nearest neighbour search kdTree

Question

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 la

Answer 1

You can use sklearn.neighbors.KDTree's query_radius() method, which returns a list of the indices of the nearest neighbours within some radius (as opposed to returning k nearest neighbours).

from sklearn.neighbors import KDTree

points = [(1, 1), (2, 2), (3, 3), (4, 4), (5, 5)]

tree = KDTree(points, leaf_size=2)
all_nn_indices = tree.query_radius(points, r=1.5)  # NNs within distance of 1.5 of point
all_nns = [[points[idx] for idx in nn_indices] for nn_indices in all_nn_indices]
for nns in all_nns:
    print(nns)

Outputs:

[(1, 1), (2, 2)]
[(1, 1), (2, 2), (3, 3)]
[(2, 2), (3, 3), (4, 4)]
[(3, 3), (4, 4), (5, 5)]
[(4, 4), (5, 5)]

Note that each point includes itself in its list of nearest neighbours within the given radius. If you want to remove these identity points, the line computing all_nns can be changed to:

all_nns = [
    [points[idx] for idx in nn_indices if idx != i]
    for i, nn_indices in enumerate(all_nn_indices)
]

Resulting in:

[(2, 2)]
[(1, 1), (3, 3)]
[(2, 2), (4, 4)]
[(3, 3), (5, 5)]
[(4, 4)]