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

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

#include <maxParentsMCBayesNetGenerator.h>

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

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

Public Member Functions
Constructors / Destructor
	MaxParentsMCBayesNetGenerator (Size nbrNodes, Size maxArcs, Size maxModality=2, Size maxParents=1, Idx iteration=5000, Idx p=30, Idx q=40)
	Constructor. More...
	MaxParentsMCBayesNetGenerator (BayesNet< GUM_SCALAR > bayesNet, Size maxParents=2, Idx iteration=5000, Idx p=30, Idx q=40)
	Constructor. More...
	~MaxParentsMCBayesNetGenerator () final
	Destructor. More...
Getters
Size	maxParents () const
	Return a constant reference to the number of maximum parents imposed on the Markov Chain BayesNetGenerator. More...
Setters
void	setMaxParents (Size maxParents)
	Modifies the value of the number of maximum parents imposed on the BayesNetGenerator. More...
BN generation methods
void	generateBN (BayesNet< GUM_SCALAR > &bayesNet) override
	Generates a random Bayesian network. More...
void	disturbBN (BayesNet< GUM_SCALAR > &bayesNetinit, Size iteration=0)
	Change randomly the topology of a specific Bayesian networks. More...
Getters
Size	iteration () const
	Return a constant reference to the number of iteration imposed on the Markov Chain BayesNetGenerator. More...
Idx	p () const
	Return a constant reference to the probabilité p imposed on the Markov Chain BayesNetGenerator. More...
Idx	q () const
	Return a constant reference to the probabilité imposed on the Markov Chain BayesNetGenerator. More...
Setters
void	setIteration (Size iteration)
	Modifies the value of the number of iterations impose on the BayesNetGenerator. More...
void	setP (Idx p)
	Modifies the value of the probability p imposed on the BayesNetGenerator. More...
void	setQ (Idx q)
	Modifies the value of the probability q imposed on the BayesNetGenerator. More...
BN generation methods
void	fillCPT ()
	function that insert random values in the CPT of each nodes according to the CPTGenerator. More...
Getters
Size	nbrNodes () const
	Return a constant reference to the number of nodes imposed on the IBayesNetGenerator. More...
Size	maxArcs () const
	Return a constant reference to the maximum number of arcs imposed on the IBayesNetGenerator. More...
Size	maxModality () const
	Return a constant reference to the maximum modality imposed on the IBayesNetGenerator. More...
Setters
void	setNbrNodes (Size nbrNodes)
	Modifies the value of the number of nodes imposed on the BayesGenerator. More...
void	setMaxArcs (Size maxArcs)
	Modifies the value of the number of nodes imposed on the BayesGenerator. More...
void	setMaxModality (Size maxModality)
	Modifies the value of the number of nodes imposed on the BayesGenerator. More...

Protected Attributes
Size	maxParents_
Size	iteration_
Idx	p_
Idx	q_
bool	disturbing_
BayesNet< GUM_SCALAR >	bayesNettemp_
HashTable< NodeId, Potential< GUM_SCALAR > *>	hashMarginal_
Size	nbrNodes_
Size	maxArcs_
Size	maxModality_
BayesNet< GUM_SCALAR >	bayesNet_

Protected Member Functions
bool	_isPolytree_ ()
	The function that verify if graph is a polytree. More...
bool	_connect_ (NodeId i, NodeId j)
	The function that verify if node i and j are connected. More...
bool	_connect_ (NodeId i, NodeId j, NodeSet &excluded)
	The internal function used by the previous connect. More...
bool	_directedPath_ (NodeId tail, NodeId head)
	The function that verify if there is a oriented path from node i to node j. More...
bool	_directedPath_ (NodeId tail, NodeId head, NodeSet &excluded)
	The internal function used by the previous directedPath. More...
void	_insertArc_ (NodeId i, NodeId j)
	The function that will insert an arc between node i to node j, but only if there isn't any cycle created. More...
void	_eraseArc_ (NodeId i, NodeId j, bool mustbeconnex=true)
	The function that will remove the arc between node i and node j. More...
void	_PMMx_poly_ ()
	In the case that the graph is a polytree, the function will, according to the probability p and q, choose which change of state must occure( AorR or AR or jump) then will assert that the imposed constraint are respected and if not, will return to the previous topology. More...
void	_PMMx_multi_ ()
	In the case that the graph is a multiconnected graph, the function will, according to the probability p and q, choose which change of state must occure( AorR or jump) then will assert that the imposed constraint are respected and if not, will return to the previous topology. More...
void	_jump_poly_ ()
	In the case that the graph is a polytree, the function will add a ramdom arc by the use of the function insertArc if the arc does not exist allready. More...
void	_jump_multi_ ()
	In the case that the graph is a multiconnect graph, the function will choose randomly two nodes and will remove the arc between them by the use of the function insertArc if the arc exists. More...
void	_AorR_ ()
	The function will add or remove a random arc in the graph using the functions insertArc and removeArc. More...
void	_AR_ ()
	The function will remove and add a random arc changing the topology of the graph but asserting its connectivity. More...
void	_chooseNodes_ (NodeId &i, NodeId &j)
	The function that randomly choose two nodes of the graph. More...
void	_chooseCloseNodes_ (NodeId &i, NodeId &j)
	The function that randomly choose two neighbours nodes of the graph. More...
void	_transformPoly_ (Idx nbiter)
	The function that randomly change the simple tree into a polytree. More...
void	_createTree_ (Size BNSize)
	The function that randomly generate a simple tree. More...
NodeId	_createPartTree_ (Size BNSize, Idx &n)
	The internal function used by createTree that randomly generate a simple tree. More...

Detailed Description

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

<agrum/BN/generator/SimpleMCayesNetGenerator.h>

Class for generating Bayesian networks with Markov chains.

This class is inherited from MCBayesNetGenerator and is an example of Markov Chain Bayesian network Generator that can be implemented. Here a constraint is added which is the maximum number of parents that a node can have. To create our own MCBayesNetGenerator with a specific constraint you must:
1) Create the class XXXXXXMCBayesNetGenerator<GUM_SCALAR,ICPTGenerator,ICPTDisturber>.
2) Add a variable specific to the constraint (here maxParents_).
3) Verify in the function checkCondition_() the wanted constraint and then launch MCBayesNetGenerator<GUM_SCALAR,ICPTGenerator,ICPTDisturber>:: checkConditions() to verify the default constraint.
4) Define the getters and setters of the constraint variable.
5) And verify in the constructor that the constraint is not in contradiction with the generator here maxParent must be >= 1.

Warning

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

Definition at line 89 of file maxParentsMCBayesNetGenerator.h.

Constructor & Destructor Documentation

MaxParentsMCBayesNetGenerator() [1/2]

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

INLINE gum::MaxParentsMCBayesNetGenerator< GUM_SCALAR, ICPTGenerator, ICPTDisturber >::MaxParentsMCBayesNetGenerator	(	Size	nbrNodes,
		Size	maxArcs,
		Size	maxModality = 2,
		Size	maxParents = 1,
		Idx	iteration = 5000,
		Idx	p = 30,
		Idx	q = 40
	)

Constructor.

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

Parameters

nbrNodes	The number of nodes in the generated BN.
maxArcs	The maximum number of Arcs.
maxModality	Each DRV has from 2 to maxModality modalities
maxParents	The number of maximum parents for each node imposed on the generator
iteration	The number of iterations wanted to repeat the algorithm
p	probability for the change of the state (see useof p and q" )
q	probability for the change of the state (see useof p and q" )

Definition at line 47 of file maxParentsMCBayesNetGenerator_tpl.h.

References gum::Set< Key, Alloc >::emplace().

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

    maxParents_ = maxParents;
    GUM_CONSTRUCTOR(MaxParentsMCBayesNetGenerator);
  }

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MaxParentsMCBayesNetGenerator() [2/2]

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

INLINE gum::MaxParentsMCBayesNetGenerator< GUM_SCALAR, ICPTGenerator, ICPTDisturber >::MaxParentsMCBayesNetGenerator ( BayesNet< GUM_SCALAR >  bayesNet, Size  maxParents = 2, Idx  iteration = 5000, Idx  p = 30, Idx  q = 40  )

explicit

Constructor.

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

Parameters

bayesNet	the IBayesNet used as reference to fill the parameters nbrNodes, maxArcs and maxModality
maxParents	The number of maximum parents for each node imposed on the generator
iteration	The number of iterations wanted to repeat the algorithm
p	probability for the change of the state (see useof p and q" )
q	probability for the change of the state (see useof p and q" )

Definition at line 69 of file maxParentsMCBayesNetGenerator_tpl.h.

References gum::Set< Key, Alloc >::emplace().

                                                             :
      MCBG(bayesNet, iteration, p, q) {
    maxParents_ = maxParents;
    GUM_CONSTRUCTOR(MaxParentsMCBayesNetGenerator);
  }

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~MaxParentsMCBayesNetGenerator()

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

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

final

Destructor.

Definition at line 86 of file maxParentsMCBayesNetGenerator_tpl.h.

References gum::Set< Key, Alloc >::emplace().

                                      {
    GUM_DESTRUCTOR(MaxParentsMCBayesNetGenerator);
  }

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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_ ( )

protectedinherited

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

Definition at line 287 of file MCBayesNetGenerator_tpl.h.

References gum::Set< Key, Alloc >::emplace().

                                                                               {
    NodeId i, j;
    _chooseNodes_(i, j);
    const DAG _dag_ = this->bayesNet_.dag();

    if (_dag_.existsArc(i, j)) {
      _eraseArc_(i, j);

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

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_AR_()

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

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

protectedinherited

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

Definition at line 305 of file MCBayesNetGenerator_tpl.h.

References gum::Set< Key, Alloc >::emplace().

                                                                             {
    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);
          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< class > class ICPTGenerator, template< class > class ICPTDisturber>

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

finalprivatevirtual

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

Returns

boolean state that verify the conditions

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

Definition at line 96 of file maxParentsMCBayesNetGenerator_tpl.h.

References gum::Set< Key, Alloc >::emplace().

                         {
    for (auto node: this->bayesNet_.nodes())
      if (this->bayesNet_.parents(node).size() > maxParents_) return false;

    return MCBG::_checkConditions_();
  }

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_chooseCloseNodes_()

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

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

protectedinherited

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

Definition at line 440 of file MCBayesNetGenerator_tpl.h.

References gum::Set< Key, Alloc >::emplace().

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

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

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

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

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

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

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_chooseNodes_()

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

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

protectedinherited

The function that randomly choose two nodes of the graph.

Definition at line 426 of file MCBayesNetGenerator_tpl.h.

References gum::Set< Key, Alloc >::emplace().

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

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

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_connect_() [1/2]

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

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

protectedinherited

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

Definition at line 552 of file MCBayesNetGenerator_tpl.h.

References gum::Set< Key, Alloc >::emplace().

                                                                                                  {
    const DAG _dag_ = this->bayesNet_.dag();

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

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

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

      return false;
    }
  }

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_connect_() [2/2]

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

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

protectedinherited

The internal function used by the previous connect.

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

Definition at line 580 of file MCBayesNetGenerator_tpl.h.

References gum::Set< Key, Alloc >::emplace().

                                                                                                   {
    const DAG _dag_ = this->bayesNet_.dag();

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

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

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

      return false;
    }
  }

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_createPartTree_()

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

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

protectedinherited

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

n : id number for node label

Definition at line 499 of file MCBayesNetGenerator_tpl.h.

References gum::Set< Key, Alloc >::emplace().

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

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

    return root;
  }

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_createTree_()

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

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

protectedinherited

The function that randomly generate a simple tree.

Definition at line 476 of file MCBayesNetGenerator_tpl.h.

References gum::Set< Key, Alloc >::emplace().

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

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

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_directedPath_() [1/2]

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

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

protectedinherited

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

Definition at line 608 of file MCBayesNetGenerator_tpl.h.

References gum::Set< Key, Alloc >::emplace().

                                                                                                  {
    const DAG _dag_ = this->bayesNet_.dag();

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

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

      return false;
    }
  }

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_directedPath_() [2/2]

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

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

protectedinherited

The internal function used by the previous directedPath.

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

Definition at line 631 of file MCBayesNetGenerator_tpl.h.

References gum::Set< Key, Alloc >::emplace().

                        {
    const DAG _dag_ = this->bayesNet_.dag();

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

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

      return false;
    }
  }

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_eraseArc_()

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

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

protectedinherited

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

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

Definition at line 397 of file MCBayesNetGenerator_tpl.h.

References gum::Set< Key, Alloc >::emplace().

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

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

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

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

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_insertArc_()

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

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

protectedinherited

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

Definition at line 379 of file MCBayesNetGenerator_tpl.h.

References gum::Set< Key, Alloc >::emplace().

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

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

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

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_isPolytree_()

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

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

protectedinherited

The function that verify if graph is a polytree.

Definition at line 542 of file MCBayesNetGenerator_tpl.h.

References gum::Set< Key, Alloc >::emplace().

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

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_jump_multi_()

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

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

protectedinherited

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

Definition at line 365 of file MCBayesNetGenerator_tpl.h.

References gum::Set< Key, Alloc >::emplace().

                                                                                     {
    NodeId i, j;
    _chooseNodes_(i, j);
    const DAG _dag_ = this->bayesNet_.dag();

    if (_dag_.existsArc(i, j)) { _eraseArc_(i, j); }
  }

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_jump_poly_()

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

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

protectedinherited

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

Definition at line 352 of file MCBayesNetGenerator_tpl.h.

References gum::Set< Key, Alloc >::emplace().

                                                                                           {
    NodeId i, j;
    _chooseNodes_(i, j);
    const DAG _dag_ = this->bayesNet_.dag();

    if (!_dag_.existsArc(i, j)) _insertArc_(i, j);
  }

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_PMMx_multi_()

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

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

protectedinherited

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

Definition at line 239 of file MCBayesNetGenerator_tpl.h.

References gum::Set< Key, Alloc >::emplace().

                                                                                     {
    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_();

      } else {
        this->bayesNet_ = bayesNettemp_;
        _PMMx_multi_();
      }
    }
  }

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_PMMx_poly_()

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

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

protectedinherited

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

Definition at line 194 of file MCBayesNetGenerator_tpl.h.

References gum::Set< Key, Alloc >::emplace().

                                                                                    {
    if (!iteration_--) return;

    Idx per = randomValue(100);

    if (per < p_) {
      _AorR_();

      if (_checkConditions_()) {
        bayesNettemp_ = this->bayesNet_;
        _PMMx_multi_();
      } else {
        this->bayesNet_ = bayesNettemp_;
        _PMMx_poly_();
      }
    } else {
      if (per < p_ + q_) {
        _AR_();

        if (!_checkConditions_()) {
          this->bayesNet_ = bayesNettemp_;
        } else
          bayesNettemp_ = this->bayesNet_;

        _PMMx_poly_();
      } else {
        _jump_poly_();

        if (_checkConditions_()) {
          bayesNettemp_ = this->bayesNet_;
          _PMMx_multi_();

        } else {
          this->bayesNet_ = bayesNettemp_;
          _PMMx_poly_();
        }
      }
    }
  }

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_transformPoly_()

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

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

protectedinherited

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

Definition at line 525 of file MCBayesNetGenerator_tpl.h.

References gum::Set< Key, Alloc >::emplace().

                                                                                                {
    while (nbiter--) {
      NodeId i, j;
      _chooseCloseNodes_(i, j);
      bayesNettemp_ = this->bayesNet_;
      _eraseArc_(i, j, false);
      this->bayesNet_.addArc(j, i);

      if (!_checkConditions_()) this->bayesNet_ = bayesNettemp_;
    }
  }

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disturbBN()

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

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

inherited

Change randomly the topology of a specific Bayesian networks.

Parameters

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

Returns

null but modify inputed Bayesian network

Exceptions

OperationNotAllow

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

Definition at line 134 of file MCBayesNetGenerator_tpl.h.

References gum::Set< Key, Alloc >::emplace().

                                        {   // 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 62 of file IBayesNetGenerator_tpl.h.

References gum::Set< Key, Alloc >::emplace().

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

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generateBN()

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

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

overridevirtualinherited

Generates a random Bayesian network.

Parameters

bayesNet

empty IBayesNet to generate.

Returns

null but modify inputed Bayesian network

Implements gum::IBayesNetGenerator< GUM_SCALAR, ICPTGenerator >.

Definition at line 112 of file MCBayesNetGenerator_tpl.h.

References gum::Set< Key, Alloc >::emplace().

                                       {
    Idx iteration = iteration_;

    // this->bayesNet_ = bayesNet;
    _createTree_(this->nbrNodes_);
    _transformPoly_(this->nbrNodes_ / 2);
    bayesNettemp_ = this->bayesNet_;
    _PMMx_poly_();

    this->fillCPT();
    iteration_ = iteration;

    bayesNet = this->bayesNet_;
  }

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iteration()

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

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

inherited

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

Definition at line 655 of file MCBayesNetGenerator_tpl.h.

References gum::Set< Key, Alloc >::emplace().

                                                                                               {
    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 78 of file IBayesNetGenerator_tpl.h.

References gum::Set< Key, Alloc >::emplace().

                                                                             {
    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 68 of file IBayesNetGenerator_tpl.h.

References gum::Set< Key, Alloc >::emplace().

                                                                                 {
    return maxModality_;
  }

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maxParents()

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

INLINE Size gum::MaxParentsMCBayesNetGenerator< GUM_SCALAR, ICPTGenerator, ICPTDisturber >::maxParents

(

)

const

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

Definition at line 109 of file maxParentsMCBayesNetGenerator_tpl.h.

References gum::Set< Key, Alloc >::emplace().

                                                                                                 {
    return maxParents_;
  }

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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 73 of file IBayesNetGenerator_tpl.h.

References gum::Set< Key, Alloc >::emplace().

                                                                              {
    return nbrNodes_;
  }

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p()

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

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

inherited

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

Definition at line 664 of file MCBayesNetGenerator_tpl.h.

References gum::Set< Key, Alloc >::emplace().

                                                                                      {
    return p_;
  }

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q()

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

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

inherited

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

Definition at line 673 of file MCBayesNetGenerator_tpl.h.

References gum::Set< Key, Alloc >::emplace().

                                                                                      {
    return q_;
  }

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setIteration()

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

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

inherited

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

Definition at line 683 of file MCBayesNetGenerator_tpl.h.

References gum::Set< Key, Alloc >::emplace().

                                                                                                 {
    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 98 of file IBayesNetGenerator_tpl.h.

References gum::Set< Key, Alloc >::emplace().

                                                                                      {
    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 83 of file IBayesNetGenerator_tpl.h.

References gum::Set< Key, Alloc >::emplace().

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

    maxModality_ = maxModality;
  }

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setMaxParents()

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

INLINE void gum::MaxParentsMCBayesNetGenerator< GUM_SCALAR, ICPTGenerator, ICPTDisturber >::setMaxParents

(

Size

maxParents

)

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

Definition at line 118 of file maxParentsMCBayesNetGenerator_tpl.h.

References gum::Set< Key, Alloc >::emplace().

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

    maxParents_ = maxParents;
  }

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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 90 of file IBayesNetGenerator_tpl.h.

References gum::Set< Key, Alloc >::emplace().

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

    nbrNodes_ = nbrNodes;
  }

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setP()

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

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

inherited

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

Definition at line 691 of file MCBayesNetGenerator_tpl.h.

References gum::Set< Key, Alloc >::emplace().

                                                                                         {
    p_ = p;

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

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setQ()

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

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

inherited

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

Definition at line 703 of file MCBayesNetGenerator_tpl.h.

References gum::Set< Key, Alloc >::emplace().

                                                                                         {
    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 166 of file IBayesNetGenerator.h.

bayesNettemp_

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

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

protectedinherited

Definition at line 289 of file MCBayesNetGenerator.h.

disturbing_

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

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

protectedinherited

Definition at line 288 of file MCBayesNetGenerator.h.

hashMarginal_

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

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

protectedinherited

Definition at line 290 of file MCBayesNetGenerator.h.

iteration_

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

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

protectedinherited

Definition at line 286 of file MCBayesNetGenerator.h.

maxArcs_

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

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

protectedinherited

Definition at line 164 of file IBayesNetGenerator.h.

maxModality_

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

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

protectedinherited

Definition at line 165 of file IBayesNetGenerator.h.

maxParents_

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

Size gum::MaxParentsMCBayesNetGenerator< GUM_SCALAR, ICPTGenerator, ICPTDisturber >::maxParents_

protected

Definition at line 179 of file maxParentsMCBayesNetGenerator.h.

nbrNodes_

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

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

protectedinherited

Definition at line 163 of file IBayesNetGenerator.h.

p_

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

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

protectedinherited

Definition at line 287 of file MCBayesNetGenerator.h.

q_

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

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

protectedinherited

Definition at line 287 of file MCBayesNetGenerator.h.

---

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

• agrum/BN/generator/maxParentsMCBayesNetGenerator.h
• agrum/BN/generator/maxParentsMCBayesNetGenerator_tpl.h