Q: How do we use constraint propagation to solve the naked twins problem?
A: With the naked twins problem, we know that when two of boxes in the same unit have the same 2 values that those values are "locked" onto those squares, even if we can't solve them directly. This rule allows us to have the constraint of seeing the two squares as "solved" for their shared units. This creates smaller subset of search space by elimating those values from their shared peers. (constrant progogation).
Q: How do we use constraint propagation to solve the diagonal sudoku problem?
A: We use constraint propagation in the diagonal sudoku in the same was as the regular sudoku problem. In the diagonal, we know we have another set of rules, the 2 diagonals, to join the rules of the rows, columns, and squares. By simply adding the diagonal units to the unitlist, we can confine the problem to more distinct subset. This is know as constrant propogation.
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.
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.
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 runningpython 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.
To visualize your solution, please only assign values to the values_dict using the assign_values function provided in solution.py
The data consists of a text file of diagonal sudokus for you to solve.