static void DisplayHelp()
{
// Create a dummy command line parser so we can display command line
// help in the usual format.
EnumParameter mode = new EnumParameter(
"mode",
"Select mode of operation.",
new string[] { "clas", "density", "regression", "ssclas" },
new string[] { "Supervised 2D classfication", "2D density estimation", "1D to 1D regression", "Semi-supervised 2D classification" },
null);
StringParameter args = new StringParameter("args...", "Other mode-specific arguments");
CommandLineParser parser = new CommandLineParser();
parser.Command = "SW";
parser.AddArgument(mode);
parser.AddArgument(args);
Console.WriteLine(
@"Sherwood decision forest library demos.
");
parser.PrintHelp();
Console.WriteLine(
@"
To get more help on a particular mode of operation, omit the arguments, e.g.
sw density
");
}
static void Main(string[] args)
{
if (args.Length == 0 || args[0] == "/?" || args[0].ToLower() == "help")
{
DisplayHelp();
return;
}
// These command line parameters are reused over several command line modes...
StringParameter trainingDataPath = new StringParameter("path", "Path of file containing training data.");
NaturalParameter T = new NaturalParameter("t", "No. of trees in the forest (default = {0}).", 10);
NaturalParameter D = new NaturalParameter("d", "Maximum tree levels (default = {0}).", 10, 20);
NaturalParameter F = new NaturalParameter("f", "No. of candidate feature responses per decision node (default = {0}).", 10);
NaturalParameter L = new NaturalParameter("l", "No. of candidate thresholds per feature response (default = {0}).", 1);
SingleParameter a = new SingleParameter("a", "The number of 'effective' prior observations (default = {0}).", true, false, 10.0f);
SingleParameter b = new SingleParameter("b", "The variance of the effective observations (default = {0}).", true, true, 400.0f);
SimpleSwitchParameter verboseSwitch = new SimpleSwitchParameter("Enables verbose progress indication.");
SingleParameter plotPaddingX = new SingleParameter("padx", "Pad plot horizontally (default = {0}).", true, false, 0.1f);
SingleParameter plotPaddingY = new SingleParameter("pady", "Pad plot vertically (default = {0}).", true, false, 0.1f);
EnumParameter split = new EnumParameter(
"s",
"Specify what kind of split function to use (default = {0}).",
new string[] { "axis", "linear" },
new string[] { "axis-aligned split", "linear split" },
"axis");
// Behaviour depends on command line mode...
string mode = args[0].ToLower(); // first argument defines the command line mode
if (mode == "clas" || mode == "class")
{
#region Supervised classification
CommandLineParser parser = new CommandLineParser();
parser.Command = "SW " + mode.ToUpper();
parser.AddArgument(trainingDataPath);
parser.AddSwitch("T", T);
parser.AddSwitch("D", D);
parser.AddSwitch("F", F);
parser.AddSwitch("L", L);
parser.AddSwitch("SPLIT", split);
parser.AddSwitch("PADX", plotPaddingX);
parser.AddSwitch("PADY", plotPaddingY);
parser.AddSwitch("VERBOSE", verboseSwitch);
// Default values up above should be fine here.
if (args.Length == 1)
{
parser.PrintHelp();
DisplayTextFiles(CLAS_DATA_PATH);
return;
}
if (parser.Parse(args, 1) == false)
{
return;
}
TrainingParameters trainingParameters = new TrainingParameters()
{
MaxDecisionLevels = D.Value - 1,
NumberOfCandidateFeatures = F.Value,
NumberOfCandidateThresholdsPerFeature = L.Value,
NumberOfTrees = T.Value,
Verbose = verboseSwitch.Used
};
PointF plotDilation = new PointF(plotPaddingX.Value, plotPaddingY.Value);
DataPointCollection trainingData = LoadTrainingData(
trainingDataPath.Value,
CLAS_DATA_PATH,
2,
DataDescriptor.HasClassLabels);
if (split.Value == "linear")
{
Forest <LinearFeatureResponse2d, HistogramAggregator> forest = ClassificationExample.Train(
trainingData,
new LinearFeatureFactory(),
trainingParameters);
using (Bitmap result = ClassificationExample.Visualize(forest, trainingData, new Size(300, 300), plotDilation))
{
ShowVisualizationImage(result);
}
}
else if (split.Value == "axis")
{
Forest <AxisAlignedFeatureResponse, HistogramAggregator> forest = ClassificationExample.Train(
trainingData,
new AxisAlignedFeatureFactory(),
trainingParameters);
using (Bitmap result = ClassificationExample.Visualize(forest, trainingData, new Size(300, 300), plotDilation))
{
ShowVisualizationImage(result);
}
}
#endregion
}
else if (mode == "density")
{
#region Density Estimation
CommandLineParser parser = new CommandLineParser();
parser.Command = "SW " + mode.ToUpper();
parser.AddArgument(trainingDataPath);
parser.AddSwitch("T", T);
parser.AddSwitch("D", D);
parser.AddSwitch("F", F);
parser.AddSwitch("L", L);
// For density estimation (and semi-supervised learning) we add
// a command line option to set the hyperparameters of the prior.
parser.AddSwitch("a", a);
parser.AddSwitch("b", b);
parser.AddSwitch("PADX", plotPaddingX);
parser.AddSwitch("PADY", plotPaddingY);
parser.AddSwitch("VERBOSE", verboseSwitch);
// Override default values for command line options.
T.Value = 1;
D.Value = 3;
F.Value = 5;
L.Value = 1;
a.Value = 0;
b.Value = 900;
if (args.Length == 1)
{
parser.PrintHelp();
DisplayTextFiles(DENSITY_DATA_PATH);
return;
}
if (parser.Parse(args, 1) == false)
{
return;
}
TrainingParameters parameters = new TrainingParameters()
{
MaxDecisionLevels = D.Value - 1,
NumberOfCandidateFeatures = F.Value,
NumberOfCandidateThresholdsPerFeature = L.Value,
NumberOfTrees = T.Value,
Verbose = verboseSwitch.Used
};
DataPointCollection trainingData = LoadTrainingData(
trainingDataPath.Value,
DENSITY_DATA_PATH,
2,
DataDescriptor.Unadorned);
Forest <AxisAlignedFeatureResponse, GaussianAggregator2d> forest = DensityEstimationExample.Train(trainingData, parameters, a.Value, b.Value);
PointF plotDilation = new PointF(plotPaddingX.Value, plotPaddingY.Value);
using (Bitmap result = DensityEstimationExample.Visualize(forest, trainingData, new Size(300, 300), plotDilation))
{
ShowVisualizationImage(result);
}
#endregion
}
else if (mode == "ssclas" || mode == "ssclas")
{
#region Semi-supervised classification
CommandLineParser parser = new CommandLineParser();
parser.Command = "SW " + mode.ToUpper();
parser.AddArgument(trainingDataPath);
parser.AddSwitch("T", T);
parser.AddSwitch("D", D);
parser.AddSwitch("F", F);
parser.AddSwitch("L", L);
parser.AddSwitch("split", split);
parser.AddSwitch("a", a);
parser.AddSwitch("b", b);
EnumParameter plotMode = new EnumParameter(
"plot",
"Determines what to plot",
new string[] { "density", "labels" },
new string[] { "plot recovered density estimate", "plot class likelihood" },
"labels");
parser.AddSwitch("plot", plotMode);
parser.AddSwitch("PADX", plotPaddingX);
parser.AddSwitch("PADY", plotPaddingY);
parser.AddSwitch("VERBOSE", verboseSwitch);
// Override default values for command line options.
T.Value = 10;
D.Value = 12 - 1;
F.Value = 30;
L.Value = 1;
if (args.Length == 1)
{
parser.PrintHelp();
DisplayTextFiles(SSCLAS_DATA_PATH);
return;
}
if (parser.Parse(args, 1) == false)
{
return;
}
DataPointCollection trainingData = LoadTrainingData(
trainingDataPath.Value,
SSCLAS_DATA_PATH,
2,
DataDescriptor.HasClassLabels);
TrainingParameters parameters = new TrainingParameters()
{
MaxDecisionLevels = D.Value - 1,
NumberOfCandidateFeatures = F.Value,
NumberOfCandidateThresholdsPerFeature = L.Value,
NumberOfTrees = T.Value,
Verbose = verboseSwitch.Used
};
Forest <LinearFeatureResponse2d, SemiSupervisedClassificationStatisticsAggregator> forest = SemiSupervisedClassificationExample.Train(
trainingData, parameters, a.Value, b.Value);
PointF plotPadding = new PointF(plotPaddingX.Value, plotPaddingY.Value);
if (plotMode.Value == "labels")
{
using (Bitmap result = SemiSupervisedClassificationExample.VisualizeLabels(forest, trainingData, new Size(300, 300), plotPadding))
{
ShowVisualizationImage(result);
}
}
else if (plotMode.Value == "density")
{
using (Bitmap result = SemiSupervisedClassificationExample.VisualizeDensity(forest, trainingData, new Size(300, 300), plotPadding))
{
ShowVisualizationImage(result);
}
}
#endregion
}
else if (mode == "regression")
{
#region Regression
CommandLineParser parser = new CommandLineParser();
parser.Command = "SW " + mode.ToUpper();
parser.AddArgument(trainingDataPath);
parser.AddSwitch("T", T);
parser.AddSwitch("D", D);
parser.AddSwitch("F", F);
parser.AddSwitch("L", L);
parser.AddSwitch("PADX", plotPaddingX);
parser.AddSwitch("PADY", plotPaddingY);
parser.AddSwitch("VERBOSE", verboseSwitch);
// Override default values for command line options
T.Value = 10;
D.Value = 2;
a.Value = 0; // prior turned off by default
b.Value = 900;
if (args.Length == 1)
{
parser.PrintHelp();
DisplayTextFiles(REGRESSION_DATA_PATH);
return;
}
if (parser.Parse(args, 1) == false)
{
return;
}
RegressionExample regressionDemo = new RegressionExample();
regressionDemo.PlotDilation.X = plotPaddingX.Value;
regressionDemo.PlotDilation.Y = plotPaddingY.Value;
regressionDemo.TrainingParameters = new TrainingParameters()
{
MaxDecisionLevels = D.Value - 1,
NumberOfCandidateFeatures = F.Value,
NumberOfCandidateThresholdsPerFeature = L.Value,
NumberOfTrees = T.Value,
Verbose = verboseSwitch.Used
};
DataPointCollection trainingData = LoadTrainingData(
trainingDataPath.Value,
REGRESSION_DATA_PATH,
1,
DataDescriptor.HasTargetValues);
using (Bitmap result = regressionDemo.Run(trainingData))
{
ShowVisualizationImage(result);
}
#endregion
}
else
{
Console.WriteLine("Unrecognized command line argument, try SW HELP.");
return;
}
}
static void Main(string[] args)
{
if (args.Length == 0 || args[0] == "/?" || args[0].ToLower() == "help")
{
DisplayHelp();
return;
}
// These command line parameters are reused over several command line modes...
StringParameter trainingDataPath = new StringParameter("path", "Path of file containing training data.");
NaturalParameter T = new NaturalParameter("t", "No. of trees in the forest (default = {0}).", 10);
NaturalParameter D = new NaturalParameter("d", "Maximum tree levels (default = {0}).", 10, 20);
NaturalParameter F = new NaturalParameter("f", "No. of candidate feature responses per decision node (default = {0}).", 10);
NaturalParameter L = new NaturalParameter("l", "No. of candidate thresholds per feature response (default = {0}).", 1);
SingleParameter a = new SingleParameter("a", "The number of 'effective' prior observations (default = {0}).", true, false, 10.0f);
SingleParameter b = new SingleParameter("b", "The variance of the effective observations (default = {0}).", true, true, 400.0f);
SimpleSwitchParameter verboseSwitch = new SimpleSwitchParameter("Enables verbose progress indication.");
SingleParameter plotPaddingX = new SingleParameter("padx", "Pad plot horizontally (default = {0}).", true, false, 0.1f);
SingleParameter plotPaddingY = new SingleParameter("pady", "Pad plot vertically (default = {0}).", true, false, 0.1f);
EnumParameter split = new EnumParameter(
"s",
"Specify what kind of split function to use (default = {0}).",
new string[] { "axis", "linear" },
new string[] { "axis-aligned split", "linear split" },
"axis");
// Behaviour depends on command line mode...
string mode = args[0].ToLower(); // first argument defines the command line mode
if (mode == "clas" || mode == "class")
{
#region Supervised classification
CommandLineParser parser = new CommandLineParser();
parser.Command = "SW " + mode.ToUpper();
parser.AddArgument(trainingDataPath);
parser.AddSwitch("T", T);
parser.AddSwitch("D", D);
parser.AddSwitch("F", F);
parser.AddSwitch("L", L);
parser.AddSwitch("SPLIT", split);
parser.AddSwitch("PADX", plotPaddingX);
parser.AddSwitch("PADY", plotPaddingY);
parser.AddSwitch("VERBOSE", verboseSwitch);
// Default values up above should be fine here.
if (args.Length == 1)
{
parser.PrintHelp();
DisplayTextFiles(CLAS_DATA_PATH);
return;
}
if (parser.Parse(args, 1) == false)
return;
TrainingParameters trainingParameters = new TrainingParameters()
{
MaxDecisionLevels = D.Value - 1,
NumberOfCandidateFeatures = F.Value,
NumberOfCandidateThresholdsPerFeature = L.Value,
NumberOfTrees = T.Value,
Verbose = verboseSwitch.Used
};
PointF plotDilation = new PointF(plotPaddingX.Value, plotPaddingY.Value);
DataPointCollection trainingData = LoadTrainingData(
trainingDataPath.Value,
CLAS_DATA_PATH,
2,
DataDescriptor.HasClassLabels);
if (split.Value == "linear")
{
Forest<LinearFeatureResponse2d, HistogramAggregator> forest = ClassificationExample.Train(
trainingData,
new LinearFeatureFactory(),
trainingParameters);
using (Bitmap result = ClassificationExample.Visualize(forest, trainingData, new Size(300, 300), plotDilation))
{
ShowVisualizationImage(result);
}
}
else if (split.Value == "axis")
{
Forest<AxisAlignedFeatureResponse, HistogramAggregator> forest = ClassificationExample.Train(
trainingData,
new AxisAlignedFeatureFactory(),
trainingParameters);
using (Bitmap result = ClassificationExample.Visualize(forest, trainingData, new Size(300, 300), plotDilation))
{
ShowVisualizationImage(result);
}
}
#endregion
}
else if (mode == "density")
{
#region Density Estimation
CommandLineParser parser = new CommandLineParser();
parser.Command = "SW " + mode.ToUpper();
parser.AddArgument(trainingDataPath);
parser.AddSwitch("T", T);
parser.AddSwitch("D", D);
parser.AddSwitch("F", F);
parser.AddSwitch("L", L);
// For density estimation (and semi-supervised learning) we add
// a command line option to set the hyperparameters of the prior.
parser.AddSwitch("a", a);
parser.AddSwitch("b", b);
parser.AddSwitch("PADX", plotPaddingX);
parser.AddSwitch("PADY", plotPaddingY);
parser.AddSwitch("VERBOSE", verboseSwitch);
// Override default values for command line options.
T.Value = 1;
D.Value = 3;
F.Value = 5;
L.Value = 1;
a.Value = 0;
b.Value = 900;
if (args.Length == 1)
{
parser.PrintHelp();
DisplayTextFiles(DENSITY_DATA_PATH);
return;
}
if (parser.Parse(args, 1) == false)
return;
TrainingParameters parameters = new TrainingParameters()
{
MaxDecisionLevels = D.Value - 1,
NumberOfCandidateFeatures = F.Value,
NumberOfCandidateThresholdsPerFeature = L.Value,
NumberOfTrees = T.Value,
Verbose = verboseSwitch.Used
};
DataPointCollection trainingData = LoadTrainingData(
trainingDataPath.Value,
DENSITY_DATA_PATH,
2,
DataDescriptor.Unadorned);
Forest<AxisAlignedFeatureResponse, GaussianAggregator2d> forest = DensityEstimationExample.Train(trainingData, parameters, a.Value, b.Value);
PointF plotDilation = new PointF(plotPaddingX.Value, plotPaddingY.Value);
using (Bitmap result = DensityEstimationExample.Visualize(forest, trainingData, new Size(300, 300), plotDilation))
{
ShowVisualizationImage(result);
}
#endregion
}
else if (mode == "ssclas" || mode == "ssclas")
{
#region Semi-supervised classification
CommandLineParser parser = new CommandLineParser();
parser.Command = "SW " + mode.ToUpper();
parser.AddArgument(trainingDataPath);
parser.AddSwitch("T", T);
parser.AddSwitch("D", D);
parser.AddSwitch("F", F);
parser.AddSwitch("L", L);
parser.AddSwitch("split", split);
parser.AddSwitch("a", a);
parser.AddSwitch("b", b);
EnumParameter plotMode = new EnumParameter(
"plot",
"Determines what to plot",
new string[] { "density", "labels" },
new string[] { "plot recovered density estimate", "plot class likelihood" },
"labels");
parser.AddSwitch("plot", plotMode);
parser.AddSwitch("PADX", plotPaddingX);
parser.AddSwitch("PADY", plotPaddingY);
parser.AddSwitch("VERBOSE", verboseSwitch);
// Override default values for command line options.
T.Value = 10;
D.Value = 12 - 1;
F.Value = 30;
L.Value = 1;
if (args.Length == 1)
{
parser.PrintHelp();
DisplayTextFiles(SSCLAS_DATA_PATH);
return;
}
if (parser.Parse(args, 1) == false)
return;
DataPointCollection trainingData = LoadTrainingData(
trainingDataPath.Value,
SSCLAS_DATA_PATH,
2,
DataDescriptor.HasClassLabels);
TrainingParameters parameters = new TrainingParameters()
{
MaxDecisionLevels = D.Value - 1,
NumberOfCandidateFeatures = F.Value,
NumberOfCandidateThresholdsPerFeature = L.Value,
NumberOfTrees = T.Value,
Verbose = verboseSwitch.Used
};
Forest<LinearFeatureResponse2d, SemiSupervisedClassificationStatisticsAggregator> forest = SemiSupervisedClassificationExample.Train(
trainingData, parameters, a.Value, b.Value);
PointF plotPadding = new PointF(plotPaddingX.Value, plotPaddingY.Value);
if (plotMode.Value == "labels")
{
using (Bitmap result = SemiSupervisedClassificationExample.VisualizeLabels(forest, trainingData, new Size(300, 300), plotPadding))
{
ShowVisualizationImage(result);
}
}
else if (plotMode.Value == "density")
{
using (Bitmap result = SemiSupervisedClassificationExample.VisualizeDensity(forest, trainingData, new Size(300, 300), plotPadding))
{
ShowVisualizationImage(result);
}
}
#endregion
}
else if (mode == "regression")
{
#region Regression
CommandLineParser parser = new CommandLineParser();
parser.Command = "SW " + mode.ToUpper();
parser.AddArgument(trainingDataPath);
parser.AddSwitch("T", T);
parser.AddSwitch("D", D);
parser.AddSwitch("F", F);
parser.AddSwitch("L", L);
parser.AddSwitch("PADX", plotPaddingX);
parser.AddSwitch("PADY", plotPaddingY);
parser.AddSwitch("VERBOSE", verboseSwitch);
// Override default values for command line options
T.Value = 10;
D.Value = 2;
a.Value = 0; // prior turned off by default
b.Value = 900;
if (args.Length == 1)
{
parser.PrintHelp();
DisplayTextFiles(REGRESSION_DATA_PATH);
return;
}
if (parser.Parse(args, 1) == false)
return;
RegressionExample regressionDemo = new RegressionExample();
regressionDemo.PlotDilation.X = plotPaddingX.Value;
regressionDemo.PlotDilation.Y = plotPaddingY.Value;
regressionDemo.TrainingParameters = new TrainingParameters()
{
MaxDecisionLevels = D.Value - 1,
NumberOfCandidateFeatures = F.Value,
NumberOfCandidateThresholdsPerFeature = L.Value,
NumberOfTrees = T.Value,
Verbose = verboseSwitch.Used
};
DataPointCollection trainingData = LoadTrainingData(
trainingDataPath.Value,
REGRESSION_DATA_PATH,
1,
DataDescriptor.HasTargetValues);
using (Bitmap result = regressionDemo.Run(trainingData))
{
ShowVisualizationImage(result);
}
#endregion
}
else
{
Console.WriteLine("Unrecognized command line argument, try SW HELP.");
return;
}
}