void RAModel<SortPolicy>::Search(const size_t k,
arma::Mat<size_t>& neighbors,
arma::mat& distances)
{
Log::Info << "Searching for " << k << " approximate nearest neighbors with ";
if (!Naive() && !SingleMode())
Log::Info << "dual-tree rank-approximate " << TreeName() << " search...";
else if (!Naive())
Log::Info << "single-tree rank-approximate " << TreeName() << " search...";
else
Log::Info << "brute-force (naive) rank-approximate search...";
Log::Info << std::endl;
switch (treeType)
{
case KD_TREE:
kdTreeRA->Search(k, neighbors, distances);
break;
case COVER_TREE:
coverTreeRA->Search(k, neighbors, distances);
break;
case R_TREE:
rTreeRA->Search(k, neighbors, distances);
break;
case R_STAR_TREE:
rStarTreeRA->Search(k, neighbors, distances);
break;
case X_TREE:
xTreeRA->Search(k, neighbors, distances);
break;
}
}
void IdePngDes::Load(const char *_filename)
{
Clear();
filename = _filename;
filetime = FileGetTime(filename);
Image m = StreamRaster::LoadFileAny(filename);
AddImage(filename, m, false);
SingleMode();
}
void IdePngDes::Create(const char *_filename)
{
Clear();
filename = _filename;
filetime = GetSysTime();
Image m = CreateImage(Size(16, 16), Null);
AddImage(filename, m, false);
SingleMode();
}
void NSModel<SortPolicy>::Search(const size_t k,
arma::Mat<size_t>& neighbors,
arma::mat& distances)
{
Log::Info << "Searching for " << k << " neighbors with ";
if (!Naive() && !SingleMode())
Log::Info << "dual-tree " << TreeName() << " search..." << std::endl;
else if (!Naive())
Log::Info << "single-tree " << TreeName() << " search..." << std::endl;
else
Log::Info << "brute-force (naive) search..." << std::endl;
if (Epsilon() != 0 && !Naive())
Log::Info << "Maximum of " << Epsilon() * 100 << "% relative error."
<< std::endl;
MonoSearchVisitor search(k, neighbors, distances);
boost::apply_visitor(search, nSearch);
}
void NSModel<SortPolicy>::Search(arma::mat&& querySet,
const size_t k,
arma::Mat<size_t>& neighbors,
arma::mat& distances)
{
// We may need to map the query set randomly.
if (randomBasis)
querySet = q * querySet;
Log::Info << "Searching for " << k << " neighbors with ";
if (!Naive() && !SingleMode())
Log::Info << "dual-tree " << TreeName() << " search..." << std::endl;
else if (!Naive())
Log::Info << "single-tree " << TreeName() << " search..." << std::endl;
else
Log::Info << "brute-force (naive) search..." << std::endl;
if (Epsilon() != 0 && !Naive())
Log::Info << "Maximum of " << Epsilon() * 100 << "% relative error."
<< std::endl;
BiSearchVisitor<SortPolicy> search(querySet, k, neighbors, distances,
leafSize);
boost::apply_visitor(search, nSearch);
}
void RAModel<SortPolicy>::Search(arma::mat&& querySet,
const size_t k,
arma::Mat<size_t>& neighbors,
arma::mat& distances)
{
// Apply the random basis if necessary.
if (randomBasis)
querySet = q * querySet;
Log::Info << "Searching for " << k << " approximate nearest neighbors with ";
if (!Naive() && !SingleMode())
Log::Info << "dual-tree rank-approximate " << TreeName() << " search...";
else if (!Naive())
Log::Info << "single-tree rank-approximate " << TreeName() << " search...";
else
Log::Info << "brute-force (naive) rank-approximate search...";
Log::Info << std::endl;
switch (treeType)
{
case KD_TREE:
if (!kdTreeRA->Naive() && !kdTreeRA->SingleMode())
{
// Build a second tree and search.
Timer::Start("tree_building");
Log::Info << "Building query tree..." << std::endl;
std::vector<size_t> oldFromNewQueries;
typename RAType<tree::KDTree>::Tree queryTree(std::move(querySet),
oldFromNewQueries, leafSize);
Log::Info << "Tree built." << std::endl;
Timer::Stop("tree_building");
arma::Mat<size_t> neighborsOut;
arma::mat distancesOut;
kdTreeRA->Search(&queryTree, k, neighborsOut, distancesOut);
// Unmap the query points.
distances.set_size(distancesOut.n_rows, distancesOut.n_cols);
neighbors.set_size(neighborsOut.n_rows, neighborsOut.n_cols);
for (size_t i = 0; i < neighborsOut.n_cols; ++i)
{
neighbors.col(oldFromNewQueries[i]) = neighborsOut.col(i);
distances.col(oldFromNewQueries[i]) = distancesOut.col(i);
}
}
else
{
// Search without building a second tree.
kdTreeRA->Search(querySet, k, neighbors, distances);
}
break;
case COVER_TREE:
// No mapping necessary.
coverTreeRA->Search(querySet, k, neighbors, distances);
break;
case R_TREE:
// No mapping necessary.
rTreeRA->Search(querySet, k, neighbors, distances);
break;
case R_STAR_TREE:
// No mapping necessary.
rStarTreeRA->Search(querySet, k, neighbors, distances);
break;
case X_TREE:
// No mapping necessary.
xTreeRA->Search(querySet, k, neighbors, distances);
break;
}
}
