线性代数矩阵转置乘法_转置矩阵| 使用Python的线性代数

线性代数矩阵转置乘法

Prerequisites:

先决条件：

• Defining a matrix

  定义矩阵

The transpose of a matrix is a matrix whose rows are the columns of the original. In mathematical terms, A(i,j) becomes A(j,i) in the new matrix. Transpose has an important role in understanding and implementing Machine Learning algorithms. Major usage by Matrix Multiplication.

矩阵的转置是其行是原始列的矩阵。 用数学术语来说， A(i，j )在新矩阵中变为A(j，i) 。 转置在理解和实现机器学习算法中具有重要作用。 矩阵乘法的主要用法。

Method 1:

方法1：

Syntax:  
    transpose_M = M.T
Parameter: 
    Matrix M
Return: 
    MT

Method 2:

方法2：

Syntax:  
    transpose_M = numpy.transpose(M)
Input Parameter: 
    Matrix M
Return: 
    MT

转置矩阵的Python代码 (Python code for transpose matrix)

# Linear Algebra Learning Sequence
# Transpose using different Method

import numpy as np

g = np.array([[2,3,4], [45,45,45]])
print("---Matrix g----\n", g)

# Transposing the Matrix g
print('\n\nTranspose as g.T----\n', g.T)
print('\n\nTranspose as np.tanspose(g)----\n', np.transpose(g))

Output:

输出：

---Matrix g----
 [[ 2  3  4]
 [45 45 45]]


Transpose as g.T----
 [[ 2 45]
 [ 3 45]
 [ 4 45]]


Transpose as np.tanspose(g)----
 [[ 2 45]
 [ 3 45]
 [ 4 45]]

翻译自: https://www.includehelp.com/python/transpose-matrix.aspx

线性代数矩阵转置乘法