Marginals.h

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/* ----------------------------------------------------------------------------

 * 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

 * -------------------------------------------------------------------------- */

#pragma once

#include <gtsam/linear/GaussianBayesTree.h>
#include <gtsam/nonlinear/NonlinearFactorGraph.h>
#include <gtsam/nonlinear/Values.h>

namespace gtsam {

class JointMarginal;

class GTSAM_EXPORT Marginals {

public:

  enum Factorization {
    CHOLESKY,
    QR
  };

protected:

  GaussianFactorGraph graph_;
  Values values_;
  Factorization factorization_;
  GaussianBayesTree bayesTree_;

public:

  Marginals(){}

  Marginals(const NonlinearFactorGraph& graph, const Values& solution, Factorization factorization = CHOLESKY);

  Marginals(const NonlinearFactorGraph& graph, const Values& solution, const Ordering& ordering,
              Factorization factorization = CHOLESKY);

  Marginals(const GaussianFactorGraph& graph, const Values& solution, Factorization factorization = CHOLESKY);

  Marginals(const GaussianFactorGraph& graph, const Values& solution, const Ordering& ordering,
              Factorization factorization = CHOLESKY);

  Marginals(const GaussianFactorGraph& graph, const VectorValues& solution, Factorization factorization = CHOLESKY);

  Marginals(const GaussianFactorGraph& graph, const VectorValues& solution, const Ordering& ordering,
              Factorization factorization = CHOLESKY);

  void print(const std::string& str = "Marginals: ", const KeyFormatter& keyFormatter = DefaultKeyFormatter) const;

  GaussianFactor::shared_ptr marginalFactor(Key variable) const;

  Matrix marginalInformation(Key variable) const;

  Matrix marginalCovariance(Key variable) const;

  JointMarginal jointMarginalCovariance(const KeyVector& variables) const;

  JointMarginal jointMarginalInformation(const KeyVector& variables) const;

  VectorValues optimize() const;
            
protected:
  
  void computeBayesTree();

  void computeBayesTree(const Ordering& ordering);

public:
  Marginals(const NonlinearFactorGraph& graph, const Values& solution, Factorization factorization,
            const Ordering& ordering) : Marginals(graph, solution, ordering, factorization) {}

  Marginals(const GaussianFactorGraph& graph, const Values& solution, Factorization factorization,
            const Ordering& ordering) : Marginals(graph, solution, ordering, factorization) {}

  Marginals(const GaussianFactorGraph& graph, const VectorValues& solution, Factorization factorization,
            const Ordering& ordering) : Marginals(graph, solution, ordering, factorization) {}

};

class GTSAM_EXPORT JointMarginal {

protected:
  SymmetricBlockMatrix blockMatrix_;
  KeyVector keys_;
  FastMap<Key, size_t> indices_;

public:
  JointMarginal() {}

  Matrix operator()(Key iVariable, Key jVariable) const {
    const auto indexI = indices_.at(iVariable);
    const auto indexJ = indices_.at(jVariable);
    return blockMatrix_.block(indexI, indexJ);
  }

  Matrix at(Key iVariable, Key jVariable) const {
    return (*this)(iVariable, jVariable);
  }

  Matrix fullMatrix() const {
    return blockMatrix_.selfadjointView();
  }

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

protected:
  JointMarginal(const Matrix& fullMatrix, const std::vector<size_t>& dims, const KeyVector& keys) :
    blockMatrix_(dims, fullMatrix), keys_(keys), indices_(Ordering(keys).invert()) {}

  friend class Marginals;

};

} /* namespace gtsam */