Multidimensional Euclidean Distance in Python

Question

I want to calculate the Euclidean distance in multiple dimensions (24 dimensions) between 2 arrays. I'm using numpy-Scipy.

Here is my code:

import numpy,scipy;

A=numpy.array([116.629, 7192.6, 4535.66, 279714, 176404, 443608, 295522, 1.18399e+07, 7.74233e+06, 2.85839e+08, 2.30168e+08, 5.6919e+08, 168989, 7.48866e+06, 1.45261e+06, 7.49496e+07, 2.13295e+07, 3.74361e+08, 54.5, 3349.39, 262.614, 16175.8, 3693.79, 205865]);

B=numpy.array([0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 151246, 6795630, 4566625, 2.0355328e+08, 1.4250515e+08, 3.2699482e+08, 95635, 4470961, 589043, 29729866, 6124073, 222.3]);

However, I used scipy.spatial.distance.cdist(A[numpy.newaxis,:],B,'euclidean') to calcuate the eucleidan distance.

But it gave me an error

raise ValueError('XB must be a 2-dimensional array.');

I don't seem to understand it.

I looked up scipy.spatial.distance.pdist but don't understand how to use it?

Is there any other better way to do it?

Answer 1

Perhaps scipy.spatial.distance.euclidean?

Examples

>>> from scipy.spatial import distance
>>> distance.euclidean([1, 0, 0], [0, 1, 0])
1.4142135623730951
>>> distance.euclidean([1, 1, 0], [0, 1, 0])
1.0