From 59c8d50653480bef3f24517296e6ddf937fdf6bc Mon Sep 17 00:00:00 2001
From: hsbt <hsbt@b2dd03c8-39d4-4d8f-98ff-823fe69b080e>
Date: Fri, 2 Nov 2018 23:07:56 +0000
Subject: Added bundler as default gems. Revisit [Feature #12733]

  * bin/*, lib/bundler/*, lib/bundler.rb, spec/bundler, man/*:
    Merge from latest stable branch of bundler/bundler repository and
    added workaround patches. I will backport them into upstream.
  * common.mk, defs/gmake.mk: Added `test-bundler` task for test suite
    of bundler.
  * tool/sync_default_gems.rb: Added sync task for bundler.

git-svn-id: svn+ssh://ci.ruby-lang.org/ruby/trunk@65509 b2dd03c8-39d4-4d8f-98ff-823fe69b080e
---
 lib/bundler/similarity_detector.rb | 63 ++++++++++++++++++++++++++++++++++++++
 1 file changed, 63 insertions(+)
 create mode 100644 lib/bundler/similarity_detector.rb

(limited to 'lib/bundler/similarity_detector.rb')

diff --git a/lib/bundler/similarity_detector.rb b/lib/bundler/similarity_detector.rb
new file mode 100644
index 0000000000..b7f3ee7afa
--- /dev/null
+++ b/lib/bundler/similarity_detector.rb
@@ -0,0 +1,63 @@
+# frozen_string_literal: true
+
+module Bundler
+  class SimilarityDetector
+    SimilarityScore = Struct.new(:string, :distance)
+
+    # initialize with an array of words to be matched against
+    def initialize(corpus)
+      @corpus = corpus
+    end
+
+    # return an array of words similar to 'word' from the corpus
+    def similar_words(word, limit = 3)
+      words_by_similarity = @corpus.map {|w| SimilarityScore.new(w, levenshtein_distance(word, w)) }
+      words_by_similarity.select {|s| s.distance <= limit }.sort_by(&:distance).map(&:string)
+    end
+
+    # return the result of 'similar_words', concatenated into a list
+    # (eg "a, b, or c")
+    def similar_word_list(word, limit = 3)
+      words = similar_words(word, limit)
+      if words.length == 1
+        words[0]
+      elsif words.length > 1
+        [words[0..-2].join(", "), words[-1]].join(" or ")
+      end
+    end
+
+  protected
+
+    # http://www.informit.com/articles/article.aspx?p=683059&seqNum=36
+    def levenshtein_distance(this, that, ins = 2, del = 2, sub = 1)
+      # ins, del, sub are weighted costs
+      return nil if this.nil?
+      return nil if that.nil?
+      dm = [] # distance matrix
+
+      # Initialize first row values
+      dm[0] = (0..this.length).collect {|i| i * ins }
+      fill = [0] * (this.length - 1)
+
+      # Initialize first column values
+      (1..that.length).each do |i|
+        dm[i] = [i * del, fill.flatten]
+      end
+
+      # populate matrix
+      (1..that.length).each do |i|
+        (1..this.length).each do |j|
+          # critical comparison
+          dm[i][j] = [
+            dm[i - 1][j - 1] + (this[j - 1] == that[i - 1] ? 0 : sub),
+            dm[i][j - 1] + ins,
+            dm[i - 1][j] + del
+          ].min
+        end
+      end
+
+      # The last value in matrix is the Levenshtein distance between the strings
+      dm[that.length][this.length]
+    end
+  end
+end
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