Q: How do we use constraint propagation to solve the naked twins problem?
A: When we have a naked twin in a unit (row, col, ...), we know that the values for the twins
can't be in other squares of this unit, so we delete the possibilities from these other squares. With this method
we can reduce the complexity of our grid and continue with our algorithm (eliminate/only_choice and search)
Q: How do we use constraint propagation to solve the diagonal sudoku problem?
A: We take a look at the diagonals of the sudoku and eliminate the values of solved squares from their peers
(other squares in the unit).
With the only_choice method we take a look at the list of possible values for every square in the diagonals
and see if there is one number that can only be placed in one square. If its the case we assign this number as the
only solution for this square (and eliminate again).
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.