Overview This project implements memory-efficient sparse matrix operations in Python. It uses a Dictionary of Keys (DOK) format to store only non-zero elements, making it suitable for large sparsematrices
Getting Started

Installation Clone this repository: git clone https://github.com/Rachel-png-coder/DSA_SparseMatrix cd dsa/sparse_matrix No additional installation needed - we only use standard Python! 📝 File Format Input files must follow this format:

rows= cols= (row, col, value) (row, col, value) ... Example:

rows=8433 cols=3180 (0, 381, -694) (0, 128, -838) (0, 639, 857) Format Rules First line: rows= Second line: cols= Following lines: (row, col, value) where: row must be between 0 and rows-1 col must be between 0 and cols-1 value must be an integer Whitespace is ignored All numbers must be integers Usage Run the program: cd code/src python main.py Select an operation: Sparse Matrix Operations

1. ➕ Add matrices
2. ➖ Subtract matrices
3. ✖️ Multiply matrices
4. 📊 Display matrix statistics
5. 👋 Exit Choose input files: Enter 1-3 for sample files Or type a full path to your own file Results will be saved in the results/ directory with timestamp Implementation Details Core Classes SparseMatrix: Main class implementing sparse matrix operations Uses Dictionary of Keys (DOK) format Only stores non-zero elements Optimized for memory efficiency Key Operations

Create from file

matrix = SparseMatrix("path/to/file.txt")

Create empty

matrix = SparseMatrix(num_rows=10, num_cols=10)

Get/Set elements

value = matrix.get_element(row, col) matrix.set_element(row, col, value)

Matrix operations

result = matrix1.add(matrix2) result = matrix1.subtract(matrix2) result = matrix1.multiply(matrix2) Error Handling The implementation handles various error cases:

Invalid file formats Out-of-bounds indices Incompatible matrix dimensions Missing or unreadable files Invalid number formats 🛠️ Matrix Operation Rules Addition/Subtraction Both matrices must have the same dimensions Results in matrix of same size Adds/subtracts corresponding elements Multiplication Matrix 1 columns must equal Matrix 2 rows Results in matrix of size (M1_rows × M2_cols) Uses efficient sparse multiplication algorithm 📈 Performance Considerations Only stores non-zero elements Optimized multiplication for sparse matrices Efficient memory usage for large sparse matrices Fast access to elements using dictionary 📄 License This project is for educational purposes. Feel free to learn from it!