如何确定Python中的多维核距离？内容来源于 Stack Overflow，并遵循CC BY-SA 3.0许可协议进行翻译与使用

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```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]);```

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

2 个回答

```def n_dimensional_euclidean_distance(a, b):
"""
Returns the euclidean distance for n>=2 dimensions
:param a: tuple with integers
:param b: tuple with integers
:return: the euclidean distance as an integer
"""
dimension = len(a) # notice, this will definitely throw a IndexError if len(a) != len(b)

return sqrt(reduce(lambda i,j: i + ((a[j] - b[j]) ** 2), range(dimension), 0))```