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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

Plotting probability densities


In addition to using jStat to generate samples from the exponential distribution, we'll also use it to calculate the probability density for the t-distribution. We can construct a simple function to wrap the jStat.studentt.pdf(t, df) function, providing the correct t-statistic and degrees of freedom to parameterize the distribution:

(defn pdf-t [t & {:keys [df]}]
  (js/jStat.studentt.pdf t df))

An advantage of using ClojureScript is that we have already written the code to calculate the t-statistic from a sample. The code, which worked in Clojure, can be compiled to ClojureScript with no changes whatsoever:

(defn t-statistic [test {:keys [mean n sd]}]
  (/ (- mean test)
     (/ sd (Math/sqrt n))))

To render the probability density, we can use B1's c/function-area-plot. This will generate an area plot from the line described by a function. The provided function simply needs to accept an x and return the corresponding y.

A slight complication is that the value...

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