Assignment 1

Question #1

An n-gram, n greater than or equal to 2, is a sequence of n non-whitespace characters. The n-gram frequency vector associated with a textfile is a vector NM indexed by the n-grams in the textfile such that the entry for n-gram x in this vector is the total number of occurrences of x in the textfile divided by the total number of n-grams in the textfile. For example, given the "ababa bba bb", the associated 2-gram frequency vector is

aa   0/7
ab   2/7
ba   3/7
bb   2/7

and the 3-gram frequency vector is

aaa  0/4
aab  0/4 
aba  2/4
abb  0/4
baa  0/4
bab  1/4
bba  1/4
bbb  0/4

Given two textfiles X and Y, let the similarity score between these textfiles be defined as Sim(X, Y) = 1.0 - (Diff(X, Y)/2.0), where Diff(X,Y) is the sum over all possible n-grams of the absolute values of the differences in frequency of occurrence of each n-gram in textfiles X and Y.

Write and document a Python script tcomp1.py which takes as command-line arguments a master textfile, a value of n, and two or more comparison textfiles and prints (1) the similarity of the master textfile and each comparison textfile and (2) the name of the comparison file that is most similar to the master textfile. Your script must work on datafiles nm1.dat, nm2.dat, nm3.dat, nm4.dat, and nm5.dat to produce the output given in typescript-file tcomp1.script. Your code must implement the similarity computation using dictionaries to encode the non-zero entries in n-gram frequency matrices. You may assume that each given textfile has at least one word, i.e., no given textfile is composed entirely of whitespace characters.

Question #2

Given two textfiles X and Y, let the similarity score between these textfiles be defined as Sim(X, Y) = 1.0 - (SD(X, Y) / (nW(X) + nW(Y))), where nW(X) and nW(Y) are the numbers of words that occur in X and Y and SD(X, Y) is the total number of words that occur uniquely in X or Y, i.e., (the number of words that occur in X that do not occur in Y) + (the number of words that occur in Y that do not occur in X). Note that nW() does not count the total number of words in a file, but rather the number of different words that occur in a file.

Write and document a Python script tcomp2.py which takes as command-line arguments a master textfile and two or more comparison textfiles and prints (1) the similarity of the master textfile and each comparison textfile and (2) the name of the comparison file that is most similar to the master textfile. Your script must work on datafiles tc1.dat, tc2.dat, tc3.dat, tc4.dat, tc5.dat, and tc6.dat to produce the output given in typescript-file tcomp2.script. Your code must implement the similarity computation using sets.

Submission

Please submit your Python script files through the course D2L / Brightspace shell by 11:59pm on the assignment due date. Note that each script file must have the following comment block at the top, where the X's are replaced with the appropriate information, followed by a docstring briefly describing the program in that script. For instance, my script for Question #1 of this assignment would begin with the following comment block:

#########################################################
##  CS 4750 (Fall 2021), Assignment #1, Question #1    ##
##   Script File Name: tcomp1.py                       ##
##       Student Name: Todd Wareham                    ##
##         Login Name: harold                          ##
##              MUN #: 8008765                         ##
#########################################################

You do not have to develop your code on our CS departmental systems. However, as your code will be compiled and tested on our CS departmental systems as part of the assignment marking process, you should ensure that your code compiles and runs correctly on at least one of these systems.

Additional Notes:

• September 20, 10:45am
  All assignment submissions will now be through the course D2L / Brightspace shell rather than e-mailing files to the instructor.

• August 3, 2:15pm
  Assignment #1 posted.

Created: August 3, 2021
Last Modified: September 20, 2021