Skip to main content

Linked List Just Not Over (Continue....)

so with the previous post talked about the Linked List functions. So its not over yet. We talked about inserting and deleting from head and tail.
So this with this one I'll post the code for deleting a node from any location.


1
 2
 3
 4
 5
 6
 7
 8
 9
10
11
12
13
14
15
16
17
public void DeleteFromAny(int del){
        //the del is the info we need to delete
        if(!isEmpty())
            if(head == tail && del ==head.info)
                head = tail = null; //if only one no no probelem
            else if (del == head.info)
                head = head.next;   //if the del matches the head info then delete head
            else{
                nodeClass prev, temp; //prev will hold the predecessor and temp will check for del match
                for(prev = head , temp = head.next; temp !=null&&temp.info !=del; prev = prev.next,temp = temp.next);
                if(temp != null){   //then search success del was found
                    prev.next = temp.next;
                    if(temp == tail)
                        tail = prev;    //if the del element is tail then set the new tail
                }
            }
    }

you have to pass a value to the method and the method will search and destroy. That it. 
P.S. Be careful and check every time if you are deleting the head or the tail. If so update the head and tail variables accordingly.
Labels:

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....
Read more

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 ...
Read more

Natural Language Processing with Python NLTK part 3 - Stemming

Natural Language Processing So this one will be about stemming. Stemming is used in NLP for various reasons Stemming is removing certain parts of the word to get the meaning of it. For example, Running when stemmed returns run, and cooking when stemmed returns cook. from nltk.stem import PorterStemmer from nltk.tokenize import word_tokenize # testing with a sentence sent = "when we run we get healthy, Running is awesome. I have ran for may miles." myWords = word_tokenize(sent) for w in myWords: print (PorterStemmer() . stem(w)) print ( "**********Custom List************" ) # Testing with several custom words listwords = [ "come" , "came" , "coming" , "run" , "running" , "added" , "adding" ] for w in listwords: print (PorterStemmer() . stem(w)) The output will be as follows:
Read more