gum::Estimator< GUM_SCALAR > Class Template Reference

#include <estimator.h>

Collaboration diagram for gum::Estimator< GUM_SCALAR >:

Public Member Functions
	Estimator ()
	Default constructor. More...
	Estimator (const IBayesNet< GUM_SCALAR > *bn)
	Constructor with Bayesian Network. More...
	~Estimator ()
GUM_SCALAR	confidence ()
	computes the maximum length of confidence interval for each possible value of each variable More...
void	update (Instantiation I, GUM_SCALAR w)
	updates the estimator with a given sample More...
const Potential< GUM_SCALAR > &	posterior (const DiscreteVariable &var)
	returns the posterior of a node More...
void	clear ()
	refresh the estimator state as empty More...
void	setFromBN (const IBayesNet< GUM_SCALAR > *bn, const NodeSet &hardEvidence)
	estimator initializing More...
void	setFromLBP (LoopyBeliefPropagation< GUM_SCALAR > *lbp, const NodeSet &hardEvidence, GUM_SCALAR virtualLBPSize)
	sets the estimatoor object with posteriors obtained by LoopyBeliefPropagation More...

Detailed Description

template<typename GUM_SCALAR>
class gum::Estimator< GUM_SCALAR >

Definition at line 37 of file estimator.h.

Constructor & Destructor Documentation

Estimator() [1/2]

template<typename GUM_SCALAR >

Estimator::Estimator

(

)

Default constructor.

Definition at line 31 of file estimator_tpl.h.

                                     {
    GUM_CONSTRUCTOR(Estimator);
    _wtotal = (GUM_SCALAR)0.;
    _ntotal = (Size)0;
    _bn = nullptr;
  }

Estimator() [2/2]

template<typename GUM_SCALAR >

Estimator::Estimator ( const IBayesNet< GUM_SCALAR > *  bn )

explicit

Constructor with Bayesian Network.

Definition at line 40 of file estimator_tpl.h.

References gum::Estimator< GUM_SCALAR >::_bn, gum::Estimator< GUM_SCALAR >::_estimator, gum::HashTable< Key, Val, Alloc >::insert(), gum::DAGmodel::nodes(), and gum::IBayesNet< GUM_SCALAR >::variable().

                                                                      :
      Estimator() {
    _bn = bn;

    for (gum::NodeGraphPartIterator iter = bn->nodes().begin();
         iter != bn->nodes().end();
         ++iter)
      _estimator.insert(
         bn->variable(*iter).name(),
         std::vector< GUM_SCALAR >(bn->variable(*iter).domainSize(), 0.0));

    GUM_CONSTRUCTOR(Estimator);
  }

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~Estimator()

template<typename GUM_SCALAR >

INLINE Estimator::~Estimator

(

)

Definition at line 56 of file estimator_tpl.h.

References gum::Estimator< GUM_SCALAR >::clear().

                                             {
    GUM_DESTRUCTOR(Estimator);
    // remove all the posteriors computed
    clear();
  }

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Member Function Documentation

clear()

template<typename GUM_SCALAR >

void Estimator::clear

(

)

refresh the estimator state as empty

this function remove all the statistics in order to restart the computations.

Definition at line 183 of file estimator_tpl.h.

References gum::Estimator< GUM_SCALAR >::__target_posteriors, gum::Estimator< GUM_SCALAR >::_estimator, gum::Estimator< GUM_SCALAR >::_ntotal, gum::Estimator< GUM_SCALAR >::_wtotal, and gum::HashTable< Key, Val, Alloc >::clear().

Referenced by gum::Estimator< GUM_SCALAR >::~Estimator().

                                      {
    _estimator.clear();
    _wtotal = (GUM_SCALAR)0;
    _ntotal = Size(0);
    for (const auto& pot : __target_posteriors)
      delete pot.second;
    __target_posteriors.clear();
  }

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confidence()

template<typename GUM_SCALAR >

GUM_SCALAR Estimator::confidence

(

)

computes the maximum length of confidence interval for each possible value of each variable

Returns

maximum length of confidence interval

Definition at line 168 of file estimator_tpl.h.

References gum::Estimator< GUM_SCALAR >::_estimator, gum::Estimator< GUM_SCALAR >::_ntotal, gum::HashTable< Key, Val, Alloc >::begin(), gum::HashTable< Key, Val, Alloc >::end(), and gum::Estimator< GUM_SCALAR >::variance().

                                                 {
    GUM_SCALAR ic_max = 0;

    for (auto iter = _estimator.begin(); iter != _estimator.end(); ++iter) {
      for (Idx i = 0; i < iter.val().size(); i++) {
        GUM_SCALAR ic = GUM_SCALAR(
           2 * 1.96 * std::sqrt(variance(iter.key(), i) / (_ntotal - 1)));
        if (ic > ic_max) ic_max = ic;
      }
    }

    return ic_max;
  }

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EV()

template<typename GUM_SCALAR >

GUM_SCALAR Estimator::EV ( std::string  name, Idx  val  )

private

returns expected value of Bernouilli variable (called by it's name) of given parameter

Returns

Expected value of Bernouilli variable (called by it's name) of given parameter

Parameters

name	variable's name, considered as a Bernouilli variable
val	the parameter of the Bernouilli variable

computes the amount of cumulative weights for paramater val over the amount of total cumulative weights

Definition at line 150 of file estimator_tpl.h.

References gum::Estimator< GUM_SCALAR >::_estimator, and gum::Estimator< GUM_SCALAR >::_wtotal.

Referenced by gum::Estimator< GUM_SCALAR >::variance().

                                                                {
    return _estimator[name][val] / _wtotal;
  }

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posterior()

template<typename GUM_SCALAR >

const Potential< GUM_SCALAR > & Estimator::posterior

(

const DiscreteVariable &

var

)

returns the posterior of a node

Returns

a constant ref to the posterior probability of the variable node

Parameters

var	the variable node which we want posterior for

returns the vector of cumulated weight bias for each value of the variable normalized as a CPT

Exceptions

NotFound

if variable node is not in estimator.

Definition at line 126 of file estimator_tpl.h.

References gum::Estimator< GUM_SCALAR >::__target_posteriors, gum::Estimator< GUM_SCALAR >::_estimator, gum::HashTable< Key, Val, Alloc >::exists(), gum::Potential< GUM_SCALAR >::fillWith(), GUM_ERROR, gum::Variable::name(), and gum::Potential< GUM_SCALAR >::normalize().

                                                                   {
    Potential< GUM_SCALAR >* p = nullptr;

    if (!_estimator.exists(var.name()))
      GUM_ERROR(NotFound, "Target variable not found");

    // check if we have already computed the posterior
    if (__target_posteriors.exists(var.name())) {
      p = __target_posteriors[var.name()];
    } else {
      p = new Potential< GUM_SCALAR >();
      *p << var;
      __target_posteriors.insert(var.name(), p);
    }

    p->fillWith(_estimator[var.name()]);
    p->normalize();
    return *p;
  }

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setFromBN()

template<typename GUM_SCALAR >

void Estimator::setFromBN	(	const IBayesNet< GUM_SCALAR > *	bn,
		const NodeSet &	hardEvidence
	)

estimator initializing

sets the estimator object with 0-filled vectors corresponding to each non evidence node

Definition at line 66 of file estimator_tpl.h.

References gum::Estimator< GUM_SCALAR >::_estimator, gum::Set< Key, Alloc >::contains(), gum::HashTable< Key, Val, Alloc >::exists(), gum::HashTable< Key, Val, Alloc >::insert(), gum::DAGmodel::nodes(), and gum::IBayesNet< GUM_SCALAR >::variable().

                                                                       {
    for (gum::NodeGraphPartIterator iter = bn->nodes().begin();
         iter != bn->nodes().end();
         ++iter) {
      auto v = bn->variable(*iter).name();

      if (!hardEvidence.contains(*iter)) {
        if (_estimator.exists(v))
          _estimator[v] = std::vector< GUM_SCALAR >(
             bn->variable(*iter).domainSize(), (GUM_SCALAR)0.0);
        else
          _estimator.insert(v,
                            std::vector< GUM_SCALAR >(
                               bn->variable(*iter).domainSize(), (GUM_SCALAR)0.0));
      }
    }
  }

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setFromLBP()

template<typename GUM_SCALAR >

void Estimator::setFromLBP	(	LoopyBeliefPropagation< GUM_SCALAR > *	lbp,
		const NodeSet &	hardEvidence,
		GUM_SCALAR	virtualLBPSize
	)

sets the estimatoor object with posteriors obtained by LoopyBeliefPropagation

Definition at line 89 of file estimator_tpl.h.

References gum::Estimator< GUM_SCALAR >::_estimator, gum::Estimator< GUM_SCALAR >::_ntotal, gum::Estimator< GUM_SCALAR >::_wtotal, gum::BayesNetInference< GUM_SCALAR >::BN(), gum::Set< Key, Alloc >::contains(), gum::Instantiation::end(), gum::HashTable< Key, Val, Alloc >::insert(), gum::MarginalTargetedInference< GUM_SCALAR >::posterior(), and gum::Instantiation::setFirst().

                                                                        {
    for (const auto& node : lbp->BN().nodes()) {
      if (!hardEvidence.contains(node)) {
        std::vector< GUM_SCALAR > v;
        auto                      p = lbp->posterior(node);
        gum::Instantiation        inst(p);

        for (inst.setFirst(); !inst.end(); ++inst) {
          v.push_back(p[inst] * virtualLBPSize);
        }

        _estimator.insert(lbp->BN().variable(node).name(), v);
      }
    }
    _ntotal = (Size)virtualLBPSize;
    _wtotal = virtualLBPSize;
  }

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update()

template<typename GUM_SCALAR >

void Estimator::update	(	Instantiation	I,
		GUM_SCALAR	w
	)

updates the estimator with a given sample

Parameters

I	the sample used to update the estimators
weight	bias for the given sample

adds the sample weight to each node's given value in the estimator

Definition at line 112 of file estimator_tpl.h.

References gum::Estimator< GUM_SCALAR >::_estimator, gum::Estimator< GUM_SCALAR >::_ntotal, gum::Estimator< GUM_SCALAR >::_wtotal, gum::HashTable< Key, Val, Alloc >::exists(), gum::Variable::name(), gum::Instantiation::nbrDim(), gum::Instantiation::val(), and gum::Instantiation::variable().

                                                                    {
    _wtotal += w;
    _ntotal += (Size)1;

    for (Idx i = 0; i < I.nbrDim(); i++) {
      if (_estimator.exists(I.variable(i).name()))
        _estimator[I.variable(i).name()][I.val(i)] += w;
    }
  }

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variance()

template<typename GUM_SCALAR >

GUM_SCALAR Estimator::variance ( std::string  name, Idx  val  )

private

returns variance of Bernouilli variable (called by it's name) of given parameter

Returns

variance of Bernouilli variable (called by it's name) of given parameter

Parameters

name	variable's name, considered as a Bernouilli variable
val	the parameter of the Bernouilli variable

computes variance for Bernouilli law using EV(name, val)

Definition at line 158 of file estimator_tpl.h.

References gum::Estimator< GUM_SCALAR >::EV().

Referenced by gum::Estimator< GUM_SCALAR >::confidence().

                                                                      {
    GUM_SCALAR p = EV(name, val);
    return p * (1 - p);
  }

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Member Data Documentation

__target_posteriors

template<typename GUM_SCALAR>

HashTable< std::string, Potential< GUM_SCALAR >* > gum::Estimator< GUM_SCALAR >::__target_posteriors

private

the set of single posteriors computed during the last inference

the posteriors are owned by LazyPropagation.

Definition at line 155 of file estimator.h.

Referenced by gum::Estimator< GUM_SCALAR >::clear(), and gum::Estimator< GUM_SCALAR >::posterior().

_bn

template<typename GUM_SCALAR>

const IBayesNet< GUM_SCALAR >* gum::Estimator< GUM_SCALAR >::_bn

private

bayesian network on which approximation is done

Definition at line 125 of file estimator.h.

Referenced by gum::Estimator< GUM_SCALAR >::Estimator().

_estimator

template<typename GUM_SCALAR>

HashTable< std::string, std::vector< GUM_SCALAR > > gum::Estimator< GUM_SCALAR >::_estimator

private

estimator represented by hashtable between each variable name and a vector of cumulative sample weights

Definition at line 116 of file estimator.h.

Referenced by gum::Estimator< GUM_SCALAR >::clear(), gum::Estimator< GUM_SCALAR >::confidence(), gum::Estimator< GUM_SCALAR >::Estimator(), gum::Estimator< GUM_SCALAR >::EV(), gum::Estimator< GUM_SCALAR >::posterior(), gum::Estimator< GUM_SCALAR >::setFromBN(), gum::Estimator< GUM_SCALAR >::setFromLBP(), and gum::Estimator< GUM_SCALAR >::update().

_ntotal

template<typename GUM_SCALAR>

Size gum::Estimator< GUM_SCALAR >::_ntotal

private

number of generated samples

Definition at line 122 of file estimator.h.

Referenced by gum::Estimator< GUM_SCALAR >::clear(), gum::Estimator< GUM_SCALAR >::confidence(), gum::Estimator< GUM_SCALAR >::setFromLBP(), and gum::Estimator< GUM_SCALAR >::update().

_wtotal

template<typename GUM_SCALAR>

GUM_SCALAR gum::Estimator< GUM_SCALAR >::_wtotal

private

cumulated weights of all samples

Definition at line 119 of file estimator.h.

Referenced by gum::Estimator< GUM_SCALAR >::clear(), gum::Estimator< GUM_SCALAR >::EV(), gum::Estimator< GUM_SCALAR >::setFromLBP(), and gum::Estimator< GUM_SCALAR >::update().

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

• agrum/BN/inference/tools/estimator.h
• agrum/BN/inference/tools/estimator_tpl.h