/* ************************************************************************* */
TEST( SubgraphPreconditioner, planarGraph )
{
// Check planar graph construction
GaussianFactorGraph A;
VectorValues xtrue;
boost::tie(A, xtrue) = planarGraph(3);
LONGS_EQUAL(13,A.size());
LONGS_EQUAL(9,xtrue.size());
DOUBLES_EQUAL(0,error(A,xtrue),1e-9); // check zero error for xtrue
// Check that xtrue is optimal
GaussianBayesNet::shared_ptr R1 = GaussianSequentialSolver(A).eliminate();
VectorValues actual = optimize(*R1);
CHECK(assert_equal(xtrue,actual));
}
/* ************************************************************************* */
TEST(GaussianFactorGraph, initialization) {
// Create empty graph
GaussianFactorGraph fg;
SharedDiagonal unit2 = noiseModel::Unit::Create(2);
fg +=
JacobianFactor(0, 10*I_2x2, -1.0*Vector::Ones(2), unit2),
JacobianFactor(0, -10*I_2x2,1, 10*I_2x2, Vector2(2.0, -1.0), unit2),
JacobianFactor(0, -5*I_2x2, 2, 5*I_2x2, Vector2(0.0, 1.0), unit2),
JacobianFactor(1, -5*I_2x2, 2, 5*I_2x2, Vector2(-1.0, 1.5), unit2);
EXPECT_LONGS_EQUAL(4, (long)fg.size());
// Test sparse, which takes a vector and returns a matrix, used in MATLAB
// Note that this the augmented vector and the RHS is in column 7
Matrix expectedIJS =
(Matrix(3, 22) << 1., 2., 1., 2., 3., 4., 3., 4., 3., 4., 5., 6., 5., 6., 5., 6., 7., 8., 7.,
8., 7., 8., 1., 2., 7., 7., 1., 2., 3., 4., 7., 7., 1., 2., 5., 6., 7., 7., 3., 4., 5., 6.,
7., 7., 10., 10., -1., -1., -10., -10., 10., 10., 2., -1., -5., -5., 5., 5., 0., 1., -5.,
-5., 5., 5., -1., 1.5).finished();
Matrix actualIJS = fg.sparseJacobian_();
EQUALITY(expectedIJS, actualIJS);
}
