Skip to main content

AngularJS Basics again


Thought of doing basics of AngularJS again and ended up following W3School tutorials which are pretty good. So here is what I learned.


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
<!doctype html>
<html lang="en">
<head>
  <meta charset="utf-8">
  <title>Back to Basics</title>
  <link rel="stylesheet" href="bower_components/bootstrap/dist/css/bootstrap.css">
  <link rel="stylesheet" href="css/app.css">
  <script src="bower_components/angular/angular.js"></script>

</head>
<body >
<!--ng-app is given in div tag and where the application begins-->
<!--ng-init initialize the application variables-->
<!-- 3rd line ng-init creating the object vehicle-->
<!-- final line is defining an array-->
<div ng-app ng-init="Numone = 3 ; NumTwo = 5 ;
                    Fname = 'KMAN' ; Lname = 'PMAN' ;
                    vehicle = {color : 'red' , wheels : '4'};
                    Lottery = [3,4,34,67,8,3]">
  <p>Adding the two numbers {{Numone + NumTwo}}</p> <!--Adding numbers-->
  <p>The names together {{Fname+" "+Lname}}</p> <!--Binding Strings-->
  <p>The names binding different way <span ng-bind="Fname+' '+ Lname"></span> </p>  <!--Binding Strings using ng-bing-->
  <p>My New vehicle color : <strong> {{vehicle.color}} </strong> and it has &lt,strong>{{vehicle.wheels}}&lt,/strong> no of wheels</p>
  <!--calling the object vehicle-->
  <p>Tonight's lottery numbers are</p>
  <p>{{Lottery[0]+" "+Lottery[3]+" "+Lottery[5]+" "+Lottery[2]}}</p><!--Just some random pick ups from the array-->
</div>
</body>
</html>

So day two done with basics again. Nothing is wasted. Ah by the way the results for above code is :
Labels:
Location: Sri Jayawardenepura Kotte, Sri Lanka

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 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