评估爱因斯坦的两个多维NumPy数组的求和惯例

在Python中，我们可以使用NumPy包的einsum()函数来计算两个给定多维数组的爱因斯坦求和惯例。

语法: numpy.einsum(subscripts, *operands, out=None)

参数:

副标题 : str

指定用于求和的下标，以逗号分隔的下标标签列表。除非包含显式指标’->’以及精确输出形式的下标标签，否则将进行隐式（经典的爱因斯坦求和）计算。

操作数 : 类似数组的列表

这些是操作的数组。

输出: ndarray, optional

如果提供，计算就会进入这个数组。

返回：基于爱因斯坦求和惯例的计算。

例子1：爱因斯坦的两个2X2矩阵的求和惯例

# Importing library
import numpy as np
  
# Creating two 2X2 matrix
matrix1 = np.array([[1, 2], [0, 2]])
matrix2 = np.array([[0, 1], [3, 4]])
  
print("Original matrix:")
print(matrix1)
print(matrix2)
  
# Output
result = np.einsum("mk,kn", matrix1, matrix2)
  
print("Einstein’s summation convention of the two matrix:")
print(result)

输出:

Original matrix:
[[1 2]
 [0 2]]
[[0 1]
 [3 4]]
Einstein’s summation convention of the two matrix:
[[6 9]
 [6 8]]

例子2：爱因斯坦的两个3X3矩阵的求和惯例

# Importing library
import numpy as np
  
# Creating two 3X3 matrix
matrix1 = np.array([[2, 3, 5], [4, 0, 2], [0, 6, 8]])
matrix2 = np.array([[0, 1, 5], [3, 4, 4], [8, 3, 0]])
  
print("Original matrix:")
print(matrix1)
print(matrix2)
  
# Output
result = np.einsum("mk,kn", matrix1, matrix2)
  
print("Einstein’s summation convention of the two matrix:")
print(result)

输出:

Original matrix:
[[2 3 5]
 [4 0 2]
 [0 6 8]]
[[0 1 5]
 [3 4 4]
 [8 3 0]]
Einstein’s summation convention of the two matrix:
[[49 29 22]
 [16 10 20]
 [82 48 24]]

例子3：爱因斯坦的两个4X4矩阵的求和惯例

# Importing library
import numpy as np
  
# Creating two 4X4 matrix
matrix1 = np.array([[1, 2, 3, 5], [4, 4, 0, 2], 
                    [0, 1, 6, 8], [0, 5, 6, 9]])
  
matrix2 = np.array([[0, 1, 9, 2], [3, 3, 4, 4], 
                    [1, 8, 3, 0], [5, 2, 1, 6]])
  
print("Original matrix:")
print(matrix1)
print(matrix2)
  
# Output
result = np.einsum("mk,kn", matrix1, matrix2)
  
print("Einstein’s summation convention of the two matrix:")
print(result)

输出:

Original matrix:
[[1 2 3 5]
 [4 4 0 2]
 [0 1 6 8]
 [0 5 6 9]]
[[0 1 9 2]
 [3 3 4 4]
 [1 8 3 0]
 [5 2 1 6]]
Einstein’s summation convention of the two matrix:
[[34 41 31 40]
 [22 20 54 36]
 [49 67 30 52]
 [66 81 47 74]]