General Tree Inference Library (GTIL), providing a reference implementation for predicting with decision trees. More...

#include <cstddef>
#include <cstdint>
#include <optional>
#include <string>
#include <variant>
#include <vector>

Include dependency graph for gtil.h:

Classes
struct	treelite::gtil::Configuration
	Configuration class. More...

Enumerations
enum class	treelite::gtil::PredictKind : std::int8_t { treelite::gtil::kPredictDefault = 0 , treelite::gtil::kPredictRaw = 1 , treelite::gtil::kPredictLeafID = 2 , treelite::gtil::kPredictPerTree = 3 }
	Prediction type. More...

Functions
template<typename InputT >
void	treelite::gtil::Predict (Model const &model, InputT const input, std::uint64_t num_row, InputT output, Configuration const &config)
	Predict with dense data. More...

template<typename InputT >
void	treelite::gtil::PredictSparse (Model const &model, InputT const data, std::uint64_t const col_ind, std::uint64_t const row_ptr, std::uint64_t num_row, InputT output, Configuration const &config)
	Predict with sparse data with CSR (compressed sparse row) layout. More...

std::vector< std::uint64_t >	treelite::gtil::GetOutputShape (Model const &model, std::uint64_t num_row, Configuration const &config)
	Given a data matrix, query the necessary shape of array to hold predictions for all data points. More...

template void	treelite::gtil::Predict< float > (Model const &, float const , std::uint64_t, float , Configuration const &)

template void	treelite::gtil::Predict< double > (Model const &, double const , std::uint64_t, double , Configuration const &)

template void	treelite::gtil::PredictSparse< float > (Model const &, float const , std::uint64_t const , std::uint64_t const , std::uint64_t, float , Configuration const &)

template void	treelite::gtil::PredictSparse< double > (Model const &, double const , std::uint64_t const , std::uint64_t const , std::uint64_t, double , Configuration const &)

Detailed Description

General Tree Inference Library (GTIL), providing a reference implementation for predicting with decision trees.

Classes