[Solved] Objective Write Function Named Matrixmul B Given Two 2d Array Matrix B Function Returns P Q37244462

Please answer using Python Pycharm 2018 programming.

  

Objective Write a function named matrix_mul(a, b). Given a two 2D array(Matrix) A and B, the function returns the product of A and B Description The product C of two matrices A and B is defined as where i is summed over for all possible values of and k. In order for matrix multiplication to be defined, the dimensions of the matrices must satisfy where (axb) denotes a matrix with a rows and b columns. Writing out the product explicitly, C1 12 Cp where c21-a21 011 (10) You should write your code in function: def matrix mul(A,B) # return an array Task 1: You use three level nested for loop(do not use numpy.dot0) Taks 2: use numpy.dot) function to compute the matrix multiplication.https://docs.scipy.org/doc/numpy-1.15.0/reference/gern Example Dialog: np.array(I[1, 2, 3], [4, 5, 6]]) Bnp.array([[1, 2], [3, 4], [5, 6]]) print(matrix mul(A, B)) A np.array ([[1, 2, 3, 4], [5, 6, 7, 8], [9, 10, 11, 1211 , [3, 4, 10], [s, 6, 11], np.array([1, 2, 91 [7, 8, 12]]) print(matrix mul(A, B)) The resultis: [[22.28.] [49.64.]] [I50. 60.110.] 114.140. 278.] [178. 220. 446.]] Show transcribed image text Objective Write a function named matrix_mul(a, b). Given a two 2D array(Matrix) A and B, the function returns the product of A and B Description The product C of two matrices A and B is defined as where i is summed over for all possible values of and k. In order for matrix multiplication to be defined, the dimensions of the matrices must satisfy where (axb) denotes a matrix with a rows and b columns. Writing out the product explicitly, C1 12 Cp where c21-a21 011 (10)
You should write your code in function: def matrix mul(A,B) # return an array Task 1: You use three level nested for loop(do not use numpy.dot0) Taks 2: use numpy.dot) function to compute the matrix multiplication.https://docs.scipy.org/doc/numpy-1.15.0/reference/gern Example Dialog: np.array(I[1, 2, 3], [4, 5, 6]]) Bnp.array([[1, 2], [3, 4], [5, 6]]) print(matrix mul(A, B)) A np.array ([[1, 2, 3, 4], [5, 6, 7, 8], [9, 10, 11, 1211 , [3, 4, 10], [s, 6, 11], np.array([1, 2, 91 [7, 8, 12]]) print(matrix mul(A, B)) The resultis: [[22.28.] [49.64.]] [I50. 60.110.] 114.140. 278.] [178. 220. 446.]]

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