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Practical Data Analysis

You're reading from  Practical Data Analysis

Product type Book
Published in Oct 2013
Publisher Packt
ISBN-13 9781783280995
Pages 360 pages
Edition 1st Edition
Languages
Author (1):
Hector Cuesta Hector Cuesta
Profile icon Hector Cuesta
Toc

Table of Contents (24) Chapters close

Practical Data Analysis
Credits
Foreword
About the Author
Acknowledgments
About the Reviewers
www.PacktPub.com
Preface
1. Getting Started 2. Working with Data 3. Data Visualization 4. Text Classification 5. Similarity-based Image Retrieval 6. Simulation of Stock Prices 7. Predicting Gold Prices 8. Working with Support Vector Machines 9. Modeling Infectious Disease with Cellular Automata 10. Working with Social Graphs 11. Sentiment Analysis of Twitter Data 12. Data Processing and Aggregation with MongoDB 13. Working with MapReduce 14. Online Data Analysis with IPython and Wakari Setting Up the Infrastructure Index

Simulation of the SIRS model in CA with D3.js


In Chapter 7, Predicting Gold Prices, we already studied the basics of a random walk simulation. In this chapter, we will implement a CA in JavaScript using D3.js to simulate the SIRS model.

In the following screenshot we can see the interface of our simulator. It's a simple interface with a grid of 15 x 15 cells (225 total cells). An Update button that applies the rules to all the cells on the grid (step). A paragraph area that will show the status of different populations in the current step, for example, Susceptible: 35 Infected: 153 Recovered: 37 Step: 4. Finally, a Statistics button that writes a list with all the statistics of each step (susceptible, infected, recovered, and so on) into a text area for plotting purpose:

Inside the head tag we need to refer to the library:

  <html>
  <head>
    <script src="http://d3js.org/d3.v3.min.js"></script>
  </head>

The code is mostly in JavaScript. First, we need to define...

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