Q: How do we use constraint propagation to solve the naked twins problem?
A: To solve the Naked twins problem, we implement the following constraint: we define naked twins as a pair of boxes of length two digits in a unit, where the digit of both boxes have equal values. The two digits in the naked twins cannot be present in other boxes in the unit, and we can as a result eliminiate them from the the other boxes. Our goal is to look at all of the individual units and apply the constraint locally, identifying the naked twins present and then remove the two twin digits from the other boxes in the unit. Going through all the units propagates the constraint to the whole grid and makes us closer to the solution. In my implementation, I scan through the units to find the naked twins and the look at the peers common to each naked twins to find where to eliminate the twin digits at each iteration in a more efficient manner.
Q: How do we use constraint propagation to solve the diagonal sudoku problem?
A: To solve for the diagonal sudoku problem, we need to give additional constraints by adding more units to the unit list, in this case the diagonal units. To do this, we add the two diagonal units to the list of units in our code: the list of units and peers will then take into account the additional diagonal units. We then can apply the strategies to reduce the size of the solution space taking into account the additional units and peers.
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.