From 675b0854c8c13c437a692be5b1e6f8df363389a5 Mon Sep 17 00:00:00 2001
From: Allen Goodman <allen@banksimple.com>
Date: Mon, 20 Jun 2011 21:24:08 -0400
Subject: [PATCH 1/2] Included recipe for matching two or more strings (i.e.
 calculating an edit distance).

---
 chapters/strings/matching-strings.md | 42 ++++++++++++++++++++++++++++
 1 file changed, 42 insertions(+)
 create mode 100644 chapters/strings/matching-strings.md

diff --git a/chapters/strings/matching-strings.md b/chapters/strings/matching-strings.md
new file mode 100644
index 0000000..7a65af3
--- /dev/null
+++ b/chapters/strings/matching-strings.md
@@ -0,0 +1,42 @@
+---
+layout:   recipe
+title:    Matching Strings
+chapter:  Strings
+---
+## Problem
+
+You want to match two or more strings.
+
+## Solution
+
+Calculate the edit distance, or number of operations required to transform one string into the other.
+
+{% highlight coffeescript %}
+Levenshtein =
+  (str1, str2) ->
+          
+    l1 = str1.length
+    l2 = str2.length
+    
+    Math.max l1, l2 if Math.min l1, l2 == 0      
+    
+    i = 0; j = 0; distance = []
+
+    for i in [0...l1 + 1]
+      distance[i] = []
+      distance[i][0] = i
+
+    distance[0][j] = j for j in [0...l2 + 1]
+    
+    for i in [1...l1 + 1]
+      for j in [1...l2 + 1]
+        distance[i][j] = Math.min distance[i - 1][j] + 1,
+          distance[i][j - 1] + 1,                         
+          distance[i - 1][j - 1] + if str1.charAt(i - 1) == str2.charAt(j - 1) then 0 else 1
+
+    distance[l1][l2]
+{% endhighlight %}
+
+## Discussion
+
+You can use either Hirschberg or Wagner–Fischer's algorithm to calculate a Levenshtein distance. This example uses Wagner–Fischer's algorithm. 
\ No newline at end of file

From d1824197c303e3239d68ae28938735f65d3129d5 Mon Sep 17 00:00:00 2001
From: Allen Goodman <allen@banksimple.com>
Date: Tue, 21 Jun 2011 03:56:46 -0400
Subject: [PATCH 2/2] added newline; repositioned parentheses to make algorithm
 slightly more idiomatic

---
 chapters/strings/matching-strings.md | 25 ++++++++++++++-----------
 1 file changed, 14 insertions(+), 11 deletions(-)

diff --git a/chapters/strings/matching-strings.md b/chapters/strings/matching-strings.md
index 7a65af3..100aefa 100644
--- a/chapters/strings/matching-strings.md
+++ b/chapters/strings/matching-strings.md
@@ -12,31 +12,34 @@ You want to match two or more strings.
 Calculate the edit distance, or number of operations required to transform one string into the other.
 
 {% highlight coffeescript %}
+
 Levenshtein =
   (str1, str2) ->
-          
+         
     l1 = str1.length
     l2 = str2.length
-    
+
     Math.max l1, l2 if Math.min l1, l2 == 0      
-    
+
     i = 0; j = 0; distance = []
 
     for i in [0...l1 + 1]
-      distance[i] = []
-      distance[i][0] = i
+     distance[i] = []
+     distance[i][0] = i
 
     distance[0][j] = j for j in [0...l2 + 1]
-    
+
     for i in [1...l1 + 1]
-      for j in [1...l2 + 1]
-        distance[i][j] = Math.min distance[i - 1][j] + 1,
-          distance[i][j - 1] + 1,                         
-          distance[i - 1][j - 1] + if str1.charAt(i - 1) == str2.charAt(j - 1) then 0 else 1
+     for j in [1...l2 + 1]
+       distance[i][j] = Math.min distance[i - 1][j] + 1,
+         distance[i][j - 1] + 1,                         
+         distance[i - 1][j - 1] + 
+           if (str1.charAt i - 1) == (str2.charAt j - 1) then 0 else 1
 
     distance[l1][l2]
+    
 {% endhighlight %}
 
 ## Discussion
 
-You can use either Hirschberg or Wagner–Fischer's algorithm to calculate a Levenshtein distance. This example uses Wagner–Fischer's algorithm. 
\ No newline at end of file
+You can use either Hirschberg or Wagner–Fischer's algorithm to calculate a Levenshtein distance. This example uses Wagner–Fischer's algorithm.