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

Computing ordinary least squares regression


The OLSMultipleLinearRegression provides Ordinary Least Squares Regression to fit the linear model Y=X*b+u. Here, Y is an n-vector regress, and X is a [n,k] matrix, where k columns are called regressors, b is k-vector of regression parameters, and u is an n-vector of error terms or residuals.

How to do it...

  1. Create a method that takes a two-dimensional double array and a one-dimensional double array:

            public void calculateOlsRegression(double[][] x, double[] y){ 
    
  2. Create an OLS regression object and add the data points x and y:

            OLSMultipleLinearRegression regression = new 
              OLSMultipleLinearRegression(); 
            regression.newSampleData(y, x); 
    
  3. Calculate various regression parameters and diagnostics using the following methods in the OLSMultipleLinearRegression class. The usage of these information depends on your task in hand. Finally, close the method:

            double[] beta = regression.estimateRegressionParameters...