python sklearn KDTree与haversine距离 [英] python sklearn KDTree with haversine distance

问题描述

我尝试创建一个 WGS84 坐标的 KD 树并找到一定半径内的邻居

I try to create a KD tree of WGS84 coordinates and find neighbors within a certain radius

from sklearn.neighbors.dist_metrics import DistanceMetric
from sklearn.neighbors.kd_tree import KDTree    
T = KDTree([[47.8665, 8.90123]], metric=DistanceMetric.get_metric('haversine'))

但得到以下错误:

ValueError: metric HaversineDistance is not valid for KDTree

如何在 KD 树中使用半正弦距离?

How can I use haversine distance in a KD-Tree?

推荐答案

k-d-tree 可以(据我所知)只能与 Minkowski 范数一起使用.

The k-d-tree can (to the best of my knowledge) only be used with Minkowski norms.

还有其他树，例如 sklearn 中的球树，或 ELKI 中的覆盖树，它们与Haversine距离一起工作，因为它是一个度量.

There are other trees such as the ball tree in sklearn, or the covertree in ELKI that work with Haversine distance because it is a metric.

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