Learn Java by Example: Java Program to Add Two Matrix Using Multi-dimensional Arrays

Hits: 38

Java Program to Add Two Matrix Using Multi-dimensional Arrays

In this program, you’ll learn to add two matrices using multi-dimensional arrays in Java.

 


Example: Program to Add Two Matrices


public class AddMatrices{

    public static void main(String[] args){
        int rows = 2, columns = 3;
        int[][] firstMatrix = { {2, 3, 4}, {5, 2, 3} };
        int[][] secondMatrix = { {-4, 5, 3}, {5, 6, 3} };

        // Adding Two matrices
        int[][] sum = new int[rows][columns];
        for(int i = 0; i < rows; i++) {
            for (int j = 0; j < columns; j++) {
                sum[i][j] = firstMatrix[i][j] + secondMatrix[i][j];
            }
        }

        // Displaying the result
        System.out.println("Sum of two matrices is: ");
        for(int[] row : sum) {
            for (int column : row) {
                System.out.print(column + "    ");
            }
            System.out.println();
        }
    }
}

Output

Sum of two matrices is:
-2    8    7    
10    8    6    

In the above program, the two matrices are stored in 2d array, namely firstMatrix and secondMatrix. We’ve also defined the number of rows and columns and stored them in variables rows and columns respectively.

Then, we initialize a new array of the given rows and columns called sum. This matrix array stores the addition of the given matrices.

We loop through each index of both arrays to add and store the result.

Finally, we loop through each element in the sum array using the for-each loop to print the elements.

 

 

Python Example for Beginners

Two Machine Learning Fields

There are two sides to machine learning:

  • Practical Machine Learning:This is about querying databases, cleaning data, writing scripts to transform data and gluing algorithm and libraries together and writing custom code to squeeze reliable answers from data to satisfy difficult and ill defined questions. It’s the mess of reality.
  • Theoretical Machine Learning: This is about math and abstraction and idealized scenarios and limits and beauty and informing what is possible. It is a whole lot neater and cleaner and removed from the mess of reality.

Data Science Resources: Data Science Recipes and Applied Machine Learning Recipes

Introduction to Applied Machine Learning & Data Science for Beginners, Business Analysts, Students, Researchers and Freelancers with Python & R Codes @ Western Australian Center for Applied Machine Learning & Data Science (WACAMLDS) !!!

Latest end-to-end Learn by Coding Recipes in Project-Based Learning:

Applied Statistics with R for Beginners and Business Professionals

Data Science and Machine Learning Projects in Python: Tabular Data Analytics

Data Science and Machine Learning Projects in R: Tabular Data Analytics

Python Machine Learning & Data Science Recipes: Learn by Coding

R Machine Learning & Data Science Recipes: Learn by Coding

Comparing Different Machine Learning Algorithms in Python for Classification (FREE)

Disclaimer: The information and code presented within this recipe/tutorial is only for educational and coaching purposes for beginners and developers. Anyone can practice and apply the recipe/tutorial presented here, but the reader is taking full responsibility for his/her actions. The author (content curator) of this recipe (code / program) has made every effort to ensure the accuracy of the information was correct at time of publication. The author (content curator) does not assume and hereby disclaims any liability to any party for any loss, damage, or disruption caused by errors or omissions, whether such errors or omissions result from accident, negligence, or any other cause. The information presented here could also be found in public knowledge domains.