Queues with Java API

And continuing with the Queues the next thing did was making a queue with java API. The thing is Queue is an interface for java. The details in the standard API can be seen here.
As usually did the standard and simplest functions just to get a hang of it.
add(value); for adding elements for the group
always remember in Queue FIFO
remove(); Removes the first in line
peek(); Gives a glance of the first/ head element without removing it
element(); The same as about except in above if the Queue is empty it will return null but in element(); if the Queue is empty it will give an exception.

import java.util.LinkedList;
import java.util.Queue;

public class QueueFromAPI  {
    public static void main(String args[]) {
        Queue A = new LinkedList();
        A.add(30);
        A.add(100);
        A.add(70);
        A.add(567);
        System.out.println("First Element "+ A.element());
        A.remove();                 // removing one element
        System.out.println("New First Element " + A.element());
    }
    
}

So as seen here I did implement it using the LinkedList class. following are few of the other known implementing classes.

AbstractQueue, ArrayBlockingQueue, ArrayDeque, ConcurrentLinkedDeque, ConcurrentLinkedQueue, DelayQueue, LinkedBlockingDeque,LinkedBlockingQueue, LinkedList, LinkedTransferQueue, PriorityBlockingQueue, PriorityQueue, SynchronousQueue

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...

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