formatting of comments, added parent() interface for Bayes Tree cliques
Alex Cunningham
parent
c7734db4fa
commit
4e2aae0121
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@ -67,6 +67,7 @@ namespace gtsam {
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of.close();
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}
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/* ************************************************************************* */
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template<class CONDITIONAL, class CLIQUE>
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void BayesTree<CONDITIONAL,CLIQUE>::saveGraph(std::ostream &s, sharedClique clique, const IndexFormatter& indexFormatter, int parentnum) const {
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static int num = 0;
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@ -101,7 +102,7 @@ namespace gtsam {
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}
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}
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/* ************************************************************************* */
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template<class CONDITIONAL, class CLIQUE>
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typename BayesTree<CONDITIONAL,CLIQUE>::CliqueStats
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BayesTree<CONDITIONAL,CLIQUE>::CliqueData::getStats() const {
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@ -117,6 +117,9 @@ namespace gtsam {
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/** return the const reference of children */
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const std::list<derived_ptr>& children() const { return children_; }
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/** return a shared_ptr to the parent clique */
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derived_ptr parent() const { return parent_.lock(); }
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/// @}
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/// @name Advanced Interface
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/// @{
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@ -40,14 +40,14 @@ Key i3002 = 3002, i2002 = 2002, i1002 = 1002;
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Key i3001 = 3001, i2001 = 2001, i1001 = 1001;
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// TODO fix Ordering::equals, because the ordering *is* correct !
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/* ************************************************************************* *
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TEST( SubgraphPreconditioner, planarOrdering )
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{
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// Check canonical ordering
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Ordering expected, ordering = planarOrdering(3);
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expected += i3003, i2003, i1003, i3002, i2002, i1002, i3001, i2001, i1001;
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CHECK(assert_equal(expected,ordering));
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}
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/* ************************************************************************* */
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//TEST( SubgraphPreconditioner, planarOrdering )
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//{
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// // Check canonical ordering
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// Ordering expected, ordering = planarOrdering(3);
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// expected += i3003, i2003, i1003, i3002, i2002, i1002, i3001, i2001, i1001;
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// CHECK(assert_equal(expected,ordering));
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//}
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/* ************************************************************************* */
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TEST( SubgraphPreconditioner, planarGraph )
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@ -66,148 +66,145 @@ TEST( SubgraphPreconditioner, planarGraph )
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CHECK(assert_equal(xtrue,actual));
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}
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/* ************************************************************************* *
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TEST( SubgraphPreconditioner, splitOffPlanarTree )
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{
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// Build a planar graph
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GaussianFactorGraph A;
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VectorValues xtrue;
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boost::tie(A, xtrue) = planarGraph(3);
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// Get the spanning tree and constraints, and check their sizes
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JacobianFactorGraph T, C;
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// TODO big mess: GFG and JFG mess !!!
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boost::tie(T, C) = splitOffPlanarTree(3, A);
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LONGS_EQUAL(9,T.size());
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LONGS_EQUAL(4,C.size());
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// Check that the tree can be solved to give the ground xtrue
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GaussianBayesNet::shared_ptr R1 = GaussianSequentialSolver(T).eliminate();
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VectorValues xbar = optimize(*R1);
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CHECK(assert_equal(xtrue,xbar));
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}
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/* ************************************************************************* *
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TEST( SubgraphPreconditioner, system )
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{
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// Build a planar graph
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JacobianFactorGraph Ab;
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VectorValues xtrue;
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size_t N = 3;
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boost::tie(Ab, xtrue) = planarGraph(N); // A*x-b
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// Get the spanning tree and corresponding ordering
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GaussianFactorGraph Ab1_, Ab2_; // A1*x-b1 and A2*x-b2
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boost::tie(Ab1_, Ab2_) = splitOffPlanarTree(N, Ab);
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SubgraphPreconditioner::sharedFG Ab1(new GaussianFactorGraph(Ab1_));
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SubgraphPreconditioner::sharedFG Ab2(new GaussianFactorGraph(Ab2_));
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// Eliminate the spanning tree to build a prior
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SubgraphPreconditioner::sharedBayesNet Rc1 = GaussianSequentialSolver(Ab1_).eliminate(); // R1*x-c1
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VectorValues xbar = optimize(*Rc1); // xbar = inv(R1)*c1
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// Create Subgraph-preconditioned system
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VectorValues::shared_ptr xbarShared(new VectorValues(xbar)); // TODO: horrible
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SubgraphPreconditioner system(Ab1, Ab2, Rc1, xbarShared);
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// Create zero config
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VectorValues zeros = VectorValues::Zero(xbar);
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// Set up y0 as all zeros
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VectorValues y0 = zeros;
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// y1 = perturbed y0
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VectorValues y1 = zeros;
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y1[i2003] = Vector_(2, 1.0, -1.0);
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// Check corresponding x values
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VectorValues expected_x1 = xtrue, x1 = system.x(y1);
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expected_x1[i2003] = Vector_(2, 2.01, 2.99);
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expected_x1[i3003] = Vector_(2, 3.01, 2.99);
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CHECK(assert_equal(xtrue, system.x(y0)));
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CHECK(assert_equal(expected_x1,system.x(y1)));
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// Check errors
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// DOUBLES_EQUAL(0,error(Ab,xtrue),1e-9); // TODO !
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// DOUBLES_EQUAL(3,error(Ab,x1),1e-9); // TODO !
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DOUBLES_EQUAL(0,error(system,y0),1e-9);
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DOUBLES_EQUAL(3,error(system,y1),1e-9);
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// Test gradient in x
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VectorValues expected_gx0 = zeros;
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VectorValues expected_gx1 = zeros;
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CHECK(assert_equal(expected_gx0,gradient(Ab,xtrue)));
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expected_gx1[i1003] = Vector_(2, -100., 100.);
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expected_gx1[i2002] = Vector_(2, -100., 100.);
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expected_gx1[i2003] = Vector_(2, 200., -200.);
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expected_gx1[i3002] = Vector_(2, -100., 100.);
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expected_gx1[i3003] = Vector_(2, 100., -100.);
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CHECK(assert_equal(expected_gx1,gradient(Ab,x1)));
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// Test gradient in y
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VectorValues expected_gy0 = zeros;
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VectorValues expected_gy1 = zeros;
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expected_gy1[i1003] = Vector_(2, 2., -2.);
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expected_gy1[i2002] = Vector_(2, -2., 2.);
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expected_gy1[i2003] = Vector_(2, 3., -3.);
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expected_gy1[i3002] = Vector_(2, -1., 1.);
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expected_gy1[i3003] = Vector_(2, 1., -1.);
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CHECK(assert_equal(expected_gy0,gradient(system,y0)));
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CHECK(assert_equal(expected_gy1,gradient(system,y1)));
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// Check it numerically for good measure
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// TODO use boost::bind(&SubgraphPreconditioner::error,&system,_1)
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// Vector numerical_g1 = numericalGradient<VectorValues> (error, y1, 0.001);
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// Vector expected_g1 = Vector_(18, 0., 0., 0., 0., 2., -2., 0., 0., -2., 2.,
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// 3., -3., 0., 0., -1., 1., 1., -1.);
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// CHECK(assert_equal(expected_g1,numerical_g1));
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}
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/* ************************************************************************* *
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TEST( SubgraphPreconditioner, conjugateGradients )
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{
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// Build a planar graph
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GaussianFactorGraph Ab;
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VectorValues xtrue;
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size_t N = 3;
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boost::tie(Ab, xtrue) = planarGraph(N); // A*x-b
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// Get the spanning tree and corresponding ordering
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GaussianFactorGraph Ab1_, Ab2_; // A1*x-b1 and A2*x-b2
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boost::tie(Ab1_, Ab2_) = splitOffPlanarTree(N, Ab);
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SubgraphPreconditioner::sharedFG Ab1(new GaussianFactorGraph(Ab1_));
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SubgraphPreconditioner::sharedFG Ab2(new GaussianFactorGraph(Ab2_));
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// Eliminate the spanning tree to build a prior
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Ordering ordering = planarOrdering(N);
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SubgraphPreconditioner::sharedBayesNet Rc1 = GaussianSequentialSolver(Ab1_).eliminate(); // R1*x-c1
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VectorValues xbar = optimize(*Rc1); // xbar = inv(R1)*c1
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// Create Subgraph-preconditioned system
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VectorValues::shared_ptr xbarShared(new VectorValues(xbar)); // TODO: horrible
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SubgraphPreconditioner system(Ab1, Ab2, Rc1, xbarShared);
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// Create zero config y0 and perturbed config y1
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VectorValues y0 = VectorValues::Zero(xbar);
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VectorValues y1 = y0;
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y1[i2003] = Vector_(2, 1.0, -1.0);
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VectorValues x1 = system.x(y1);
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// Solve for the remaining constraints using PCG
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ConjugateGradientParameters parameters;
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// VectorValues actual = gtsam::conjugateGradients<SubgraphPreconditioner,
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// VectorValues, Errors>(system, y1, verbose, epsilon, epsilon, maxIterations);
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// CHECK(assert_equal(y0,actual));
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// Compare with non preconditioned version:
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VectorValues actual2 = conjugateGradientDescent(Ab, x1, parameters);
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CHECK(assert_equal(xtrue,actual2,1e-4));
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}
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/* ************************************************************************* */
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//TEST( SubgraphPreconditioner, splitOffPlanarTree )
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//{
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// // Build a planar graph
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// GaussianFactorGraph A;
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// VectorValues xtrue;
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// boost::tie(A, xtrue) = planarGraph(3);
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//
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// // Get the spanning tree and constraints, and check their sizes
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// JacobianFactorGraph T, C;
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// // TODO big mess: GFG and JFG mess !!!
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// boost::tie(T, C) = splitOffPlanarTree(3, A);
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// LONGS_EQUAL(9,T.size());
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// LONGS_EQUAL(4,C.size());
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//
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// // Check that the tree can be solved to give the ground xtrue
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// GaussianBayesNet::shared_ptr R1 = GaussianSequentialSolver(T).eliminate();
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// VectorValues xbar = optimize(*R1);
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// CHECK(assert_equal(xtrue,xbar));
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//}
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/* ************************************************************************* */
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int main() {
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TestResult tr;
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return TestRegistry::runAllTests(tr);
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}
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//TEST( SubgraphPreconditioner, system )
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//{
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// // Build a planar graph
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// JacobianFactorGraph Ab;
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// VectorValues xtrue;
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// size_t N = 3;
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// boost::tie(Ab, xtrue) = planarGraph(N); // A*x-b
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//
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// // Get the spanning tree and corresponding ordering
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// GaussianFactorGraph Ab1_, Ab2_; // A1*x-b1 and A2*x-b2
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// boost::tie(Ab1_, Ab2_) = splitOffPlanarTree(N, Ab);
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// SubgraphPreconditioner::sharedFG Ab1(new GaussianFactorGraph(Ab1_));
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// SubgraphPreconditioner::sharedFG Ab2(new GaussianFactorGraph(Ab2_));
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//
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// // Eliminate the spanning tree to build a prior
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// SubgraphPreconditioner::sharedBayesNet Rc1 = GaussianSequentialSolver(Ab1_).eliminate(); // R1*x-c1
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// VectorValues xbar = optimize(*Rc1); // xbar = inv(R1)*c1
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//
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// // Create Subgraph-preconditioned system
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// VectorValues::shared_ptr xbarShared(new VectorValues(xbar)); // TODO: horrible
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// SubgraphPreconditioner system(Ab1, Ab2, Rc1, xbarShared);
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//
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// // Create zero config
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// VectorValues zeros = VectorValues::Zero(xbar);
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//
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// // Set up y0 as all zeros
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// VectorValues y0 = zeros;
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//
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// // y1 = perturbed y0
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// VectorValues y1 = zeros;
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// y1[i2003] = Vector_(2, 1.0, -1.0);
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//
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// // Check corresponding x values
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// VectorValues expected_x1 = xtrue, x1 = system.x(y1);
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// expected_x1[i2003] = Vector_(2, 2.01, 2.99);
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// expected_x1[i3003] = Vector_(2, 3.01, 2.99);
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// CHECK(assert_equal(xtrue, system.x(y0)));
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// CHECK(assert_equal(expected_x1,system.x(y1)));
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//
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// // Check errors
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//// DOUBLES_EQUAL(0,error(Ab,xtrue),1e-9); // TODO !
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//// DOUBLES_EQUAL(3,error(Ab,x1),1e-9); // TODO !
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// DOUBLES_EQUAL(0,error(system,y0),1e-9);
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// DOUBLES_EQUAL(3,error(system,y1),1e-9);
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//
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// // Test gradient in x
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// VectorValues expected_gx0 = zeros;
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// VectorValues expected_gx1 = zeros;
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// CHECK(assert_equal(expected_gx0,gradient(Ab,xtrue)));
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// expected_gx1[i1003] = Vector_(2, -100., 100.);
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// expected_gx1[i2002] = Vector_(2, -100., 100.);
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// expected_gx1[i2003] = Vector_(2, 200., -200.);
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// expected_gx1[i3002] = Vector_(2, -100., 100.);
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// expected_gx1[i3003] = Vector_(2, 100., -100.);
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// CHECK(assert_equal(expected_gx1,gradient(Ab,x1)));
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//
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// // Test gradient in y
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// VectorValues expected_gy0 = zeros;
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// VectorValues expected_gy1 = zeros;
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// expected_gy1[i1003] = Vector_(2, 2., -2.);
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// expected_gy1[i2002] = Vector_(2, -2., 2.);
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// expected_gy1[i2003] = Vector_(2, 3., -3.);
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// expected_gy1[i3002] = Vector_(2, -1., 1.);
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// expected_gy1[i3003] = Vector_(2, 1., -1.);
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// CHECK(assert_equal(expected_gy0,gradient(system,y0)));
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// CHECK(assert_equal(expected_gy1,gradient(system,y1)));
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//
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// // Check it numerically for good measure
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// // TODO use boost::bind(&SubgraphPreconditioner::error,&system,_1)
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// // Vector numerical_g1 = numericalGradient<VectorValues> (error, y1, 0.001);
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// // Vector expected_g1 = Vector_(18, 0., 0., 0., 0., 2., -2., 0., 0., -2., 2.,
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// // 3., -3., 0., 0., -1., 1., 1., -1.);
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// // CHECK(assert_equal(expected_g1,numerical_g1));
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//}
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/* ************************************************************************* */
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//TEST( SubgraphPreconditioner, conjugateGradients )
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//{
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// // Build a planar graph
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// GaussianFactorGraph Ab;
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// VectorValues xtrue;
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// size_t N = 3;
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// boost::tie(Ab, xtrue) = planarGraph(N); // A*x-b
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//
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// // Get the spanning tree and corresponding ordering
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// GaussianFactorGraph Ab1_, Ab2_; // A1*x-b1 and A2*x-b2
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// boost::tie(Ab1_, Ab2_) = splitOffPlanarTree(N, Ab);
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// SubgraphPreconditioner::sharedFG Ab1(new GaussianFactorGraph(Ab1_));
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// SubgraphPreconditioner::sharedFG Ab2(new GaussianFactorGraph(Ab2_));
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//
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// // Eliminate the spanning tree to build a prior
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// Ordering ordering = planarOrdering(N);
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// SubgraphPreconditioner::sharedBayesNet Rc1 = GaussianSequentialSolver(Ab1_).eliminate(); // R1*x-c1
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// VectorValues xbar = optimize(*Rc1); // xbar = inv(R1)*c1
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//
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// // Create Subgraph-preconditioned system
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// VectorValues::shared_ptr xbarShared(new VectorValues(xbar)); // TODO: horrible
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// SubgraphPreconditioner system(Ab1, Ab2, Rc1, xbarShared);
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//
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// // Create zero config y0 and perturbed config y1
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// VectorValues y0 = VectorValues::Zero(xbar);
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//
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// VectorValues y1 = y0;
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// y1[i2003] = Vector_(2, 1.0, -1.0);
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// VectorValues x1 = system.x(y1);
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//
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// // Solve for the remaining constraints using PCG
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// ConjugateGradientParameters parameters;
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//// VectorValues actual = gtsam::conjugateGradients<SubgraphPreconditioner,
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//// VectorValues, Errors>(system, y1, verbose, epsilon, epsilon, maxIterations);
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//// CHECK(assert_equal(y0,actual));
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//
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// // Compare with non preconditioned version:
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// VectorValues actual2 = conjugateGradientDescent(Ab, x1, parameters);
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// CHECK(assert_equal(xtrue,actual2,1e-4));
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//}
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/* ************************************************************************* */
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int main() { TestResult tr; return TestRegistry::runAllTests(tr); }
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/* ************************************************************************* */
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