SubrangeKernelTutorial.cpp

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#include <shark/Algorithms/Trainers/NormalizeKernelUnitVariance.h>
#include <shark/Models/Kernels/GaussianRbfKernel.h>
#include <shark/Models/Kernels/WeightedSumKernel.h>
#include <shark/Models/Kernels/PolynomialKernel.h>
#include <shark/Models/Kernels/LinearKernel.h>
#include <shark/Models/Kernels/SubrangeKernel.h>
#include <shark/Data/DataDistribution.h>
#include <shark/Core/Random.h>
 
using namespace shark;
 
//our problem
class UniformPoints : public DataDistribution<RealVector>
{
public:
    UniformPoints(std::size_t dimensions){
        m_dimensions = dimensions;
    }
 
    void draw(RealVector& input)const{
        input.resize(m_dimensions);
        for ( std::size_t j=0; j<m_dimensions; j++ ) {
            input(j) = random::uni(random::globalRng, -1,1);
        }
    }
 
protected:
    std::size_t m_dimensions;
};
 
int main()
{
    std::cout << "\n ----- Starting MklKernel normalization demo ---- \n\n" << std::flush;
    
    std::size_t num_dims = 9;
    std::size_t num_points = 200;
    UniformPoints problem(num_dims);
    UnlabeledData<RealVector> data = problem.generateDataset(num_points);
    
    DenseRbfKernel        basekernel1(0.1);
    DenseLinearKernel      basekernel2;
    DensePolynomialKernel  basekernel3(2, 1.0);
    
    std::vector< AbstractKernelFunction<RealVector> * > kernels;
    kernels.push_back(&basekernel1);
    kernels.push_back(&basekernel2);
    kernels.push_back(&basekernel3);
    
    std::vector< std::pair< std::size_t, std::size_t > > frs;
    frs.push_back( std::make_pair( 0,3 ) );
    frs.push_back( std::make_pair( 3,6 ) );
    frs.push_back( std::make_pair( 6,9 ) );
    
    DenseSubrangeKernel kernel( kernels, frs );
    DenseScaledKernel scale( &kernel );
    
    NormalizeKernelUnitVariance<> normalizer;
    normalizer.train( scale, data );
    std::cout << "    Done training. Factor is " << scale.factor() << std::endl;
    std::cout << "    Mean                   = " << normalizer.mean() << std::endl;
    std::cout << "    Trace                  = " << normalizer.trace() << std::endl << std::endl;
    //check in feature space
    double control = 0.0;
    for ( std::size_t i=0; i<num_points; i++ ) {
        control += scale.eval(data.element(i), data.element(i));
        for ( std::size_t j=0; j<num_points; j++ ) {
            control -= scale.eval(data.element(i), data.element(j)) / num_points;
        }
    }
    control /= num_points;
    std::cout << "    Variance of scaled MklKernel: " << control << std::endl;
}