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tf-idf

Term Frequency–Inverse Document Frequency (tf-idf) is implemented to determine how important a word (or words) is to a document relative to a corpus. The following example will add four documents to a corpus and determine the weight of the word "crystal" and then the weight of the word "ruby" in each document.

Installation

  1. Add the dependency to your shard.yml:

    dependencies:
  cadmium_tfidf:
    github: cadmiumcr/tfidf

  2. Run shards install

Usage

require "cadmium_tfidf"

tfidf = Cadmium.tf_idf.new
tfidf.add_document("this document is about crystal.")
tfidf.add_document("this document is about ruby.")
tfidf.add_document("this document is about ruby and crystal.")
tfidf.add_document("this document is about crystal. it has crystal examples")

puts "crystal --------------------------------"
tfidf.tfidfs("crystal") do |i, measure, key|
  puts "document ##{i} is #{measure}"
end

puts "ruby --------------------------------"
tfidf.tfidfs("ruby") do |i, measure, key|
  puts "document ##{i} is #{measure}"
end

# =>  crystal --------------------------------
      document #0 is 1
      document #1 is 0
      document #2 is 1
      document #3 is 2
      ruby --------------------------------
      document #0 is 0
      document #1 is 1.2876820724517808
      document #2 is 1.2876820724517808
      document #3 is 0

Contributing

  1. Fork it (https://github.com/cadmiumcr/tfidf/fork)
  2. Create your feature branch (git checkout -b my-new-feature)
  3. Commit your changes (git commit -am 'Add some feature')
  4. Push to the branch (git push origin my-new-feature)
  5. Create a new Pull Request

Contributors