大型数组的numpy向量化运算

问题描述

我正在尝试通过python3对numpy数组进行一些计算。

数组：

   c0 c1 c2 c3
r0 1  5  2  7
r1 3  9  4  6
r2 8  2  1  3

“ cx”和“ rx”分别是列名和行名。

如果元素不在给定的列列表中，我需要逐行计算每个元素的差异。

例如

 given a column list  [0,2,1] # they are column indices
 which means that 
    for r0,we need to calculate the difference between the c0 and all other columns,so we have 

    [1,5-1,2-1,7-1]

    for r1,we need to calculate the difference between the c2 and all other columns,so we have 

    [3-4,9-4,4,6-4]

    for r2,we need to calculate the difference between the c1 and all other columns,so we have 

    [8-2,1-2,3-2]

所以，结果应该是

   1 4 1 6
   -1 5 4 2
   6 2 -1 1

由于该数组可能非常大，我想通过numpy向量化操作进行计算，例如广播。

BuT，我不确定如何有效地做到这一点。

我已经检查了Vectorizing operation on numpy array，Vectorizing a Numpy slice operation，Vectorize large NumPy multiplication，Replace For Loop with Numpy Vectorized Operation，Vectorize numpy array for loop。

但是，他们都不适合我。

感谢您的帮助！

解决方法

首先从数组中提取值，然后进行减法：

import numpy as np

a = np.array([[1,5,2,7],[3,9,4,6],[8,1,3]])

cols = [0,1]

# create the index for advanced indexing
idx = np.arange(len(a)),cols

# extract values 
vals = a[idx]

# subtract array by the values
a -= vals[:,None]

# add original values back to corresponding position
a[idx] += vals 

print(a)

#[[ 1  4  1  6]
# [-1  5  4  2]
# [ 6  2 -1  1]]

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