问题
A short note: This question relates to another I asked previously, but since asking multiple questions within a single Q&A is concidered bad SO-style I splitted it up.
Setup
I have the following two implementations of a matrix-calculation:
- The first implementation uses a
matrix of shape (n, m)
and the calculation is repeated in a for-loop forrepetition
-times:
import numpy as np
def foo():
for i in range(1, n):
for j in range(1, m):
_deleteA = (
matrix[i, j] +
#some constants added here
)
_deleteB = (
matrix[i, j-1] +
#some constants added here
)
matrix[i, j] = min(_deleteA, _deleteB)
return matrix
repetition = 3
for x in range(repetition):
foo()
2. The second implementation avoids the extra for-loop and, hence, includes repetition = 3
into the matrix, which is then of shape (repetition, n, m)
:
def foo():
for i in range(1, n):
for j in range(1, m):
_deleteA = (
matrix[:, i, j] +
#some constants added here
)
_deleteB = (
matrix[:, i, j-1] +
#some constants added here
)
matrix[:, i, j] = np.amin(np.stack((_deleteA, _deleteB), axis=1), axis=1)
return matrix
Question
Regarding both implementations, I discovered this regarding their performance with %timeit
in iPython:
- The first implementation is faster (in my test-case with n=1000, m=1000: 17sec vs. 26sec). Why is
numpy
such slower when working on three instead of two dimensions?
来源:https://stackoverflow.com/questions/57055257/performance-decreases-with-increasing-nesting-of-array-elements