(self, observed_entries, unkonwn_entries, batch_size=1, **kwargs)
return [tpl[i] for i in perm]
Select a subset from the unlabeled set randomly.

        Parameters
        ----------
        observed_entries: {list, np.ndarray, MultiLabelIndexCollection}
            The indexes of labeled samples. It should be a 1d array of indexes (column major, start from 0)
            or MultiLabelIndexCollection or a list of tuples with 2 elements, in which,
            the 1st element is the index of instance and the 2nd element is the index of features.

        unkonwn_entries: {list, np.ndarray, MultiLabelIndexCollection}
            The indexes of unlabeled samples. It should be a 1d array of indexes (column major, start from 0)
            or MultiLabelIndexCollection or a list of tuples with 2 elements, in which,
            the 1st element is the index of instance and the 2nd element is the index of features.

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

        Returns
        -------
        selected_feature: list
            The selected features, it is a list of tuples.
            Note that, the index is a subset of unkonwn_entries.
Select a subset from the unlabeled set randomly.
Select a subset from the unlabeled set randomly .
def select(self, observed_entries, unkonwn_entries, batch_size=1, **kwargs):
        """Select a subset from the unlabeled set randomly.

        Parameters
        ----------
        observed_entries: {list, np.ndarray, MultiLabelIndexCollection}
            The indexes of labeled samples. It should be a 1d array of indexes (column major, start from 0)
            or MultiLabelIndexCollection or a list of tuples with 2 elements, in which,
            the 1st element is the index of instance and the 2nd element is the index of features.

        unkonwn_entries: {list, np.ndarray, MultiLabelIndexCollection}
            The indexes of unlabeled samples. It should be a 1d array of indexes (column major, start from 0)
            or MultiLabelIndexCollection or a list of tuples with 2 elements, in which,
            the 1st element is the index of instance and the 2nd element is the index of features.

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

        Returns
        -------
        selected_feature: list
            The selected features, it is a list of tuples.
            Note that, the index is a subset of unkonwn_entries.
        """
        # build map from value to index
        if len(unkonwn_entries) <= 1:
            return unkonwn_entries
        unkonwn_entries = self._check_feature_ind(unkonwn_entries)
        perm = randperm(len(unkonwn_entries) - 1, batch_size)
        tpl = list(unkonwn_entries.index)
        return [tpl[i] for i in perm]
def select ( self , observed_entries , unkonwn_entries , batch_size = 1 , * * kwargs ) : # build map from value to index if len ( unkonwn_entries ) <= 1 : return unkonwn_entries unkonwn_entries = self . _check_feature_ind ( unkonwn_entries ) perm = randperm ( len ( unkonwn_entries ) - 1 , batch_size ) tpl = list ( unkonwn_entries . index ) return [ tpl [ i ] for i in perm ]