gtsam/inference/SymbolicSequentialSolver.h

62 lines
1.8 KiB
C++

/* ----------------------------------------------------------------------------
* GTSAM Copyright 2010, Georgia Tech Research Corporation,
* Atlanta, Georgia 30332-0415
* All Rights Reserved
* Authors: Frank Dellaert, et al. (see THANKS for the full author list)
* See LICENSE for the license information
* -------------------------------------------------------------------------- */
/**
* @file SymbolicSequentialSolver.h
* @brief
* @author Richard Roberts
* @created Oct 21, 2010
*/
#pragma once
#include <gtsam/inference/GenericSequentialSolver.h>
#include <gtsam/inference/SymbolicFactorGraph.h>
namespace gtsam {
// The base class provides all of the needed functionality
typedef GenericSequentialSolver<IndexFactor> SymbolicSequentialSolver;
//class SymbolicSequentialSolver : GenericSequentialSolver<IndexFactor> {
//
//protected:
//
// typedef GenericSequentialSolver<IndexFactor> Base;
//
//public:
//
// SymbolicSequentialSolver(const FactorGraph<IndexFactor>& factorGraph);
//
// /**
// * Eliminate the factor graph sequentially. Uses a column elimination tree
// * to recursively eliminate.
// */
// BayesNet<IndexConditional>::shared_ptr eliminate() const;
//
// /**
// * Compute the marginal Gaussian density over a variable, by integrating out
// * all of the other variables. This function returns the result as a factor.
// */
// IndexFactor::shared_ptr marginal(Index j) const;
//
// /**
// * Compute the marginal joint over a set of variables, by integrating out
// * all of the other variables. This function returns the result as an upper-
// * triangular R factor and right-hand-side, i.e. a GaussianBayesNet with
// * R*x = d. To get a mean and covariance matrix, use jointStandard(...)
// */
// SymbolicFactorGraph::shared_ptr joint(const std::vector<Index>& js) const;
//
//};
}