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..
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build_metadata.rb
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capistrano.rb
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cli
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cli.rb
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compact_index_client
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compact_index_client.rb
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constants.rb
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current_ruby.rb
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definition.rb
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dep_proxy.rb
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dependency.rb
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deployment.rb
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deprecate.rb
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dsl.rb
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endpoint_specification.rb
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env.rb
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environment_preserver.rb
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errors.rb
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feature_flag.rb
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fetcher
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fetcher.rb
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friendly_errors.rb
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gem_helper.rb
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gem_helpers.rb
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gem_tasks.rb
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gem_version_promoter.rb
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gemdeps.rb
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graph.rb
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index.rb
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injector.rb
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inline.rb
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installer
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installer.rb
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lazy_specification.rb
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lockfile_generator.rb
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lockfile_parser.rb
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man
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match_platform.rb
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mirror.rb
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plugin
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plugin.rb
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process_lock.rb
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psyched_yaml.rb
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remote_specification.rb
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resolver
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resolver.rb
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retry.rb
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ruby_dsl.rb
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ruby_version.rb
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rubygems_ext.rb
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rubygems_gem_installer.rb
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rubygems_integration.rb
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runtime.rb
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settings
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settings.rb
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setup.rb
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shared_helpers.rb
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similarity_detector.rb
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source
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source.rb
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source_list.rb
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source_map.rb
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spec_set.rb
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stub_specification.rb
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templates
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ui
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ui.rb
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uri_credentials_filter.rb
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vendor
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vendored_fileutils.rb
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vendored_molinillo.rb
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vendored_persistent.rb
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vendored_thor.rb
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vendored_tmpdir.rb
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vendored_uri.rb
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version.rb
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version_ranges.rb
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vlad.rb
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worker.rb
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yaml_serializer.rb
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Editing: similarity_detector.rb
# 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 # https://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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