Skip to main content

Starting with Queues

So in this post no more about stack its all about queues. When talking about queues the first thing that comes in to our mind is the movie queue. What happens there is the earlier you come the best chance of getting a good seat. Likewise in here this data structure is also like a movie queue. It serves elements in First In First Out (FIFO) basis. The elements are added in the rear and must be remove prom the front.
That's right in stacks we had our concern only on the top element but here we need to keep our concern on two elements rear and front




1
 2
 3
 4
 5
 6
 7
 8
 9
10
11
12
13
14
15
16
17
18
19
20
21
22
23
24
25
26
27
28
29
30
31
32
33
34
35
36
37
38
39
40
41
42
43
44
45
46
47
48
49
50
51
52
53
54
55
56
57
58
59
60
61
62
63
64
65
66
public class QueueOne {
    private int MaxSize;    // size of the array
    private int[] arr;      // the queue
    private int rear;       // the back element
    private int front;      // the front element
    private int elementnum; // number of elements
    
    public QueueOne(int x){ // initiating the queue with constructor
        MaxSize = x;
        arr = new int[MaxSize];
        front = 0;          // no front element
        rear = -1;          //no back element
        elementnum = 0;     // initially 0 elemets
    }
    /* ============== functions ============= */
    public boolean isFull(){
        return (elementnum == MaxSize);
    }
    public boolean isEmpty(){
        return (elementnum == 0);
    } 
    public void insert(int i){
        if (isFull() == false){  // chacks stack is full
            arr[++rear] = i;
            elementnum++;
        }else
            System.out.println("This stack is full");
    }
    public int remove(){
        if(isEmpty() == false){   
            elementnum--;           //reduze the size by one
            return (arr[front++]);
        }else
            System.out.println("This stack is Empty");
            return 0;
    }
    public int peekFront(){
        if(isEmpty() == false){
            return (arr[front]);
        }else
            return 0;
    }
    public int peekRear(){
        if (isEmpty() == false){
            return (arr[rear]);
        }else
            return 0;
    }
    public int size(){
        return(elementnum); // return the queue size
    }
    public static void main(String[] args) {
        QueueOne Q = new QueueOne(6);
        Q.insert(20);
        Q.insert(25);
        Q.insert(70);
        Q.insert(125);
        System.out.println("Front "+Q.peekFront());
        System.out.println("rear "+Q.peekRear());
        System.out.println("Size " +Q.size());
        Q.remove();
        System.out.println("Front "+Q.peekFront());
        System.out.println("rear "+Q.peekRear());
        System.out.println("Size " +Q.size());
    }
}

The results would be as like this:

Highlighted ones are before removing an element

Popular posts from this blog

Natural Language Processing with Python NLTK part 5 - Chunking and Chinking

Natural Language Processing Using regular expression modifiers we can chunk out the PoS tagged words from the earlier example. The chunking is done with regular expressions defining a chunk rule. The Chinking defines what we need to exclude from the selection. Here are list of modifiers for Python: {1,3} = for digits, u expect 1-3 counts of digits, or "places" + = match 1 or more ? = match 0 or 1 repetitions. * = match 0 or MORE repetitions $ = matches at the end of string ^ = matches start of a string | = matches either/or. Example x|y = will match either x or y [] = range, or "variance" {x} = expect to see this amount of the preceding code. {x,y} = expect to see this x-y amounts of the preceding code source: https://pythonprogramming.net/regular-expressions-regex-tutorial-python-3/ Chunking import nltk from nltk.tokenize import word_tokenize # POS tagging sent = "This will be chunked. This is for Test. World is awesome. Hello world....

Natural Language Processing with Python NLTK part 1 - Tokenizer

Natural Language Processing Starting with the NLP articles first we will try the  tokenizer  in the NLTK package. Tokenizer breaks a paragraph into the relevant sub strings or sentences based on the tokenizer you used. In this I will use the Sent tokenizer, word_tokenizer and TweetTokenizer which has its specific work to do. import nltk from nltk.tokenize import sent_tokenize, word_tokenize, TweetTokenizer para = "Hello there this is the blog about NLP. In this blog I have made some posts. " \ "I can come up with new content." tweet = "#Fun night. :) Feeling crazy #TGIF" # tokenizing the paragraph into sentences and words sent = sent_tokenize(para) word = word_tokenize(para) # printing the output print ( "this paragraph has " + str(len(sent)) + " sentences and " + str(len(word)) + " words" ) # print each sentence k = 1 for i in sent: print ( "sentence ...

Natural Language Processing with Python NLTK part 6 - Named Entity Recognition

Natural Language Processing - NER Named entities are specific reference to something. As a part of recognizing text NLTK has allowed us to used the named entity recognition and recognize certain types of entities. Those types are as follows NE Type Examples ORGANIZATION Georgia-Pacific Corp. ,  WHO PERSON Eddy Bonte ,  President Obama LOCATION Murray River ,  Mount Everest DATE June ,  2008-06-29 TIME two fifty a m ,  1:30 p.m. MONEY 175 million Canadian Dollars ,  GBP 10.40 PERCENT twenty pct ,  18.75 % FACILITY Washington Monument ,  Stonehenge GPE South East Asia ,  Midlothian Source:  http://www.nltk.org/book/ch07.html Simple example on NER: import nltk from nltk.tokenize import word_tokenize, sent_tokenize para = " America is a country. John is a name. " sent = sent_tokenize(para) for s in sent: word = word_tokenize(s) tag = nltk . pos_tag(word) n...