Q: How do we use constraint propagation to solve the naked twins problem?
A: The naked twins is a constraining technique. For every pair of boxes in the same unit and with the same two values, we remove these two values from all their SHARED peers. The boxes may fall into one or two units simultaneously. We add this technique to the reduction iteration, after eliminate and only choice. Note: Unlike the single-value elimination, eliminating for the naked twins can stall if implemented naively. In particular, if a unit has more than one pair of twins, the elimination of the values of the first one from the shared peers might eliminate a value from one of the remaining pairs. So, there should be a check for this condition while iterating throught the twin pairs of the unit.
Q: How do we use constraint propagation to solve the diagonal sudoku problem?
A: We simply add the diagonals to the unit list. This results in different boxes having different numbers of peers and units, but apart from the unit tests, there is no other change that needs to be made to solve diagonal sudoku. Only some single-solution sudoku have diagonal solutions.
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_values
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](https://project-assistant.udacity.com/auth_tokens/jwt_login 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.