<agrum/BN/generator/MCayesNetGenerator.h> More...

#include <MCBayesNetGenerator.h>

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

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

Public Member Functions
Constructors / Destructor
	MCBayesNetGenerator (Size nbrNodes, Size maxArcs, Idx maxModality=2, Size iteration=NB_INIT_ITERATIONS, Idx p=30, Idx q=40)
	Constructor.
	MCBayesNetGenerator (BayesNet< GUM_SCALAR > bayesNet, Size iteration=NB_INIT_ITERATIONS, Idx p=30, Idx q=40)
	Constructor.
	~MCBayesNetGenerator () override
	Destructor.
BN generation methods
void	generateBN (BayesNet< GUM_SCALAR > &bayesNet) override
	Generates a random Bayesian network.
Getters
Size	iteration () const
	Return a constant reference to the number of iteration imposed on the Markov Chain BayesNetGenerator.
Idx	p () const
	Return a constant reference to the probabilité p imposed on the Markov Chain BayesNetGenerator.
Idx	q () const
	Return a constant reference to the probabilité imposed on the Markov Chain BayesNetGenerator.
Setters
void	setIteration (Size iteration)
	Modifies the value of the number of iterations impose on the BayesNetGenerator.
void	setP (Idx p)
	Modifies the value of the probability p imposed on the BayesNetGenerator.
void	setQ (Idx q)
	Modifies the value of the probability q imposed on the BayesNetGenerator.
BN generation methods
void	fromDAG (BayesNet< GUM_SCALAR > &bayesNet)
	Virtual function that Generates a Bayesian networks.
void	fillCPT (BayesNet< GUM_SCALAR > &bn) const
	function that insert random values in the CPT of each nodes according to the CPTGenerator.
Getters
Size	nbrNodes () const
	Return a constant reference to the number of nodes imposed on the IBayesNetGenerator.
Size	maxArcs () const
	Return a constant reference to the maximum number of arcs imposed on the IBayesNetGenerator.
Size	maxModality () const
	Return a constant reference to the maximum modality imposed on the IBayesNetGenerator.
Setters
void	setNbrNodes (Size nbrNodes)
	Modifies the value of the number of nodes imposed on the BayesGenerator.
void	setMaxArcs (Size maxArcs)
	Modifies the value of the number of nodes imposed on the BayesGenerator.
void	setMaxModality (Size maxModality)
	Modifies the value of the number of nodes imposed on the BayesGenerator.
CPT generation methods
void	generateCPT (const Idx &varId, const Tensor< GUM_SCALAR > &cpt) const override
	Generates a CPT using floats.
CPT disturbing methods
void	disturbReducCPT (NodeId node, BayesNet< GUM_SCALAR > &bayesNet, Tensor< GUM_SCALAR > &src, Tensor< GUM_SCALAR > &marg) override
	Disturb a CPT using GUM_SCALAR when removing parent varIdi.
void	disturbAugmCPT (NodeId node, BayesNet< GUM_SCALAR > &bayesNet, Tensor< GUM_SCALAR > &src, GUM_SCALAR variation) override
	Disturb a CPT using GUM_SCALAR when inserting a new parent new_parent.

Protected Member Functions
bool	_isPolytree_ ()
	The function that verify if graph is a polytree.
bool	_connect_ (NodeId i, NodeId j)
	The function that verify if node i and j are connected.
bool	_directedPath_ (NodeId tail, NodeId head)
	The function that verify if there is a oriented path from node i to node j.
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.
void	_eraseArc_ (NodeId i, NodeId j, bool mustbeconnex=true)
	The function that will remove the arc between node i and node j.
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 occur (AorR or AR or jump) then will assert that the imposed constraints are respected and if not, will return to the previous topology.
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 occur (AorR or jump) then will assert that the imposed constraint are respected and if not, will return to the previous topology.
void	_jump_poly_ ()
	In the case that the graph is a polytree, the function will add a random arc by the use of the function insertArc if the arc does not exist already.
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.
void	_Add_or_Remove_ ()
	The function will add or remove a random arc in the graph using the functions insertArc and removeArc.
void	_Add_and_Remove_ ()
	The function will remove and add a random arc changing the topology of the graph but asserting its connectivity.
virtual bool	_checkConditions_ ()
	The boolean function that will assert the respect of the constraint.
bool	_is_connected_ (const NodeId i, const NodeId j, NodeSet &excluded)
	The internal function used by the previous is_connected.
bool	_directedPath_ (NodeId tail, NodeId head, NodeSet &excluded)
	The internal function used by the previous directedPath.
void	_chooseNodes_ (NodeId &i, NodeId &j)
	The function that randomly choose two nodes of the graph.
void	_chooseCloseNodes_ (NodeId &i, NodeId &j)
	The function that randomly choose two neighbours nodes of the graph.
void	_transformPoly_ (Idx nbiter)
	The function that randomly change the simple tree into a polytree.
void	_createTree_ (Size BNSize)
	The function that randomly generate a simple tree.
NodeId	_createPartTree_ (Size BNSize, Idx &n)
	The internal function used by createTree that randomly generate a simple tree.

Protected Attributes
Size	iteration_
Idx	p_
Idx	q_
Size	nbrNodes_
Size	maxArcs_
Size	maxModality_
DAG	dag_

Detailed Description

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

Class for generating Bayesian networks with Markov chains.

This class randomly generates a Bayesian network given 6 parameters: the number of nodes, the maximum number of arcs the and of iterations the maximum modality.

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.

This Generation method require the use of two probability parameters(p, q) defining the choice of processes that will provoke the change of state. You can see in the graph below how the probabilities are used.

Definition at line 169 of file MCBayesNetGenerator.h.

Constructor & Destructor Documentation

◆ MCBayesNetGenerator() [1/2]

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

gum::MCBayesNetGenerator< GUM_SCALAR, ICPTGenerator, ICPTDisturber >::MCBayesNetGenerator	(	Size	nbrNodes,
		Size	maxArcs,
		Idx	maxModality = 2,
		Size	iteration = NB_INIT_ITERATIONS,
		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
iteration	The number of iterations wanted to repeat the algorithm
p	probability for the change of the state (see use of p and q )
q	probability for the change of the state (see use of p and q )

Definition at line 70 of file MCBayesNetGenerator_tpl.h.

              : IBNG(nbrNodes, maxArcs, maxModality) {
    if (p + q > 100)
      GUM_ERROR(OperationNotAllowed,
                "the sum of the probabilities p and q must be at most equal to 100");
 
    iteration_ = iteration;
    p_         = p;
    q_         = q;
 
    GUM_CONSTRUCTOR(MCBayesNetGenerator);
  }

References MCBayesNetGenerator(), GUM_ERROR, IBNG, iteration(), iteration_, gum::IBayesNetGenerator< GUM_SCALAR, SimpleCPTGenerator >::maxArcs(), gum::IBayesNetGenerator< GUM_SCALAR, SimpleCPTGenerator >::maxModality(), gum::IBayesNetGenerator< GUM_SCALAR, SimpleCPTGenerator >::nbrNodes(), p(), p_, q(), and q_.

Referenced by MCBayesNetGenerator(), MCBayesNetGenerator(), and ~MCBayesNetGenerator().

Here is the call graph for this function:

Here is the caller graph for this function:

◆ MCBayesNetGenerator() [2/2]

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

gum::MCBayesNetGenerator< GUM_SCALAR, ICPTGenerator, ICPTDisturber >::MCBayesNetGenerator	(	BayesNet< GUM_SCALAR >	bayesNet,
		Size	iteration = NB_INIT_ITERATIONS,
		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
iteration	The number of iterations wanted to repeat the algorithm
p	probability for the change of the state (see probability_p_q )
q	probability for the change of the state (see probability_p_q )

Definition at line 91 of file MCBayesNetGenerator_tpl.h.

                                :
      MCBayesNetGenerator(bayesNet.size(),
                          (Size)(bayesNet.sizeArcs() * 1.1),
                          getMaxModality(bayesNet)) {
    iteration_ = iteration;
    p_         = p;
    q_         = q;
  }

References MCBayesNetGenerator(), gum::getMaxModality(), iteration(), iteration_, p(), p_, q(), and q_.

Here is the call graph for this function:

◆ ~MCBayesNetGenerator()

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

gum::MCBayesNetGenerator< GUM_SCALAR, ICPTGenerator, ICPTDisturber >::~MCBayesNetGenerator ( )

override

Destructor.

Definition at line 108 of file MCBayesNetGenerator_tpl.h.

                                                                                        {
    GUM_DESTRUCTOR(MCBayesNetGenerator);
  }

References MCBayesNetGenerator().

Here is the call graph for this function:

Member Function Documentation

◆ _Add_and_Remove_()

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

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

protected

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

Definition at line 290 of file MCBayesNetGenerator_tpl.h.

                                                                                         {
    NodeId i, j, head, tail;
    _chooseNodes_(i, j);
 
    if (this->dag_.existsArc(i, j) || this->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: this->dag_.parents(j)) {
        NodeSet excluded;
        excluded.insert(j);
 
        if (_is_connected_(node, i, excluded)) {
          this->dag_.eraseArc(Arc(node, j));
          this->dag_.addArc(head, tail);
          return;
        }
      }
 
      for (auto node: this->dag_.children(j)) {
        NodeSet excluded;
        excluded.insert(j);
 
        if (_is_connected_(node, i, excluded)) {
          this->dag_.eraseArc(Arc{j, node});
          this->dag_.addArc(head, tail);
          return;
        }
      }
    }
  }

References _chooseNodes_().

Here is the call graph for this function:

◆ _Add_or_Remove_()

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

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

protected

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

Definition at line 277 of file MCBayesNetGenerator_tpl.h.

                                                                                        {
    NodeId i, j;
    _chooseNodes_(i, j);
    if (this->dag_.existsArc(i, j)) {
      _eraseArc_(i, j);
 
      return;
    } else _insertArc_(i, j);
  }

References _chooseNodes_(), _eraseArc_(), _insertArc_(), and gum::IBayesNetGenerator< GUM_SCALAR, SimpleCPTGenerator >::dag_.

Here is the call graph for this function:

◆ _checkConditions_()

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

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

protectedvirtual

The boolean function that will assert the respect of the constraint.

Reimplemented in gum::MaxInducedWidthMCBayesNetGenerator< GUM_SCALAR, ICPTGenerator, ICPTDisturber >, and gum::MaxParentsMCBayesNetGenerator< GUM_SCALAR, ICPTGenerator, ICPTDisturber >.

Definition at line 182 of file MCBayesNetGenerator_tpl.h.

                                                                                                 {
    return this->maxArcs_ >= this->dag_.sizeArcs();
  }

References gum::IBayesNetGenerator< GUM_SCALAR, SimpleCPTGenerator >::dag_, and gum::IBayesNetGenerator< GUM_SCALAR, SimpleCPTGenerator >::maxArcs_.

Referenced by gum::MaxParentsMCBayesNetGenerator< GUM_SCALAR, ICPTGenerator, ICPTDisturber >::_checkConditions_(), and _transformPoly_().

Here is the caller graph for this function:

◆ _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 )

protected

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

Definition at line 413 of file MCBayesNetGenerator_tpl.h.

                 {
    NodeId temp = randomValue(this->dag_.size());
    Size   co   = 0;
 
    if (this->dag_.parents(temp).size()) {
      j       = temp;
      auto it = this->dag_.parents(j).begin();
      co      = randomValue(this->dag_.parents(j).size());
 
      while (co--) {
        ++it;
      }
 
      i = *it;
    } else if (this->dag_.children(temp).size()) {
      i       = temp;
      auto it = this->dag_.children(i).begin();
      co      = randomValue(this->dag_.children(i).size());
 
      while (co--) {
        ++it;
      }
 
      j = *it;
    } else {
      GUM_ERROR(FatalError, "Sorry Misconstructed BN because of isolated node.")
    }
  }

References gum::IBayesNetGenerator< GUM_SCALAR, SimpleCPTGenerator >::dag_, and gum::randomValue().

Referenced by _transformPoly_().

Here is the call graph for this function:

Here is the caller graph for this function:

◆ _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 )

protected

The function that randomly choose two nodes of the graph.

Definition at line 398 of file MCBayesNetGenerator_tpl.h.

                                                                                                {
    if (this->dag_.size() < 3) {
      GUM_ERROR(ArgumentError, "This dag has only " << this->dag_.size() << " nodes.")
    }
    i = randomValue(this->dag_.size());
    j = randomValue(this->dag_.size());
 
    while (i == j)
      j = randomValue(this->dag_.size());
  }

References gum::IBayesNetGenerator< GUM_SCALAR, SimpleCPTGenerator >::dag_.

Referenced by _Add_and_Remove_(), _Add_or_Remove_(), and _jump_poly_().

Here is the caller graph for this function:

◆ _connect_()

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

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

protected

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

Definition at line 514 of file MCBayesNetGenerator_tpl.h.

                                                                                                  {
    if (this->dag_.existsArc(i, j) || this->dag_.existsArc(j, i)) return true;
    else {
      NodeSet excluded;
      excluded.insert(i);
 
      for (auto par: this->dag_.parents(i)) {
        if (!excluded.exists(par) && _is_connected_(par, j, excluded)) return true;
      }
 
      for (auto chi: this->dag_.children(i)) {
        if (!excluded.exists(chi) && _is_connected_(chi, j, excluded)) return true;
      }
 
      return false;
    }
  }

References _is_connected_(), gum::IBayesNetGenerator< GUM_SCALAR, SimpleCPTGenerator >::dag_, gum::Set< Key >::exists(), and gum::Set< Key >::insert().

Here is the call graph for this function:

◆ _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 )

protected

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

n : id number for node label

Definition at line 465 of file MCBayesNetGenerator_tpl.h.

                                                                                                {
    /*
    Size              nb_mod = 2 + randomValue(this->maxModality_ - 1);
    std::stringstream strBuff;
    strBuff << "n_" << n++;
    NodeId root     = this->bayesNet_.add(LabelizedVariable(strBuff.str(), "", nb_mod));
     */
    NodeId root     = this->dag_.addNode();
    Size   maxNodes = BNSize - 1;
    Size   SubG     = 0;
 
    while (maxNodes) {
      SubG         = randomValue(maxNodes) + 1;
      maxNodes     = maxNodes - SubG;
      NodeId rootS = _createPartTree_(SubG, n);
      this->dag_.addArc(root, rootS);
    }
 
    return root;
  }

References _createPartTree_(), gum::IBayesNetGenerator< GUM_SCALAR, SimpleCPTGenerator >::dag_, and gum::randomValue().

Referenced by _createPartTree_(), and _createTree_().

Here is the call graph for this function:

Here is the caller graph for this function:

◆ _createTree_()

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

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

protected

The function that randomly generate a simple tree.

Definition at line 447 of file MCBayesNetGenerator_tpl.h.

                                                                                                {
    Idx    n        = 0;
    NodeId root     = this->dag_.addNode();
    Size   maxNodes = BNSize - 1;
    Size   SubG     = 0;
 
    while (maxNodes) {
      SubG         = randomValue(maxNodes) + 1;
      maxNodes     = maxNodes - SubG;
      NodeId rootS = _createPartTree_(SubG, n);
      this->dag_.addArc(root, rootS);
    }
  }

References _createPartTree_(), gum::IBayesNetGenerator< GUM_SCALAR, SimpleCPTGenerator >::dag_, and gum::randomValue().

Referenced by generateBN().

Here is the call graph for this function:

Here is the caller graph for this function:

◆ _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 )

protected

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

Definition at line 560 of file MCBayesNetGenerator_tpl.h.

                                                                                                   {
    if (this->dag_.existsArc(tail, head)) return true;
    else {
      NodeSet excluded;
      excluded.insert(tail);
 
      for (auto node: this->dag_.children(tail)) {
        if (_directedPath_(node, head, excluded)) return true;
      }
 
      return false;
    }
  }

References _directedPath_(), gum::IBayesNetGenerator< GUM_SCALAR, SimpleCPTGenerator >::dag_, and gum::Set< Key >::insert().

Referenced by _directedPath_(), and _directedPath_().

Here is the call graph for this function:

Here is the caller graph for this function:

◆ _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 )

protected

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 578 of file MCBayesNetGenerator_tpl.h.

                         {
    if (this->dag_.existsArc(tail, head)) return true;
    else {
      excluded.insert(tail);
 
      for (auto node: this->dag_.children(tail)) {
        if (!excluded.exists(node) && _directedPath_(node, head, excluded)) return true;
      }
 
      return false;
    }
  }

References _directedPath_(), gum::IBayesNetGenerator< GUM_SCALAR, SimpleCPTGenerator >::dag_, gum::Set< Key >::exists(), and gum::Set< Key >::insert().

Here is the call graph for this function:

◆ _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 )

protected

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 371 of file MCBayesNetGenerator_tpl.h.

                           {
    /*if (disturbing_) {
      const BayesNet< GUM_SCALAR > bayesNet(this->bayesNet_);
      Tensor< 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->dag_.eraseArc(Arc(i, j));
 
      if (!_connect_(i, j) && mustbeconnex) { this->dag_.addArc(i, j); }
    }
  }

Referenced by _Add_or_Remove_(), and _transformPoly_().

Here is the caller graph for this function:

◆ _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 )

protected

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 355 of file MCBayesNetGenerator_tpl.h.

                                                                                             {
    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->dag_.addArc(i, j);
  }

Referenced by _Add_or_Remove_().

Here is the caller graph for this function:

◆ _is_connected_()

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

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

protected

The internal function used by the previous is_connected.

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

Definition at line 536 of file MCBayesNetGenerator_tpl.h.

                             {
    if (this->dag_.existsArc(i, j) || this->dag_.existsArc(j, i)) return true;
    else {
      excluded.insert(i);
 
      for (auto par: this->dag_.parents(i)) {
        if (!excluded.exists(par) && _is_connected_(par, j, excluded)) return true;
      }
 
      for (auto chi: this->dag_.children(i)) {
        if (!excluded.exists(chi) && _is_connected_(chi, j, excluded)) return true;
      }
 
      return false;
    }
  }

References _is_connected_(), gum::IBayesNetGenerator< GUM_SCALAR, SimpleCPTGenerator >::dag_, gum::Set< Key >::exists(), and gum::Set< Key >::insert().

Referenced by _connect_(), and _is_connected_().

Here is the call graph for this function:

Here is the caller graph for this function:

◆ _isPolytree_()

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

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

protected

The function that verify if graph is a polytree.

Definition at line 507 of file MCBayesNetGenerator_tpl.h.

                                                                                            {
    return this->dag_.size() - 1 == this->dag_.sizeArcs();
  }

References gum::IBayesNetGenerator< GUM_SCALAR, SimpleCPTGenerator >::dag_.

◆ _jump_multi_()

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

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

protected

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 344 of file MCBayesNetGenerator_tpl.h.

                                                                                     {
    NodeId i, j;
    _chooseNodes_(i, j);
 
    if (this->dag_.existsArc(i, j)) { _eraseArc_(i, j); }
  }

◆ _jump_poly_()

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

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

protected

In the case that the graph is a polytree, the function will add a random arc by the use of the function insertArc if the arc does not exist already.

Definition at line 334 of file MCBayesNetGenerator_tpl.h.

                                                                                           {
    NodeId i, j;
    _chooseNodes_(i, j);
 
    if (!this->dag_.existsArc(i, j)) _insertArc_(i, j);
  }

References _chooseNodes_().

Here is the call graph for this function:

◆ _PMMx_multi_()

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

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

protected

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 occur (AorR or jump) then will assert that the imposed constraint are respected and if not, will return to the previous topology.

Definition at line 235 of file MCBayesNetGenerator_tpl.h.

                                                                                     {
    while (true) {
      if (!iteration_--) return;
      DAG tmp_dag = this->dag_;
 
      Idx per = randomValue(100);
 
      if (per < p_ + q_) {
        _Add_or_Remove_();
        if (_checkConditions_()) {
          if (_isPolytree_()) {
            if (per < p_) {
              tmp_dag = this->dag_;
              _PMMx_poly_();
              break;
            } else {
              this->dag_ = tmp_dag;
            }
          } else {
            tmp_dag = this->dag_;
          }
        } else {
          this->dag_ = tmp_dag;
        }
      } else {
        _jump_multi_();
        if (_checkConditions_()) {
          tmp_dag = this->dag_;
          if (_isPolytree_()) {
            _PMMx_poly_();
            break;
          }
        } else {
          this->dag_ = tmp_dag;
        }
      }
    }
  }

◆ _PMMx_poly_()

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

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

protected

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 occur (AorR or AR or jump) then will assert that the imposed constraints are respected and if not, will return to the previous topology.

Definition at line 192 of file MCBayesNetGenerator_tpl.h.

                                                                                    {
    while (true) {
      if (!iteration_--) return;
      DAG tmp_dag = this->dag_;
      Idx per     = randomValue(100);
 
      if (per < p_) {
        _Add_or_Remove_();
 
        if (_checkConditions_()) {
          tmp_dag = this->dag_;
          _PMMx_multi_();
          break;
        } else {
          this->dag_ = tmp_dag;
        }
      } else {
        if (per < p_ + q_) {
          _Add_and_Remove_();
 
          if (!_checkConditions_()) {
            this->dag_ = tmp_dag;
          } else {
            tmp_dag = this->dag_;
          }
        } else {
          _jump_poly_();
 
          if (_checkConditions_()) {
            tmp_dag = this->dag_;
            _PMMx_multi_();
            break;
          } else {
            this->dag_ = tmp_dag;
          }
        }
      }
    }
  }

Referenced by generateBN().

Here is the caller graph for this function:

◆ _transformPoly_()

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

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

protected

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

Definition at line 492 of file MCBayesNetGenerator_tpl.h.

                                                                                                 {
    while (nbiter--) {
      NodeId i, j;
      _chooseCloseNodes_(i, j);
      auto dag_tmp = this->dag_;
      _eraseArc_(i, j, false);
      this->dag_.addArc(j, i);
 
      if (!_checkConditions_()) this->dag_ = dag_tmp;
    }
  }

References _checkConditions_(), _chooseCloseNodes_(), _eraseArc_(), and gum::IBayesNetGenerator< GUM_SCALAR, SimpleCPTGenerator >::dag_.

Referenced by generateBN().

Here is the call graph for this function:

Here is the caller graph for this function:

◆ disturbAugmCPT()

template<typename GUM_SCALAR>

void gum::SimpleCPTDisturber< GUM_SCALAR >::disturbAugmCPT	(	NodeId	node,
		BayesNet< GUM_SCALAR > &	bayesNet,
		Tensor< GUM_SCALAR > &	src,
		GUM_SCALAR	variation )

overrideinherited

Disturb a CPT using GUM_SCALAR when inserting a new parent new_parent.

Parameters

node	A reference on the CPT owner.
bayesNet	the Bayesian network.
src	copy of the CPT before augmentation.
variation	degree of variation from the initial probability.

Definition at line 94 of file simpleCPTDisturber_tpl.h.

                                                                                           {
    Instantiation i(bayesNet.cpt(node));
 
    for (i.setFirst(); !i.end(); ++i)
      bayesNet.cpt(node).set(i, src.get(i) + GUM_SCALAR(randomProba()) * variation);
 
    bayesNet.cpt(node).normalizeAsCPT();
  }

◆ disturbReducCPT()

template<typename GUM_SCALAR>

void gum::SimpleCPTDisturber< GUM_SCALAR >::disturbReducCPT	(	NodeId	node,
		BayesNet< GUM_SCALAR > &	bayesNet,
		Tensor< GUM_SCALAR > &	src,
		Tensor< GUM_SCALAR > &	marg )

overrideinherited

Disturb a CPT using GUM_SCALAR when removing parent varIdi.

Parameters

node	The variable on the CPT owner.
bayesNet	the Bayesian network.
src	copy of the CPT before reduction.
marg	of the inference before reduction on the node varIdi.

Definition at line 70 of file simpleCPTDisturber_tpl.h.

                                                                                       {
    Instantiation i_src(src);
    Instantiation i_dest(bayesNet.cpt(node));
    Instantiation i_marg(marg);
 
    for (i_dest.setFirst(); !i_dest.end(); ++i_dest) {
      GUM_SCALAR potval = 0;
 
      i_src.setVals(i_dest);
      for (i_marg.setFirst(); !i_marg.end(); ++i_marg) {
        i_src.setVals(i_marg);
        potval += src.get(i_src) * marg.get(i_marg);
      }
 
      bayesNet.cpt(node).set(i_dest, potval);
    }
 
    bayesNet.cpt(node).normalizeAsCPT();
  }

References gum::Instantiation::end(), gum::Instantiation::setFirst(), and gum::Instantiation::setVals().

Here is the call graph for this function:

◆ fillCPT()

void gum::IBayesNetGenerator< GUM_SCALAR, SimpleCPTGenerator >::fillCPT ( BayesNet< GUM_SCALAR > & bn ) const

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 130 of file IBayesNetGenerator_tpl.h.

                                                                                                {
    for (auto node: bn.nodes())
      this->generateCPT(bn.cpt(node).pos(bn.variable(node)), bn.cpt(node));
  }

References dag_, maxModality_, and gum::randomValue().

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

Here is the call graph for this function:

Here is the caller graph for this function:

◆ fromDAG()

INLINE void gum::IBayesNetGenerator< GUM_SCALAR, SimpleCPTGenerator >::fromDAG ( BayesNet< GUM_SCALAR > & bayesNet )

inherited

Virtual function that Generates a Bayesian networks.

Parameters

bayesNet Bayesian network to be completed after initialisation

Returns: null but modify inputed empty Bayesian network

Definition at line 122 of file IBayesNetGenerator_tpl.h.

                                                                                                 {
    bn.clear();
 
    const auto  width = (this->dag_.size() >= 100) ? 3 : 2;
    int         n     = 0;
    const auto& topo  = this->dag_.topologicalOrder();
    for (const auto node: topo) {
      std::stringstream strBuff;
      strBuff << "X" << std::setfill('0') << std::setw(width) << n++;
      bn.add(RangeVariable(strBuff.str(), "", 0, long(1 + randomValue(this->maxModality_ - 1))),
             node);
    }
    bn.beginTopologyTransformation();
    for (auto arc: this->dag_.arcs()) {
      bn.addArc(arc.tail(), arc.head());
    }
    bn.endTopologyTransformation();
  }

References maxArcs(), and maxArcs_.

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

Here is the call graph for this function:

Here is the caller graph for this function:

◆ 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 )

overridevirtual

Generates a random Bayesian network.

Parameters

bayesNet empty IBayesNet to generate.

Returns: null but modify inputed Bayesian network

Implements gum::IBayesNetGenerator< GUM_SCALAR, SimpleCPTGenerator >.

Definition at line 115 of file MCBayesNetGenerator_tpl.h.

                                        {
    Idx   iteration = iteration_;
    Timer timer;
    _createTree_(this->nbrNodes_);
    _transformPoly_(this->nbrNodes_ / 2);
    _PMMx_poly_();
    this->fromDAG(bayesNet);
 
    this->fromDAG(bayesNet);
    this->fillCPT(bayesNet);
    iteration_ = iteration;
  }

References _createTree_(), _PMMx_poly_(), _transformPoly_(), gum::IBayesNetGenerator< GUM_SCALAR, SimpleCPTGenerator >::fillCPT(), gum::IBayesNetGenerator< GUM_SCALAR, SimpleCPTGenerator >::fromDAG(), iteration(), iteration_, and gum::IBayesNetGenerator< GUM_SCALAR, SimpleCPTGenerator >::nbrNodes_.

Here is the call graph for this function:

◆ generateCPT()

template<typename GUM_SCALAR>

void gum::SimpleCPTGenerator< GUM_SCALAR >::generateCPT	(	const Idx &	varId,
		const Tensor< GUM_SCALAR > &	cpt ) const

overrideinherited

Generates a CPT using floats.

Parameters

varId	The variable id of the CPT owner.
cpt	A reference on the CPT to fill.

Definition at line 72 of file simpleCPTGenerator_tpl.h.

                                                                                            {
    cpt.random().normalizeAsCPT(varId);
  }

◆ iteration()

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

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

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

Definition at line 597 of file MCBayesNetGenerator_tpl.h.

                                                                                               {
    return iteration_;
  }

References iteration_.

Referenced by MCBayesNetGenerator(), MCBayesNetGenerator(), generateBN(), and setIteration().

Here is the caller graph for this function:

◆ maxArcs()

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

inherited

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

Definition at line 151 of file IBayesNetGenerator_tpl.h.

                                                                             {
    return maxArcs_;
  }

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

Here is the caller graph for this function:

◆ maxModality()

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

inherited

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

Definition at line 157 of file IBayesNetGenerator_tpl.h.

                                                                                 {
    return maxModality_;
  }

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

Here is the caller graph for this function:

◆ nbrNodes()

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

inherited

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

Definition at line 145 of file IBayesNetGenerator_tpl.h.

                                                                              {
    return nbrNodes_;
  }

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

Here is the caller graph for this function:

◆ p()

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

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

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

Definition at line 604 of file MCBayesNetGenerator_tpl.h.

                                                                                      {
    return p_;
  }

References p_.

Referenced by MCBayesNetGenerator(), MCBayesNetGenerator(), and setP().

Here is the caller graph for this function:

◆ q()

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

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

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

Definition at line 611 of file MCBayesNetGenerator_tpl.h.

                                                                                      {
    return q_;
  }

References q_.

Referenced by MCBayesNetGenerator(), MCBayesNetGenerator(), and setQ().

Here is the caller graph for this function:

◆ setIteration()

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

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

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

Definition at line 618 of file MCBayesNetGenerator_tpl.h.

                      {
    iteration_ = iteration;
  }

References iteration(), and iteration_.

Here is the call graph for this function:

◆ setMaxArcs()

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

inherited

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

Definition at line 172 of file IBayesNetGenerator_tpl.h.

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

◆ setMaxModality()

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

inherited

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

Definition at line 177 of file IBayesNetGenerator_tpl.h.

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

◆ setNbrNodes()

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

inherited

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

Definition at line 167 of file IBayesNetGenerator_tpl.h.

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

◆ setP()

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

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

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

Definition at line 626 of file MCBayesNetGenerator_tpl.h.

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

References GUM_ERROR, p(), p_, and q_.

Here is the call graph for this function:

◆ setQ()

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

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

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

Definition at line 637 of file MCBayesNetGenerator_tpl.h.

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

References GUM_ERROR, p_, q(), and q_.

Here is the call graph for this function:

Member Data Documentation

◆ dag_

DAG gum::IBayesNetGenerator< GUM_SCALAR, SimpleCPTGenerator >::dag_

protectedinherited

Definition at line 187 of file IBayesNetGenerator.h.

◆ iteration_

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

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

protected

Definition at line 298 of file MCBayesNetGenerator.h.

Referenced by MCBayesNetGenerator(), MCBayesNetGenerator(), generateBN(), iteration(), and setIteration().

◆ maxArcs_

Size gum::IBayesNetGenerator< GUM_SCALAR, SimpleCPTGenerator >::maxArcs_

protectedinherited

Definition at line 185 of file IBayesNetGenerator.h.

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

◆ maxModality_

Size gum::IBayesNetGenerator< GUM_SCALAR, SimpleCPTGenerator >::maxModality_

protectedinherited

Definition at line 186 of file IBayesNetGenerator.h.

Referenced by ~IBayesNetGenerator(), and fillCPT().

◆ nbrNodes_

Size gum::IBayesNetGenerator< GUM_SCALAR, SimpleCPTGenerator >::nbrNodes_

protectedinherited

Definition at line 184 of file IBayesNetGenerator.h.

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

◆ p_

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

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

protected

Definition at line 299 of file MCBayesNetGenerator.h.

Referenced by MCBayesNetGenerator(), MCBayesNetGenerator(), p(), setP(), and setQ().

◆ q_

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

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

protected

Definition at line 299 of file MCBayesNetGenerator.h.

Referenced by MCBayesNetGenerator(), MCBayesNetGenerator(), q(), setP(), and setQ().

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

agrum/BN/generator/MCBayesNetGenerator.h
agrum/BN/generator/MCBayesNetGenerator_tpl.h

Public Member Functions

Protected Member Functions

Protected Attributes

Detailed Description

Constructor & Destructor Documentation

◆ MCBayesNetGenerator() [1/2]

◆ MCBayesNetGenerator() [2/2]

◆ ~MCBayesNetGenerator()

Member Function Documentation

◆ _Add_and_Remove_()

◆ _Add_or_Remove_()

◆ _checkConditions_()

◆ _chooseCloseNodes_()

◆ _chooseNodes_()

◆ _connect_()

◆ _createPartTree_()

◆ _createTree_()

◆ _directedPath_() [1/2]

◆ _directedPath_() [2/2]

◆ _eraseArc_()

◆ _insertArc_()

◆ _is_connected_()

◆ _isPolytree_()

◆ _jump_multi_()

◆ _jump_poly_()

◆ _PMMx_multi_()

◆ _PMMx_poly_()

◆ _transformPoly_()

◆ disturbAugmCPT()

◆ disturbReducCPT()

◆ fillCPT()

◆ fromDAG()

◆ generateBN()

◆ generateCPT()

◆ iteration()

◆ maxArcs()

◆ maxModality()

◆ nbrNodes()

◆ p()

◆ q()

◆ setIteration()

◆ setMaxArcs()

◆ setMaxModality()

◆ setNbrNodes()

◆ setP()

◆ setQ()

Member Data Documentation

◆ dag_

◆ iteration_

◆ maxArcs_

◆ maxModality_

◆ nbrNodes_

◆ p_

◆ q_