예제 #1
0
        public void GetSimilarity_TwoDifferentVectors_ExpectedResult()
        {
            JaccardSimilarity js = new JaccardSimilarity();
            Article           x  = new Article();
            Article           y  = new Article();

            x.Vector[1] = 1;
            x.Vector[2] = 3;
            x.Vector[3] = 5;

            y.Vector[1] = 2;
            y.Vector[2] = 4;
            y.Vector[5] = 3;

            double expected = 0.28571428571428571428571428571429;
            double actual   = js.GetSimilarity(x, y);

            Assert.IsTrue(Math.Abs(expected - actual) < 0.001);
        }
예제 #2
0
        public static double ApproximatelyEquals(this string firstWord, string secondWord, SimMetricType simMetricType = SimMetricType.Levenstein)
        {
            switch (simMetricType)
            {
            case SimMetricType.BlockDistance:
                var sim2 = new BlockDistance();
                return(sim2.GetSimilarity(firstWord, secondWord));

            case SimMetricType.ChapmanLengthDeviation:
                var sim3 = new ChapmanLengthDeviation();
                return(sim3.GetSimilarity(firstWord, secondWord));

            case SimMetricType.CosineSimilarity:
                var sim4 = new CosineSimilarity();
                return(sim4.GetSimilarity(firstWord, secondWord));

            case SimMetricType.DiceSimilarity:
                var sim5 = new DiceSimilarity();
                return(sim5.GetSimilarity(firstWord, secondWord));

            case SimMetricType.EuclideanDistance:
                var sim6 = new EuclideanDistance();
                return(sim6.GetSimilarity(firstWord, secondWord));

            case SimMetricType.JaccardSimilarity:
                var sim7 = new JaccardSimilarity();
                return(sim7.GetSimilarity(firstWord, secondWord));

            case SimMetricType.Jaro:
                var sim8 = new Jaro();
                return(sim8.GetSimilarity(firstWord, secondWord));

            case SimMetricType.JaroWinkler:
                var sim9 = new JaroWinkler();
                return(sim9.GetSimilarity(firstWord, secondWord));

            case SimMetricType.MatchingCoefficient:
                var sim10 = new MatchingCoefficient();
                return(sim10.GetSimilarity(firstWord, secondWord));

            case SimMetricType.MongeElkan:
                var sim11 = new MongeElkan();
                return(sim11.GetSimilarity(firstWord, secondWord));

            case SimMetricType.NeedlemanWunch:
                var sim12 = new NeedlemanWunch();
                return(sim12.GetSimilarity(firstWord, secondWord));

            case SimMetricType.OverlapCoefficient:
                var sim13 = new OverlapCoefficient();
                return(sim13.GetSimilarity(firstWord, secondWord));

            case SimMetricType.QGramsDistance:
                var sim14 = new QGramsDistance();
                return(sim14.GetSimilarity(firstWord, secondWord));

            case SimMetricType.SmithWaterman:
                var sim15 = new SmithWaterman();
                return(sim15.GetSimilarity(firstWord, secondWord));

            case SimMetricType.SmithWatermanGotoh:
                var sim16 = new SmithWatermanGotoh();
                return(sim16.GetSimilarity(firstWord, secondWord));

            case SimMetricType.SmithWatermanGotohWindowedAffine:
                var sim17 = new SmithWatermanGotohWindowedAffine();
                return(sim17.GetSimilarity(firstWord, secondWord));

            case SimMetricType.ChapmanMeanLength:
                var sim18 = new ChapmanMeanLength();
                return(sim18.GetSimilarity(firstWord, secondWord));

            default:
                var sim1 = new Levenstein();
                return(sim1.GetSimilarity(firstWord, secondWord));
            }
        }