scipy.spatial.KDTree.query中distance_upper_bound参数的度量标准是什么? [英] What is the metric for distance_upper_bound parameter in scipy.spatial.KDTree.query?
问题描述
我不熟悉scipy。我似乎不太清楚在 scipy.spatial.KDTree.query
中 distance_upper_bound
的指标。是公里还是弧度?
I am new to using scipy. I can't quite seem to figure out the metric of distance_upper_bound
in scipy.spatial.KDTree.query
. Is it in Kilometers or Radians?
推荐答案
这就是值根据您决定使用参数 p
的度量标准。
It's exactly the value in terms of the chosen metric on which you decided using parameter p
.
如果确实选择了 p = 1
,又称曼哈顿距离,向量的距离: x = [1,2,3]
和 y = [2,3,4]
是 3
。如果您使用 distance_upper_bound = 2
并且 y
是 x的下一个邻居
您在寻找,不要期望正确的结果。
If you did chose p=1
, aka manhattan-distance, the distance of the vectors: x=[1,2,3]
and y=[2,3,4]
is 3
. If you would have used distance_upper_bound=2
and y
is the next neighbor to x
you are looking for, don't expect a correct result.
备注:您正在谈论的此参数设置为 inf
默认情况。
Remark: this parameter you are talking about is set to inf
by default.
您的任务似乎与纬度/经度有关。在这种情况下,我认为您想使用 Haversine-metric (免责声明:我不是这方面的专家)。
Your task seems to be about latitude/longitude points. In this case, i think you want to use the Haversine-metric (disclaimer: i'm no expert in this area).
遗憾的是,根据 p规范,这是scipy的邻居搜索中唯一支持的规范!
Sadly, this metric is imho not available in terms of a p-norm, the only ones supported in scipy's neighbor-searches!
但是:sklearn的 BallTree 可以与Haversine一起使用! (KDTree不会!仍然是p范数!)
But: sklearn's BallTree can work with Haversine! (KDTree does not! Still p-norms!)
大概有一个很好的理由(无论是数学还是实际表现)为何KDTree不支持Haversine,而BallTree却支持。不要试图盲目地滚动自己的Haversine-KDTree!
There is probably a good reason (either math or practical performance) why KDTree is not supporting Haversine, while BallTree does. Don't try to roll your own Haversine-KDTree blindly!
来自:
From here:
二叉搜索树无法通过设计处理极坐标表示的环绕。您可能需要将坐标转换为3D笛卡尔空间,然后应用自己喜欢的搜索算法,例如kD-Tree,Octree等。
A binary search tree cannot handle the wraparound of the polar representation by design. You might need to transform the coordinates to a 3D cartesian space and then apply your favorite search algorithm, e.g., kD-Tree, Octree etc.
from sklearn.neighbors import KDTree, BallTree
KDTree.valid_metrics
# ['euclidean', 'l2', 'minkowski', 'p', 'manhattan', 'cityblock', 'l1', 'chebyshev',
# 'infinity']
BallTree.valid_metrics
# ['euclidean', 'l2', 'minkowski', 'p', 'manhattan', 'cityblock', 'l1', 'chebyshev',
# 'infinity', 'seuclidean', 'mahalanobis', 'wminkowski', 'hamming', 'canberra',
# 'braycurtis', 'matching', 'jaccard', 'dice', 'kulsinski', 'rogerstanimoto', 'russellrao',
# 'sokalmichener', 'sokalsneath', 'haversine', 'pyfunc']
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