Classe « cKDTree »

Méthode scipy.spatial.cKDTree.query

Signature de la méthode query

Description

query.__doc__

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

        Query the kd-tree for nearest neighbors

        Parameters
        ----------
        x : array_like, last dimension self.m
            An array of points to query.
        k : list of integer or integer
            The list of k-th nearest neighbors to return. If k is an
            integer it is treated as a list of [1, ... k] (range(1, k+1)).
            Note that the counting starts from 1.
        eps : non-negative float
            Return approximate nearest neighbors; the k-th returned value
            is guaranteed to be no further than (1+eps) times the
            distance to the real k-th nearest neighbor.
        p : float, 1<=p<=infinity
            Which Minkowski p-norm to use.
            1 is the sum-of-absolute-values "Manhattan" distance
            2 is the usual Euclidean distance
            infinity is the maximum-coordinate-difference distance
            A finite large p may cause a ValueError if overflow can occur.
        distance_upper_bound : nonnegative float
            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.

            .. versionchanged:: 1.6.0
               The "n_jobs" argument was renamed "workers". The old name
               "n_jobs" is deprecated and will stop working in SciPy 1.8.0.

        Returns
        -------
        d : 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.
        i : ndarray of ints
            The index of each neighbor in ``self.data``.
            If ``x`` has shape ``tuple+(self.m,)``, then ``i`` has shape ``tuple+(k,)``.
            When k == 1, the last dimension of the output is squeezed.
            Missing neighbors are indicated with ``self.n``.

        Notes
        -----
        If the KD-Tree is periodic, the position ``x`` is wrapped into the
        box.

        When the input k is a list, a query for arange(max(k)) is performed, but
        only columns that store the requested values of k are preserved. This is
        implemented in a manner that reduces memory usage.

        Examples
        --------

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