Q: How do we use constraint propagation to solve the naked twins problem?
A: The Naked Twins problem requires addition of a new constraint that represents the "Naked Twin" relationship. This relationship says that if two boxes are peers within a common unit and share the same two possiblities for their values, then the two possiblities may be removed from all other peers within the unit.
More formally: "for each box b_i with only two value posibilities [a_1, a_2] , if a box b_j within a unit of b_i's peers "u_k" shares the same possiblities then remove [a_1,a_2] from the possiblities of all members of u_k, expect b_i and b_j."
Q: How do we use constraint propagation to solve the diagonal sudoku problem?
A: Diagonal Sudoku introduces an additional set of peers for some boxes in the puzzle. In this case, the constraint relationships should now be applied along the sets of diagonal elements on the puzzle, in addition to the existing units. Thus, new units must be formed to represent the diagonal relationship, and all constraints may now be applied to the new 'diagonal' units in adition to the existing conventional sudoku units.
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.