Method Example of Multiplication Operation of Matrix by Pure python

This paper introduces the pure python matrix multiplication method example, to share with you, as follows:

def matrixMultiply(A, B):
  #  Get A Number of rows and columns of 
  A_row, A_col = shape(A)
  #  Get B Number of rows and columns of 
  B_row, B_col = shape(B)

  #  Judgment of inability to calculate 
  if(A_col != B_row):
    raise ValueError

  #  Final matrix 
  result = []

  # zip  After unpacking, it is a transposed tuple, which is strongly transformed into list,  Deposit result Medium 
  BT = [list(row) for row in zip(*B)] 

  #  Start doing product operation  
  for A_index in range(A_row):
    #  Used to record each row element of the new matrix 
    rowItem = []
    for B_index in range(len(BT)): 
      # num  Used for accumulation 
      num = 0   
      for Br in range(len(BT[B_index])): 
        num += A[A_index][Br] * BT[B_index][Br]
      #  After the accumulation is completed, store the data in the rows of the new matrix 
      rowItem.append(num) 
    result.append(rowItem) 
  return result

A Matrix and B Matrix multiplication, finally get a new matrix X, ideas

First, determine whether multiplication is possible: the premise is that the columns of A are the same as the rows of B We can draw a picture to understand: If A is 3 rows and 5 columns, B is 5 rows and 2 columns, the multiplication result is 3 rows and 2 columns When B is transposed, it is 2 rows and 5 columns, which we call BT, so that A and BT are both 5 columns Then the i-th element in each row of A * the i-th element in each row of BT are added to form a new row of the new matrix X, and the A row is circulated for a total of 3 rows, then the new row is gradually added to the new matrix X, and the new matrix X is obtained after the circulation is completed