gum::MaxInducedWidthMCBayesNetGenerator< GUM_SCALAR, ICPTGenerator, ICPTDisturber > Class Template Reference

maxInducedWidthMCBayesNetGenerator.h <agrum/BN/generator/SimpleMCayesNetGenerator.h> More...

#include <maxInducedWidthMCBayesNetGenerator.h>

Inheritance diagram for gum::MaxInducedWidthMCBayesNetGenerator< GUM_SCALAR, ICPTGenerator, ICPTDisturber >:

Collaboration diagram for gum::MaxInducedWidthMCBayesNetGenerator< GUM_SCALAR, ICPTGenerator, ICPTDisturber >:

Public Member Functions
Constructors / Destructor
	MaxInducedWidthMCBayesNetGenerator (Size nbrNodes, Size maxArcs, Size maxModality=2, Size maxInducedWidth=3, Idx iteration=5000, Idx p=30, Idx q=40)
	Constructor. More...
	MaxInducedWidthMCBayesNetGenerator (BayesNet< GUM_SCALAR > bayesNet, Size maxInducedWidth=3, Idx iteration=5000, Idx p=30, Idx q=40)
	Constructor. More...
	~MaxInducedWidthMCBayesNetGenerator () final
	Destructor. More...
Getters
Size	maxlog10InducedWidth () const
	Return a constant reference to the number of maximum parents imposed on the Markov Chain BayesNetGenerator. More...
Setters
void	setMaxlog10InducedWidth (Size maxlog10InducedWidth)
	Modifies the value of the number of maximum parents imposed on the BayesNetGenerator. More...
BN generation methods
void	generateBN (BayesNet< GUM_SCALAR > &bayesNet) override
	Generates a random bayesian network. More...
void	disturbBN (BayesNet< GUM_SCALAR > &bayesNetinit, Size iteration=0)
	Change randomly the topology of a specific bayesian networks. More...
Getters
Size	iteration () const
	Return a constant reference to the number of iteration imposed on the Markov Chain BayesNetGenerator. More...
Idx	p () const
	Return a constant reference to the probabilité p imposed on the Markov Chain BayesNetGenerator. More...
Idx	q () const
	Return a constant reference to the probabilité imposed on the Markov Chain BayesNetGenerator. More...
Setters
void	setIteration (Size iteration)
	Modifies the value of the number of iterations impose on the BayesNetGenerator. More...
void	setP (Idx p)
	Modifies the value of the probability p imposed on the BayesNetGenerator. More...
void	setQ (Idx q)
	Modifies the value of the probability q imposed on the BayesNetGenerator. More...
BN generation methods
void	fillCPT ()
	function that insert random values in the CPT of each nodes according to the CPTGenerator. More...
Getters
Size	nbrNodes () const
	Return a constant reference to the number of nodes imposed on the IBayesNetGenerator. More...
Size	maxArcs () const
	Return a constant reference to the maximum number of arcs imposed on the IBayesNetGenerator. More...
Size	maxModality () const
	Return a constant reference to the maximum modality imposed on the IBayesNetGenerator. More...
Setters
void	setNbrNodes (Size nbrNodes)
	Modifies the value of the number of nodes imposed on the BayesGenerator. More...
void	setMaxArcs (Size maxArcs)
	Modifies the value of the number of nodes imposed on the BayesGenerator. More...
void	setMaxModality (Size maxModality)
	Modifies the value of the number of nodes imposed on the BayesGenerator. More...

Protected Attributes
Size	_maxlog10InducedWidth
Size	_iteration
Idx	_p
Idx	_q
bool	_disturbing
BayesNet< GUM_SCALAR >	_bayesNettemp
HashTable< NodeId, Potential< GUM_SCALAR > *>	_hashMarginal
Size	_nbrNodes
Size	_maxArcs
Size	_maxModality
BayesNet< GUM_SCALAR >	_bayesNet

Protected Member Functions
bool	__isPolytree ()
	The function that verify if graph is a polytree. More...
bool	__connect (NodeId i, NodeId j)
	The function that verify if node i and j are connected. More...
bool	__connect (NodeId i, NodeId j, NodeSet &excluded)
	The internal function used by the previous __connect. More...
bool	__directedPath (NodeId tail, NodeId head)
	The function that verify if there is a oriented path from node i to node j. More...
bool	__directedPath (NodeId tail, NodeId head, NodeSet &excluded)
	The internal function used by the previous __directedPath. More...
void	__insertArc (NodeId i, NodeId j)
	The function that will insert an arc between node i to node j, but only if there isn't any cycle created. More...
void	__eraseArc (NodeId i, NodeId j, bool mustbeconnex=true)
	The function that will remove the arc between node i and node j. More...
void	__PMMx_poly ()
	In the case that the graph is a polytree, the function will, according to the probability p and q, choose which change of state must occure( AorR or AR or jump) then will assert that the imposed constraint are respected and if not, will return to the previous topology. More...
void	__PMMx_multi ()
	In the case that the graph is a multiconnected graph, the function will, according to the probability p and q, choose which change of state must occure( AorR or jump) then will assert that the imposed constraint are respected and if not, will return to the previous topology. More...
void	__jump_poly ()
	In the case that the graph is a polytree, the function will add a ramdom arc by the use of the function __insertArc if the arc does not exist allready. More...
void	__jump_multi ()
	In the case that the graph is a multiconnect graph, the function will choose randomly two nodes and will remove the arc between them by the use of the function __insertArc if the arc exists. More...
void	__AorR ()
	The function will add or remove a random arc in the graph using the functions __insertArc and __removeArc. More...
void	__AR ()
	The function will remove and add a random arc changing the topology of the graph but asserting its connectivity. More...
void	__chooseNodes (NodeId &i, NodeId &j)
	The function that randomly choose two nodes of the graph. More...
void	__chooseCloseNodes (NodeId &i, NodeId &j)
	The function that randomly choose two neighbours nodes of the graph. More...
void	__transformPoly (Idx nbiter)
	The function that randomly change the simple tree into a polytree. More...
void	__createTree (Size BNSize)
	The function that randomly generate a simple tree. More...
NodeId	__createPartTree (Size BNSize, Idx &n)
	The internal function used by __createTree that randomly generate a simple tree. More...

Detailed Description

template<typename GUM_SCALAR, template< typename > class ICPTGenerator = SimpleCPTGenerator, template< typename > class ICPTDisturber = SimpleCPTDisturber>
class gum::MaxInducedWidthMCBayesNetGenerator< GUM_SCALAR, ICPTGenerator, ICPTDisturber >

maxInducedWidthMCBayesNetGenerator.h <agrum/BN/generator/SimpleMCayesNetGenerator.h>

Class for generating bayesian networks with Markov chains.

This class is inherited from MCBayesNetGenerator and is an example of Markov Chain Bayesian Network Generator that can be implemented. Here a constraint is added which is the maximum number of parents that nodes can have.

Warning

Be careful when entering the parameters, high Values may cause the density of the Bayesian Network to be too high resulting in the failure of most of the inference Methods.

Definition at line 76 of file maxInducedWidthMCBayesNetGenerator.h.

Constructor & Destructor Documentation

MaxInducedWidthMCBayesNetGenerator() [1/2]

template<typename GUM_SCALAR , template< typename > class ICPTGenerator, template< typename > class ICPTDisturber>

INLINE gum::MaxInducedWidthMCBayesNetGenerator< GUM_SCALAR, ICPTGenerator, ICPTDisturber >::MaxInducedWidthMCBayesNetGenerator	(	Size	nbrNodes,
		Size	maxArcs,
		Size	maxModality = 2,
		Size	maxInducedWidth = 3,
		Idx	iteration = 5000,
		Idx	p = 30,
		Idx	q = 40
	)

Constructor.

Use by default the SimpleCPTGenerator for generating the BNs CPT and the SimpleCPTDisturber to tweak the CPT when the dimension of the table changes.

Parameters

nbrNodes	The number of nodes in the generated BN.
maxArcs	The maximum number of Arcs.
maxModality	Each DRV has from 2 to maxModality modalities
maxInducedWidth	The number of maximum variable allow in the cliques of the junction tree of the bayesian Network.
iteration	The number of iterations wanted to repeat the algorithm
p	probability for the change of the state (see useof p and q" )
q	probability for the change of the state (see useof p and q" )

Definition at line 50 of file maxInducedWidthMCBayesNetGenerator_tpl.h.

References gum::MaxInducedWidthMCBayesNetGenerator< GUM_SCALAR, ICPTGenerator, ICPTDisturber >::_maxlog10InducedWidth, and GUM_ERROR.

                                                                 :
      MCBG(nbrNodes, maxArcs, maxModality, iteration, p, q) {
    if (maxInducedWidth == 0)
      GUM_ERROR(OperationNotAllowed,
                "maxInducedWidth must be at least equal "
                "to 1 to have a connexe graph");

    _maxlog10InducedWidth = maxInducedWidth;
    GUM_CONSTRUCTOR(MaxInducedWidthMCBayesNetGenerator);
  }

MaxInducedWidthMCBayesNetGenerator() [2/2]

template<typename GUM_SCALAR , template< typename > class ICPTGenerator, template< typename > class ICPTDisturber>

INLINE gum::MaxInducedWidthMCBayesNetGenerator< GUM_SCALAR, ICPTGenerator, ICPTDisturber >::MaxInducedWidthMCBayesNetGenerator ( BayesNet< GUM_SCALAR >  bayesNet, Size  maxInducedWidth = 3, Idx  iteration = 5000, Idx  p = 30, Idx  q = 40  )

explicit

Constructor.

Use by default the SimpleCPTGenerator for generating the BNs CPT and the SimpleCPTDisturber to tweak the CPT when the dimension of the table changes.

Parameters

bayesNet	the IBayesNet used as reference to fill the parameters nbrNodes, maxArcs and maxModality
maxInducedWidth	The number of maximum variable allow in the cliques of the junction tree of the bayesian Network.
iteration	The number of iterations wanted to repeat the algorithm
p	probability for the change of the state (see useof p and q" )
q	probability for the change of the state (see useof p and q" )

Definition at line 75 of file maxInducedWidthMCBayesNetGenerator_tpl.h.

References gum::MaxInducedWidthMCBayesNetGenerator< GUM_SCALAR, ICPTGenerator, ICPTDisturber >::_maxlog10InducedWidth.

                                                                  :
      MCBG(bayesNet, iteration, p, q) {
    _maxlog10InducedWidth = maxInducedWidth;
    GUM_CONSTRUCTOR(MaxInducedWidthMCBayesNetGenerator);
  }

~MaxInducedWidthMCBayesNetGenerator()

template<typename GUM_SCALAR , template< typename > class ICPTGenerator, template< typename > class ICPTDisturber>

INLINE gum::MaxInducedWidthMCBayesNetGenerator< GUM_SCALAR, ICPTGenerator, ICPTDisturber >::~MaxInducedWidthMCBayesNetGenerator ( )

final

Destructor.

Definition at line 108 of file maxInducedWidthMCBayesNetGenerator_tpl.h.

                                                          {
    GUM_DESTRUCTOR(MaxInducedWidthMCBayesNetGenerator);
    //    delete BayesNetGenerator<GUM_SCALAR>::_cptGenerator;
  }

Member Function Documentation

__AorR()

template<typename GUM_SCALAR , template< typename > class ICPTGenerator, template< typename > class ICPTDisturber>

void gum::MCBayesNetGenerator< GUM_SCALAR, ICPTGenerator, ICPTDisturber >::__AorR ( )

protectedinherited

The function will add or remove a random arc in the graph using the functions __insertArc and __removeArc.

Definition at line 295 of file MCBayesNetGenerator_tpl.h.

References gum::MCBayesNetGenerator< GUM_SCALAR, ICPTGenerator, ICPTDisturber >::__chooseNodes(), gum::MCBayesNetGenerator< GUM_SCALAR, ICPTGenerator, ICPTDisturber >::__eraseArc(), gum::MCBayesNetGenerator< GUM_SCALAR, ICPTGenerator, ICPTDisturber >::__insertArc(), gum::IBayesNetGenerator< GUM_SCALAR, ICPTGenerator >::_bayesNet, and gum::ArcGraphPart::existsArc().

Referenced by gum::MCBayesNetGenerator< GUM_SCALAR, ICPTGenerator, ICPTDisturber >::__PMMx_multi(), and gum::MCBayesNetGenerator< GUM_SCALAR, ICPTGenerator, ICPTDisturber >::__PMMx_poly().

                                                                               {
    NodeId i, j;
    __chooseNodes(i, j);
    const DAG __dag = this->_bayesNet.dag();

    if (__dag.existsArc(i, j)) {
      __eraseArc(i, j);

      return;
    } else
      __insertArc(i, j);
  }

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__AR()

template<typename GUM_SCALAR , template< typename > class ICPTGenerator, template< typename > class ICPTDisturber>

void gum::MCBayesNetGenerator< GUM_SCALAR, ICPTGenerator, ICPTDisturber >::__AR ( )

protectedinherited

The function will remove and add a random arc changing the topology of the graph but asserting its connectivity.

Definition at line 313 of file MCBayesNetGenerator_tpl.h.

References gum::MCBayesNetGenerator< GUM_SCALAR, ICPTGenerator, ICPTDisturber >::__chooseNodes(), gum::MCBayesNetGenerator< GUM_SCALAR, ICPTGenerator, ICPTDisturber >::__connect(), gum::MCBayesNetGenerator< GUM_SCALAR, ICPTGenerator, ICPTDisturber >::__jump_poly(), gum::IBayesNetGenerator< GUM_SCALAR, ICPTGenerator >::_bayesNet, gum::ArcGraphPart::children(), gum::ArcGraphPart::existsArc(), gum::Set< Key, Alloc >::insert(), gum::ArcGraphPart::parents(), and gum::randomValue().

Referenced by gum::MCBayesNetGenerator< GUM_SCALAR, ICPTGenerator, ICPTDisturber >::__PMMx_poly().

                                                                             {
    NodeId i, j, head, tail;
    __chooseNodes(i, j);
    const DAG __dag = this->_bayesNet.dag();

    if (__dag.existsArc(i, j) || __dag.existsArc(j, i)) {
      return;
    } else {
      Idx per = randomValue(100);

      if (per < 50) {
        head = i;
        tail = j;
      } else {
        head = j;
        tail = i;
      }

      for (auto node : __dag.parents(j)) {
        NodeSet excluded;
        excluded.insert(j);

        if (__connect(node, i, excluded)) {
          this->_bayesNet.eraseArc(node, j);   // TODO reflect
          this->_bayesNet.addArc(head, tail);
          return;
        }
      }

      for (auto node : __dag.children(j)) {
        NodeSet excluded;
        excluded.insert(j);

        if (__connect(node, i, excluded)) {
          this->_bayesNet.eraseArc(j, node);
          this->_bayesNet.addArc(head, tail);
          return;
        }
      }
    }
  }

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__checkConditions()

template<typename GUM_SCALAR , template< typename > class ICPTGenerator, template< typename > class ICPTDisturber>

bool gum::MaxInducedWidthMCBayesNetGenerator< GUM_SCALAR, ICPTGenerator, ICPTDisturber >::__checkConditions ( )

finalprivatevirtual

function to holding the the specification wanted for the Bayesian markov.

Returns

boolean state that verify the conditions

Reimplemented from gum::MCBayesNetGenerator< GUM_SCALAR, ICPTGenerator, ICPTDisturber >.

Definition at line 120 of file maxInducedWidthMCBayesNetGenerator_tpl.h.

References gum::IBayesNetGenerator< GUM_SCALAR, ICPTGenerator >::_bayesNet, gum::MaxInducedWidthMCBayesNetGenerator< GUM_SCALAR, ICPTGenerator, ICPTDisturber >::_maxlog10InducedWidth, gum::HashTable< Key, Val, Alloc >::insert(), and gum::Triangulation::maxLog10CliqueDomainSize().

                                                                              {
    NodeProperty< Size > __modalitiesMap;

    for (auto node : this->_bayesNet.nodes())
      __modalitiesMap.insert(node, this->_bayesNet.variable(node).domainSize());

    DefaultTriangulation tri(&(this->_bayesNet.moralGraph()), &__modalitiesMap);

    if (tri.maxLog10CliqueDomainSize() > _maxlog10InducedWidth) return false;

    return MCBG::__checkConditions();
  }

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__chooseCloseNodes()

template<typename GUM_SCALAR , template< typename > class ICPTGenerator, template< typename > class ICPTDisturber>

void gum::MCBayesNetGenerator< GUM_SCALAR, ICPTGenerator, ICPTDisturber >::__chooseCloseNodes ( NodeId &  i, NodeId &  j  )

protectedinherited

The function that randomly choose two neighbours nodes of the graph.

Definition at line 449 of file MCBayesNetGenerator_tpl.h.

References gum::IBayesNetGenerator< GUM_SCALAR, ICPTGenerator >::_bayesNet, GUM_ERROR, and gum::randomValue().

Referenced by gum::MCBayesNetGenerator< GUM_SCALAR, ICPTGenerator, ICPTDisturber >::__chooseNodes(), and gum::MCBayesNetGenerator< GUM_SCALAR, ICPTGenerator, ICPTDisturber >::__transformPoly().

                                              {
    NodeId temp = randomValue(this->_bayesNet.size());
    Size   co = 0;

    if (this->_bayesNet.parents(temp).size()) {
      j = temp;
      auto it = this->_bayesNet.parents(j).begin();
      co = randomValue(this->_bayesNet.parents(j).size());

      while (co--) {
        ++it;
      }

      i = *it;
    } else if (this->_bayesNet.children(temp).size()) {
      i = temp;
      auto it = this->_bayesNet.children(i).begin();
      co = randomValue(this->_bayesNet.children(i).size());

      while (co--) {
        ++it;
      }

      j = *it;
    } else {
      GUM_ERROR(FatalError, "Sorry Misconstructed BN because of isolated node.");
    }
  }

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__chooseNodes()

template<typename GUM_SCALAR , template< typename > class ICPTGenerator, template< typename > class ICPTDisturber>

INLINE void gum::MCBayesNetGenerator< GUM_SCALAR, ICPTGenerator, ICPTDisturber >::__chooseNodes ( NodeId &  i, NodeId &  j  )

protectedinherited

The function that randomly choose two nodes of the graph.

Definition at line 435 of file MCBayesNetGenerator_tpl.h.

References gum::MCBayesNetGenerator< GUM_SCALAR, ICPTGenerator, ICPTDisturber >::__chooseCloseNodes(), gum::IBayesNetGenerator< GUM_SCALAR, ICPTGenerator >::_bayesNet, and gum::randomValue().

Referenced by gum::MCBayesNetGenerator< GUM_SCALAR, ICPTGenerator, ICPTDisturber >::__AorR(), gum::MCBayesNetGenerator< GUM_SCALAR, ICPTGenerator, ICPTDisturber >::__AR(), gum::MCBayesNetGenerator< GUM_SCALAR, ICPTGenerator, ICPTDisturber >::__eraseArc(), gum::MCBayesNetGenerator< GUM_SCALAR, ICPTGenerator, ICPTDisturber >::__jump_multi(), and gum::MCBayesNetGenerator< GUM_SCALAR, ICPTGenerator, ICPTDisturber >::__jump_poly().

                                         {
    i = randomValue(this->_bayesNet.size());
    j = randomValue(this->_bayesNet.size());

    while (i == j)
      j = randomValue(this->_bayesNet.size());
  }

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__connect() [1/2]

template<typename GUM_SCALAR , template< typename > class ICPTGenerator, template< typename > class ICPTDisturber>

bool gum::MCBayesNetGenerator< GUM_SCALAR, ICPTGenerator, ICPTDisturber >::__connect ( NodeId  i, NodeId  j  )

protectedinherited

The function that verify if node i and j are connected.

Definition at line 563 of file MCBayesNetGenerator_tpl.h.

References gum::IBayesNetGenerator< GUM_SCALAR, ICPTGenerator >::_bayesNet, gum::ArcGraphPart::children(), gum::Set< Key, Alloc >::exists(), gum::ArcGraphPart::existsArc(), gum::Set< Key, Alloc >::insert(), and gum::ArcGraphPart::parents().

Referenced by gum::MCBayesNetGenerator< GUM_SCALAR, ICPTGenerator, ICPTDisturber >::__AR(), gum::MCBayesNetGenerator< GUM_SCALAR, ICPTGenerator, ICPTDisturber >::__connect(), and gum::MCBayesNetGenerator< GUM_SCALAR, ICPTGenerator, ICPTDisturber >::__eraseArc().

                                     {
    const DAG __dag = this->_bayesNet.dag();

    if (__dag.existsArc(i, j) || __dag.existsArc(j, i))
      return true;
    else {
      NodeSet excluded;
      excluded.insert(i);

      for (auto par : __dag.parents(i)) {
        if (!excluded.exists(par) && __connect(par, j, excluded)) return true;
      }

      for (auto chi : __dag.children(i)) {
        if (!excluded.exists(chi) && __connect(chi, j, excluded)) return true;
      }

      return false;
    }
  }

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__connect() [2/2]

template<typename GUM_SCALAR , template< typename > class ICPTGenerator, template< typename > class ICPTDisturber>

bool gum::MCBayesNetGenerator< GUM_SCALAR, ICPTGenerator, ICPTDisturber >::__connect ( NodeId  i, NodeId  j, NodeSet &  excluded  )

protectedinherited

The internal function used by the previous __connect.

It asserts the existence of an unoriented path between node i and node j avoiding passing through nodes listed in excluded.

Definition at line 590 of file MCBayesNetGenerator_tpl.h.

References gum::MCBayesNetGenerator< GUM_SCALAR, ICPTGenerator, ICPTDisturber >::__connect(), gum::MCBayesNetGenerator< GUM_SCALAR, ICPTGenerator, ICPTDisturber >::__directedPath(), gum::IBayesNetGenerator< GUM_SCALAR, ICPTGenerator >::_bayesNet, gum::ArcGraphPart::children(), gum::Set< Key, Alloc >::exists(), gum::ArcGraphPart::existsArc(), gum::Set< Key, Alloc >::insert(), and gum::ArcGraphPart::parents().

                                                        {
    const DAG __dag = this->_bayesNet.dag();

    if (__dag.existsArc(i, j) || __dag.existsArc(j, i))
      return true;
    else {
      excluded.insert(i);

      for (auto par : __dag.parents(i)) {
        if (!excluded.exists(par) && __connect(par, j, excluded)) return true;
      }

      for (auto chi : __dag.children(i)) {
        if (!excluded.exists(chi) && __connect(chi, j, excluded)) return true;
      }

      return false;
    }
  }

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__createPartTree()

template<typename GUM_SCALAR , template< typename > class ICPTGenerator, template< typename > class ICPTDisturber>

NodeId gum::MCBayesNetGenerator< GUM_SCALAR, ICPTGenerator, ICPTDisturber >::__createPartTree ( Size  BNSize, Idx &  n  )

protectedinherited

The internal function used by __createTree that randomly generate a simple tree.

n : id number for node label

Definition at line 509 of file MCBayesNetGenerator_tpl.h.

References gum::MCBayesNetGenerator< GUM_SCALAR, ICPTGenerator, ICPTDisturber >::__transformPoly(), gum::IBayesNetGenerator< GUM_SCALAR, ICPTGenerator >::_bayesNet, gum::IBayesNetGenerator< GUM_SCALAR, ICPTGenerator >::_maxModality, and gum::randomValue().

Referenced by gum::MCBayesNetGenerator< GUM_SCALAR, ICPTGenerator, ICPTDisturber >::__createTree().

                                           {
    Size              nb_mod = 2 + randomValue(this->_maxModality - 1);
    std::stringstream strBuff;
    strBuff << "n_" << n++;
    NodeId root =
       this->_bayesNet.add(LabelizedVariable(strBuff.str(), "", nb_mod));
    Size maxNodes = BNSize - 1;
    Size SubG = 0;

    while (maxNodes) {
      SubG = randomValue(maxNodes) + 1;
      maxNodes = maxNodes - SubG;
      NodeId rootS = __createPartTree(SubG, n);
      this->_bayesNet.addArc(root, rootS);
    }

    return root;
  }

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__createTree()

template<typename GUM_SCALAR , template< typename > class ICPTGenerator, template< typename > class ICPTDisturber>

void gum::MCBayesNetGenerator< GUM_SCALAR, ICPTGenerator, ICPTDisturber >::__createTree ( Size  BNSize )

protectedinherited

The function that randomly generate a simple tree.

Definition at line 484 of file MCBayesNetGenerator_tpl.h.

References gum::MCBayesNetGenerator< GUM_SCALAR, ICPTGenerator, ICPTDisturber >::__createPartTree(), gum::IBayesNetGenerator< GUM_SCALAR, ICPTGenerator >::_bayesNet, gum::IBayesNetGenerator< GUM_SCALAR, ICPTGenerator >::_maxModality, and gum::randomValue().

Referenced by gum::MCBayesNetGenerator< GUM_SCALAR, ICPTGenerator, ICPTDisturber >::generateBN().

                     {
    Idx               n = 0;
    Size              nb_mod = 2 + randomValue(this->_maxModality - 1);
    std::stringstream strBuff;
    strBuff << "n_" << n++;
    NodeId root =
       this->_bayesNet.add(LabelizedVariable(strBuff.str(), "", nb_mod));
    Size maxNodes = BNSize - 1;
    Size SubG = 0;

    while (maxNodes) {
      SubG = randomValue(maxNodes) + 1;
      maxNodes = maxNodes - SubG;
      NodeId rootS = __createPartTree(SubG, n);
      this->_bayesNet.addArc(root, rootS);
    }
  }

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__directedPath() [1/2]

template<typename GUM_SCALAR , template< typename > class ICPTGenerator, template< typename > class ICPTDisturber>

bool gum::MCBayesNetGenerator< GUM_SCALAR, ICPTGenerator, ICPTDisturber >::__directedPath ( NodeId  tail, NodeId  head  )

protectedinherited

The function that verify if there is a oriented path from node i to node j.

Definition at line 617 of file MCBayesNetGenerator_tpl.h.

References gum::IBayesNetGenerator< GUM_SCALAR, ICPTGenerator >::_bayesNet, gum::ArcGraphPart::children(), gum::ArcGraphPart::existsArc(), and gum::Set< Key, Alloc >::insert().

Referenced by gum::MCBayesNetGenerator< GUM_SCALAR, ICPTGenerator, ICPTDisturber >::__connect(), gum::MCBayesNetGenerator< GUM_SCALAR, ICPTGenerator, ICPTDisturber >::__directedPath(), and gum::MCBayesNetGenerator< GUM_SCALAR, ICPTGenerator, ICPTDisturber >::__insertArc().

                                              {
    const DAG __dag = this->_bayesNet.dag();

    if (__dag.existsArc(tail, head))
      return true;
    else {
      NodeSet excluded;
      excluded.insert(tail);

      for (auto node : __dag.children(tail)) {
        if (__directedPath(node, head, excluded)) return true;
      }

      return false;
    }
  }

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__directedPath() [2/2]

template<typename GUM_SCALAR , template< typename > class ICPTGenerator, template< typename > class ICPTDisturber>

bool gum::MCBayesNetGenerator< GUM_SCALAR, ICPTGenerator, ICPTDisturber >::__directedPath ( NodeId  tail, NodeId  head, NodeSet &  excluded  )

protectedinherited

The internal function used by the previous __directedPath.

It asserts the existence of an oriented path between node i and node j avoiding passing through nodes listed in excluded.

Definition at line 640 of file MCBayesNetGenerator_tpl.h.

References gum::MCBayesNetGenerator< GUM_SCALAR, ICPTGenerator, ICPTDisturber >::__directedPath(), gum::IBayesNetGenerator< GUM_SCALAR, ICPTGenerator >::_bayesNet, gum::ArcGraphPart::children(), gum::Set< Key, Alloc >::exists(), gum::ArcGraphPart::existsArc(), and gum::Set< Key, Alloc >::insert().

                                                                 {
    const DAG __dag = this->_bayesNet.dag();

    if (__dag.existsArc(tail, head))
      return true;
    else {
      excluded.insert(tail);

      for (auto node : __dag.children(tail)) {
        if (!excluded.exists(node) && __directedPath(node, head, excluded))
          return true;
      }

      return false;
    }
  }

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__eraseArc()

template<typename GUM_SCALAR , template< typename > class ICPTGenerator, template< typename > class ICPTDisturber>

INLINE void gum::MCBayesNetGenerator< GUM_SCALAR, ICPTGenerator, ICPTDisturber >::__eraseArc ( NodeId  i, NodeId  j, bool  mustbeconnex = true  )

protectedinherited

The function that will remove the arc between node i and node j.

If the boolean parameter mustbeconnex is true, the function will assert that the graph remain connected and will restore the arc otherwise.

Definition at line 408 of file MCBayesNetGenerator_tpl.h.

References gum::MCBayesNetGenerator< GUM_SCALAR, ICPTGenerator, ICPTDisturber >::__chooseNodes(), gum::MCBayesNetGenerator< GUM_SCALAR, ICPTGenerator, ICPTDisturber >::__connect(), gum::IBayesNetGenerator< GUM_SCALAR, ICPTGenerator >::_bayesNet, gum::MCBayesNetGenerator< GUM_SCALAR, ICPTGenerator, ICPTDisturber >::_disturbing, gum::MCBayesNetGenerator< GUM_SCALAR, ICPTGenerator, ICPTDisturber >::_hashMarginal, and gum::MultiDimContainer< GUM_SCALAR >::copy().

Referenced by gum::MCBayesNetGenerator< GUM_SCALAR, ICPTGenerator, ICPTDisturber >::__AorR(), gum::MCBayesNetGenerator< GUM_SCALAR, ICPTGenerator, ICPTDisturber >::__jump_multi(), and gum::MCBayesNetGenerator< GUM_SCALAR, ICPTGenerator, ICPTDisturber >::__transformPoly().

                                               {
    if (_disturbing) {
      const BayesNet< GUM_SCALAR > bayesNet(this->_bayesNet);
      Potential< GUM_SCALAR >      potj;
      potj.copy(this->_bayesNet.cpt(j));
      this->_bayesNet.eraseArc(i, j);

      if (__connect(i, j) || !mustbeconnex) {
        auto marg = *_hashMarginal[i];

        this->disturbReducCPT(j, this->_bayesNet, potj, marg);
      } else
        this->_bayesNet.addArc(i, j);
    } else {
      this->_bayesNet.eraseArc(i, j);

      if (!__connect(i, j) && mustbeconnex) { this->_bayesNet.addArc(i, j); }
    }
  }

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__insertArc()

template<typename GUM_SCALAR , template< typename > class ICPTGenerator, template< typename > class ICPTDisturber>

INLINE void gum::MCBayesNetGenerator< GUM_SCALAR, ICPTGenerator, ICPTDisturber >::__insertArc ( NodeId  i, NodeId  j  )

protectedinherited

The function that will insert an arc between node i to node j, but only if there isn't any cycle created.

Definition at line 389 of file MCBayesNetGenerator_tpl.h.

References gum::MCBayesNetGenerator< GUM_SCALAR, ICPTGenerator, ICPTDisturber >::__directedPath(), gum::IBayesNetGenerator< GUM_SCALAR, ICPTGenerator >::_bayesNet, and gum::MCBayesNetGenerator< GUM_SCALAR, ICPTGenerator, ICPTDisturber >::_disturbing.

Referenced by gum::MCBayesNetGenerator< GUM_SCALAR, ICPTGenerator, ICPTDisturber >::__AorR(), and gum::MCBayesNetGenerator< GUM_SCALAR, ICPTGenerator, ICPTDisturber >::__jump_poly().

                            {
    if (__directedPath(j, i)) return;

    if (_disturbing) {
      auto potj = this->_bayesNet.cpt(j);
      this->_bayesNet.addArc(i, j);

      this->disturbAugmCPT(j, this->_bayesNet, potj, (GUM_SCALAR)0.5);
    } else
      this->_bayesNet.addArc(i, j);
  }

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__isPolytree()

template<typename GUM_SCALAR , template< typename > class ICPTGenerator, template< typename > class ICPTDisturber>

INLINE bool gum::MCBayesNetGenerator< GUM_SCALAR, ICPTGenerator, ICPTDisturber >::__isPolytree ( )

protectedinherited

The function that verify if graph is a polytree.

Definition at line 553 of file MCBayesNetGenerator_tpl.h.

References gum::IBayesNetGenerator< GUM_SCALAR, ICPTGenerator >::_bayesNet.

Referenced by gum::MCBayesNetGenerator< GUM_SCALAR, ICPTGenerator, ICPTDisturber >::__PMMx_multi(), gum::MCBayesNetGenerator< GUM_SCALAR, ICPTGenerator, ICPTDisturber >::__transformPoly(), and gum::MCBayesNetGenerator< GUM_SCALAR, ICPTGenerator, ICPTDisturber >::disturbBN().

                    {
    const DAG __dag = this->_bayesNet.dag();
    return this->_bayesNet.size() - 1 == this->_bayesNet.sizeArcs();
  }

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__jump_multi()

template<typename GUM_SCALAR , template< typename > class ICPTGenerator, template< typename > class ICPTDisturber>

void gum::MCBayesNetGenerator< GUM_SCALAR, ICPTGenerator, ICPTDisturber >::__jump_multi ( )

protectedinherited

In the case that the graph is a multiconnect graph, the function will choose randomly two nodes and will remove the arc between them by the use of the function __insertArc if the arc exists.

Definition at line 375 of file MCBayesNetGenerator_tpl.h.

References gum::MCBayesNetGenerator< GUM_SCALAR, ICPTGenerator, ICPTDisturber >::__chooseNodes(), gum::MCBayesNetGenerator< GUM_SCALAR, ICPTGenerator, ICPTDisturber >::__eraseArc(), gum::IBayesNetGenerator< GUM_SCALAR, ICPTGenerator >::_bayesNet, and gum::ArcGraphPart::existsArc().

Referenced by gum::MCBayesNetGenerator< GUM_SCALAR, ICPTGenerator, ICPTDisturber >::__jump_poly(), and gum::MCBayesNetGenerator< GUM_SCALAR, ICPTGenerator, ICPTDisturber >::__PMMx_multi().

                    {
    NodeId i, j;
    __chooseNodes(i, j);
    const DAG __dag = this->_bayesNet.dag();

    if (__dag.existsArc(i, j)) { __eraseArc(i, j); }
  }

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__jump_poly()

template<typename GUM_SCALAR , template< typename > class ICPTGenerator, template< typename > class ICPTDisturber>

INLINE void gum::MCBayesNetGenerator< GUM_SCALAR, ICPTGenerator, ICPTDisturber >::__jump_poly ( )

protectedinherited

In the case that the graph is a polytree, the function will add a ramdom arc by the use of the function __insertArc if the arc does not exist allready.

Definition at line 361 of file MCBayesNetGenerator_tpl.h.

References gum::MCBayesNetGenerator< GUM_SCALAR, ICPTGenerator, ICPTDisturber >::__chooseNodes(), gum::MCBayesNetGenerator< GUM_SCALAR, ICPTGenerator, ICPTDisturber >::__insertArc(), gum::MCBayesNetGenerator< GUM_SCALAR, ICPTGenerator, ICPTDisturber >::__jump_multi(), gum::IBayesNetGenerator< GUM_SCALAR, ICPTGenerator >::_bayesNet, and gum::ArcGraphPart::existsArc().

Referenced by gum::MCBayesNetGenerator< GUM_SCALAR, ICPTGenerator, ICPTDisturber >::__AR(), and gum::MCBayesNetGenerator< GUM_SCALAR, ICPTGenerator, ICPTDisturber >::__PMMx_poly().

                   {
    NodeId i, j;
    __chooseNodes(i, j);
    const DAG __dag = this->_bayesNet.dag();

    if (!__dag.existsArc(i, j)) __insertArc(i, j);
  }

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__PMMx_multi()

template<typename GUM_SCALAR , template< typename > class ICPTGenerator, template< typename > class ICPTDisturber>

void gum::MCBayesNetGenerator< GUM_SCALAR, ICPTGenerator, ICPTDisturber >::__PMMx_multi ( )

protectedinherited

In the case that the graph is a multiconnected graph, the function will, according to the probability p and q, choose which change of state must occure( AorR or jump) then will assert that the imposed constraint are respected and if not, will return to the previous topology.

Definition at line 247 of file MCBayesNetGenerator_tpl.h.

References gum::MCBayesNetGenerator< GUM_SCALAR, ICPTGenerator, ICPTDisturber >::__AorR(), gum::MCBayesNetGenerator< GUM_SCALAR, ICPTGenerator, ICPTDisturber >::__checkConditions(), gum::MCBayesNetGenerator< GUM_SCALAR, ICPTGenerator, ICPTDisturber >::__isPolytree(), gum::MCBayesNetGenerator< GUM_SCALAR, ICPTGenerator, ICPTDisturber >::__jump_multi(), gum::MCBayesNetGenerator< GUM_SCALAR, ICPTGenerator, ICPTDisturber >::__PMMx_poly(), gum::IBayesNetGenerator< GUM_SCALAR, ICPTGenerator >::_bayesNet, gum::MCBayesNetGenerator< GUM_SCALAR, ICPTGenerator, ICPTDisturber >::_bayesNettemp, gum::MCBayesNetGenerator< GUM_SCALAR, ICPTGenerator, ICPTDisturber >::_iteration, gum::MCBayesNetGenerator< GUM_SCALAR, ICPTGenerator, ICPTDisturber >::_p, gum::MCBayesNetGenerator< GUM_SCALAR, ICPTGenerator, ICPTDisturber >::_q, and gum::randomValue().

Referenced by gum::MCBayesNetGenerator< GUM_SCALAR, ICPTGenerator, ICPTDisturber >::__PMMx_poly(), and gum::MCBayesNetGenerator< GUM_SCALAR, ICPTGenerator, ICPTDisturber >::disturbBN().

                    {
    if (!_iteration--) return;

    Idx per = randomValue(100);

    if (per < _p + _q) {
      __AorR();

      if (__checkConditions()) {
        if (__isPolytree()) {
          if (per < _p) {
            _bayesNettemp = this->_bayesNet;
            __PMMx_poly();
          } else {
            this->_bayesNet = _bayesNettemp;
            __PMMx_multi();
          }
        } else {
          _bayesNettemp = this->_bayesNet;
          __PMMx_multi();
        }
      } else {
        this->_bayesNet = _bayesNettemp;
        __PMMx_multi();
      }
    } else {
      __jump_multi();

      if (__checkConditions()) {
        _bayesNettemp = this->_bayesNet;

        if (__isPolytree())
          __PMMx_poly();
        else
          __PMMx_multi();   // TODO verification required

      } else {
        this->_bayesNet = _bayesNettemp;
        __PMMx_multi();
      }
    }
  }

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__PMMx_poly()

template<typename GUM_SCALAR , template< typename > class ICPTGenerator, template< typename > class ICPTDisturber>

void gum::MCBayesNetGenerator< GUM_SCALAR, ICPTGenerator, ICPTDisturber >::__PMMx_poly ( )

protectedinherited

In the case that the graph is a polytree, the function will, according to the probability p and q, choose which change of state must occure( AorR or AR or jump) then will assert that the imposed constraint are respected and if not, will return to the previous topology.

Definition at line 201 of file MCBayesNetGenerator_tpl.h.

References gum::MCBayesNetGenerator< GUM_SCALAR, ICPTGenerator, ICPTDisturber >::__AorR(), gum::MCBayesNetGenerator< GUM_SCALAR, ICPTGenerator, ICPTDisturber >::__AR(), gum::MCBayesNetGenerator< GUM_SCALAR, ICPTGenerator, ICPTDisturber >::__checkConditions(), gum::MCBayesNetGenerator< GUM_SCALAR, ICPTGenerator, ICPTDisturber >::__jump_poly(), gum::MCBayesNetGenerator< GUM_SCALAR, ICPTGenerator, ICPTDisturber >::__PMMx_multi(), gum::IBayesNetGenerator< GUM_SCALAR, ICPTGenerator >::_bayesNet, gum::MCBayesNetGenerator< GUM_SCALAR, ICPTGenerator, ICPTDisturber >::_bayesNettemp, gum::MCBayesNetGenerator< GUM_SCALAR, ICPTGenerator, ICPTDisturber >::_iteration, gum::MCBayesNetGenerator< GUM_SCALAR, ICPTGenerator, ICPTDisturber >::_p, gum::MCBayesNetGenerator< GUM_SCALAR, ICPTGenerator, ICPTDisturber >::_q, and gum::randomValue().

Referenced by gum::MCBayesNetGenerator< GUM_SCALAR, ICPTGenerator, ICPTDisturber >::__checkConditions(), gum::MCBayesNetGenerator< GUM_SCALAR, ICPTGenerator, ICPTDisturber >::__PMMx_multi(), gum::MCBayesNetGenerator< GUM_SCALAR, ICPTGenerator, ICPTDisturber >::disturbBN(), and gum::MCBayesNetGenerator< GUM_SCALAR, ICPTGenerator, ICPTDisturber >::generateBN().

                   {
    if (!_iteration--) return;

    Idx per = randomValue(100);

    if (per < _p) {
      __AorR();

      if (__checkConditions()) {
        _bayesNettemp = this->_bayesNet;
        __PMMx_multi();
      } else {
        this->_bayesNet = _bayesNettemp;
        __PMMx_poly();
      }
    } else {
      if (per < _p + _q) {
        __AR();

        if (!__checkConditions()) {
          this->_bayesNet = _bayesNettemp;
        } else
          _bayesNettemp = this->_bayesNet;

        __PMMx_poly();
      } else {
        __jump_poly();

        if (__checkConditions()) {
          _bayesNettemp = this->_bayesNet;
          __PMMx_multi();

        } else {
          this->_bayesNet = _bayesNettemp;
          __PMMx_poly();
        }
      }
    }
  }

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__transformPoly()

template<typename GUM_SCALAR , template< typename > class ICPTGenerator, template< typename > class ICPTDisturber>

void gum::MCBayesNetGenerator< GUM_SCALAR, ICPTGenerator, ICPTDisturber >::__transformPoly ( Idx  nbiter )

protectedinherited

The function that randomly change the simple tree into a polytree.

Definition at line 535 of file MCBayesNetGenerator_tpl.h.

References gum::MCBayesNetGenerator< GUM_SCALAR, ICPTGenerator, ICPTDisturber >::__checkConditions(), gum::MCBayesNetGenerator< GUM_SCALAR, ICPTGenerator, ICPTDisturber >::__chooseCloseNodes(), gum::MCBayesNetGenerator< GUM_SCALAR, ICPTGenerator, ICPTDisturber >::__eraseArc(), gum::MCBayesNetGenerator< GUM_SCALAR, ICPTGenerator, ICPTDisturber >::__isPolytree(), gum::IBayesNetGenerator< GUM_SCALAR, ICPTGenerator >::_bayesNet, and gum::MCBayesNetGenerator< GUM_SCALAR, ICPTGenerator, ICPTDisturber >::_bayesNettemp.

Referenced by gum::MCBayesNetGenerator< GUM_SCALAR, ICPTGenerator, ICPTDisturber >::__createPartTree(), and gum::MCBayesNetGenerator< GUM_SCALAR, ICPTGenerator, ICPTDisturber >::generateBN().

                                 {
    while (nbiter--) {
      NodeId i, j;
      __chooseCloseNodes(i, j);
      _bayesNettemp = this->_bayesNet;
      __eraseArc(i, j, false);
      this->_bayesNet.addArc(j, i);

      if (!__checkConditions()) this->_bayesNet = _bayesNettemp;
    }
  }

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disturbBN()

template<typename GUM_SCALAR , template< typename > class ICPTGenerator, template< typename > class ICPTDisturber>

void gum::MCBayesNetGenerator< GUM_SCALAR, ICPTGenerator, ICPTDisturber >::disturbBN ( BayesNet< GUM_SCALAR > &  bayesNetinit, Size  iteration = 0  )

inherited

Change randomly the topology of a specific bayesian networks.

Parameters

bayesNetinit	IBayesNet to be modify
iteration	The number of iterations wanted to repeat the algorithm

Returns

null but modify inputed Bayesian Network

Exceptions

OperationNotAllow

if the initial state of the IBayesNet is not respecting the wanted conditions if iteration = 0, it is assumed that the number of iteration wanted is the same as the one specified in the constructor

Definition at line 137 of file MCBayesNetGenerator_tpl.h.

References gum::MCBayesNetGenerator< GUM_SCALAR, ICPTGenerator, ICPTDisturber >::__checkConditions(), gum::MCBayesNetGenerator< GUM_SCALAR, ICPTGenerator, ICPTDisturber >::__isPolytree(), gum::MCBayesNetGenerator< GUM_SCALAR, ICPTGenerator, ICPTDisturber >::__PMMx_multi(), gum::MCBayesNetGenerator< GUM_SCALAR, ICPTGenerator, ICPTDisturber >::__PMMx_poly(), gum::IBayesNetGenerator< GUM_SCALAR, ICPTGenerator >::_bayesNet, gum::MCBayesNetGenerator< GUM_SCALAR, ICPTGenerator, ICPTDisturber >::_bayesNettemp, gum::MCBayesNetGenerator< GUM_SCALAR, ICPTGenerator, ICPTDisturber >::_disturbing, gum::MCBayesNetGenerator< GUM_SCALAR, ICPTGenerator, ICPTDisturber >::_hashMarginal, gum::MCBayesNetGenerator< GUM_SCALAR, ICPTGenerator, ICPTDisturber >::_iteration, gum::MultiDimContainer< GUM_SCALAR >::copy(), GUM_ERROR, gum::MCBayesNetGenerator< GUM_SCALAR, ICPTGenerator, ICPTDisturber >::iteration(), gum::BayesNetInference< GUM_SCALAR >::makeInference(), gum::DAGmodel::nodes(), and gum::JointTargetedInference< GUM_SCALAR >::posterior().

                                        {   // insert option for the variation
    _disturbing = true;
    Size iter = _iteration;

    if (iteration) _iteration = iteration;

    this->_bayesNet = bayesNetinit;

    if (__checkConditions()) {
      LazyPropagation< GUM_SCALAR > inf(&bayesNetinit);
      inf.makeInference();

      for (auto node : bayesNetinit.nodes()) {
        auto pottemp = new Potential< GUM_SCALAR >();
        pottemp->copy(inf.posterior(node));
        _hashMarginal.insert(node, pottemp);
      }

      _bayesNettemp = this->_bayesNet;

      if (__isPolytree())
        __PMMx_poly();
      else
        __PMMx_multi();

      bayesNetinit = (this->_bayesNet);

      while (_hashMarginal.size()) {
        delete (_hashMarginal.begin().val());
        _hashMarginal.erase(
           _hashMarginal.beginSafe());   // safe iterator needed here.
      }

    } else {
      std::cout << this->_bayesNet.toDot() << std::endl;
      GUM_ERROR(OperationNotAllowed,
                "BN is not valid cause it does not respect constraint ");
    }

    _iteration = iter;
    _disturbing = false;
  }

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fillCPT()

template<typename GUM_SCALAR , template< typename > class ICPTGenerator>

void gum::IBayesNetGenerator< GUM_SCALAR, ICPTGenerator >::fillCPT ( )

inherited

function that insert random values in the CPT of each nodes according to the CPTGenerator.

Returns

null but modify inputed empty Bayesian Network

Definition at line 64 of file IBayesNetGenerator_tpl.h.

References gum::IBayesNetGenerator< GUM_SCALAR, ICPTGenerator >::_bayesNet.

Referenced by gum::SimpleBayesNetGenerator< GUM_SCALAR, ICPTGenerator >::generateBN(), and gum::MCBayesNetGenerator< GUM_SCALAR, ICPTGenerator, ICPTDisturber >::generateBN().

                                                                {
    for (auto node : _bayesNet.nodes())
      this->generateCPT(_bayesNet.cpt(node).pos(_bayesNet.variable(node)),
                        _bayesNet.cpt(node));   // TODO ASSERT THE LINE
  }

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generateBN()

template<typename GUM_SCALAR , template< typename > class ICPTGenerator, template< typename > class ICPTDisturber>

void gum::MCBayesNetGenerator< GUM_SCALAR, ICPTGenerator, ICPTDisturber >::generateBN ( BayesNet< GUM_SCALAR > &  bayesNet )

overridevirtualinherited

Generates a random bayesian network.

Parameters

bayesNet

empty IBayesNet to generate.

Returns

null but modify inputed Bayesian Network

Implements gum::IBayesNetGenerator< GUM_SCALAR, ICPTGenerator >.

Definition at line 115 of file MCBayesNetGenerator_tpl.h.

References gum::MCBayesNetGenerator< GUM_SCALAR, ICPTGenerator, ICPTDisturber >::__createTree(), gum::MCBayesNetGenerator< GUM_SCALAR, ICPTGenerator, ICPTDisturber >::__PMMx_poly(), gum::MCBayesNetGenerator< GUM_SCALAR, ICPTGenerator, ICPTDisturber >::__transformPoly(), gum::IBayesNetGenerator< GUM_SCALAR, ICPTGenerator >::_bayesNet, gum::MCBayesNetGenerator< GUM_SCALAR, ICPTGenerator, ICPTDisturber >::_bayesNettemp, gum::MCBayesNetGenerator< GUM_SCALAR, ICPTGenerator, ICPTDisturber >::_iteration, gum::IBayesNetGenerator< GUM_SCALAR, ICPTGenerator >::_nbrNodes, gum::IBayesNetGenerator< GUM_SCALAR, ICPTGenerator >::fillCPT(), and gum::MCBayesNetGenerator< GUM_SCALAR, ICPTGenerator, ICPTDisturber >::iteration().

                                       {
    Idx iteration = _iteration;

    // this->_bayesNet = bayesNet;
    __createTree(this->_nbrNodes);
    __transformPoly(this->_nbrNodes / 2);
    _bayesNettemp = this->_bayesNet;
    __PMMx_poly();

    this->fillCPT();
    _iteration = iteration;

    bayesNet = this->_bayesNet;
  }

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iteration()

template<typename GUM_SCALAR , template< typename > class ICPTGenerator, template< typename > class ICPTDisturber>

INLINE Size gum::MCBayesNetGenerator< GUM_SCALAR, ICPTGenerator, ICPTDisturber >::iteration ( ) const

inherited

Return a constant reference to the number of iteration imposed on the Markov Chain BayesNetGenerator.

Definition at line 663 of file MCBayesNetGenerator_tpl.h.

References gum::MCBayesNetGenerator< GUM_SCALAR, ICPTGenerator, ICPTDisturber >::_iteration.

Referenced by gum::MCBayesNetGenerator< GUM_SCALAR, ICPTGenerator, ICPTDisturber >::disturbBN(), gum::MCBayesNetGenerator< GUM_SCALAR, ICPTGenerator, ICPTDisturber >::generateBN(), gum::MCBayesNetGenerator< GUM_SCALAR, ICPTGenerator, ICPTDisturber >::MCBayesNetGenerator(), and gum::MCBayesNetGenerator< GUM_SCALAR, ICPTGenerator, ICPTDisturber >::setIteration().

              {
    return _iteration;
  }

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maxArcs()

template<typename GUM_SCALAR , template< typename > class ICPTGenerator>

INLINE Size gum::IBayesNetGenerator< GUM_SCALAR, ICPTGenerator >::maxArcs ( ) const

inherited

Return a constant reference to the maximum number of arcs imposed on the IBayesNetGenerator.

Definition at line 82 of file IBayesNetGenerator_tpl.h.

References gum::IBayesNetGenerator< GUM_SCALAR, ICPTGenerator >::_maxArcs.

Referenced by gum::IBayesNetGenerator< GUM_SCALAR, ICPTGenerator >::IBayesNetGenerator(), and gum::IBayesNetGenerator< GUM_SCALAR, ICPTGenerator >::setMaxArcs().

                                                                             {
    return _maxArcs;
  }

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maxlog10InducedWidth()

template<typename GUM_SCALAR , template< typename > class ICPTGenerator, template< typename > class ICPTDisturber>

INLINE Size gum::MaxInducedWidthMCBayesNetGenerator< GUM_SCALAR, ICPTGenerator, ICPTDisturber >::maxlog10InducedWidth

(

)

const

Return a constant reference to the number of maximum parents imposed on the Markov Chain BayesNetGenerator.

Definition at line 141 of file maxInducedWidthMCBayesNetGenerator_tpl.h.

References gum::MaxInducedWidthMCBayesNetGenerator< GUM_SCALAR, ICPTGenerator, ICPTDisturber >::_maxlog10InducedWidth.

Referenced by gum::MaxInducedWidthMCBayesNetGenerator< GUM_SCALAR, ICPTGenerator, ICPTDisturber >::setMaxlog10InducedWidth().

              {
    return _maxlog10InducedWidth;
  }

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maxModality()

template<typename GUM_SCALAR , template< typename > class ICPTGenerator>

INLINE Size gum::IBayesNetGenerator< GUM_SCALAR, ICPTGenerator >::maxModality ( ) const

inherited

Return a constant reference to the maximum modality imposed on the IBayesNetGenerator.

Definition at line 72 of file IBayesNetGenerator_tpl.h.

References gum::IBayesNetGenerator< GUM_SCALAR, ICPTGenerator >::_maxModality.

Referenced by gum::IBayesNetGenerator< GUM_SCALAR, ICPTGenerator >::IBayesNetGenerator(), and gum::IBayesNetGenerator< GUM_SCALAR, ICPTGenerator >::setMaxModality().

                                                                            {
    return _maxModality;
  }

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nbrNodes()

template<typename GUM_SCALAR , template< typename > class ICPTGenerator>

INLINE Size gum::IBayesNetGenerator< GUM_SCALAR, ICPTGenerator >::nbrNodes ( ) const

inherited

Return a constant reference to the number of nodes imposed on the IBayesNetGenerator.

Definition at line 77 of file IBayesNetGenerator_tpl.h.

References gum::IBayesNetGenerator< GUM_SCALAR, ICPTGenerator >::_nbrNodes.

Referenced by gum::IBayesNetGenerator< GUM_SCALAR, ICPTGenerator >::IBayesNetGenerator(), and gum::IBayesNetGenerator< GUM_SCALAR, ICPTGenerator >::setNbrNodes().

                                                                              {
    return _nbrNodes;
  }

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p()

template<typename GUM_SCALAR , template< typename > class ICPTGenerator, template< typename > class ICPTDisturber>

INLINE Idx gum::MCBayesNetGenerator< GUM_SCALAR, ICPTGenerator, ICPTDisturber >::p ( ) const

inherited

Return a constant reference to the probabilité p imposed on the Markov Chain BayesNetGenerator.

Definition at line 674 of file MCBayesNetGenerator_tpl.h.

References gum::MCBayesNetGenerator< GUM_SCALAR, ICPTGenerator, ICPTDisturber >::_p.

Referenced by gum::MCBayesNetGenerator< GUM_SCALAR, ICPTGenerator, ICPTDisturber >::MCBayesNetGenerator(), and gum::MCBayesNetGenerator< GUM_SCALAR, ICPTGenerator, ICPTDisturber >::setP().

                                                                                  {
    return _p;
  }

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q()

template<typename GUM_SCALAR , template< typename > class ICPTGenerator, template< typename > class ICPTDisturber>

INLINE Idx gum::MCBayesNetGenerator< GUM_SCALAR, ICPTGenerator, ICPTDisturber >::q ( ) const

inherited

Return a constant reference to the probabilité imposed on the Markov Chain BayesNetGenerator.

Definition at line 684 of file MCBayesNetGenerator_tpl.h.

References gum::MCBayesNetGenerator< GUM_SCALAR, ICPTGenerator, ICPTDisturber >::_q.

Referenced by gum::MCBayesNetGenerator< GUM_SCALAR, ICPTGenerator, ICPTDisturber >::MCBayesNetGenerator(), and gum::MCBayesNetGenerator< GUM_SCALAR, ICPTGenerator, ICPTDisturber >::setQ().

                                                                                  {
    return _q;
  }

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setIteration()

template<typename GUM_SCALAR , template< typename > class ICPTGenerator, template< typename > class ICPTDisturber>

INLINE void gum::MCBayesNetGenerator< GUM_SCALAR, ICPTGenerator, ICPTDisturber >::setIteration ( Size  iteration )

inherited

Modifies the value of the number of iterations impose on the BayesNetGenerator.

Definition at line 694 of file MCBayesNetGenerator_tpl.h.

References gum::MCBayesNetGenerator< GUM_SCALAR, ICPTGenerator, ICPTDisturber >::_iteration, and gum::MCBayesNetGenerator< GUM_SCALAR, ICPTGenerator, ICPTDisturber >::iteration().

                        {
    _iteration = iteration;
  }

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setMaxArcs()

template<typename GUM_SCALAR , template< typename > class ICPTGenerator>

INLINE void gum::IBayesNetGenerator< GUM_SCALAR, ICPTGenerator >::setMaxArcs ( Size  maxArcs )

inherited

Modifies the value of the number of nodes imposed on the BayesGenerator.

Definition at line 106 of file IBayesNetGenerator_tpl.h.

References gum::IBayesNetGenerator< GUM_SCALAR, ICPTGenerator >::_maxArcs, gum::IBayesNetGenerator< GUM_SCALAR, ICPTGenerator >::_nbrNodes, GUM_ERROR, and gum::IBayesNetGenerator< GUM_SCALAR, ICPTGenerator >::maxArcs().

                                                                             {
    if (maxArcs < _nbrNodes - 1 || maxArcs > (_nbrNodes * (_nbrNodes - 1)) / 2)
      GUM_ERROR(OperationNotAllowed, " maxArcs value not possible ");

    _maxArcs = maxArcs;
  }

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setMaxlog10InducedWidth()

template<typename GUM_SCALAR , template< typename > class ICPTGenerator, template< typename > class ICPTDisturber>

INLINE void gum::MaxInducedWidthMCBayesNetGenerator< GUM_SCALAR, ICPTGenerator, ICPTDisturber >::setMaxlog10InducedWidth

(

Size

maxlog10InducedWidth

)

Modifies the value of the number of maximum parents imposed on the BayesNetGenerator.

Definition at line 153 of file maxInducedWidthMCBayesNetGenerator_tpl.h.

References gum::MaxInducedWidthMCBayesNetGenerator< GUM_SCALAR, ICPTGenerator, ICPTDisturber >::_maxlog10InducedWidth, GUM_ERROR, and gum::MaxInducedWidthMCBayesNetGenerator< GUM_SCALAR, ICPTGenerator, ICPTDisturber >::maxlog10InducedWidth().

                                                                       {
    if (maxlog10InducedWidth == 0)
      GUM_ERROR(OperationNotAllowed,
                "maxInducedWidth must be at least equal "
                "to 1 to have a connexe graph");

    _maxlog10InducedWidth = maxlog10InducedWidth;
  }

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setMaxModality()

template<typename GUM_SCALAR , template< typename > class ICPTGenerator>

INLINE void gum::IBayesNetGenerator< GUM_SCALAR, ICPTGenerator >::setMaxModality ( Size  maxModality )

inherited

Modifies the value of the number of nodes imposed on the BayesGenerator.

Definition at line 87 of file IBayesNetGenerator_tpl.h.

References gum::IBayesNetGenerator< GUM_SCALAR, ICPTGenerator >::_maxModality, GUM_ERROR, and gum::IBayesNetGenerator< GUM_SCALAR, ICPTGenerator >::maxModality().

                       {
    if (maxModality < 2)
      GUM_ERROR(OperationNotAllowed,
                " maxModality must be at least equal to two ");

    _maxModality = maxModality;
  }

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setNbrNodes()

template<typename GUM_SCALAR , template< typename > class ICPTGenerator>

INLINE void gum::IBayesNetGenerator< GUM_SCALAR, ICPTGenerator >::setNbrNodes ( Size  nbrNodes )

inherited

Modifies the value of the number of nodes imposed on the BayesGenerator.

Definition at line 97 of file IBayesNetGenerator_tpl.h.

References gum::IBayesNetGenerator< GUM_SCALAR, ICPTGenerator >::_maxArcs, gum::IBayesNetGenerator< GUM_SCALAR, ICPTGenerator >::_nbrNodes, GUM_ERROR, and gum::IBayesNetGenerator< GUM_SCALAR, ICPTGenerator >::nbrNodes().

                                                                               {
    if ((_maxArcs < nbrNodes - 1) || (_maxArcs > (nbrNodes * (nbrNodes - 1)) / 2))
      GUM_ERROR(OperationNotAllowed, " nbrNodes value not possible ");

    _nbrNodes = nbrNodes;
  }

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setP()

template<typename GUM_SCALAR , template< typename > class ICPTGenerator, template< typename > class ICPTDisturber>

INLINE void gum::MCBayesNetGenerator< GUM_SCALAR, ICPTGenerator, ICPTDisturber >::setP ( Idx  p )

inherited

Modifies the value of the probability p imposed on the BayesNetGenerator.

Definition at line 704 of file MCBayesNetGenerator_tpl.h.

References gum::MCBayesNetGenerator< GUM_SCALAR, ICPTGenerator, ICPTDisturber >::_p, gum::MCBayesNetGenerator< GUM_SCALAR, ICPTGenerator, ICPTDisturber >::_q, GUM_ERROR, and gum::MCBayesNetGenerator< GUM_SCALAR, ICPTGenerator, ICPTDisturber >::p().

                                                                                {
    _p = p;

    if (p + _q > 100)
      GUM_ERROR(
         OperationNotAllowed,
         "the sum of the probabilities p and q must be at most equal to 100");
  }

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setQ()

template<typename GUM_SCALAR , template< typename > class ICPTGenerator, template< typename > class ICPTDisturber>

INLINE void gum::MCBayesNetGenerator< GUM_SCALAR, ICPTGenerator, ICPTDisturber >::setQ ( Idx  q )

inherited

Modifies the value of the probability q imposed on the BayesNetGenerator.

Definition at line 718 of file MCBayesNetGenerator_tpl.h.

References gum::MCBayesNetGenerator< GUM_SCALAR, ICPTGenerator, ICPTDisturber >::_p, gum::MCBayesNetGenerator< GUM_SCALAR, ICPTGenerator, ICPTDisturber >::_q, GUM_ERROR, and gum::MCBayesNetGenerator< GUM_SCALAR, ICPTGenerator, ICPTDisturber >::q().

                                                                                {
    _q = q;

    if (_p + q > 100)
      GUM_ERROR(
         OperationNotAllowed,
         "the sum of the probabilities p and q must be at most equal to 100");
  }

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Member Data Documentation

_bayesNet

template<typename GUM_SCALAR , template< typename > class ICPTGenerator>

BayesNet< GUM_SCALAR > gum::IBayesNetGenerator< GUM_SCALAR, ICPTGenerator >::_bayesNet

protectedinherited

Definition at line 167 of file IBayesNetGenerator.h.

Referenced by gum::MCBayesNetGenerator< GUM_SCALAR, ICPTGenerator, ICPTDisturber >::__AorR(), gum::MCBayesNetGenerator< GUM_SCALAR, ICPTGenerator, ICPTDisturber >::__AR(), gum::MaxInducedWidthMCBayesNetGenerator< GUM_SCALAR, ICPTGenerator, ICPTDisturber >::__checkConditions(), gum::MaxParentsMCBayesNetGenerator< GUM_SCALAR, ICPTGenerator, ICPTDisturber >::__checkConditions(), gum::MCBayesNetGenerator< GUM_SCALAR, ICPTGenerator, ICPTDisturber >::__checkConditions(), gum::MCBayesNetGenerator< GUM_SCALAR, ICPTGenerator, ICPTDisturber >::__chooseCloseNodes(), gum::MCBayesNetGenerator< GUM_SCALAR, ICPTGenerator, ICPTDisturber >::__chooseNodes(), gum::MCBayesNetGenerator< GUM_SCALAR, ICPTGenerator, ICPTDisturber >::__connect(), gum::MCBayesNetGenerator< GUM_SCALAR, ICPTGenerator, ICPTDisturber >::__createPartTree(), gum::MCBayesNetGenerator< GUM_SCALAR, ICPTGenerator, ICPTDisturber >::__createTree(), gum::MCBayesNetGenerator< GUM_SCALAR, ICPTGenerator, ICPTDisturber >::__directedPath(), gum::MCBayesNetGenerator< GUM_SCALAR, ICPTGenerator, ICPTDisturber >::__eraseArc(), gum::MCBayesNetGenerator< GUM_SCALAR, ICPTGenerator, ICPTDisturber >::__insertArc(), gum::MCBayesNetGenerator< GUM_SCALAR, ICPTGenerator, ICPTDisturber >::__isPolytree(), gum::MCBayesNetGenerator< GUM_SCALAR, ICPTGenerator, ICPTDisturber >::__jump_multi(), gum::MCBayesNetGenerator< GUM_SCALAR, ICPTGenerator, ICPTDisturber >::__jump_poly(), gum::MCBayesNetGenerator< GUM_SCALAR, ICPTGenerator, ICPTDisturber >::__PMMx_multi(), gum::MCBayesNetGenerator< GUM_SCALAR, ICPTGenerator, ICPTDisturber >::__PMMx_poly(), gum::MCBayesNetGenerator< GUM_SCALAR, ICPTGenerator, ICPTDisturber >::__transformPoly(), gum::MCBayesNetGenerator< GUM_SCALAR, ICPTGenerator, ICPTDisturber >::disturbBN(), gum::IBayesNetGenerator< GUM_SCALAR, ICPTGenerator >::fillCPT(), gum::SimpleBayesNetGenerator< GUM_SCALAR, ICPTGenerator >::generateBN(), and gum::MCBayesNetGenerator< GUM_SCALAR, ICPTGenerator, ICPTDisturber >::generateBN().

_bayesNettemp

template<typename GUM_SCALAR , template< typename > class ICPTGenerator = SimpleCPTGenerator, template< typename > class ICPTDisturber = SimpleCPTDisturber>

BayesNet< GUM_SCALAR > gum::MCBayesNetGenerator< GUM_SCALAR, ICPTGenerator, ICPTDisturber >::_bayesNettemp

protectedinherited

Definition at line 290 of file MCBayesNetGenerator.h.

Referenced by gum::MCBayesNetGenerator< GUM_SCALAR, ICPTGenerator, ICPTDisturber >::__PMMx_multi(), gum::MCBayesNetGenerator< GUM_SCALAR, ICPTGenerator, ICPTDisturber >::__PMMx_poly(), gum::MCBayesNetGenerator< GUM_SCALAR, ICPTGenerator, ICPTDisturber >::__transformPoly(), gum::MCBayesNetGenerator< GUM_SCALAR, ICPTGenerator, ICPTDisturber >::disturbBN(), and gum::MCBayesNetGenerator< GUM_SCALAR, ICPTGenerator, ICPTDisturber >::generateBN().

_disturbing

template<typename GUM_SCALAR , template< typename > class ICPTGenerator = SimpleCPTGenerator, template< typename > class ICPTDisturber = SimpleCPTDisturber>

bool gum::MCBayesNetGenerator< GUM_SCALAR, ICPTGenerator, ICPTDisturber >::_disturbing

protectedinherited

Definition at line 289 of file MCBayesNetGenerator.h.

Referenced by gum::MCBayesNetGenerator< GUM_SCALAR, ICPTGenerator, ICPTDisturber >::__eraseArc(), gum::MCBayesNetGenerator< GUM_SCALAR, ICPTGenerator, ICPTDisturber >::__insertArc(), gum::MCBayesNetGenerator< GUM_SCALAR, ICPTGenerator, ICPTDisturber >::disturbBN(), and gum::MCBayesNetGenerator< GUM_SCALAR, ICPTGenerator, ICPTDisturber >::MCBayesNetGenerator().

_hashMarginal

template<typename GUM_SCALAR , template< typename > class ICPTGenerator = SimpleCPTGenerator, template< typename > class ICPTDisturber = SimpleCPTDisturber>

HashTable< NodeId, Potential< GUM_SCALAR >* > gum::MCBayesNetGenerator< GUM_SCALAR, ICPTGenerator, ICPTDisturber >::_hashMarginal

protectedinherited

Definition at line 291 of file MCBayesNetGenerator.h.

Referenced by gum::MCBayesNetGenerator< GUM_SCALAR, ICPTGenerator, ICPTDisturber >::__eraseArc(), and gum::MCBayesNetGenerator< GUM_SCALAR, ICPTGenerator, ICPTDisturber >::disturbBN().

_iteration

template<typename GUM_SCALAR , template< typename > class ICPTGenerator = SimpleCPTGenerator, template< typename > class ICPTDisturber = SimpleCPTDisturber>

Size gum::MCBayesNetGenerator< GUM_SCALAR, ICPTGenerator, ICPTDisturber >::_iteration

protectedinherited

Definition at line 287 of file MCBayesNetGenerator.h.

Referenced by gum::MCBayesNetGenerator< GUM_SCALAR, ICPTGenerator, ICPTDisturber >::__PMMx_multi(), gum::MCBayesNetGenerator< GUM_SCALAR, ICPTGenerator, ICPTDisturber >::__PMMx_poly(), gum::MCBayesNetGenerator< GUM_SCALAR, ICPTGenerator, ICPTDisturber >::disturbBN(), gum::MCBayesNetGenerator< GUM_SCALAR, ICPTGenerator, ICPTDisturber >::generateBN(), gum::MCBayesNetGenerator< GUM_SCALAR, ICPTGenerator, ICPTDisturber >::iteration(), gum::MCBayesNetGenerator< GUM_SCALAR, ICPTGenerator, ICPTDisturber >::MCBayesNetGenerator(), and gum::MCBayesNetGenerator< GUM_SCALAR, ICPTGenerator, ICPTDisturber >::setIteration().

_maxArcs

template<typename GUM_SCALAR , template< typename > class ICPTGenerator>

Size gum::IBayesNetGenerator< GUM_SCALAR, ICPTGenerator >::_maxArcs

protectedinherited

Definition at line 165 of file IBayesNetGenerator.h.

Referenced by gum::MCBayesNetGenerator< GUM_SCALAR, ICPTGenerator, ICPTDisturber >::__checkConditions(), gum::SimpleBayesNetGenerator< GUM_SCALAR, ICPTGenerator >::generateBN(), gum::IBayesNetGenerator< GUM_SCALAR, ICPTGenerator >::IBayesNetGenerator(), gum::IBayesNetGenerator< GUM_SCALAR, ICPTGenerator >::maxArcs(), gum::IBayesNetGenerator< GUM_SCALAR, ICPTGenerator >::setMaxArcs(), and gum::IBayesNetGenerator< GUM_SCALAR, ICPTGenerator >::setNbrNodes().

_maxlog10InducedWidth

template<typename GUM_SCALAR , template< typename > class ICPTGenerator = SimpleCPTGenerator, template< typename > class ICPTDisturber = SimpleCPTDisturber>

Size gum::MaxInducedWidthMCBayesNetGenerator< GUM_SCALAR, ICPTGenerator, ICPTDisturber >::_maxlog10InducedWidth

protected

Definition at line 166 of file maxInducedWidthMCBayesNetGenerator.h.

Referenced by gum::MaxInducedWidthMCBayesNetGenerator< GUM_SCALAR, ICPTGenerator, ICPTDisturber >::__checkConditions(), gum::MaxInducedWidthMCBayesNetGenerator< GUM_SCALAR, ICPTGenerator, ICPTDisturber >::MaxInducedWidthMCBayesNetGenerator(), gum::MaxInducedWidthMCBayesNetGenerator< GUM_SCALAR, ICPTGenerator, ICPTDisturber >::maxlog10InducedWidth(), and gum::MaxInducedWidthMCBayesNetGenerator< GUM_SCALAR, ICPTGenerator, ICPTDisturber >::setMaxlog10InducedWidth().

_maxModality

template<typename GUM_SCALAR , template< typename > class ICPTGenerator>

Size gum::IBayesNetGenerator< GUM_SCALAR, ICPTGenerator >::_maxModality

protectedinherited

Definition at line 166 of file IBayesNetGenerator.h.

Referenced by gum::MCBayesNetGenerator< GUM_SCALAR, ICPTGenerator, ICPTDisturber >::__createPartTree(), gum::MCBayesNetGenerator< GUM_SCALAR, ICPTGenerator, ICPTDisturber >::__createTree(), gum::SimpleBayesNetGenerator< GUM_SCALAR, ICPTGenerator >::generateBN(), gum::IBayesNetGenerator< GUM_SCALAR, ICPTGenerator >::IBayesNetGenerator(), gum::IBayesNetGenerator< GUM_SCALAR, ICPTGenerator >::maxModality(), and gum::IBayesNetGenerator< GUM_SCALAR, ICPTGenerator >::setMaxModality().

_nbrNodes

template<typename GUM_SCALAR , template< typename > class ICPTGenerator>

Size gum::IBayesNetGenerator< GUM_SCALAR, ICPTGenerator >::_nbrNodes

protectedinherited

Definition at line 164 of file IBayesNetGenerator.h.

Referenced by gum::SimpleBayesNetGenerator< GUM_SCALAR, ICPTGenerator >::generateBN(), gum::MCBayesNetGenerator< GUM_SCALAR, ICPTGenerator, ICPTDisturber >::generateBN(), gum::IBayesNetGenerator< GUM_SCALAR, ICPTGenerator >::IBayesNetGenerator(), gum::IBayesNetGenerator< GUM_SCALAR, ICPTGenerator >::nbrNodes(), gum::IBayesNetGenerator< GUM_SCALAR, ICPTGenerator >::setMaxArcs(), and gum::IBayesNetGenerator< GUM_SCALAR, ICPTGenerator >::setNbrNodes().

_p

template<typename GUM_SCALAR , template< typename > class ICPTGenerator = SimpleCPTGenerator, template< typename > class ICPTDisturber = SimpleCPTDisturber>

Idx gum::MCBayesNetGenerator< GUM_SCALAR, ICPTGenerator, ICPTDisturber >::_p

protectedinherited

Definition at line 288 of file MCBayesNetGenerator.h.

Referenced by gum::MCBayesNetGenerator< GUM_SCALAR, ICPTGenerator, ICPTDisturber >::__PMMx_multi(), gum::MCBayesNetGenerator< GUM_SCALAR, ICPTGenerator, ICPTDisturber >::__PMMx_poly(), gum::MCBayesNetGenerator< GUM_SCALAR, ICPTGenerator, ICPTDisturber >::MCBayesNetGenerator(), gum::MCBayesNetGenerator< GUM_SCALAR, ICPTGenerator, ICPTDisturber >::p(), gum::MCBayesNetGenerator< GUM_SCALAR, ICPTGenerator, ICPTDisturber >::setP(), and gum::MCBayesNetGenerator< GUM_SCALAR, ICPTGenerator, ICPTDisturber >::setQ().

_q

template<typename GUM_SCALAR , template< typename > class ICPTGenerator = SimpleCPTGenerator, template< typename > class ICPTDisturber = SimpleCPTDisturber>

Idx gum::MCBayesNetGenerator< GUM_SCALAR, ICPTGenerator, ICPTDisturber >::_q

protectedinherited

Definition at line 288 of file MCBayesNetGenerator.h.

Referenced by gum::MCBayesNetGenerator< GUM_SCALAR, ICPTGenerator, ICPTDisturber >::__PMMx_multi(), gum::MCBayesNetGenerator< GUM_SCALAR, ICPTGenerator, ICPTDisturber >::__PMMx_poly(), gum::MCBayesNetGenerator< GUM_SCALAR, ICPTGenerator, ICPTDisturber >::MCBayesNetGenerator(), gum::MCBayesNetGenerator< GUM_SCALAR, ICPTGenerator, ICPTDisturber >::q(), gum::MCBayesNetGenerator< GUM_SCALAR, ICPTGenerator, ICPTDisturber >::setP(), and gum::MCBayesNetGenerator< GUM_SCALAR, ICPTGenerator, ICPTDisturber >::setQ().

---

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

• agrum/BN/generator/maxInducedWidthMCBayesNetGenerator.h
• agrum/BN/generator/maxInducedWidthMCBayesNetGenerator_tpl.h