public static Bitmap VisualizeDensity(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
int index = 0;
for (int j = 0; j < PlotSize.Height; j++)
{
for (int i = 0; i < PlotSize.Width; i++)
{
// Map pixel coordinate (i,j) in visualization image back to point in input space
float x = plotCanvas.plotRangeX.Item1 + i * plotCanvas.stepX;
float y = plotCanvas.plotRangeY.Item1 + j * plotCanvas.stepY;
// Aggregate statistics for this sample over all trees
double probability = 0.0;
for (int t = 0; t < forest.TreeCount; t++)
{
int leafIndex = leafNodeIndices[t][index];
probability += forest.GetTree(t).GetNode(leafIndex).TrainingDataStatistics.GaussianAggregator2d.GetPdf().GetProbability(x, y);
}
probability /= forest.TreeCount;
float l = (float)(LuminanceScaleFactor * probability);
if (l < 0)
{
l = 0;
}
else if (l > 255)
{
l = 255;
}
Color c = Color.FromArgb(255, (byte)(l), 0, 0);
result.SetPixel(i, j, c);
index++;
}
}
PaintTrainingData(trainingData, plotCanvas, result);
return(result);
}
static void PaintTrainingData(DataPointCollection trainingData, PlotCanvas plotCanvas, Bitmap result)
{
// First few colours are same as those in the book, remainder are random.
Color[] colors = new Color[Math.Max(trainingData.CountClasses(), 4)];
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);
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));
}
// Also plot the original training data (a little bigger for clarity)
using (Graphics g = Graphics.FromImage(result))
{
g.InterpolationMode = System.Drawing.Drawing2D.InterpolationMode.HighQualityBicubic;
g.SmoothingMode = System.Drawing.Drawing2D.SmoothingMode.HighQuality;
// Paint unlabelled data
for (int s = 0; s < trainingData.Count(); s++)
{
if (trainingData.GetIntegerLabel(s) == DataPointCollection.UnknownClassLabel) // unlabelled
{
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 - 2.0f, x.Y - 2.0f, 4.0f, 4.0f);
g.FillRectangle(new SolidBrush(UnlabelledDataPointColor), rectangle);
g.DrawRectangle(new Pen(Color.Black), rectangle.X, rectangle.Y, rectangle.Width, rectangle.Height);
}
}
// Paint labelled data on top
for (int s = 0; s < trainingData.Count(); s++)
{
if (trainingData.GetIntegerLabel(s) != DataPointCollection.UnknownClassLabel)
{
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 - 5.0f, x.Y - 5.0f, 10.0f, 10.0f);
g.FillRectangle(new SolidBrush(colors[trainingData.GetIntegerLabel(s)]), rectangle);
g.DrawRectangle(new Pen(Color.White, 2), rectangle.X, rectangle.Y, rectangle.Width, rectangle.Height);
}
}
}
}
public static Bitmap Visualize(
Forest <AxisAlignedFeatureResponse, GaussianAggregator2d> 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);
// Compute normalization factors per node
int nTrainingPoints = (int)(trainingData.Count()); // could also count over tree nodes if training data no longer accessible
double[][] normalizationFactors = new double[forest.TreeCount][];
for (int t = 0; t < forest.TreeCount; t++)
{
normalizationFactors[t] = new double[forest.GetTree(t).NodeCount];
ComputeNormalizationFactorsRecurse(forest.GetTree(t), 0, nTrainingPoints, new Bounds(2), normalizationFactors[t]);
}
Bitmap result = new Bitmap(PlotSize.Width, PlotSize.Height);
// Paint the test data
int index = 0;
for (int j = 0; j < PlotSize.Height; j++)
{
for (int i = 0; i < PlotSize.Width; i++)
{
// Map pixel coordinate (i,j) in visualization image back to point in input space
float x = plotCanvas.plotRangeX.Item1 + i * plotCanvas.stepX;
float y = plotCanvas.plotRangeY.Item1 + j * plotCanvas.stepY;
// Aggregate statistics for this sample over all trees
double probability = 0.0;
for (int t = 0; t < forest.TreeCount; t++)
{
int leafIndex = leafNodeIndices[t][index];
probability += normalizationFactors[t][leafIndex] * forest.GetTree(t).GetNode(leafIndex).TrainingDataStatistics.GetPdf().GetProbability(x, y);
}
probability /= forest.TreeCount;
// 'Gamma correct' probability density for better display
float l = (float)(LuminanceScaleFactor * Math.Pow(probability, Gamma));
if (l < 0)
{
l = 0;
}
else if (l > 255)
{
l = 255;
}
Color c = Color.FromArgb(255, (byte)(l), 0, 0);
result.SetPixel(i, j, c);
index++;
}
}
// Also plot 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 - 2.0f, x.Y - 2.0f, 4.0f, 4.0f);
g.FillRectangle(new SolidBrush(DataPointColor), rectangle);
g.DrawRectangle(new Pen(Color.Black), rectangle.X, rectangle.Y, rectangle.Width, rectangle.Height);
}
}
return(result);
}
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 Bitmap Run(DataPointCollection trainingData)
{
// Train the forest
Console.WriteLine("Training the forest...");
Random random = new Random();
ITrainingContext <AxisAlignedFeatureResponse, LinearFitAggregator1d> regressionTrainingContext = new RegressionTrainingContext();
var forest = ForestTrainer <AxisAlignedFeatureResponse, LinearFitAggregator1d> .TrainForest(
random,
TrainingParameters,
regressionTrainingContext,
trainingData);
// Generate some test samples in a grid pattern (a useful basis for creating visualization images)
PlotCanvas plotCanvas = new PlotCanvas(trainingData.GetRange(0), trainingData.GetTargetRange(), PlotSize, PlotDilation);
DataPointCollection testData = DataPointCollection.Generate1dGrid(plotCanvas.plotRangeX, PlotSize.Width);
// Apply the trained forest to the test data
Console.WriteLine("\nApplying the forest to test data...");
int[][] leafNodeIndices = forest.Apply(testData);
#region Generate Visualization Image
Bitmap result = new Bitmap(PlotSize.Width, PlotSize.Height);
// Plot the learned density
Color inverseDensityColor = Color.FromArgb(255, 255 - DensityColor.R, 255 - DensityColor.G, 255 - DensityColor.B);
double[] mean_y_given_x = new double[PlotSize.Width];
int index = 0;
for (int i = 0; i < PlotSize.Width; i++)
{
double totalProbability = 0.0;
for (int j = 0; j < PlotSize.Height; j++)
{
// Map pixel coordinate (i,j) in visualization image back to point in input space
float x = plotCanvas.plotRangeX.Item1 + i * plotCanvas.stepX;
float y = plotCanvas.plotRangeY.Item1 + j * plotCanvas.stepY;
double probability = 0.0;
// Aggregate statistics for this sample over all trees
for (int t = 0; t < forest.TreeCount; t++)
{
Node <AxisAlignedFeatureResponse, LinearFitAggregator1d> leafNodeCopy = forest.GetTree(t).GetNode(leafNodeIndices[t][i]);
LinearFitAggregator1d leafStatistics = leafNodeCopy.TrainingDataStatistics;
probability += leafStatistics.GetProbability(x, y);
}
probability /= forest.TreeCount;
mean_y_given_x[i] += probability * y;
totalProbability += probability;
float scale = 10.0f * (float)probability;
Color weightedColor = Color.FromArgb(
255,
(byte)(Math.Min(scale * inverseDensityColor.R + 0.5f, 255.0f)),
(byte)(Math.Min(scale * inverseDensityColor.G + 0.5f, 255.0f)),
(byte)(Math.Min(scale * inverseDensityColor.B + 0.5f, 255.0f)));
Color c = Color.FromArgb(255, 255 - weightedColor.R, 255 - weightedColor.G, 255 - weightedColor.G);
result.SetPixel(i, j, c);
index++;
}
// NB We don't really compute the mean over y, just over the region of y that is plotted
mean_y_given_x[i] /= totalProbability;
}
// Also plot the mean curve and the original training data
using (Graphics g = Graphics.FromImage(result))
{
g.InterpolationMode = System.Drawing.Drawing2D.InterpolationMode.HighQualityBicubic;
g.SmoothingMode = System.Drawing.Drawing2D.SmoothingMode.HighQuality;
using (Pen meanPen = new Pen(MeanColor, 2))
{
for (int i = 0; i < PlotSize.Width - 1; i++)
{
g.DrawLine(
meanPen,
(float)(i),
(float)((mean_y_given_x[i] - plotCanvas.plotRangeY.Item1) / plotCanvas.stepY),
(float)(i + 1),
(float)((mean_y_given_x[i + 1] - plotCanvas.plotRangeY.Item1) / plotCanvas.stepY));
}
}
using (Brush dataPointBrush = new SolidBrush(DataPointColor))
using (Pen dataPointBorderPen = new Pen(DataPointBorderColor))
{
for (int s = 0; s < trainingData.Count(); s++)
{
// Map sample coordinate back to a pixel coordinate in the visualization image
PointF x = new PointF(
(trainingData.GetDataPoint(s)[0] - plotCanvas.plotRangeX.Item1) / plotCanvas.stepX,
(trainingData.GetTarget(s) - plotCanvas.plotRangeY.Item1) / plotCanvas.stepY);
RectangleF rectangle = new RectangleF(x.X - 2.0f, x.Y - 2.0f, 4.0f, 4.0f);
g.FillRectangle(dataPointBrush, rectangle);
g.DrawRectangle(dataPointBorderPen, rectangle.X, rectangle.Y, rectangle.Width, rectangle.Height);
}
}
}
return(result);
#endregion
}
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);
}