Split Namespace Reference

Functions
template<typename T >
ArrayXb	threshold_mask (const T &x, const float &threshold)
	Applies a learned threshold to a feature, returning a mask.
template<typename T > requires same_as<typename T::Scalar, bool>
ArrayXb	threshold_mask (const T &x, const float &threshold)
	Applies a learned threshold to a feature, returning a mask.
template<typename T > requires same_as<typename T::Scalar, bJet>
ArrayXb	threshold_mask (const T &x, const float &threshold)
	Applies a learned threshold to a feature, returning a mask.
template<typename T > requires same_as<typename T::Scalar, float>
ArrayXb	threshold_mask (const T &x, const float &threshold)
	Applies a learned threshold to a feature, returning a mask.
template<typename T > requires same_as<typename T::Scalar, fJet>
ArrayXb	threshold_mask (const T &x, const float &threshold)
	Applies a learned threshold to a feature, returning a mask.
template<typename T > requires same_as<typename T::Scalar, int>
ArrayXb	threshold_mask (const T &x, const float &threshold)
	Applies a learned threshold to a feature, returning a mask.
template<typename T > requires same_as<typename T::Scalar, iJet>
ArrayXb	threshold_mask (const T &x, const float &threshold)
	Applies a learned threshold to a feature, returning a mask.
float	gini_impurity_index (const ArrayXf &classes, const vector< float > &uc)
float	gain (const ArrayXf &lsplit, const ArrayXf &rsplit, bool classification, vector< float > unique_classes)
template<typename T >
vector< float >	get_thresholds (const T &x)
tuple< string, float >	get_best_variable_and_threshold (const Dataset &d, TreeNode &tn)
template<typename T >
tuple< float, float >	best_threshold (const T &x, const ArrayXf &y, bool classification)
template<typename T >
void	get_best_threshold_by_type (const Dataset &d, auto &results)
template<typename Ts , std::size_t... Is>
auto	get_best_thresholds (const Dataset &d, std::index_sequence< Is... >)
template<typename T >
T	stitch (array< T, 2 > &child_outputs, const ArrayXb &mask)
	Stitches together outputs from left or right child based on threshold.

Function Documentation

best_threshold()

template<typename T >

tuple< float, float > Split::best_threshold	(	const T &	x,
		const ArrayXf &	y,
		bool	classification )

Definition at line 61 of file split.h.

Here is the call graph for this function:

Here is the caller graph for this function:

gain()

float Split::gain	(	const ArrayXf &	lsplit,
		const ArrayXf &	rsplit,
		bool	classification,
		vector< float >	unique_classes )

Here is the caller graph for this function:

get_best_threshold_by_type()

template<typename T >

void Split::get_best_threshold_by_type	(	const Dataset &	d,
		auto &	results )

Definition at line 115 of file split.h.

Here is the call graph for this function:

get_best_thresholds()

template<typename Ts , std::size_t... Is>

auto Split::get_best_thresholds	(	const Dataset &	d,
		std::index_sequence< Is... >	)

Definition at line 153 of file split.h.

Here is the call graph for this function:

get_best_variable_and_threshold()

tuple< string, float > Split::get_best_variable_and_threshold	(	const Dataset &	d,
		TreeNode &	tn )

Here is the caller graph for this function:

get_thresholds()

template<typename T >

vector< float > Split::get_thresholds

(

const T &

x

)

Here is the caller graph for this function:

gini_impurity_index()

float Split::gini_impurity_index	(	const ArrayXf &	classes,
		const vector< float > &	uc )

stitch()

template<typename T >

T Split::stitch	(	array< T, 2 > &	child_outputs,
		const ArrayXb &	mask )

Stitches together outputs from left or right child based on threshold.

Definition at line 172 of file split.h.

Here is the call graph for this function:

Here is the caller graph for this function:

threshold_mask() [1/7]

template<typename T >

ArrayXb Split::threshold_mask	(	const T &	x,
		const float &	threshold )

Applies a learned threshold to a feature, returning a mask.

Definition at line 15 of file split.h.

Here is the call graph for this function:

Here is the caller graph for this function:

threshold_mask() [2/7]

template<typename T >
requires same_as<typename T::Scalar, bool>

ArrayXb Split::threshold_mask	(	const T &	x,
		const float &	threshold )

Applies a learned threshold to a feature, returning a mask.

Definition at line 15 of file split.h.

threshold_mask() [3/7]

template<typename T >
requires same_as<typename T::Scalar, bJet>

ArrayXb Split::threshold_mask	(	const T &	x,
		const float &	threshold )

Applies a learned threshold to a feature, returning a mask.

Definition at line 19 of file split.h.

Here is the call graph for this function:

threshold_mask() [4/7]

template<typename T >
requires same_as<typename T::Scalar, float>

ArrayXb Split::threshold_mask	(	const T &	x,
		const float &	threshold )

Applies a learned threshold to a feature, returning a mask.

Definition at line 26 of file split.h.

Here is the call graph for this function:

threshold_mask() [5/7]

template<typename T >
requires same_as<typename T::Scalar, fJet>

ArrayXb Split::threshold_mask	(	const T &	x,
		const float &	threshold )

Applies a learned threshold to a feature, returning a mask.

Definition at line 30 of file split.h.

Here is the call graph for this function:

threshold_mask() [6/7]

template<typename T >
requires same_as<typename T::Scalar, int>

ArrayXb Split::threshold_mask	(	const T &	x,
		const float &	threshold )

Applies a learned threshold to a feature, returning a mask.

Definition at line 39 of file split.h.

Here is the call graph for this function:

threshold_mask() [7/7]

template<typename T >
requires same_as<typename T::Scalar, iJet>

ArrayXb Split::threshold_mask	(	const T &	x,
		const float &	threshold )

Applies a learned threshold to a feature, returning a mask.

Definition at line 44 of file split.h.

Here is the call graph for this function:

Here is the caller graph for this function: