CIS22C Lab 3 Revision Due Date Changed To 87-100 Pm Addition

Cis22c Lab 3 Revision Due Date Changed To 87 100 Pm Additional

Revised Scoring Rubric: You may select which parts of the lab you wish to do in any order. The first 3 parts you choose will be worth 6 points each. After doing 3 parts, additional parts are worth 2 points each. This means you can earn from 6 points to 24 points depending on which parts you choose to answer.

PART A: Write a C++ function to compute the balance factor of a tree. If it is called initially with a root pointer, it should determine the balance factor of the entire tree. If it is called with a pointer to a subtree, it should determine if the tree is balanced or not. In balanced trees, the height difference of the left versus the right subtrees must not differ by more than 1. Upload your function to Catalyst to a file named determineBalance.cpp. Test with unbalanced and balanced preorder trees as specified.

PART B: Write a C++ program that adds a node with value 29 to the balanced tree from PART A. Implement your code using the given algorithm. Your result should indicate that a new pointer is created from node C pointing to a location that contains the value 29. Upload your function to addBstNode.cpp and upload a screenshot of your program execution.

PART C: Write a C++ program that reads a list of names and telephone numbers from a text file and inserts them into a BST. Provide a menu for searching, inserting, deleting, or printing the list. Save the updated list back to the file. Test with the provided file and upload your code and output screenshot as specified.

PART D: Write an algorithm to combine two heaps into a third, with Heap A at the bottom and Heap B on top. Upload your algorithm to heapCombineAlgorithm.txt.

PART E: Fill out the adjacency matrix for the given graph manually, using 1 for edges and 0 for no edges. Upload the matrix to adjacencyMatrix.txt.

PART F: Write and test a program to find friends of specific people based on a list of friendship relations. Upload your program as myFriends.cpp and the output screenshot as myFriendsOutput.jpg or Word document.

Paper For Above instruction

Efficient data structures such as trees, heaps, and graphs are fundamental to computer science, enabling effective data storage, retrieval, and manipulation. In this assignment, various aspects of these structures are explored through programming tasks involving binary trees, heaps, and graphs, emphasizing both implementation skills and understanding of core concepts.

Part A focuses on the implementation of a recursive function in C++ to determine the balance factor of a binary tree, which assesses whether the tree is height-balanced. The balance factor is crucial in self-balancing trees like AVL trees, where maintaining balance ensures optimal search times. The function should compute the height of subtrees and evaluate their height differences, returning an indication of balance status. Testing involves unbalanced and balanced trees to verify correctness.

Part B involves enhancing a balanced binary search tree (BST) by inserting a node with a specific value. This tests understanding of BST insertion algorithms, especially in maintaining the tree's balance during insertion. The process includes pointer manipulations, ensuring that the new node correctly links to existing nodes, preserving BST properties.

Part C extends the application to real-world data: managing a phone list stored in a text file. Building a BST from file data involves reading and parsing input, inserting nodes into the tree. Providing a menu for operations such as search, insert, delete, and print facilitates user interaction, with changes saved back to the file to ensure data persistence. This task combines file I/O with dynamic tree management.

Part D addresses the merging of two heaps, data structures optimized for priority queue operations. Combining heaps typically involves merging two binary heap structures into a single heap, preserving heap properties. The algorithm ensures that the combined heap correctly maintains its shape and order properties.

Part E requires constructing an adjacency matrix manually for a given graph, represented as a 2D boolean matrix. This task involves understanding graph representations, where the matrix indicates the presence or absence of edges between vertices. Accurate formation of the matrix is vital for graph algorithms such as traversal, shortest path, and connectivity analysis.

Part F involves using a graph to model relationships among individuals and their friendships. By representing people as vertices and friendships as edges, the program can identify all friends of a specific individual through adjacency lists or matrices. The implementation includes input data definition, graph construction, and querying functions, demonstrating graph traversal and relationship querying.

Together, these programming tasks cover core data structures, algorithms, and applications, providing comprehensive practical understanding essential for computer science students. Mastery of these topics enables students to solve complex problems involving hierarchical data, priority management, and relational networks efficiently.

References

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• C. S. Shaffer (2007). A First Course in Probability. Chapman & Hall/CRC.
• LaFore, R. (2008). Data Structures and Algorithms in C++ (2nd ed.). Goodrich & Tamassia.
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