用函数给定的值初始化numpy数组的最快方法 [英] Fastest way to initialize numpy array with values given by function

问题描述

我主要对((d1，d2))numpy数组(矩阵)感兴趣，但是这个问题对于具有更多轴的数组有意义.我有函数f(i，j)，我想通过此函数的一些操作来初始化数组

I am mainly interested in ((d1,d2)) numpy arrays (matrices) but the question makes sense for arrays with more axes. I have function f(i,j) and I'd like to initialize an array by some operation of this function

A=np.empty((d1,d2))
for i in range(d1):
    for j in range(d2):
        A[i,j]=f(i,j)

这是可读且可行的，但是我想知道是否有一种更快的方法，因为我的数组A将非常大，因此我必须优化此位.

This is readable and works but I am wondering if there is a faster way since my array A will be very large and I have to optimize this bit.

推荐答案

一种方法是使用

One way is to use np.fromfunction. Your code can be replaced with the line:

np.fromfunction(f, shape=(d1, d2))

这已实现在NumPy函数的术语，因此对于大型数组，它应该比Python for循环快很多.

This is implemented in terms of NumPy functions and so should be quite a bit faster than Python for loops for larger arrays.

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