Graph Traversal ( Due 22 Apr 2022 )

In this assignment you will be creating a graph from an input data file called graph.txt. The first line in that file will be a single integer v. This number will denote the number of vertices to follow. The next v lines will be the labels for the vertices. There will be one label to a line. Assume that the labels are unique. The next line after the labels for vertices will be a single number e. This number will denote the number of edges to follow. There will be one edge per line. Each edge will be of the form - fromVertex, toVertex, and weight. If the weight is not given, assign a default weight of 1 to that edge. After the list of edges there will be a label for the starting vertex. This will be the starting vertex for both the Depth First Search and Breadth First Search. After that there will be two cities and you will have to delete the edges connecting the two cities and print the adjacency matrix. Then there will be a single city and you will delete this vertex and all edges from and to this vertex. You will print the list of vertices and the adjacency matrix showing all edges from it and all edges to it have been deleted. To delete a vertex from the graph - remove it from the vertex list and remove the corresponding row and column for this vertex.

Here is the outline of the code that we explained in class that you will be modifying. You can add an Edge class if you want to. You will be adding the following functions to the Graph class and the following test cases to your main program. 

class Graph (object):
  # check if a vertex is already in the graph
  def has_vertex (self, label):

  # get the index from the vertex label
  def get_index (self, label):

  # add a Vertex object with a given label to the graph
  def add_vertex (self, label):
  
  # add weighted directed edge to graph
  def add_directed_edge (self, start, finish, weight = 1):

  # add weighted undirected edge to graph
  def add_undirected_edge (self, start, finish, weight = 1):

  # get edge weight between two vertices
  # return -1 if edge does not exist
  def get_edge_weight (self, fromVertexLabel, toVertexLabel):

  # get a list of immediate neighbors that you can go to from a vertex
  # return a list of indices or an empty list if there are none
  def get_neighbors (self, vertexLabel):

  # return an index to an unvisited vertex adjacent to vertex v (index)
  def get_adj_unvisited_vertex (self, v):

  # get a copy of the list of Vertex objects
  def get_vertices (self):

  # do a depth first search in a graph starting at vertex v (index)
  def dfs (self, v):

  # do a breadth first search in a graph starting at vertex v (index)
  def bfs (self, v):

  # delete an edge from the adjacency matrix
  # delete a single edge if the graph is directed
  # delete two edges if the graph is undirected
  def delete_edge (self, fromVertexLabel, toVertexLabel):

  # delete a vertex from the vertex list and all edges from and
  # to it in the adjacency matrix
  def delete_vertex (self, vertexLabel):

def main():
  # test depth first search

  # test breadth first search

  # test deletion of an edge

  # test deletion of a vertex

if __name__ == "__main__":
  main()

Here is sample output. For grading purpose, please follow this output format. Matching the provided sample prompt will expedite grading and prevent unexpected grading error. 

Depth First Search
Houston
Atlanta
Kansas City
Los Angeles
San Francisco
Seattle
Denver
Chicago
Boston
New York
Dallas
Miami

Breadth First Search
Houston
Atlanta
Miami
Dallas
Kansas City
New York
Los Angeles
Denver
Chicago
Boston
San Francisco
Seattle

Deletion of an edge

Adjacency Matrix
0 1 0 1 0 1 0 0 0 0 0 0
1 0 1 1 0 0 0 0 0 0 0 0
0 1 0 1 1 0 0 0 0 0 1 0
1 1 1 0 1 1 0 0 0 0 0 0
0 0 1 1 0 1 0 1 1 0 1 0
1 0 0 1 1 0 1 1 0 0 0 0
0 0 0 0 0 1 0 1 0 0 0 0
0 0 0 0 1 1 1 0 1 0 0 0
0 0 0 0 1 0 0 1 0 1 0 1
0 0 0 0 0 0 0 0 1 0 0 1
0 0 1 0 1 0 0 0 0 0 0 1
0 0 0 0 0 0 0 0 1 1 1 0

Deletion of a vertex

List of Vertices
Seattle
San Francisco
Los Angeles
Kansas City
Chicago
Boston
New York
Atlanta
Miami
Dallas
Houston

Adjacency Matrix
0 1 0 0 1 0 0 0 0 0 0
1 0 1 0 0 0 0 0 0 0 0
0 1 0 1 0 0 0 0 0 1 0
0 0 1 0 1 0 1 1 0 1 0
1 0 0 1 0 1 1 0 0 0 0
0 0 0 0 1 0 1 0 0 0 0
0 0 0 1 1 1 0 1 0 0 0
0 0 0 1 0 0 1 0 1 0 1
0 0 0 0 0 0 0 1 0 0 1
0 0 1 1 0 0 0 0 0 0 1
0 0 0 0 0 0 0 1 1 1 0

For this assignment you may work with a partner. Both of you must read the paper on Pair Programming. .

The file that you will be uploading will be called Graph.py. We are looking for clean and structured design. The file will have a header of the following form: 

#  File: Graph.py

#  Description:

#  Student Name:

#  Student UT EID:

#  Partner Name:

#  Partner UT EID:

#  Course Name: CS 313E

#  Unique Number: 

#  Date Created:

#  Date Last Modified:

If you are working with a partner only one of you will be submitting your program but make sure that you have your partner's name and eid in your program. If you are working alone, then remove the two lines that has the partner's name and eid in the header.

Use the Canvas system to submit your Graph.py file. We should receive your work by 11 PM on Friday, 22 Apr 2022. There will be substantial penalties if you do not adhere to the guidelines. Remember Python is case sensitive. The name of your file must match exactly what we have specified.