Book Image

Java Data Science Cookbook

By : Shams
Book Image

Java Data Science Cookbook

By: 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

Calculating covariance of two sets of data points


Unbiased covariances are given by the formula cov(X, Y) = sum [(xi - E(X))(yi - E(Y))] / (n - 1), where E(X) is the mean of X and E(Y) is the mean of the Y values. Non-bias-corrected estimates use n in place of n - 1. To determine if the covariance is bias corrected or not, we need to set an additional, optional parameter called biasCorrected which is set to true by default.

How to do it...

  1. Create a method that takes two one-dimensional double arrays. Each array represents a set of data points:

            public void calculateCov(double[] x, double[] y){ 
    
  2. Calculate the covariance of the two sets of data points as follows:

            double covariance = new Covariance().covariance(x, y, false); 
    

    Note

    For this recipe, we have used non-bias-corrected covariance, and therefore, we have used three parameters in the covariace() method. To use unbiased covariance between two double arrays, remove the third parameter, double covariance = new Covariance...