Q: How do we use constraint propagation to solve the naked twins problem? A: The naked twins problem arises when two boxes in the same unit share the same two possible values. These boxes are referred as naked twins. Since there are only two identical options for two boxes, these options can't be used by any other box(apart from the twins)in the unit, otherwise one of the twins would end up with no possible value. Constraint propagation is then used by determining if a sudoku unit contains naked twins, and if it does, by propagating the value of the twins to the other boxes of that same unit. This is accomplished by removing the twins values from all other boxes in that unit.
Q: How do we use constraint propagation to solve the diagonal sudoku problem?
A: Constraing propagation is used by creating two new units that further constrain the boxes that are
present on the diagonal. Once a the value of a box in one of these two units is determined, the other potential values of
other boxes in the same unit are updated.
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.