Common_Word_Finder is a command line Java application to find the π most common words in a document. Written from scratch by me as a final project for my data structures class, it implements three of the data structures we developed for previous programming assignments. BSTMap, AVLTreeMap, and MyHashMap implement the MyMap interface. Rspectively, these class names refer to Binary Search Tree, AVL Tree, and Hash Map data structures. Using polymorphism, I create any one of these data structures and refer to it from a MyMap reference. Though all three classes implement the same interface, their methods are implemented very differently, leading to different execution times on the computer.
I only included the Common_Word_Finder class in this repository because of the many classes used by this program, this is the only one that was written entirely from scratch by me. In past programming assignments for the class to create implementations of the BST, AVL Tree, and Hash Map data structures, as well as other classes used by this program such as Entry (a class for encapsulating a key-value entry in a map), and Node (a class for a Node containing a key-value mapping), I either filled in empty methods in an otherwise functional program, or was provided a template, and didn't want to put these on my public GitHub without the permission of the professor. While the program is not functional without these other classes, I included it on my GitHub as an example of the type of code that can be expected of me to write.
After validating all command line arguments, my program instantiates either a BSTMap, AVLTreeMap, or MyHashMap. The key-value pairs are String-to-Integer, where String is the word and Integer is the number of times the word is found in the document. Lowercase letters (a-z) and single quotes (') are legal characters for words. Hyphens (-) are legal too, as long as they are not the first character in a word. Uppercase letters (A-Z) are converted to lowercase letters before putting them into a word. Words are separated by end-of-line characters and spaces. Every time the program parses a word for the first time, it is inserted into the map with a count of 1. If it has been seen before, the count associated with it is incremented by 1. The first line of output displays the following: "Total unique words: " Then up to words and their counts are displayed, right-aligned to the width of the largest number. The word in all lowercase letters appears after the period, left-aligned with one space between the period and the word. If two lowercase words have the same count, the words are alphabetized in the output. Finally, the count of the words appears at the end of each line, with one space between the longest word and the count.
The results of taking the average of 10 running time commands on a .txt file of the Bible for each terminal command below are as follows:
time java CommonWordFinder Bible.txt bst 20000
real 0m0.393s | user 0m0.372s | sys 0m0.053s
time java CommonWordFinder Bible.txt avl 20000
real 0m0.346s | user 0m0.421s | sys 0m0.042s
time java CommonWordFinder Bible.txt hash 20000
real 0m0.231s | user 0m0.354s | sys 0m0.042s
From these results, the Hash Map performed the fastest, followed by AVL, and then BST. Although, considering individual time tests the AVL Tree is sometimes faster than BST and BST is sometimes faster than AVL Tree. Hash Map was consistently faster than the other two data structures. This matched my expectations as I expected the AVL Tree data structure to outperform BST but didn't expect there to be a huge time difference between BST and AVL Tree as in our implementation of these two classes, entries are sorted by key. In this case, this means the BST is in increasing alphabetical ordering for right subtrees and decreasing alphabetical ordering for left subtrees so the BST should never really become widely imbalanced as long as there is a similar count of words starting with the beginning of the alphabet and words starting with the end of the alphabet. I also expected Hash Map to consistently perform the best, as search and insert operations are being called 13680 times each for Bible.txt, so its average Theta(1) time search and insert are much faster than the average Theta(logn) for AVL Tree and BST.