NumPy数组中滑动窗口中的最大值 [英] Max in a sliding window in NumPy array

问题描述

我想创建一个数组，该数组保存在给定numpy数组中移动的窗口的所有max() es.很抱歉，这听起来令人困惑.我举一个例子.输入:

I want to create an array which holds all the max()es of a window moving through a given numpy array. I'm sorry if this sounds confusing. I'll give an example. Input:

[ 6,4,8,7,1,4,3,5,7,2,4,6,2,1,3,5,6,3,4,7,1,9,4,3,2 ]

我的窗口宽度为5的输出应为:

My output with a window width of 5 shall be this:

[     8,8,8,7,7,7,7,7,7,6,6,6,6,6,6,7,7,9,9,9,9     ]

每个数字应为输入数组宽度5的子数组的最大值:

Each number shall be the max of a subarray of width 5 of the input array:

[ 6,4,8,7,1,4,3,5,7,2,4,6,2,1,3,5,6,3,4,7,1,9,4,3,2 ]
  \       /                 \       /
   \     /                   \     /
    \   /                     \   /
     \ /                       \ /
[     8,8,8,7,7,7,7,7,7,6,6,6,6,6,6,7,7,9,9,9,9     ]

我没有在numpy中找到一个开箱即用的函数来做到这一点(但是如果有一个，我不会感到惊讶；我并不是一直以numpy开发人员的想法来思考).我考虑过为输入创建偏移的2D版本:

I did not find an out-of-the-box function within numpy which would do this (but I would not be surprised if there was one; I'm not always thinking in the terms the numpy developers thought). I considered creating a shifted 2D-version of my input:

[ [ 6,4,8,7,1,4,3,5,7,8,4,6,2,1,3,5,6,3,4,7,1 ]
  [ 4,8,7,1,4,3,5,7,8,4,6,2,1,3,5,6,3,4,7,1,9 ]
  [ 8,7,1,4,3,5,7,8,4,6,2,1,3,5,6,3,4,7,1,9,4 ]
  [ 7,1,4,3,5,7,8,4,6,2,1,3,5,6,3,4,7,1,9,4,3 ]
  [ 1,4,3,5,7,8,4,6,2,1,3,5,6,3,4,7,1,9,4,3,2 ] ]

然后我可以对此应用np.max(input, 0)并得到我的结果.但这对我来说似乎并不高效，因为我的数组和窗口宽度都可能很大(> 1000000个条目和> 100000个窗口宽度).数据会因窗口宽度而变大或变小.

Then I could apply np.max(input, 0) on this and would get my results. But this does not seem efficient in my case because both my array and my window width can be large (>1000000 entries and >100000 window width). The data would be blown up more or less by a factor of the window width.

我还考虑过以某种方式使用np.convolve()，但无法找到一种方法来实现我的目标.

I also considered using np.convolve() in some fashion but couldn't figure out a way to achieve my goal with it.

任何想法如何有效地做到这一点?

Any ideas how to do this efficiently?

推荐答案

Pandas具有用于Series和DataFrame的滚动方法，可以在此处使用:

Pandas has a rolling method for both Series and DataFrames, and that could be of use here:

import pandas as pd

lst = [6,4,8,7,1,4,3,5,7,8,4,6,2,1,3,5,6,3,4,7,1,9,4,3,2]
lst1 = pd.Series(lst).rolling(5).max().dropna().tolist()

# [8.0, 8.0, 8.0, 7.0, 7.0, 8.0, 8.0, 8.0, 8.0, 8.0, 6.0, 6.0, 6.0, 6.0, 6.0, 7.0, 7.0, 9.0, 9.0, 9.0, 9.0]

为了保持一致性，您可以将lst1的每个元素强制为int:

For consistency, you can coerce each element of lst1 to int:

[int(x) for x in lst1]

# [8, 8, 8, 7, 7, 8, 8, 8, 8, 8, 6, 6, 6, 6, 6, 7, 7, 9, 9, 9, 9]

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