public static float calculateSingleKernel(TrainingUnit xi, TrainingUnit xj,SVM ProblemSolution)
{
ProblemConfig problemConfig = ProblemSolution.ProblemCfg;
// Vectors size check
//if (xi.getDimension() != xj.getDimension()) return 0;
// Linear: u'*v (inner product)
if (problemConfig.kernelType == ProblemConfig.KernelType.Linear)
{
float sum = 0;
for (int i = 0; i < xi.getDimension(); i++)
{
sum += xi.xVector[i] * xj.xVector[i];
}
return sum;
}
// Radial basis function: exp(-gamma*|u-v|^2)
if (problemConfig.kernelType == ProblemConfig.KernelType.RBF)
{
// Gamma is, by choice, 1 / (number of features).
float sum = 0, temp;
for (int i = 0; i < xi.getDimension(); i++)
{
temp = xi.xVector[i] - xj.xVector[i];
sum += temp * temp;
}
return (float)Math.Exp(-ProblemSolution.ProblemCfg.lambda * sum);
}
return 0;
}
/// <summary>Adds a new training unit to the set</summary>
/// <param name="newTrainingUnit">New training unit to add</param>
public void addTrainingUnit(TrainingUnit newTrainingUnit)
{
if (p != 0)
{
if (p == newTrainingUnit.getDimension())
{
trainingArray.Add(newTrainingUnit);
}
else
{
// Invalid entry, not equal in size to the others training units
// Do nothing
}
}
else // The first training set is being added
{
p = newTrainingUnit.getDimension();
trainingArray.Add(newTrainingUnit);
}
}
