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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

Creating term frequency vectors

To calculate the Euclidean distance, let's first create a vector from our dictionary and document. This will allow us to easily compare the term frequencies between documents because they will occupy the same index of the vector.

(defn term-id [dict term]
  (get-in @dict [:terms term]))

(defn term-frequencies [dict terms]
  (->> (map #(term-id dict %) terms)
       (remove nil?)
       (frequencies)))

(defn map->vector [dictionary id-counts]
  (let [zeros (vec (replicate (:count @dictionary) 0))]
    (-> (reduce #(apply assoc! %1 %2) (transient zeros) id-counts)
        (persistent!))))

(defn tf-vector [dict document]
  (map->vector dict (term-frequencies dict document)))

The term-frequencies function creates a map of term ID to frequency count for each term in the document. The map->vector function simply takes this map and associates the frequency count at the index of the vector given by the term ID. Since there may be many terms...

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