/**************************************************************************************************
* Procedure *
* *
* Description: getSigmaPoints *
* Class : UnscentedExpectedImprovement *
**************************************************************************************************/
void UnscentedExpectedImprovement::getSigmaPoints(const vectord& x ,
const double scale ,
const int dim ,
const matrixd& matrix_noise ,
std::vector<vectord>& xx ,
std::vector<double>& w ,
const bool matrix_convert)
{
const size_t n = dim;
assert(matrix_noise.size1() == n);
assert(matrix_noise.size2() == n);
assert(x.size() == n);
matrixd px;
if (matrix_convert) px = UnscentedExpectedImprovement::convertMatrixNoise(matrix_noise, scale, dim);
else px = matrix_noise;
// Output variable intialization
xx = std::vector<vectord>();
w = std::vector<double>();
xx.push_back(x);
w .push_back(scale / (dim + scale));
// Calculate query_i
for (size_t col = 0; col < n; col += 1)
{
xx.push_back(x - boost::numeric::ublas::column(px, col));
xx.push_back(x + boost::numeric::ublas::column(px, col));
w .push_back(0.5 / (dim + scale));
w .push_back(0.5 / (dim + scale));
}
}
double HierarchicalGaussianProcess::negativeTotalLogLikelihood()
{
/*This is the restricted version. For the unrestricted, make p=0
and remove the last term of loglik*/
const matrixd K = computeCorrMatrix();
const size_t n = K.size1();
const size_t p = mFeatM.size1();
matrixd L(n,n);
utils::cholesky_decompose(K,L);
matrixd KF(ublas::trans(mFeatM));
inplace_solve(L,KF,ublas::lower_tag());
matrixd FKF = prod(ublas::trans(KF),KF);
matrixd L2(p,p);
utils::cholesky_decompose(FKF,L2);
vectord Ky(mGPY);
inplace_solve(L,Ky,ublas::lower_tag());
vectord wML = prod(Ky,KF);
utils::cholesky_solve(L2,wML,ublas::lower());
vectord alpha = mGPY - prod(wML,mFeatM);
inplace_solve(L,alpha,ublas::lower_tag());
double sigma = ublas::inner_prod(alpha,alpha)/(n-p);
double loglik = .5*(n-p)*log(ublas::inner_prod(alpha,alpha))
+ utils::log_trace(L) + utils::log_trace(L2);
return loglik;
}
inline void MeanModel::addNewPoint(const vectord &x)
{
using boost::numeric::ublas::column;
mFeatM.resize(mFeatM.size1(),mFeatM.size2()+1);
column(mFeatM,mFeatM.size2()-1) = mMean->getFeatures(x);
}
double GaussianProcess::negativeLogLikelihood()
{
const matrixd K = computeCorrMatrix();
const size_t n = K.size1();
matrixd L(n,n);
utils::cholesky_decompose(K,L);
vectord alpha(mData.mY-mMean.muTimesFeat());
inplace_solve(L,alpha,ublas::lower_tag());
double loglik = ublas::inner_prod(alpha,alpha)/(2*mSigma);
loglik += utils::log_trace(L);
return loglik;
}
/**************************************************************************************************
* Procedure *
* *
* Description: convertMatrixToParams *
* Class : UnscentedExpectedImprovement *
**************************************************************************************************/
void UnscentedExpectedImprovement::convertMatrixToParams(bopt_params& params, const matrixd px)
{
if (px.size1() != px.size2()) return;
size_t dim = px.size1();
for (size_t row = 0; row < dim; ++row)
{
for (size_t col = 0; col < dim; ++col)
{
params.crit_params[4 + col + (row * dim)] = px(row, col);
params.input.noise[0 + col + (row * dim)] = px(row, col);
}
}
}
void Dataset::setSamples(const matrixd &x)
{
for (size_t i=0; i<x.size1(); ++i)
{
mX.push_back(row(x,i));
}
};
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;
}
}
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);
}
}
void Dataset::setSamples(const matrixd &x, const vectord &y)
{
// WARNING: It assumes mX is empty
mY = y;
for (size_t i=0; i<x.size1(); ++i)
{
mX.push_back(row(x,i));
updateMinMax(i);
}
};
/**************************************************************************************************
* Procedure *
* *
* Description: isDiag *
* Class : UnscentedExpectedImprovement *
**************************************************************************************************/
bool UnscentedExpectedImprovement::isDiag(matrixd matrix)
{
if (matrix.size1() == matrix.size2())
{
for (size_t row = 0; row < matrix.size1(); ++row)
{
for (size_t col = 0; col < matrix.size2(); ++col)
{
if (row != col)
{
if (std::abs(matrix(row, col)) > std::numeric_limits<double>::epsilon())
{
return false;
}
}
}
}
return true;
}
return false;
}
