64 lines
1.8 KiB
Ruby
64 lines
1.8 KiB
Ruby
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module Bundler
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class SimilarityDetector
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SimilarityScore = Struct.new(:string, :distance)
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# initialize with an array of words to be matched against
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def initialize(corpus)
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@corpus = corpus
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end
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# return an array of words similar to 'word' from the corpus
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def similar_words(word, limit=3)
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words_by_similarity = @corpus.map{|w| SimilarityScore.new(w, levenshtein_distance(word, w))}
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words_by_similarity.select{|s| s.distance<=limit}.sort_by(&:distance).map(&:string)
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end
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# return the result of 'similar_words', concatenated into a list
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# (eg "a, b, or c")
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def similar_word_list(word, limit=3)
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words = similar_words(word,limit)
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if words.length==1
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words[0]
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elsif words.length>1
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[words[0..-2].join(', '), words[-1]].join(' or ')
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end
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end
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protected
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# http://www.informit.com/articles/article.aspx?p=683059&seqNum=36
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def levenshtein_distance(this, that, ins=2, del=2, sub=1)
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# ins, del, sub are weighted costs
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return nil if this.nil?
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return nil if that.nil?
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dm = [] # distance matrix
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# Initialize first row values
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dm[0] = (0..this.length).collect { |i| i * ins }
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fill = [0] * (this.length - 1)
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# Initialize first column values
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for i in 1..that.length
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dm[i] = [i * del, fill.flatten]
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end
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# populate matrix
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for i in 1..that.length
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for j in 1..this.length
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# critical comparison
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dm[i][j] = [
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dm[i-1][j-1] +
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(this[j-1] == that[i-1] ? 0 : sub),
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dm[i][j-1] + ins,
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dm[i-1][j] + del
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].min
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end
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end
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# The last value in matrix is the Levenshtein distance between the strings
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dm[that.length][this.length]
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end
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end
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end
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