1033 lines
39 KiB
C++
1033 lines
39 KiB
C++
/**
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* @file testGaussianISAM2.cpp
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* @brief Unit tests for GaussianISAM2
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* @author Michael Kaess
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*/
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#include <gtsam/base/debug.h>
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#include <gtsam/base/TestableAssertions.h>
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#include <gtsam/base/LieVector.h>
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#include <gtsam/geometry/Point2.h>
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#include <gtsam/geometry/Pose2.h>
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#include <gtsam/inference/SymbolicFactorGraph.h>
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#include <gtsam/linear/GaussianBayesNet.h>
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#include <gtsam/linear/GaussianSequentialSolver.h>
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#include <gtsam/linear/GaussianBayesTree.h>
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#include <gtsam/linear/GaussianFactorGraph.h>
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#include <gtsam/nonlinear/Ordering.h>
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#include <gtsam/nonlinear/Values.h>
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#include <gtsam/nonlinear/NonlinearFactorGraph.h>
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#include <gtsam/nonlinear/ISAM2.h>
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#include <gtsam/slam/PriorFactor.h>
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#include <gtsam/slam/BetweenFactor.h>
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#include <gtsam/slam/BearingRangeFactor.h>
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#include <tests/smallExample.h>
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#include <CppUnitLite/TestHarness.h>
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#include <boost/foreach.hpp>
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#include <boost/assign/std/list.hpp> // for operator +=
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#include <boost/assign.hpp>
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using namespace boost::assign;
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using namespace std;
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using namespace gtsam;
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using boost::shared_ptr;
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const double tol = 1e-4;
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// SETDEBUG("ISAM2 update", true);
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// SETDEBUG("ISAM2 update verbose", true);
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// SETDEBUG("ISAM2 recalculate", true);
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// Set up parameters
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SharedDiagonal odoNoise = noiseModel::Diagonal::Sigmas(Vector_(3, 0.1, 0.1, M_PI/100.0));
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SharedDiagonal brNoise = noiseModel::Diagonal::Sigmas(Vector_(2, M_PI/100.0, 0.1));
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ISAM2 createSlamlikeISAM2(
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boost::optional<Values&> init_values = boost::none,
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boost::optional<NonlinearFactorGraph&> full_graph = boost::none,
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const ISAM2Params& params = ISAM2Params(ISAM2GaussNewtonParams(0.001), 0.0, 0, false, true)) {
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// These variables will be reused and accumulate factors and values
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ISAM2 isam(params);
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Values fullinit;
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NonlinearFactorGraph fullgraph;
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// i keeps track of the time step
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size_t i = 0;
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// Add a prior at time 0 and update isam
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{
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NonlinearFactorGraph newfactors;
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newfactors.add(PriorFactor<Pose2>(0, Pose2(0.0, 0.0, 0.0), odoNoise));
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fullgraph.push_back(newfactors);
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Values init;
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init.insert((0), Pose2(0.01, 0.01, 0.01));
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fullinit.insert((0), Pose2(0.01, 0.01, 0.01));
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isam.update(newfactors, init);
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}
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// Add odometry from time 0 to time 5
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for( ; i<5; ++i) {
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NonlinearFactorGraph newfactors;
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newfactors.add(BetweenFactor<Pose2>(i, i+1, Pose2(1.0, 0.0, 0.0), odoNoise));
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fullgraph.push_back(newfactors);
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Values init;
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init.insert((i+1), Pose2(double(i+1)+0.1, -0.1, 0.01));
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fullinit.insert((i+1), Pose2(double(i+1)+0.1, -0.1, 0.01));
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isam.update(newfactors, init);
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}
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// Add odometry from time 5 to 6 and landmark measurement at time 5
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{
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NonlinearFactorGraph newfactors;
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newfactors.add(BetweenFactor<Pose2>(i, i+1, Pose2(1.0, 0.0, 0.0), odoNoise));
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newfactors.add(BearingRangeFactor<Pose2,Point2>(i, 100, Rot2::fromAngle(M_PI/4.0), 5.0, brNoise));
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newfactors.add(BearingRangeFactor<Pose2,Point2>(i, 101, Rot2::fromAngle(-M_PI/4.0), 5.0, brNoise));
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fullgraph.push_back(newfactors);
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Values init;
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init.insert((i+1), Pose2(1.01, 0.01, 0.01));
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init.insert(100, Point2(5.0/sqrt(2.0), 5.0/sqrt(2.0)));
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init.insert(101, Point2(5.0/sqrt(2.0), -5.0/sqrt(2.0)));
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fullinit.insert((i+1), Pose2(1.01, 0.01, 0.01));
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fullinit.insert(100, Point2(5.0/sqrt(2.0), 5.0/sqrt(2.0)));
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fullinit.insert(101, Point2(5.0/sqrt(2.0), -5.0/sqrt(2.0)));
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isam.update(newfactors, init);
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++ i;
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}
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// Add odometry from time 6 to time 10
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for( ; i<10; ++i) {
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NonlinearFactorGraph newfactors;
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newfactors.add(BetweenFactor<Pose2>(i, i+1, Pose2(1.0, 0.0, 0.0), odoNoise));
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fullgraph.push_back(newfactors);
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Values init;
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init.insert((i+1), Pose2(double(i+1)+0.1, -0.1, 0.01));
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fullinit.insert((i+1), Pose2(double(i+1)+0.1, -0.1, 0.01));
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isam.update(newfactors, init);
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}
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// Add odometry from time 10 to 11 and landmark measurement at time 10
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{
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NonlinearFactorGraph newfactors;
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newfactors.add(BetweenFactor<Pose2>(i, i+1, Pose2(1.0, 0.0, 0.0), odoNoise));
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newfactors.add(BearingRangeFactor<Pose2,Point2>(i, 100, Rot2::fromAngle(M_PI/4.0 + M_PI/16.0), 4.5, brNoise));
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newfactors.add(BearingRangeFactor<Pose2,Point2>(i, 101, Rot2::fromAngle(-M_PI/4.0 + M_PI/16.0), 4.5, brNoise));
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fullgraph.push_back(newfactors);
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Values init;
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init.insert((i+1), Pose2(6.9, 0.1, 0.01));
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fullinit.insert((i+1), Pose2(6.9, 0.1, 0.01));
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isam.update(newfactors, init);
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++ i;
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}
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if (full_graph)
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*full_graph = fullgraph;
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if (init_values)
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*init_values = fullinit;
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return isam;
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}
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/* ************************************************************************* */
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TEST_UNSAFE(ISAM2, ImplAddVariables) {
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// Create initial state
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Values theta;
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theta.insert(0, Pose2(.1, .2, .3));
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theta.insert(100, Point2(.4, .5));
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Values newTheta;
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newTheta.insert(1, Pose2(.6, .7, .8));
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VectorValues delta;
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delta.insert(0, Vector_(3, .1, .2, .3));
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delta.insert(1, Vector_(2, .4, .5));
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VectorValues deltaNewton;
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deltaNewton.insert(0, Vector_(3, .1, .2, .3));
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deltaNewton.insert(1, Vector_(2, .4, .5));
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VectorValues deltaRg;
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deltaRg.insert(0, Vector_(3, .1, .2, .3));
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deltaRg.insert(1, Vector_(2, .4, .5));
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vector<bool> replacedKeys(2, false);
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Ordering ordering; ordering += 100, 0;
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// Verify initial state
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LONGS_EQUAL(0, ordering[100]);
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LONGS_EQUAL(1, ordering[0]);
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EXPECT(assert_equal(delta[0], delta[ordering[100]]));
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EXPECT(assert_equal(delta[1], delta[ordering[0]]));
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// Create expected state
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Values thetaExpected;
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thetaExpected.insert(0, Pose2(.1, .2, .3));
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thetaExpected.insert(100, Point2(.4, .5));
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thetaExpected.insert(1, Pose2(.6, .7, .8));
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VectorValues deltaExpected;
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deltaExpected.insert(0, Vector_(3, .1, .2, .3));
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deltaExpected.insert(1, Vector_(2, .4, .5));
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deltaExpected.insert(2, Vector_(3, 0.0, 0.0, 0.0));
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VectorValues deltaNewtonExpected;
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deltaNewtonExpected.insert(0, Vector_(3, .1, .2, .3));
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deltaNewtonExpected.insert(1, Vector_(2, .4, .5));
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deltaNewtonExpected.insert(2, Vector_(3, 0.0, 0.0, 0.0));
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VectorValues deltaRgExpected;
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deltaRgExpected.insert(0, Vector_(3, .1, .2, .3));
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deltaRgExpected.insert(1, Vector_(2, .4, .5));
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deltaRgExpected.insert(2, Vector_(3, 0.0, 0.0, 0.0));
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vector<bool> replacedKeysExpected(3, false);
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Ordering orderingExpected; orderingExpected += 100, 0, 1;
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// Expand initial state
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ISAM2::Impl::AddVariables(newTheta, theta, delta, deltaNewton, deltaRg, replacedKeys, ordering);
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EXPECT(assert_equal(thetaExpected, theta));
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EXPECT(assert_equal(deltaExpected, delta));
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EXPECT(assert_equal(deltaNewtonExpected, deltaNewton));
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EXPECT(assert_equal(deltaRgExpected, deltaRg));
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EXPECT(assert_container_equality(replacedKeysExpected, replacedKeys));
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EXPECT(assert_equal(orderingExpected, ordering));
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}
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/* ************************************************************************* */
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TEST_UNSAFE(ISAM2, ImplRemoveVariables) {
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// Create initial state
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Values theta;
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theta.insert(0, Pose2(.1, .2, .3));
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theta.insert(1, Pose2(.6, .7, .8));
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theta.insert(100, Point2(.4, .5));
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SymbolicFactorGraph sfg;
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sfg.push_back(boost::make_shared<IndexFactor>(Index(0), Index(2)));
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sfg.push_back(boost::make_shared<IndexFactor>(Index(0), Index(1)));
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VariableIndex variableIndex(sfg);
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VectorValues delta;
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delta.insert(0, Vector_(3, .1, .2, .3));
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delta.insert(1, Vector_(3, .4, .5, .6));
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delta.insert(2, Vector_(2, .7, .8));
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VectorValues deltaNewton;
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deltaNewton.insert(0, Vector_(3, .1, .2, .3));
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deltaNewton.insert(1, Vector_(3, .4, .5, .6));
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deltaNewton.insert(2, Vector_(2, .7, .8));
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VectorValues deltaRg;
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deltaRg.insert(0, Vector_(3, .1, .2, .3));
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deltaRg.insert(1, Vector_(3, .4, .5, .6));
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deltaRg.insert(2, Vector_(2, .7, .8));
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vector<bool> replacedKeys(3, false);
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replacedKeys[0] = true;
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replacedKeys[1] = false;
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replacedKeys[2] = true;
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Ordering ordering; ordering += 100, 1, 0;
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ISAM2::Nodes nodes(3);
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// Verify initial state
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LONGS_EQUAL(0, ordering[100]);
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LONGS_EQUAL(1, ordering[1]);
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LONGS_EQUAL(2, ordering[0]);
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// Create expected state
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Values thetaExpected;
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thetaExpected.insert(0, Pose2(.1, .2, .3));
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thetaExpected.insert(100, Point2(.4, .5));
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SymbolicFactorGraph sfgRemoved;
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sfgRemoved.push_back(boost::make_shared<IndexFactor>(Index(0), Index(1)));
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sfgRemoved.push_back(SymbolicFactorGraph::sharedFactor()); // Add empty factor to keep factor indices consistent
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VariableIndex variableIndexExpected(sfgRemoved);
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VectorValues deltaExpected;
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deltaExpected.insert(0, Vector_(3, .1, .2, .3));
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deltaExpected.insert(1, Vector_(2, .7, .8));
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VectorValues deltaNewtonExpected;
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deltaNewtonExpected.insert(0, Vector_(3, .1, .2, .3));
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deltaNewtonExpected.insert(1, Vector_(2, .7, .8));
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VectorValues deltaRgExpected;
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deltaRgExpected.insert(0, Vector_(3, .1, .2, .3));
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deltaRgExpected.insert(1, Vector_(2, .7, .8));
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vector<bool> replacedKeysExpected(2, true);
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Ordering orderingExpected; orderingExpected += 100, 0;
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ISAM2::Nodes nodesExpected(2);
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// Reduce initial state
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FastSet<Key> unusedKeys;
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unusedKeys.insert(1);
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vector<size_t> removedFactorsI; removedFactorsI.push_back(1);
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SymbolicFactorGraph removedFactors; removedFactors.push_back(sfg[1]);
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variableIndex.remove(removedFactorsI, removedFactors);
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GaussianFactorGraph linearFactors;
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FastSet<Key> fixedVariables;
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ISAM2::Impl::RemoveVariables(unusedKeys, ISAM2::sharedClique(), theta, variableIndex, delta, deltaNewton, deltaRg,
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replacedKeys, ordering, nodes, linearFactors, fixedVariables);
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EXPECT(assert_equal(thetaExpected, theta));
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EXPECT(assert_equal(variableIndexExpected, variableIndex));
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EXPECT(assert_equal(deltaExpected, delta));
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EXPECT(assert_equal(deltaNewtonExpected, deltaNewton));
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EXPECT(assert_equal(deltaRgExpected, deltaRg));
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EXPECT(assert_container_equality(replacedKeysExpected, replacedKeys));
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EXPECT(assert_equal(orderingExpected, ordering));
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}
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/* ************************************************************************* */
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//TEST(ISAM2, IndicesFromFactors) {
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//
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// using namespace gtsam::planarSLAM;
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// typedef GaussianISAM2<Values>::Impl Impl;
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//
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// Ordering ordering; ordering += (0), (0), (1);
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// NonlinearFactorGraph graph;
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// graph.add(PriorFactor<Pose2>((0), Pose2(), noiseModel::Unit::Create(Pose2::dimension));
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// graph.addRange((0), (0), 1.0, noiseModel::Unit::Create(1));
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//
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// FastSet<Index> expected;
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// expected.insert(0);
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// expected.insert(1);
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//
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// FastSet<Index> actual = Impl::IndicesFromFactors(ordering, graph);
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//
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// EXPECT(assert_equal(expected, actual));
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//}
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/* ************************************************************************* */
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//TEST(ISAM2, CheckRelinearization) {
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//
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// typedef GaussianISAM2<Values>::Impl Impl;
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//
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// // Create values where indices 1 and 3 are above the threshold of 0.1
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// VectorValues values;
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// values.reserve(4, 10);
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// values.push_back_preallocated(Vector_(2, 0.09, 0.09));
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// values.push_back_preallocated(Vector_(3, 0.11, 0.11, 0.09));
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// values.push_back_preallocated(Vector_(3, 0.09, 0.09, 0.09));
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// values.push_back_preallocated(Vector_(2, 0.11, 0.11));
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//
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// // Create a permutation
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// Permutation permutation(4);
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// permutation[0] = 2;
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// permutation[1] = 0;
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// permutation[2] = 1;
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// permutation[3] = 3;
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//
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// Permuted<VectorValues> permuted(permutation, values);
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//
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// // After permutation, the indices above the threshold are 2 and 2
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// FastSet<Index> expected;
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// expected.insert(2);
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// expected.insert(3);
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//
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// // Indices checked by CheckRelinearization
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// FastSet<Index> actual = Impl::CheckRelinearization(permuted, 0.1);
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//
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// EXPECT(assert_equal(expected, actual));
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//}
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/* ************************************************************************* */
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TEST(ISAM2, optimize2) {
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// Create initialization
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Values theta;
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theta.insert((0), Pose2(0.01, 0.01, 0.01));
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// Create conditional
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Vector d(3); d << -0.1, -0.1, -0.31831;
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Matrix R(3,3); R <<
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10, 0.0, 0.0,
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0.0, 10, 0.0,
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0.0, 0.0, 31.8309886;
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GaussianConditional::shared_ptr conditional(new GaussianConditional(0, d, R, Vector::Ones(3)));
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// Create ordering
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Ordering ordering; ordering += (0);
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// Expected vector
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VectorValues expected(1, 3);
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conditional->solveInPlace(expected);
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// Clique
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ISAM2::sharedClique clique(
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ISAM2::Clique::Create(make_pair(conditional,GaussianFactor::shared_ptr())));
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VectorValues actual(theta.dims(ordering));
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internal::optimizeInPlace<ISAM2::Base>(clique, actual);
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// expected.print("expected: ");
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// actual.print("actual: ");
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EXPECT(assert_equal(expected, actual));
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}
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/* ************************************************************************* */
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bool isam_check(const NonlinearFactorGraph& fullgraph, const Values& fullinit, const ISAM2& isam, Test& test, TestResult& result) {
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TestResult& result_ = result;
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const std::string name_ = test.getName();
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Values actual = isam.calculateEstimate();
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Ordering ordering = isam.getOrdering(); // *fullgraph.orderingCOLAMD(fullinit).first;
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GaussianFactorGraph linearized = *fullgraph.linearize(fullinit, ordering);
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// linearized.print("Expected linearized: ");
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GaussianBayesNet gbn = *GaussianSequentialSolver(linearized).eliminate();
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// gbn.print("Expected bayesnet: ");
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VectorValues delta = optimize(gbn);
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Values expected = fullinit.retract(delta, ordering);
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bool isamEqual = assert_equal(expected, actual);
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// The following two checks make sure that the cached gradients are maintained and used correctly
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// Check gradient at each node
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bool nodeGradientsOk = true;
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typedef ISAM2::sharedClique sharedClique;
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BOOST_FOREACH(const sharedClique& clique, isam.nodes()) {
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// Compute expected gradient
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FactorGraph<JacobianFactor> jfg;
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jfg.push_back(JacobianFactor::shared_ptr(new JacobianFactor(*clique->conditional())));
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VectorValues expectedGradient(*allocateVectorValues(isam));
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gradientAtZero(jfg, expectedGradient);
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// Compare with actual gradients
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int variablePosition = 0;
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for(GaussianConditional::const_iterator jit = clique->conditional()->begin(); jit != clique->conditional()->end(); ++jit) {
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const int dim = clique->conditional()->dim(jit);
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Vector actual = clique->gradientContribution().segment(variablePosition, dim);
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bool gradOk = assert_equal(expectedGradient[*jit], actual);
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EXPECT(gradOk);
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nodeGradientsOk = nodeGradientsOk && gradOk;
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variablePosition += dim;
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}
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bool dimOk = clique->gradientContribution().rows() == variablePosition;
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EXPECT(dimOk);
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nodeGradientsOk = nodeGradientsOk && dimOk;
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}
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// Check gradient
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VectorValues expectedGradient(*allocateVectorValues(isam));
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gradientAtZero(FactorGraph<JacobianFactor>(isam), expectedGradient);
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VectorValues expectedGradient2(gradient(FactorGraph<JacobianFactor>(isam), VectorValues::Zero(expectedGradient)));
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VectorValues actualGradient(*allocateVectorValues(isam));
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gradientAtZero(isam, actualGradient);
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bool expectedGradOk = assert_equal(expectedGradient2, expectedGradient);
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EXPECT(expectedGradOk);
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bool totalGradOk = assert_equal(expectedGradient, actualGradient);
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EXPECT(totalGradOk);
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return nodeGradientsOk && expectedGradOk && totalGradOk && isamEqual;
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}
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/* ************************************************************************* */
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TEST(ISAM2, slamlike_solution_gaussnewton)
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{
|
|
// These variables will be reused and accumulate factors and values
|
|
Values fullinit;
|
|
NonlinearFactorGraph fullgraph;
|
|
ISAM2 isam = createSlamlikeISAM2(fullinit, fullgraph, ISAM2Params(ISAM2GaussNewtonParams(0.001), 0.0, 0, false));
|
|
|
|
// Compare solutions
|
|
CHECK(isam_check(fullgraph, fullinit, isam, *this, result_));
|
|
}
|
|
|
|
/* ************************************************************************* */
|
|
TEST(ISAM2, slamlike_solution_dogleg)
|
|
{
|
|
// These variables will be reused and accumulate factors and values
|
|
Values fullinit;
|
|
NonlinearFactorGraph fullgraph;
|
|
ISAM2 isam = createSlamlikeISAM2(fullinit, fullgraph, ISAM2Params(ISAM2DoglegParams(1.0), 0.0, 0, false));
|
|
|
|
// Compare solutions
|
|
CHECK(isam_check(fullgraph, fullinit, isam, *this, result_));
|
|
}
|
|
|
|
/* ************************************************************************* */
|
|
TEST(ISAM2, slamlike_solution_gaussnewton_qr)
|
|
{
|
|
// These variables will be reused and accumulate factors and values
|
|
Values fullinit;
|
|
NonlinearFactorGraph fullgraph;
|
|
ISAM2 isam = createSlamlikeISAM2(fullinit, fullgraph, ISAM2Params(ISAM2GaussNewtonParams(0.001), 0.0, 0, false, false, ISAM2Params::QR));
|
|
|
|
// Compare solutions
|
|
CHECK(isam_check(fullgraph, fullinit, isam, *this, result_));
|
|
}
|
|
|
|
/* ************************************************************************* */
|
|
TEST(ISAM2, slamlike_solution_dogleg_qr)
|
|
{
|
|
// These variables will be reused and accumulate factors and values
|
|
Values fullinit;
|
|
NonlinearFactorGraph fullgraph;
|
|
ISAM2 isam = createSlamlikeISAM2(fullinit, fullgraph, ISAM2Params(ISAM2DoglegParams(1.0), 0.0, 0, false, false, ISAM2Params::QR));
|
|
|
|
// Compare solutions
|
|
CHECK(isam_check(fullgraph, fullinit, isam, *this, result_));
|
|
}
|
|
|
|
/* ************************************************************************* */
|
|
TEST(ISAM2, clone) {
|
|
|
|
ISAM2 clone1;
|
|
|
|
{
|
|
ISAM2 isam = createSlamlikeISAM2();
|
|
clone1 = isam;
|
|
|
|
ISAM2 clone2(isam);
|
|
|
|
// Modify original isam
|
|
NonlinearFactorGraph factors;
|
|
factors.add(BetweenFactor<Pose2>(0, 10,
|
|
isam.calculateEstimate<Pose2>(0).between(isam.calculateEstimate<Pose2>(10)), noiseModel::Unit::Create(3)));
|
|
isam.update(factors);
|
|
|
|
CHECK(assert_equal(createSlamlikeISAM2(), clone2));
|
|
}
|
|
|
|
// This is to (perhaps unsuccessfully) try to currupt unallocated memory referenced
|
|
// if the references in the iSAM2 copy point to the old instance which deleted at
|
|
// the end of the {...} section above.
|
|
ISAM2 temp = createSlamlikeISAM2();
|
|
|
|
CHECK(assert_equal(createSlamlikeISAM2(), clone1));
|
|
CHECK(assert_equal(clone1, temp));
|
|
|
|
// Check clone empty
|
|
ISAM2 isam;
|
|
clone1 = isam;
|
|
CHECK(assert_equal(ISAM2(), clone1));
|
|
}
|
|
|
|
/* ************************************************************************* */
|
|
TEST(ISAM2, permute_cached) {
|
|
typedef boost::shared_ptr<ISAM2Clique> sharedISAM2Clique;
|
|
|
|
// Construct expected permuted BayesTree (variable 2 has been changed to 1)
|
|
BayesTree<GaussianConditional, ISAM2Clique> expected;
|
|
expected.insert(sharedISAM2Clique(new ISAM2Clique(make_pair(
|
|
boost::make_shared<GaussianConditional>(pair_list_of
|
|
(3, Matrix_(1,1,1.0))
|
|
(4, Matrix_(1,1,2.0)),
|
|
2, Vector_(1,1.0), Vector_(1,1.0)), // p(3,4)
|
|
HessianFactor::shared_ptr())))); // Cached: empty
|
|
expected.insert(sharedISAM2Clique(new ISAM2Clique(make_pair(
|
|
boost::make_shared<GaussianConditional>(pair_list_of
|
|
(2, Matrix_(1,1,1.0))
|
|
(3, Matrix_(1,1,2.0)),
|
|
1, Vector_(1,1.0), Vector_(1,1.0)), // p(2|3)
|
|
boost::make_shared<HessianFactor>(3, Matrix_(1,1,1.0), Vector_(1,1.0), 0.0))))); // Cached: p(3)
|
|
expected.insert(sharedISAM2Clique(new ISAM2Clique(make_pair(
|
|
boost::make_shared<GaussianConditional>(pair_list_of
|
|
(0, Matrix_(1,1,1.0))
|
|
(2, Matrix_(1,1,2.0)),
|
|
1, Vector_(1,1.0), Vector_(1,1.0)), // p(0|2)
|
|
boost::make_shared<HessianFactor>(1, Matrix_(1,1,1.0), Vector_(1,1.0), 0.0))))); // Cached: p(1)
|
|
// Change variable 2 to 1
|
|
expected.root()->children().front()->conditional()->keys()[0] = 1;
|
|
expected.root()->children().front()->children().front()->conditional()->keys()[1] = 1;
|
|
|
|
// Construct unpermuted BayesTree
|
|
BayesTree<GaussianConditional, ISAM2Clique> actual;
|
|
actual.insert(sharedISAM2Clique(new ISAM2Clique(make_pair(
|
|
boost::make_shared<GaussianConditional>(pair_list_of
|
|
(3, Matrix_(1,1,1.0))
|
|
(4, Matrix_(1,1,2.0)),
|
|
2, Vector_(1,1.0), Vector_(1,1.0)), // p(3,4)
|
|
HessianFactor::shared_ptr())))); // Cached: empty
|
|
actual.insert(sharedISAM2Clique(new ISAM2Clique(make_pair(
|
|
boost::make_shared<GaussianConditional>(pair_list_of
|
|
(2, Matrix_(1,1,1.0))
|
|
(3, Matrix_(1,1,2.0)),
|
|
1, Vector_(1,1.0), Vector_(1,1.0)), // p(2|3)
|
|
boost::make_shared<HessianFactor>(3, Matrix_(1,1,1.0), Vector_(1,1.0), 0.0))))); // Cached: p(3)
|
|
actual.insert(sharedISAM2Clique(new ISAM2Clique(make_pair(
|
|
boost::make_shared<GaussianConditional>(pair_list_of
|
|
(0, Matrix_(1,1,1.0))
|
|
(2, Matrix_(1,1,2.0)),
|
|
1, Vector_(1,1.0), Vector_(1,1.0)), // p(0|2)
|
|
boost::make_shared<HessianFactor>(2, Matrix_(1,1,1.0), Vector_(1,1.0), 0.0))))); // Cached: p(2)
|
|
|
|
// Create permutation that changes variable 2 -> 0
|
|
Permutation permutation = Permutation::Identity(5);
|
|
permutation[2] = 1;
|
|
|
|
// Permute BayesTree
|
|
actual.root()->permuteWithInverse(permutation);
|
|
|
|
// Check
|
|
EXPECT(assert_equal(expected, actual));
|
|
}
|
|
|
|
/* ************************************************************************* */
|
|
TEST(ISAM2, removeFactors)
|
|
{
|
|
// This test builds a graph in the same way as the "slamlike" test above, but
|
|
// then removes the 2nd-to-last landmark measurement
|
|
|
|
// These variables will be reused and accumulate factors and values
|
|
Values fullinit;
|
|
NonlinearFactorGraph fullgraph;
|
|
ISAM2 isam = createSlamlikeISAM2(fullinit, fullgraph, ISAM2Params(ISAM2GaussNewtonParams(0.001), 0.0, 0, false));
|
|
|
|
// Remove the 2nd measurement on landmark 0 (Key 100)
|
|
FastVector<size_t> toRemove;
|
|
toRemove.push_back(12);
|
|
isam.update(NonlinearFactorGraph(), Values(), toRemove);
|
|
|
|
// Remove the factor from the full system
|
|
fullgraph.remove(12);
|
|
|
|
// Compare solutions
|
|
CHECK(isam_check(fullgraph, fullinit, isam, *this, result_));
|
|
}
|
|
|
|
/* ************************************************************************* */
|
|
TEST_UNSAFE(ISAM2, removeVariables)
|
|
{
|
|
// These variables will be reused and accumulate factors and values
|
|
Values fullinit;
|
|
NonlinearFactorGraph fullgraph;
|
|
ISAM2 isam = createSlamlikeISAM2(fullinit, fullgraph, ISAM2Params(ISAM2GaussNewtonParams(0.001), 0.0, 0, false));
|
|
|
|
// Remove the measurement on landmark 0 (Key 100)
|
|
FastVector<size_t> toRemove;
|
|
toRemove.push_back(7);
|
|
toRemove.push_back(14);
|
|
isam.update(NonlinearFactorGraph(), Values(), toRemove);
|
|
|
|
// Remove the factors and variable from the full system
|
|
fullgraph.remove(7);
|
|
fullgraph.remove(14);
|
|
fullinit.erase(100);
|
|
|
|
// Compare solutions
|
|
CHECK(isam_check(fullgraph, fullinit, isam, *this, result_));
|
|
}
|
|
|
|
/* ************************************************************************* */
|
|
TEST_UNSAFE(ISAM2, swapFactors)
|
|
{
|
|
// This test builds a graph in the same way as the "slamlike" test above, but
|
|
// then swaps the 2nd-to-last landmark measurement with a different one
|
|
|
|
Values fullinit;
|
|
NonlinearFactorGraph fullgraph;
|
|
ISAM2 isam = createSlamlikeISAM2(fullinit, fullgraph);
|
|
|
|
// Remove the measurement on landmark 0 and replace with a different one
|
|
{
|
|
size_t swap_idx = isam.getFactorsUnsafe().size()-2;
|
|
FastVector<size_t> toRemove;
|
|
toRemove.push_back(swap_idx);
|
|
fullgraph.remove(swap_idx);
|
|
|
|
NonlinearFactorGraph swapfactors;
|
|
// swapfactors.add(BearingRange<Pose2,Point2>(10, 100, Rot2::fromAngle(M_PI/4.0 + M_PI/16.0), 4.5, brNoise); // original factor
|
|
swapfactors.add(BearingRangeFactor<Pose2,Point2>(10, 100, Rot2::fromAngle(M_PI/4.0 + M_PI/16.0), 5.0, brNoise));
|
|
fullgraph.push_back(swapfactors);
|
|
isam.update(swapfactors, Values(), toRemove);
|
|
}
|
|
|
|
// Compare solutions
|
|
EXPECT(assert_equal(fullgraph, NonlinearFactorGraph(isam.getFactorsUnsafe())));
|
|
EXPECT(isam_check(fullgraph, fullinit, isam, *this, result_));
|
|
|
|
// Check gradient at each node
|
|
typedef ISAM2::sharedClique sharedClique;
|
|
BOOST_FOREACH(const sharedClique& clique, isam.nodes()) {
|
|
// Compute expected gradient
|
|
FactorGraph<JacobianFactor> jfg;
|
|
jfg.push_back(JacobianFactor::shared_ptr(new JacobianFactor(*clique->conditional())));
|
|
VectorValues expectedGradient(*allocateVectorValues(isam));
|
|
gradientAtZero(jfg, expectedGradient);
|
|
// Compare with actual gradients
|
|
int variablePosition = 0;
|
|
for(GaussianConditional::const_iterator jit = clique->conditional()->begin(); jit != clique->conditional()->end(); ++jit) {
|
|
const int dim = clique->conditional()->dim(jit);
|
|
Vector actual = clique->gradientContribution().segment(variablePosition, dim);
|
|
EXPECT(assert_equal(expectedGradient[*jit], actual));
|
|
variablePosition += dim;
|
|
}
|
|
EXPECT_LONGS_EQUAL(clique->gradientContribution().rows(), variablePosition);
|
|
}
|
|
|
|
// Check gradient
|
|
VectorValues expectedGradient(*allocateVectorValues(isam));
|
|
gradientAtZero(FactorGraph<JacobianFactor>(isam), expectedGradient);
|
|
VectorValues expectedGradient2(gradient(FactorGraph<JacobianFactor>(isam), VectorValues::Zero(expectedGradient)));
|
|
VectorValues actualGradient(*allocateVectorValues(isam));
|
|
gradientAtZero(isam, actualGradient);
|
|
EXPECT(assert_equal(expectedGradient2, expectedGradient));
|
|
EXPECT(assert_equal(expectedGradient, actualGradient));
|
|
}
|
|
|
|
/* ************************************************************************* */
|
|
TEST(ISAM2, constrained_ordering)
|
|
{
|
|
// These variables will be reused and accumulate factors and values
|
|
ISAM2 isam(ISAM2Params(ISAM2GaussNewtonParams(0.001), 0.0, 0, false));
|
|
Values fullinit;
|
|
NonlinearFactorGraph fullgraph;
|
|
|
|
// We will constrain x3 and x4 to the end
|
|
FastMap<Key, int> constrained;
|
|
constrained.insert(make_pair((3), 1));
|
|
constrained.insert(make_pair((4), 2));
|
|
|
|
// i keeps track of the time step
|
|
size_t i = 0;
|
|
|
|
// Add a prior at time 0 and update isam
|
|
{
|
|
NonlinearFactorGraph newfactors;
|
|
newfactors.add(PriorFactor<Pose2>(0, Pose2(0.0, 0.0, 0.0), odoNoise));
|
|
fullgraph.push_back(newfactors);
|
|
|
|
Values init;
|
|
init.insert((0), Pose2(0.01, 0.01, 0.01));
|
|
fullinit.insert((0), Pose2(0.01, 0.01, 0.01));
|
|
|
|
isam.update(newfactors, init);
|
|
}
|
|
|
|
CHECK(isam_check(fullgraph, fullinit, isam, *this, result_));
|
|
|
|
// Add odometry from time 0 to time 5
|
|
for( ; i<5; ++i) {
|
|
NonlinearFactorGraph newfactors;
|
|
newfactors.add(BetweenFactor<Pose2>(i, i+1, Pose2(1.0, 0.0, 0.0), odoNoise));
|
|
fullgraph.push_back(newfactors);
|
|
|
|
Values init;
|
|
init.insert((i+1), Pose2(double(i+1)+0.1, -0.1, 0.01));
|
|
fullinit.insert((i+1), Pose2(double(i+1)+0.1, -0.1, 0.01));
|
|
|
|
if(i >= 3)
|
|
isam.update(newfactors, init, FastVector<size_t>(), constrained);
|
|
else
|
|
isam.update(newfactors, init);
|
|
}
|
|
|
|
// Add odometry from time 5 to 6 and landmark measurement at time 5
|
|
{
|
|
NonlinearFactorGraph newfactors;
|
|
newfactors.add(BetweenFactor<Pose2>(i, i+1, Pose2(1.0, 0.0, 0.0), odoNoise));
|
|
newfactors.add(BearingRangeFactor<Pose2,Point2>(i, 100, Rot2::fromAngle(M_PI/4.0), 5.0, brNoise));
|
|
newfactors.add(BearingRangeFactor<Pose2,Point2>(i, 101, Rot2::fromAngle(-M_PI/4.0), 5.0, brNoise));
|
|
fullgraph.push_back(newfactors);
|
|
|
|
Values init;
|
|
init.insert((i+1), Pose2(1.01, 0.01, 0.01));
|
|
init.insert(100, Point2(5.0/sqrt(2.0), 5.0/sqrt(2.0)));
|
|
init.insert(101, Point2(5.0/sqrt(2.0), -5.0/sqrt(2.0)));
|
|
fullinit.insert((i+1), Pose2(1.01, 0.01, 0.01));
|
|
fullinit.insert(100, Point2(5.0/sqrt(2.0), 5.0/sqrt(2.0)));
|
|
fullinit.insert(101, Point2(5.0/sqrt(2.0), -5.0/sqrt(2.0)));
|
|
|
|
isam.update(newfactors, init, FastVector<size_t>(), constrained);
|
|
++ i;
|
|
}
|
|
|
|
// Add odometry from time 6 to time 10
|
|
for( ; i<10; ++i) {
|
|
NonlinearFactorGraph newfactors;
|
|
newfactors.add(BetweenFactor<Pose2>(i, i+1, Pose2(1.0, 0.0, 0.0), odoNoise));
|
|
fullgraph.push_back(newfactors);
|
|
|
|
Values init;
|
|
init.insert((i+1), Pose2(double(i+1)+0.1, -0.1, 0.01));
|
|
fullinit.insert((i+1), Pose2(double(i+1)+0.1, -0.1, 0.01));
|
|
|
|
isam.update(newfactors, init, FastVector<size_t>(), constrained);
|
|
}
|
|
|
|
// Add odometry from time 10 to 11 and landmark measurement at time 10
|
|
{
|
|
NonlinearFactorGraph newfactors;
|
|
newfactors.add(BetweenFactor<Pose2>(i, i+1, Pose2(1.0, 0.0, 0.0), odoNoise));
|
|
newfactors.add(BearingRangeFactor<Pose2,Point2>(i, 100, Rot2::fromAngle(M_PI/4.0 + M_PI/16.0), 4.5, brNoise));
|
|
newfactors.add(BearingRangeFactor<Pose2,Point2>(i, 101, Rot2::fromAngle(-M_PI/4.0 + M_PI/16.0), 4.5, brNoise));
|
|
fullgraph.push_back(newfactors);
|
|
|
|
Values init;
|
|
init.insert((i+1), Pose2(6.9, 0.1, 0.01));
|
|
fullinit.insert((i+1), Pose2(6.9, 0.1, 0.01));
|
|
|
|
isam.update(newfactors, init, FastVector<size_t>(), constrained);
|
|
++ i;
|
|
}
|
|
|
|
// Compare solutions
|
|
EXPECT(isam_check(fullgraph, fullinit, isam, *this, result_));
|
|
|
|
// Check that x3 and x4 are last, but either can come before the other
|
|
EXPECT(isam.getOrdering()[(3)] == 12 && isam.getOrdering()[(4)] == 13);
|
|
|
|
// Check gradient at each node
|
|
typedef ISAM2::sharedClique sharedClique;
|
|
BOOST_FOREACH(const sharedClique& clique, isam.nodes()) {
|
|
// Compute expected gradient
|
|
FactorGraph<JacobianFactor> jfg;
|
|
jfg.push_back(JacobianFactor::shared_ptr(new JacobianFactor(*clique->conditional())));
|
|
VectorValues expectedGradient(*allocateVectorValues(isam));
|
|
gradientAtZero(jfg, expectedGradient);
|
|
// Compare with actual gradients
|
|
int variablePosition = 0;
|
|
for(GaussianConditional::const_iterator jit = clique->conditional()->begin(); jit != clique->conditional()->end(); ++jit) {
|
|
const int dim = clique->conditional()->dim(jit);
|
|
Vector actual = clique->gradientContribution().segment(variablePosition, dim);
|
|
EXPECT(assert_equal(expectedGradient[*jit], actual));
|
|
variablePosition += dim;
|
|
}
|
|
LONGS_EQUAL(clique->gradientContribution().rows(), variablePosition);
|
|
}
|
|
|
|
// Check gradient
|
|
VectorValues expectedGradient(*allocateVectorValues(isam));
|
|
gradientAtZero(FactorGraph<JacobianFactor>(isam), expectedGradient);
|
|
VectorValues expectedGradient2(gradient(FactorGraph<JacobianFactor>(isam), VectorValues::Zero(expectedGradient)));
|
|
VectorValues actualGradient(*allocateVectorValues(isam));
|
|
gradientAtZero(isam, actualGradient);
|
|
EXPECT(assert_equal(expectedGradient2, expectedGradient));
|
|
EXPECT(assert_equal(expectedGradient, actualGradient));
|
|
}
|
|
|
|
/* ************************************************************************* */
|
|
TEST(ISAM2, slamlike_solution_partial_relinearization_check)
|
|
{
|
|
// These variables will be reused and accumulate factors and values
|
|
Values fullinit;
|
|
NonlinearFactorGraph fullgraph;
|
|
ISAM2Params params(ISAM2GaussNewtonParams(0.001), 0.0, 0, false);
|
|
params.enablePartialRelinearizationCheck = true;
|
|
ISAM2 isam = createSlamlikeISAM2(fullinit, fullgraph, params);
|
|
|
|
// Compare solutions
|
|
CHECK(isam_check(fullgraph, fullinit, isam, *this, result_));
|
|
}
|
|
|
|
namespace {
|
|
bool checkMarginalizeLeaves(ISAM2& isam, const FastList<Key>& leafKeys) {
|
|
Matrix expectedAugmentedHessian, expected3AugmentedHessian;
|
|
vector<Index> toKeep;
|
|
const Index lastVar = isam.getOrdering().size() - 1;
|
|
for(Index i=0; i<=lastVar; ++i)
|
|
if(find(leafKeys.begin(), leafKeys.end(), isam.getOrdering().key(i)) == leafKeys.end())
|
|
toKeep.push_back(i);
|
|
|
|
// Calculate expected marginal from iSAM2 tree
|
|
GaussianFactorGraph isamAsGraph(isam);
|
|
GaussianSequentialSolver solver(isamAsGraph);
|
|
GaussianFactorGraph marginalgfg = *solver.jointFactorGraph(toKeep);
|
|
expectedAugmentedHessian = marginalgfg.augmentedHessian();
|
|
|
|
//// Calculate expected marginal from cached linear factors
|
|
//assert(isam.params().cacheLinearizedFactors);
|
|
//GaussianSequentialSolver solver2(isam.linearFactors_, isam.params().factorization == ISAM2Params::QR);
|
|
//expected2AugmentedHessian = solver2.jointFactorGraph(toKeep)->augmentedHessian();
|
|
|
|
// Calculate expected marginal from original nonlinear factors
|
|
GaussianSequentialSolver solver3(
|
|
*isam.getFactorsUnsafe().linearize(isam.getLinearizationPoint(), isam.getOrdering()),
|
|
isam.params().factorization == ISAM2Params::QR);
|
|
expected3AugmentedHessian = solver3.jointFactorGraph(toKeep)->augmentedHessian();
|
|
|
|
// Do marginalization
|
|
isam.marginalizeLeaves(leafKeys);
|
|
|
|
// Check
|
|
GaussianFactorGraph actualMarginalGraph(isam);
|
|
Matrix actualAugmentedHessian = actualMarginalGraph.augmentedHessian();
|
|
//Matrix actual2AugmentedHessian = linearFactors_.augmentedHessian();
|
|
Matrix actual3AugmentedHessian = isam.getFactorsUnsafe().linearize(
|
|
isam.getLinearizationPoint(), isam.getOrdering())->augmentedHessian();
|
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assert(actualAugmentedHessian.unaryExpr(std::ptr_fun(&std::isfinite<double>)).all());
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// Check full marginalization
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//cout << "treeEqual" << endl;
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bool treeEqual = assert_equal(expectedAugmentedHessian, actualAugmentedHessian, 1e-6);
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//bool linEqual = assert_equal(expected2AugmentedHessian, actualAugmentedHessian, 1e-6);
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//cout << "nonlinEqual" << endl;
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bool nonlinEqual = assert_equal(expected3AugmentedHessian, actualAugmentedHessian, 1e-6);
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//bool linCorrect = assert_equal(expected3AugmentedHessian, expected2AugmentedHessian, 1e-6);
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//actual2AugmentedHessian.bottomRightCorner(1,1) = expected3AugmentedHessian.bottomRightCorner(1,1); bool afterLinCorrect = assert_equal(expected3AugmentedHessian, actual2AugmentedHessian, 1e-6);
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//cout << "nonlinCorrect" << endl;
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actual3AugmentedHessian.bottomRightCorner(1,1) = expected3AugmentedHessian.bottomRightCorner(1,1); bool afterNonlinCorrect = assert_equal(expected3AugmentedHessian, actual3AugmentedHessian, 1e-6);
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|
|
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bool ok = treeEqual && /*linEqual &&*/ nonlinEqual && /*linCorrect &&*/ /*afterLinCorrect &&*/ afterNonlinCorrect;
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return ok;
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}
|
|
}
|
|
|
|
/* ************************************************************************* */
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TEST_UNSAFE(ISAM2, marginalizeLeaves1)
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|
{
|
|
ISAM2 isam;
|
|
|
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NonlinearFactorGraph factors;
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factors.add(PriorFactor<LieVector>(0, LieVector(0.0), noiseModel::Unit::Create(1)));
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|
|
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factors.add(BetweenFactor<LieVector>(0, 1, LieVector(0.0), noiseModel::Unit::Create(1)));
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factors.add(BetweenFactor<LieVector>(1, 2, LieVector(0.0), noiseModel::Unit::Create(1)));
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factors.add(BetweenFactor<LieVector>(0, 2, LieVector(0.0), noiseModel::Unit::Create(1)));
|
|
|
|
Values values;
|
|
values.insert(0, LieVector(0.0));
|
|
values.insert(1, LieVector(0.0));
|
|
values.insert(2, LieVector(0.0));
|
|
|
|
FastMap<Key,int> constrainedKeys;
|
|
constrainedKeys.insert(make_pair(0,0));
|
|
constrainedKeys.insert(make_pair(1,1));
|
|
constrainedKeys.insert(make_pair(2,2));
|
|
|
|
isam.update(factors, values, FastVector<size_t>(), constrainedKeys);
|
|
|
|
FastList<Key> leafKeys;
|
|
leafKeys.push_back(isam.getOrdering().key(0));
|
|
EXPECT(checkMarginalizeLeaves(isam, leafKeys));
|
|
}
|
|
|
|
/* ************************************************************************* */
|
|
TEST_UNSAFE(ISAM2, marginalizeLeaves2)
|
|
{
|
|
ISAM2 isam;
|
|
|
|
NonlinearFactorGraph factors;
|
|
factors.add(PriorFactor<LieVector>(0, LieVector(0.0), noiseModel::Unit::Create(1)));
|
|
|
|
factors.add(BetweenFactor<LieVector>(0, 1, LieVector(0.0), noiseModel::Unit::Create(1)));
|
|
factors.add(BetweenFactor<LieVector>(1, 2, LieVector(0.0), noiseModel::Unit::Create(1)));
|
|
factors.add(BetweenFactor<LieVector>(0, 2, LieVector(0.0), noiseModel::Unit::Create(1)));
|
|
factors.add(BetweenFactor<LieVector>(2, 3, LieVector(0.0), noiseModel::Unit::Create(1)));
|
|
|
|
Values values;
|
|
values.insert(0, LieVector(0.0));
|
|
values.insert(1, LieVector(0.0));
|
|
values.insert(2, LieVector(0.0));
|
|
values.insert(3, LieVector(0.0));
|
|
|
|
FastMap<Key,int> constrainedKeys;
|
|
constrainedKeys.insert(make_pair(0,0));
|
|
constrainedKeys.insert(make_pair(1,1));
|
|
constrainedKeys.insert(make_pair(2,2));
|
|
constrainedKeys.insert(make_pair(3,3));
|
|
|
|
isam.update(factors, values, FastVector<size_t>(), constrainedKeys);
|
|
|
|
FastList<Key> leafKeys;
|
|
leafKeys.push_back(isam.getOrdering().key(0));
|
|
EXPECT(checkMarginalizeLeaves(isam, leafKeys));
|
|
}
|
|
|
|
/* ************************************************************************* */
|
|
TEST_UNSAFE(ISAM2, marginalizeLeaves3)
|
|
{
|
|
ISAM2 isam;
|
|
|
|
NonlinearFactorGraph factors;
|
|
factors.add(PriorFactor<LieVector>(0, LieVector(0.0), noiseModel::Unit::Create(1)));
|
|
|
|
factors.add(BetweenFactor<LieVector>(0, 1, LieVector(0.0), noiseModel::Unit::Create(1)));
|
|
factors.add(BetweenFactor<LieVector>(1, 2, LieVector(0.0), noiseModel::Unit::Create(1)));
|
|
factors.add(BetweenFactor<LieVector>(0, 2, LieVector(0.0), noiseModel::Unit::Create(1)));
|
|
|
|
factors.add(BetweenFactor<LieVector>(2, 3, LieVector(0.0), noiseModel::Unit::Create(1)));
|
|
|
|
factors.add(BetweenFactor<LieVector>(3, 4, LieVector(0.0), noiseModel::Unit::Create(1)));
|
|
factors.add(BetweenFactor<LieVector>(4, 5, LieVector(0.0), noiseModel::Unit::Create(1)));
|
|
factors.add(BetweenFactor<LieVector>(3, 5, LieVector(0.0), noiseModel::Unit::Create(1)));
|
|
|
|
Values values;
|
|
values.insert(0, LieVector(0.0));
|
|
values.insert(1, LieVector(0.0));
|
|
values.insert(2, LieVector(0.0));
|
|
values.insert(3, LieVector(0.0));
|
|
values.insert(4, LieVector(0.0));
|
|
values.insert(5, LieVector(0.0));
|
|
|
|
FastMap<Key,int> constrainedKeys;
|
|
constrainedKeys.insert(make_pair(0,0));
|
|
constrainedKeys.insert(make_pair(1,1));
|
|
constrainedKeys.insert(make_pair(2,2));
|
|
constrainedKeys.insert(make_pair(3,3));
|
|
constrainedKeys.insert(make_pair(4,4));
|
|
constrainedKeys.insert(make_pair(5,5));
|
|
|
|
isam.update(factors, values, FastVector<size_t>(), constrainedKeys);
|
|
|
|
FastList<Key> leafKeys;
|
|
leafKeys.push_back(isam.getOrdering().key(0));
|
|
EXPECT(checkMarginalizeLeaves(isam, leafKeys));
|
|
}
|
|
|
|
/* ************************************************************************* */
|
|
TEST_UNSAFE(ISAM2, marginalizeLeaves4)
|
|
{
|
|
ISAM2 isam;
|
|
|
|
NonlinearFactorGraph factors;
|
|
factors.add(PriorFactor<LieVector>(0, LieVector(0.0), noiseModel::Unit::Create(1)));
|
|
factors.add(BetweenFactor<LieVector>(0, 2, LieVector(0.0), noiseModel::Unit::Create(1)));
|
|
factors.add(BetweenFactor<LieVector>(1, 2, LieVector(0.0), noiseModel::Unit::Create(1)));
|
|
|
|
Values values;
|
|
values.insert(0, LieVector(0.0));
|
|
values.insert(1, LieVector(0.0));
|
|
values.insert(2, LieVector(0.0));
|
|
|
|
FastMap<Key,int> constrainedKeys;
|
|
constrainedKeys.insert(make_pair(0,0));
|
|
constrainedKeys.insert(make_pair(1,1));
|
|
constrainedKeys.insert(make_pair(2,2));
|
|
|
|
isam.update(factors, values, FastVector<size_t>(), constrainedKeys);
|
|
|
|
FastList<Key> leafKeys;
|
|
leafKeys.push_back(isam.getOrdering().key(1));
|
|
EXPECT(checkMarginalizeLeaves(isam, leafKeys));
|
|
}
|
|
|
|
/* ************************************************************************* */
|
|
TEST_UNSAFE(ISAM2, marginalizeLeaves5)
|
|
{
|
|
// Create isam2
|
|
ISAM2 isam = createSlamlikeISAM2();
|
|
|
|
// Marginalize
|
|
FastList<Key> marginalizeKeys;
|
|
marginalizeKeys.push_back(isam.getOrdering().key(0));
|
|
EXPECT(checkMarginalizeLeaves(isam, marginalizeKeys));
|
|
}
|
|
|
|
/* ************************************************************************* */
|
|
int main() { TestResult tr; return TestRegistry::runAllTests(tr);}
|
|
/* ************************************************************************* */
|