The writeup is broken up into two parts that will need to be completed. Here, I will describe your task to complete the main program portion of the assignment. Part B will describe the development of an AVL Tree data structure for creating a more-efficient index of documents. That will be released on Monday once you have completed the LinkedBST implementation.
This week's lab will further the development of a plagiarism detection algorithm. Given a set of documents your algorithm will identify significant matches between pairs of documents that indicate shared use of the same material. Specifically, we will test your program on a set of essays submitted by high school students. You can peruse the data set here in
/home/soni/public/cs35/cheatDetector/data/Beware, the writing quality may induce blood-filled tears.
Your main program will be implemented in cheatDetector.cpp. For each document you will report the essay with largest number of hits (i.e., matching phrases). The design of your main program will be largely left up to you, but we have broken down the requirements into the following parts:
Ensure your design is easily modified to use either an AVLTree or LinkedBST to represent one document. While you have not implemented the AVLTree, a good design will make it very easy to incorporate the second representation in part B. The user will specify which they want to use at run-time. If you understand polymorphism, this is pretty simple to accomplish.
TODO:
First, run update35 to create your labs/08 directory and obtain the central file cheatDetector.cpp. You'll also get test files in the input directory and Makefile that accounts for the AVLTree. You'll also want to copy over the files from Lab 07 for use this week (except the Makefile). To do so, follow this script:
$ cd ~/cs35/labs/08/ $ cp -r ../07/* . cp: overwrite `./Makefile'? noThis will let you use your LinkedBST implementation this week. Note that you should be sure to fix any errors in that implementation otherwise your main program may not work properly. Once you start adding in elements for the AVLTree you'll have to modify the Makefile; you'll find instructions in Part B.
TODO:
To avoid user-interaction during run-time, a common too for obtaining input is by using command-line arguments. These are values given when executing from the command-line. To illustrate by example, our usual execution without command-line arguments:
$ ./cheatDetectorAn example run for this lab would instead be:
$ ./cheatDetector inputs/smallList.txt 3 0
To process command-line arguments in C++ you need to add two arguments to your main function:
int main(int argc, char* argv[])argc is an int representing the number of arguments entered (including the program name itself), and argv is an array of character arrays, with each character array containing one argument to the program. You can then use argc and argv inside your program to access the command-line arguments. For example, in the run above, argc has the value 4 meaning that argv is an array with four c-string values. argv[0] is always the program being run ("cheatDetector"), argv[1] is "inputs/smallList.txt", argv[2] is "3" and argv[3] is "0". argc is useful to check if the user entered the correct number of arguments. For example, if the user forgets the arguments you can print an error:
$ ./cheatDetector Incorrect number of arguments Usage: cheatDetector file-list phrase-size useAVL
We have provided the central set up already; the provided code converts argv[2] to the phraseSize (using atoi which converts c-strings to ints) and argv[3] to the boolean useAVL. You are responsible for obtaining the name of the file listing essays. Remember, argv is a c-string array, so if you want to store the file name as a string, use the string constructor. For example, if we wanted to store the third parameter as a string, we'd use string useAVL(argv[3]);
TODO:
One naive solution to this lab would be to compare all phrases in one document against all phrases in all other documents. That can be extremely costly (O(w^2) for w words in the corpus!). Instead, we will index web pages into a binary search tree to quickly query phrases. The next section will discuss how to load all documents. For now, assume you are attempting to load one document, e.g., data/tyc6.txt. To do so, first check that the file exists, exiting if it does not.
Given the file exists, you will index the document by loading all phrases into a binary search tree. That is, your key will be an n-word phrase string (where n is specified on the command-line) and the value is the number of times that phrase occurs in the document. You should overlap all overlapping phrases. For example, for two word phrases, the sentence: "Computer Science 35 rules" leads to the 3 following keys being inserted into the BST: "Computer Science","Science 35","35 rules" .
In addition to allowing phrases of different size, you should determine whether to use a LinkedBST or a AVLTree depending on the boolean value useAVL given as a command-line argument.
The provided command-line argument is a list of the locations of all essays. Each line specifies the location of one document. You can read the entire line in as one long string.
For each document, (1) check that the file exists, (2) load the document into its own tree (see above) and (3) store the BST into some meta-data structure. That last part is up to you to figure out. One way to accomplish this is...with another tree! For instance, you could use each file name as a key, and a pointer to the essay's phrase index tree as the value. This might have the type AVLTree< string, AVLTree< string,int >* > There are other reasonable options if this data type is scarier than your Halloween costume.
For each essay i:
For each other essay j:
Count the number of matches between i and j
If this is the most matches to i, save the result
Output i and the j with maximum overlap
You'll also want to report the size of the overlap for the optimal hit.
For counting matches, you'll want to take every phrase in essay i and
determine if it is in essay j's tree. For every match, increase the
total number of matches for that pair.
After your program has completed finding and outputting the largest overlap for each essay, you should output three statistics to give us an understanding of the efficiency difference between AVLTrees and LinkedBSTs
Sample runs:
For the following applications,which of the 4 traversal algorithms in class would best provide a solution (i.e., either Level, Pre-Order, Post-Order, or In-Order). You do not need to explain.
$ du -h cs35/labs/ 472K cs35/labs/06 6.0M cs35/labs/01/input 6.0M cs35/labs/01 2.2M cs35/labs/04 44K cs35/labs/03 48K cs35/labs/02 16K cs35/labs/05/inputs 120K cs35/labs/05 28K cs35/labs/07/library 20K cs35/labs/07/testFiles 304K cs35/labs/07 9.2M cs35/labs/
*
/ \
+ 2
/ \
* 1
/ \
3 5
What traversal would produce the correct order of operations?