inline vectord KernelModel::computeCrossCorrelation(const vecOfvec& XX,
const vectord &query)
{
vectord knx(XX.size());
computeCrossCorrelation(XX,query,knx);
return knx;
}
ProbabilityDistribution* StudentTProcessNIG::prediction(const vectord &query)
{
double kq = computeSelfCorrelation(query);
vectord kn = computeCrossCorrelation(query);
vectord phi = mMean.getFeatures(query);
vectord v(kn);
inplace_solve(mL,v,ublas::lower_tag());
vectord rq = phi - prod(v,mKF);
vectord rho(rq);
inplace_solve(mD,rho,ublas::lower_tag());
double yPred = inner_prod(phi,mWMap) + inner_prod(v,mVf);
double sPred = sqrt( mSigma * (kq - inner_prod(v,v)
+ inner_prod(rho,rho)));
if ((boost::math::isnan(yPred)) || (boost::math::isnan(sPred)))
{
throw std::runtime_error("Error in prediction. NaN found.");
}
d_->setMeanAndStd(yPred,sPred);
return d_;
}
ProbabilityDistribution*
GaussianProcessNormal::prediction(const vectord &query)
{
double kq = (*mKernel)(query, query);;
vectord kn = computeCrossCorrelation(query);
vectord phi = mMean->getFeatures(query);
vectord v(kn);
inplace_solve(mL,v,ublas::lower_tag());
vectord rq = phi - prod(v,mKF);
vectord rho(rq);
inplace_solve(mD,rho,ublas::lower_tag());
double yPred = inner_prod(phi,mWMap) + inner_prod(v,mVf);
double sPred = sqrt( mSigma * (kq - inner_prod(v,v)
+ inner_prod(rho,rho)));
if ((boost::math::isnan(yPred)) || (boost::math::isnan(sPred)))
{
FILE_LOG(logERROR) << "Error in prediction. NaN found.";
exit(EXIT_FAILURE);
}
d_->setMeanAndStd(yPred,sPred);
return d_;
}
ProbabilityDistribution* GaussianProcess::prediction(const vectord &query)
{
const double kq = computeSelfCorrelation(query);
const vectord kn = computeCrossCorrelation(query);
vectord vd(kn);
inplace_solve(mL,vd,ublas::lower_tag());
double basisPred = mMean.muTimesFeat(query);
double yPred = basisPred + ublas::inner_prod(vd,mAlphaV);
double sPred = sqrt(mSigma*(kq - ublas::inner_prod(vd,vd)));
d_->setMeanAndStd(yPred,sPred);
return d_;
}
ProbabilityDistribution* GaussianProcessML::prediction( const vectord &query )
{
double kq = computeSelfCorrelation(query);
vectord kn = computeCrossCorrelation(query);
vectord phi = mMean.getFeatures(query);
vectord v(kn);
inplace_solve(mL,v,ublas::lower_tag());
vectord rq = phi - prod(v,mKF);
vectord rho(rq);
inplace_solve(mL2,rho,ublas::lower_tag());
double yPred = inner_prod(phi,mWML) + inner_prod(v,mAlphaF);
double sPred = sqrt( mSigma * (kq - inner_prod(v,v)
+ inner_prod(rho,rho)));
d_->setMeanAndStd(yPred,sPred);
return d_;
}
