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gtsam::HybridGaussianFactorGraph Class Reference
Hybrid

## Detailed Description

## Hybrid Gaussian Factor Graph

This is the linearized version of a hybrid factor graph.

Inheritance diagram for gtsam::HybridGaussianFactorGraph:

## Public Member Functions

Constructors
| HybridGaussianFactorGraph ()=default
| Default constructor.
template<class DERIVEDFACTOR>
| HybridGaussianFactorGraph (const FactorGraph< DERIVEDFACTOR > &graph)
| Implicit copy/downcast constructor to override explicit template container constructor.
Standard Interface
AlgebraicDecisionTree< Key > | error (const VectorValues &continuousValues) const
| Compute error for each discrete assignment, and return as a tree.
AlgebraicDecisionTree< Key > | probPrime (const VectorValues &continuousValues) const
| Compute unnormalized probability \( P(X | M, Z) \) for each discrete assignment, and return as a tree.
double | probPrime (const HybridValues &values) const
| Compute the unnormalized posterior probability for a continuous vector values given a specific assignment.
GaussianFactorGraphTree | assembleGraphTree () const
| Create a decision tree of factor graphs out of this hybrid factor graph.
Public Member Functions inherited from gtsam::HybridFactorGraph
| HybridFactorGraph ()=default
| Default constructor.
template<class DERIVEDFACTOR>
| HybridFactorGraph (const FactorGraph< DERIVEDFACTOR > &graph)
| Implicit copy/downcast constructor to override explicit template container constructor.
std::set< DiscreteKey > | discreteKeys () const
| Get all the discrete keys in the factor graph.
KeySet | discreteKeySet () const
| Get all the discrete keys in the factor graph, as a set.
std::unordered_map< Key, DiscreteKey > | discreteKeyMap () const
| Get a map from Key to corresponding DiscreteKey.
const KeySet | continuousKeySet () const
| Get all the continuous keys in the factor graph.
Public Member Functions inherited from gtsam::FactorGraph< Factor >
virtual | ~FactorGraph ()=default
| Default destructor Public and virtual so boost serialization can call it.
| FactorGraph (std::initializer_list< boost::shared_ptr< DERIVEDFACTOR > > sharedFactors)
| Constructor that takes an initializer list of shared pointers.
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 > | emplace_shared (Args &&... args)
| Emplace a shared pointer to factor of given type.
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 > | 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.
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.
virtual void | print (const std::string &s="FactorGraph", const KeyFormatter &formatter=DefaultKeyFormatter) const
| Print out graph to std::cout, with optional key formatter.
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).
sharedFactor & | at (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).
sharedFactor & | operator[] (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.
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 Member Functions inherited from gtsam::EliminateableFactorGraph< HybridGaussianFactorGraph >
boost::shared_ptr< BayesNetType > | eliminateSequential (OptionalOrderingType orderingType=boost::none, const Eliminate &function=EliminationTraitsType::DefaultEliminate, OptionalVariableIndex variableIndex=boost::none) const
| Do sequential elimination of all variables to produce a Bayes net.
boost::shared_ptr< BayesTreeType > | eliminateMultifrontal (OptionalOrderingType orderingType=boost::none, const Eliminate &function=EliminationTraitsType::DefaultEliminate, OptionalVariableIndex variableIndex=boost::none) const
| Do multifrontal elimination of all variables to produce a Bayes tree.
std::pair< boost::shared_ptr< BayesNetType >, boost::shared_ptr< FactorGraphType > > | eliminatePartialSequential (const Ordering &ordering, const Eliminate &function=EliminationTraitsType::DefaultEliminate, OptionalVariableIndex variableIndex=boost::none) const
| Do sequential elimination of some variables, in `ordering` provided, to produce a Bayes net and a remaining factor graph.
std::pair< boost::shared_ptr< BayesTreeType >, boost::shared_ptr< FactorGraphType > > | eliminatePartialMultifrontal (const Ordering &ordering, const Eliminate &function=EliminationTraitsType::DefaultEliminate, OptionalVariableIndex variableIndex=boost::none) const
| Do multifrontal elimination of some variables, in `ordering` provided, to produce a Bayes tree and a remaining factor graph.
boost::shared_ptr< BayesNetType > | marginalMultifrontalBayesNet (boost::variant< const Ordering &, const KeyVector & > variables, const Eliminate &function=EliminationTraitsType::DefaultEliminate, OptionalVariableIndex variableIndex=boost::none) const
| Compute the marginal of the requested variables and return the result as a Bayes net.
boost::shared_ptr< BayesTreeType > | marginalMultifrontalBayesTree (boost::variant< const Ordering &, const KeyVector & > variables, const Eliminate &function=EliminationTraitsType::DefaultEliminate, OptionalVariableIndex variableIndex=boost::none) const
| Compute the marginal of the requested variables and return the result as a Bayes tree.
boost::shared_ptr< FactorGraphType > | marginal (const KeyVector &variables, const Eliminate &function=EliminationTraitsType::DefaultEliminate, OptionalVariableIndex variableIndex=boost::none) const
| Compute the marginal factor graph of the requested variables.

## Public Types

using | Base = HybridFactorGraph
using | This = HybridGaussianFactorGraph
| this class
using | BaseEliminateable
| for elimination
using | shared_ptr = boost::shared_ptr<This>
| shared_ptr to This
using | Values = gtsam::Values
| backwards compatibility
using | Indices = KeyVector
| map from keys to values
Public Types inherited from gtsam::HybridFactorGraph
using | Base = FactorGraph<Factor>
using | This = HybridFactorGraph
| this class
using | shared_ptr = boost::shared_ptr<This>
| shared_ptr to This
using | Values = gtsam::Values
| backwards compatibility
using | Indices = KeyVector
Public Types inherited from gtsam::FactorGraph< Factor >
typedef Factor | FactorType
| factor type
typedef boost::shared_ptr< Factor > | sharedFactor
| Shared pointer to a factor.
typedef sharedFactor | value_type
typedef FastVector< sharedFactor >::iterator | iterator
typedef FastVector< sharedFactor >::const_iterator | const_iterator
Public Types inherited from gtsam::EliminateableFactorGraph< HybridGaussianFactorGraph >
typedef EliminationTraits< FactorGraphType > | EliminationTraitsType
| Typedef to the specific EliminationTraits for this graph.
typedef EliminationTraitsType::ConditionalType | ConditionalType
| Conditional type stored in the Bayes net produced by elimination.
typedef EliminationTraitsType::BayesNetType | BayesNetType
| Bayes net type produced by sequential elimination.
typedef EliminationTraitsType::EliminationTreeType | EliminationTreeType
| Elimination tree type that can do sequential elimination of this graph.
typedef EliminationTraitsType::BayesTreeType | BayesTreeType
| Bayes tree type produced by multifrontal elimination.
typedef EliminationTraitsType::JunctionTreeType | JunctionTreeType
| Junction tree type that can do multifrontal elimination of this graph.
typedef std::pair< boost::shared_ptr< ConditionalType >, boost::shared_ptr< _FactorType > > | EliminationResult
| The pair of conditional and remaining factor produced by a single dense elimination step on a subgraph.
typedef std::function< EliminationResult(const FactorGraphType &, const Ordering &)> | Eliminate
| The function type that does a single dense elimination step on a subgraph.
typedef boost::optional< const VariableIndex & > | OptionalVariableIndex
| Typedef for an optional variable index as an argument to elimination functions.
typedef boost::optional< Ordering::OrderingType > | OptionalOrderingType
| Typedef for an optional ordering type.

## Protected Types

template<typename FACTOR>
using | IsGaussian
| Check if FACTOR type is derived from GaussianFactor.

## Additional Inherited Members

Protected Member Functions inherited from gtsam::FactorGraph< Factor >
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< Factor >
FastVector< sharedFactor > | factors_
| concept check, makes sure FACTOR defines print and equals

## Member Typedef Documentation

## BaseEliminateable

using gtsam::HybridGaussianFactorGraph::BaseEliminateable

Initial value:
EliminateableFactorGraph<This>
gtsam::EliminateableFactorGraph
EliminateableFactorGraph is a base class for factor graphs that contains elimination algorithms.
Definition EliminateableFactorGraph.h:57

for elimination

## IsGaussian

template<typename FACTOR>

using gtsam::HybridGaussianFactorGraph::IsGaussian

protected

Initial value:
typename std::enable_if<
std::is_base_of<GaussianFactor, FACTOR>::value>::type

Check if FACTOR type is derived from GaussianFactor.

## Constructor & Destructor Documentation

## HybridGaussianFactorGraph()

template<class DERIVEDFACTOR>

gtsam::HybridGaussianFactorGraph::HybridGaussianFactorGraph | ( | const FactorGraph< DERIVEDFACTOR > & | graph | ) |

inline

Implicit copy/downcast constructor to override explicit template container constructor.

In BayesTree this is used for: cachedSeparatorMarginal_.reset(*separatorMarginal)

## Member Function Documentation

## assembleGraphTree()

GaussianFactorGraphTree gtsam::HybridGaussianFactorGraph::assembleGraphTree | ( |  | ) | const

Create a decision tree of factor graphs out of this hybrid factor graph.

For example, if there are two mixture factors, one with a discrete key A and one with a discrete key B, then the decision tree will have two levels, one for A and one for B. The leaves of the tree will be the Gaussian factors that have only continuous keys.

## error()

AlgebraicDecisionTree< Key > gtsam::HybridGaussianFactorGraph::error | ( | const VectorValues & | continuousValues | ) | const

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

Error \( e = \Vert x - \mu \Vert_{\Sigma} \).

Parameters

  continuousValues | Continuous values at which to compute the error.

Returns
  AlgebraicDecisionTree<Key>

## probPrime() [1/2]

double gtsam::HybridGaussianFactorGraph::probPrime | ( | const HybridValues & | values | ) | const

Compute the unnormalized posterior probability for a continuous vector values given a specific assignment.

Returns
  double

## probPrime() [2/2]

AlgebraicDecisionTree< Key > gtsam::HybridGaussianFactorGraph::probPrime | ( | const VectorValues & | continuousValues | ) | const

Compute unnormalized probability \( P(X | M, Z) \) for each discrete assignment, and return as a tree.

Parameters

  continuousValues | Continuous values at which to compute the probability.

Returns
  AlgebraicDecisionTree<Key>

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

- /build/gtsam-Xg031d/gtsam-4.2.0+dfsg/gtsam/hybrid/HybridGaussianFactorGraph.h
- /build/gtsam-Xg031d/gtsam-4.2.0+dfsg/gtsam/hybrid/HybridGaussianFactorGraph.cpp