In this lab, you will complete a provided partial implementation of a topological sort that mimics the Unix command tsort. You should begin by reading about the tsort command and then using it until you have a good understanding of what it is and does. You can learn a bit more about tsort by entering man tsort at the command line. Press q to quit. You can learn even more by entering info coreutils 'tsort invocation' at the command line. Press q to quit.
A topological sort of a directed acyclic graph (DAG) is an ordering of its vertices such that, if there is a path from vertex v1 to vertex v2, then v1 must come before v2 in the ordering. The graph must be acyclic (without cycles) because in a graph with a cycle there exist vertices with paths to themselves. This would imply that a vertex must come before itself (which it cannot). A simple algorithm for finding a topological sort is:
• Build an adjacency list for all of the vertices and include each vertex's in degree (number of incoming edges) as well as the specific vertices adjacent to it.
o Store the adjacency list using a dictionary where the key is the string name of the vertex
• Push all vertices with an in degree of zero on to a stack. Push the vertices in the order in which they were encountered while building the adjacency list. o For the implementation of a stack data structure, you must use your Stack class from stack_arry.py from Lab 2 (and also used in Project 2). You must add, commit, and push a correct implementation of this file.
o In order to keep track of the order in which the vertices were encountered, you should use a separate data structure. This will not necessarily be in alphabetical order, and this order cannot be determined by the adjacency list described above.
• While the stack is not empty:
o Pop and output a vertex.
o Reduce the in degree of the vertices that were adjacent to the just-popped vertex.
▪ If reducing the in degree of a vertex results in a value of 0, push the vertex immediately.
The following starter files are available on GitHub. Complete the implementations and ensure that your implementations are committed and pushed to GitHub.
