GaussianBayesTree.cpp

Go to the documentation of this file.

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

 * 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

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

#include <gtsam/base/treeTraversal-inst.h>
#include <gtsam/inference/BayesTree-inst.h>
#include <gtsam/inference/BayesTreeCliqueBase-inst.h>
#include <gtsam/linear/linearAlgorithms-inst.h>
#include <gtsam/linear/GaussianBayesTree.h>
#include <gtsam/linear/GaussianBayesNet.h>
#include <gtsam/linear/VectorValues.h>

namespace gtsam {

  // Instantiate base class
  template class BayesTreeCliqueBase<GaussianBayesTreeClique, GaussianFactorGraph>;
  template class BayesTree<GaussianBayesTreeClique>;

  /* ************************************************************************* */
  namespace internal
  {
    /* ************************************************************************* */
  double logDeterminant(const GaussianBayesTreeClique::shared_ptr& clique,
                        double& parentSum) {
    parentSum += clique->conditional()
                     ->R()
                     .diagonal()
                     .unaryExpr([](double x) { return log(x); })
                     .sum();
    return 0;
  }
  }  // namespace internal

  /* ************************************************************************* */
  bool GaussianBayesTree::equals(const This& other, double tol) const
  {
    return Base::equals(other, tol);
  }

  /* ************************************************************************* */
  VectorValues GaussianBayesTree::optimize() const
  {
    return internal::linearAlgorithms::optimizeBayesTree(*this);
  }

  /* ************************************************************************* */
  VectorValues GaussianBayesTree::optimizeGradientSearch() const
  {
    gttic(GaussianBayesTree_optimizeGradientSearch);
    return GaussianFactorGraph(*this).optimizeGradientSearch();
  }

  /* ************************************************************************* */
  VectorValues GaussianBayesTree::gradient(const VectorValues& x0) const {
    return GaussianFactorGraph(*this).gradient(x0);
  }

  /* ************************************************************************* */
  VectorValues GaussianBayesTree::gradientAtZero() const {
    return GaussianFactorGraph(*this).gradientAtZero();
  }

  /* ************************************************************************* */
  double GaussianBayesTree::error(const VectorValues& x) const {
    return GaussianFactorGraph(*this).error(x);
  }

  /* ************************************************************************* */
  double GaussianBayesTree::logDeterminant() const
  {
    if(this->roots_.empty()) {
      return 0.0;
    } else {
      double sum = 0.0;
      treeTraversal::DepthFirstForest(*this, sum, internal::logDeterminant);
      return sum;
    }
  }

  /* ************************************************************************* */
  double GaussianBayesTree::determinant() const
  {
    return exp(logDeterminant());
  }

  /* ************************************************************************* */
  Matrix GaussianBayesTree::marginalCovariance(Key key) const
  {
    return marginalFactor(key)->information().inverse();
  }


} // \namespace gtsam