Q: How do we use constraint propagation to solve the naked twins problem?
A: To find naked twins, we need to look for two boxes in the same unit which have the same value and it's length should be 2. let say we have found two boxes 'A3' and 'A5' in the row_unit with same value '23'. so that means 2 and 3 are the only options for squares 'A3' and 'A5'. and not other boxes in the same unit can have value '2' or '3'. so we must eliminate '2' and '3' from the values of all boxes which contain these values and those boxes must have more than 1 values. So in the programming part, i have taken each units in to consideration and tried to find a possible twins. if there is a match in the specific unit. then i have applied eliminate strategy with value of twin box to all boxes in the same unit.

Q: How do we use constraint propagation to solve the diagonal sudoku problem?
A: In the diagonal sudoku,we need to take two main diagonals into consideration. the numbers 1 to 9 should all appear exactly once in each diagonal. so that means when we calculate peers for those boxes which are in the two main diagonals, we need to consider boxes in diagonals. So in the programming part, i have added one more constraint called diagonal_units. and i have added diagonal_units with the main unit list which contains row_units,col_units and box_units. so at the time peer calculation, the diagonal_units should be taken in to consideration.

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 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.

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.