A Diagonal Sudoku solver implemented with Python.
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README.md

Artificial Intelligence Nanodegree

Introductory Project: Diagonal Sudoku Solver

Question 1 (Naked Twins)

Q: How do we use constraint propagation to solve the naked twins problem?
A: The “naked twins constraint” is used as an additional strategy to reduce the search space at each search step. This is similar to the “eliminate” and “only choice” strategies that were implemented earlier. For each unit, we remove digits that are part of the naked twins found in the same unit.

Question 2 (Diagonal Sudoku)

Q: How do we use constraint propagation to solve the diagonal sudoku problem?
A: The current model implements a nice abstraction about “units”, which are related constraints over boxes in the grid. By adding a vector of units for diagonals (DUNITS), we can extend the pruning of each strategy without changing their code.

Install

This project requires Python 3.

We recommend students install Anaconda, a pre-packaged Python distribution that contains all of the necessary libraries and software for this project. Please try using the environment we provided in the Anaconda lesson of the Nanodegree.

Optional: Pygame

Optionally, you can also install pygame if you want to see your visualization. If you’ve followed our instructions for setting up our conda environment, you should be all set.

If not, please see how to download pygame here.

Code

  • solutions.py - You’ll fill this in as part of your solution.
  • solution_test.py - Do not modify this. You can test your solution by running python solution_test.py.
  • PySudoku.py - Do not modify this. This is code for visualizing your solution.
  • visualize.py - Do not modify this. This is code for visualizing your solution.

Visualizing

To visualize your solution, please only assign values to the values_dict using the assign_values function provided in solution.py

Data

The data consists of a text file of diagonal sudokus for you to solve.