public override double GetSimilarity(string firstWord, string secondWord)
{
if (firstWord == null || secondWord == null)
{
return(DefaultMismatchScore);
}
Collection <string> collection = Tokeniser.Tokenize(firstWord);
Collection <string> collection2 = Tokeniser.Tokenize(secondWord);
double num = 0.0;
for (int i = 0; i < collection.Count; i++)
{
string str = collection[i];
double num3 = 0.0;
for (int j = 0; j < collection2.Count; j++)
{
string str2 = collection2[j];
double similarity = _internalStringMetric.GetSimilarity(str, str2);
if (similarity > num3)
{
num3 = similarity;
}
}
num += num3;
}
return(num / collection.Count);
}
/// <summary>
/// gets the similarity of the two strings using Monge Elkan.
/// </summary>
/// <param name="firstWord"></param>
/// <param name="secondWord"></param>
/// <returns>a value between 0-1 of the similarity</returns>
public override double GetSimilarity(string firstWord, string secondWord)
{
if ((firstWord != null) && (secondWord != null))
{
Collection <string> firstTokens = tokeniser.Tokenize(firstWord);
Collection <string> secondTokens = tokeniser.Tokenize(secondWord);
double sumMatches = 0.0;
for (int i = 0; i < firstTokens.Count; i++)
{
string sToken = firstTokens[i];
double maxFound = 0.0;
for (int j = 0; j < secondTokens.Count; j++)
{
string tToken = secondTokens[j];
double found = internalStringMetric.GetSimilarity(sToken, tToken);
if (found > maxFound)
{
maxFound = found;
}
}
sumMatches += maxFound;
}
return(sumMatches / firstTokens.Count);
}
return(defaultMismatchScore);
}
/// <summary>
/// gets the un-normalised similarity measure of the metric for the given strings.</summary>
/// <param name="firstWord">first word</param>
/// <param name="secondWord">second word</param>
/// <returns> returns the score of the similarity measure (un-normalised)</returns>
public override double GetUnnormalisedSimilarity(string firstWord, string secondWord)
{
Collection <string> firstTokens = tokeniser.Tokenize(firstWord);
Collection <string> secondTokens = tokeniser.Tokenize(secondWord);
tokenUtilities.CreateMergedList(firstTokens, secondTokens);
return(GetActualSimilarity(firstTokens, secondTokens));
}
public override double GetSimilarity(string firstWord, string secondWord)
{
if (firstWord != null && secondWord != null)
{
_tokenUtilities.CreateMergedSet(_tokeniser.Tokenize(firstWord), _tokeniser.Tokenize(secondWord));
return(_tokenUtilities.CommonSetTerms() / (double)Math.Min(_tokenUtilities.FirstSetTokenCount, _tokenUtilities.SecondSetTokenCount));
}
return(DefaultMismatchScore);
}
public double GetEuclidDistance(string firstWord, string secondWord)
{
if (firstWord != null && secondWord != null)
{
Collection <string> firstTokens = _tokeniser.Tokenize(firstWord);
Collection <string> secondTokens = _tokeniser.Tokenize(secondWord);
return(GetActualDistance(firstTokens, secondTokens));
}
return(0.0);
}
public override double GetSimilarityTimingEstimated(string firstWord, string secondWord)
{
if (firstWord != null && secondWord != null)
{
double count = _tokeniser.Tokenize(firstWord).Count;
double num2 = _tokeniser.Tokenize(secondWord).Count;
return(num2 * count * _estimatedTimingConstant);
}
return(DefaultMismatchScore);
}
/// <summary>
/// gets the estimated time in milliseconds it takes to perform a similarity timing.
/// </summary>
/// <param name="firstWord"></param>
/// <param name="secondWord"></param>
/// <returns>the estimated time in milliseconds taken to perform the similarity measure</returns>
public override double GetSimilarityTimingEstimated(string firstWord, string secondWord)
{
if ((firstWord != null) && (secondWord != null))
{
double firstTokens = tokeniser.Tokenize(firstWord).Count;
double secondTokens = tokeniser.Tokenize(secondWord).Count;
return(secondTokens * firstTokens * estimatedTimingConstant);
}
return(0.0);
}
/// <summary>
/// gets the actual euclidean distance ie not the value between 0-1.
/// </summary>
/// <param name="firstWord"></param>
/// <param name="secondWord"></param>
/// <returns>the actual euclidean distance</returns>
public double GetEuclidDistance(string firstWord, string secondWord)
{
if ((firstWord != null) && (secondWord != null))
{
Collection <string> firstTokens = tokeniser.Tokenize(firstWord);
Collection <string> secondTokens = tokeniser.Tokenize(secondWord);
return(GetActualDistance(firstTokens, secondTokens));
}
return(defaultMismatchScore);
}
public override double GetSimilarity(string firstWord, string secondWord)
{
if (firstWord != null && secondWord != null)
{
Collection <string> collection = _tokenUtilities.CreateMergedSet(_tokeniser.Tokenize(firstWord), _tokeniser.Tokenize(secondWord));
if (collection.Count > 0)
{
return(_tokenUtilities.CommonSetTerms() / (double)collection.Count);
}
}
return(DefaultMismatchScore);
}
/// <summary>
/// gets the similarity of the two strings using OverlapCoefficient
/// </summary>
/// <param name="firstWord"></param>
/// <param name="secondWord"></param>
/// <returns>a value between 0-1 of the similarity</returns>
/// <remarks>overlap_coefficient(q,r) = ( | q and r | ) / min{ | q | , | r | }.</remarks>
public override double GetSimilarity(string firstWord, string secondWord)
{
if ((firstWord != null) && (secondWord != null))
{
//Collection<string> allTokens =
tokenUtilities.CreateMergedSet(tokeniser.Tokenize(firstWord), tokeniser.Tokenize(secondWord));
return
(tokenUtilities.CommonSetTerms() /
(double)Math.Min(tokenUtilities.FirstSetTokenCount, tokenUtilities.SecondSetTokenCount));
}
return(defaultMismatchScore);
}
/// <summary>
/// gets the similarity of the two strings using DiceSimilarity
/// </summary>
/// <param name="firstWord"></param>
/// <param name="secondWord"></param>
/// <returns>a value between 0-1 of the similarity</returns>
/// <remarks>Dices coefficient = (2*Common Terms) / (Number of terms in String1 + Number of terms in String2).</remarks>
public override double GetSimilarity(string firstWord, string secondWord)
{
if ((firstWord != null) && (secondWord != null))
{
if (tokenUtilities.CreateMergedSet(tokeniser.Tokenize(firstWord), tokeniser.Tokenize(secondWord)).Count > 0)
{
return
((2.0 * tokenUtilities.CommonSetTerms()) /
(tokenUtilities.FirstSetTokenCount + tokenUtilities.SecondSetTokenCount));
}
}
return(0.0);
}
/// <summary>
/// gets the similarity of the two strings using JaccardSimilarity.
/// </summary>
/// <param name="firstWord"></param>
/// <param name="secondWord"></param>
/// <returns>a value between 0-1 of the similarity</returns>
/// <remarks>Each instance is represented as a Jaccard vector similarity function. The Jaccard between two vectors X and Y is
/// (X*Y) / (|X||Y|-(X*Y))
/// where (X*Y) is the inner product of X and Y, and |X| = (X*X)^1/2, i.e. the Euclidean norm of X.
/// This can more easily be described as ( |X and Y| ) / ( | X or Y | )</remarks>
public override double GetSimilarity(string firstWord, string secondWord)
{
if ((firstWord != null) && (secondWord != null))
{
Collection <string> allTokens =
tokenUtilities.CreateMergedSet(tokeniser.Tokenize(firstWord), tokeniser.Tokenize(secondWord));
if (allTokens.Count > 0)
{
return((double)tokenUtilities.CommonSetTerms() / (double)allTokens.Count);
}
}
return(defaultMismatchScore);
}
/// <summary>
/// gets the similarity of the two strings using BlockDistance.
/// </summary>
/// <param name="firstWord"></param>
/// <param name="secondWord"></param>
/// <returns>a 0-1 similarity score</returns>
public override double GetSimilarity(string firstWord, string secondWord)
{
Collection <string> firstTokens = tokeniser.Tokenize(firstWord);
Collection <string> secondTokens = tokeniser.Tokenize(secondWord);
int totalPossible = firstTokens.Count + secondTokens.Count;
double totalDistance = GetActualSimilarity(firstTokens, secondTokens);
return((totalPossible - totalDistance) / totalPossible);
}
public override double GetSimilarity(string firstWord, string secondWord)
{
Collection <string> firstTokens = _tokeniser.Tokenize(firstWord);
Collection <string> secondTokens = _tokeniser.Tokenize(secondWord);
int num = firstTokens.Count + secondTokens.Count;
double actualSimilarity = GetActualSimilarity(firstTokens, secondTokens);
return((num - actualSimilarity) / num);
}
public override double GetSimilarity(string firstWord, string secondWord)
{
if (firstWord != null && secondWord != null && _tokenUtilities.CreateMergedSet(_tokeniser.Tokenize(firstWord), _tokeniser.Tokenize(secondWord)).Count > 0)
{
return(_tokenUtilities.CommonSetTerms() / (Math.Pow(_tokenUtilities.FirstSetTokenCount, 0.5) * Math.Pow(_tokenUtilities.SecondSetTokenCount, 0.5)));
}
return(0.0);
}
