gtsam 4.2.0
gtsam
gtsam::HybridBayesNet Class Reference

Detailed Description

A hybrid Bayes net is a collection of HybridConditionals, which can have discrete conditionals, Gaussian mixtures, or pure Gaussian conditionals.

+ Inheritance diagram for gtsam::HybridBayesNet:

Public Member Functions

Standard Constructors
 HybridBayesNet ()=default
 Construct empty Bayes net.
 
Testable
void print (const std::string &s="", const KeyFormatter &formatter=DefaultKeyFormatter) const override
 GTSAM-style printing. More...
 
bool equals (const This &fg, double tol=1e-9) const
 GTSAM-style equals.
 
Standard Interface
void push_back (boost::shared_ptr< HybridConditional > conditional)
 Add a hybrid conditional using a shared_ptr. More...
 
template<class Conditional >
void emplace_back (Conditional *conditional)
 Preferred: add a conditional directly using a pointer. More...
 
void push_back (HybridConditional &&conditional)
 Add a conditional using a shared_ptr, using implicit conversion to a HybridConditional. More...
 
GaussianBayesNet choose (const DiscreteValues &assignment) const
 Get the Gaussian Bayes Net which corresponds to a specific discrete value assignment. More...
 
double evaluate (const HybridValues &values) const
 Evaluate hybrid probability density for given HybridValues.
 
double operator() (const HybridValues &values) const
 Evaluate hybrid probability density for given HybridValues, sugar.
 
HybridValues optimize () const
 Solve the HybridBayesNet by first computing the MPE of all the discrete variables and then optimizing the continuous variables based on the MPE assignment. More...
 
VectorValues optimize (const DiscreteValues &assignment) const
 Given the discrete assignment, return the optimized estimate for the selected Gaussian BayesNet. More...
 
DecisionTreeFactor::shared_ptr discreteConditionals () const
 Get all the discrete conditionals as a decision tree factor. More...
 
HybridValues sample (const HybridValues &given, std::mt19937_64 *rng) const
 Sample from an incomplete BayesNet, given missing variables. More...
 
HybridValues sample (std::mt19937_64 *rng) const
 Sample using ancestral sampling. More...
 
HybridValues sample (const HybridValues &given) const
 Sample from an incomplete BayesNet, use default rng. More...
 
HybridValues sample () const
 Sample using ancestral sampling, use default rng. More...
 
HybridBayesNet prune (size_t maxNrLeaves)
 Prune the Hybrid Bayes Net such that we have at most maxNrLeaves leaves.
 
AlgebraicDecisionTree< KeylogProbability (const VectorValues &continuousValues) const
 Compute conditional error for each discrete assignment, and return as a tree. More...
 
AlgebraicDecisionTree< Keyevaluate (const VectorValues &continuousValues) const
 Compute unnormalized probability q(μ|M), for each discrete assignment, and return as a tree. More...
 
HybridGaussianFactorGraph toFactorGraph (const VectorValues &measurements) const
 Convert a hybrid Bayes net to a hybrid Gaussian factor graph by converting all conditionals with instantiated measurements into likelihood factors.
 
double logProbability (const HybridValues &x) const
 
- Public Member Functions inherited from gtsam::BayesNet< HybridConditional >
void print (const std::string &s="BayesNet", const KeyFormatter &formatter=DefaultKeyFormatter) const override
 print out graph More...
 
void dot (std::ostream &os, const KeyFormatter &keyFormatter=DefaultKeyFormatter, const DotWriter &writer=DotWriter()) const
 Output to graphviz format, stream version.
 
std::string dot (const KeyFormatter &keyFormatter=DefaultKeyFormatter, const DotWriter &writer=DotWriter()) const
 Output to graphviz format string.
 
void saveGraph (const std::string &filename, const KeyFormatter &keyFormatter=DefaultKeyFormatter, const DotWriter &writer=DotWriter()) const
 output to file with graphviz format.
 
double logProbability (const HybridValues &x) const
 
double evaluate (const HybridValues &c) const
 
- Public Member Functions inherited from gtsam::FactorGraph< HybridConditional >
 FactorGraph (std::initializer_list< boost::shared_ptr< DERIVEDFACTOR > > sharedFactors)
 Constructor that takes an initializer list of shared pointers. More...
 
virtual ~FactorGraph ()=default
 Default destructor Public and virtual so boost serialization can call it.
 
void reserve (size_t size)
 Reserve space for the specified number of factors if you know in advance how many there will be (works like FastVector::reserve).
 
IsDerived< DERIVEDFACTOR > push_back (boost::shared_ptr< DERIVEDFACTOR > factor)
 Add a factor directly using a shared_ptr.
 
IsDerived< DERIVEDFACTOR > push_back (const DERIVEDFACTOR &factor)
 Add a factor by value, will be copy-constructed (use push_back with a shared_ptr to avoid the copy).
 
IsDerived< DERIVEDFACTOR > emplace_shared (Args &&... args)
 Emplace a shared pointer to factor of given type.
 
IsDerived< DERIVEDFACTOR > add (boost::shared_ptr< DERIVEDFACTOR > factor)
 add is a synonym for push_back.
 
std::enable_if< std::is_base_of< FactorType, DERIVEDFACTOR >::value, boost::assign::list_inserter< RefCallPushBack< This > > >::type operator+= (boost::shared_ptr< DERIVEDFACTOR > factor)
 += works well with boost::assign list inserter.
 
HasDerivedElementType< ITERATOR > push_back (ITERATOR firstFactor, ITERATOR lastFactor)
 Push back many factors with an iterator over shared_ptr (factors are not copied)
 
HasDerivedValueType< ITERATOR > push_back (ITERATOR firstFactor, ITERATOR lastFactor)
 Push back many factors with an iterator (factors are copied)
 
HasDerivedElementType< CONTAINER > push_back (const CONTAINER &container)
 Push back many factors as shared_ptr's in a container (factors are not copied)
 
HasDerivedValueType< CONTAINER > push_back (const CONTAINER &container)
 Push back non-pointer objects in a container (factors are copied).
 
void add (const FACTOR_OR_CONTAINER &factorOrContainer)
 Add a factor or container of factors, including STL collections, BayesTrees, etc.
 
boost::assign::list_inserter< CRefCallPushBack< This > > operator+= (const FACTOR_OR_CONTAINER &factorOrContainer)
 Add a factor or container of factors, including STL collections, BayesTrees, etc.
 
std::enable_if< std::is_base_of< This, typenameCLIQUE::FactorGraphType >::value >::type push_back (const BayesTree< CLIQUE > &bayesTree)
 Push back a BayesTree as a collection of factors. More...
 
FactorIndices add_factors (const CONTAINER &factors, bool useEmptySlots=false)
 Add new factors to a factor graph and returns a list of new factor indices, optionally finding and reusing empty factor slots.
 
bool equals (const This &fg, double tol=1e-9) const
 Check equality up to tolerance.
 
size_t size () const
 return the number of factors (including any null factors set by remove() ).
 
bool empty () const
 Check if the graph is empty (null factors set by remove() will cause this to return false).
 
const sharedFactor at (size_t i) const
 Get a specific factor by index (this checks array bounds and may throw an exception, as opposed to operator[] which does not).
 
sharedFactorat (size_t i)
 Get a specific factor by index (this checks array bounds and may throw an exception, as opposed to operator[] which does not).
 
const sharedFactor operator[] (size_t i) const
 Get a specific factor by index (this does not check array bounds, as opposed to at() which does).
 
sharedFactoroperator[] (size_t i)
 Get a specific factor by index (this does not check array bounds, as opposed to at() which does).
 
const_iterator begin () const
 Iterator to beginning of factors.
 
const_iterator end () const
 Iterator to end of factors.
 
sharedFactor front () const
 Get the first factor.
 
sharedFactor back () const
 Get the last factor.
 
double error (const HybridValues &values) const
 Add error for all factors.
 
iterator begin ()
 non-const STL-style begin()
 
iterator end ()
 non-const STL-style end()
 
virtual void resize (size_t size)
 Directly resize the number of factors in the graph. More...
 
void remove (size_t i)
 delete factor without re-arranging indexes by inserting a nullptr pointer
 
void replace (size_t index, sharedFactor factor)
 replace a factor by index
 
iterator erase (iterator item)
 Erase factor and rearrange other factors to take up the empty space.
 
iterator erase (iterator first, iterator last)
 Erase factors and rearrange other factors to take up the empty space.
 
void dot (std::ostream &os, const KeyFormatter &keyFormatter=DefaultKeyFormatter, const DotWriter &writer=DotWriter()) const
 Output to graphviz format, stream version.
 
std::string dot (const KeyFormatter &keyFormatter=DefaultKeyFormatter, const DotWriter &writer=DotWriter()) const
 Output to graphviz format string.
 
void saveGraph (const std::string &filename, const KeyFormatter &keyFormatter=DefaultKeyFormatter, const DotWriter &writer=DotWriter()) const
 output to file with graphviz format.
 
size_t nrFactors () const
 return the number of non-null factors
 
KeySet keys () const
 Potentially slow function to return all keys involved, sorted, as a set.
 
KeyVector keyVector () const
 Potentially slow function to return all keys involved, sorted, as a vector.
 
bool exists (size_t idx) const
 MATLAB interface utility: Checks whether a factor index idx exists in the graph and is a live pointer.
 

Public Types

using Base = BayesNet< HybridConditional >
 
using This = HybridBayesNet
 
using ConditionalType = HybridConditional
 
using shared_ptr = boost::shared_ptr< HybridBayesNet >
 
using sharedConditional = boost::shared_ptr< ConditionalType >
 
- Public Types inherited from gtsam::BayesNet< HybridConditional >
typedef boost::shared_ptr< HybridConditionalsharedConditional
 A shared pointer to a conditional.
 
- Public Types inherited from gtsam::FactorGraph< HybridConditional >
typedef HybridConditional FactorType
 factor type
 
typedef boost::shared_ptr< HybridConditionalsharedFactor
 Shared pointer to a factor.
 
typedef sharedFactor value_type
 
typedef FastVector< sharedFactor >::iterator iterator
 
typedef FastVector< sharedFactor >::const_iterator const_iterator
 

Friends

class boost::serialization::access
 Serialization function.
 

Additional Inherited Members

- Protected Member Functions inherited from gtsam::BayesNet< HybridConditional >
 BayesNet ()
 Default constructor as an empty BayesNet.
 
 BayesNet (ITERATOR firstConditional, ITERATOR lastConditional)
 Construct from iterator over conditionals.
 
 BayesNet (std::initializer_list< sharedConditional > conditionals)
 Constructor that takes an initializer list of shared pointers. More...
 
- Protected Member Functions inherited from gtsam::FactorGraph< HybridConditional >
bool isEqual (const FactorGraph &other) const
 Check exact equality of the factor pointers. Useful for derived ==.
 
 FactorGraph ()
 Default constructor.
 
 FactorGraph (ITERATOR firstFactor, ITERATOR lastFactor)
 Constructor from iterator over factors (shared_ptr or plain objects)
 
 FactorGraph (const CONTAINER &factors)
 Construct from container of factors (shared_ptr or plain objects)
 
- Protected Attributes inherited from gtsam::FactorGraph< HybridConditional >
FastVector< sharedFactorfactors_
 concept check, makes sure FACTOR defines print and equals More...
 

Member Function Documentation

◆ choose()

GaussianBayesNet gtsam::HybridBayesNet::choose ( const DiscreteValues assignment) const

Get the Gaussian Bayes Net which corresponds to a specific discrete value assignment.

Parameters
assignmentThe discrete value assignment for the discrete keys.
Returns
GaussianBayesNet

◆ discreteConditionals()

DecisionTreeFactor::shared_ptr gtsam::HybridBayesNet::discreteConditionals ( ) const

Get all the discrete conditionals as a decision tree factor.

Returns
DecisionTreeFactor::shared_ptr

◆ emplace_back()

template<class Conditional >
void gtsam::HybridBayesNet::emplace_back ( Conditional conditional)
inline

Preferred: add a conditional directly using a pointer.

Examples: hbn.emplace_back(new GaussianMixture(...))); hbn.emplace_back(new GaussianConditional(...))); hbn.emplace_back(new DiscreteConditional(...)));

◆ evaluate()

AlgebraicDecisionTree< Key > gtsam::HybridBayesNet::evaluate ( const VectorValues continuousValues) const

Compute unnormalized probability q(μ|M), for each discrete assignment, and return as a tree.

q(μ|M) is the unnormalized probability at the MLE point μ, conditioned on the discrete variables.

Parameters
continuousValuesContinuous values at which to compute the probability.
Returns
AlgebraicDecisionTree<Key>

◆ logProbability()

AlgebraicDecisionTree< Key > gtsam::HybridBayesNet::logProbability ( const VectorValues continuousValues) const

Compute conditional error for each discrete assignment, and return as a tree.

Parameters
continuousValuesContinuous values at which to compute the error.
Returns
AlgebraicDecisionTree<Key>

◆ optimize() [1/2]

HybridValues gtsam::HybridBayesNet::optimize ( ) const

Solve the HybridBayesNet by first computing the MPE of all the discrete variables and then optimizing the continuous variables based on the MPE assignment.

Returns
HybridValues

◆ optimize() [2/2]

VectorValues gtsam::HybridBayesNet::optimize ( const DiscreteValues assignment) const

Given the discrete assignment, return the optimized estimate for the selected Gaussian BayesNet.

Parameters
assignmentAn assignment of discrete values.
Returns
Values

◆ print()

void gtsam::HybridBayesNet::print ( const std::string &  s = "",
const KeyFormatter formatter = DefaultKeyFormatter 
) const
overridevirtual

GTSAM-style printing.

Reimplemented from gtsam::FactorGraph< HybridConditional >.

◆ push_back() [1/2]

void gtsam::HybridBayesNet::push_back ( boost::shared_ptr< HybridConditional conditional)
inline

Add a hybrid conditional using a shared_ptr.

This is the "native" push back, as this class stores hybrid conditionals.

◆ push_back() [2/2]

void gtsam::HybridBayesNet::push_back ( HybridConditional &&  conditional)
inline

Add a conditional using a shared_ptr, using implicit conversion to a HybridConditional.

This is useful when you create a conditional shared pointer as you need it somewhere else.

Example: auto shared_ptr_to_a_conditional = boost::make_shared<GaussianMixture>(...); hbn.push_back(shared_ptr_to_a_conditional);

◆ sample() [1/4]

HybridValues gtsam::HybridBayesNet::sample ( ) const

Sample using ancestral sampling, use default rng.

Returns
HybridValues

◆ sample() [2/4]

HybridValues gtsam::HybridBayesNet::sample ( const HybridValues given) const

Sample from an incomplete BayesNet, use default rng.

Parameters
givenValues of missing variables.
Returns
HybridValues

◆ sample() [3/4]

HybridValues gtsam::HybridBayesNet::sample ( const HybridValues given,
std::mt19937_64 *  rng 
) const

Sample from an incomplete BayesNet, given missing variables.

Example: std::mt19937_64 rng(42); VectorValues given = ...; auto sample = bn.sample(given, &rng);

Parameters
givenValues of missing variables.
rngThe pseudo-random number generator.
Returns
HybridValues

◆ sample() [4/4]

HybridValues gtsam::HybridBayesNet::sample ( std::mt19937_64 *  rng) const

Sample using ancestral sampling.

Example: std::mt19937_64 rng(42); auto sample = bn.sample(&rng);

Parameters
rngThe pseudo-random number generator.
Returns
HybridValues

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