Crazy Word Creator

Overview

Fun Part:

Were going to create crazy names similar to what github and repl.it do when suggesting names for repls or repos. Github suggests things like: musical-journey or cautious-waffle or bug-free-succotash. Whereas repl.it suggests things like: LikelyTatteredGreatwhiteshark or PepperySunnyMaggot or ClearcutLongtermCrocodile or RowdyWorthwhileBird. I'm not sure what thier algorithm is, but I'm sure we can come up with something better (or at least similar).

Data Structures Part:

After we generate about 10000 names, we are going to compare the performance of a Binary Search Tree vs an AVL tree when it comes to finding those named components in each tree.

I think most of us remember what types of words there are:

ad.jec.tive noun verb adverb
ajaktiv noun varb ad varb
a word or phrase naming an attribute, added to or grammatically related to a noun to modify or describe it. a word (other than a pronoun) used to identify any of a class of people, places, or things common noun, or to name a particular one of these proper noun. a word used to describe an action, state, or occurrence, and forming the main part of the predicate of a sentence, such as hear, become, happen. qualifies an adjective, verb, or other adverb or a word group, expressing a relation of place, time, circumstance, manner, cause, degree, etc. (e.g., gently, quite, then, there )

There are a few more word categories (I think) but we don't really need those to make our crazy words. What we do have are words lists for the above categories + a list of animal names:

  • adjectives
  • nouns
  • verbs
  • adverbs
  • animals

Motivation

Binary search tree's are only good when they are balanced. So we are going to compare an AVL implementation of a Binary Search Tree to a regular implementation that is not balanced.

Why Crazy Words?

Because there are a lot of words and we need data to load into both of our data structures in order to compare performance. The word lists only number in the thousands, so we won't be bogging down our computers trying to handle the data, but its enough data to motivate us to be efficient. And crazy words are funny.

Requirements

Part 1

  • Come up with some heuristic that will create a crazy name using your word lists. If we look at some of the examples from above:
    • RowdyWorthwhileBird = adjective adjective noun
    • bug-free-succotash = noun adjective noun
    • PepperySunnyMaggot = adjective adjective noun
    • cautious-waffle = adjective noun
  • There are no verbs or adverbs! I want you to find a way to incorporate all the word groups into your crazy word generator.
  • You don't have to have every group in every crazy word, but try to randomly cycle through all the groups.
  • How you do this is up to you, however you need to generate at least 10000 crazy words that are made up with 3-5 word parts.
  • Just for your information, animals make words funnier. Totally my opinion, but I'm grading your programs so ...
  • Your 10000 words will be stored in a file, one crazy word per line, with spaces between each word component.
  • No repeat words!
crazywords.txt
rowdy worthwhile bird
bug free succotash
peppery sunny maggot
cautious waffle

Part 2

  • Assuming you have loaded all of your words into an AVL tree and a Binary Search Tree (BST).
  • Read in all of your 10000 crazy words and lookup each portion of the word in the AVL and BST data structures.
  • Compare the performance of the BST and AVL trees by adding up the number of comparisons necessary to lookup each word component.
  • Load both data structures with the same order that the files are listed above.
  • Output will look like:
BST Comparisons = xxx
AVL Comparisons = xxx
Number of Adjectives = xxx
Number of Adverbs = xxx
Number of Nouns = xxx
Number of Verbs = xxx

Big Picture

1.Read each of the word files into some structure (possibly an array).

2.Build your crazy random words (with no repeats) and save them into tenthousandwords.txt

3.Load all of the word files into your BSTree and AVLtree in this order:

adjectives
adverbs
animals
nouns
animals
verbs

4.Make sure you don't load duplicates into either of your trees!!

5.The adjectives file should start with an M word.

6.Open your tenthousandwords.txt and as you read in each crazy word: look up each word component in the BSTree and AVLtree to count comparisons.

7.Output your results to screen and file.

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