#include <HybridBayesNet.h>
Public Types | |
using | Base = BayesNet< HybridConditional > |
using | ConditionalType = HybridConditional |
using | shared_ptr = std::shared_ptr< HybridBayesNet > |
using | sharedConditional = std::shared_ptr< ConditionalType > |
using | This = HybridBayesNet |
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typedef std::shared_ptr< HybridConditional > | sharedConditional |
A shared pointer to a conditional. More... | |
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typedef FastVector< sharedFactor >::const_iterator | const_iterator |
typedef HybridConditional | FactorType |
factor type More... | |
typedef FastVector< sharedFactor >::iterator | iterator |
typedef std::shared_ptr< HybridConditional > | sharedFactor |
Shared pointer to a factor. More... | |
typedef sharedFactor | value_type |
Public Member Functions | |
Standard Constructors | |
HybridBayesNet ()=default | |
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. More... | |
Standard Interface | |
void | push_back (std::shared_ptr< HybridConditional > conditional) |
Add a hybrid conditional using a shared_ptr. More... | |
template<class Conditional > | |
void | emplace_back (Conditional *conditional) |
void | push_back (HybridConditional &&conditional) |
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. More... | |
double | operator() (const HybridValues &values) const |
Evaluate hybrid probability density for given HybridValues, sugar. More... | |
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... | |
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. More... | |
AlgebraicDecisionTree< Key > | errorTree (const VectorValues &continuousValues) const |
Compute conditional error for each discrete assignment, and return as a tree. More... | |
AlgebraicDecisionTree< Key > | logProbability (const VectorValues &continuousValues) const |
Compute log probability for each discrete assignment, and return as a tree. More... | |
AlgebraicDecisionTree< Key > | 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. More... | |
HybridGaussianFactorGraph | toFactorGraph (const VectorValues &measurements) const |
double | logProbability (const HybridValues &x) const |
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void | print (const std::string &s="BayesNet", const KeyFormatter &formatter=DefaultKeyFormatter) const override |
void | dot (std::ostream &os, const KeyFormatter &keyFormatter=DefaultKeyFormatter, const DotWriter &writer=DotWriter()) const |
Output to graphviz format, stream version. More... | |
std::string | dot (const KeyFormatter &keyFormatter=DefaultKeyFormatter, const DotWriter &writer=DotWriter()) const |
Output to graphviz format string. More... | |
void | saveGraph (const std::string &filename, const KeyFormatter &keyFormatter=DefaultKeyFormatter, const DotWriter &writer=DotWriter()) const |
output to file with graphviz format. More... | |
double | logProbability (const HybridValues &x) const |
double | evaluate (const HybridValues &c) const |
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FactorGraph (std::initializer_list< std::shared_ptr< DERIVEDFACTOR >> sharedFactors) | |
virtual | ~FactorGraph ()=default |
void | reserve (size_t size) |
IsDerived< DERIVEDFACTOR > | push_back (std::shared_ptr< DERIVEDFACTOR > factor) |
Add a factor directly using a shared_ptr. More... | |
IsDerived< DERIVEDFACTOR > | push_back (const DERIVEDFACTOR &factor) |
IsDerived< DERIVEDFACTOR > | emplace_shared (Args &&... args) |
Emplace a shared pointer to factor of given type. More... | |
IsDerived< DERIVEDFACTOR > | add (std::shared_ptr< DERIVEDFACTOR > factor) |
add is a synonym for push_back. More... | |
std::enable_if< std::is_base_of< FactorType, DERIVEDFACTOR >::value, This >::type & | operator+= (std::shared_ptr< DERIVEDFACTOR > factor) |
Append factor to factor graph. More... | |
std::enable_if< std::is_base_of< FactorType, DERIVEDFACTOR >::value, This >::type & | operator, (std::shared_ptr< DERIVEDFACTOR > factor) |
Overload comma operator to allow for append chaining. More... | |
HasDerivedElementType< ITERATOR > | push_back (ITERATOR firstFactor, ITERATOR lastFactor) |
HasDerivedValueType< ITERATOR > | push_back (ITERATOR firstFactor, ITERATOR lastFactor) |
Push back many factors with an iterator (factors are copied) More... | |
HasDerivedElementType< CONTAINER > | push_back (const CONTAINER &container) |
HasDerivedValueType< CONTAINER > | push_back (const CONTAINER &container) |
Push back non-pointer objects in a container (factors are copied). More... | |
void | add (const FACTOR_OR_CONTAINER &factorOrContainer) |
This & | operator+= (const FACTOR_OR_CONTAINER &factorOrContainer) |
std::enable_if< std::is_base_of< This, typename CLIQUE::FactorGraphType >::value >::type | push_back (const BayesTree< CLIQUE > &bayesTree) |
FactorIndices | add_factors (const CONTAINER &factors, bool useEmptySlots=false) |
bool | equals (const This &fg, double tol=1e-9) const |
Check equality up to tolerance. More... | |
size_t | size () const |
bool | empty () const |
const sharedFactor | at (size_t i) const |
sharedFactor & | at (size_t i) |
std::shared_ptr< F > | at (size_t i) |
const std::shared_ptr< F > | at (size_t i) const |
Const version of templated at method. More... | |
const sharedFactor | operator[] (size_t i) const |
sharedFactor & | operator[] (size_t i) |
const_iterator | begin () const |
const_iterator | end () const |
sharedFactor | front () const |
sharedFactor | back () const |
double | error (const HybridValues &values) const |
iterator | begin () |
iterator | end () |
virtual void | resize (size_t size) |
void | remove (size_t i) |
void | replace (size_t index, sharedFactor factor) |
iterator | erase (iterator item) |
iterator | erase (iterator first, iterator last) |
void | dot (std::ostream &os, const KeyFormatter &keyFormatter=DefaultKeyFormatter, const DotWriter &writer=DotWriter()) const |
Output to graphviz format, stream version. More... | |
std::string | dot (const KeyFormatter &keyFormatter=DefaultKeyFormatter, const DotWriter &writer=DotWriter()) const |
Output to graphviz format string. More... | |
void | saveGraph (const std::string &filename, const KeyFormatter &keyFormatter=DefaultKeyFormatter, const DotWriter &writer=DotWriter()) const |
output to file with graphviz format. More... | |
size_t | nrFactors () const |
KeySet | keys () const |
KeyVector | keyVector () const |
bool | exists (size_t idx) const |
Private Member Functions | |
DecisionTreeFactor | pruneDiscreteConditionals (size_t maxNrLeaves) |
Prune all the discrete conditionals. More... | |
Additional Inherited Members | |
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BayesNet () | |
BayesNet (ITERATOR firstConditional, ITERATOR lastConditional) | |
BayesNet (std::initializer_list< sharedConditional > conditionals) | |
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bool | isEqual (const FactorGraph &other) const |
Check exact equality of the factor pointers. Useful for derived ==. More... | |
FactorGraph () | |
FactorGraph (ITERATOR firstFactor, ITERATOR lastFactor) | |
FactorGraph (const CONTAINER &factors) | |
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FastVector< sharedFactor > | factors_ |
A hybrid Bayes net is a collection of HybridConditionals, which can have discrete conditionals, Gaussian mixtures, or pure Gaussian conditionals.
Definition at line 35 of file HybridBayesNet.h.
Definition at line 37 of file HybridBayesNet.h.
Definition at line 39 of file HybridBayesNet.h.
using gtsam::HybridBayesNet::shared_ptr = std::shared_ptr<HybridBayesNet> |
Definition at line 40 of file HybridBayesNet.h.
using gtsam::HybridBayesNet::sharedConditional = std::shared_ptr<ConditionalType> |
Definition at line 41 of file HybridBayesNet.h.
Definition at line 38 of file HybridBayesNet.h.
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default |
Construct empty Bayes net
GaussianBayesNet gtsam::HybridBayesNet::choose | ( | const DiscreteValues & | assignment | ) | const |
Get the Gaussian Bayes Net which corresponds to a specific discrete value assignment.
assignment | The discrete value assignment for the discrete keys. |
Definition at line 201 of file HybridBayesNet.cpp.
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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(...)));
Definition at line 82 of file HybridBayesNet.h.
GTSAM-style equals.
Definition at line 36 of file HybridBayesNet.cpp.
AlgebraicDecisionTree< Key > gtsam::HybridBayesNet::errorTree | ( | const VectorValues & | continuousValues | ) | const |
Compute conditional error for each discrete assignment, and return as a tree.
continuousValues | Continuous values at which to compute the error. |
Definition at line 285 of file HybridBayesNet.cpp.
double gtsam::HybridBayesNet::evaluate | ( | const HybridValues & | values | ) | const |
Evaluate hybrid probability density for given HybridValues.
Definition at line 354 of file HybridBayesNet.cpp.
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.
continuousValues | Continuous values at which to compute the probability. |
Definition at line 347 of file HybridBayesNet.cpp.
double gtsam::BayesNet< CONDITIONAL >::logProbability |
Definition at line 94 of file BayesNet-inst.h.
AlgebraicDecisionTree< Key > gtsam::HybridBayesNet::logProbability | ( | const VectorValues & | continuousValues | ) | const |
Compute log probability for each discrete assignment, and return as a tree.
continuousValues | Continuous values at which to compute the log probability. |
Definition at line 315 of file HybridBayesNet.cpp.
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inline |
Evaluate hybrid probability density for given HybridValues, sugar.
Definition at line 117 of file HybridBayesNet.h.
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.
Definition at line 221 of file HybridBayesNet.cpp.
VectorValues gtsam::HybridBayesNet::optimize | ( | const DiscreteValues & | assignment | ) | const |
Given the discrete assignment, return the optimized estimate for the selected Gaussian BayesNet.
assignment | An assignment of discrete values. |
Definition at line 238 of file HybridBayesNet.cpp.
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overridevirtual |
GTSAM-style printing.
Reimplemented from gtsam::FactorGraph< HybridConditional >.
Definition at line 30 of file HybridBayesNet.cpp.
HybridBayesNet gtsam::HybridBayesNet::prune | ( | size_t | maxNrLeaves | ) |
Prune the Hybrid Bayes Net such that we have at most maxNrLeaves leaves.
Definition at line 167 of file HybridBayesNet.cpp.
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private |
Prune all the discrete conditionals.
maxNrLeaves |
Definition at line 129 of file HybridBayesNet.cpp.
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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 = std::make_shared<GaussianMixture>(...); hbn.push_back(shared_ptr_to_a_conditional);
Definition at line 99 of file HybridBayesNet.h.
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inline |
Add a hybrid conditional using a shared_ptr.
This is the "native" push back, as this class stores hybrid conditionals.
Definition at line 69 of file HybridBayesNet.h.
HybridValues gtsam::HybridBayesNet::sample | ( | ) | const |
Sample using ancestral sampling, use default rng.
Definition at line 280 of file HybridBayesNet.cpp.
HybridValues gtsam::HybridBayesNet::sample | ( | const HybridValues & | given | ) | const |
Sample from an incomplete BayesNet, use default rng.
given | Values of missing variables. |
Definition at line 275 of file HybridBayesNet.cpp.
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);
given | Values of missing variables. |
rng | The pseudo-random number generator. |
Definition at line 250 of file HybridBayesNet.cpp.
HybridValues gtsam::HybridBayesNet::sample | ( | std::mt19937_64 * | rng | ) | const |
Sample using ancestral sampling.
Example: std::mt19937_64 rng(42); auto sample = bn.sample(&rng);
rng | The pseudo-random number generator. |
Definition at line 269 of file HybridBayesNet.cpp.
HybridGaussianFactorGraph gtsam::HybridBayesNet::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.
Definition at line 359 of file HybridBayesNet.cpp.