(y, n_subsamples, random_state=None)
return sample_without_replacement(n_samples, n_subsamples,
                                      random_state=random_state)
Bootstrap without replacement, irrespective of label. It is a wrapper around
    sklearn.utils.random.sample_without_replacement.

    Parameters
    ----------
    y : array of size [n_subsamples,]
        True labels
    n_subsamples : int
        Number of subsamples in the bootstrap sample
    random_state : int, RandomState instance or None, optional, default=None
        Pseudo random number generator state used for random uniform sampling
        from lists of possible values instead of scipy.stats distributions.
        If int, random_state is the seed used by the random number generator;
        If RandomState instance, random_state is the random number generator;
        If None, the random number generator is the RandomState instance used
        by `np.random`.

    Returns
    -------
    out : array of size [n_subsamples,]
            The sampled subsets of integer. The subset of selected integer might
            not be randomized, see the method argument.
Bootstrap without replacement, irrespective of label. It is a wrapper around
    sklearn.utils.random.sample_without_replacement.
Bootstrap without replacement irrespective of label . It is a wrapper around sklearn . utils . random . sample_without_replacement .
def bootstrap_without_replacement(y, n_subsamples, random_state=None):
    """
    Bootstrap without replacement, irrespective of label. It is a wrapper around
    sklearn.utils.random.sample_without_replacement.

    Parameters
    ----------
    y : array of size [n_subsamples,]
        True labels
    n_subsamples : int
        Number of subsamples in the bootstrap sample
    random_state : int, RandomState instance or None, optional, default=None
        Pseudo random number generator state used for random uniform sampling
        from lists of possible values instead of scipy.stats distributions.
        If int, random_state is the seed used by the random number generator;
        If RandomState instance, random_state is the random number generator;
        If None, the random number generator is the RandomState instance used
        by `np.random`.

    Returns
    -------
    out : array of size [n_subsamples,]
            The sampled subsets of integer. The subset of selected integer might
            not be randomized, see the method argument.
    """
    n_samples = y.shape[0]
    return sample_without_replacement(n_samples, n_subsamples,
                                      random_state=random_state)
def bootstrap_without_replacement ( y , n_subsamples , random_state = None ) : n_samples = y . shape [ 0 ] return sample_without_replacement ( n_samples , n_subsamples , random_state = random_state )