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
About the Author
About the Reviewer
Customer Feedback

Computing generalized least squares regression

In this recipe, we will see another variant of least squares regression named generalized least squares regression. GLSMultipleLinearRegression implements Generalized Least Squares to fit the linear model Y=X*b+u.

How to do it...

  1. Create a method that takes a two-dimensional double array, a one-dimensional double array, and a two-dimensional double array for the regression's omega parameter:

            public void calculateGlsRegression(double[][] x, double[] y, 
              double[][] omega){ 
  2. Create a GLS regression object, the data points, and the omega parameter:

            GLSMultipleLinearRegression regression = new  
            regression.newSampleData(y, x, omega); 
  3. Using the methods of the GLSMultipleLinearRegression class, compute various statistics of the regression and finally, close the method:

            double[] beta = regression.estimateRegressionParameters();