- (a)
S A C B E F G K L D - (b)
S.neighbors = [A] C.neighbors = [A] A.neighbors = [S, C, B, E] B.neighbors = [A, E] E.neighbors = [B, A] F.neighbors = [E, G] G.neighbors = [F, K] K.neighbors = [G, L] L.neighbors = [K, D] D.neighbors = [L] - (c)
- (a) 25 Nodes
- (b) Adjacency lists of "touching" nodes
- (c) Undirected, Acyclic, Connected, Unweighted
- (d)
- (a)
Graph.py - (b)
main.py - (c)
main.py - (d)
GraphSearch.py - (e)
GraphSearch.py - (f)
GraphSearch.py - (g)
GraphSearch.py - (h)
main.py- i. I ran into a
RecursionError: maximum recursion depth exceeded in comparison. The code needs to run n recursions (where n is number of nodes in the graph) when it is a LinkedList.
- i. I ran into a
- (i)
main.py- i. Since there are no recursive calls made, there is no reason for the callstack to be overflowed.
- (a) A DAG is a connected graph where at least one node has 0 incoming nodes and at least one node has 0 outgoing nodes. There are no cycles in a DAG. In the previous graph every connected node could traverse to and from each other. In a DAG they only move in one direction.
- (b)
DirectedGraph.py - (c)
main.py - (d)
TopSort.py - (e)
TopSort.py
- (a) Each edge is positively weighted and the graph is connected.
- (b)
WeightedGraph.py - (c)
main.py - (d)
main.py - (e)
TreadmillMazeSolver.py
- (a)
GridGraph.py - (b)
main.py - (c) The Manhattan Distance is a heuristic that I can use to solve the maze with A*. It is consistent because it is always less than estimated distance. Since the maze can only be solved by moving up, down, left, and right the diagonal distance will always be shorter than the shortest possible path distance. By this definition it is also admissible because it was also never overestimate the shortest distance.
- (d)
main.py - (e)
main.py
- (a) In a graph with 1000 nodes, the A* algorithm only visits 370 nodes, while the Dijkstra algorithm visits all 1000. The A* algorithm only visits nodes that are the highest chance of being part of the shortest path, while the Dijkstra algorithm visits all the nodes it can before reaching the destination node to compare its results and create the shortest path.