The original approach for the solution of lab1 is explained in this README
After receiving the first review i tried to implement the suggestion proposed in te Issues of my peer, in particular i added:
- Added some usefull comment in my code other than the readme in order to make it more readable and understandable
- Changed my approach using an ad hoc class
PrioritizedSetinstead using a tuple representing(priority,list) - Added a file
Dijkstra.pyimplementing a vanilla version of Dijkstra's algorithm in order to find a globally optimal solution for values of$N <= 20$
In order to review the code produced by some of my peer i opened a pull request or used the Issues tool provided by github,improving some aspects of the code and suggesting some other changes I was assigned to review the code produced by Omid Mahdavii. The other peer review were chosen randomly from the list of github repos provided by the professor.
For this lab i reviewed:
- Omid Mahdavii forking his repo and creating a new pull request that can be found here
- Shayan Taghinezhad Roudbaraki using github Issues, my review can be found here
- RaminHedayatmehr also using github Issues, my review can be found here
The original approach for the solution of lab2 is explained in this README
TODO
In order to review the code produced by some of my peer i used the Issues tool provided by github, suggesting some changes and improvements. For this lab i reviewed:
- Enrico Magliano using github Issues, my review can be found here
- Peppepelle99 using github Issues, my review can be found here
The approach for the solution of lab3 is explained in this README
TODO
In order to review the code produced by some of my peer i used the Issues tool provided by github, suggesting some changes and improvements. For this lab i reviewed:
- Leonor Gomes creating a pull request since in the README asked to fix the
minimax_pruningfunction, my pull request can be found here - Omid Mahdavii using github Issues, my review can be found here