he that the adjacency list of each node is sorted alphabetic cerm parent in the question is synonymous to predecessor.
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- class Dijkstra(): """ A fully connected directed graph with edge weights """ def __init__(self, vertex_count): self.vertex_count = vertex_count self.graph = [[0 for _ in range(vertex_count)] for _ in range(vertex_count)] def min_distance(self, dist, min_dist_set): """ Find the vertex that is closest to the visited set """ min_dist = float("inf") for target in range(self.vertex_count): if min_dist_set[target]: continue if dist[target] < min_dist: min_dist = dist[target] min_index = target return min_index def dijkstra(self, src): """ Given a node, returns the shortest distance to every other node """ dist = [float("inf")] * self.vertex_count dist[src] = 0 min_dist_set = [False] * self.vertex_count for _ in range(self.vertex_count): #minimum distance vertex that is not processed…the number of edges in a complete 2 of 2 undirected graph of 80 vertices is.? Select one: a. exactly equals 3160 b. None c. less than or equals 3160 d. less than or equals 6320 e. exactly equals 6320 Assume that you have a doubly link list. Pointer x is pointing to the last node in the link list and pointer y is pointing to before the last node. which of the following will remove the node pointed by x from the list? Select one: O a. delete x; b. y->prev->next = y->next; X->prev= y->prev; delete x; O C. X->prev->next = x->next; y->prev= x->prev; delete x; O d. x->next = y; y->prev = x->prev; delete x;Exercise # 2 - Detecting Cycle between List of N Airports The following graph is an example from Rosen (2011). It shows the flights and distances between some of the major airports in the United States. Dallas 200 1300 200 Austin Washington Denver 1400 Atlanta 160 800 800 Chicago Houston Write a function that takes a list of N airports and checks if they form a cycle of size N. {A cycle is a directed path that starts and ends at the same vertex. Before writing code, make sure you can identify cycles yourself} >> check_cycles (G, ['Austin','Houston', 'Atlanta','Washington','Dallas'l) Yes >>> check_cycles (G, ['Austin', 'Houston','Atlanta','Washington']) No 600 600 780 0000 000L 006
- Question 11 Complete the function body below. def max_neighbours(g): Input : adjacency matrix of graph (g) Out put: vertex with maximum number of neighbours in g # your code here #6. Write a java Program to implement Breadth First Search traversal of Graph.Enter the number of vertices in graph us 4.Q5. Figure below shows an unweighted graph of 6 vertices. Draw a spanning tree for the graph where each node has at most 2 children. Implement the binary tree by using array index referencing method and print the content of each node following inorder traversal. Upload the drawing of the spanning tree as an image file and the complete C program as .txt file. * 3 6. 1 2 4 1 Add File Q6. Extract 3 fundamental cycles from figure in Q5 and form 3 sets of vertices. By using linear bitmap implementation of sets, determine the intersection between the three sets and print the intersection element(s) to the screen. Upload the complete C program as .txt file and the screenshot of the output as an image file. * 1 Add File
- using vertex 'A' as the starting vertex, perform BFS traversal on the graph, and collect the visited nodes in a list. Break ties alphabetically (i.e., the adjacent nodes from node E should be tried in the order A, B, C, D, H). Note: write a single vertex at one text field, e.g., A, without any space and quotation marks.Sorting Create a MyLinkedList class with inner Node class, data fields, and the insert(element) method. Implement a toString method to return all nodes in MyLinkedList. Implement a recursive sorting and a non-recursive sorting method.ADT Graph Iterator: Create either a DFS graph iterator or a BFS graph iterator by implementing java.util.lterator interface. An ADT graph iterator should have a reference to an ADT graph. It should also have a DFS search method or a BFS search method.
- 5. Select all true statements about DFS and BFS in the box below. Assume no other algorithms are involved: for example, "DFS can explore a tree" means DFS can achieve it alone without involving with any other algorithms. A. When exploring a wide and shallow tree, DFS is more memory efficient than BFS. B. DFS can find words with increasing order of length in auto-completion. C. BFS can find all the vertices that are reachable from a given vertex, but DFS can not. D. Both DFS and BFS can be used to detect cycles in a graph. E. DFS is generally faster than BFS when exploring a graph.Data Structures Weighted Graph Applications Demonstration Look at Figure 29.23 which illustrates a weighted graph with 6 verticies and 8 weighted edges. Simply provide: Minimal Spanning Tree as an illustration or a textual list of edges (in our standard vertex order). Single-Source Shortest Path route from vertex 0 to the other 5 (described as one path/route for each). You may draw the two solutions and attach the illustration or describe them in text (a list of edges for the one and the vertex to vertex path the other). Be sure the final trees or path lists are clearly visible in your solution.7 4 2 4 2 Tree #1 1 5 Tree #2 6 3 6 8 8 9 5 7 9 1 For parts a through g, implement the given method and demonstrate the method working with the two trees provided earlier. All code implemented in this assignment should be in a class called Homework 6. You may use the data structures and algorithm code from the lecture notes. Hint: Consider using recursion. To do so implement a private helper method that takes a Node and then recursively calls itself to traverse the tree. The public method would call the private method passing the tree's root as the Node.