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Haskell Data Analysis cookbook

You're reading from   Haskell Data Analysis cookbook Explore intuitive data analysis techniques and powerful machine learning methods using over 130 practical recipes

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Product type Paperback
Published in Jun 2014
Publisher
ISBN-13 9781783286331
Length 334 pages
Edition 1st Edition
Languages
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Author (1):
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Nishant Shukla Nishant Shukla
Author Profile Icon Nishant Shukla
Nishant Shukla
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Toc

Table of Contents (14) Chapters Close

Preface 1. The Hunt for Data FREE CHAPTER 2. Integrity and Inspection 3. The Science of Words 4. Data Hashing 5. The Dance with Trees 6. Graph Fundamentals 7. Statistics and Analysis 8. Clustering and Classification 9. Parallel and Concurrent Design 10. Real-time Data 11. Visualizing Data 12. Exporting and Presenting Index

Using Directed Acyclic Word Graphs


We use Directed Acyclic Word Graphs (DAWG) to retrieve very quickly from a large corpus of strings at an extremely small cost in space complexity. Imagine compressing all words in a dictionary using a DAWG to perform efficient lookups for words. It is a powerful data structure that can come in handy when dealing with a large corpus of words. A very nice introduction to DAWGs can be found in Steve Hanov's blog post here: http://stevehanov.ca/blog/index.php?id=115.

We can use this recipe to incorporate a DAWG in our code.

Getting ready

Install the DAWG package using cabal:

$ cabal install dawg

How to do it...

We name a new file Main.hs and insert the following code:

  1. Import the following packages:

    import qualified Data.DAWG.Static as D
    import Network.HTTP ( simpleHTTP, getRequest,  
                          getResponseBody)
    import Data.Char (toLower, isAlphaNum, isSpace)
    import Data.Maybe (isJust)
  2. In main, download a large corpus of text to store:

    main = do
      let url...
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