ml::DecisionTrees::SplitNode< Y > Struct Template Reference

Non-terminal node, which splits data depending on a threshold value of some feature. More...

#include <DecisionTreeNodes.hpp>

Public Types
using	arg_type = typename Node< Y >::arg_type

Public Member Functions
	SplitNode (double n_error, Y n_value, SplitNode< Y > *n_parent, double n_threshold, unsigned int n_feature_index)
	Constructor. More...
Y	operator() (arg_type x) const override
	Returns a prediction given a feature vector. More...
unsigned int	count_lower_nodes () const override
	Total number of nodes reachable from this one. More...
unsigned int	count_leaf_nodes () const override
	Total number of leaf nodes reachable from this one, including itself. More...
double	total_leaf_error () const override
	Total error of the training samples seen by the leaf nodes reachable from this node (including its own if leaf). More...
SplitNode< Y > *	clone (SplitNode< Y > *cloned_parent) const override
	Make a perfect copy of the node. Function works recursively from root to leafs. More...
bool	is_leaf () const override
	Return true if node is a leaf. More...
void	collect_lowest_split_nodes (std::unordered_set< SplitNode< Y > * > &s) override
	Adds all lowest split nodes. More...

Public Attributes
std::unique_ptr< Node< Y > >	lower
std::unique_ptr< Node< Y > >	higher
double	threshold
unsigned int	feature_index

Detailed Description

template<class Y>
struct ml::DecisionTrees::SplitNode< Y >

Non-terminal node, which splits data depending on a threshold value of some feature.

Member Typedef Documentation

arg_type

template<class Y >

using ml::DecisionTrees::SplitNode< Y >::arg_type = typename Node<Y>::arg_type

Type for feature vector.

Constructor & Destructor Documentation

SplitNode()

template<class Y >

ml::DecisionTrees::SplitNode< Y >::SplitNode ( double  n_error, Y  n_value, SplitNode< Y > *  n_parent, double  n_threshold, unsigned int  n_feature_index  )

inline

Constructor.

Parameters

[in]	n_error	Prediction error value on training data which reached this node.
[in]	n_value	Prediction value assigned to this node.
[in]	n_parent	Link to parent node.
[in]	n_threshold	Split threshold value.
[in]	n_feature_index	Index of the feature on which this node splits data.

Exceptions

std::domain_error

If n_error < 0.

Member Function Documentation

operator()()

template<class Y >

Y ml::DecisionTrees::SplitNode< Y >::operator() ( arg_type  x ) const

inlineoverride

Returns a prediction given a feature vector.

Parameters

x	Feature vector.

Returns

Prediction value.

count_lower_nodes()

template<class Y >

unsigned int ml::DecisionTrees::SplitNode< Y >::count_lower_nodes ( ) const

inlineoverride

Total number of nodes reachable from this one.

count_leaf_nodes()

template<class Y >

unsigned int ml::DecisionTrees::SplitNode< Y >::count_leaf_nodes ( ) const

inlineoverride

Total number of leaf nodes reachable from this one, including itself.

total_leaf_error()

template<class Y >

double ml::DecisionTrees::SplitNode< Y >::total_leaf_error ( ) const

inlineoverride

Total error of the training samples seen by the leaf nodes reachable from this node (including its own if leaf).

Has the invariant total_leaf_error() <= error.

clone()

template<class Y >

SplitNode<Y>* ml::DecisionTrees::SplitNode< Y >::clone ( SplitNode< Y > *  cloned_parent ) const

inlineoverride

Make a perfect copy of the node. Function works recursively from root to leafs.

Parameters

[in]

cloned_parent

Pointer to already cloned parent.

is_leaf()

template<class Y >

bool ml::DecisionTrees::SplitNode< Y >::is_leaf ( ) const

inlineoverride

Return true if node is a leaf.

collect_lowest_split_nodes()

template<class Y >

void ml::DecisionTrees::SplitNode< Y >::collect_lowest_split_nodes ( std::unordered_set< SplitNode< Y > * > &  s )

inlineoverride

Adds all lowest split nodes.

Walks over this node and all below it, adding to s every split node which has only leaf nodes as children.

Parameters

[out]

s

Set of pointers to split nodes.

Member Data Documentation

lower

template<class Y >

std::unique_ptr<Node<Y> > ml::DecisionTrees::SplitNode< Y >::lower

Followed if x[feature_index] < threshold.

higher

template<class Y >

std::unique_ptr<Node<Y> > ml::DecisionTrees::SplitNode< Y >::higher

Followed if x[feature_index] >= threshold.

threshold

template<class Y >

double ml::DecisionTrees::SplitNode< Y >::threshold

Split threshold value.

feature_index

template<class Y >

unsigned int ml::DecisionTrees::SplitNode< Y >::feature_index

Index of the feature on which this node splits data.

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The documentation for this struct was generated from the following file:

• ML/DecisionTreeNodes.hpp