(self, unlabel_index, predict, batch_size=1)
return unlabel_index[nlargestarg(score, batch_size)]
Select indexes from the unlabel_index for querying.

        Parameters
        ----------
        unlabel_index: {list, np.ndarray, IndexCollection}
            The indexes of unlabeled samples. Should be one-to-one
            correspondence to the prediction matrix.

        predict: list
            The prediction matrix for each committee.
            Each committee predict matrix should have the shape [n_samples, n_classes] for probabilistic output
            or [n_samples] for class output.

        batch_size: int, optional (default=1)
            Selection batch size.

        Returns
        -------
        selected_idx: list
            The selected indexes which is a subset of unlabel_index.
Select indexes from the unlabel_index for querying.
Select indexes from the unlabel_index for querying .
def select_by_prediction_mat(self, unlabel_index, predict, batch_size=1):
        """Select indexes from the unlabel_index for querying.

        Parameters
        ----------
        unlabel_index: {list, np.ndarray, IndexCollection}
            The indexes of unlabeled samples. Should be one-to-one
            correspondence to the prediction matrix.

        predict: list
            The prediction matrix for each committee.
            Each committee predict matrix should have the shape [n_samples, n_classes] for probabilistic output
            or [n_samples] for class output.

        batch_size: int, optional (default=1)
            Selection batch size.

        Returns
        -------
        selected_idx: list
            The selected indexes which is a subset of unlabel_index.
        """
        assert (batch_size > 0)
        assert (isinstance(unlabel_index, collections.Iterable))
        unlabel_index = np.asarray(unlabel_index)
        if len(unlabel_index) <= batch_size:
            return unlabel_index

        if self._disagreement == 'vote_entropy':
            score = self.calc_vote_entropy(predict)
        else:
            score = self.calc_avg_KL_divergence(predict)
        return unlabel_index[nlargestarg(score, batch_size)]
def select_by_prediction_mat ( self , unlabel_index , predict , batch_size = 1 ) : assert ( batch_size > 0 ) assert ( isinstance ( unlabel_index , collections . Iterable ) ) unlabel_index = np . asarray ( unlabel_index ) if len ( unlabel_index ) <= batch_size : return unlabel_index if self . _disagreement == 'vote_entropy' : score = self . calc_vote_entropy ( predict ) else : score = self . calc_avg_KL_divergence ( predict ) return unlabel_index [ nlargestarg ( score , batch_size ) ]