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Removed redundant test

release/4.3a0
Richard Roberts 2013-08-05 22:31:15 +00:00
parent 0e80fe6418
commit ac0f108106
1 changed files with 0 additions and 89 deletions
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tests/testInferenceB.cpp
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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
* -------------------------------------------------------------------------- */
/**
* @file testInferenceB.cpp
* @brief Unit tests for functionality declared in inference.h
* @author Frank Dellaert
*/
#include <CppUnitLite/TestHarness.h>
#include <gtsam/slam/PriorFactor.h>
#include <gtsam/slam/BetweenFactor.h>
#include <gtsam/slam/BearingRangeFactor.h>
#include <gtsam/nonlinear/NonlinearFactorGraph.h>
#include <gtsam/nonlinear/Values.h>
#include <gtsam/nonlinear/Symbol.h>
#include <gtsam/linear/GaussianSequentialSolver.h>
#include <gtsam/linear/GaussianMultifrontalSolver.h>
#include <gtsam/geometry/Pose2.h>
#include <gtsam/geometry/Point2.h>
#include <gtsam/geometry/Rot2.h>
#include <tests/smallExample.h>
using namespace std;
using namespace gtsam;
// Convenience for named keys
using symbol_shorthand::X;
using symbol_shorthand::L;
/* ************************************************************************* */
// The tests below test the *generic* inference algorithms. Some of these have
// specialized versions in the derived classes GaussianFactorGraph etc...
/* ************************************************************************* */
/* ************************************************************************* */
TEST( inference, marginals )
{
using namespace example;
// create and marginalize a small Bayes net on "x"
GaussianBayesNetOrdered cbn = createSmallGaussianBayesNet();
vector<Index> xvar; xvar.push_back(0);
GaussianBayesNetOrdered actual = *GaussianSequentialSolver(
*GaussianSequentialSolver(GaussianFactorGraphOrdered(cbn)).jointFactorGraph(xvar)).eliminate();
// expected is just scalar Gaussian on x
GaussianBayesNetOrdered expected = scalarGaussian(0, 4, sqrt(2.0));
CHECK(assert_equal(expected,actual));
}
/* ************************************************************************* */
TEST( inference, marginals2)
{
NonlinearFactorGraph fg;
SharedDiagonal poseModel(noiseModel::Isotropic::Sigma(3, 0.1));
SharedDiagonal pointModel(noiseModel::Isotropic::Sigma(2, 0.1));
fg.add(PriorFactor<Pose2>(X(0), Pose2(), poseModel));
fg.add(BetweenFactor<Pose2>(X(0), X(1), Pose2(1.0,0.0,0.0), poseModel));
fg.add(BetweenFactor<Pose2>(X(1), X(2), Pose2(1.0,0.0,0.0), poseModel));
fg.add(BearingRangeFactor<Pose2, Point2>(X(0), L(0), Rot2(), 1.0, pointModel));
fg.add(BearingRangeFactor<Pose2, Point2>(X(1), L(0), Rot2(), 1.0, pointModel));
fg.add(BearingRangeFactor<Pose2, Point2>(X(2), L(0), Rot2(), 1.0, pointModel));
Values init;
init.insert(X(0), Pose2(0.0,0.0,0.0));
init.insert(X(1), Pose2(1.0,0.0,0.0));
init.insert(X(2), Pose2(2.0,0.0,0.0));
init.insert(L(0), Point2(1.0,1.0));
OrderingOrdered ordering(*fg.orderingCOLAMD(init));
FactorGraphOrdered<GaussianFactorOrdered>::shared_ptr gfg(fg.linearize(init, ordering));
GaussianMultifrontalSolver solver(*gfg);
solver.marginalFactor(ordering[L(0)]);
}
/* ************************************************************************* */
int main() { TestResult tr; return TestRegistry::runAllTests(tr);}
/* ************************************************************************* */