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Contents
#pragma once
#include <Eigen/Dense>
#include <Eigen/Cholesky>
#include "serializer.hpp"
namespace tomoto
{
namespace math
{
template<typename _Ty = float>
struct MultiNormalDistribution
{
static constexpr _Ty log2pi = (_Ty)1.83787706641;
Eigen::Matrix<_Ty, -1, 1> mean;
Eigen::Matrix<_Ty, -1, -1> cov, l;
_Ty logDet = 0;
MultiNormalDistribution(size_t k = 0) :
mean{ Eigen::Matrix<_Ty, -1, 1>::Zero(k) },
cov{ Eigen::Matrix<_Ty, -1, -1>::Identity(k, k) },
l{ Eigen::Matrix<_Ty, -1, -1>::Identity(k, k) }
{
}
_Ty getLL(const Eigen::Matrix<_Ty, -1, 1>& x) const
{
_Ty ll = -((x - mean).transpose() * cov.inverse() * (x - mean))[0] / 2;
ll -= log2pi * mean.size() / 2 + logDet;
return ll;
}
const Eigen::Matrix<_Ty, -1, -1>& getCovL() const
{
return l;
}
template<typename _List>
static MultiNormalDistribution<_Ty> estimate(_List list, size_t len)
{
MultiNormalDistribution<_Ty> newDist;
if (len)
{
newDist.mean = list(0);
for (size_t i = 1; i < len; ++i) newDist.mean += list(i);
newDist.mean /= len;
newDist.cov = Eigen::Matrix<_Ty, -1, -1>::Identity(newDist.mean.size(), newDist.mean.size());
for (size_t i = 0; i < len; ++i)
{
Eigen::Matrix<_Ty, -1, 1> o = list(i) - newDist.mean;
newDist.cov += o * o.transpose();
}
if (len > 1) newDist.cov /= len - 1;
}
Eigen::MatrixXd l = newDist.cov.template cast<double>().llt().matrixL();
newDist.l = l.template cast<float>();
newDist.logDet = l.diagonal().array().log().sum();
return newDist;
}
DEFINE_SERIALIZER_CALLBACK(onRead, mean, cov);
private:
void onRead()
{
l = cov.llt().matrixL();
logDet = l.diagonal().array().log().sum();
}
};
}
}
Version data entries
6 entries across 6 versions & 1 rubygems