# BLG 223E DATA STRUCTURES HOMEWORK -2 solved

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## Description

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In this assignment, you are required to build up an extension of the linked list data structure.
A diagram of the structure is given below.
The general way of generating a sequence of text is to train a model to predict the next
word/character given all previous words/characters. Such model is called a Language Model.
Language models also allows you to calculate the possibility of predicting the next
word/character given all previous words/characters.
An example calculation of the character probability is:
𝑃(𝑎|𝑑) =
𝐶(𝑑 ∩ 𝑎)
𝐶(𝑑)
The 𝑷(𝒂|𝒅) is the probability the character “a” comes right after the character “d”.
The 𝑪(𝒅 ∩ 𝒂) is the total count of concurrence “da” characters seen in given sentences sequence.
The 𝑪(𝒅) is the total count of “d”’s seen in given sentences sequence.
For this homework, you will be given a set of sentences (input.txt) and ask to perform a basic
character based language model by using linked list structure.
Workflow:
1. You will read the input.txt and split each unique character, cases of letters will be
neglected (case insensitive) meaning both “A” and “a” will be counted as same. You will
also see special characters (in input.txt file) such as dots (.), commas (,) or whitespaces
(), these special characters will be added to your vocabulary list as well.
2. Then add each unique character to the vocab_list structure by alphabetical order. You
should also add special characters to your vocab_list. The order of special characters are
not important just they should be placed to the end of the alphabetical characters (after
“z”).
3. Then, trace your input again but this time add occurrences of each unique character by
each unique next character. This structure will contain occurrences of each character
pairs together and it will be stored by order of appearance. Meaning for the character
“a”, you will add each unique characters which comes next to “a” and keep their
Explanation of the workflow by an example sentence:
“Dilara bugün okula gitti. ”
The given sentence is split by spaces, and for this sentence the vocab_list contains 15 nodes
(one is for “.”(dot) and one is for the space character () seen in between and at the end of
sentences).
“D i l a r a b u g ü n o k u l a g i t t i .
While traversing all sentencesfillthe vocab_list with alphabetical order. When you finish adding
all the vocabulary, for the next time traverse the input.txt to add the occurrences. For example,
for the character “a” in the input sentence “a-r” (occurrence is 1), “a-”(occurrence is 2)
union co-occurrences have been found, and they are added to the occurrence structure by
order of appearance. For the characters “i”, “l”, “r”, “.” and “” (white space) diagram is
filled as an example, the rest characters are left blank on purpose.
The probability character sequence of the “la” will be calculated as:
𝑃(𝑎|𝑙) =
𝐶(𝑙 ∩ 𝑎)
𝐶(𝑙)
=
2
2
= 1
Or
The probability character sequence of the “ar” will be calculated as:
𝑃(𝑟|𝑎) =
𝐶(𝑎 ∩ 𝑟)
𝐶(𝑎)
=
1
3
= 0.33
Or
The probability character sequence of the “il” will be calculated as:
𝑃(𝑙|𝑖) =
𝐶(𝑖 ∩ 𝑙)
𝐶(𝑖)
=
1
3
= 0.33
Hint1: calculation of C(x) (the total count of “x”’s seen in given sentences sequence) could be
calculated as adding all the union co-occurrences of x with other characters. For example for C(a);
𝐶(𝑎)= 𝐶(𝑎 ∩ 𝑟) + 𝐶(𝑎 ∩< 𝑆𝑃 >)
Hint2: 𝑃(𝑟|𝑎) ≠ 𝑃(𝑎|𝑟) probabilities of “ar” and “ra” are not same.
Structure:
struct vocab_node {
char character;
vocab_node *next;
occur_node *list;
}
struct occur_node {
char character;
occur_node *next;
int occurence;
};
struct vocab_list
{
void create();
void print();
int get_occurence(char );
int get_union_occurence (char , char );
};
struct language_model {
vocab_list *vocabularylist;
double calculateProbability (char, char);
};
Implementation:
Implement the following methods with appropriate arguments and return types for your
structure:
a. create(): Creates vocab_list.
functions and fills entire language model.
c. print(): prints entire language model in the form:
character:

Example:
a:

<,2>
b:

d:

d. add_char(): gets one character from txt file add all the unique characters in alphabetical
order to vocab_list. Meaning it first checks the character if it exists in vocab_list, if not it adds it
to vocab_list. If it already exists then continues with the next character.
e. add_occurence(): gets two union co-occurrence characters from txt file traces the first
character in vocab_list, finds it and searches second character in its occur_node, if not found
add this second character in its occur_node and initializes occurrence as 1, if found then only
increases occurrence by one.
f. get_occurence(): returns the total occurrence count of given character. Use hint to
calculate this method.
g. get_union_occurence(): returns the total union co-occurrence count of given two
characters.
h. calculateProbability(): returns calculated probability of given two characters.
Submission
1. Make sure you write your name and number in all of the files of your project, in the following
format:
/* @Author
* Student Name:
* Student ID :
* Date: */
2. Use comments wherever necessary in your code to explain what you did.
3. Your program should compile and run on Linux environment using g++ (version 4.8.5 or
later). You can test your program on ITU’s Linux Server using SSH protocol.
To compile the code, you can use the following command:
g++ lm.cpp –o lm
The program should produce the probability of co-occurrence two characters if the program
is executed with three command line arguments which are
. However, the program should print the whole language model only if given as single argument.
./lm input.txt a r (output will be probability of co-occurrence “ar” First character =a,
second character =r)
./lm input.txt (output will be printed entire language model in given form)
4. After you make sure that everything is compiled smoothly, archive all files into a zip file.
Submit this file through www.ninova.itu.edu.tr. Ninova enables you to change your