(y=None, label_shape=None, test_ratio=0.3, initial_label_rate=0.05,
                      split_count=10, all_class=True, saving_path='.')
return __split_data_matrix(data_matrix=y, matrix_shape=label_shape, test_ratio=test_ratio,
                        initial_label_rate=initial_label_rate,
                        split_count=split_count, all_class=all_class, partially_labeled=False,
                        saving_path=saving_path)
Split given data matrix with shape like [n_samples, n_labels]
    Giving one of matrix or matrix_shape to split the data.

    Parameters
    ----------
    y: array-like, optional
        Labels of given data, shape like [n_samples, n_labels]

    label_shape: tuple, optional (default=None)
        The shape of data_matrix, should be a tuple with 2 elements.
        The first one is the number of instances, and the other is the
        number of _labels.

    test_ratio: float, optional (default=0.3)
        Ratio of test set

    initial_label_rate: float, optional (default=0.05)
        Ratio of initial label set
        e.g. Initial_labelset*(1-test_ratio)*n_samples

    split_count: int, optional (default=10)
        Random split data _split_count times

    all_class: bool, optional (default=True)
        Whether each split will contain at least one instance for each class.
        If False, a totally random split will be performed.

    saving_path: str, optional (default='.')
        Giving None to disable saving.

    Returns
    -------
    train_idx: list
        index of training set, shape like [n_split_count, n_training_indexes]

    test_idx: list
        index of testing set, shape like [n_split_count, n_testing_indexes]

    label_idx: list
        index of labeling set, shape like [n_split_count, n_labeling_indexes]

    unlabel_idx: list
        index of unlabeling set, shape like [n_split_count, n_unlabeling_indexes]
Split given data matrix with shape like [n_samples, n_labels]
    Giving one of matrix or matrix_shape to split the data.
Split given data matrix with shape like [ n_samples n_labels ] Giving one of matrix or matrix_shape to split the data .
def split_multi_label(y=None, label_shape=None, test_ratio=0.3, initial_label_rate=0.05,
                      split_count=10, all_class=True, saving_path='.'):
    """Split given data matrix with shape like [n_samples, n_labels]
    Giving one of matrix or matrix_shape to split the data.

    Parameters
    ----------
    y: array-like, optional
        Labels of given data, shape like [n_samples, n_labels]

    label_shape: tuple, optional (default=None)
        The shape of data_matrix, should be a tuple with 2 elements.
        The first one is the number of instances, and the other is the
        number of _labels.

    test_ratio: float, optional (default=0.3)
        Ratio of test set

    initial_label_rate: float, optional (default=0.05)
        Ratio of initial label set
        e.g. Initial_labelset*(1-test_ratio)*n_samples

    split_count: int, optional (default=10)
        Random split data _split_count times

    all_class: bool, optional (default=True)
        Whether each split will contain at least one instance for each class.
        If False, a totally random split will be performed.

    saving_path: str, optional (default='.')
        Giving None to disable saving.

    Returns
    -------
    train_idx: list
        index of training set, shape like [n_split_count, n_training_indexes]

    test_idx: list
        index of testing set, shape like [n_split_count, n_testing_indexes]

    label_idx: list
        index of labeling set, shape like [n_split_count, n_labeling_indexes]

    unlabel_idx: list
        index of unlabeling set, shape like [n_split_count, n_unlabeling_indexes]

    """
    return __split_data_matrix(data_matrix=y, matrix_shape=label_shape, test_ratio=test_ratio,
                        initial_label_rate=initial_label_rate,
                        split_count=split_count, all_class=all_class, partially_labeled=False,
                        saving_path=saving_path)
def split_multi_label ( y = None , label_shape = None , test_ratio = 0.3 , initial_label_rate = 0.05 , split_count = 10 , all_class = True , saving_path = '.' ) : return __split_data_matrix ( data_matrix = y , matrix_shape = label_shape , test_ratio = test_ratio , initial_label_rate = initial_label_rate , split_count = split_count , all_class = all_class , partially_labeled = False , saving_path = saving_path )