Q: How do we use constraint propagation to solve the naked twins problem?
A: The naked twins are unsolved boxes in a unit that have the same potential solutions, for example, '23' and '23'.
In this particular example, since we know that '2' and '3' must be in one of these two boxes, we can remove these values
from the rest of the unsolved boxes in the unit, simplifying the problem at hand.
In my solution of the naked twins problem, I look for values in a unit that are of length 2 and are found more than once
(the constraints). If any are found, I proceed to remove digits found in these values from the rest of the boxes in the
unit.
Q: How do we use constraint propagation to solve the diagonal sudoku problem?
A: Diagonal sudoku includes diagnals as units (not just rows, columns and square units). Introducing this adds one more
set of constraints to the problem, narrowing down the number of possible solutions for each unsolved box.
In my solution of the diagonal sudoku problem, I get the diagonals from zipping the rows' ids with columns' ids and
rows' ids with reversed columns ids since the indices of the ids' positions correspond in both cases (the constraint).
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.