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Clojure for Data Science

You're reading from   Clojure for Data Science Statistics, big data, and machine learning for Clojure programmers

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Product type Paperback
Published in Sep 2015
Publisher
ISBN-13 9781784397180
Length 608 pages
Edition 1st Edition
Languages
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Author (1):
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Henry Garner Henry Garner
Author Profile Icon Henry Garner
Henry Garner
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Table of Contents (12) Chapters Close

Preface 1. Statistics FREE CHAPTER 2. Inference 3. Correlation 4. Classification 5. Big Data 6. Clustering 7. Recommender Systems 8. Network Analysis 9. Time Series 10. Visualization Index

The normal equation


Now that we've covered the basics of matrix and vector manipulation we're in a position to study the normal equation. This is an equation that uses matrix algebra to calculate the coefficients of our OLS linear regression model:

We read "to find β, multiply the inverse of X transpose X, by X transpose y" where X is the matrix of independent variables (including the intercept term) for our sample and y is a vector containing the dependent variables for our sample. The result β contains the calculated coefficients. This normal equation is relatively easy to derive from the equation of multiple regression, applying the rules of matrix multiplication, but the mathematics is beyond the scope of this book.

We can implement the normal equation with Incanter using only the functions we have just encountered:

(defn normal-equation [x y]
  (let [xtx  (i/mmult (i/trans x) x)
        xtxi (i/solve xtx)
        xty  (i/mmult (i/trans x) y)]
    (i/mmult xtxi xty)))

This normal equation...

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