Matrix multiplication

I implemented a function in Python to calculate the matrix multiplication, vector inner product, transposed matrix, and matrix product.

• Regarding the matrix product, if you build an algorithm that imitates the manual calculation familiar in textbooks, the loop seems to be amazing, so once create the transposed matrix B_t of B, the row vector of A and the row vector of B_t The inner product of and is calculated, and the value is embedded in the (i, j) component of the result.
• In Python, initialization of a multidimensional array is a nested way of writing list comprehensions.

matrix.py

#!/usr/bin/env python                                                                                                                   
# -*- coding : utf-8 -*-

def get_matrix_scalar_mul(matrix, n): 
    if isinstance(matrix[0], list):
        return [get_matrix_scalar_mul(matrix[i], n) for i in range(len(matrix))]

    return [x*n for x in matrix]

def get_inner_prod(raw, col):
    return sum([x*y for (x,y) in zip(raw,col)])

def get_matrix_trans(matrix):
    # malloc n*m matrix
    matrix_t = [[0 for j in range(len(matrix))] for i in range(len(matrix[0]))]

    for i in range(len(matrix)):
        for j in range(len(matrix[i])):
            matrix_t[j][i] = matrix[i][j]

    return matrix_t

def get_matrix_mul(matrix1, matrix2):
    # malloc A_m*B_n matrix
    result = [[0 for j in range(len(matrix2[0]))] for i in range(len(matrix1))]
    matrix2_t = get_matrix_trans(matrix2)

    for i in range(len(result)):
        for j in range(len(result[0])):
            result[i][j] = get_inner_prod(matrix1[i], matrix2_t[j])

    return result

if __name__ != '__main__':
    exit(None)
#
# main
#
A = [ [1,2], \
      [0,1], \
      [5,1]]

B = [[1,-2,4,0], \
     [0,-1,2,3]]

print '2A = '
print get_matrix_scalar_mul(A, 2)

print 'A x B = '
print get_matrix_mul(A, B)

exit(None) 

Execution result

$ python sample.py 
A = 
[[1, 2], [0, 1], [5, 1]]
B = 
[[1, -2, 4, 0], [0, -1, 2, 3]]
2A = 
[[2, 4], [0, 2], [10, 2]]
A_t = 
[[1, 0, 5], [2, 1, 1]]
A x B = 
[[1, -4, 8, 6], [0, -1, 2, 3], [5, -11, 22, 3]]