#include <DecisionTree.h>

Classes
struct	Choice

struct	Leaf

struct	Node

Public Types
using	LabelFormatter = std::function< std::string(L)>

using	ValueFormatter = std::function< std::string(Y)>

using	CompareFunc = std::function< bool(const Y &, const Y &)>

using	Unary = std::function< Y(const Y &)>

using	UnaryAssignment = std::function< Y(const Assignment< L > &, const Y &)>

using	Binary = std::function< Y(const Y &, const Y &)>

using	LabelC = std::pair< L, size_t >

using	NodePtr = typename Node::Ptr

Public Member Functions
Standard Constructors
	DecisionTree ()

	DecisionTree (const Y &y)

	DecisionTree (const L &label, const Y &y1, const Y &y2)
	Create tree with 2 assignments `y1`, `y2`, splitting on variable `label`

	DecisionTree (const LabelC &label, const Y &y1, const Y &y2)

	DecisionTree (const std::vector< LabelC > &labelCs, const std::vector< Y > &ys)

	DecisionTree (const std::vector< LabelC > &labelCs, const std::string &table)

template<typename Iterator >
	DecisionTree (Iterator begin, Iterator end, const L &label)

	DecisionTree (const L &label, const DecisionTree &f0, const DecisionTree &f1)

template<typename X , typename Func >
	DecisionTree (const DecisionTree< L, X > &other, Func Y_of_X)
	Convert from a different value type. More...

template<typename M , typename X , typename Func >
	DecisionTree (const DecisionTree< M, X > &other, const std::map< M, L > &map, Func Y_of_X)
	Convert from a different value type X to value type Y, also transate labels via map from type M to L. More...

Testable
void	print (const std::string &s, const LabelFormatter &labelFormatter, const ValueFormatter &valueFormatter) const
	GTSAM-style print. More...

bool	equals (const DecisionTree &other, const CompareFunc &compare=&DefaultCompare) const

Standard Interface
virtual	~DecisionTree ()=default
	Make virtual.

bool	empty () const
	Check if tree is empty.

bool	operator== (const DecisionTree &q) const

const Y &	operator() (const Assignment< L > &x) const

template<typename Func >
void	visit (Func f) const
	Visit all leaves in depth-first fashion. More...

template<typename Func >
void	visitLeaf (Func f) const
	Visit all leaves in depth-first fashion. More...

template<typename Func >
void	visitWith (Func f) const
	Visit all leaves in depth-first fashion. More...

size_t	nrLeaves () const
	Return the number of leaves in the tree.

template<typename Func , typename X >
X	fold (Func f, X x0) const
	Fold a binary function over the tree, returning accumulator. More...

std::set< L >	labels () const

DecisionTree	apply (const Unary &op) const

DecisionTree	apply (const UnaryAssignment &op) const
	Apply Unary operation "op" to f while also providing the corresponding assignment. More...

DecisionTree	apply (const DecisionTree &g, const Binary &op) const

DecisionTree	choose (const L &label, size_t index) const

DecisionTree	combine (const L &label, size_t cardinality, const Binary &op) const

DecisionTree	combine (const LabelC &labelC, const Binary &op) const

void	dot (std::ostream &os, const LabelFormatter &labelFormatter, const ValueFormatter &valueFormatter, bool showZero=true) const

void	dot (const std::string &name, const LabelFormatter &labelFormatter, const ValueFormatter &valueFormatter, bool showZero=true) const

std::string	dot (const LabelFormatter &labelFormatter, const ValueFormatter &valueFormatter, bool showZero=true) const

Advanced Interface
	DecisionTree (const NodePtr &root)

template<typename Iterator >
NodePtr	compose (Iterator begin, Iterator end, const L &label) const

Public Attributes
NodePtr	root_
	A DecisionTree just contains the root. TODO(dellaert): make protected.

Protected Member Functions
template<typename It , typename ValueIt >
NodePtr	create (It begin, It end, ValueIt beginY, ValueIt endY) const

template<typename M , typename X >
NodePtr	convertFrom (const typename DecisionTree< M, X >::NodePtr &f, std::function< L(const M &)> L_of_M, std::function< Y(const X &)> Y_of_X) const
	Convert from a DecisionTree<M, X> to DecisionTree<L, Y>. More...

Static Protected Member Functions
static bool	DefaultCompare (const Y &a, const Y &b)
	Default method for comparison of two objects of type Y.

Detailed Description

template<typename L, typename Y>
class gtsam::DecisionTree< L, Y >

Decision Tree L = label for variables Y = function range (any algebra), e.g., bool, int, double

Member Typedef Documentation

◆ LabelC

template<typename L, typename Y>

using gtsam::DecisionTree< L, Y >::LabelC = std::pair<L, size_t>

A label annotated with cardinality

◆ NodePtr

template<typename L, typename Y>

using gtsam::DecisionTree< L, Y >::NodePtr = typename Node::Ptr

------------------—— Node base class ---------------------—— A function is a shared pointer to the root of a DT

◆ Unary

template<typename L, typename Y>

using gtsam::DecisionTree< L, Y >::Unary = std::function<Y(const Y&)>

Handy typedefs for unary and binary function types

Constructor & Destructor Documentation

◆ DecisionTree() [1/9]

template<typename L , typename Y >

gtsam::DecisionTree< L, Y >::DecisionTree ( )

Default constructor (for serialization)

◆ DecisionTree() [2/9]

template<typename L , typename Y>

gtsam::DecisionTree< L, Y >::DecisionTree ( const Y & y )

explicit

Create a constant

◆ DecisionTree() [3/9]

template<typename L, typename Y>

gtsam::DecisionTree< L, Y >::DecisionTree	(	const LabelC &	label,
		const Y &	y1,
		const Y &	y2
	)

Allow Label+Cardinality for convenience

◆ DecisionTree() [4/9]

template<typename L, typename Y>

gtsam::DecisionTree< L, Y >::DecisionTree	(	const std::vector< LabelC > &	labelCs,
		const std::vector< Y > &	ys
	)

Create from keys and a corresponding vector of values

◆ DecisionTree() [5/9]

template<typename L, typename Y>

gtsam::DecisionTree< L, Y >::DecisionTree	(	const std::vector< LabelC > &	labelCs,
		const std::string &	table
	)

Create from keys and string table

◆ DecisionTree() [6/9]

template<typename L, typename Y >

template<typename Iterator >

gtsam::DecisionTree< L, Y >::DecisionTree	(	Iterator	begin,
		Iterator	end,
		const L &	label
	)

Create DecisionTree from others

◆ DecisionTree() [7/9]

template<typename L, typename Y>

gtsam::DecisionTree< L, Y >::DecisionTree	(	const L &	label,
		const DecisionTree< L, Y > &	f0,
		const DecisionTree< L, Y > &	f1
	)

Create DecisionTree from two others

◆ DecisionTree() [8/9]

template<typename L, typename Y >

template<typename X , typename Func >

gtsam::DecisionTree< L, Y >::DecisionTree	(	const DecisionTree< L, X > &	other,
		Func	Y_of_X
	)

Convert from a different value type.

Template Parameters

X	The previous value type.

Parameters

other	The DecisionTree to convert from.
Y_of_X	Functor to convert from value type X to type Y.

◆ DecisionTree() [9/9]

template<typename L, typename Y >

template<typename M , typename X , typename Func >

gtsam::DecisionTree< L, Y >::DecisionTree	(	const DecisionTree< M, X > &	other,
		const std::map< M, L > &	map,
		Func	Y_of_X
	)

Convert from a different value type X to value type Y, also transate labels via map from type M to L.

Template Parameters

M	Previous label type.
X	Previous value type.

Parameters

other	The decision tree to convert.
L_of_M	Map from label type M to type L.
Y_of_X	Functor to convert from type X to type Y.

Member Function Documentation

◆ apply() [1/3]

template<typename L , typename Y >

DecisionTree< L, Y > gtsam::DecisionTree< L, Y >::apply ( const Unary & op ) const

apply Unary operation "op" to f

◆ apply() [2/3]

template<typename L , typename Y >

DecisionTree< L, Y > gtsam::DecisionTree< L, Y >::apply ( const UnaryAssignment & op ) const

Apply Unary operation "op" to f while also providing the corresponding assignment.

Apply unary operator with assignment.

Parameters

op	Function which takes Assignment<L> and Y as input and returns object of type Y.

Returns: DecisionTree

◆ apply() [3/3]

template<typename L , typename Y >

DecisionTree< L, Y > gtsam::DecisionTree< L, Y >::apply	(	const DecisionTree< L, Y > &	g,
		const Binary &	op
	)		const

apply binary operation "op" to f and g

◆ choose()

template<typename L, typename Y>

DecisionTree gtsam::DecisionTree< L, Y >::choose	(	const L &	label,
		size_t	index
	)		const

inline

create a new function where value(label)==index It's like "restrict" in Darwiche09book pg329, 330?

◆ combine() [1/2]

template<typename L, typename Y >

DecisionTree< L, Y > gtsam::DecisionTree< L, Y >::combine	(	const L &	label,
		size_t	cardinality,
		const Binary &	op
	)		const

combine subtrees on key with binary operation "op"

◆ combine() [2/2]

template<typename L, typename Y>

DecisionTree gtsam::DecisionTree< L, Y >::combine	(	const LabelC &	labelC,
		const Binary &	op
	)		const

inline

combine with LabelC for convenience

◆ convertFrom()

template<typename L, typename Y>

template<typename M , typename X >

DecisionTree< L, Y >::NodePtr gtsam::DecisionTree< L, Y >::convertFrom	(	const typename DecisionTree< M, X >::NodePtr &	f,
		std::function< L(const M &)>	L_of_M,
		std::function< Y(const X &)>	Y_of_X
	)		const

protected

Convert from a DecisionTree<M, X> to DecisionTree<L, Y>.

Template Parameters

M	The previous label type.
X	The previous value type.

Parameters

f	The node pointer to the root of the previous DecisionTree.
L_of_M	Functor to convert from label type M to type L.
Y_of_X	Functor to convert from value type X to type Y.

Returns: NodePtr

◆ create()

template<typename L , typename Y >

template<typename It , typename ValueIt >

DecisionTree< L, Y >::NodePtr gtsam::DecisionTree< L, Y >::create	(	It	begin,
		It	end,
		ValueIt	beginY,
		ValueIt	endY
	)		const

protected

Internal recursive function to create from keys, cardinalities, and Y values

◆ dot() [1/3]

template<typename L , typename Y >

void gtsam::DecisionTree< L, Y >::dot	(	std::ostream &	os,
		const LabelFormatter &	labelFormatter,
		const ValueFormatter &	valueFormatter,
		bool	showZero = `true`
	)		const

output to graphviz format, stream version

◆ dot() [2/3]

template<typename L , typename Y >

void gtsam::DecisionTree< L, Y >::dot	(	const std::string &	name,
		const LabelFormatter &	labelFormatter,
		const ValueFormatter &	valueFormatter,
		bool	showZero = `true`
	)		const

output to graphviz format, open a file

◆ dot() [3/3]

template<typename L , typename Y >

std::string gtsam::DecisionTree< L, Y >::dot	(	const LabelFormatter &	labelFormatter,
		const ValueFormatter &	valueFormatter,
		bool	showZero = `true`
	)		const

output to graphviz format string

◆ fold()

template<typename L , typename Y >

template<typename Func , typename X >

X gtsam::DecisionTree< L, Y >::fold	(	Func	f,
		X	x0
	)		const

Fold a binary function over the tree, returning accumulator.

Template Parameters

X	type for accumulator.

Parameters

f	binary function: Y * X -> X returning an updated accumulator.
x0	initial value for accumulator.

Returns: X final value for accumulator.

Note: X is always passed by value.; Due to pruning, leaves might not exhaust choices.

Example: auto add = [](const double& y, double x) { return y + x; }; double sum = tree.fold(add, 0.0);

◆ labels()

template<typename L , typename Y >

std::set< L > gtsam::DecisionTree< L, Y >::labels ( ) const

Retrieve all unique labels as a set.

Get (partial) labels by performing a visit.

This method performs a depth-first search to go to every leaf and records the keys assignment which leads to that leaf. Since the tree can be pruned, there might be a leaf at a lower depth which results in a partial assignment (i.e. not all keys are specified).

E.g. given a tree with 3 keys, there may be a branch where the 3rd key has the same values for all the leaves. This leads to the branch being pruned so we get a leaf which is arrived at by just the first 2 keys and their assignments.

◆ operator()()

template<typename L, typename Y >

const Y & gtsam::DecisionTree< L, Y >::operator() ( const Assignment< L > & x ) const

evaluate

◆ operator==()

template<typename L , typename Y >

bool gtsam::DecisionTree< L, Y >::operator== ( const DecisionTree< L, Y > & q ) const

equality

◆ print()

template<typename L , typename Y >

void gtsam::DecisionTree< L, Y >::print	(	const std::string &	s,
		const LabelFormatter &	labelFormatter,
		const ValueFormatter &	valueFormatter
	)		const

GTSAM-style print.

Parameters

s	Prefix string.
labelFormatter	Functor to format the node label.
valueFormatter	Functor to format the node value.

◆ visit()

template<typename L , typename Y >

template<typename Func >

void gtsam::DecisionTree< L, Y >::visit ( Func f ) const

Visit all leaves in depth-first fashion.

Parameters

f	(side-effect) Function taking the value of the leaf node.

Note: Due to pruning, the number of leaves may not be the same as the number of assignments. E.g. if we have a tree on 2 binary variables with all values being 1, then there are 2^2=4 assignments, but only 1 leaf.

Example: int sum = 0; auto visitor = [&](int y) { sum += y; }; tree.visit(visitor);

◆ visitLeaf()

template<typename L , typename Y >

template<typename Func >

void gtsam::DecisionTree< L, Y >::visitLeaf ( Func f ) const

Visit all leaves in depth-first fashion.

Parameters

f	(side-effect) Function taking the leaf node pointer.

Note: Due to pruning, the number of leaves may not be the same as the number of assignments. E.g. if we have a tree on 2 binary variables with all values being 1, then there are 2^2=4 assignments, but only 1 leaf.

Example: int sum = 0; auto visitor = [&](const Leaf& leaf) { sum += leaf.constant(); }; tree.visitLeaf(visitor);

◆ visitWith()

template<typename L , typename Y >

template<typename Func >

void gtsam::DecisionTree< L, Y >::visitWith ( Func f ) const

Visit all leaves in depth-first fashion.

Parameters

f	(side-effect) Function taking an assignment and a value.

Note: Due to pruning, the number of leaves may not be the same as the number of assignments. E.g. if we have a tree on 2 binary variables with all values being 1, then there are 2^2=4 assignments, but only 1 leaf.

Example: int sum = 0; auto visitor = [&](const Assignment<L>& assignment, int y) { sum += y; }; tree.visitWith(visitor);

The documentation for this class was generated from the following files:

/home/docs/checkouts/readthedocs.org/user_builds/gtsam-jlblanco-docs/checkouts/latest/gtsam/discrete/DecisionTree.h
/home/docs/checkouts/readthedocs.org/user_builds/gtsam-jlblanco-docs/checkouts/latest/gtsam/discrete/DecisionTree-inl.h

Classes

Public Types

Public Member Functions

Public Attributes

Protected Member Functions

Static Protected Member Functions

Detailed Description

template<typename L, typename Y> class gtsam::DecisionTree< L, Y >

Member Typedef Documentation

◆ LabelC

◆ NodePtr

◆ Unary

Constructor & Destructor Documentation

◆ DecisionTree() [1/9]

◆ DecisionTree() [2/9]

◆ DecisionTree() [3/9]

◆ DecisionTree() [4/9]

◆ DecisionTree() [5/9]

◆ DecisionTree() [6/9]

◆ DecisionTree() [7/9]

◆ DecisionTree() [8/9]

◆ DecisionTree() [9/9]

Member Function Documentation

◆ apply() [1/3]

◆ apply() [2/3]

◆ apply() [3/3]

◆ choose()

◆ combine() [1/2]

◆ combine() [2/2]

◆ convertFrom()

◆ create()

◆ dot() [1/3]

◆ dot() [2/3]

◆ dot() [3/3]

◆ fold()

◆ labels()

◆ operator()()

◆ operator==()

◆ print()

◆ visit()

◆ visitLeaf()

◆ visitWith()

template<typename L, typename Y>
class gtsam::DecisionTree< L, Y >