/// <summary>
/// Initializes a new instance of the <see cref="BaseSupportVectorLearning"/> class.
/// </summary>
///
/// <param name="machine">The machine to be learned.</param>
/// <param name="inputs">The input data.</param>
/// <param name="outputs">The corresponding output data.</param>
///
protected BaseSupportVectorLearning(SupportVectorMachine machine, double[][] inputs, int[] outputs)
{
// Initial argument checking
SupportVectorLearningHelper.CheckArgs(machine, inputs, outputs);
// Machine
this.machine = machine;
// Kernel (if applicable)
KernelSupportVectorMachine ksvm = machine as KernelSupportVectorMachine;
if (ksvm == null)
{
isLinear = true;
Linear linear = new Linear(0);
kernel = linear;
}
else
{
Linear linear = ksvm.Kernel as Linear;
isLinear = linear != null;
kernel = ksvm.Kernel;
}
// Learning data
this.inputs = inputs;
this.outputs = outputs;
}
/// <summary>
/// Constructs a new Least Squares SVM (LS-SVM) learning algorithm.
/// </summary>
///
/// <param name="machine">A support vector machine.</param>
/// <param name="inputs">The input data points as row vectors.</param>
/// <param name="outputs">The output label for each input point. Values must be either -1 or +1.</param>
///
public LeastSquaresLearning(SupportVectorMachine machine, double[][] inputs, int[] outputs)
{
SupportVectorLearningHelper.CheckArgs(machine, inputs, outputs);
// Set the machine
this.machine = machine;
// Grab the machine kernel
KernelSupportVectorMachine ksvm = machine as KernelSupportVectorMachine;
this.kernel = (ksvm == null) ? new Linear() : ksvm.Kernel;
// Kernel cache
this.cacheSize = inputs.Length;
// Get learning data
this.inputs = inputs;
this.outputs = outputs;
this.ones = Matrix.Vector(outputs.Length, 1);
}
