脾气暴躁的“:"运营商广播问题 [英] Numpy ":" operator broadcasting issues

问题描述

在下面的代码中，我编写了2种方法(在我看来)在理论上应该做同样的事情.不幸的是，他们没有这样做，我无法找出为什么他们在numpy文档中没有做同样的事情.

In the following code I have written 2 methods that theoretically(in my mind) should do the same thing. Unfortunately they don't, I am unable to find out why they don't do the same thing per the numpy documentation.

import numpy as np


dW = np.zeros((20, 10))
y = [1 for _ in range(100)]
X =  np.ones((100, 20))

# ===================
# Method 1  (works!)
# ===================
for i in range(len(y)):
  dW[:, y[i]] -=  X[i]


# ===================
# Method 2 (does not work)
# ===================
dW[:, y] -=  X.T

推荐答案

如前所述，由于NumPy中的缓冲工作原理，原则上您不能在同一操作中对同一元素进行多次操作.为此，有 at 函数，该函数可以在几乎所有标准NumPy函数上使用( add ， subtract 等).对于您的情况，您可以执行以下操作:

As indicated, in principle you cannot operate multiple times over the same element in a single operation, due to how buffering works in NumPy. For that purpose there is the at function, which can be used on about any standard NumPy function (add, subtract, etc.). For your case, you can do:

import numpy as np

dW = np.zeros((20, 10))
y = [1 for _ in range(100)]
X =  np.ones((100, 20))
# at modifies in place dW, does not return a new array
np.subtract.at(dW, (slice(None), y), X.T)

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