salvaged old test
Frank Dellaert
parent
47279b56e0
commit
6727ae9ae5
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@ -15,29 +15,42 @@
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* @author nikai
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*/
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#include <CppUnitLite/TestHarness.h>
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#if 0
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#include <tests/smallExample.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 <gtsam/slam/BetweenFactor.h>
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#include <gtsam/slam/PriorFactor.h>
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#include <gtsam/geometry/Pose2.h>
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#include <gtsam/nonlinear/NonlinearFactorGraph.h>
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#include <gtsam/nonlinear/Values.h>
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#include <gtsam/nonlinear/Ordering.h>
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#include <gtsam/inference/Symbol.h>
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#include <gtsam/linear/GaussianBayesNet.h>
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#include <gtsam/linear/GaussianConditional.h>
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#include <gtsam/linear/GaussianFactor.h>
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#include <gtsam/linear/GaussianFactorGraph.h>
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#include <gtsam/linear/GaussianEliminationTree.h>
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#include <gtsam/linear/GaussianJunctionTree.h>
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#include <gtsam/linear/HessianFactor.h>
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#include <gtsam/linear/JacobianFactor.h>
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#include <gtsam/linear/NoiseModel.h>
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#include <gtsam/linear/VectorValues.h>
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#include <gtsam/symbolic/SymbolicEliminationTree.h>
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#include <gtsam/inference/BayesTree.h>
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#include <gtsam/geometry/Pose2.h>
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#include <gtsam/base/TestableAssertions.h>
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#include <gtsam/base/debug.h>
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#include <gtsam/base/cholesky.h>
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#include <gtsam/inference/ClusterTree.h>
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#include <gtsam/inference/Ordering.h>
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#include <gtsam/inference/Symbol.h>
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#include <gtsam/base/Matrix.h>
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#include <gtsam/base/Testable.h>
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#include <boost/assign/list_of.hpp>
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#include <boost/assign/std/list.hpp> // for operator +=
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#include <boost/assign/std/set.hpp> // for operator +=
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#include <CppUnitLite/TestHarness.h>
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#include <boost/shared_ptr.hpp>
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#include <boost/tuple/tuple.hpp>
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#include <boost/assign/std/vector.hpp>
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#include <cmath>
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#include <list>
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#include <utility>
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#include <vector>
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using namespace boost::assign;
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#include <iostream>
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@ -59,180 +72,188 @@ using symbol_shorthand::L;
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TEST( GaussianJunctionTreeB, constructor2 )
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{
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// create a graph
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NonlinearFactorGraph nlfg;
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Values values;
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boost::tie(nlfg,values) = createNonlinearSmoother(7);
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SymbolicFactorGraph::shared_ptr symbolic = nlfg.symbolic();
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// linearize
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GaussianFactorGraph::shared_ptr fg = nlfg.linearize(values);
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Ordering ordering; ordering += X(1),X(3),X(5),X(7),X(2),X(6),X(4);
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GaussianFactorGraph fg = createSmoother(7, ordering).first;
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// create an ordering
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GaussianJunctionTree actual(fg);
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GaussianEliminationTree etree(*fg, ordering);
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SymbolicEliminationTree stree(*symbolic,ordering);
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GaussianJunctionTree actual(etree);
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vector<Index> frontal1; frontal1 += ordering[X(5)], ordering[X(6)], ordering[X(4)];
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vector<Index> frontal2; frontal2 += ordering[X(3)], ordering[X(2)];
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vector<Index> frontal3; frontal3 += ordering[X(1)];
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vector<Index> frontal4; frontal4 += ordering[X(7)];
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vector<Index> sep1;
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vector<Index> sep2; sep2 += ordering[X(4)];
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vector<Index> sep3; sep3 += ordering[X(2)];
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vector<Index> sep4; sep4 += ordering[X(6)];
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EXPECT(assert_equal(frontal1, actual.root()->frontal));
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EXPECT(assert_equal(sep1, actual.root()->separator));
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LONGS_EQUAL(5, actual.root()->size());
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list<GaussianJunctionTree::sharedClique>::const_iterator child0it = actual.root()->children().begin();
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list<GaussianJunctionTree::sharedClique>::const_iterator child1it = child0it; ++child1it;
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GaussianJunctionTree::sharedClique child0 = *child0it;
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GaussianJunctionTree::sharedClique child1 = *child1it;
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EXPECT(assert_equal(frontal2, child0->frontal));
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EXPECT(assert_equal(sep2, child0->separator));
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LONGS_EQUAL(4, child0->size());
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EXPECT(assert_equal(frontal3, child0->children().front()->frontal));
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EXPECT(assert_equal(sep3, child0->children().front()->separator));
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LONGS_EQUAL(2, child0->children().front()->size());
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EXPECT(assert_equal(frontal4, child1->frontal));
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EXPECT(assert_equal(sep4, child1->separator));
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LONGS_EQUAL(2, child1->size());
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Ordering frontal1; frontal1 += X(3), X(2), X(4);
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Ordering frontal2; frontal2 += X(5), X(6);
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Ordering frontal3; frontal3 += X(7);
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Ordering frontal4; frontal4 += X(1);
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// Can no longer test these:
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// Ordering sep1;
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// Ordering sep2; sep2 += X(4);
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// Ordering sep3; sep3 += X(6);
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// Ordering sep4; sep4 += X(2);
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GaussianJunctionTree::sharedNode root = actual.roots().front();
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FastVector<GaussianJunctionTree::sharedNode>::const_iterator child0it = root->children.begin();
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FastVector<GaussianJunctionTree::sharedNode>::const_iterator child1it = child0it; ++child1it;
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GaussianJunctionTree::sharedNode child0 = *child0it;
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GaussianJunctionTree::sharedNode child1 = *child1it;
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EXPECT(assert_equal(frontal1, root->orderedFrontalKeys));
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LONGS_EQUAL(5, root->factors.size());
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EXPECT(assert_equal(frontal2, child0->orderedFrontalKeys));
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LONGS_EQUAL(4, child0->factors.size());
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EXPECT(assert_equal(frontal3, child0->children.front()->orderedFrontalKeys));
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LONGS_EQUAL(2, child0->children.front()->factors.size());
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EXPECT(assert_equal(frontal4, child1->orderedFrontalKeys));
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LONGS_EQUAL(2, child1->factors.size());
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}
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/* ************************************************************************* */
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TEST( GaussianJunctionTreeB, optimizeMultiFrontal )
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{
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// create a graph
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GaussianFactorGraph fg;
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Ordering ordering;
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boost::tie(fg,ordering) = createSmoother(7);
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// optimize the graph
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GaussianJunctionTree tree(fg);
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VectorValues actual = tree.optimize(&EliminateQR);
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// verify
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VectorValues expected(vector<size_t>(7,2)); // expected solution
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Vector v = Vector2(0., 0.);
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for (int i=1; i<=7; i++)
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expected[ordering[X(i)]] = v;
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EXPECT(assert_equal(expected,actual));
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}
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/* ************************************************************************* */
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TEST( GaussianJunctionTreeB, optimizeMultiFrontal2)
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{
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// create a graph
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example::Graph nlfg = createNonlinearFactorGraph();
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Values noisy = createNoisyValues();
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Ordering ordering; ordering += X(1),X(2),L(1);
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GaussianFactorGraph fg = *nlfg.linearize(noisy, ordering);
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// optimize the graph
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GaussianJunctionTree tree(fg);
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VectorValues actual = tree.optimize(&EliminateQR);
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// verify
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VectorValues expected = createCorrectDelta(ordering); // expected solution
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EXPECT(assert_equal(expected,actual));
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}
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/* ************************************************************************* */
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TEST(GaussianJunctionTreeB, slamlike) {
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Values init;
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NonlinearFactorGraph newfactors;
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NonlinearFactorGraph fullgraph;
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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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size_t i = 0;
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newfactors = NonlinearFactorGraph();
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newfactors.add(PriorFactor<Pose2>(X(0), Pose2(0.0, 0.0, 0.0), odoNoise));
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init.insert(X(0), Pose2(0.01, 0.01, 0.01));
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fullgraph.push_back(newfactors);
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for( ; i<5; ++i) {
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newfactors = NonlinearFactorGraph();
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newfactors.add(BetweenFactor<Pose2>(X(i), X(i+1), Pose2(1.0, 0.0, 0.0), odoNoise));
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init.insert(X(i+1), Pose2(double(i+1)+0.1, -0.1, 0.01));
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fullgraph.push_back(newfactors);
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}
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newfactors = NonlinearFactorGraph();
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newfactors.add(BetweenFactor<Pose2>(X(i), X(i+1), Pose2(1.0, 0.0, 0.0), odoNoise));
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newfactors.add(BearingRangeFactor<Pose2,Point2>(X(i), L(0), Rot2::fromAngle(M_PI/4.0), 5.0, brNoise));
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newfactors.add(BearingRangeFactor<Pose2,Point2>(X(i), L(1), Rot2::fromAngle(-M_PI/4.0), 5.0, brNoise));
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init.insert(X(i+1), Pose2(1.01, 0.01, 0.01));
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init.insert(L(0), Point2(5.0/sqrt(2.0), 5.0/sqrt(2.0)));
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init.insert(L(1), Point2(5.0/sqrt(2.0), -5.0/sqrt(2.0)));
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fullgraph.push_back(newfactors);
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++ i;
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for( ; i<5; ++i) {
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newfactors = NonlinearFactorGraph();
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newfactors.add(BetweenFactor<Pose2>(X(i), X(i+1), Pose2(1.0, 0.0, 0.0), odoNoise));
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init.insert(X(i+1), Pose2(double(i+1)+0.1, -0.1, 0.01));
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fullgraph.push_back(newfactors);
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}
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newfactors = NonlinearFactorGraph();
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newfactors.add(BetweenFactor<Pose2>(X(i), X(i+1), Pose2(1.0, 0.0, 0.0), odoNoise));
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newfactors.add(BearingRangeFactor<Pose2,Point2>(X(i), L(0), Rot2::fromAngle(M_PI/4.0 + M_PI/16.0), 4.5, brNoise));
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newfactors.add(BearingRangeFactor<Pose2,Point2>(X(i), L(1), Rot2::fromAngle(-M_PI/4.0 + M_PI/16.0), 4.5, brNoise));
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init.insert(X(i+1), Pose2(6.9, 0.1, 0.01));
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fullgraph.push_back(newfactors);
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++ i;
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// Compare solutions
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Ordering ordering = *fullgraph.orderingCOLAMD(init);
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GaussianFactorGraph linearized = *fullgraph.linearize(init, ordering);
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GaussianJunctionTree gjt(linearized);
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VectorValues deltaactual = gjt.optimize(&EliminateQR);
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Values actual = init.retract(deltaactual, ordering);
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GaussianBayesNet gbn = *GaussianSequentialSolver(linearized).eliminate();
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VectorValues delta = optimize(gbn);
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Values expected = init.retract(delta, ordering);
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EXPECT(assert_equal(expected, actual));
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}
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/* ************************************************************************* */
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TEST(GaussianJunctionTreeB, simpleMarginal) {
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typedef BayesTree<GaussianConditional> GaussianBayesTree;
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// Create a simple graph
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NonlinearFactorGraph fg;
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fg.add(PriorFactor<Pose2>(X(0), Pose2(), noiseModel::Isotropic::Sigma(3, 10.0)));
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fg.add(BetweenFactor<Pose2>(X(0), X(1), Pose2(1.0, 0.0, 0.0), noiseModel::Diagonal::Sigmas(Vector3(10.0, 1.0, 1.0))));
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Values init;
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init.insert(X(0), Pose2());
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init.insert(X(1), Pose2(1.0, 0.0, 0.0));
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Ordering ordering;
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ordering += X(1), X(0);
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GaussianFactorGraph gfg = *fg.linearize(init, ordering);
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// Compute marginals with both sequential and multifrontal
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Matrix expected = GaussianSequentialSolver(gfg).marginalCovariance(1);
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Matrix actual1 = GaussianMultifrontalSolver(gfg).marginalCovariance(1);
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// Compute marginal directly from marginal factor
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GaussianFactor::shared_ptr marginalFactor = GaussianMultifrontalSolver(gfg).marginalFactor(1);
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JacobianFactor::shared_ptr marginalJacobian = boost::dynamic_pointer_cast<JacobianFactor>(marginalFactor);
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Matrix actual2 = inverse(marginalJacobian->getA(marginalJacobian->begin()).transpose() * marginalJacobian->getA(marginalJacobian->begin()));
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// Compute marginal directly from BayesTree
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GaussianBayesTree gbt;
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gbt.insert(GaussianJunctionTree(gfg).eliminate(EliminateCholesky));
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marginalFactor = gbt.marginalFactor(1, EliminateCholesky);
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marginalJacobian = boost::dynamic_pointer_cast<JacobianFactor>(marginalFactor);
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Matrix actual3 = inverse(marginalJacobian->getA(marginalJacobian->begin()).transpose() * marginalJacobian->getA(marginalJacobian->begin()));
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EXPECT(assert_equal(expected, actual1));
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EXPECT(assert_equal(expected, actual2));
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EXPECT(assert_equal(expected, actual3));
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}
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#endif
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///* ************************************************************************* */
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//TEST( GaussianJunctionTreeB, optimizeMultiFrontal )
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//{
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// // create a graph
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// GaussianFactorGraph fg;
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// Ordering ordering;
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// boost::tie(fg,ordering) = createSmoother(7);
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//
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// // optimize the graph
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// GaussianJunctionTree tree(fg);
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// VectorValues actual = tree.optimize(&EliminateQR);
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//
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// // verify
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// VectorValues expected(vector<size_t>(7,2)); // expected solution
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// Vector v = Vector2(0., 0.);
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// for (int i=1; i<=7; i++)
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// expected[ordering[X(i)]] = v;
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// EXPECT(assert_equal(expected,actual));
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//}
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//
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///* ************************************************************************* */
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//TEST( GaussianJunctionTreeB, optimizeMultiFrontal2)
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//{
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// // create a graph
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// example::Graph nlfg = createNonlinearFactorGraph();
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// Values noisy = createNoisyValues();
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// Ordering ordering; ordering += X(1),X(2),L(1);
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// GaussianFactorGraph fg = *nlfg.linearize(noisy, ordering);
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//
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// // optimize the graph
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// GaussianJunctionTree tree(fg);
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// VectorValues actual = tree.optimize(&EliminateQR);
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//
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// // verify
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// VectorValues expected = createCorrectDelta(ordering); // expected solution
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// EXPECT(assert_equal(expected,actual));
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//}
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//
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///* ************************************************************************* */
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//TEST(GaussianJunctionTreeB, slamlike) {
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// Values init;
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// NonlinearFactorGraph newfactors;
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// NonlinearFactorGraph fullgraph;
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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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//
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// size_t i = 0;
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//
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// newfactors = NonlinearFactorGraph();
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// newfactors.add(PriorFactor<Pose2>(X(0), Pose2(0.0, 0.0, 0.0), odoNoise));
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// init.insert(X(0), Pose2(0.01, 0.01, 0.01));
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// fullgraph.push_back(newfactors);
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//
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// for( ; i<5; ++i) {
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// newfactors = NonlinearFactorGraph();
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// newfactors.add(BetweenFactor<Pose2>(X(i), X(i+1), Pose2(1.0, 0.0, 0.0), odoNoise));
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// init.insert(X(i+1), Pose2(double(i+1)+0.1, -0.1, 0.01));
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// fullgraph.push_back(newfactors);
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// }
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//
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// newfactors = NonlinearFactorGraph();
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// newfactors.add(BetweenFactor<Pose2>(X(i), X(i+1), Pose2(1.0, 0.0, 0.0), odoNoise));
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// newfactors.add(BearingRangeFactor<Pose2,Point2>(X(i), L(0), Rot2::fromAngle(M_PI/4.0), 5.0, brNoise));
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// newfactors.add(BearingRangeFactor<Pose2,Point2>(X(i), L(1), Rot2::fromAngle(-M_PI/4.0), 5.0, brNoise));
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// init.insert(X(i+1), Pose2(1.01, 0.01, 0.01));
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// init.insert(L(0), Point2(5.0/sqrt(2.0), 5.0/sqrt(2.0)));
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// init.insert(L(1), Point2(5.0/sqrt(2.0), -5.0/sqrt(2.0)));
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// fullgraph.push_back(newfactors);
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// ++ i;
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//
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// for( ; i<5; ++i) {
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// newfactors = NonlinearFactorGraph();
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// newfactors.add(BetweenFactor<Pose2>(X(i), X(i+1), Pose2(1.0, 0.0, 0.0), odoNoise));
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// init.insert(X(i+1), Pose2(double(i+1)+0.1, -0.1, 0.01));
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// fullgraph.push_back(newfactors);
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// }
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//
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// newfactors = NonlinearFactorGraph();
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// newfactors.add(BetweenFactor<Pose2>(X(i), X(i+1), Pose2(1.0, 0.0, 0.0), odoNoise));
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// newfactors.add(BearingRangeFactor<Pose2,Point2>(X(i), L(0), Rot2::fromAngle(M_PI/4.0 + M_PI/16.0), 4.5, brNoise));
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// newfactors.add(BearingRangeFactor<Pose2,Point2>(X(i), L(1), Rot2::fromAngle(-M_PI/4.0 + M_PI/16.0), 4.5, brNoise));
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// init.insert(X(i+1), Pose2(6.9, 0.1, 0.01));
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// fullgraph.push_back(newfactors);
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// ++ i;
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//
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// // Compare solutions
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// Ordering ordering = *fullgraph.orderingCOLAMD(init);
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// GaussianFactorGraph linearized = *fullgraph.linearize(init, ordering);
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//
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// GaussianJunctionTree gjt(linearized);
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// VectorValues deltaactual = gjt.optimize(&EliminateQR);
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// Values actual = init.retract(deltaactual, ordering);
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//
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// GaussianBayesNet gbn = *GaussianSequentialSolver(linearized).eliminate();
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// VectorValues delta = optimize(gbn);
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// Values expected = init.retract(delta, ordering);
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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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///* ************************************************************************* */
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//TEST(GaussianJunctionTreeB, simpleMarginal) {
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//
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// typedef BayesTree<GaussianConditional> GaussianBayesTree;
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//
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// // Create a simple graph
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// NonlinearFactorGraph fg;
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// fg.add(PriorFactor<Pose2>(X(0), Pose2(), noiseModel::Isotropic::Sigma(3, 10.0)));
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// fg.add(BetweenFactor<Pose2>(X(0), X(1), Pose2(1.0, 0.0, 0.0), noiseModel::Diagonal::Sigmas(Vector3(10.0, 1.0, 1.0))));
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//
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// Values init;
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// init.insert(X(0), Pose2());
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// init.insert(X(1), Pose2(1.0, 0.0, 0.0));
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//
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// Ordering ordering;
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// ordering += X(1), X(0);
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//
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// GaussianFactorGraph gfg = *fg.linearize(init, ordering);
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//
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// // Compute marginals with both sequential and multifrontal
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// Matrix expected = GaussianSequentialSolver(gfg).marginalCovariance(1);
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//
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// Matrix actual1 = GaussianMultifrontalSolver(gfg).marginalCovariance(1);
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//
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// // Compute marginal directly from marginal factor
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// GaussianFactor::shared_ptr marginalFactor = GaussianMultifrontalSolver(gfg).marginalFactor(1);
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// JacobianFactor::shared_ptr marginalJacobian = boost::dynamic_pointer_cast<JacobianFactor>(marginalFactor);
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// Matrix actual2 = inverse(marginalJacobian->getA(marginalJacobian->begin()).transpose() * marginalJacobian->getA(marginalJacobian->begin()));
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//
|
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// // Compute marginal directly from BayesTree
|
||||
// GaussianBayesTree gbt;
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||||
// gbt.insert(GaussianJunctionTree(gfg).eliminate(EliminateCholesky));
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// marginalFactor = gbt.marginalFactor(1, EliminateCholesky);
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// marginalJacobian = boost::dynamic_pointer_cast<JacobianFactor>(marginalFactor);
|
||||
// Matrix actual3 = inverse(marginalJacobian->getA(marginalJacobian->begin()).transpose() * marginalJacobian->getA(marginalJacobian->begin()));
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||||
//
|
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// EXPECT(assert_equal(expected, actual1));
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// EXPECT(assert_equal(expected, actual2));
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// EXPECT(assert_equal(expected, actual3));
|
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//}
|
||||
|
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/* ************************************************************************* */
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int main() { TestResult tr; return TestRegistry::runAllTests(tr);}
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|
|
|
|||
Loading…
Reference in New Issue