void StudentTProcessNIG::precomputePrediction()
{
size_t n = mData.getNSamples();
size_t p = mMean.nFeatures();
mKF = trans(mMean.mFeatM);
inplace_solve(mL,mKF,ublas::lower_tag());
//TODO: make one line
matrixd DD(p,p);
DD = prod(trans(mKF),mKF);
utils::add_to_diagonal(DD,mInvVarW);
utils::cholesky_decompose(DD,mD);
vectord vn = mData.mY;
inplace_solve(mL,vn,ublas::lower_tag());
mWMap = prod(mMean.mFeatM,vn) + utils::ublas_elementwise_prod(mInvVarW,mW0);
utils::cholesky_solve(mD,mWMap,ublas::lower());
mVf = mData.mY - prod(trans(mMean.mFeatM),mWMap);
inplace_solve(mL,mVf,ublas::lower_tag());
vectord v0 = mData.mY - prod(trans(mMean.mFeatM),mW0);
//TODO: check for "cheaper" version
//matrixd KK = prod(mL,trans(mL));
matrixd KK = computeCorrMatrix();
matrixd WW = zmatrixd(p,p); //TODO: diagonal matrix
utils::add_to_diagonal(WW,mInvVarW);
const matrixd FW = prod(trans(mMean.mFeatM),WW);
KK += prod(FW,mMean.mFeatM);
matrixd BB(n,n);
utils::cholesky_decompose(KK,BB);
inplace_solve(BB,v0,ublas::lower_tag());
mSigma = (mBeta/mAlpha + inner_prod(v0,v0))/(n+2*mAlpha);
int dof = static_cast<int>(n+2*mAlpha);
if ((boost::math::isnan(mWMap(0))) || (boost::math::isnan(mSigma)))
{
throw std::runtime_error("Error in precomputed prediction. NaN found.");
}
if (dof <= 0)
{
dof = n;
FILE_LOG(logERROR) << "ERROR: Incorrect alpha. Dof invalid."
<< "Forcing Dof <= num of points.";
}
d_->setDof(dof);
}
double GaussianProcessNormal::negativeLogLikelihood()
{
matrixd KK = computeCorrMatrix();
const size_t n = KK.size1();
const size_t p = mMean->nFeatures();
vectord v0 = mGPY - prod(trans(mFeatM),mW0);
matrixd WW = zmatrixd(p,p); //TODO: diagonal matrix
utils::addToDiagonal(WW,mInvVarW);
matrixd FW = prod(trans(mFeatM),WW);
KK += prod(FW,mFeatM);
matrixd BB(n,n);
utils::cholesky_decompose(KK,BB);
inplace_solve(BB,v0,ublas::lower_tag());
double zz = inner_prod(v0,v0);
double lik = 1/(2*mSigma) * zz;
lik += utils::log_trace(BB);
return lik;
}
double StudentTProcessNIG::negativeLogLikelihood()
{
matrixd KK = computeCorrMatrix();
const size_t n = KK.size1();
const size_t p = mMean.nFeatures();
const size_t nalpha = (n+2*mAlpha);
vectord v0 = mData.mY - prod(trans(mMean.mFeatM),mW0);
matrixd WW = zmatrixd(p,p); //TODO: diagonal matrix
utils::add_to_diagonal(WW,mInvVarW);
matrixd FW = prod(trans(mMean.mFeatM),WW);
KK += prod(FW,mMean.mFeatM);
matrixd BB(n,n);
utils::cholesky_decompose(KK,BB);
inplace_solve(BB,v0,ublas::lower_tag());
double zz = inner_prod(v0,v0);
double sigmaMap = (mBeta/mAlpha + zz)/nalpha;
double lik = nalpha/2 * std::log(1+zz/(2*mBeta*sigmaMap));
lik += utils::log_trace(BB);
lik += n/2 * std::log(sigmaMap);
return lik;
}
