CS 112 Project 9: Binary Search Trees

Objectives:

1. Practice building BST operations.
2. Practice building recursive operations.
3. Learn more about binary search trees.

Introduction

This week's project is a single person project. The project is to:

• Add a new method to your BST class:
  • bst.getHeight() should return the height of the tree in bst. For example, if bst is empty, it should return 0; if bst contains a single item, it should return 1; if it contains 2 items, it should return 2; if it is balanced and contains 3 items, it should return 2, otherwise, it should return 3; if it is balanced and contains 4 items, it should return 3, otherwise, it should return 4; etc.
• Convert your BST into a template; and
• Use your BST<Item> to conduct an experiment.

The Experiment

The height of a BST determines the speed of operations like insert() and contains(). If we build a "large" BST of N items (e.g., ~2²⁰), and that tree is balanced, then its height should be the minimal O(lg(N)) (e.g., 20).

This week's problem is to conduct a simple experiment to see how "tall" BSTs get, given random values. Our objective is to see how close we come to lg(N) height if we build large trees of N randomly ordered values.

In the ulab, the folder:

/home/cs/112/proj/09

Your task is to write a simple program that prompts the user for the name of an input file, reads each value from that file, and inserts it into a BST. Your program should then display the height of the resulting BST.

There are three minor details to keep in mind:

• There may be duplicate values within an input file, and inserting a duplicate value into our BST will throw an exception. Your program should count and output the number of duplicate values in a given file by catching and handling the exception.

• Each of these input files is about 20 MB in size. To avoid killing your disk-space quota, your program should open and read the files directly from that folder. (Hint: use a file's full file-name to open it: e.g., /home/cs/112/proj/09/randomInts01.txt.)

• Since these files were generated in the ulab (which has 64-bit machines), the integers in these files are 64-bit integers. This should not be an issue if you compile/run your program in the ulab, but on a 32-bit machine, you will need to find a way to read and store these 64-bit numbers...

Since there are ten input files, you will need to run your program ten times. (Or find a clever way to automate this.) For each file, record the corresponding tree-height in a spreadsheet, and the number of duplicate values that were found. Given all ten heights, use the spreadsheet to calculate the minimum height, the maximum height, the average height, the median height, and the standard deviation of the ten heights. (Your spreadsheet will make each of these computations easy.) Finally, use your spreadsheet to create a bar-chart showing each of the ten heights, as a visualization of your data. Save this chart in the worksheet where you recorded and analyzed your data.

Finally

1. Is lg(N) or N a better approximation for your measured heights? Why?
2. How much variance is there in the height of these trees? Is this surprising? Why or why not?
3. How many duplicate values were there (on average)? Is this surprising? Why or why not?
4. The "big Oh" notation O(f(n)) implies that there are constants a and b, such that a * f(n) + b describes the reality for which O(f(n)) is an approximation. A balanced BST of N items has height O(lg(N)). Theoretically, there should be values a and b such that a * lg(N) + b describes the actual height of a BST built using random data. Using your experimental results, what is a reasonable value for a?

Submit

• A hard copy of this grade sheet, attached to:
  1. a hard copy of a script file that lists the files in your project, shows that it compiles/links correctly, and shows it correctly running and providing the required functionality.
  2. as many hard copies as necessary to show your spreadsheet's data and each of its charts.
• An electronic copy of your project (including your spreadsheet), in your /home/cs/112/current/yourUserName/proj9 folder.

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