在Python中获取具有不同维度的两个数组的克罗内克积

要获取具有不同尺寸的两个数组的克罗内克积，请使用Python Numpy中的numpy.kron()方法。计算克罗内克积，这是由第二个数组的块按第一个数组缩放而成的复合数组

该函数假设a和b的维度数相同，如有必要，用1填充最小值。如果a.shape = (r0,r1,..,rN)和b.shape = (s0,s1,…,sN)，则克罗内克积的形状为(r0s0, r1s1, …, rN*SN)。元素是来自a和b的元素的乘积，按−明确组织。

# kron(a,b)[k0,k1,...,kN] = a[i0,i1,...,iN] * b[j0,j1,...,jN]

步骤

首先，导入所需的库-

import numpy as np

使用arange()和reshape()方法创建具有不同维度的两个numpy数组−

arr1 = np.arange(20).reshape((2,5,2))
arr2 = np.arange(6).reshape((2,3))

显示数组 –

print("Array1...\n",arr1)
print("\nArray2...\n",arr2)

检查两个数组的尺寸 –

print("\nDimensions of Array1...\n",arr1.ndim)
print("\nDimensions of Array2...\n",arr2.ndim)

检查两个数组的形状 –

print("\nShape of Array1...\n",arr1.shape)
print("\nShape of Array2...\n",arr2.shape)

要获得两个数组的Kronecker乘积，请在Python中使用numpy.kron()方法−

print("\nResult (Kronecker product)...\n",np.kron(arr1, arr2))

示例

import numpy as np

# Creating two numpy arrays with different dimensions using the arange() and reshape() method
arr1 = np.arange(20).reshape((2,5,2))
arr2 = np.arange(6).reshape((2,3))

# Display the arrays
print("Array1...\n",arr1)
print("\nArray2...\n",arr2)

# Check the Dimensions of both the array
print("\nDimensions of Array1...\n",arr1.ndim)
print("\nDimensions of Array2...\n",arr2.ndim)

# Check the Shape of both the array
print("\nShape of Array1...\n",arr1.shape)
print("\nShape of Array2...\n",arr2.shape)

# To get the Kronecker product of two arrays, use the numpy.kron() method in Python Numpy
print("\nResult (Kronecker product)...\n",np.kron(arr1, arr2))

输出

Array1...
[[[ 0 1]
[ 2 3]
[ 4 5]
[ 6 7]
[ 8 9]]

[[10 11]
[12 13]
[14 15]
[16 17]
[18 19]]]

Array2...
[[0 1 2]
[3 4 5]]

Dimensions of Array1...
3

Dimensions of Array2...
2

Shape of Array1...
(2, 5, 2)

Shape of Array2...
(2, 3)

Result (Kronecker product)...
[[[ 0 0 0 0 1 2]
[ 0 0 0 3 4 5]
[ 0 2 4 0 3 6]
[ 6 8 10 9 12 15]
[ 0 4 8 0 5 10]
[12 16 20 15 20 25]
[ 0 6 12 0 7 14]
[18 24 30 21 28 35]
[ 0 8 16 0 9 18]
[24 32 40 27 36 45]]

[[ 0 10 20 0 11 22]
[30 40 50 33 44 55]
[ 0 12 24 0 13 26]
[36 48 60 39 52 65]
[ 0 14 28 0 15 30]
[42 56 70 45 60 75]
[ 0 16 32 0 17 34]
[48 64 80 51 68 85]
[ 0 18 36 0 19 38]
[54 72 90 57 76 95]]]