Book Image

Java Data Science Cookbook

By : Rushdi Shams
Book Image

Java Data Science Cookbook

By: Rushdi Shams

Overview of this book

If you are looking to build data science models that are good for production, Java has come to the rescue. With the aid of strong libraries such as MLlib, Weka, DL4j, and more, you can efficiently perform all the data science tasks you need to. This unique book provides modern recipes to solve your common and not-so-common data science-related problems. We start with recipes to help you obtain, clean, index, and search data. Then you will learn a variety of techniques to analyze, learn from, and retrieve information from data. You will also understand how to handle big data, learn deeply from data, and visualize data. Finally, you will work through unique recipes that solve your problems while taking data science to production, writing distributed data science applications, and much more - things that will come in handy at work.
Table of Contents (16 chapters)
Java Data Science Cookbook
Credits
About the Author
About the Reviewer
www.PacktPub.com
Customer Feedback
Preface

Generating summary statistics from multiple distributions


In this recipe, we will be creating an AggregateSummaryStatistics instance to accumulate the overall statistics and SummaryStatistics for the sample data.

How to do it...

  1. Create a method that takes two double array arguments. Each array will contain two different sets of data:

            public void getAggregateStats(double[] values1, double[] 
              values2){ 
    
  2. Create an object of class AggregateSummaryStatistics:

            AggregateSummaryStatistics aggregate = new  
            AggregateSummaryStatistics(); 
    
  3. To generate summary statistics from the two distributions, create two objects of the SummaryStatistics class:

            SummaryStatistics firstSet = 
              aggregate.createContributingStatistics(); 
            SummaryStatistics secondSet = 
              aggregate.createContributingStatistics(); 
    
  4. Add the values of the two distributions in the two objects created in the preceding step:

            for(int...