From 8a5c4831adaa68dc155ec3abcf58ce809a3ba33b Mon Sep 17 00:00:00 2001
From: Mispy <^_^@mispy.me>
Date: Wed, 7 May 2014 16:45:17 +1000
Subject: [PATCH] 2.2.5 - encoding: utf-8

---
 bin/ebooks                   | 1 +
 lib/twitter_ebooks/bot.rb    | 1 +
 lib/twitter_ebooks/suffix.rb | 6 ++++--
 3 files changed, 6 insertions(+), 2 deletions(-)

diff --git a/bin/ebooks b/bin/ebooks
index a2309c3..e1950cc 100755
--- a/bin/ebooks
+++ b/bin/ebooks
@@ -1,4 +1,5 @@
 #!/usr/bin/env ruby
+# encoding: utf-8
 
 require 'twitter_ebooks'
 
diff --git a/lib/twitter_ebooks/bot.rb b/lib/twitter_ebooks/bot.rb
index 7b08929..bace4be 100644
--- a/lib/twitter_ebooks/bot.rb
+++ b/lib/twitter_ebooks/bot.rb
@@ -1,4 +1,5 @@
 #!/usr/bin/env ruby
+# encoding: utf-8
 require 'twitter'
 require 'tweetstream'
 require 'rufus/scheduler'
diff --git a/lib/twitter_ebooks/suffix.rb b/lib/twitter_ebooks/suffix.rb
index 9a4bd04..0b10f64 100644
--- a/lib/twitter_ebooks/suffix.rb
+++ b/lib/twitter_ebooks/suffix.rb
@@ -1,3 +1,5 @@
+# encoding: utf-8
+
 module Ebooks
   # This generator uses data identical to the markov model, but
   # instead of making a chain by looking up bigrams it uses the
@@ -50,7 +52,7 @@ module Ebooks
         tokens.each_with_index do |token, i|
           next_token = tokens[i+1]
           break if next_token.nil?
-          
+
           alternatives = (n == :unigrams) ? @unigrams[next_token] : @bigrams[token][next_token]
           # Filter out suffixes from previous sentences
           alternatives.reject! { |a| a[1] == INTERIM || used.include?(a[0]) }
@@ -66,7 +68,7 @@ module Ebooks
             verbatim << @sentences[alt[0]]
             suffix = @sentences[alt[0]][alt[1]..-1]
             potential = tokens[0..start+1] + suffix
-            
+
             # Ensure we're not just rebuilding some segment of another sentence
             unless verbatim.find { |v| NLP.subseq?(v, potential) || NLP.subseq?(potential, v) }
               used << alt[0]