterations,
            xrange(self._num_docs))
dataset/ETHPy150Open oppia/oppia/core/domain/classifier_services.py/StringClassifier.load_examples/swapped operands of `-`