numpy中多个向量的逐个元素最小值 [英] Element-wise minimum of multiple vectors in numpy

问题描述

我知道在numpy中，我可以使用以下公式计算两个向量的逐元素最小值:

I know that in numpy I can compute the element-wise minimum of two vectors with

numpy.minimum(v1, v2)

如果我有一个等维向量列表，V = [v1, v2, v3, v4](但是列表，而不是数组)怎么办?接受numpy.minimum(*V)不起作用.相反，首选的操作是什么?

What if I have a list of vectors of equal dimension, V = [v1, v2, v3, v4] (but a list, not an array)? Taking numpy.minimum(*V) doesn't work. What's the preferred thing to do instead?

推荐答案

*V将起作用. np.minimum是ufunc，带有2个参数.

*V works if V has only 2 arrays. np.minimum is a ufunc and takes 2 arguments.

作为ufunc，它具有.reduce方法，因此可以重复应用于列表输入.

As a ufunc it has a .reduce method, so it can apply repeated to a list inputs.

In [321]: np.minimum.reduce([np.arange(3), np.arange(2,-1,-1), np.ones((3,))])
Out[321]: array([ 0.,  1.,  0.])

我怀疑np.min方法更快，但这可能取决于数组和列表的大小.

I suspect the np.min approach is faster, but that could depend on the array and list size.

In [323]: np.array([np.arange(3), np.arange(2,-1,-1), np.ones((3,))]).min(axis=0)
Out[323]: array([ 0.,  1.,  0.])

ufunc还有一个accumulate，它可以向我们显示还原的每个阶段的结果.这不是很有趣，但是我可以调整输入来更改它.

The ufunc also has an accumulate which can show us the results of each stage of the reduction. Here's it's not to interesting, but I could tweak the inputs to change that.

In [325]: np.minimum.accumulate([np.arange(3), np.arange(2,-1,-1), np.ones((3,))])
     ...: 
Out[325]: 
array([[ 0.,  1.,  2.],
       [ 0.,  1.,  0.],
       [ 0.,  1.,  0.]])

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