コード例 #1
0
        Plot CreateIntraClusterPlot(ModelAnalyzer analyzer)
        {
            Plot intraClusterPlot = new Plot();

            int pointsCount = analyzer.AnalyzedSongs.Count;

            double[] xValues = new double[pointsCount];
            double[] yValues = new double[pointsCount];

            int currentClusterIndex = analyzer.AnalyzedSongs[0].ClusterIndex;

            for (int i = 0; i < pointsCount; i++)
            {
                AnalyzedSong point = analyzer.AnalyzedSongs[i];

                xValues[i] = i;
                yValues[i] = point.CentroidDistance;

                if (currentClusterIndex != point.ClusterIndex)
                {
                    double value = (i + (i - 1)) / 2.0;
                    string label = $"cluster {currentClusterIndex}";
                    intraClusterPlot.PlotVLine(value, VerticalDividerColor, label: label);
                }

                currentClusterIndex = point.ClusterIndex;
            }

            intraClusterPlot.PlotScatter(xValues, yValues, IntraClusterDistanceColor);
            intraClusterPlot.PlotHLine(analyzer.IntraClusterMeanDistance, IntraClustersMeanDistanceColor);

            return(intraClusterPlot);
        }
コード例 #2
0
        void AnalyzeIntraClusterDistance()
        {
            List <Song> centroids = Model.Centroids;

            IntraClusterDistances    = new List <double>(centroids.Capacity);
            IntraClusterMeanDistance = 0.0;

            List <int> clusterCounts = new List <int>(centroids.Capacity);
            int        songsCount    = 0;

            for (int i = 0; i < centroids.Count; i++)
            {
                IntraClusterDistances.Add(0.0);
                clusterCounts.Add(0);
            }

            AnalyzedSongs = Model.ClusterizedSongs.Select(song =>
            {
                double distance = Model.DistanceFunc.GetDistance(song.Song, centroids[song.ClusterIndex]);
                int index       = song.ClusterIndex;

                AnalyzedSong analyzedSong = new AnalyzedSong
                {
                    AssociatedSong   = song.Song,
                    ClusterIndex     = index,
                    CentroidDistance = distance,
                };

                IntraClusterDistances[index] += distance;
                IntraClusterMeanDistance     += distance;

                clusterCounts[index] += 1;
                songsCount           += 1;

                return(analyzedSong);
            }).ToList();

            for (int i = 0; i < IntraClusterDistances.Count; i++)
            {
                IntraClusterDistances[i] /= clusterCounts[i];
            }

            IntraClusterMeanDistance /= songsCount;
        }