Q: How do we use constraint propagation to solve the naked twins problem?
A: For naked twins we are using constraint propagation by leveraging the fact that a number can only
appear once among peer boxes. When we have two peer boxes that contain only the same two integers we
know that we can remove those integers from the peers that those two boxes share. Basically, we are using
the rule (constraint) of one integer per box of peers to reduce the quantity of the possible integers
for boxes which facilitates arriving at a solution.
Q: How do we use constraint propagation to solve the diagonal sudoku problem?
A: In much the same way as naked twins, by utilizing the rule (constraint) of one integer per box in a
group of peers, we are able to identify boxes with only one integer then get their peers (including their
diagonal peers in this case) and remove those single integers from their peers - as we know those integers can
only appear once. This is very similar to applying a "safe move" with a "greedy" algorithm - based on this
rule we can be certain that is it safe to to remove these integers from their peers, thus reducing the
complexity of solving the overall puzzle.
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.