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 2 - Stop Words

Natural Language Processing Stop words are the words which we ignore due to the fact that they do not generate any specific meaning to the sentence. Words like the, is, at etc. can be removed to extract the meaning of the sentence more easily. So NLTK has introduced us a stop words filter we can easily use. Let's see how it works. from nltk.corpus import stopwords from nltk.tokenize import word_tokenize sent = "As you can see this is the blog of myself which is written by Anjula" w = word_tokenize(sent) # set English stop words stop_words = set(stopwords . words( 'english' )) # list of standard stop words in English print (stop_words) # making empty arrays to store stop words and others stop_words_in_sent = [] non_stop_words = [] # Loop through to get the stop words for x in w: if x not in stop_words: non_stop_words . append(x) else : stop_words_in_sent . append(x) # print result ...
Read more

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...
Read more