public double ComputeError(TInput[] inputs, int[] expectedOutputs)
{
var classifier = (IClassifier <TInput, bool>)Model;
var loss = new ZeroOneLoss(Classes.Decide(expectedOutputs))
{
Mean = true
};
return(loss.Loss(classifier.Decide(inputs)));
}
public void kaggle_digits_with_compress()
{
string root = TestContext.CurrentContext.TestDirectory;
var training = Properties.Resources.trainingsample;
var validation = Properties.Resources.validationsample;
var tset = readData(training);
var observations = tset.Item1;
var labels = tset.Item2;
var teacher = new MulticlassSupportVectorLearning <Linear>();
#if MONO
teacher.ParallelOptions.MaxDegreeOfParallelism = 1;
#endif
var svm = teacher.Learn(observations, labels);
Assert.AreEqual(50, svm.Models[0][0].SupportVectors.Length);
Assert.AreEqual(127, svm.Models[1][0].SupportVectors.Length);
svm.Compress();
Assert.AreEqual(1, svm.Models[0][0].SupportVectors.Length);
Assert.AreEqual(1, svm.Models[1][0].SupportVectors.Length);
{
var trainingLoss = new ZeroOneLoss(labels)
{
Mean = true
};
double error = trainingLoss.Loss(svm.Decide(observations));
Assert.AreEqual(0.054, error);
}
{
var vset = readData(validation);
var validationData = vset.Item1;
var validationLabels = vset.Item2;
var validationLoss = new ZeroOneLoss(validationLabels)
{
Mean = true
};
double val = validationLoss.Loss(svm.Decide(validationData));
Assert.AreEqual(0.082, val);
}
}
public void kaggle_digits_old_style()
{
string root = Environment.CurrentDirectory;
var training = Properties.Resources.trainingsample;
var validation = Properties.Resources.validationsample;
var tset = readData(training);
var observations = tset.Item1;
var labels = tset.Item2;
var teacher = new MulticlassSupportVectorLearning();
#if MONO
teacher.ParallelOptions.MaxDegreeOfParallelism = 1;
#endif
var svm = teacher.Learn(observations, labels);
{
var trainingLoss = new ZeroOneLoss(labels)
{
Mean = true
};
double error = trainingLoss.Loss(svm.Decide(observations));
Assert.AreEqual(0.054, error);
}
{
var vset = readData(validation);
var validationData = vset.Item1;
var validationLabels = vset.Item2;
var validationLoss = new ZeroOneLoss(validationLabels)
{
Mean = true
};
double val = validationLoss.Loss(svm.Decide(validationData));
Assert.AreEqual(0.082, val);
}
}
public void kaggle_digits_with_compress()
{
string root = Environment.CurrentDirectory;
var training = Properties.Resources.trainingsample;
var validation = Properties.Resources.validationsample;
var tset = readData(training);
var observations = tset.Item1;
var labels = tset.Item2;
var teacher = new MulticlassSupportVectorLearning<Linear>();
var svm = teacher.Learn(observations, labels);
Assert.AreEqual(50, svm.Models[0][0].SupportVectors.Length);
Assert.AreEqual(127, svm.Models[1][0].SupportVectors.Length);
svm.Compress();
Assert.AreEqual(1, svm.Models[0][0].SupportVectors.Length);
Assert.AreEqual(1, svm.Models[1][0].SupportVectors.Length);
{
var trainingLoss = new ZeroOneLoss(labels)
{
Mean = true
};
double error = trainingLoss.Loss(svm.Decide(observations));
Assert.AreEqual(0.054, error);
}
{
var vset = readData(validation);
var validationData = vset.Item1;
var validationLabels = vset.Item2;
var validationLoss = new ZeroOneLoss(validationLabels)
{
Mean = true
};
double val = validationLoss.Loss(svm.Decide(validationData));
Assert.AreEqual(0.082, val);
}
}
