在Python中使用 NumPy 计算两个给定向量的外积

在Python中，我们可以使用 NumPy 包的 outer()函数来查找两个矩阵的外积。

Syntax : numpy.outer(a, b, out = None)

Parameters :

a : [array_like] First input vector. Input is flattened if not already 1-dimensional.

b : [array_like] Second input vector. Input is flattened if not already 1-dimensional.

out : [ndarray, optional] A location where the result is stored.

Return : [ndarray] Returns the outer product of two vectors. out[i, j] = a[i] * b[j]

示例 1：一维数组的外积

Python3

# Importing library
import numpy as np
  
# Creating two 1-D arrays
array1 = np.array([6,2])
array2 = np.array([2,5])
print("Original 1-D arrays:")
print(array1)
print(array2)
  
# Output
print("Outer Product of the two array is:")
result = np.outer(array1, array2)
print(result)

输出：

Original 1-D arrays:
[6 2]
[2 5]
Outer Product of the two array is:
[[12 30]
 [ 4 10]]

示例 2： 2X2 矩阵的外积

Python3

# Importing library
import numpy as np
  
# Creating two 2-D matrix
matrix1 = np.array([[1, 3], [2, 6]])
matrix2 = np.array([[0, 1], [1, 9]])
print("Original 2-D matrix:")
print(matrix1)
print(matrix2)
  
# Output
print("Outer Product of the two matrix is:")
result = np.outer(matrix1, matrix2)
print(result)

输出：

Original 2-D matrix:
[[1 3]
 [2 6]]
[[0 1]
 [1 9]]
Outer Product of the two matrix is:
[[ 0  1  1  9]
 [ 0  3  3 27]
 [ 0  2  2 18]
 [ 0  6  6 54]]

示例 3： 3X3 矩阵的外积

Python3

# Importing library
import numpy as np
  
# Creating two 3-D matrix
matrix1 = np.array([[2, 8, 2], [3, 4, 8], [0, 2, 1]])
matrix2 = np.array([[2, 1, 1], [0, 1, 0], [2, 3, 0]])
print("Original 3-D matrix:")
print(matrix1)
print(matrix2)
  
# Output
print("Outer Product of the two matrix is:")
result = np.outer(matrix1, matrix2)
print(result)

输出：

Original 3-D matrix:
[[2 8 2]
 [3 4 8]
 [0 2 1]]
[[2 1 1]
 [0 1 0]
 [2 3 0]]
Outer Product of the two matrix is:
[[ 4  2  2  0  2  0  4  6  0]
 [16  8  8  0  8  0 16 24  0]
 [ 4  2  2  0  2  0  4  6  0]
 [ 6  3  3  0  3  0  6  9  0]
 [ 8  4  4  0  4  0  8 12  0]
 [16  8  8  0  8  0 16 24  0]
 [ 0  0  0  0  0  0  0  0  0]
 [ 4  2  2  0  2  0  4  6  0]
 [ 2  1  1  0  1  0  2  3  0]]