changgyhub / sudoku

A Scalable Sudoku and Constraint Satisfaction Problem Solver

Sudoku

A scalable sudoku solver with templates for all constraint satisfaction problems.

Author

Chang Gao
Bruce Tan
Rimoun Ghaly

Usage

python main.py <input> <output> <timeout> <tokens>

1. <input>: Raw input file of sudoku problems, see /ExampleSudokuFiles for samples. For sudoku data, we support different size of board, with digits or/and upperCase letters.

2. <output>: Name and location of output file.

3. <timeout>: Maximum time of backtracking (in seconds).

4. <tokens>: Backtracking methods to use, we support:

• BT: Default backtracking.
• FC, ACP, NKD, NKT: Consistency checking methods to use. FC for Forward Checking, ACP for Arc Consistency, NKD for Naked Double, and NKT for Naked Triple. If multiple methods are set, they will be considered in order.
• MRV, DH: Variable selection heuristic to use. MRV for Minimum Remaining Values, and DH for Degree Heuristic. We only support one heuristic at the same time.
• LCV: Value selection heuristic to use. LCV for Least Constraining Value.

Generate a new Sudoku puzzle

python problem_generator.py <input> <output>

The input file will consist of 4 numbers

M N P Q

• M is the number of cells that will be initialized
• N is the size of the NXN grid
• P is the number of rows in each block
• Q is the number of columns in each block

EXAMPLE: 25 9 3 3

Default Testing

For default test on all solver options, you can simply type

python main.py

You may customize your test in main.test()

Note

• Modified based on rimoung/PythonSudoku.
• Do not copy contents of this repo for course assignments. You should take the responsibility for any form of plagiarism.