inline void KernelModel::computeCrossCorrelation(const vecOfvec& XX,
const vectord &query,
vectord& knx)
{
std::vector<vectord>::const_iterator x_it = XX.begin();
vectord::iterator k_it = knx.begin();
while(x_it != XX.end())
{ *k_it++ = (*mKernel)(*x_it++, query); }
}
virtual matrixd getAllFeatures(const vecOfvec& x)
{
size_t nf = nFeatures();
matrixd result(nf,x.size());
for(size_t ii=0; ii< x.size(); ++ii)
{
column(result,ii) = getFeatures(x[ii]);
}
return result;
};
virtual vectord operator()(const vecOfvec& x)
{
vectord result(x.size());
std::vector<vectord>::const_iterator x_it = x.begin();
std::vector<vectord>::const_iterator x_end = x.end();
vectord::iterator res_it = result.begin();
while(x_it != x_end)
{
*res_it++ = getMean(*x_it++);
}
return result;
};
inline vectord KernelModel::computeCrossCorrelation(const vecOfvec& XX,
const vectord &query)
{
vectord knx(XX.size());
computeCrossCorrelation(XX,query,knx);
return knx;
}
void KernelModel::computeCorrMatrix(const vecOfvec& XX, matrixd& corrMatrix,
double nugget)
{
assert(corrMatrix.size1() == XX.size());
assert(corrMatrix.size2() == XX.size());
const size_t nSamples = XX.size();
for (size_t ii=0; ii< nSamples; ++ii)
{
for (size_t jj=0; jj < ii; ++jj)
{
corrMatrix(ii,jj) = (*mKernel)(XX[ii], XX[jj]);
corrMatrix(jj,ii) = corrMatrix(ii,jj);
}
corrMatrix(ii,ii) = (*mKernel)(XX[ii],XX[ii]) + nugget;
}
}
inline void MCMCSampler::printParticles()
{
for(size_t i=0; i<mParticles.size(); ++i)
{
FILE_LOG(logDEBUG) << i << "->" << mParticles[i]
<< " | Log-lik " << -obj->evaluate(mParticles[i]);
}
}
void KernelModel::computeDerivativeCorrMatrix(const vecOfvec& XX,
matrixd& corrMatrix,
int dth_index)
{
assert(corrMatrix.size1() == XX.size());
assert(corrMatrix.size2() == XX.size());
const size_t nSamples = XX.size();
for (size_t ii=0; ii< nSamples; ++ii)
{
for (size_t jj=0; jj < ii; ++jj)
{
corrMatrix(ii,jj) = mKernel->gradient(XX[ii],XX[jj],
dth_index);
corrMatrix(jj,ii) = corrMatrix(ii,jj);
}
corrMatrix(ii,ii) = mKernel->gradient(XX[ii],XX[ii],dth_index);
}
}
