the class for computing the NML penalty used by MIIC More...

#include <kNML.h>

Inheritance diagram for gum::learning::KNML:

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Public Member Functions
Constructors / Destructors
	KNML (const DBRowGeneratorParser &parser, const Prior &prior, const std::vector< std::pair< std::size_t, std::size_t > > &ranges, const Bijection< NodeId, std::size_t > &nodeId2columns=Bijection< NodeId, std::size_t >())
	default constructor
	KNML (const DBRowGeneratorParser &parser, const Prior &prior, const Bijection< NodeId, std::size_t > &nodeId2columns=Bijection< NodeId, std::size_t >())
	default constructor
	KNML (const KNML &from)
	copy constructor
	KNML (KNML &&from)
	move constructor
virtual KNML *	clone () const
	virtual copy constructor
	~KNML () override
	destructor
Operators
KNML &	operator= (const KNML &from)
	copy operator
KNML &	operator= (KNML &&from)
	move operator
Accessors / Modifiers
double	score (NodeId var1, NodeId var2)
	returns the kNML penalty for a pair of nodes
double	score (NodeId var1, NodeId var2, const std::vector< NodeId > &rhs_ids)
	returns the kNML penalty for a pair of nodes given conditioning nodes
void	clear () override
	clears all the data structures from memory, including the C_n^r cache
void	clearCache () override
	clears the current C_n^r cache
void	useCache (const bool on_off) override
	turn on/off the use of the C_n^r cache
Accessors / Modifiers
void	setNumberOfThreads (Size nb) override
	sets the number max of threads that can be used
Size	getNumberOfThreads () const override
	returns the current max number of threads of the scheduler
bool	isGumNumberOfThreadsOverriden () const override
	indicates whether the user set herself the number of threads
virtual void	setMinNbRowsPerThread (const std::size_t nb) const
	changes the number min of rows a thread should process in a multithreading context
virtual std::size_t	minNbRowsPerThread () const
	returns the minimum of rows that each thread should process
void	setRanges (const std::vector< std::pair< std::size_t, std::size_t > > &new_ranges)
	sets new ranges to perform the counts
void	clearRanges ()
	reset the ranges to the one range corresponding to the whole database
const std::vector< std::pair< std::size_t, std::size_t > > &	ranges () const
	returns the current ranges
const Bijection< NodeId, std::size_t > &	nodeId2Columns () const
	return the mapping between the columns of the database and the node ids
const DatabaseTable &	database () const
	return the database used by the score

Protected Attributes
const double	one_log2_ {M_LOG2E}
	1 / log(2)
Prior *	prior_ {nullptr}
	the expert knowledge prior added to the contingency tables
RecordCounter	counter_
	the record counter used for the counts over discrete variables
ScoringCache	cache_
	the scoring cache
bool	use_cache_ {true}
	a Boolean indicating whether we wish to use the cache
const std::vector< NodeId >	empty_ids_
	an empty vector

Detailed Description

the class for computing the NML penalty used by MIIC

Definition at line 67 of file kNML.h.

Constructor & Destructor Documentation

◆ KNML() [1/4]

gum::learning::KNML::KNML	(	const DBRowGeneratorParser &	parser,
		const Prior &	prior,
		const std::vector< std::pair< std::size_t, std::size_t > > &	ranges,
		const Bijection< NodeId, std::size_t > &	nodeId2columns = Bijection< NodeId, std::size_t >() )

default constructor

Parameters

parser	the parser used to parse the database
prior	An prior that we add to the computation of the score (this should come from expert knowledge): this consists in adding numbers to counts in the contingency tables
ranges	a set of pairs {(X1,Y1),...,(Xn,Yn)} of database's rows indices. The counts are then performed only on the union of the rows [Xi,Yi), i in {1,...,n}. This is useful, e.g, when performing cross validation tasks, in which part of the database should be ignored. An empty set of ranges is equivalent to an interval [X,Y) ranging over the whole database.
nodeId2Columns	a mapping from the ids of the nodes in the graphical model to the corresponding column in the DatabaseTable parsed by the parser. This enables estimating from a database in which variable A corresponds to the 2nd column the parameters of a BN in which variable A has a NodeId of 5. An empty nodeId2Columns bijection means that the mapping is an identity, i.e., the value of a NodeId is equal to the index of the column in the DatabaseTable.

Warning: If nodeId2columns is not empty, then only the scores over the ids belonging to this bijection can be computed: applying method score() over other ids will raise exception NotFound.

References gum::learning::CachedContingencyCounter::ranges().

Referenced by KNML(), KNML(), clone(), operator=(), and operator=().

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◆ KNML() [2/4]

gum::learning::KNML::KNML	(	const DBRowGeneratorParser &	parser,
		const Prior &	prior,
		const Bijection< NodeId, std::size_t > &	nodeId2columns = Bijection< NodeId, std::size_t >() )

default constructor

Parameters

parser	the parser used to parse the database
prior	An prior that we add to the computation of the score (this should come from expert knowledge): this consists in adding numbers to counts in the contingency tables
nodeId2Columns	a mapping from the ids of the nodes in the graphical model to the corresponding column in the DatabaseTable parsed by the parser. This enables estimating from a database in which variable A corresponds to the 2nd column the parameters of a BN in which variable A has a NodeId of 5. An empty nodeId2Columns bijection means that the mapping is an identity, i.e., the value of a NodeId is equal to the index of the column in the DatabaseTable.

Warning: If nodeId2columns is not empty, then only the scores over the ids belonging to this bijection can be computed: applying method score() over other ids will raise exception NotFound.

◆ KNML() [3/4]

gum::learning::KNML::KNML ( const KNML & from )

copy constructor

References KNML().

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◆ KNML() [4/4]

gum::learning::KNML::KNML ( KNML && from )

move constructor

References KNML().

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◆ ~KNML()

gum::learning::KNML::~KNML ( )

override

destructor

Member Function Documentation

◆ clear()

void gum::learning::KNML::clear ( )

overridevirtual

clears all the data structures from memory, including the C_n^r cache

Reimplemented from gum::learning::CachedContingencyCounter.

◆ clearCache()

void gum::learning::KNML::clearCache ( )

overridevirtual

clears the current C_n^r cache

Reimplemented from gum::learning::CachedContingencyCounter.

◆ clearRanges()

void gum::learning::CachedContingencyCounter::clearRanges ( )

inherited

reset the ranges to the one range corresponding to the whole database

◆ clone()

virtual KNML * gum::learning::KNML::clone ( ) const

nodiscardvirtual

virtual copy constructor

References KNML().

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◆ database()

const DatabaseTable & gum::learning::CachedContingencyCounter::database ( ) const

inherited

return the database used by the score

◆ getNumberOfThreads()

Size gum::learning::CachedContingencyCounter::getNumberOfThreads ( ) const

overridevirtualinherited

returns the current max number of threads of the scheduler

Implements gum::IThreadNumberManager.

◆ isGumNumberOfThreadsOverriden()

bool gum::learning::CachedContingencyCounter::isGumNumberOfThreadsOverriden ( ) const

overridevirtualinherited

indicates whether the user set herself the number of threads

Implements gum::IThreadNumberManager.

◆ minNbRowsPerThread()

virtual std::size_t gum::learning::CachedContingencyCounter::minNbRowsPerThread ( ) const

virtualinherited

returns the minimum of rows that each thread should process

◆ nodeId2Columns()

const Bijection< NodeId, std::size_t > & gum::learning::CachedContingencyCounter::nodeId2Columns ( ) const

inherited

return the mapping between the columns of the database and the node ids

Warning: An empty nodeId2Columns bijection means that the mapping is an identity, i.e., the value of a NodeId is equal to the index of the column in the DatabaseTable.

◆ operator=() [1/2]

KNML & gum::learning::KNML::operator= ( const KNML & from )

copy operator

References KNML().

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◆ operator=() [2/2]

KNML & gum::learning::KNML::operator= ( KNML && from )

move operator

References KNML().

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◆ ranges()

const std::vector< std::pair< std::size_t, std::size_t > > & gum::learning::CachedContingencyCounter::ranges ( ) const

inherited

returns the current ranges

Referenced by CachedContingencyCounter(), gum::learning::IndependenceTest::IndependenceTest(), gum::learning::IndepTestChi2::IndepTestChi2(), gum::learning::IndepTestG2::IndepTestG2(), and gum::learning::KNML::KNML().

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◆ score() [1/2]

double gum::learning::KNML::score	(	NodeId	var1,
		NodeId	var2 )

returns the kNML penalty for a pair of nodes

◆ score() [2/2]

double gum::learning::KNML::score	(	NodeId	var1,
		NodeId	var2,
		const std::vector< NodeId > &	rhs_ids )

returns the kNML penalty for a pair of nodes given conditioning nodes

◆ setMinNbRowsPerThread()

virtual void gum::learning::CachedContingencyCounter::setMinNbRowsPerThread ( const std::size_t nb ) const

virtualinherited

changes the number min of rows a thread should process in a multithreading context

◆ setNumberOfThreads()

void gum::learning::CachedContingencyCounter::setNumberOfThreads ( Size nb )

overridevirtualinherited

sets the number max of threads that can be used

Implements gum::IThreadNumberManager.

◆ setRanges()

void gum::learning::CachedContingencyCounter::setRanges ( const std::vector< std::pair< std::size_t, std::size_t > > & new_ranges )

inherited

sets new ranges to perform the counts

◆ useCache()

void gum::learning::KNML::useCache ( const bool on_off )

overridevirtual

turn on/off the use of the C_n^r cache

Reimplemented from gum::learning::CachedContingencyCounter.

Member Data Documentation

◆ cache_

ScoringCache gum::learning::CachedContingencyCounter::cache_

protectedinherited

the scoring cache

Definition at line 171 of file cachedContingencyCounter.h.

◆ counter_

RecordCounter gum::learning::CachedContingencyCounter::counter_

protectedinherited

the record counter used for the counts over discrete variables

Definition at line 168 of file cachedContingencyCounter.h.

◆ empty_ids_

const std::vector< NodeId > gum::learning::CachedContingencyCounter::empty_ids_

protectedinherited

an empty vector

Definition at line 177 of file cachedContingencyCounter.h.

◆ one_log2_

const double gum::learning::CachedContingencyCounter::one_log2_ {M_LOG2E}

protectedinherited

1 / log(2)

Definition at line 162 of file cachedContingencyCounter.h.

162{M_LOG2E};

M_LOG2E

#define M_LOG2E

Definition math_utils.h:55

◆ prior_

Prior* gum::learning::CachedContingencyCounter::prior_ {nullptr}

protectedinherited

the expert knowledge prior added to the contingency tables

Definition at line 165 of file cachedContingencyCounter.h.

165{nullptr};

◆ use_cache_

bool gum::learning::CachedContingencyCounter::use_cache_ {true}

protectedinherited

a Boolean indicating whether we wish to use the cache

Definition at line 174 of file cachedContingencyCounter.h.

174{true};

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

agrum/base/stattests/kNML.h

Public Member Functions

Protected Attributes

Detailed Description

Constructor & Destructor Documentation

◆ KNML() [1/4]

◆ KNML() [2/4]

◆ KNML() [3/4]

◆ KNML() [4/4]

◆ ~KNML()

Member Function Documentation

◆ clear()

◆ clearCache()

◆ clearRanges()

◆ clone()

◆ database()

◆ getNumberOfThreads()

◆ isGumNumberOfThreadsOverriden()

◆ minNbRowsPerThread()

◆ nodeId2Columns()

◆ operator=() [1/2]

◆ operator=() [2/2]

◆ ranges()

◆ score() [1/2]

◆ score() [2/2]

◆ setMinNbRowsPerThread()

◆ setNumberOfThreads()

◆ setRanges()

◆ useCache()

Member Data Documentation

◆ cache_

◆ counter_

◆ empty_ids_

◆ one_log2_

◆ prior_

◆ use_cache_