# Nick Grattan's Blog

About Microsoft SharePoint, .NET, Natural Language Processing and Machine Learning

## Summary – Document and String Similarity/Distances Measures in C#  This post summarizes a number of recent posts on this blog showing how to calculate similarity and distance measures in C# for documents and strings.

First, documents can be represented by a “Bag of Words” (a list of the unique words in a document) or a “Frequency Distribution” (a list of the unique words in a document together with the occurrence frequency).

Bag of Words and Frequency Distributions in C#

The simplest similarity measure covered in this series is the Jaccard Similarity measure. This uses a bag of words and compares the number of common words between two documents with the overall number of words. This does not take into account the relative frequency nor the order of the words in the two documents.

Jaccard Similarity Index for measuring Document Similarity

Three measures using Frequency Distributions are described. In all these cases a n-dimensional space is created from the Frequency Distribution with a dimension for each word in the documents being compared. They are:

1. Euclidean Distance – The shortest distance between two documents in the Frequency Distribution space.
2. Manhattan Distance – The sum of all the sides in the hyper rectangle formed around two documents in the Frequency Distribution Space.
3. Cosine Distance – The cosine of the angle subtended at the origin between two documents in the Frequency Distribution Space.

These measures take into account the word frequency, but Cosine Distance cannot distinguish between documents where the relative frequency of words is the same (rather than the absolute frequency). They do not take into account the order of the words in the documents.

Euclidean, Manhattan and Cosine Distance Measures in C#

The final measure is the Levenshtein Minimum Edit Distance. This measure aligns two documents and calculates the number of inserts, deletes or substitutions that are required to change the first document into the second document, which may not necessarily be the same length. This measure takes into account the words, the frequency of words and the order of words in the document.

Levenshtein Minimum Edit Distance in C#

By using MinHash and Locality Sensitivity Hashing, similar documents can be identified very, very efficiently – these techniques are related to the Jaccard Similarity Index.

MinHash for Document Fingerprinting in C#

Locality Sensitivity Hashing for finding similar documents in C#

Written by Nick Grattan

July 10, 2014 at 6:21 pm

## Levenshtein Minimum Edit Distance in C# From Lesk p.254 – “The Levenstein, or edit distance , defined between two strings of not necessarily equal length, is the minimum number of ‘edit operations’ required to change one string into the other. An edit operation is a deletion, insertion or alteration [substitution] of a single character in either sequence “. Thus the edit distance between two strings or documents takes into account not only the relative frequency of characters/words but the position as well. Strings can be aligned too. For example, here’s an alignment of two nucleotide sequences where ‘-‘ represents an insertion:

```ag-tcc
cgctca```

For these two strings the edit distance is 3 (2 substitutions and 1 insertion/deletion).  In the case above the substitutions and inserts/deletes (“indels”) have the same weight. Often, substitutions are given a weight of 2 and indels 1 resulting in an edit distance of 5 for these strings. Substitutions are really an insert with a delete, hence the double weight.

The edit distance calculation uses Dynamic Programming. The algorithm is well described in Jurafsky & Martin p.107. and summarized in these PowerPoint slides.  This class implements the edit distance algorithm and text alignment in C#:

```    ///
<summary>
/// Calculates the minimum edit distance (Levenshtein) between two lists of items
/// </summary>

/// <typeparam name="T">Type of item (e.g. characters or string)</typeparam>
public class MinEditDistance<T>
{
public MinEditDistance(List<T> list1, List<T> list2)
{
InsCost = 1;
DelCost = 1;
SubCost = 2;
List1 = list1;
List2 = list2;
}

List<T> List1;
List<T> List2;

Distance[,] D;

// The costs of inserts, deletes, substitutions. Normally run SubCost with 2, others at 1
public int InsCost { get; set; }
public int DelCost { get; set; }
public int SubCost { get; set; }

///
<summary>
/// Calculates the minimum edit distance using weights in InsCost etc.
/// </summary>

/// <returns></returns>
public int CalcMinEditDistance()
{
if (List1 == null || List2 == null)
{
throw new ArgumentNullException();
}
if (List1.Count == 0 || List2.Count == 0)
{
throw new ArgumentException("Zero length list");
}
// prepend dummy value
List1.Insert(0, default(T));
List2.Insert(0, default(T));

int lenList1 = List1.Count;
int lenList2 = List2.Count;
D = new Distance[lenList1, lenList2];
for (int i = 0; i < lenList1; i++)
{
D[i, 0].val = DelCost * i;
D[i, 0].backTrace = Direction.Left;
}
for (int j = 0; j < lenList2; j++)
{
D[0, j].val = InsCost * j;
D[0, j].backTrace = Direction.Up;
}
for (int i = 1; i < lenList1; i++)
for (int j = 1; j < lenList2; j++)
{
int d1 = D[i - 1, j].val + DelCost;
int d2 = D[i, j - 1].val + InsCost;
int d3 = (EqualityComparer<T>.Default.Equals(List1[i], List2[j])) ? D[i - 1, j - 1].val : D[i - 1, j - 1].val + SubCost;
D[i, j].val = Math.Min(d1, Math.Min(d2, d3));
// back trace
if (D[i, j].val == d1)
{
D[i, j].backTrace |= Direction.Left;
}
if (D[i, j].val == d2)
{
D[i, j].backTrace |= Direction.Up;
}
if (D[i, j].val == d3)
{
D[i, j].backTrace |= Direction.Diag;
}
}
return D[lenList1 - 1, lenList2 - 1].val;
}

///
<summary>
/// Returns alignment strings. The default value for T indicates an insertion or deletion in the appropriate string. align1 and align2 will
/// have the same length padded with default(T) regardless of the input string lengths.
/// </summary>

/// <param name="align1">First string alignment</param>
/// <param name="align2">Second string alignment</param>
public void Alignment(out List<T> align1, out List<T> align2)
{
if (D == null)
{
throw new Exception("Distance matrix is null");
}
int i = List1.Count - 1;
int j = List2.Count - 1;

align1 = new List<T>();
align2 = new List<T>();
while (i > 0 || j > 0)
{
Direction dir = D[i, j].backTrace;
int dVal, dDiag = int.MaxValue, dLeft = int.MaxValue, dUp = int.MaxValue;
if ((dir & Direction.Diag) == Direction.Diag)
{
// always favour diagonal as this is a match on items
dDiag = -1;
}
if ((dir & Direction.Up) == Direction.Up)
{
dUp = D[i, j - 1].val;
}
if ((dir & Direction.Left) == Direction.Left)
{
dLeft = D[i - 1, j].val;
}
dVal = Math.Min(dDiag, Math.Min(dLeft, dUp));
if (dVal == dDiag)
{
i--;
j--;
}
else if (dVal == dUp)
{
j--;
}
else if (dVal == dLeft)
{
i--;
}
}
align1.Reverse();
align2.Reverse();
}

///
<summary>
/// Writes out the "D" matrix showing the edit distances and the back tracing directions
/// </summary>

public void Write()
{
int lenList1 = List1.Count;
int lenList2 = List2.Count;
Console.Write("      ");
for (int i = 0; i < lenList1; i++)
{
if (i == 0)
Console.Write(string.Format("{0, 6}", "*"));
else
Console.Write(string.Format("{0, 6}", List1[i]));
}
Console.WriteLine();
for (int j = 0; j < lenList2; j++)
{
if (j == 0)
Console.Write(string.Format("{0, 6}", "*"));
else
Console.Write(string.Format("{0, 6}", List2[j]));
for (int i = 0; i < lenList1; i++)
{
Console.Write(string.Format("{0,6:###}", D[i, j].val));
}
Console.WriteLine();
Console.Write("      ");
for (int i = 0; i < lenList1; i++)
{
WriteBackTrace(D[i, j].backTrace);
}
Console.WriteLine();
Console.WriteLine();
}
}

///
<summary>
/// Writes out one backtrace item
/// </summary>

/// <param name="d"></param>
void WriteBackTrace(Direction d)
{
string s = string.Empty;
s += ((d & Direction.Diag) == Direction.Diag) ? "\u2196" : "";
s += ((d & Direction.Up) == Direction.Up) ? "\u2191" : "";
s += ((d & Direction.Left) == Direction.Left) ? "\u2190" : "";
Console.Write(string.Format("{0,6}", s));
}

///
<summary>
/// Represents one cell in the 'D' matrix
/// </summary>

public struct Distance
{
public int val;
public Direction backTrace;
}

///
<summary>
/// Direction(s) for one cell in the 'D' matrix.
/// </summary>

[FlagsAttribute]
public enum Direction
{
None = 0,
Diag = 1,
Left = 2,
Up = 4
}
}
```

The following code shows how this class can be used:

```
// example from Speach & Language Processing, Jurafsky & Martin P. 108, cites Krushkal (1983)
[TestMethod]
public void LevenshteinAltDistance()
{
string s1 = "EXECUTION";
string s2 = "INTENTION";
int m = Align(s1, s2, 2, 1, 1, "*EXECUTION", "INTE*NTION");
Assert.AreEqual(8, m);
}

private int Align(string s1, string s2, int subCost = 1, int delCost = 1, int insCost = 1, string alignment1 = null, string alignment2 = null)
{
List<char> l1 = s1.ToList();
List<char> l2 = s2.ToList();
MinEditDistance<char> med = new MinEditDistance<char>(l1, l2);
List<char> a1, a2;
med.SubCost = subCost;
med.DelCost = delCost;
med.InsCost = insCost;
int m = med.CalcMinEditDistance();
Console.WriteLine("Min edit distance: " + m);
med.Write();

med.Alignment(out a1, out a2);
foreach (char c in a1)
{
if (c == default(char))
Console.Write("*");
else
Console.Write(c);
}
Console.WriteLine();
foreach (char c in a2)
{
if (c == default(char))
Console.Write("*");
else
Console.Write(c);
}
Console.WriteLine();
if (alignment1 != null && alignment2 != null)
{
Assert.AreEqual(alignment1, new string(a1.ToArray()).Replace('\0', '*'));
Assert.AreEqual(alignment2, new string(a2.ToArray()).Replace('\0', '*'));
}
return m;
}

```

The output from this test reports the edit distance to be 8 and the alignment is:

```*EXECUTION
INTE*NTION```

The “D” matrix with backtrack information is also displayed: Note that there may be several different possible alignments since backtracking allows multiple routes through the matrix. This web site http://odur.let.rug.nl/kleiweg/lev/ provides an online tool for calculating the edit distance.

 “Introduction to Bioinfomatics”, Arthur M. Lesk, 3rd Edition 2008, Oxford University Press

 “Speech and Language Processing” D.Jurafsky, J.Martin, 2nd Edition 2009, Prentice Hall

Written by Nick Grattan

June 21, 2014 at 8:10 am