Q: How do we use constraint propagation to solve the naked twins problem?
A: Constraint Propagation is all about using local constraints in a space (in the case of Sudoku, the constraints of each square) to dramatically reduce the search space. In this case, we can simply chain different strategies together (elimination, only choice, naked twins) to reduce search space in order to solve the problem. Just like the code in method reduce_puzzle, we can add naked_twins after the last existing strategy only_choice.
The Naked Twins is a heuristic for sudoku problem. Bascially, if we ever see there are two same boxes within the same unit and having the same value (exactly two number in a box), we can assume they are Naked Twins. The Naked Twins eliminated the possbilities of having any of the those numbers (exists in the twins) in other boxes within the same unit. For instance, if 27 is the value of naked twins, then within the same unit, 2 and 7 can't exist in other boxes.
Q: How do we use constraint propagation to solve the diagonal sudoku problem?
A: We can reuse the existing logic of constraint propagation, the only thing missing that needs to be added is the new constraint which is the diagonal unit.
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.