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

<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=5000, Idx p=30, Idx q=40)
	Constructor. More...
	MCBayesNetGenerator (BayesNet< GUM_SCALAR > bayesNet, Size iteration=5000, Idx p=30, Idx q=40)
	Constructor. More...
	~MCBayesNetGenerator () override
	Destructor. 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	_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	__directedPath (NodeId tail, NodeId head)
	The function that verify if there is a oriented path from node i to node j. 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...
virtual bool	__checkConditions ()
	The boolean function that will assert the respect of the constraint. More...
bool	__connect (NodeId i, NodeId j, NodeSet &excluded)
	The internal function used by the previous __connect. More...
bool	__directedPath (NodeId tail, NodeId head, NodeSet &excluded)
	The internal function used by the previous __directedPath. 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::MCBayesNetGenerator< GUM_SCALAR, ICPTGenerator, ICPTDisturber >

<agrum/BN/generator/MCayesNetGenerator.h>

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 157 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 = 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
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 56 of file MCBayesNetGenerator_tpl.h.

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

Referenced by gum::getMaxModality().

                                 :
      IBNG(nbrNodes, maxArcs, maxModality),
      _bayesNettemp(), _hashMarginal() {
    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;
    _disturbing = false;

    GUM_CONSTRUCTOR(MCBayesNetGenerator);
  }

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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 = 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
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 83 of file MCBayesNetGenerator_tpl.h.

References gum::MCBayesNetGenerator< GUM_SCALAR, ICPTGenerator, ICPTDisturber >::_disturbing, gum::MCBayesNetGenerator< GUM_SCALAR, ICPTGenerator, ICPTDisturber >::_iteration, gum::MCBayesNetGenerator< GUM_SCALAR, ICPTGenerator, ICPTDisturber >::_p, gum::MCBayesNetGenerator< GUM_SCALAR, ICPTGenerator, ICPTDisturber >::_q, gum::MCBayesNetGenerator< GUM_SCALAR, ICPTGenerator, ICPTDisturber >::iteration(), gum::MCBayesNetGenerator< GUM_SCALAR, ICPTGenerator, ICPTDisturber >::p(), gum::MCBayesNetGenerator< GUM_SCALAR, ICPTGenerator, ICPTDisturber >::q(), and gum::MCBayesNetGenerator< GUM_SCALAR, ICPTGenerator, ICPTDisturber >::~MCBayesNetGenerator().

                                                   :
      MCBayesNetGenerator(bayesNet.size(),
                          (Size)(bayesNet.sizeArcs() * 1.1),
                          getMaxModality(bayesNet)) {
    _iteration = iteration;
    _p = p;
    _q = q;
    _disturbing = false;
  }

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~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 103 of file MCBayesNetGenerator_tpl.h.

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

                            {
    GUM_DESTRUCTOR(MCBayesNetGenerator);
  }

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Member Function Documentation

__AorR()

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

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

protected

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

Definition at line 292 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 ( )

protected

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

Definition at line 310 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>

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

protectedvirtual

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

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

Definition at line 185 of file MCBayesNetGenerator_tpl.h.

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

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

                         {
    return this->_maxArcs >= this->_bayesNet.sizeArcs();
  }

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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  )

protected

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

Definition at line 446 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  )

protected

The function that randomly choose two nodes of the graph.

Definition at line 432 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  )

protected

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

Definition at line 560 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  )

protected

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 587 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  )

protected

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

n : id number for node label

Definition at line 506 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 )

protected

The function that randomly generate a simple tree.

Definition at line 481 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  )

protected

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

Definition at line 614 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  )

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 637 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  )

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 405 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  )

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 386 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 ( )

protected

The function that verify if graph is a polytree.

Definition at line 550 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 ( )

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 372 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 ( )

protected

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 358 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 ( )

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 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 244 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 ( )

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 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 198 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 )

protected

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

Definition at line 532 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
	)

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 134 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 61 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 )

overridevirtual

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 112 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

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

Definition at line 660 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 79 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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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 69 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 74 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

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

Definition at line 671 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

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

Definition at line 681 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

)

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

Definition at line 691 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 103 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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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 84 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 94 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

)

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

Definition at line 701 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

)

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

Definition at line 715 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 165 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

protected

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 >::__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

protected

Definition at line 286 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

protected

Definition at line 288 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

protected

Definition at line 284 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 163 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().

_maxModality

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

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

protectedinherited

Definition at line 164 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 162 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

protected

Definition at line 285 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

protected

Definition at line 285 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/MCBayesNetGenerator.h
• agrum/BN/generator/MCBayesNetGenerator_tpl.h