(self, label_index, unlabel_index, batch_size=1, **kwargs)
return select_index
Select the unlabel data in batch mode.
        Parameters
        ----------
        label_index: {list, np.ndarray, IndexCollection}
            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 labels.

        unlabel_index: {list, np.ndarray, IndexCollection}
            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 labels.

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

        Returns
        -------
        selected_ind: list
            The selected indexes.
Select the unlabel data in batch mode.
        Parameters
        ----------
        label_index: {list, np.ndarray, IndexCollection}
            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 labels.
Select the unlabel data in batch mode . Parameters ---------- label_index : { list np . ndarray IndexCollection } 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 labels .
def select(self, label_index, unlabel_index, batch_size=1, **kwargs):
        """
            Select the unlabel data in batch mode.
        Parameters
        ----------
        label_index: {list, np.ndarray, IndexCollection}
            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 labels.

        unlabel_index: {list, np.ndarray, IndexCollection}
            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 labels.

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

        Returns
        -------
        selected_ind: list
            The selected indexes.
        """
        if isinstance(label_index, (list, np.ndarray)):
            label_index = IndexCollection(label_index)
        elif isinstance(label_index, MultiLabelIndexCollection):
            label_index = IndexCollection(label_index.get_unbroken_instances())
        elif not isinstance(label_index,IndexCollection):
            raise TypeError("index type error")
        if isinstance(unlabel_index, (list, np.ndarray)):
            unlabel_index = IndexCollection(unlabel_index)
        elif isinstance(unlabel_index, MultiLabelIndexCollection):
            unlabel_index = IndexCollection(unlabel_index.get_unbroken_instances())
        elif not isinstance(unlabel_index,IndexCollection):
            raise TypeError("index type error")
        
        if len(unlabel_index) <= batch_size:
            return list(unlabel_index)
        
        select_index = []
        for i in range(batch_size):
            selected = self.sequential_select(label_index, unlabel_index)
            label_index.update(selected)
            unlabel_index.difference_update(selected)
            select_index.append((selected, ))

        return select_index
def select ( self , label_index , unlabel_index , batch_size = 1 , * * kwargs ) : if isinstance ( label_index , ( list , np . ndarray ) ) : label_index = IndexCollection ( label_index ) elif isinstance ( label_index , MultiLabelIndexCollection ) : label_index = IndexCollection ( label_index . get_unbroken_instances ( ) ) elif not isinstance ( label_index , IndexCollection ) : raise TypeError ( "index type error" ) if isinstance ( unlabel_index , ( list , np . ndarray ) ) : unlabel_index = IndexCollection ( unlabel_index ) elif isinstance ( unlabel_index , MultiLabelIndexCollection ) : unlabel_index = IndexCollection ( unlabel_index . get_unbroken_instances ( ) ) elif not isinstance ( unlabel_index , IndexCollection ) : raise TypeError ( "index type error" ) if len ( unlabel_index ) <= batch_size : return list ( unlabel_index ) select_index = [ ] for i in range ( batch_size ) : selected = self . sequential_select ( label_index , unlabel_index ) label_index . update ( selected ) unlabel_index . difference_update ( selected ) select_index . append ( ( selected , ) ) return select_index