public void CompareStringMetrics(string s1, string s2)
{
var consine = new F23.StringSimilarity.Cosine().Distance(s1, s2);
var damerau = new F23.StringSimilarity.Damerau().Distance(s1, s2);
var jaccard = new F23.StringSimilarity.Jaccard().Distance(s1, s2);
var jarodwinkler = new F23.StringSimilarity.JaroWinkler().Distance(s1, s2);
var levenshtein = new F23.StringSimilarity.Levenshtein().Distance(s1, s2);
var lcs = new F23.StringSimilarity.LongestCommonSubsequence().Distance(s1, s2);
var mlcs = new F23.StringSimilarity.MetricLCS().Distance(s1, s2);
var ng = new F23.StringSimilarity.NGram().Distance(s1, s2);
var nl = new F23.StringSimilarity.NormalizedLevenshtein().Distance(s1, s2);
var osa = new F23.StringSimilarity.OptimalStringAlignment().Distance(s1, s2);
var qg = new F23.StringSimilarity.QGram().Distance(s1, s2);
//var ng = new F23.StringSimilarity.ShingleBased
var sd = new F23.StringSimilarity.SorensenDice().Distance(s1, s2);
//var wl = new F23.StringSimilarity.WeightedLevenshtein().Distance
Console.WriteLine("consine score = {0}", consine.ToString());
Console.WriteLine("damerau score = {0}", damerau.ToString());
Console.WriteLine("jaccard score = {0}", jaccard.ToString());
Console.WriteLine("jarodwinkler score = {0}", jarodwinkler.ToString());
Console.WriteLine("levenshtein score = {0}", levenshtein.ToString());
Console.WriteLine("lcs score = {0}", lcs.ToString());
Console.WriteLine("mlcs score = {0}", mlcs.ToString());
Console.WriteLine("ng score = {0}", ng.ToString());
Console.WriteLine("nl score = {0}", nl.ToString());
Console.WriteLine("osa score = {0}", osa.ToString());
Console.WriteLine("qg score = {0}", qg.ToString());
//Console.WriteLine("ng score = {0}",ng.ToString());
Console.WriteLine("sd score = {0}", sd.ToString());
//Console.WriteLine("wl score = {0}",wl.ToString());
}
public double CalculateSimilarity(string source, string target)
{
//F23.StringSimilarity.Cosine cosine = new F23.StringSimilarity.Cosine();
//F23.StringSimilarity.Levenshtein levenshtein = new F23.StringSimilarity.Levenshtein();
F23.StringSimilarity.NGram ngram = new F23.StringSimilarity.NGram();
//F23.StringSimilarity.JaroWinkler jaroWinkler = new F23.StringSimilarity.JaroWinkler();
//double cosineRes = cosine.Distance(source, target);
//double levenshteinRes = (1.0 - ((double)levenshtein.Distance(source, target) / (double)Math.Max(source.Length, target.Length)));
double ngramRes = 1 - ngram.Distance(source, target);
//double jaroWinklerRes = 1- jaroWinkler.Distance(source, target);
if ((source.Length * 2 <= target.Length || target.Length * 2 <= source.Length) &&
(source.Contains(target) || target.Contains(source)))
{
return(2.5);
}
if ((source.Length * 2 <= target.Length || target.Length * 2 <= source.Length) &&
(!source.Contains(target) || !target.Contains(source)))
{
return(0);
}
//if ((source == null) || (target == null)) return 0.0;
//if ((source.Length == 0) || (target.Length == 0)) return 0.0;
//if (source == target) return 1.0;
//int stepsToSame = ComputeLevenshteinDistance(source, target);
if (source.Length == 1 && target == source || target.Length == 1 && target == source)
{
return(1);
}
if (source.Length == 1 && target.Length > 1 || source.Length == 1 && target != source ||
target.Length == 1 && source.Length > 1 || target.Length == 1 && target != source)
{
return(0);
}
//return (1.0 - ((double)stepsToSame / (double)Math.Max(source.Length, target.Length)));
return(ngramRes);
}