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Clojure Data Analysis Cookbook - Second Edition

You're reading from   Clojure Data Analysis Cookbook - Second Edition Dive into data analysis with Clojure through over 100 practical recipes for every stage of the analysis and collection process

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Product type Paperback
Published in Jan 2015
Publisher
ISBN-13 9781784390297
Length 372 pages
Edition 2nd Edition
Languages
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Author (1):
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Eric Richard Rochester Eric Richard Rochester
Author Profile Icon Eric Richard Rochester
Eric Richard Rochester
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Table of Contents (14) Chapters Close

Preface 1. Importing Data for Analysis FREE CHAPTER 2. Cleaning and Validating Data 3. Managing Complexity with Concurrent Programming 4. Improving Performance with Parallel Programming 5. Distributed Data Processing with Cascalog 6. Working with Incanter Datasets 7. Statistical Data Analysis with Incanter 8. Working with Mathematica and R 9. Clustering, Classifying, and Working with Weka 10. Working with Unstructured and Textual Data 11. Graphing in Incanter 12. Creating Charts for the Web Index

Parallelizing processing with Incanter

In the upcoming chapters, many recipes will feature Incanter. One of its good features is that it uses the Parallel Colt Java library (http://sourceforge.net/projects/parallelcolt/) to actually handle its processing. So when you use a lot of matrix, statistical, or other functions, they're automatically executed on multiple threads.

For this, we'll revisit the Virginia housing-unit census data from the Managing program complexity with STM recipe in Chapter 3, Managing Complexity with Concurrent Programming. This time, we'll fit it to a linear regression.

Getting ready

We need to add Incanter to our list of dependencies in our Leiningen project.clj file:

(defproject parallel-data "0.1.0"
  :dependencies [[org.clojure/clojure "1.6.0"]
                 [incanter "1.5.5"]])

We also need to pull these libraries into our REPL or script:

(use '(incanter core datasets io optimize charts stats))

We'll use the...

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