WhiteNoiseKernel.h

Go to the documentation of this file.

#ifndef WHITENOISEKERNEL_H
#define WHITENOISEKERNEL_H
 
#include "MUQ/Approximation/GaussianProcesses/KernelImpl.h"
 
 
namespace muq
{
namespace Approximation
{
 
class WhiteNoiseKernel : public KernelImpl<WhiteNoiseKernel>
{
 
public:
 
    WhiteNoiseKernel(unsigned     dim,
                     const double sigma2In) : WhiteNoiseKernel(dim, sigma2In, {0.0, std::numeric_limits<double>::infinity()}){}
 
    WhiteNoiseKernel(unsigned     dim,
                     const double sigma2In,
                     const Eigen::Vector2d sigmaBounds) : KernelImpl<WhiteNoiseKernel>(dim, 1, 1)
    {
      paramBounds.resize(2,1);
      paramBounds(0) = sigmaBounds[0]; // lower bound on sigma2
      paramBounds(1) = sigmaBounds[1]; // upper bound on sigma2
 
      cachedParams.resize(1);
      cachedParams(0) = sigma2In;
    };
 
    virtual ~WhiteNoiseKernel(){};
 
    template<typename ScalarType1, typename ScalarType2, typename ScalarType3>
    void FillBlockImpl(Eigen::Ref<const Eigen::Matrix<ScalarType1, Eigen::Dynamic, 1>> const& x1,
                       Eigen::Ref<const Eigen::Matrix<ScalarType1, Eigen::Dynamic, 1>> const& x2,
                       Eigen::Ref<const Eigen::Matrix<ScalarType2, Eigen::Dynamic, 1>> const& params,
                       Eigen::Ref<Eigen::Matrix<ScalarType3,Eigen::Dynamic, Eigen::Dynamic>>  block) const
    {
      ScalarType1 squaredDist = (x1-x2).squaredNorm();
      block(0,0) = (squaredDist<1e-14) ? params(0) : 0.0;
    }
};
 
 
}
}
 
#endif