Classe « KDTree »

Méthode scipy.spatial.KDTree.query

Signature de la méthode query

def query(self, x, k=1, eps=0, p=2, distance_upper_bound=inf, workers=1) 

Description

query.__doc__

Query the kd-tree for nearest neighbors.

        Parameters
        ----------
        x : array_like, last dimension self.m
            An array of points to query.
        k : int or Sequence[int], optional
            Either the number of nearest neighbors to return, or a list of the
            k-th nearest neighbors to return, starting from 1.
        eps : nonnegative float, optional
            Return approximate nearest neighbors; the kth returned value
            is guaranteed to be no further than (1+eps) times the
            distance to the real kth nearest neighbor.
        p : float, 1<=p<=infinity, optional
            Which Minkowski p-norm to use.
            1 is the sum-of-absolute-values distance ("Manhattan" distance).
            2 is the usual Euclidean distance.
            infinity is the maximum-coordinate-difference distance.
            A large, finite p may cause a ValueError if overflow can occur.
        distance_upper_bound : nonnegative float, optional
            Return only neighbors within this distance. This is used to prune
            tree searches, so if you are doing a series of nearest-neighbor
            queries, it may help to supply the distance to the nearest neighbor
            of the most recent point.
        workers : int, optional
            Number of workers to use for parallel processing. If -1 is given
            all CPU threads are used. Default: 1.

            .. versionadded:: 1.6.0

        Returns
        -------
        d : float or array of floats
            The distances to the nearest neighbors.
            If ``x`` has shape ``tuple+(self.m,)``, then ``d`` has shape
            ``tuple+(k,)``.
            When k == 1, the last dimension of the output is squeezed.
            Missing neighbors are indicated with infinite distances.
            Hits are sorted by distance (nearest first).

            .. deprecated:: 1.6.0
               If ``k=None``, then ``d`` is an object array of shape ``tuple``,
               containing lists of distances. This behavior is deprecated and
               will be removed in SciPy 1.8.0, use ``query_ball_point``
               instead.

        i : integer or array of integers
            The index of each neighbor in ``self.data``.
            ``i`` is the same shape as d.
            Missing neighbors are indicated with ``self.n``.

        Examples
        --------

        >>> import numpy as np
        >>> from scipy.spatial import KDTree
        >>> x, y = np.mgrid[0:5, 2:8]
        >>> tree = KDTree(np.c_[x.ravel(), y.ravel()])

        To query the nearest neighbours and return squeezed result, use

        >>> dd, ii = tree.query([[0, 0], [2.1, 2.9]], k=1)
        >>> print(dd, ii)
        [2.         0.14142136] [ 0 13]

        To query the nearest neighbours and return unsqueezed result, use

        >>> dd, ii = tree.query([[0, 0], [2.1, 2.9]], k=[1])
        >>> print(dd, ii)
        [[2.        ]
         [0.14142136]] [[ 0]
         [13]]

        To query the second nearest neighbours and return unsqueezed result,
        use

        >>> dd, ii = tree.query([[0, 0], [2.1, 2.9]], k=[2])
        >>> print(dd, ii)
        [[2.23606798]
         [0.90553851]] [[ 6]
         [12]]

        To query the first and second nearest neighbours, use

        >>> dd, ii = tree.query([[0, 0], [2.1, 2.9]], k=2)
        >>> print(dd, ii)
        [[2.         2.23606798]
         [0.14142136 0.90553851]] [[ 0  6]
         [13 12]]

        or, be more specific

        >>> dd, ii = tree.query([[0, 0], [2.1, 2.9]], k=[1, 2])
        >>> print(dd, ii)
        [[2.         2.23606798]
         [0.14142136 0.90553851]] [[ 0  6]
         [13 12]]