|| || || || || || || ||

---

KernelFunctions.h

Go to the documentation of this file.

#ifndef Impala_Core_Training_KernelFunctions_h
#define Impala_Core_Training_KernelFunctions_h

#include "Core/Vector/VectorSet.h"

namespace Impala
{
namespace Core
{
namespace Training
{

double Chi2Distance(const Vector::VectorTem<double>& v1, const Vector::VectorTem<double>& v2)
{
    int length=v1.Size();
    double accumulator = 0;
    for(int i=0 ; i<length ; ++i)
    {
        double sum = v1[i] + v2[i];
        if(sum)
            accumulator += (v1[i] - v2[i]) * (v1[i] - v2[i]) / sum;
    }
    // make it between 0..1, instead of 0..2 (this statement is only true if the input vectors sum to one)
    return accumulator / 2.0;
}

class MpoChi2Kernel
{
public:
    MpoChi2Kernel(const std::vector<double>& weights,
                  const std::vector<double>& averages)
    {
        int length = weights.size();
        mNormalisation = 0;
        int i;
        for(i=0 ; i<length ; ++i)
            mNormalisation += weights[i];
        mNormalisation = 1.0 / mNormalisation;

        mWeights.resize(length);
        for(i=0 ; i<length ; ++i)
            mWeights[i] = -1 * weights[i] / averages[i];
    }
                  
    double DoIt(const std::vector<double>& d) //kernel distance
    {
        double sum=0;
        int l = d.size();
        for(int i=0 ; i<l ; ++i)
        {
            sum += d[i] * mWeights[i];
        }
        return exp(sum / mNormalisation);
    }

private:
    double mNormalisation;
    std::vector<double> mWeights;
};

} // namespace Vector
} // namespace Core
} // namespace Impala


#endif

---