#include <DiscreteDistribution.h>

Inheritance diagram for gtsam::DiscreteDistribution:

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Collaboration diagram for gtsam::DiscreteDistribution:

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Public Types
using	Base = DiscreteConditional

typedef DiscreteConditional	This
	Typedef to this class.

typedef std::shared_ptr< This >	shared_ptr
	shared_ptr to this class

typedef DecisionTreeFactor	BaseFactor
	Typedef to our factor base class.

typedef Conditional< BaseFactor, This >	BaseConditional
	Typedef to our conditional base class.

using	Values = DiscreteValues
	backwards compatibility

typedef AlgebraicDecisionTree< Key >	ADT

typedef KeyVector::iterator	iterator
	Iterator over keys.

typedef KeyVector::const_iterator	const_iterator
	Const iterator over keys.

using	LabelFormatter = std::function< std::string(Key)>

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

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

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

using	UnaryAssignment = std::function< double(const Assignment< Key > &, const double &)>

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

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

using	NodePtr = typename Node::Ptr

typedef std::pair< typename DecisionTreeFactor ::const_iterator, typename DecisionTreeFactor ::const_iterator >	ConstFactorRange

typedef ConstFactorRangeIterator	Frontals

typedef ConstFactorRangeIterator	Parents

Wrapper support
using	Names = DiscreteValues::Names
	Translation table from values to strings.

Public Member Functions
AlgebraicDecisionTree	operator* (const AlgebraicDecisionTree &g) const

void	print (const std::string &s="", const typename Base::LabelFormatter &labelFormatter=&DefaultFormatter) const
	print method customized to value type `double`.

bool	equals (const AlgebraicDecisionTree &other, double tol=1e-9) const
	Equality method customized to value type `double`.

AlgebraicDecisionTree	operator/ (const AlgebraicDecisionTree &g) const

AlgebraicDecisionTree	sum (const Key &label, size_t cardinality) const

AlgebraicDecisionTree	sum (const typename Base::LabelC &labelC) const

AlgebraicDecisionTree	operator+ (const AlgebraicDecisionTree &g) const

Standard Constructors
	DiscreteDistribution ()
	Default constructor needed for serialization.

	DiscreteDistribution (const DecisionTreeFactor &f)
	Constructor from factor.

	DiscreteDistribution (const Signature &s)

	DiscreteDistribution (const DiscreteKey &key, const std::vector< double > &spec)

	DiscreteDistribution (const DiscreteKey &key, const std::string &spec)

Testable
void	print (const std::string &s="Discrete Prior: ", const KeyFormatter &formatter=DefaultKeyFormatter) const override
	GTSAM-style print.

Standard interface
double	operator() (size_t value) const
	Evaluate given a single value.

std::vector< double >	pmf () const
	We also want to keep the Base version, taking DiscreteValues: More...

Standard Constructors
DiscreteConditional	operator* (const DiscreteConditional &other) const
	Combine two conditionals, yielding a new conditional with the union of the frontal keys, ordered by gtsam::Key. More...

DiscreteConditional	marginal (Key key) const

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

Testable
bool	equals (const DiscreteFactor &other, double tol=1e-9) const override
	GTSAM-style equals.

Testable
bool	equals (const This &other, double tol=1e-9) const
	check equality

virtual void	printKeys (const std::string &s="Factor", const KeyFormatter &formatter=DefaultKeyFormatter) const
	print only keys

Testable
bool	equals (const This &c, double tol=1e-9) const

Standard Interface
double	logProbability (const DiscreteValues &x) const
	Log-probability is just -error(x).

void	printSignature (const std::string &s="Discrete Conditional: ", const KeyFormatter &formatter=DefaultKeyFormatter) const
	print index signature only

double	evaluate (const DiscreteValues &values) const
	Evaluate, just look up in AlgebraicDecisonTree.

shared_ptr	choose (const DiscreteValues &given) const
	< DiscreteValues version More...

DecisionTreeFactor::shared_ptr	likelihood (const DiscreteValues &frontalValues) const

DecisionTreeFactor::shared_ptr	likelihood (size_t frontal) const

size_t	sample (const DiscreteValues &parentsValues) const

size_t	sample (size_t parent_value) const
	Single parent version.

size_t	sample () const
	Zero parent version.

size_t	argmax () const
	Return assignment that maximizes distribution. More...

HybridValues methods.
double	logProbability (const HybridValues &x) const override
	< HybridValues version More...

double	evaluate (const HybridValues &x) const override

double	logNormalizationConstant () const override

Standard Interface
DecisionTree	choose (const Key &label, size_t index) const

const double &	operator() (const Assignment< Key > &x) 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	combine (const Key &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

bool	empty () const
	Check if tree is empty.

bool	operator== (const DecisionTree &q) const

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

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

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.

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

std::set< Key >	labels () const

Advanced Interface
void	sampleInPlace (DiscreteValues *parentsValues) const
	sample in place, stores result in partial solution

std::vector< DiscreteValues >	frontalAssignments () const
	Return all assignments for frontal variables.

std::vector< DiscreteValues >	allAssignments () const
	Return all assignments for frontal and parent variables.

Wrapper support
std::string	markdown (const KeyFormatter &keyFormatter=DefaultKeyFormatter, const Names &names={}) const override
	Render as markdown table.

std::string	html (const KeyFormatter &keyFormatter=DefaultKeyFormatter, const Names &names={}) const override
	Render as html table.

Standard Interface
double	operator() (const HybridValues &x) const
	Evaluate probability density, sugar.

size_t	nrFrontals () const

size_t	nrParents () const

Key	firstFrontalKey () const

Frontals	frontals () const

Parents	parents () const

double	normalizationConstant () const

HybridValues methods.
double	error (const HybridValues &values) const override

Advanced Interface
DecisionTreeFactor	apply (const DecisionTreeFactor &f, ADT::Binary op) const

shared_ptr	combine (size_t nrFrontals, ADT::Binary op) const

shared_ptr	combine (const Ordering &keys, ADT::Binary op) const

std::vector< std::pair< DiscreteValues, double > >	enumerate () const
	Enumerate all values into a map from values to double.

DiscreteKeys	discreteKeys () const
	Return all the discrete keys associated with this factor.

DecisionTreeFactor	prune (size_t maxNrAssignments) const
	Prune the decision tree of discrete variables. More...

Wrapper support
void	dot (std::ostream &os, const KeyFormatter &keyFormatter=DefaultKeyFormatter, bool showZero=true) const

void	dot (const std::string &name, const KeyFormatter &keyFormatter=DefaultKeyFormatter, bool showZero=true) const

std::string	dot (const KeyFormatter &keyFormatter=DefaultKeyFormatter, bool showZero=true) const

Standard Interface
bool	empty () const
	Whether the factor is empty (involves zero variables).

Key	front () const
	First key.

Key	back () const
	Last key.

const_iterator	find (Key key) const
	find

const KeyVector &	keys () const
	Access the factor's involved variable keys.

const_iterator	begin () const

const_iterator	end () const

size_t	size () const

Advanced Interface
KeyVector &	keys ()

iterator	begin ()

iterator	end ()

Advanced Interface
NodePtr	compose (Iterator begin, Iterator end, const Key &label) const

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

Protected Member Functions
DiscreteConditional::ADT	choose (const DiscreteValues &given, bool forceComplete) const
	Internal version of choose.

NodePtr	create (It begin, It end, ValueIt beginY, ValueIt endY) const

NodePtr	convertFrom (const typename DecisionTree< M, X >::NodePtr &f, std::function< Key(const M &)> L_of_M, std::function< double(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 double &a, const double &b)
	Default method for comparison of two objects of type Y.

Standard Constructors
template<typename CONTAINER >
static Factor	FromKeys (const CONTAINER &keys)

template<typename ITERATOR >
static Factor	FromIterators (ITERATOR first, ITERATOR last)

Protected Attributes
std::map< Key, size_t >	cardinalities_

KeyVector	keys_
	The keys involved in this factor.

size_t	nrFrontals_

Standard Interface
DecisionTreeFactor	operator* (const DecisionTreeFactor &f) const override
	multiply two factors

double	operator() (const DiscreteValues &values) const override
	Evaluate probability distribution, sugar.

double	error (const DiscreteValues &values) const
	Calculate error for DiscreteValues `x`, is -log(probability).

size_t	cardinality (Key j) const

DecisionTreeFactor	operator/ (const DecisionTreeFactor &f) const
	divide by factor f (safely)

DecisionTreeFactor	toDecisionTreeFactor () const override
	Convert into a decisiontree.

shared_ptr	sum (size_t nrFrontals) const
	Create new factor by summing all values with the same separator values.

shared_ptr	sum (const Ordering &keys) const
	Create new factor by summing all values with the same separator values.

shared_ptr	max (size_t nrFrontals) const
	Create new factor by maximizing over all values with the same separator.

shared_ptr	max (const Ordering &keys) const
	Create new factor by maximizing over all values with the same separator.

static double	safe_div (const double &a, const double &b)

Advanced Interface
size_t &	nrFrontals ()

DecisionTreeFactor ::const_iterator	beginFrontals () const

DecisionTreeFactor ::iterator	beginFrontals ()

DecisionTreeFactor ::const_iterator	endFrontals () const

DecisionTreeFactor ::iterator	endFrontals ()

DecisionTreeFactor ::const_iterator	beginParents () const

DecisionTreeFactor ::iterator	beginParents ()

DecisionTreeFactor ::const_iterator	endParents () const

DecisionTreeFactor ::iterator	endParents ()

static bool	CheckInvariants (const DiscreteConditional &conditional, const VALUES &x)

Detailed Description

A prior probability on a set of discrete variables. Derives from DiscreteConditional

Member Typedef Documentation

◆ ConstFactorRange

typedef std::pair<typename DecisionTreeFactor ::const_iterator, typename DecisionTreeFactor ::const_iterator> gtsam::Conditional< DecisionTreeFactor , DiscreteConditional >::ConstFactorRange

inherited

A mini implementation of an iterator range, to share const views of frontals and parents.

◆ Frontals

typedef ConstFactorRangeIterator gtsam::Conditional< DecisionTreeFactor , DiscreteConditional >::Frontals

inherited

View of the frontal keys (call frontals())

◆ LabelC

using gtsam::DecisionTree< Key , double >::LabelC = std::pair<Key , size_t>

inherited

A label annotated with cardinality

◆ NodePtr

using gtsam::DecisionTree< Key , double >::NodePtr = typename Node::Ptr

inherited

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

◆ Parents

typedef ConstFactorRangeIterator gtsam::Conditional< DecisionTreeFactor , DiscreteConditional >::Parents

inherited

View of the separator keys (call parents())

◆ Unary

using gtsam::DecisionTree< Key , double >::Unary = std::function<double (const double &)>

inherited

Handy typedefs for unary and binary function types

Constructor & Destructor Documentation

◆ DiscreteDistribution() [1/3]

gtsam::DiscreteDistribution::DiscreteDistribution ( const Signature & s )

inlineexplicit

Construct from a Signature.

Example: DiscreteDistribution P(D % "3/2");

◆ DiscreteDistribution() [2/3]

gtsam::DiscreteDistribution::DiscreteDistribution	(	const DiscreteKey &	key,
		const std::vector< double > &	spec
	)

inline

Construct from key and a vector of floats specifying the probability mass function (PMF).

Example: DiscreteDistribution P(D, {0.4, 0.6});

◆ DiscreteDistribution() [3/3]

gtsam::DiscreteDistribution::DiscreteDistribution	(	const DiscreteKey &	key,
		const std::string &	spec
	)

inline

Construct from key and a string specifying the probability mass function (PMF).

Example: DiscreteDistribution P(D, "9/1 2/8 3/7 1/9");

Member Function Documentation

◆ apply() [1/4]

DecisionTreeFactor gtsam::DecisionTreeFactor::apply	(	const DecisionTreeFactor &	f,
		ADT::Binary	op
	)		const

inherited

Apply binary operator (*this) "op" f

Parameters

f	the second argument for op
op	a binary operator that operates on AlgebraicDecisionTree

◆ apply() [2/4]

DecisionTree< Key , double > gtsam::DecisionTree< Key , double >::apply ( const Unary & op ) const

inherited

apply Unary operation "op" to f

◆ apply() [3/4]

DecisionTree< Key , double > gtsam::DecisionTree< Key , double >::apply ( const UnaryAssignment & op ) const

inherited

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() [4/4]

DecisionTree< Key , double > gtsam::DecisionTree< Key , double >::apply	(	const DecisionTree< Key, double > &	g,
		const Binary &	op
	)		const

inherited

apply binary operation "op" to f and g

◆ argmax()

size_t gtsam::DiscreteConditional::argmax ( ) const

inherited

Return assignment that maximizes distribution.

Returns: Optimal assignment (1 frontal variable).

◆ begin() [1/2]

const_iterator gtsam::Factor::begin ( ) const

inlineinherited

Iterator at beginning of involved variable keys

◆ begin() [2/2]

iterator gtsam::Factor::begin ( )

inlineinherited

Iterator at beginning of involved variable keys

◆ beginFrontals() [1/2]

DecisionTreeFactor ::const_iterator gtsam::Conditional< DecisionTreeFactor , DiscreteConditional >::beginFrontals ( ) const

inlineinherited

Iterator pointing to first frontal key.

◆ beginFrontals() [2/2]

DecisionTreeFactor ::iterator gtsam::Conditional< DecisionTreeFactor , DiscreteConditional >::beginFrontals ( )

inlineinherited

Mutable iterator pointing to first frontal key.

◆ beginParents() [1/2]

DecisionTreeFactor ::const_iterator gtsam::Conditional< DecisionTreeFactor , DiscreteConditional >::beginParents ( ) const

inlineinherited

Iterator pointing to the first parent key.

◆ beginParents() [2/2]

DecisionTreeFactor ::iterator gtsam::Conditional< DecisionTreeFactor , DiscreteConditional >::beginParents ( )

inlineinherited

Mutable iterator pointing to the first parent key.

◆ CheckInvariants()

bool gtsam::Conditional< DecisionTreeFactor , DiscreteConditional >::CheckInvariants	(	const DiscreteConditional< DecisionTreeFactor, DiscreteConditional > &	conditional,
		const VALUES &	x
	)

staticinherited

Check invariants of this conditional, given the values x. It tests:

evaluate >= 0.0
evaluate(x) == conditional(x)
exp(logProbability(x)) == evaluate(x)
logNormalizationConstant() = log(normalizationConstant())
error >= 0.0
logProbability(x) == logNormalizationConstant() - error(x)

Parameters

conditional The conditional to test, as a reference to the derived type.

Template Parameters

VALUES HybridValues, or a more narrow type like DiscreteValues.

◆ choose() [1/2]

shared_ptr gtsam::DiscreteConditional::choose ( const DiscreteValues & given ) const

inherited

< DiscreteValues version

restrict to given parent values.

Note: does not need be complete set. Examples:

Returns: a shared_ptr to a new DiscreteConditional

◆ choose() [2/2]

DecisionTree gtsam::DecisionTree< Key , double >::choose	(	const Key &	label,
		size_t	index
	)		const

inlineinherited

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

◆ combine() [1/4]

shared_ptr gtsam::DecisionTreeFactor::combine	(	size_t	nrFrontals,
		ADT::Binary	op
	)		const

inherited

Combine frontal variables using binary operator "op"

Parameters

nrFrontals	nr. of frontal to combine variables in this factor
op	a binary operator that operates on AlgebraicDecisionTree

Returns: shared pointer to newly created DecisionTreeFactor

◆ combine() [2/4]

shared_ptr gtsam::DecisionTreeFactor::combine	(	const Ordering &	keys,
		ADT::Binary	op
	)		const

inherited

Combine frontal variables in an Ordering using binary operator "op"

Parameters

nrFrontals	nr. of frontal to combine variables in this factor
op	a binary operator that operates on AlgebraicDecisionTree

Returns: shared pointer to newly created DecisionTreeFactor

◆ combine() [3/4]

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

inherited

combine subtrees on key with binary operation "op"

◆ combine() [4/4]

DecisionTree gtsam::DecisionTree< Key , double >::combine	(	const LabelC &	labelC,
		const Binary &	op
	)		const

inlineinherited

combine with LabelC for convenience

◆ convertFrom()

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

protectedinherited

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()

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

protectedinherited

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

◆ dot() [1/6]

void gtsam::DecisionTreeFactor::dot	(	std::ostream &	os,
		const KeyFormatter &	keyFormatter = `DefaultKeyFormatter`,
		bool	showZero = `true`
	)		const

inherited

output to graphviz format, stream version

◆ dot() [2/6]

void gtsam::DecisionTreeFactor::dot	(	const std::string &	name,
		const KeyFormatter &	keyFormatter = `DefaultKeyFormatter`,
		bool	showZero = `true`
	)		const

inherited

output to graphviz format, open a file

◆ dot() [3/6]

std::string gtsam::DecisionTreeFactor::dot	(	const KeyFormatter &	keyFormatter = `DefaultKeyFormatter`,
		bool	showZero = `true`
	)		const

inherited

output to graphviz format string

◆ dot() [4/6]

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

inherited

output to graphviz format, stream version

◆ dot() [5/6]

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

inherited

output to graphviz format, open a file

◆ dot() [6/6]

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

inherited

output to graphviz format string

◆ end() [1/2]

const_iterator gtsam::Factor::end ( ) const

inlineinherited

Iterator at end of involved variable keys

◆ end() [2/2]

iterator gtsam::Factor::end ( )

inlineinherited

Iterator at end of involved variable keys

◆ endFrontals() [1/2]

DecisionTreeFactor ::const_iterator gtsam::Conditional< DecisionTreeFactor , DiscreteConditional >::endFrontals ( ) const

inlineinherited

Iterator pointing past the last frontal key.

◆ endFrontals() [2/2]

DecisionTreeFactor ::iterator gtsam::Conditional< DecisionTreeFactor , DiscreteConditional >::endFrontals ( )

inlineinherited

Mutable iterator pointing past the last frontal key.

◆ endParents() [1/2]

DecisionTreeFactor ::const_iterator gtsam::Conditional< DecisionTreeFactor , DiscreteConditional >::endParents ( ) const

inlineinherited

Iterator pointing past the last parent key.

◆ endParents() [2/2]

DecisionTreeFactor ::iterator gtsam::Conditional< DecisionTreeFactor , DiscreteConditional >::endParents ( )

inlineinherited

Mutable iterator pointing past the last parent key.

◆ equals()

bool gtsam::Conditional< DecisionTreeFactor , DiscreteConditional >::equals	(	const This &	c,
		double	tol = `1e-9`
	)		const

inherited

check equality

◆ error()

double gtsam::DecisionTreeFactor::error ( const HybridValues & values ) const

overridevirtualinherited

Calculate error for HybridValues x, is -log(probability) Simply dispatches to DiscreteValues version.

Reimplemented from gtsam::Factor.

◆ evaluate()

double gtsam::DiscreteConditional::evaluate ( const HybridValues & x ) const

overridevirtualinherited

Calculate probability for HybridValues x. Dispatches to DiscreteValues version.

Reimplemented from gtsam::Conditional< DecisionTreeFactor, DiscreteConditional >.

◆ firstFrontalKey()

Key gtsam::Conditional< DecisionTreeFactor , DiscreteConditional >::firstFrontalKey ( ) const

inlineinherited

Convenience function to get the first frontal key

◆ fold()

X gtsam::DecisionTree< Key , double >::fold	(	Func	f,
		X	x0
	)		const

inherited

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);

◆ FromIterators()

template<typename ITERATOR >

static Factor gtsam::Factor::FromIterators	(	ITERATOR	first,
		ITERATOR	last
	)

inlinestaticprotectedinherited

Construct factor from iterator keys. This is called internally from derived factor static factor methods, as a workaround for not being able to call the protected constructors above.

◆ FromKeys()

template<typename CONTAINER >

static Factor gtsam::Factor::FromKeys ( const CONTAINER & keys )

inlinestaticprotectedinherited

Construct factor from container of keys. This is called internally from derived factor static factor methods, as a workaround for not being able to call the protected constructors above.

◆ frontals()

Frontals gtsam::Conditional< DecisionTreeFactor , DiscreteConditional >::frontals ( ) const

inlineinherited

return a view of the frontal keys

◆ keys()

KeyVector& gtsam::Factor::keys ( )

inlineinherited

Returns: keys involved in this factor

◆ labels()

std::set< Key > gtsam::DecisionTree< Key , double >::labels ( ) const

inherited

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.

◆ likelihood() [1/2]

DecisionTreeFactor::shared_ptr gtsam::DiscreteConditional::likelihood ( const DiscreteValues & frontalValues ) const

inherited

Convert to a likelihood factor by providing value before bar.

◆ likelihood() [2/2]

DecisionTreeFactor::shared_ptr gtsam::DiscreteConditional::likelihood ( size_t frontal ) const

inherited

Single variable version of likelihood.

◆ logNormalizationConstant()

double gtsam::DiscreteConditional::logNormalizationConstant ( ) const

inlineoverridevirtualinherited

logNormalizationConstant K is just zero, such that logProbability(x) = log(evaluate(x)) = - error(x) and hence error(x) = - log(evaluate(x)) > 0 for all x.

Reimplemented from gtsam::Conditional< DecisionTreeFactor, DiscreteConditional >.

◆ logProbability()

double gtsam::DiscreteConditional::logProbability ( const HybridValues & x ) const

inlineoverridevirtualinherited

< HybridValues version

Calculate log-probability log(evaluate(x)) for HybridValues x. This is actually just -error(x).

Reimplemented from gtsam::Conditional< DecisionTreeFactor, DiscreteConditional >.

◆ marginal()

DiscreteConditional gtsam::DiscreteConditional::marginal ( Key key ) const

inherited

Calculate marginal on given key, no parent case.

◆ normalizationConstant()

double gtsam::Conditional< DecisionTreeFactor , DiscreteConditional >::normalizationConstant ( ) const

inherited

Non-virtual, exponentiate logNormalizationConstant.

◆ nrFrontals() [1/2]

size_t gtsam::Conditional< DecisionTreeFactor , DiscreteConditional >::nrFrontals ( ) const

inlineinherited

return the number of frontals

◆ nrFrontals() [2/2]

size_t& gtsam::Conditional< DecisionTreeFactor , DiscreteConditional >::nrFrontals ( )

inlineinherited

Mutable version of nrFrontals

◆ nrParents()

size_t gtsam::Conditional< DecisionTreeFactor , DiscreteConditional >::nrParents ( ) const

inlineinherited

return the number of parents

◆ operator()()

const double & gtsam::DecisionTree< Key , double >::operator() ( const Assignment< Key > & x ) const

inherited

evaluate

◆ operator*() [1/2]

DiscreteConditional gtsam::DiscreteConditional::operator* ( const DiscreteConditional & other ) const

inherited

Combine two conditionals, yielding a new conditional with the union of the frontal keys, ordered by gtsam::Key.

The two conditionals must make a valid Bayes net fragment, i.e., the frontal variables cannot overlap, and must be acyclic: Example of correct use: P(A,B) = P(A|B) * P(B) P(A,B|C) = P(A|B) * P(B|C) P(A,B,C) = P(A,B|C) * P(C) Example of incorrect use: P(A|B) * P(A|C) = ? P(A|B) * P(B|A) = ? We check for overlapping frontals, but do not check for cyclic.

◆ operator*() [2/2]

AlgebraicDecisionTree gtsam::AlgebraicDecisionTree< Key >::operator* ( const AlgebraicDecisionTree< Key > & g ) const

inlineinherited

product

◆ operator+()

AlgebraicDecisionTree gtsam::AlgebraicDecisionTree< Key >::operator+ ( const AlgebraicDecisionTree< Key > & g ) const

inlineinherited

sum

◆ operator/()

AlgebraicDecisionTree gtsam::AlgebraicDecisionTree< Key >::operator/ ( const AlgebraicDecisionTree< Key > & g ) const

inlineinherited

division

◆ operator==()

bool gtsam::DecisionTree< Key , double >::operator== ( const DecisionTree< Key, double > & q ) const

inherited

equality

◆ parents()

Parents gtsam::Conditional< DecisionTreeFactor , DiscreteConditional >::parents ( ) const

inlineinherited

return a view of the parent keys

◆ pmf()

std::vector<double> gtsam::DiscreteDistribution::pmf ( ) const

We also want to keep the Base version, taking DiscreteValues:

Return entire probability mass function.

◆ print()

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

inherited

GTSAM-style print.

Parameters

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

◆ prune()

DecisionTreeFactor gtsam::DecisionTreeFactor::prune ( size_t maxNrAssignments ) const

inherited

Prune the decision tree of discrete variables.

Pruning will set the leaves to be "pruned" to 0 indicating a 0 probability. An assignment is pruned if it is not in the top maxNrAssignments values.

A violation can occur if there are more duplicate values than maxNrAssignments. A violation here is the need to un-prune the decision tree (e.g. all assignment values are 1.0). We could have another case where some subset of duplicates exist (e.g. for a tree with 8 assignments we have 1, 1, 1, 1, 0.8, 0.7, 0.6, 0.5), but this is not a violation since the for maxNrAssignments=5 the top values are (1, 0.8).

Parameters

maxNrAssignments The maximum number of assignments to keep.

Returns: DecisionTreeFactor

◆ sample()

size_t gtsam::DiscreteConditional::sample ( const DiscreteValues & parentsValues ) const

inherited

sample

Parameters

parentsValues Known values of the parents

Returns: sample from conditional

◆ size()

size_t gtsam::Factor::size ( ) const

inlineinherited

Returns: the number of variables involved in this factor

◆ sum() [1/2]

AlgebraicDecisionTree gtsam::AlgebraicDecisionTree< Key >::sum	(	const Key &	label,
		size_t	cardinality
	)		const

inlineinherited

sum out variable

◆ sum() [2/2]

AlgebraicDecisionTree gtsam::AlgebraicDecisionTree< Key >::sum ( const typename Base::LabelC & labelC ) const

inlineinherited

sum out variable

◆ visit()

void gtsam::DecisionTree< Key , double >::visit ( Func f ) const

inherited

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()

void gtsam::DecisionTree< Key , double >::visitLeaf ( Func f ) const

inherited

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()

void gtsam::DecisionTree< Key , double >::visitWith ( Func f ) const

inherited

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);

Member Data Documentation

◆ nrFrontals_

size_t gtsam::Conditional< DecisionTreeFactor , DiscreteConditional >::nrFrontals_

protectedinherited

The first nrFrontal variables are frontal and the rest are parents.

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

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

Public Types

Public Member Functions

Public Attributes

Protected Member Functions

Static Protected Member Functions

Protected Attributes

Standard Interface

Advanced Interface

Detailed Description

Member Typedef Documentation

◆ ConstFactorRange

◆ Frontals

◆ LabelC

◆ NodePtr

◆ Parents

◆ Unary

Constructor & Destructor Documentation

◆ DiscreteDistribution() [1/3]

◆ DiscreteDistribution() [2/3]

◆ DiscreteDistribution() [3/3]

Member Function Documentation

◆ apply() [1/4]

◆ apply() [2/4]

◆ apply() [3/4]

◆ apply() [4/4]

◆ argmax()

◆ begin() [1/2]

◆ begin() [2/2]

◆ beginFrontals() [1/2]

◆ beginFrontals() [2/2]

◆ beginParents() [1/2]

◆ beginParents() [2/2]

◆ CheckInvariants()

◆ choose() [1/2]

◆ choose() [2/2]

◆ combine() [1/4]

◆ combine() [2/4]

◆ combine() [3/4]

◆ combine() [4/4]

◆ convertFrom()

◆ create()

◆ dot() [1/6]

◆ dot() [2/6]

◆ dot() [3/6]

◆ dot() [4/6]

◆ dot() [5/6]

◆ dot() [6/6]

◆ end() [1/2]

◆ end() [2/2]

◆ endFrontals() [1/2]

◆ endFrontals() [2/2]

◆ endParents() [1/2]

◆ endParents() [2/2]

◆ equals()

◆ error()

◆ evaluate()

◆ firstFrontalKey()

◆ fold()

◆ FromIterators()

◆ FromKeys()

◆ frontals()

◆ keys()

◆ labels()

◆ likelihood() [1/2]

◆ likelihood() [2/2]

◆ logNormalizationConstant()

◆ logProbability()

◆ marginal()

◆ normalizationConstant()

◆ nrFrontals() [1/2]

◆ nrFrontals() [2/2]

◆ nrParents()

◆ operator()()

◆ operator*() [1/2]

◆ operator*() [2/2]

◆ operator+()

◆ operator/()

◆ operator==()

◆ parents()

◆ pmf()