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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

Mathematical folds with Tesser


We should now understand how to use folds to calculate parallel implementations of simple algorithms. Hopefully, we should also have some appreciation for the ingenuity required to find efficient solutions that will perform the minimum number of iterations over the data.

Fortunately, the Clojure library Tesser (https://github.com/aphyr/tesser) includes implementations for common mathematical folds, including the mean, standard deviation, and covariance. To see how to use Tesser, let's consider the covariance of two fields from the IRS dataset: the salaries and wages, A00200, the unemployment compensation, A02300.

Calculating covariance with Tesser

We encountered covariance in Chapter 3, Correlation, as a measure of how two sequences of data vary together. The formula is reproduced as follows:

A covariance fold is included in tesser.math. In the following code, we'll include tesser.math as m and tesser.core as t:

(defn ex-5-17 []
  (let [data (into [] (load-data...
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