Q: How do we use constraint propagation to solve the naked twins problem?
A: The naked twins problem is identification of pairs of pairs :) in a unit and eliminating those from the rest of the unit.
We can make a additional improvement to void all units where changes has been seen to ensure we propagate the values accurately,
in a integrated with the rest of the Sudoku problem this may not matter, since we can apply the eliminate funtion after
each run.
Additionally I also implemented the hidden_twin function as explained http://www.sudokudragon.com/sudokustrategy.htm and a test is in the comments section above the function.
Q: How do we use constraint propagation to solve the diagonal sudoku problem?
A: As a first step implement the sudoku solver for a regular sudoku. This is what was covered in the regular class examples.
Once this is done we add the constraints for the diagonal check, this can be achieved by updating the unitlist to include
the 2 additional units. The rest of the code i.e. eliminate, only_choice, reduce_puzzle and search is agnostic to this change
and works with no issues.
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.
solution.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_value function provided in solution.py
Before submitting your solution to a reviewer, you are required to submit your project to Udacity's Project Assistant, which will provide some initial feedback.
The setup is simple. If you have not installed the client tool already, then you may do so with the command pip install udacity-pa.
To submit your code to the project assistant, run udacity submit from within the top-level directory of this project. You will be prompted for a username and password. If you login using google or facebook, visit this link for alternate login instructions.
This process will create a zipfile in your top-level directory named sudoku-.zip. This is the file that you should submit to the Udacity reviews system.