public static Bitmap Visualize <F>(
            Forest <F, HistogramAggregator> forest,
            DataPointCollection trainingData,
            Size PlotSize,
            PointF PlotDilation) where F : IFeatureResponse
        {
            // Size PlotSize = new Size(300, 300), PointF PlotDilation = new PointF(0.1f, 0.1f)
            // Generate some test samples in a grid pattern (a useful basis for creating visualization images)
            PlotCanvas plotCanvas = new PlotCanvas(trainingData.GetRange(0), trainingData.GetRange(1), PlotSize, PlotDilation);

            DataPointCollection testData = DataPointCollection.Generate2dGrid(plotCanvas.plotRangeX, PlotSize.Width, plotCanvas.plotRangeY, PlotSize.Height);

            Console.WriteLine("\nApplying the forest to test data...");
            int[][] leafNodeIndices = forest.Apply(testData);

            // Form a palette of random colors, one per class
            Color[] colors = new Color[Math.Max(trainingData.CountClasses(), 4)];

            // First few colours are same as those in the book, remainder are random.
            colors[0] = Color.FromArgb(183, 170, 8);
            colors[1] = Color.FromArgb(194, 32, 14);
            colors[2] = Color.FromArgb(4, 154, 10);
            colors[3] = Color.FromArgb(13, 26, 188);

            Color grey = Color.FromArgb(255, 127, 127, 127);

            System.Random r = new Random(0); // same seed every time so colours will be consistent
            for (int c = 4; c < colors.Length; c++)
            {
                colors[c] = Color.FromArgb(255, r.Next(0, 255), r.Next(0, 255), r.Next(0, 255));
            }

            // Create a visualization image
            Bitmap result = new Bitmap(PlotSize.Width, PlotSize.Height);

            // For each pixel...
            int index = 0;

            for (int j = 0; j < PlotSize.Height; j++)
            {
                for (int i = 0; i < PlotSize.Width; i++)
                {
                    // Aggregate statistics for this sample over all leaf nodes reached
                    HistogramAggregator h = new HistogramAggregator(trainingData.CountClasses());
                    for (int t = 0; t < forest.TreeCount; t++)
                    {
                        int leafIndex = leafNodeIndices[t][index];
                        h.Aggregate(forest.GetTree(t).GetNode(leafIndex).TrainingDataStatistics);
                    }

                    // Let's muddy the colors with grey where the entropy is high.
                    float mudiness = 0.5f * (float)(h.Entropy());

                    float R = 0.0f, G = 0.0f, B = 0.0f;

                    for (int b = 0; b < trainingData.CountClasses(); b++)
                    {
                        float p = (1.0f - mudiness) * h.GetProbability(b); // NB probabilities sum to 1.0 over the classes

                        R += colors[b].R * p;
                        G += colors[b].G * p;
                        B += colors[b].B * p;
                    }

                    R += grey.R * mudiness;
                    G += grey.G * mudiness;
                    B += grey.B * mudiness;

                    Color c = Color.FromArgb(255, (byte)(R), (byte)(G), (byte)(B));

                    result.SetPixel(i, j, c); // painfully slow but safe

                    index++;
                }
            }

            // Also draw the original training data
            using (Graphics g = Graphics.FromImage(result))
            {
                g.InterpolationMode = System.Drawing.Drawing2D.InterpolationMode.HighQualityBicubic;
                g.SmoothingMode     = System.Drawing.Drawing2D.SmoothingMode.HighQuality;

                for (int s = 0; s < trainingData.Count(); s++)
                {
                    PointF x = new PointF(
                        (trainingData.GetDataPoint(s)[0] - plotCanvas.plotRangeX.Item1) / plotCanvas.stepX,
                        (trainingData.GetDataPoint(s)[1] - plotCanvas.plotRangeY.Item1) / plotCanvas.stepY);

                    RectangleF rectangle = new RectangleF(x.X - 3.0f, x.Y - 3.0f, 6.0f, 6.0f);
                    g.FillRectangle(new SolidBrush(colors[trainingData.GetIntegerLabel(s)]), rectangle);
                    g.DrawRectangle(new Pen(Color.Black), rectangle.X, rectangle.Y, rectangle.Width, rectangle.Height);
                }
            }

            return(result);
        }
        public static Bitmap VisualizeLabels(Forest <LinearFeatureResponse2d, SemiSupervisedClassificationStatisticsAggregator> forest, DataPointCollection trainingData, Size PlotSize, PointF PlotDilation)
        {
            // Generate some test samples in a grid pattern (a useful basis for creating visualization images)
            PlotCanvas plotCanvas = new PlotCanvas(trainingData.GetRange(0), trainingData.GetRange(1), PlotSize, PlotDilation);

            // Apply the trained forest to the test data
            Console.WriteLine("\nApplying the forest to test data...");

            DataPointCollection testData = DataPointCollection.Generate2dGrid(plotCanvas.plotRangeX, PlotSize.Width, plotCanvas.plotRangeY, PlotSize.Height);

            int[][] leafNodeIndices = forest.Apply(testData);

            Bitmap result = new Bitmap(PlotSize.Width, PlotSize.Height);

            // Paint the test data
            GaussianPdf2d[][] leafDistributions = new GaussianPdf2d[forest.TreeCount][];
            for (int t = 0; t < forest.TreeCount; t++)
            {
                leafDistributions[t] = new GaussianPdf2d[forest.GetTree(t).NodeCount];
                for (int i = 0; i < forest.GetTree(t).NodeCount; i++)
                {
                    Node <LinearFeatureResponse2d, SemiSupervisedClassificationStatisticsAggregator> nodeCopy = forest.GetTree(t).GetNode(i);

                    if (nodeCopy.IsLeaf)
                    {
                        leafDistributions[t][i] = nodeCopy.TrainingDataStatistics.GaussianAggregator2d.GetPdf();
                    }
                }
            }

            // Form a palette of random colors, one per class
            Color[] colors = new Color[Math.Max(trainingData.CountClasses(), 4)];

            // First few colours are same as those in the book, remainder are random.
            colors[0] = Color.FromArgb(183, 170, 8);
            colors[1] = Color.FromArgb(194, 32, 14);
            colors[2] = Color.FromArgb(4, 154, 10);
            colors[3] = Color.FromArgb(13, 26, 188);

            Color grey = Color.FromArgb(255, 127, 127, 127);

            System.Random r = new Random(0); // same seed every time so colours will be consistent
            for (int c = 4; c < colors.Length; c++)
            {
                colors[c] = Color.FromArgb(255, r.Next(0, 255), r.Next(0, 255), r.Next(0, 255));
            }

            int index = 0;

            for (int j = 0; j < PlotSize.Height; j++)
            {
                for (int i = 0; i < PlotSize.Width; i++)
                {
                    // Aggregate statistics for this sample over all leaf nodes reached
                    HistogramAggregator h = new HistogramAggregator(trainingData.CountClasses());
                    for (int t = 0; t < forest.TreeCount; t++)
                    {
                        int leafIndex = leafNodeIndices[t][index];

                        SemiSupervisedClassificationStatisticsAggregator a = forest.GetTree(t).GetNode(leafIndex).TrainingDataStatistics;

                        h.Aggregate(a.HistogramAggregator);
                    }

                    // Let's muddy the colors with a little grey where entropy is high.
                    float mudiness = 0.5f * (float)(h.Entropy());

                    float R = 0.0f, G = 0.0f, B = 0.0f;

                    for (int b = 0; b < trainingData.CountClasses(); b++)
                    {
                        float p = (1.0f - mudiness) * h.GetProbability(b); // NB probabilities sum to 1.0 over the classes

                        R += colors[b].R * p;
                        G += colors[b].G * p;
                        B += colors[b].B * p;
                    }

                    R += grey.R * mudiness;
                    G += grey.G * mudiness;
                    B += grey.B * mudiness;

                    Color c = Color.FromArgb(255, (byte)(R), (byte)(G), (byte)(B));

                    result.SetPixel(i, j, c);

                    index++;
                }
            }

            PaintTrainingData(trainingData, plotCanvas, result);

            return(result);
        }