I have a numpy.ndarray of 3d-points, i.e. the np.shape of it is (4350,3) and such a second numpy.ndarray of 3d-points of np.shape (10510,3). Now I am trying to find the right p
Here is the KDTree way :
KDTree
from scipy.spatial import KDTree data= np.random.rand(10510,3) sample= np.random.rand(4350,3) kdtree=KDTree(data)
Then dist,points=kdtree.query(sample,2) will give you the 2 best neighbors for the 4350 candidates in about one second.
dist,points=kdtree.query(sample,2)