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Java Data Science Cookbook

You're reading from   Java Data Science Cookbook Explore the power of MLlib, DL4j, Weka, and more

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Product type Paperback
Published in Mar 2017
Publisher Packt
ISBN-13 9781787122536
Length 372 pages
Edition 1st Edition
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Author (1):
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Rushdi Shams Rushdi Shams
Author Profile Icon Rushdi Shams
Rushdi Shams
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Toc

Table of Contents (10) Chapters Close

Preface 1. Obtaining and Cleaning Data FREE CHAPTER 2. Indexing and Searching Data 3. Analyzing Data Statistically 4. Learning from Data - Part 1 5. Learning from Data - Part 2 6. Retrieving Information from Text Data 7. Handling Big Data 8. Learn Deeply from Data 9. Visualizing Data

Computing simple regression


The SimpleRegression class supports ordinary least squares regression with one independent variable: y = intercept + slope * x, where intercept is an optional parameter. The class is also capable of providing standard error for intercept. Observations (x,y) pairs can either be added to the model one at a time or they can be provided in a two-dimensional array. In this recipe, the data points are added one at a time.

Note

The observations are not stored in memory and therefore there is no limit on the number of observations that can be added to the model.

How to do it...

  1. To compute simple regression, create a method that takes a two-dimensional double array. The array represents a series of (x,y) values:

            public void calculateRegression(double[][] data){ 
    
  2. Create a SimpleRegression object, and add the data:

            SimpleRegression regression = new SimpleRegression(); 
            regression.addData(data); 
    

    Note

    If you do not have interception or if...

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