gtsam 4.1.1
gtsam

A class for computing Gaussian marginals of variables in a NonlinearFactorGraph.
Public Member Functions  
Marginals ()  
Default constructor only for wrappers.  
Marginals (const NonlinearFactorGraph &graph, const Values &solution, Factorization factorization=CHOLESKY)  
Construct a marginals class from a nonlinear factor graph. More...  
Marginals (const NonlinearFactorGraph &graph, const Values &solution, const Ordering &ordering, Factorization factorization=CHOLESKY)  
Construct a marginals class from a nonlinear factor graph. More...  
Marginals (const GaussianFactorGraph &graph, const Values &solution, Factorization factorization=CHOLESKY)  
Construct a marginals class from a linear factor graph. More...  
Marginals (const GaussianFactorGraph &graph, const Values &solution, const Ordering &ordering, Factorization factorization=CHOLESKY)  
Construct a marginals class from a linear factor graph. More...  
Marginals (const GaussianFactorGraph &graph, const VectorValues &solution, Factorization factorization=CHOLESKY)  
Construct a marginals class from a linear factor graph. More...  
Marginals (const GaussianFactorGraph &graph, const VectorValues &solution, const Ordering &ordering, Factorization factorization=CHOLESKY)  
Construct a marginals class from a linear factor graph. More...  
void  print (const std::string &str="Marginals: ", const KeyFormatter &keyFormatter=DefaultKeyFormatter) const 
print  
GaussianFactor::shared_ptr  marginalFactor (Key variable) const 
Compute the marginal factor of a single variable.  
Matrix  marginalInformation (Key variable) const 
Compute the marginal information matrix of a single variable. More...  
Matrix  marginalCovariance (Key variable) const 
Compute the marginal covariance of a single variable.  
JointMarginal  jointMarginalCovariance (const KeyVector &variables) const 
Compute the joint marginal covariance of several variables.  
JointMarginal  jointMarginalInformation (const KeyVector &variables) const 
Compute the joint marginal information of several variables.  
VectorValues  optimize () const 
Optimize the bayes tree.  
Public Types  
enum  Factorization { CHOLESKY , QR } 
The linear factorization mode  either CHOLESKY (faster and suitable for most problems) or QR (slower but more numerically stable for poorlyconditioned problems).  
Protected Member Functions  
void  computeBayesTree () 
Compute the Bayes Tree as a helper function to the constructor.  
void  computeBayesTree (const Ordering &ordering) 
Compute the Bayes Tree as a helper function to the constructor.  
Protected Attributes  
GaussianFactorGraph  graph_ 
Values  values_ 
Factorization  factorization_ 
GaussianBayesTree  bayesTree_ 
gtsam::Marginals::Marginals  (  const NonlinearFactorGraph &  graph, 
const Values &  solution,  
Factorization  factorization = CHOLESKY 

) 
Construct a marginals class from a nonlinear factor graph.
graph  The factor graph defining the full joint density on all variables. 
solution  The linearization point about which to compute Gaussian marginals (usually the MLE as obtained from a NonlinearOptimizer). 
factorization  The linear decomposition mode  either Marginals::CHOLESKY (faster and suitable for most problems) or Marginals::QR (slower but more numerically stable for poorlyconditioned problems). 
gtsam::Marginals::Marginals  (  const NonlinearFactorGraph &  graph, 
const Values &  solution,  
const Ordering &  ordering,  
Factorization  factorization = CHOLESKY 

) 
Construct a marginals class from a nonlinear factor graph.
graph  The factor graph defining the full joint density on all variables. 
solution  The linearization point about which to compute Gaussian marginals (usually the MLE as obtained from a NonlinearOptimizer). 
factorization  The linear decomposition mode  either Marginals::CHOLESKY (faster and suitable for most problems) or Marginals::QR (slower but more numerically stable for poorlyconditioned problems). 
ordering  The ordering for elimination. 
gtsam::Marginals::Marginals  (  const GaussianFactorGraph &  graph, 
const Values &  solution,  
Factorization  factorization = CHOLESKY 

) 
Construct a marginals class from a linear factor graph.
graph  The factor graph defining the full joint density on all variables. 
solution  The solution point to compute Gaussian marginals. 
factorization  The linear decomposition mode  either Marginals::CHOLESKY (faster and suitable for most problems) or Marginals::QR (slower but more numerically stable for poorlyconditioned problems). 
gtsam::Marginals::Marginals  (  const GaussianFactorGraph &  graph, 
const Values &  solution,  
const Ordering &  ordering,  
Factorization  factorization = CHOLESKY 

) 
Construct a marginals class from a linear factor graph.
graph  The factor graph defining the full joint density on all variables. 
solution  The solution point to compute Gaussian marginals. 
factorization  The linear decomposition mode  either Marginals::CHOLESKY (faster and suitable for most problems) or Marginals::QR (slower but more numerically stable for poorlyconditioned problems). 
ordering  The ordering for elimination. 
gtsam::Marginals::Marginals  (  const GaussianFactorGraph &  graph, 
const VectorValues &  solution,  
Factorization  factorization = CHOLESKY 

) 
Construct a marginals class from a linear factor graph.
graph  The factor graph defining the full joint density on all variables. 
solution  The solution point to compute Gaussian marginals. 
factorization  The linear decomposition mode  either Marginals::CHOLESKY (faster and suitable for most problems) or Marginals::QR (slower but more numerically stable for poorlyconditioned problems). 
ordering  An optional variable ordering for elimination. 
gtsam::Marginals::Marginals  (  const GaussianFactorGraph &  graph, 
const VectorValues &  solution,  
const Ordering &  ordering,  
Factorization  factorization = CHOLESKY 

) 
Construct a marginals class from a linear factor graph.
graph  The factor graph defining the full joint density on all variables. 
solution  The solution point to compute Gaussian marginals. 
factorization  The linear decomposition mode  either Marginals::CHOLESKY (faster and suitable for most problems) or Marginals::QR (slower but more numerically stable for poorlyconditioned problems). 
ordering  An optional variable ordering for elimination. 
Matrix gtsam::Marginals::marginalInformation  (  Key  variable  )  const 
Compute the marginal information matrix of a single variable.
Use LLt(const Matrix&) or RtR(const Matrix&) to obtain the squareroot information matrix.