Module « scipy.spatial »

Classe « cKDTree »

Informations générales

Héritage

builtins.object
    cKDTree

Définition

class cKDTree(builtins.object):

Description _{[extrait de cKDTree.__doc__]}

    cKDTree(data, leafsize=16, compact_nodes=True, copy_data=False,
            balanced_tree=True, boxsize=None)

    kd-tree for quick nearest-neighbor lookup

    This class provides an index into a set of k-dimensional points
    which can be used to rapidly look up the nearest neighbors of any
    point.

    .. note::
       `cKDTree` is functionally identical to `KDTree`. Prior to SciPy
       v1.6.0, `cKDTree` had better performance and slightly different
       functionality but now the two names exist only for
       backward-compatibility reasons. If compatibility with SciPy < 1.6 is not
       a concern, prefer `KDTree`.

    Parameters
    ----------
    data : array_like, shape (n,m)
        The n data points of dimension m to be indexed. This array is
        not copied unless this is necessary to produce a contiguous
        array of doubles, and so modifying this data will result in
        bogus results. The data are also copied if the kd-tree is built
        with copy_data=True.
    leafsize : positive int, optional
        The number of points at which the algorithm switches over to
        brute-force. Default: 16.
    compact_nodes : bool, optional
        If True, the kd-tree is built to shrink the hyperrectangles to
        the actual data range. This usually gives a more compact tree that
        is robust against degenerated input data and gives faster queries
        at the expense of longer build time. Default: True.
    copy_data : bool, optional
        If True the data is always copied to protect the kd-tree against
        data corruption. Default: False.
    balanced_tree : bool, optional
        If True, the median is used to split the hyperrectangles instead of
        the midpoint. This usually gives a more compact tree and
        faster queries at the expense of longer build time. Default: True.
    boxsize : array_like or scalar, optional
        Apply a m-d toroidal topology to the KDTree.. The topology is generated
        by :math:`x_i + n_i L_i` where :math:`n_i` are integers and :math:`L_i`
        is the boxsize along i-th dimension. The input data shall be wrapped
        into :math:`[0, L_i)`. A ValueError is raised if any of the data is
        outside of this bound.

    Notes
    -----
    The algorithm used is described in Maneewongvatana and Mount 1999.
    The general idea is that the kd-tree is a binary tree, each of whose
    nodes represents an axis-aligned hyperrectangle. Each node specifies
    an axis and splits the set of points based on whether their coordinate
    along that axis is greater than or less than a particular value.

    During construction, the axis and splitting point are chosen by the
    "sliding midpoint" rule, which ensures that the cells do not all
    become long and thin.

    The tree can be queried for the r closest neighbors of any given point
    (optionally returning only those within some maximum distance of the
    point). It can also be queried, with a substantial gain in efficiency,
    for the r approximate closest neighbors.

    For large dimensions (20 is already large) do not expect this to run
    significantly faster than brute force. High-dimensional nearest-neighbor
    queries are a substantial open problem in computer science.

    Attributes
    ----------
    data : ndarray, shape (n,m)
        The n data points of dimension m to be indexed. This array is
        not copied unless this is necessary to produce a contiguous
        array of doubles. The data are also copied if the kd-tree is built
        with `copy_data=True`.
    leafsize : positive int
        The number of points at which the algorithm switches over to
        brute-force.
    m : int
        The dimension of a single data-point.
    n : int
        The number of data points.
    maxes : ndarray, shape (m,)
        The maximum value in each dimension of the n data points.
    mins : ndarray, shape (m,)
        The minimum value in each dimension of the n data points.
    tree : object, class cKDTreeNode
        This attribute exposes a Python view of the root node in the cKDTree
        object. A full Python view of the kd-tree is created dynamically
        on the first access. This attribute allows you to create your own
        query functions in Python.
    size : int
        The number of nodes in the tree.

    

Constructeur(s)

Signature du constructeur	Description
__new__(*args, **kwargs)	Create and return a new object. See help(type) for accurate signature. [extrait de __new__.__doc__]
__init__(self, /, *args, **kwargs)	Initialize self. See help(type(self)) for accurate signature. [extrait de __init__.__doc__]

Liste des opérateurs

Opérateurs hérités de la classe object

__eq__, __ge__, __gt__, __le__, __lt__, __ne__

Méthodes héritées de la classe object

__delattr__, __dir__, __format__, __getattribute__, __hash__, __init_subclass__, __reduce__, __reduce_ex__, __repr__, __setattr__, __sizeof__, __str__, __subclasshook__