# 矩阵乘法
import numpy as np
a = np.arange(20).reshape(4,5)
a = np.asmatrix(a)
print(type(a))
b = np.matrix('1.0 2.0;3.0 4.0')
print(type(b))
# 另一种b
b = np.arange(2,45,3).reshape(5, 3)
b = np.mat(b)

print('matrix a:')
print(a)
print('matrix b:')
print(b)

c = a*b
print('matrix c:')
print(c)

<class 'numpy.matrix'>
<class 'numpy.matrix'>
matrix a:
[[ 0  1  2  3  4]
 [ 5  6  7  8  9]
 [10 11 12 13 14]
 [15 16 17 18 19]]
matrix b:
[[ 2  5  8]
 [11 14 17]
 [20 23 26]
 [29 32 35]
 [38 41 44]]
matrix c:
[[ 290  320  350]
 [ 790  895 1000]
 [1290 1470 1650]
 [1790 2045 2300]]

# Program to multiply two matrices using nested loops

# 4x5 matrix
X = [[ 0 , 1 , 2 , 3 , 4],
 [ 5 , 6 , 7 , 8,  9],
 [10 ,11 ,12 ,13 ,14],
 [15 ,16, 17 ,18, 19]]
# 5x3 matrix
Y = [[ 2 , 5 , 8],
 [11, 14, 17],
 [20, 23, 26],
 [29,32 ,35],
 [38, 41, 44]]
# result is 4x3
result = [[0,0,0],
         [0,0,0],
          [0,0,0],
         [0,0,0]]

# iterate through rows of X
for i in range(len(X)):
   # iterate through columns of Y
   for j in range(len(Y[0])):
       # iterate through rows of Y
       for k in range(len(Y)):
           result[i][j] += X[i][k] * Y[k][j]

for r in result:
   print(r) 

[290, 320, 350]
[790, 895, 1000]
[1290, 1470, 1650]
[1790, 2045, 2300]