Class used to store optimum IBayesNet during some inference algorithms. More...

#include <agrum/CN/varMod2BNsMap.h>

Collaboration diagram for gum::credal::VarMod2BNsMap< GUM_SCALAR >:

Public Member Functions
bool	insert (const std::vector< bool > &bn, const std::vector< Size > &key)
	Insert for thread fusion. More...

bool	insert (const std::vector< Size > &key, const bool isBetter)
	Thread insert. More...

Constructors / Destructors
	VarMod2BNsMap (const CredalNet< GUM_SCALAR > &cn)
	Thread constructor. More...

	VarMod2BNsMap ()
	Default constructor that should be used only by InferenceEngine since it has a member variable. More...

	~VarMod2BNsMap ()
	Destructor. More...

Getters and setters
void	setCNet (const CredalNet< GUM_SCALAR > &cn)
	Initialize sampleDef from the CredalNet. More...

void	setCurrentSample (const std::vector< std::vector< std::vector< bool > > > &sample)
	Set the current thread sample and it's hash. More...

const dBN &	getCurrentSample ()
	Get the current sample as a vector of bits without structure. More...

const std::vector< std::vector< std::vector< bool > > > &	getSampleDef ()
	Get the sample structure. More...

const std::vector< dBN *>	getBNOptsFromKey (const std::vector< Size > &key)
	Get optimum IBayesNet (s) without structure of the given variable, modality for min or max. More...

std::vector< std::vector< std::vector< std::vector< bool > > > >	getFullBNOptsFromKey (const std::vector< Size > &key)
	Get optimum IBayesNet (s) with structure of the given variable, modality for min or max. More...

Size	getEntrySize () const
	Get the number of IBayesNet stored. More...

Protected Attributes
hashNet	myHashNet_
	Map id - dBN : get a net from it's id. More...

varHashs	myVarHashs_
	Map varKey - list(id) : get all optimal nets id from the given key : variable, modality and min=0 ( or max=1 ). More...

hashVars	myHashVars_
	Map id - list(varKey) : get all variables, modalities associated to this optimal net id. More...

std::vector< std::vector< std::vector< bool > > >	sampleDef_
	Since all samples have the same structure, this will be used as default initialization (copy constructor) for any sample. More...

dBN	currentSample_
	The current sampled IBayesNet. More...

Size	currentHash_
	The current sampled IBayesNet hash. More...

std::hash< std::vector< bool > >	vectHash_
	The hash fuction. More...

const CredalNet< GUM_SCALAR > *	cnet
	A pointer to the CredalNet to be used. More...

Detailed Description

template<typename GUM_SCALAR>
class gum::credal::VarMod2BNsMap< GUM_SCALAR >

Class used to store optimum IBayesNet during some inference algorithms.

Template Parameters

GUM_SCALAR A floating type ( float, double, long double ... ).

Author: Matthieu HOURBRACQ and Pierre-Henri WUILLEMIN()

Definition at line 55 of file varMod2BNsMap.h.

Member Typedef Documentation

◆ dBN

template<typename GUM_SCALAR >

typedef std::vector< bool > gum::credal::VarMod2BNsMap< GUM_SCALAR >::dBN

private

Definition at line 57 of file varMod2BNsMap.h.

◆ hashNet

template<typename GUM_SCALAR >

typedef gum::HashTable< Size, dBN > gum::credal::VarMod2BNsMap< GUM_SCALAR >::hashNet

private

Definition at line 61 of file varMod2BNsMap.h.

◆ hashVars

template<typename GUM_SCALAR >

typedef gum::HashTable< Size, std::list< varKey > > gum::credal::VarMod2BNsMap< GUM_SCALAR >::hashVars

private

Definition at line 65 of file varMod2BNsMap.h.

◆ varHashs

template<typename GUM_SCALAR >

typedef gum::HashTable< varKey, std::list< Size > > gum::credal::VarMod2BNsMap< GUM_SCALAR >::varHashs

private

Definition at line 63 of file varMod2BNsMap.h.

◆ varKey

template<typename GUM_SCALAR >

typedef std::vector< Size > gum::credal::VarMod2BNsMap< GUM_SCALAR >::varKey

private

Definition at line 59 of file varMod2BNsMap.h.

Constructor & Destructor Documentation

◆ VarMod2BNsMap() [1/2]

template<typename GUM_SCALAR >

gum::credal::VarMod2BNsMap< GUM_SCALAR >::VarMod2BNsMap ( const CredalNet< GUM_SCALAR > & cn )

explicit

Thread constructor.

Initialize sampleDef from the CredalNet.

Parameters

cn	The CredalNet to be used.

Definition at line 35 of file varMod2BNsMap_tpl.h.

                                                                                 {
       setCNet(cn);
 
       GUM_CONSTRUCTOR(VarMod2BNsMap);
     }

◆ VarMod2BNsMap() [2/2]

template<typename GUM_SCALAR >

gum::credal::VarMod2BNsMap< GUM_SCALAR >::VarMod2BNsMap ( )

Default constructor that should be used only by InferenceEngine since it has a member variable.

Definition at line 28 of file varMod2BNsMap_tpl.h.

                                                {
       cnet = nullptr;
 
       GUM_CONSTRUCTOR(VarMod2BNsMap);
     }

◆ ~VarMod2BNsMap()

template<typename GUM_SCALAR >

gum::credal::VarMod2BNsMap< GUM_SCALAR >::~VarMod2BNsMap ( )

Destructor.

Definition at line 42 of file varMod2BNsMap_tpl.h.

                                                 {
       GUM_DESTRUCTOR(VarMod2BNsMap);
     }

Member Function Documentation

◆ getBNOptsFromKey()

template<typename GUM_SCALAR >

const std::vector< std::vector< bool > *> gum::credal::VarMod2BNsMap< GUM_SCALAR >::getBNOptsFromKey ( const std::vector< Size > & key )

Get optimum IBayesNet (s) without structure of the given variable, modality for min or max.

Parameters

key	The constant reference to the variable, modality, min or max.

Returns: The constant vector of not yet constant pointers to the nets.

Definition at line 187 of file varMod2BNsMap_tpl.h.

                                                                                  {
       // return something even if key does not exist
       if (!myVarHashs_.exists(key)) return std::vector< std::vector< bool >* >();
 
       std::list< Size >& netsHash = myVarHashs_[key];   //.at(key);
 
       std::vector< dBN* > nets;
       nets.resize(netsHash.size());
 
       std::list< Size >::iterator it = netsHash.begin();
 
       for (Size i = 0; i < netsHash.size(); i++, ++it) {
         nets[i] = &myHashNet_ /*.at(*/[*it];   //);
       }
 
       return nets;
     }

◆ getCurrentSample()

template<typename GUM_SCALAR >

const std::vector< bool > & gum::credal::VarMod2BNsMap< GUM_SCALAR >::getCurrentSample ( )

Get the current sample as a vector of bits without structure.

Returns: The constant reference to the sampled IBayesNet without structure.

Definition at line 175 of file varMod2BNsMap_tpl.h.

                                                                            {
       return currentSample_;
     }

◆ getEntrySize()

template<typename GUM_SCALAR >

Size gum::credal::VarMod2BNsMap< GUM_SCALAR >::getEntrySize ( ) const

Get the number of IBayesNet stored.

Definition at line 240 of file varMod2BNsMap_tpl.h.

                                                          {
       return myHashNet_.size();
     }

◆ getFullBNOptsFromKey()

template<typename GUM_SCALAR >

std::vector< std::vector< std::vector< std::vector< bool > > > > gum::credal::VarMod2BNsMap< GUM_SCALAR >::getFullBNOptsFromKey ( const std::vector< Size > & key )

Get optimum IBayesNet (s) with structure of the given variable, modality for min or max.

Parameters

key	The constant reference to the variable, modality, min or max.

Returns: The vector of not yet constant pointers to the nets.

Definition at line 207 of file varMod2BNsMap_tpl.h.

                                                                                      {
       if (cnet == nullptr)
         GUM_ERROR(OperationNotAllowed,
                   "No CredalNet associated to me ! Can't get FullBNOptsFromKey : " << key);
 
       if (!myVarHashs_.exists(key))
         return std::vector< std::vector< std::vector< std::vector< bool > > > >();
 
       std::list< Size >& netsHash = myVarHashs_[key];   //.at(key);
 
       std::vector< std::vector< std::vector< std::vector< bool > > > > nets;
       nets.resize(netsHash.size(), sampleDef_);
 
       std::list< Size >::iterator it = netsHash.begin();
 
       for (Size i = 0; i < netsHash.size(); i++, ++it) {
         // std::vector< std::vector< std::vector < bool > > > net(sampleDef_);
         dBN::iterator it2 = myHashNet_ /*.at(*/[*it] /*)*/.begin();
 
         for (Size j = 0; j < sampleDef_.size(); j++) {
           for (Size k = 0; k < sampleDef_[j].size(); k++) {
             for (Size l = 0; l < sampleDef_[j][k].size(); l++) {
               nets[i][j][k][l] = *it2;
               ++it2;
             }
           }
         }
       }
 
       return nets;
     }

◆ getSampleDef()

template<typename GUM_SCALAR >

const std::vector< std::vector< std::vector< bool > > > & gum::credal::VarMod2BNsMap< GUM_SCALAR >::getSampleDef ( )

Get the sample structure.

Returns: The constant reference to the sample structure.

Definition at line 181 of file varMod2BNsMap_tpl.h.

                                                  {
       return sampleDef_;
     }

◆ insert() [1/2]

template<typename GUM_SCALAR >

bool gum::credal::VarMod2BNsMap< GUM_SCALAR >::insert	(	const std::vector< bool > &	bn,
		const std::vector< Size > &	key
	)

Insert for thread fusion.

All inserted nets are optimums and none will be deleted because a better one is being inserted.

Parameters

bn	The constant reference to the net to be inserted.
key	The constant reference to the key at which we will insert the net.

Definition at line 69 of file varMod2BNsMap_tpl.h.

                                                                            {
       currentHash_            = Size(vectHash_(bn));
       std::list< Size >& nets = myVarHashs_.getWithDefault(key, std::list< Size >());   //[ key ];
 
       for (std::list< Size >::iterator it = nets.begin(); it != nets.end(); ++it) {
         if (*it == currentHash_) return false;
       }
 
       // add it
       myHashNet_.set(currentHash_, bn);   //[currentHash_] = bn;
       // insert net hash in our key net list
       nets.push_back(currentHash_);
       // insert out key in the hash key list
       myHashVars_.getWithDefault(currentHash_,
                                  std::list< varKey >()) /*[currentHash_]*/.push_back(key);
       return true;
     }

◆ insert() [2/2]

template<typename GUM_SCALAR >

bool gum::credal::VarMod2BNsMap< GUM_SCALAR >::insert	(	const std::vector< Size > &	key,
		const bool	isBetter
	)

Thread insert.

Parameters

key	The key at which the net will be inserted.
isBetter	`True` if the net is a better one, i.e. it gave better marginals.

Returns: True if the net was inserted, false otherwise.

Definition at line 89 of file varMod2BNsMap_tpl.h.

                                                                                               {
       if (isBetter) {
         // get all nets of this key (maybe entry does not exists)
         std::list< Size >& old_nets
            = myVarHashs_.getWithDefault(key, std::list< Size >());   //[ key ];
 
         // for each one
         for (std::list< Size >::iterator it = old_nets.begin(); it != old_nets.end(); ++it) {
           // get all keys associated to this net
           std::list< varKey >& netKeys
              = myHashVars_.getWithDefault(*it, std::list< varKey >());   //[ *it ];
 
           // if we are the sole user, delete the net entry
           if (netKeys.size() == 1) {
             myHashVars_.erase(*it);
           }
           // other keys use the net, delete our key from list
           else {
             for (std::list< varKey >::iterator it2 = netKeys.begin(); it2 != netKeys.end(); ++it2) {
               if (*it2 == key) {
                 netKeys.erase(it2);
                 break;
               }
             }
           }
         }   // end of : for each old_net
 
         // clear all old_nets
         old_nets.clear();
         // insert new net with it's hash
         myHashNet_.set(currentHash_,
                        currentSample_);   //[currentHash_] = currentSample_;
         // insert net hash in our key net list
         old_nets.push_back(currentHash_);
         // insert out key in the hash key list
         myHashVars_
            .getWithDefault(currentHash_, std::list< varKey >()) /*[currentHash_]*/
            .push_back(key);
         return true;
 
       }   // end of isBetter
       // another opt net
       else {
         // check that we didn't add it for this key
         std::list< Size >& nets = myVarHashs_.getWithDefault(key, std::list< Size >());   //[ key ];
 
         for (std::list< Size >::iterator it = nets.begin(); it != nets.end(); ++it) {
           if (*it == currentHash_) return false;
         }
 
         // add it
         myHashNet_.set(currentHash_, currentSample_);
         // insert net hash in our key net list
         nets.push_back(currentHash_);
         // insert out key in the hash key list
         myHashVars_.getWithDefault(currentHash_, std::list< varKey >()).push_back(key);
 
         /*
               // add it
               myHashNet_[currentHash_] = currentSample_;
               // insert net hash in our key net list
               nets.push_back(currentHash_);
               // insert out key in the hash key list
               myHashVars_[currentHash_].push_back(key);
         */
         return true;
       }   // end of ! isBetter
     }

◆ setCNet()

template<typename GUM_SCALAR >

void gum::credal::VarMod2BNsMap< GUM_SCALAR >::setCNet ( const CredalNet< GUM_SCALAR > & cn )

Initialize sampleDef from the CredalNet.

Shoud only be used by InferenceEngine to initialize it's member variable.

Parameters

cn	The CredalNet to be used.

Definition at line 47 of file varMod2BNsMap_tpl.h.

                                                                                {
       auto* cpt    = &cn.credalNet_currentCpt();
       auto  nNodes = cpt->size();
       sampleDef_.resize(nNodes);
 
       for (NodeId node = 0; node < nNodes; node++) {
         auto pConfs = (*cpt)[node].size();
         sampleDef_[node].resize(pConfs);
 
         for (Size pconf = 0; pconf < pConfs; pconf++) {
           Size          nVertices = Size((*cpt)[node][pconf].size());
           unsigned long b, c;   // needed by superiorPow
           superiorPow(static_cast< unsigned long >(nVertices), b, c);
           Size nBits = Size(b);
           sampleDef_[node][pconf].resize(nBits);
         }
       }
 
       cnet = &cn;
     }

◆ setCurrentSample()

template<typename GUM_SCALAR >

void gum::credal::VarMod2BNsMap< GUM_SCALAR >::setCurrentSample ( const std::vector< std::vector< std::vector< bool > > > & sample )

Set the current thread sample and it's hash.

Parameters

sample The constant reference to the sample with structure.

Definition at line 159 of file varMod2BNsMap_tpl.h.

                                                                   {
       currentSample_.clear();
 
       for (Size i = 0; i < sample.size(); i++)
         for (Size j = 0; j < sample[j].size(); j++)
           for (Size k = 0; k < sample[i][j].size(); k++)
             currentSample_.push_back(sample[i][j][k]);
 
       // std::cout << sample << std::endl;
       // std::cout << currentSample_ << std::endl;
 
       currentHash_ = Size(vectHash_(currentSample_));
     }

Member Data Documentation

◆ cnet

template<typename GUM_SCALAR >

const CredalNet< GUM_SCALAR >* gum::credal::VarMod2BNsMap< GUM_SCALAR >::cnet

protected

A pointer to the CredalNet to be used.

Definition at line 91 of file varMod2BNsMap.h.

◆ currentHash_

template<typename GUM_SCALAR >

Size gum::credal::VarMod2BNsMap< GUM_SCALAR >::currentHash_

protected

The current sampled IBayesNet hash.

Definition at line 86 of file varMod2BNsMap.h.

◆ currentSample_

template<typename GUM_SCALAR >

dBN gum::credal::VarMod2BNsMap< GUM_SCALAR >::currentSample_

protected

The current sampled IBayesNet.

Definition at line 84 of file varMod2BNsMap.h.

◆ myHashNet_

template<typename GUM_SCALAR >

hashNet gum::credal::VarMod2BNsMap< GUM_SCALAR >::myHashNet_

protected

Map id - dBN : get a net from it's id.

Definition at line 69 of file varMod2BNsMap.h.

◆ myHashVars_

template<typename GUM_SCALAR >

hashVars gum::credal::VarMod2BNsMap< GUM_SCALAR >::myHashVars_

protected

Map id - list(varKey) : get all variables, modalities associated to this optimal net id.

Definition at line 76 of file varMod2BNsMap.h.

◆ myVarHashs_

template<typename GUM_SCALAR >

varHashs gum::credal::VarMod2BNsMap< GUM_SCALAR >::myVarHashs_

protected

Map varKey - list(id) : get all optimal nets id from the given key : variable, modality and min=0 ( or max=1 ).

Definition at line 72 of file varMod2BNsMap.h.

◆ sampleDef_

template<typename GUM_SCALAR >

std::vector< std::vector< std::vector< bool > > > gum::credal::VarMod2BNsMap< GUM_SCALAR >::sampleDef_

protected

Since all samples have the same structure, this will be used as default initialization (copy constructor) for any sample.

Definition at line 81 of file varMod2BNsMap.h.

◆ vectHash_

template<typename GUM_SCALAR >

std::hash< std::vector< bool > > gum::credal::VarMod2BNsMap< GUM_SCALAR >::vectHash_

protected

The hash fuction.

Requires c++11/0x.

Definition at line 88 of file varMod2BNsMap.h.

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

agrum/CN/tools/varMod2BNsMap.h
agrum/CN/tools/varMod2BNsMap_tpl.h

Public Member Functions

Protected Attributes

Detailed Description

template<typename GUM_SCALAR> class gum::credal::VarMod2BNsMap< GUM_SCALAR >

Member Typedef Documentation

◆ dBN

◆ hashNet

◆ hashVars

◆ varHashs

◆ varKey

Constructor & Destructor Documentation

◆ VarMod2BNsMap() [1/2]

◆ VarMod2BNsMap() [2/2]

◆ ~VarMod2BNsMap()

Member Function Documentation

◆ getBNOptsFromKey()

◆ getCurrentSample()

◆ getEntrySize()

◆ getFullBNOptsFromKey()

◆ getSampleDef()

◆ insert() [1/2]

◆ insert() [2/2]

◆ setCNet()

◆ setCurrentSample()

Member Data Documentation

◆ cnet

◆ currentHash_

◆ currentSample_

◆ myHashNet_

◆ myHashVars_

◆ myVarHashs_

◆ sampleDef_

◆ vectHash_

template<typename GUM_SCALAR>
class gum::credal::VarMod2BNsMap< GUM_SCALAR >