bounding.py 文件源码

def _friends_bootstrap_radius(args):
    """Internal method used to compute the radius (half-side-length) for each
    ball (cube) used in :class:`RadFriends` (:class:`SupFriends`) using
    bootstrapping."""

    # Unzipping.
    points, ftype = args
    rstate = np.random

    # Resampling.
    npoints, ndim = points.shape
    idxs = rstate.randint(npoints, size=npoints)  # resample
    idx_in = np.unique(idxs)  # selected objects
    sel = np.ones(npoints, dtype='bool')
    sel[idx_in] = False
    idx_out = np.arange(npoints)[sel]  # "missing" objects
    if len(idx_out) < 2:  # edge case
        idx_out = np.append(idx_out, [0, 1])
    points_in, points_out = points[idx_in], points[idx_out]

    # Construct KDTree to enable quick nearest-neighbor lookup for
    # our resampled objects.
    kdtree = spatial.KDTree(points_in)

    if ftype == 'balls':
        # Compute distances from our "missing" points its closest neighbor
        # among the resampled points using the Euclidean norm
        # (i.e. "radius" of n-sphere).
        dists, ids = kdtree.query(points_out, k=1, eps=0, p=2)
    elif ftype == 'cubes':
        # Compute distances from our "missing" points its closest neighbor
        # among the resampled points using the Euclidean norm
        # (i.e. "half-side-length" of n-cube).
        dists, ids = kdtree.query(points_out, k=1, eps=0, p=np.inf)

    # Conservative upper-bound on radius.
    dist = max(dists)

    return dist