Finishing with Stacks

So this will be the last one on stacks. Here I'll talk about the places where stacks will be used.
One most popular place this is used is the undo and redo buttons. When you change something the changes are placed in a stack and for some reason when you undo it, it'll pop the top change back.

Another place would be in the web browser. The history of you visited page addresses will be in a stack. When you click back it will direct to the previous page.

JVM itself is a stack oriented VM just like most of them.

In operating systems the stacks keeps the return addresses when functions are called from another function. The stack will provide the jump address when its done.

That will be all about stacks. So if you are interested do Experiments .

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 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 ...

Design Patterns 1 : Introduction

Design Patterns : Introduction So its the holiday time and thought of starting with the Design patterns. In this post I'll talk about What are design patterns?, What good to us using them?, Why and when use them? and many more. So why wait? Lets start the journey to Design patterns. What are Design patterns? So over the years when programmers tried to build systems that can solve problems often they encountered problems that were difficult to overcome. So after finding a solution what they did was presenting it as an future guideline where other programmers when encountered the same problem can easily surpass that. These are what we called as Design Patterns. To see how it all started we have to go back in the past. History of Design Patterns? All these patterns buzz started in about 1977/79 when Christopher Alexander showed interest in using using pattern in architecture. That's right not in computer, in architectural ...

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