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Mailcheck uses the sift3 string distance algorithm by default. We are always on the lookout for better alternatives, so send in your pull requests! Meanwhile, we have consolidated contributed alternatives below. You can pass them into the Kicksend.mailcheck method.

Levenshtein Distance

Returns the strings' edit distance taking into account deletion, insertion, and substitution.

Contributed by hpshelton.

function levenshteinDistance(s, t) {
  // Determine the Levenshtein distance between s and t
  if (!s || !t) {
    return 99;
  }
  var m = s.length;
  var n = t.length;

  /* For all i and j, d[i][j] holds the Levenshtein distance between
   * the first i characters of s and the first j characters of t.
   * Note that the array has (m+1)x(n+1) values.
   */
  var d = new Array();
  for (var i = 0; i <= m; i++) {
    d[i] = new Array();
    d[i][0] = i;
  }
  for (var j = 0; j <= n; j++) {
    d[0][j] = j;
  }

  // Determine substring distances
  var cost = 0;
  for (var j = 1; j <= n; j++) {
    for (var i = 1; i <= m; i++) {
      cost = (s.charAt(i-1) == t.charAt(j-1)) ? 0 : 1;  // Subtract one to start at strings' index zero instead of index one
      d[i][j] = Math.min(d[i][j-1] + 1,                 // insertion
                         Math.min(d[i-1][j] + 1,        // deletion
                                  d[i-1][j-1] + cost)); // substitution                              
    }
  }

  // Return the strings' distance
  return d[m][n];
}

'Optimal' String-Alignment Distance

This is a variation of the Damerau-Levenshtein distance that returns the strings' edit distance taking into account deletion, insertion, substitution, and transposition, under the condition that no substring is edited more than once.

For example, optimalStringAlignmentDistance('ca', 'abc') == 3 because if the transposition 'ca' -> 'ac' is used, it is not possible to use the insertion 'ac' -> 'abc'. The shortest sequence of operations is 'ca' -> 'a' -> 'ab' -> 'abc'.

Contributed by hpshelton.

function optimalStringAlignmentDistance(s, t) {
  // Determine the "optimal" string-alignment distance between s and t
  if (!s || !t) {
    return 99;
  }
  var m = s.length;
  var n = t.length;

  /* For all i and j, d[i][j] holds the string-alignment distance
   * between the first i characters of s and the first j characters of t.
   * Note that the array has (m+1)x(n+1) values.
   */
  var d = new Array();
  for (var i = 0; i <= m; i++) {
    d[i] = new Array();
    d[i][0] = i;
  }
  for (var j = 0; j <= n; j++) {
    d[0][j] = j;
  }

  // Determine substring distances
  var cost = 0;
  for (var j = 1; j <= n; j++) {
    for (var i = 1; i <= m; i++) {
      cost = (s.charAt(i-1) == t.charAt(j-1)) ? 0 : 1;   // Subtract one to start at strings' index zero instead of index one
      d[i][j] = Math.min(d[i][j-1] + 1,                  // insertion
                         Math.min(d[i-1][j] + 1,         // deletion
                                  d[i-1][j-1] + cost));  // substitution

      if(i > 1 && j > 1 && s.charAt(i-1) == t.charAt(j-2) && s.charAt(i-2) == t.charAt(j-1)) {
        d[i][j] = Math.min(d[i][j], d[i-2][j-2] + cost); // transposition
      }
    }
  }

  // Return the strings' distance
  return d[m][n];
}

Damerau-Levenshtein Distance

Returns the strings' edit distance taking into account deletion, insertion, substitution, and transposition, without the condition imposed by the 'optimal' string alignment distance algorithm above.

For example, damerauLevenshteinDistance('ca', 'abc') == 2 because 'ca' -> 'ac' -> 'abc'.

Contributed by hpshelton.

function damerauLevenshteinDistance(s, t) {
  // Determine the Damerau-Levenshtein distance between s and t
  if (!s || !t) {
    return 99;
  }
  var m = s.length;
  var n = t.length;      
  var charDictionary = new Object();

  /* For all i and j, d[i][j] holds the Damerau-Levenshtein distance
   * between the first i characters of s and the first j characters of t.
   * Note that the array has (m+1)x(n+1) values.
   */
  var d = new Array();
  for (var i = 0; i <= m; i++) {
    d[i] = new Array();
    d[i][0] = i;
  }
  for (var j = 0; j <= n; j++) {
    d[0][j] = j;
  }

  // Populate a dictionary with the alphabet of the two strings
  for (var i = 0; i < m; i++) {
    charDictionary[s.charAt(i)] = 0;
  }
  for (var j = 0; j < n; j++) {
    charDictionary[t.charAt(j)] = 0;
  }

  // Determine substring distances
  for (var i = 1; i <= m; i++) {
    var db = 0;
    for (var j = 1; j <= n; j++) {
      var i1 = charDictionary[t.charAt(j-1)];
      var j1 = db;
      var cost = 0;

      if (s.charAt(i-1) == t.charAt(j-1)) { // Subtract one to start at strings' index zero instead of index one
        db = j;
      } else {
        cost = 1;
      }
      d[i][j] = Math.min(d[i][j-1] + 1,                 // insertion
                         Math.min(d[i-1][j] + 1,        // deletion
                                  d[i-1][j-1] + cost)); // substitution
      if(i1 > 0 && j1 > 0) {
        d[i][j] = Math.min(d[i][j], d[i1-1][j1-1] + (i-i1-1) + (j-j1-1) + 1); //transposition
      }
    }
    charDictionary[s.charAt(i-1)] = i;
  }

  // Return the strings' distance
  return d[m][n];
}

Performance

1000 randomly-generated email addresses were compared against the built-in domain list using the specified algorithm to determine an average comparison time. These average comparison times were then averaged over multiple experiments for some sort of semi-scientific-y conclusion. The following times were determined in Chrome on a MacBook Pro (3,1) with a 2.4 GHz Intel Core 2 Duo processor and 2 GB of RAM. Actual performance will vary by email addresses, domains, and client machine.

Algorithm Runtime
Sift3 Distance 0.03-0.04ms
Levenshtein Distance 0.10-0.20ms
Optimal String Alignment Distance 0.20-0.30ms
Damerau-Levenshtein Distance 0.40-0.50ms
Qwerty Keyboard Distance 1.00-1.55ms*

*Caching affects the performance of this method, which results in very different runtimes depending on the input; the algorithm can perform as well as the Damerau-Levenshtein algorithm in some cases.

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