我正在尝试理解python代码，该代码numpy.einsum()用于将4维numpy数组转换为2维A或3维数组。传递给的下标numpy.einsum()如下：
Mat1 = np.einsum('aabb->ab', A)
Mat2 = np.einsum('abab->ab', A)
Mat3 = np.einsum('abba->ab', A)
T1 = np.einsum('abcb->abc' A)
T2 = np.einsum('abbc->abc', A)
例如，在遵循了（理解NumPy的einsum）和（Python-Sum 4D数组）的回答之后，我试图numpy.sum()用来理解上述下标的含义，Mat1 = np.sum(A, axis=(0,3))但是我无法复制得到的结果numpy.einsum()。有人可以解释这些下标的解释方式numpy.einsum()吗？
我建议您阅读Wikipedia上的Einstein符号。
这是您问题的简短答案：
np.einsum('aabb->ab', A)
手段：
res = np.empty((max_a, max_b), dtype=A.dtype)
for a in range(max_a):
for b in range(max_b):
res[a, b] = A[a, a, b, b]
return res
简短说明：
aabb表示索引及其相等性（请参阅A[a, a, b, b]）；
->ab表示形状是(max_a, max_b)并且您不需要在这两个索引上有两个具有和。（如果他们c也是，那么您应该将所有内容加起来，c因为后面没有显示->）
您的其他示例：
np.einsum('abab->ab', A)
# Same as (by logic, not by actual code)
res = np.empty((max_a, max_b), dtype=A.dtype)
for a in range(max_a):
for b in range(max_b):
res[a, b] = A[a, b, a, b]
return res
np.einsum('abba->ab', A)
# Same as (by logic, not by actual code)
res = np.empty((max_a, max_b), dtype=A.dtype)
for a in range(max_a):
for b in range(max_b):
res[a, b] = A[a, b, b, a]
return res
np.einsum('abcb->abc', A)
# Same as (by logic, not by actual code)
res = np.empty((max_a, max_b, max_c), dtype=A.dtype)
for a in range(max_a):
for b in range(max_b):
for c in range(max_c):
res[a, b, c] = A[a, b, c, b]
return res
np.einsum('abbc->abc', A)
# Same as (by logic, not by actual code)
res = np.empty((max_a, max_b, max_c), dtype=A.dtype)
for a in range(max_a):
for b in range(max_b):
for c in range(max_c):
res[a, b, c] = A[a, b, b, c]
return res
一些代码来检查它是否真实：
import numpy as np
max_a = 2
max_b = 3
max_c = 5
shape_1 = (max_a, max_b, max_c, max_b)
A = np.arange(1, np.prod(shape_1) + 1).reshape(shape_1)
print(A)
print()
print(np.einsum('abcb->abc', A))
print()
res = np.empty((max_a, max_b, max_c), dtype=A.dtype)
for a in range(max_a):
for b in range(max_b):
for c in range(max_c):
res[a, b, c] = A[a, b, c, b]
print(res)
print()
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我来说两句