ppforest2::types Namespace Reference

Core numeric type aliases for the ppforest2 library. More...

Typedefs
using	Feature = float
	Scalar type for feature values.
using	FeatureMatrix = Eigen::Matrix<Feature, Eigen::Dynamic, Eigen::Dynamic>
	Dynamic-size matrix of feature values.
using	FeatureVector = Eigen::Matrix<Feature, Eigen::Dynamic, 1>
	Dynamic-size column vector of feature values.
using	GroupId = int
	Scalar type for internal group labels (integer). Used as map keys, set elements, and partition indices.
using	GroupIdVector = Eigen::Matrix<GroupId, Eigen::Dynamic, 1>
	Dynamic-size column vector of internal group labels.
template<typename T>
using	Matrix = Eigen::Matrix<T, Eigen::Dynamic, Eigen::Dynamic>
	Generic dynamic-size matrix.
using	Names = std::vector<std::string>
	Vector of name strings — used uniformly for class labels (group_names[i] is the label for GroupId == i) and feature names (feature_names[j] is the column name at index j).
using	Outcome = Feature
	Scalar type for predictions (float for both classification and regression).
using	OutcomeVector = Eigen::Matrix<Outcome, Eigen::Dynamic, 1>
	Dynamic-size column vector of predictions.
template<typename T>
using	Vector = Eigen::Matrix<T, Eigen::Dynamic, 1>
	Generic dynamic-size column vector.

Enumerations
enum class	Mode : uint8_t { Classification , Regression }
	Training mode. More...

Functions
bool	is_classification (Mode mode)
	Whether mode is Classification.
bool	is_regression (Mode mode)
	Whether mode is Regression.
Mode	mode_from_string (std::string_view s)
	Training mode from string.
std::string	to_string (Mode mode)
	Canonical string form of a training mode.

Detailed Description

Core numeric type aliases for the ppforest2 library.

All matrix and vector types are Eigen dynamic-size types. Feature precision is single-precision (float), which is sufficient for classification. If a future strategy (e.g. regression) needs higher precision internally, it can cast to double within its own scope.

GroupId is the internal integer type for group labels, partition keys, and confusion matrices. Outcome is the prediction type — currently an alias for int, but will become Feature (float) to support regression.

Typedef Documentation

Feature

using ppforest2::types::Feature = float

Scalar type for feature values.

FeatureMatrix

using ppforest2::types::FeatureMatrix = Eigen::Matrix<Feature, Eigen::Dynamic, Eigen::Dynamic>

Dynamic-size matrix of feature values.

FeatureVector

using ppforest2::types::FeatureVector = Eigen::Matrix<Feature, Eigen::Dynamic, 1>

Dynamic-size column vector of feature values.

GroupId

using ppforest2::types::GroupId = int

Scalar type for internal group labels (integer). Used as map keys, set elements, and partition indices.

GroupIdVector

using ppforest2::types::GroupIdVector = Eigen::Matrix<GroupId, Eigen::Dynamic, 1>

Dynamic-size column vector of internal group labels.

Matrix

template<typename T>

using ppforest2::types::Matrix = Eigen::Matrix<T, Eigen::Dynamic, Eigen::Dynamic>

Generic dynamic-size matrix.

Names

using ppforest2::types::Names = std::vector<std::string>

Vector of name strings — used uniformly for class labels (group_names[i] is the label for GroupId == i) and feature names (feature_names[j] is the column name at index j).

Outcome

using ppforest2::types::Outcome = Feature

Scalar type for predictions (float for both classification and regression).

OutcomeVector

using ppforest2::types::OutcomeVector = Eigen::Matrix<Outcome, Eigen::Dynamic, 1>

Dynamic-size column vector of predictions.

Vector

template<typename T>

using ppforest2::types::Vector = Eigen::Matrix<T, Eigen::Dynamic, 1>

Generic dynamic-size column vector.

Enumeration Type Documentation

Mode

enum class ppforest2::types::Mode : uint8_t

strong

Training mode.

Enumerator
Classification
Regression

Function Documentation

is_classification()

bool ppforest2::types::is_classification ( Mode mode )

inline

Whether mode is Classification.

is_regression()

bool ppforest2::types::is_regression ( Mode mode )

inline

Whether mode is Regression.

mode_from_string()

Mode ppforest2::types::mode_from_string ( std::string_view s )

inline

Training mode from string.

to_string()

std::string ppforest2::types::to_string ( Mode mode )

inline

Canonical string form of a training mode.