https://github.com/lucko515/sudoku-ai-solver
This is the first project for Artificial Intelligence Nanodegree @Udacity. Sudoku solver.
https://github.com/lucko515/sudoku-ai-solver
aind artificial-intelligence constraint-satisfaction-problem sudoku-solver
Last synced: 1 day ago
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This is the first project for Artificial Intelligence Nanodegree @Udacity. Sudoku solver.
- Host: GitHub
- URL: https://github.com/lucko515/sudoku-ai-solver
- Owner: lucko515
- Created: 2017-07-21T21:32:45.000Z (about 8 years ago)
- Default Branch: master
- Last Pushed: 2017-07-21T21:34:09.000Z (about 8 years ago)
- Last Synced: 2025-03-27T12:49:18.991Z (7 months ago)
- Topics: aind, artificial-intelligence, constraint-satisfaction-problem, sudoku-solver
- Language: Python
- Size: 175 KB
- Stars: 0
- Watchers: 1
- Forks: 0
- Open Issues: 0
-
Metadata Files:
- Readme: README.md
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README
# 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: The naked twins constraint helps to simplify our sudoku table and make it easier to solve.
The constraint of naked twins is that no boxes in unit(s) with naked twins in them have digits
which are in the naked twins boxes. By removing those digits from all boxes in a unit except
the boxes which make the naked twins, we are making sudoku easier to solve.
# Question 2 (Diagonal Sudoku)
Q: How do we use constraint propagation to solve the diagonal sudoku problem?
A: To introduce this constraint to our problem is very easy, our job is to add two more
units to our unit list (one per diagonal). By doing so, we are adding more peers for each box
and thats our new constraint to be aware of. In solving process nothing is changed, we apply
our solving strategies (Elimination, only-choice, naked twins) as before.
### Install
This project requires **Python 3**.
We recommend students install [Anaconda](https://www.continuum.io/downloads), 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](http://www.pygame.org/download.shtml).
### 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.