Data Structure
Networking
RDBMS
Operating System
Java
MS Excel
iOS
HTML
CSS
Android
Python
C Programming
C++
C#
MongoDB
MySQL
Javascript
PHP
Physics
Chemistry
Biology
Mathematics
English
Economics
Psychology
Social Studies
Fashion Studies
Legal Studies
- Selected Reading
- UPSC IAS Exams Notes
- Developer's Best Practices
- Questions and Answers
- Effective Resume Writing
- HR Interview Questions
- Computer Glossary
- Who is Who
Multiplication of two Matrices in Single line using Numpy in Python
Matrix multiplication is a lengthy process where each element from each row and column of the matrixes are to be multiplied and added in a certain way. For matrix multiplication, the number of columns in the first matrix must be equal to the number of rows in the second matrix. The result matrix has the number of rows of the first and the number of columns of the second matrix.
For smaller matrices we may design nested for loops and find the result. For bigger matrices we need some built in functionality in python to tackle this. We will see both approaches below.
Using for Loop
We take two matrices of dimension 2x3 and 3x2 (rows x columns). The result of matrix multiplication is a 2x2 matrix. We have a nested for loop designed to go through the columns of A and rows of B and add the products of the values in those rows and columns.
Example
#matrix A with 2 rows
A = ([5,10,15],[20,25,30])
#matrix B with 2 columns
B = ([4,8],[12,10],[14,16])
result = [[0 for x in range(2)] for y in range(2)]
for i in range(len(A)):
# iterate through columns of A
for j in range(len(B[0])):
# iterate through rows of B
for k in range(len(B)):
result[i][j] += A[i][k] * B[k][j]
for r in result:
print(r)
Output
Running the above code gives us the following result:
[350, 380] [800, 890]
Using Numpy
Numpy has a in-built function named dot, that carries out the matrix multiplication. Our number of lines of program becomes very less and the syntax is also very simple.
Example
import numpy as np #matrix A matrix_A = ([5,10,15],[20,25,30]) #matrix B matrix_B = ([4,8],[12,10],[14,16]) result = np.dot(matrix_A,matrix_B) # Result print(result)
Output
Running the above code gives us the following result:
[[350 380] [800 890]]
512 Views
