如何使用 NumPy 计算欧几里得距离? [英] How can the Euclidean distance be calculated with NumPy?

问题描述

我在 3D 中有两点:

(xa, ya, za)(xb, yb, zb)

我想计算距离:

dist = sqrt((xa-xb)^2 + (ya-yb)^2 + (za-zb)^2)

使用 NumPy 或一般的 Python 执行此操作的最佳方法是什么?我有:

导入numpya = numpy.array((xa,ya, za))b = numpy.array((xb, yb, zb))

解决方案

使用

I have two points in 3D:

(xa, ya, za)
(xb, yb, zb)

And I want to calculate the distance:

dist = sqrt((xa-xb)^2 + (ya-yb)^2 + (za-zb)^2)

What's the best way to do this with NumPy, or with Python in general? I have:

import numpy
a = numpy.array((xa ,ya, za))
b = numpy.array((xb, yb, zb))

解决方案

Use numpy.linalg.norm:

dist = numpy.linalg.norm(a-b)

You can find the theory behind this in Introduction to Data Mining

This works because the Euclidean distance is the l2 norm, and the default value of the ord parameter in numpy.linalg.norm is 2.

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