gtsam/tests/testSubgraphSolver.cpp

118 lines
3.9 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 testSubgraphSolver.cpp
* @brief Unit tests for SubgraphSolver
* @author Yong-Dian Jian
**/
#include <CppUnitLite/TestHarness.h>
#if 0
#include <tests/smallExample.h>
#include <gtsam/inference/Symbol.h>
#include <gtsam/linear/GaussianBayesNet.h>
#include <gtsam/linear/iterative.h>
#include <gtsam/linear/GaussianFactorGraph.h>
#include <gtsam/linear/SubgraphSolver.h>
#include <gtsam/inference/Ordering.h>
#include <gtsam/base/numericalDerivative.h>
#include <boost/foreach.hpp>
#include <boost/tuple/tuple.hpp>
#include <boost/assign/std/list.hpp>
using namespace boost::assign;
using namespace std;
using namespace gtsam;
using namespace example;
/* ************************************************************************* */
/** unnormalized error */
static double error(const GaussianFactorGraph& fg, const VectorValues& x) {
double total_error = 0.;
BOOST_FOREACH(const GaussianFactor::shared_ptr& factor, fg)
total_error += factor->error(x);
return total_error;
}
/* ************************************************************************* */
TEST( SubgraphSolver, constructor1 )
{
// Build a planar graph
GaussianFactorGraph Ab;
VectorValues xtrue;
size_t N = 3;
boost::tie(Ab, xtrue) = planarGraph(N); // A*x-b
// The first constructor just takes a factor graph (and parameters)
// and it will split the graph into A1 and A2, where A1 is a spanning tree
SubgraphSolverParameters parameters;
SubgraphSolver solver(Ab, parameters);
VectorValues optimized = solver.optimize(); // does PCG optimization
DOUBLES_EQUAL(0.0, error(Ab, optimized), 1e-5);
}
/* ************************************************************************* */
TEST( SubgraphSolver, constructor2 )
{
// Build a planar graph
GaussianFactorGraph Ab;
VectorValues xtrue;
size_t N = 3;
boost::tie(Ab, xtrue) = planarGraph(N); // A*x-b
// Get the spanning tree and corresponding ordering
GaussianFactorGraph Ab1_, Ab2_; // A1*x-b1 and A2*x-b2
boost::tie(Ab1_, Ab2_) = splitOffPlanarTree(N, Ab);
// The second constructor takes two factor graphs,
// so the caller can specify the preconditioner (Ab1) and the constraints that are left out (Ab2)
SubgraphSolverParameters parameters;
SubgraphSolver solver(Ab1_, Ab2_, parameters);
VectorValues optimized = solver.optimize();
DOUBLES_EQUAL(0.0, error(Ab, optimized), 1e-5);
}
/* ************************************************************************* */
TEST( SubgraphSolver, constructor3 )
{
// Build a planar graph
GaussianFactorGraph Ab;
VectorValues xtrue;
size_t N = 3;
boost::tie(Ab, xtrue) = planarGraph(N); // A*x-b
// Get the spanning tree and corresponding ordering
GaussianFactorGraph Ab1_, Ab2_; // A1*x-b1 and A2*x-b2
boost::tie(Ab1_, Ab2_) = splitOffPlanarTree(N, Ab);
// The caller solves |A1*x-b1|^2 == |R1*x-c1|^2 via QR factorization, where R1 is square UT
GaussianBayesNet::shared_ptr Rc1 = //
EliminationTree<GaussianFactor>::Create(Ab1_)->eliminate(&EliminateQR);
// The third constructor allows the caller to pass an already solved preconditioner Rc1_
// as a Bayes net, in addition to the "loop closing constraints" Ab2, as before
SubgraphSolverParameters parameters;
SubgraphSolver solver(Rc1, Ab2_, parameters);
VectorValues optimized = solver.optimize();
DOUBLES_EQUAL(0.0, error(Ab, optimized), 1e-5);
}
#endif
/* ************************************************************************* */
int main() { TestResult tr; return TestRegistry::runAllTests(tr); }
/* ************************************************************************* */