llathrop/aind-sudoku

Artificial Intelligence Nanodegree

Introductory Project: Diagonal Sudoku Solver

Question 1 (Naked Twins)

Q: How do we use constraint propagation to solve the naked twins problem?
A: With the Naked Twins problem, we have added the constraint that any unit with a matched set of options/numbers in an unsolved cell may no longer have those numbers as an option for other unsolved cells. We do this by:

• cycling through each box without a solution and attempting to find matching box in the same unit.
• if a twin is found track it's cell location
• we then remove each of the individual digits from all the other unsolved cells, if present, with the exception of our twins.
• we add this as a step to the original reduce_puzzle (after eliminate and only_choice), to assist in solving the puzzle

Question 2 (Diagonal Sudoku)

Q: How do we use constraint propagation to solve the diagonal sudoku problem?
A: By adding the requirement of solving the diagonal, we have constrained the problem to only have a solution if that diagonal is solved. We carry this out in our example by simply adding a new unit type and using that unit type along with the other units in the unit list. no other changes were needed.

Install

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.

Optional: Pygame

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.

Code

• 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 running python 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.

Visualizing

To visualize your solution, please only assign values to the values_dict using the assign_values function provided in solution.py

Submission

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.

initial submit:4/19/2017