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Clojure for Data Science

You're reading from  Clojure for Data Science

Product type Book
Published in Sep 2015
Publisher
ISBN-13 9781784397180
Pages 608 pages
Edition 1st Edition
Languages
Author (1):
Henry Garner Henry Garner
Profile icon Henry Garner

Table of Contents (18) Chapters

Clojure for Data Science
Credits
About the Author
Acknowledgments
About the Reviewer
www.PacktPub.com
Preface
1. Statistics 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, and...

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