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TermFrequencyTable.java
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TermFrequencyTable.java
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// Maxime Gavronsky
// stores the words from two Strings (i.e., documents) in such a way that you can easily
// calculate the "cosine" similarity of the two.
public class TermFrequencyTable{
public static int arraysize = 107;
public Node [] A = new Node[arraysize];
public Node root = null;
public class Node {
int[] termFreq = new int[2];
public String term;
public Node next;
public Node(String term, int[] freq, Node n) {
this.termFreq = freq;
this.term = term;
this.next = n;
}
}
private final String [] blackList = { "the", "of", "and", "a", "to", "in", "is",
"you", "that", "it", "he", "was", "for", "on", "are", "as", "with",
"his", "they", "i", "at", "be", "this", "have", "from", "or", "one",
"had", "by", "word", "but", "not", "what", "all", "were", "we", "when",
"your", "can", "said", "there", "use", "an", "each", "which", "she",
"do", "how", "their", "if", "will", "up", "other", "about", "out", "many",
"then", "them", "these", "so", "some", "her", "would", "make", "like",
"him", "into", "time", "has", "look", "two", "more", "write", "go", "see",
"number", "no", "way", "could", "people", "my", "than", "first", "water",
"been", "call", "who", "oil", "its", "now", "find", "long", "down", "day",
"did", "get", "come", "made", "may", "part" };
private char[] charsToRemove = { '.' , ',', ':', ';', '!', '?', '"', '\'', '/', '-', '(', ')', '~' };
//inserting string term into the hash table and incrementing the proper termFrew value
public void insert(String term, int docNum) {
String []master = remove(term);
for(int i = 0; i < master.length; i++)
{
if(master[i] == "")
continue;
else
A[hash(master[i])] = insertHelper(master[i], docNum, A[hash(master[i])]);
}
}
//worked with Selina Gerosa to obtain a recursive solution to insertion
//helper function for inserting a term
private Node insertHelper(String term, int docNum , Node p) {
int[] termFreq = new int [2];
if (p == null){
termFreq[docNum] = 1; // insert 1 to the docNum in termFreq
p = new Node (term, termFreq, null);
return p;
}
//if term is already in the table then the proper termFreq value will be changed
//without inserting the element
else if (term.compareTo(p.term) == 0) {
p.termFreq[docNum] += 1; //add 1 to the docNum indew in termFreq
return p;
}
else{
p.next = insertHelper(term, docNum, p.next);
return p;
}
}
//research.cs.vt.edu/AVresearch/hashing/strings.php
int hash(String x) {
char ch[];
ch = x.toCharArray();
int xlength = x.length();
int i, sum;
for (sum=0, i=0; i < x.length(); i++)
sum += ch[i];
return sum % arraysize;
}
//removes puncuation, makes it lower case and removes black listed words
private String[] remove(String term){
term = term.toLowerCase();
for(int i = 0; i < charsToRemove.length; ++i)
{
String s = Character.toString(charsToRemove[i]);
term = term.replace(s, "");
}
String [] arrayTerm = term.split("\\s+");
for(int j = 0; j < blackList.length; ++j){
for( int z = 0; z < arrayTerm.length; ++z){
if (arrayTerm[z].equals(blackList[j]))
arrayTerm[z] = "";
}
}
return arrayTerm;
}
//finds the cosine Similarity of the two documnets
public double cosineSimilarity() {
int aa = 0;
int bb = 0;
int ab = 0;
for(int i = 0; i < arraysize; i++){
if (A[i] != null){
aa += dotProductAA(A[i]);
bb += dotProductBB(A[i]);
ab += dotProductAB(A[i]);
}
}
double similarity = ab / (Math.sqrt(aa) * Math.sqrt(bb));
return similarity;
}
//helps interate through the hash table for doc 0
private int dotProductAA(Node p){
int product = 0;
while (p!=null){
product += (p.termFreq[0] * p.termFreq[0]);
p = p.next;
}
return product;
}
//helps interate through the hash table for doc 1
private int dotProductBB(Node p){
int product = 0;
while (p!=null){
product += (p.termFreq[1] * p.termFreq[1]);
p = p.next;
}
return product;
}
//helps interate through the hash table for doc 0 and doc1
private int dotProductAB(Node p){
int product = 0;
while (p!=null){
product += (p.termFreq[0] * p.termFreq[1]);
p = p.next;
}
return product;
}
//printing method for the hash table and the term frequency
public void print(){
String printout = "";
for(int i = 0; i < arraysize; i++){
if (A[i] != null)
printHelper(printout, A[i]);
}
}
//helper method for printing
private void printHelper(String printout, Node p){
while (p != null){
printout = (p.term + ": is present " + p.termFreq[0] + " times in Document 0 and " + p.termFreq[1] + " times in Document 1");
System.err.println(printout);
p = p.next;
}
}
public static void main(String args[]){
TermFrequencyTable T = new TermFrequencyTable();
String doc0 = "hello is this a test test test, just just checking";
String doc1 = "that this and the other thing, TESTING test! just checking?";
T.insert(doc0, 0);
T.insert(doc1, 1);
System.out.println("should print testing: \nis present 0 times in Document 0 and 1 times in Document 1 \nhello: is present 1 times in Document 0 and 0 times in Document 1 \ntest: is present 3 times in Document 0 and 1 times in Document 1 \nthing: is present 0 times in Document 0 and 1 times in Document 1 \njust: is present 2 times in Document 0 and 1 times in Document 1 \nchecking: is present 1 times in Document 0 and 1 times in Document 1");
T.print();
TermFrequencyTable F = new TermFrequencyTable();
String doc2 = "A A B B";
String doc3 = "A B";
F.insert(doc2, 0);
F.insert(doc3, 1);
System.out.println("\nShould print: \nb: is present 2 times in Document 0 and 1 times in Document 1");
F.print();
System.out.println("\nShould print: \n1.0");
System.out.println(F.cosineSimilarity());
TermFrequencyTable J = new TermFrequencyTable();
String doc4 = "A B";
String doc5 = "C D";
J.insert(doc4, 0);
J.insert(doc5, 1);
System.out.println("\nShould print: \nb: is present 1 times in Document 0 and 0 times in Document 1 \nc: is present 0 times in Document 0 and 1 times in Document 1 \nd: is present 0 times in Document 0 and 1 times in Document 1");
J.print();
System.out.println("\nShould print: \n0.0");
System.out.println(J.cosineSimilarity());
TermFrequencyTable P = new TermFrequencyTable();
String doc6 = "CS112 HW10";
String doc7 = "CS112 HW10 HW10";
P.insert(doc6, 0);
P.insert(doc7, 1);
System.out.println("\nShould print: \ncs112: is present 1 times in Document 0 and 1 times in Document 1 \nhw10: is present 1 times in Document 0 and 2 times in Document 1");
P.print();
System.out.println("\nShould print: \n0.9487");
System.out.printf("%.4f", P.cosineSimilarity());
}
}