decision_forest.hpp File Reference

#include <cuml/experimental/fil/constants.hpp>
#include <cuml/experimental/fil/detail/device_initialization.hpp>
#include <cuml/experimental/fil/detail/forest.hpp>
#include <cuml/experimental/fil/detail/index_type.hpp>
#include <cuml/experimental/fil/detail/infer.hpp>
#include <cuml/experimental/fil/detail/postprocessor.hpp>
#include <cuml/experimental/fil/detail/raft_proto/buffer.hpp>
#include <cuml/experimental/fil/detail/raft_proto/cuda_stream.hpp>
#include <cuml/experimental/fil/detail/raft_proto/exceptions.hpp>
#include <cuml/experimental/fil/detail/specialization_types.hpp>
#include <cuml/experimental/fil/exceptions.hpp>
#include <cuml/experimental/fil/infer_kind.hpp>
#include <cuml/experimental/fil/postproc_ops.hpp>
#include <cuml/experimental/fil/tree_layout.hpp>
#include <stddef.h>
#include <stdint.h>
#include <algorithm>
#include <cstddef>
#include <limits>
#include <optional>
#include <variant>

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Classes
struct	ML::experimental::fil::decision_forest< layout_v, threshold_t, index_t, metadata_storage_t, offset_t >

Namespaces
	ML
	ML::experimental
	ML::experimental::fil
	ML::experimental::fil::detail

Typedefs
template<tree_layout layout, bool double_precision, bool large_trees>
using	ML::experimental::fil::detail::preset_decision_forest = decision_forest< layout, typename specialization_types< layout, double_precision, large_trees >::threshold_type, typename specialization_types< layout, double_precision, large_trees >::index_type, typename specialization_types< layout, double_precision, large_trees >::metadata_type, typename specialization_types< layout, double_precision, large_trees >::offset_type >
using	ML::experimental::fil::decision_forest_variant = std::variant< detail::preset_decision_forest< std::variant_alternative_t< 0, detail::specialization_variant >::layout, std::variant_alternative_t< 0, detail::specialization_variant >::is_double_precision, std::variant_alternative_t< 0, detail::specialization_variant >::has_large_trees >, detail::preset_decision_forest< std::variant_alternative_t< 1, detail::specialization_variant >::layout, std::variant_alternative_t< 1, detail::specialization_variant >::is_double_precision, std::variant_alternative_t< 1, detail::specialization_variant >::has_large_trees >, detail::preset_decision_forest< std::variant_alternative_t< 2, detail::specialization_variant >::layout, std::variant_alternative_t< 2, detail::specialization_variant >::is_double_precision, std::variant_alternative_t< 2, detail::specialization_variant >::has_large_trees >, detail::preset_decision_forest< std::variant_alternative_t< 3, detail::specialization_variant >::layout, std::variant_alternative_t< 3, detail::specialization_variant >::is_double_precision, std::variant_alternative_t< 3, detail::specialization_variant >::has_large_trees >, detail::preset_decision_forest< std::variant_alternative_t< 4, detail::specialization_variant >::layout, std::variant_alternative_t< 4, detail::specialization_variant >::is_double_precision, std::variant_alternative_t< 4, detail::specialization_variant >::has_large_trees >, detail::preset_decision_forest< std::variant_alternative_t< 5, detail::specialization_variant >::layout, std::variant_alternative_t< 5, detail::specialization_variant >::is_double_precision, std::variant_alternative_t< 5, detail::specialization_variant >::has_large_trees >, detail::preset_decision_forest< std::variant_alternative_t< 6, detail::specialization_variant >::layout, std::variant_alternative_t< 6, detail::specialization_variant >::is_double_precision, std::variant_alternative_t< 6, detail::specialization_variant >::has_large_trees >, detail::preset_decision_forest< std::variant_alternative_t< 7, detail::specialization_variant >::layout, std::variant_alternative_t< 7, detail::specialization_variant >::is_double_precision, std::variant_alternative_t< 7, detail::specialization_variant >::has_large_trees > >

Functions
auto	ML::experimental::fil::get_forest_variant_index (bool use_double_thresholds, index_type max_node_offset, index_type num_features, index_type num_categorical_nodes=index_type{}, index_type max_num_categories=index_type{}, index_type num_vector_leaves=index_type{}, tree_layout layout=preferred_tree_layout)