Resurrected tests
Frank Dellaert
parent
6b637bda9e
commit
3889b29305
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@ -17,12 +17,11 @@
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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/inference/Symbol.h>
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#include <gtsam/linear/iterative.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/SubgraphPreconditioner.h>
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#include <gtsam/inference/Ordering.h>
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#include <gtsam/base/numericalDerivative.h>
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@ -49,7 +48,7 @@ TEST( SubgraphPreconditioner, planarOrdering ) {
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key(3, 3), key(2, 3), key(1, 3),
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key(3, 2), key(2, 2), key(1, 2),
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key(3, 1), key(2, 1), key(1, 1);
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CHECK(assert_equal(expected,ordering));
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EXPECT(assert_equal(expected,ordering));
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}
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/* ************************************************************************* */
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@ -73,9 +72,9 @@ TEST( SubgraphPreconditioner, planarGraph )
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DOUBLES_EQUAL(0,error(A,xtrue),1e-9); // check zero error for xtrue
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// Check that xtrue is optimal
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GaussianBayesNet::shared_ptr R1 = GaussianSequentialSolver(A).eliminate();
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VectorValues actual = optimize(*R1);
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CHECK(assert_equal(xtrue,actual));
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GaussianBayesNet::shared_ptr R1 = A.eliminateSequential();
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VectorValues actual = R1->optimize();
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EXPECT(assert_equal(xtrue,actual));
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}
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/* ************************************************************************* */
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@ -87,19 +86,18 @@ TEST( SubgraphPreconditioner, splitOffPlanarTree )
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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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GaussianFactorGraph T, C;
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GaussianFactorGraph::shared_ptr T, C;
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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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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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GaussianBayesNet::shared_ptr R1 = T->eliminateSequential();
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VectorValues xbar = R1->optimize();
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EXPECT(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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@ -108,71 +106,128 @@ TEST( SubgraphPreconditioner, system )
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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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// Get the spanning tree and remaining graph
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GaussianFactorGraph::shared_ptr Ab1, Ab2; // A1*x-b1 and A2*x-b2
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boost::tie(Ab1, Ab2) = splitOffPlanarTree(N, Ab);
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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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const Ordering ord = planarOrdering(N);
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auto Rc1 = Ab1->eliminateSequential(ord); // R1*x-c1
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VectorValues xbar = Rc1->optimize(); // 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(Ab2, Rc1, xbarShared);
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const SubgraphPreconditioner system(Ab2, Rc1, xbarShared);
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// Get corresponding matrices for tests. Add dummy factors to Ab2 to make
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// sure it works with the ordering.
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Ordering ordering = Rc1->ordering(); // not ord in general!
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Ab2->add(key(1,1),Z_2x2, Z_2x1);
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Ab2->add(key(1,2),Z_2x2, Z_2x1);
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Ab2->add(key(1,3),Z_2x2, Z_2x1);
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Matrix A, A1, A2;
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Vector b, b1, b2;
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std::tie(A,b) = Ab.jacobian(ordering);
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std::tie(A1,b1) = Ab1->jacobian(ordering);
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std::tie(A2,b2) = Ab2->jacobian(ordering);
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Matrix R1 = Rc1->matrix(ordering).first;
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Matrix Abar(13 * 2, 9 * 2);
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Abar.topRows(9 * 2) = Matrix::Identity(9 * 2, 9 * 2);
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Abar.bottomRows(4 * 2) = A2 * R1.inverse();
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// Helper function to vectorize in correct order, which is the order in which
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// we eliminated the spanning tree.
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auto vec = [ordering](const VectorValues& x) { return x.vector(ordering);};
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// Create zero config
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VectorValues zeros = VectorValues::Zero(xbar);
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const 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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const VectorValues y0 = zeros;
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// y1 = perturbed y0
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VectorValues y1 = zeros;
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y1[1] = Vector2(1.0, -1.0);
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y1[key(3,3)] = Vector2(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[1] = Vector2(2.01, 2.99);
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expected_x1[0] = Vector2(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 backSubstituteTranspose works with R1
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VectorValues actual = Rc1->backSubstituteTranspose(y1);
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Vector expected = R1.transpose().inverse() * vec(y1);
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EXPECT(assert_equal(expected, vec(actual)));
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// Check corresponding x values
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// for y = 0, we get xbar:
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EXPECT(assert_equal(xbar, system.x(y0)));
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// for non-zero y, answer is x = xbar + inv(R1)*y
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const Vector expected_x1 = vec(xbar) + R1.inverse() * vec(y1);
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const VectorValues x1 = system.x(y1);
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EXPECT(assert_equal(expected_x1, vec(x1)));
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// Check errors
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DOUBLES_EQUAL(0,error(Ab,xtrue),1e-9);
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DOUBLES_EQUAL(3,error(Ab,x1),1e-9);
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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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DOUBLES_EQUAL(0,error(Ab,xbar),1e-9);
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DOUBLES_EQUAL(0,system.error(y0),1e-9);
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DOUBLES_EQUAL(2,error(Ab,x1),1e-9);
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DOUBLES_EQUAL(2,system.error(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[2] = Vector2(-100., 100.);
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expected_gx1[4] = Vector2(-100., 100.);
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expected_gx1[1] = Vector2(200., -200.);
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expected_gx1[3] = Vector2(-100., 100.);
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expected_gx1[0] = Vector2(100., -100.);
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CHECK(assert_equal(expected_gx1,gradient(Ab,x1)));
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// Check that transposeMultiplyAdd <=> y += alpha * Abar' * e
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// We check for e1 =[1;0] and e2=[0;1] corresponding to T and C
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const double alpha = 0.5;
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Errors e1,e2;
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for (size_t i=0;i<13;i++) {
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e1 += i<9 ? Vector2(1, 1) : Vector2(0, 0);
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e2 += i>=9 ? Vector2(1, 1) : Vector2(0, 0);
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}
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Vector ee1(13*2), ee2(13*2);
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ee1 << Vector::Ones(9*2), Vector::Zero(4*2);
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ee2 << Vector::Zero(9*2), Vector::Ones(4*2);
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// Check transposeMultiplyAdd for e1
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VectorValues y = zeros;
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system.transposeMultiplyAdd(alpha, e1, y);
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Vector expected_y = alpha * Abar.transpose() * ee1;
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EXPECT(assert_equal(expected_y, vec(y)));
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// Check transposeMultiplyAdd for e2
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y = zeros;
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system.transposeMultiplyAdd(alpha, e2, y);
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expected_y = alpha * Abar.transpose() * ee2;
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EXPECT(assert_equal(expected_y, vec(y)));
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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[2] = Vector2(2., -2.);
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expected_gy1[4] = Vector2(-2., 2.);
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expected_gy1[1] = Vector2(3., -3.);
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expected_gy1[3] = Vector2(-1., 1.);
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expected_gy1[0] = Vector2(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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auto g = system.gradient(y0);
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Vector expected_g = Vector::Zero(18);
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EXPECT(assert_equal(expected_g, vec(g)));
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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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// Test raw vector interface
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TEST( SubgraphPreconditioner, RawVectorAPI )
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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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SubgraphPreconditioner system;
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// Call build, a non-const method needed to make solve work :-(
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KeyInfo keyInfo(Ab);
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std::map<Key,Vector> lambda;
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system.build(Ab, keyInfo, lambda);
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const auto ordering1 = system.Rc1()->ordering(); // build changed R1 !
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const auto ordering2 = keyInfo.ordering();
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const Matrix R1 = system.Rc1()->matrix(ordering1).first;
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// Test that 'solve' does implement x = R^{-1} y
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Vector y2 = Vector::Zero(18), x2(18), x3(18);
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y2.head(2) << 100, -100;
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system.solve(y2, x2);
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EXPECT(assert_equal(R1.inverse() * y2, x2));
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// I can't get test below to pass!
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// Test that transposeSolve does implement x = R^{-T} y
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// system.transposeSolve(y2, x3);
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// EXPECT(assert_equal(R1.transpose().inverse() * y2, x3));
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}
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/* ************************************************************************* */
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@ -184,16 +239,13 @@ TEST( SubgraphPreconditioner, conjugateGradients )
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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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// Get the spanning tree
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GaussianFactorGraph::shared_ptr Ab1, Ab2; // A1*x-b1 and A2*x-b2
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boost::tie(Ab1, Ab2) = splitOffPlanarTree(N, Ab);
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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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SubgraphPreconditioner::sharedBayesNet Rc1 = Ab1->eliminateSequential(); // R1*x-c1
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VectorValues xbar = Rc1->optimize(); // 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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@ -203,22 +255,20 @@ TEST( SubgraphPreconditioner, conjugateGradients )
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VectorValues y0 = VectorValues::Zero(xbar);
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VectorValues y1 = y0;
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y1[1] = Vector2(1.0, -1.0);
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y1[key(2, 2)] = Vector2(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 = conjugateGradients<SubgraphPreconditioner,
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VectorValues, Errors>(system, y1, parameters);
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CHECK(assert_equal(y0,actual));
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EXPECT(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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EXPECT(assert_equal(xtrue,actual2,1e-4));
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}
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#endif
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/* ************************************************************************* */
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int main() { TestResult tr; return TestRegistry::runAllTests(tr); }
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/* ************************************************************************* */
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