KTA-tutorial.cpp

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#include <shark/Data/DataDistribution.h>
#include <shark/Models/Kernels/GaussianRbfKernel.h>
#include <shark/ObjectiveFunctions/KernelTargetAlignment.h>
#include <shark/Algorithms/GradientDescent/Rprop.h>
 
using namespace shark;
using namespace std;
 
int main(int argc, char** argv)
{
    // generate dataset
    Chessboard problem;          // artificial benchmark problem
    LabeledData<RealVector, unsigned int> data = problem.generateDataset(1000);
 
    // define the family of kernel functions
    double gamma = 0.5;          // initial guess of the parameter value
    GaussianRbfKernel<RealVector> kernel(gamma);
 
    // set up kernel target alignment as a function of the kernel parameters
    // on the given data
    KernelTargetAlignment<RealVector> kta(data,&kernel);
 
    // optimize parameters for best alignment
    Rprop<> rprop;
    rprop.init(kta);
    cout << "initial parameter: " << kernel.gamma() << endl;
    for (size_t i=0; i<50; i++)
    {
        rprop.step(kta);
        cout << "parameter after step " << (i+1) << ": " << kernel.gamma() << endl;
    }
    cout << "final parameter: " << kernel.gamma() << endl;
}