Vous êtes un professionnel et vous avez besoin d'une formation ? Machine Learning
avec Scikit-Learn Voir le programme détaillé

Classe « KDTree »

Méthode scipy.spatial.KDTree.query_ball_tree

Signature de la méthode query_ball_tree

def query_ball_tree(self, other, r, p=2.0, eps=0) 

Description

help(KDTree.query_ball_tree)

Find all pairs of points between `self` and `other` whose distance is
at most r.

Parameters
----------
other : KDTree instance
    The tree containing points to search against.
r : float
    The maximum distance, has to be positive.
p : float, optional
    Which Minkowski norm to use.  `p` has to meet the condition
    ``1 <= p <= infinity``.
eps : float, optional
    Approximate search.  Branches of the tree are not explored
    if their nearest points are further than ``r/(1+eps)``, and
    branches are added in bulk if their furthest points are nearer
    than ``r * (1+eps)``.  `eps` has to be non-negative.

Returns
-------
results : list of lists
    For each element ``self.data[i]`` of this tree, ``results[i]`` is a
    list of the indices of its neighbors in ``other.data``.

Examples
--------
You can search all pairs of points between two kd-trees within a distance:

>>> import matplotlib.pyplot as plt
>>> import numpy as np
>>> from scipy.spatial import KDTree
>>> rng = np.random.default_rng()
>>> points1 = rng.random((15, 2))
>>> points2 = rng.random((15, 2))
>>> plt.figure(figsize=(6, 6))
>>> plt.plot(points1[:, 0], points1[:, 1], "xk", markersize=14)
>>> plt.plot(points2[:, 0], points2[:, 1], "og", markersize=14)
>>> kd_tree1 = KDTree(points1)
>>> kd_tree2 = KDTree(points2)
>>> indexes = kd_tree1.query_ball_tree(kd_tree2, r=0.2)
>>> for i in range(len(indexes)):
...     for j in indexes[i]:
...         plt.plot([points1[i, 0], points2[j, 0]],
...             [points1[i, 1], points2[j, 1]], "-r")
>>> plt.show()

Vous êtes un professionnel et vous avez besoin d'une formation ? Machine Learning
avec Scikit-Learn Voir le programme détaillé