[英]Calculate Euclidean Distance within points in numpy array
I have 3D array as 我有3D阵列
A = [[x1 y1 z1]
[x2 y2 z2]
[x3 y3 z3]]
I have to find euclidean distance between each points so that I'll get output with only 3 distance between (row0,row1)
, (row1,row2)
and (row0,row2)
. 我必须找到每个点之间的欧几里德距离,这样我才能得到输出(row0,row1)
, (row1,row2)
和(row0,row2)
之间只有3个距离。
I have some code 我有一些代码
dist = scipy.spatial.distance.cdist(A,A, 'euclidean')
but it will give distance in matrix form as 但它会以矩阵形式给出距离
dist= [[0 a b]
[a 0 c]
[b c 0]]
I want results as [abc]
. 我希望结果为[abc]
。
Consider using scipy.spatial.distance.pdist . 考虑使用scipy.spatial.distance.pdist 。
You can do like this. 你可以这样做。
>>> A = np.array([[1, 2, 3], [4, 5, 6], [10, 20, 30]])
>>> scipy.spatial.distance.pdist(A)
array([ 5.19615242, 33.67491648, 28.93095228])
But be careful the order of the output distance is (row0,row1),(row0,row2) and (row1,row2). 但要注意输出距离的顺序是(row0,row1),(row0,row2)和(row1,row2)。
You can do something like this: 你可以这样做:
>>> import numpy as np
>>> from itertools import combinations
>>> A = np.array([[1, 2, 3], [4, 5, 6], [10, 20, 30]])
>>> [np.linalg.norm(a-b) for a, b in combinations(A, 2)]
[5.196152422706632, 33.674916480965472, 28.930952282978865]
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