public void multilabelSVM()
{
var teacher = new MulticlassSupportVectorLearning <Gaussian>()
{
// Configure the learning algorithm to use SMO to train the
// underlying SVMs in each of the binary class subproblems.
Learner = (param) => new SequentialMinimalOptimization <Gaussian>()
{
// Estimate a suitable guess for the Gaussian kernel's parameters.
// This estimate can serve as a starting point for a grid search.
UseKernelEstimation = true
}
};
// Learn a machine
var machine = teacher.Learn(inputs, outputs);
// Create the multi-class learning algorithm for the machine
var calibration = new MulticlassSupportVectorLearning <Gaussian>()
{
Model = machine, // We will start with an existing machine
// Configure the learning algorithm to use Platt's calibration
Learner = (param) => new ProbabilisticOutputCalibration <Gaussian>()
{
Model = param.Model // Start with an existing machine
}
};
// Configure parallel execution options
calibration.ParallelOptions.MaxDegreeOfParallelism = 1;
// Learn a machine
calibration.Learn(inputs, outputs);
// Obtain class predictions for each sample
int[] predicted = machine.Decide(inputs);
// Get class scores for each sample
double[] scores = machine.Score(inputs);
// Get log-likelihoods (should be same as scores)
double[][] logl = machine.LogLikelihoods(inputs);
// Get probability for each sample
double[][] prob = machine.Probabilities(inputs);
// Compute classification error
double error = new ZeroOneLoss(outputs).Loss(predicted);
double loss = new CategoryCrossEntropyLoss(outputs).Loss(prob);
message += "SVM Validacja\n";
message += "error " + error.ToString() + "\n";
message += "loss " + loss.ToString() + "\n\n";
}
public void Learn_Clicked(object sender, EventArgs args)
{
Task.Factory.StartNew(() =>
{
var bow = CreateBow();
foreach (var image in Images)
{
TrainingData.Add(GetData(image, bow));
}
var kernel = new Polynomial();
var teacher = new MulticlassSupportVectorLearning <IKernel>()
{
Kernel = kernel,
Learner = (param) => new SequentialMinimalOptimization <IKernel>()
{
Kernel = kernel
}
};
var svm = teacher.Learn(TrainingData.ToArray(), Tags.ToArray());
var error = new ZeroOneLoss(Tags.ToArray()).Loss(svm.Decide(TrainingData.ToArray()));
Error.Dispatcher.Invoke(() => Error.Text = error.ToString());
//var kernel = new Polynomial(16, 5);
//var complexity = CalculateComplexity(kernel);
//var ml = new MulticlassSupportVectorLearning<IKernel>()
//{
// Learner = (param) => new SequentialMinimalOptimization<IKernel>()
// {
// Complexity = complexity,
// Kernel = kernel
// }
//};
//machine = ml.Learn(TrainingData.ToArray(), Tags.ToArray());
//var result = machine.Decide(TrainingData.ToArray());
//var error = new ZeroOneLoss(Tags.ToArray())
//{
// Mean = true
//}.Loss(result);
//Error.Dispatcher.Invoke(() => Error.Text = error.ToString());
});
}
