To a list of N
points [(x_1,y_1), (x_2,y_2), ... ]
I am trying to find the nearest neighbours to each point based on distance. My dataset is too large to use a brute force approach so a KDtree seems best.
Rather than implement one from scratch I see that sklearn.neighbors.KDTree
can find the nearest neighbours. Can this be used to find the nearest neighbours of each particle, i.e return a dim(N)
list?
This question is very broad and missing details. It's unclear what you did try, how your data looks like and what a nearest-neighbor is (identity?).
Assuming you are not interested in the identity (with distance 0), you can query the two nearest-neighbors and drop the first column. This is probably the easiest approach here.
import numpy as np
from sklearn.neighbors import KDTree
np.random.seed(0)
X = np.random.random((5, 2)) # 5 points in 2 dimensions
tree = KDTree(X)
nearest_dist, nearest_ind = tree.query(X, k=2) # k=2 nearest neighbors where k1 = identity
print(X)
print(nearest_dist[:, 1]) # drop id; assumes sorted -> see args!
print(nearest_ind[:, 1]) # drop id
[[ 0.5488135 0.71518937]
[ 0.60276338 0.54488318]
[ 0.4236548 0.64589411]
[ 0.43758721 0.891773 ]
[ 0.96366276 0.38344152]]
[ 0.14306129 0.1786471 0.14306129 0.20869372 0.39536284]
[2 0 0 0 1]