Moved GaussianBayesTree tests into their own file, out of testGaussianISAM
Richard Roberts
parent
eb21cf0911
commit
f3a2887af1
|
|
@ -0,0 +1,322 @@
|
||||||
|
/* ----------------------------------------------------------------------------
|
||||||
|
|
||||||
|
* GTSAM Copyright 2010, Georgia Tech Research Corporation,
|
||||||
|
* Atlanta, Georgia 30332-0415
|
||||||
|
* All Rights Reserved
|
||||||
|
* Authors: Frank Dellaert, et al. (see THANKS for the full author list)
|
||||||
|
|
||||||
|
* See LICENSE for the license information
|
||||||
|
|
||||||
|
* -------------------------------------------------------------------------- */
|
||||||
|
|
||||||
|
/**
|
||||||
|
* @file testGaussianISAM.cpp
|
||||||
|
* @brief Unit tests for GaussianISAM
|
||||||
|
* @author Michael Kaess
|
||||||
|
*/
|
||||||
|
|
||||||
|
#include <tests/smallExample.h>
|
||||||
|
#include <gtsam/nonlinear/Ordering.h>
|
||||||
|
#include <gtsam/nonlinear/Symbol.h>
|
||||||
|
#include <gtsam/linear/GaussianSequentialSolver.h>
|
||||||
|
#include <gtsam/linear/GaussianMultifrontalSolver.h>
|
||||||
|
#include <gtsam/geometry/Rot2.h>
|
||||||
|
|
||||||
|
#include <CppUnitLite/TestHarness.h>
|
||||||
|
|
||||||
|
#include <boost/foreach.hpp>
|
||||||
|
#include <boost/assign/std/list.hpp> // for operator +=
|
||||||
|
using namespace boost::assign;
|
||||||
|
|
||||||
|
using namespace std;
|
||||||
|
using namespace gtsam;
|
||||||
|
using namespace example;
|
||||||
|
|
||||||
|
using symbol_shorthand::X;
|
||||||
|
using symbol_shorthand::L;
|
||||||
|
|
||||||
|
/* ************************************************************************* */
|
||||||
|
// Some numbers that should be consistent among all smoother tests
|
||||||
|
|
||||||
|
static double sigmax1 = 0.786153, /*sigmax2 = 1.0/1.47292,*/ sigmax3 = 0.671512, sigmax4 =
|
||||||
|
0.669534 /*, sigmax5 = sigmax3, sigmax6 = sigmax2*/, sigmax7 = sigmax1;
|
||||||
|
|
||||||
|
static const double tol = 1e-4;
|
||||||
|
|
||||||
|
/* ************************************************************************* *
|
||||||
|
Bayes tree for smoother with "natural" ordering:
|
||||||
|
C1 x6 x7
|
||||||
|
C2 x5 : x6
|
||||||
|
C3 x4 : x5
|
||||||
|
C4 x3 : x4
|
||||||
|
C5 x2 : x3
|
||||||
|
C6 x1 : x2
|
||||||
|
**************************************************************************** */
|
||||||
|
TEST_UNSAFE( BayesTree, linear_smoother_shortcuts )
|
||||||
|
{
|
||||||
|
// Create smoother with 7 nodes
|
||||||
|
Ordering ordering;
|
||||||
|
GaussianFactorGraph smoother;
|
||||||
|
boost::tie(smoother, ordering) = createSmoother(7);
|
||||||
|
|
||||||
|
GaussianBayesTree bayesTree = *GaussianMultifrontalSolver(smoother).eliminate();
|
||||||
|
|
||||||
|
// Create the Bayes tree
|
||||||
|
LONGS_EQUAL(6, bayesTree.size());
|
||||||
|
|
||||||
|
// Check the conditional P(Root|Root)
|
||||||
|
GaussianBayesNet empty;
|
||||||
|
GaussianBayesTree::sharedClique R = bayesTree.root();
|
||||||
|
GaussianBayesNet actual1 = R->shortcut(R, EliminateCholesky);
|
||||||
|
EXPECT(assert_equal(empty,actual1,tol));
|
||||||
|
|
||||||
|
// Check the conditional P(C2|Root)
|
||||||
|
GaussianBayesTree::sharedClique C2 = bayesTree[ordering[X(5)]];
|
||||||
|
GaussianBayesNet actual2 = C2->shortcut(R, EliminateCholesky);
|
||||||
|
EXPECT(assert_equal(empty,actual2,tol));
|
||||||
|
|
||||||
|
// Check the conditional P(C3|Root)
|
||||||
|
double sigma3 = 0.61808;
|
||||||
|
Matrix A56 = Matrix_(2,2,-0.382022,0.,0.,-0.382022);
|
||||||
|
GaussianBayesNet expected3;
|
||||||
|
push_front(expected3,ordering[X(5)], zero(2), eye(2)/sigma3, ordering[X(6)], A56/sigma3, ones(2));
|
||||||
|
GaussianBayesTree::sharedClique C3 = bayesTree[ordering[X(4)]];
|
||||||
|
GaussianBayesNet actual3 = C3->shortcut(R, EliminateCholesky);
|
||||||
|
EXPECT(assert_equal(expected3,actual3,tol));
|
||||||
|
|
||||||
|
// Check the conditional P(C4|Root)
|
||||||
|
double sigma4 = 0.661968;
|
||||||
|
Matrix A46 = Matrix_(2,2,-0.146067,0.,0.,-0.146067);
|
||||||
|
GaussianBayesNet expected4;
|
||||||
|
push_front(expected4, ordering[X(4)], zero(2), eye(2)/sigma4, ordering[X(6)], A46/sigma4, ones(2));
|
||||||
|
GaussianBayesTree::sharedClique C4 = bayesTree[ordering[X(3)]];
|
||||||
|
GaussianBayesNet actual4 = C4->shortcut(R, EliminateCholesky);
|
||||||
|
EXPECT(assert_equal(expected4,actual4,tol));
|
||||||
|
}
|
||||||
|
|
||||||
|
/* ************************************************************************* *
|
||||||
|
Bayes tree for smoother with "nested dissection" ordering:
|
||||||
|
|
||||||
|
Node[x1] P(x1 | x2)
|
||||||
|
Node[x3] P(x3 | x2 x4)
|
||||||
|
Node[x5] P(x5 | x4 x6)
|
||||||
|
Node[x7] P(x7 | x6)
|
||||||
|
Node[x2] P(x2 | x4)
|
||||||
|
Node[x6] P(x6 | x4)
|
||||||
|
Node[x4] P(x4)
|
||||||
|
|
||||||
|
becomes
|
||||||
|
|
||||||
|
C1 x5 x6 x4
|
||||||
|
C2 x3 x2 : x4
|
||||||
|
C3 x1 : x2
|
||||||
|
C4 x7 : x6
|
||||||
|
|
||||||
|
************************************************************************* */
|
||||||
|
TEST_UNSAFE( BayesTree, balanced_smoother_marginals )
|
||||||
|
{
|
||||||
|
// Create smoother with 7 nodes
|
||||||
|
Ordering ordering;
|
||||||
|
ordering += X(1),X(3),X(5),X(7),X(2),X(6),X(4);
|
||||||
|
GaussianFactorGraph smoother = createSmoother(7, ordering).first;
|
||||||
|
|
||||||
|
// Create the Bayes tree
|
||||||
|
GaussianBayesTree bayesTree = *GaussianMultifrontalSolver(smoother).eliminate();
|
||||||
|
|
||||||
|
VectorValues expectedSolution(VectorValues::Zero(7,2));
|
||||||
|
VectorValues actualSolution = optimize(bayesTree);
|
||||||
|
EXPECT(assert_equal(expectedSolution,actualSolution,tol));
|
||||||
|
|
||||||
|
LONGS_EQUAL(4,bayesTree.size());
|
||||||
|
|
||||||
|
double tol=1e-5;
|
||||||
|
|
||||||
|
// Check marginal on x1
|
||||||
|
GaussianBayesNet expected1 = simpleGaussian(ordering[X(1)], zero(2), sigmax1);
|
||||||
|
GaussianBayesNet actual1 = *bayesTree.marginalBayesNet(ordering[X(1)], EliminateCholesky);
|
||||||
|
Matrix expectedCovarianceX1 = eye(2,2) * (sigmax1 * sigmax1);
|
||||||
|
Matrix actualCovarianceX1;
|
||||||
|
GaussianFactor::shared_ptr m = bayesTree.marginalFactor(ordering[X(1)], EliminateCholesky);
|
||||||
|
actualCovarianceX1 = bayesTree.marginalFactor(ordering[X(1)], EliminateCholesky)->information().inverse();
|
||||||
|
EXPECT(assert_equal(expectedCovarianceX1, actualCovarianceX1, tol));
|
||||||
|
EXPECT(assert_equal(expected1,actual1,tol));
|
||||||
|
|
||||||
|
// Check marginal on x2
|
||||||
|
double sigx2 = 0.68712938; // FIXME: this should be corrected analytically
|
||||||
|
GaussianBayesNet expected2 = simpleGaussian(ordering[X(2)], zero(2), sigx2);
|
||||||
|
GaussianBayesNet actual2 = *bayesTree.marginalBayesNet(ordering[X(2)], EliminateCholesky);
|
||||||
|
Matrix expectedCovarianceX2 = eye(2,2) * (sigx2 * sigx2);
|
||||||
|
Matrix actualCovarianceX2;
|
||||||
|
actualCovarianceX2 = bayesTree.marginalFactor(ordering[X(2)], EliminateCholesky)->information().inverse();
|
||||||
|
EXPECT(assert_equal(expectedCovarianceX2, actualCovarianceX2, tol));
|
||||||
|
EXPECT(assert_equal(expected2,actual2,tol));
|
||||||
|
|
||||||
|
// Check marginal on x3
|
||||||
|
GaussianBayesNet expected3 = simpleGaussian(ordering[X(3)], zero(2), sigmax3);
|
||||||
|
GaussianBayesNet actual3 = *bayesTree.marginalBayesNet(ordering[X(3)], EliminateCholesky);
|
||||||
|
Matrix expectedCovarianceX3 = eye(2,2) * (sigmax3 * sigmax3);
|
||||||
|
Matrix actualCovarianceX3;
|
||||||
|
actualCovarianceX3 = bayesTree.marginalFactor(ordering[X(3)], EliminateCholesky)->information().inverse();
|
||||||
|
EXPECT(assert_equal(expectedCovarianceX3, actualCovarianceX3, tol));
|
||||||
|
EXPECT(assert_equal(expected3,actual3,tol));
|
||||||
|
|
||||||
|
// Check marginal on x4
|
||||||
|
GaussianBayesNet expected4 = simpleGaussian(ordering[X(4)], zero(2), sigmax4);
|
||||||
|
GaussianBayesNet actual4 = *bayesTree.marginalBayesNet(ordering[X(4)], EliminateCholesky);
|
||||||
|
Matrix expectedCovarianceX4 = eye(2,2) * (sigmax4 * sigmax4);
|
||||||
|
Matrix actualCovarianceX4;
|
||||||
|
actualCovarianceX4 = bayesTree.marginalFactor(ordering[X(4)], EliminateCholesky)->information().inverse();
|
||||||
|
EXPECT(assert_equal(expectedCovarianceX4, actualCovarianceX4, tol));
|
||||||
|
EXPECT(assert_equal(expected4,actual4,tol));
|
||||||
|
|
||||||
|
// Check marginal on x7 (should be equal to x1)
|
||||||
|
GaussianBayesNet expected7 = simpleGaussian(ordering[X(7)], zero(2), sigmax7);
|
||||||
|
GaussianBayesNet actual7 = *bayesTree.marginalBayesNet(ordering[X(7)], EliminateCholesky);
|
||||||
|
Matrix expectedCovarianceX7 = eye(2,2) * (sigmax7 * sigmax7);
|
||||||
|
Matrix actualCovarianceX7;
|
||||||
|
actualCovarianceX7 = bayesTree.marginalFactor(ordering[X(7)], EliminateCholesky)->information().inverse();
|
||||||
|
EXPECT(assert_equal(expectedCovarianceX7, actualCovarianceX7, tol));
|
||||||
|
EXPECT(assert_equal(expected7,actual7,tol));
|
||||||
|
}
|
||||||
|
|
||||||
|
/* ************************************************************************* */
|
||||||
|
TEST_UNSAFE( BayesTree, balanced_smoother_shortcuts )
|
||||||
|
{
|
||||||
|
// Create smoother with 7 nodes
|
||||||
|
Ordering ordering;
|
||||||
|
ordering += X(1),X(3),X(5),X(7),X(2),X(6),X(4);
|
||||||
|
GaussianFactorGraph smoother = createSmoother(7, ordering).first;
|
||||||
|
|
||||||
|
// Create the Bayes tree
|
||||||
|
GaussianBayesTree bayesTree = *GaussianMultifrontalSolver(smoother).eliminate();
|
||||||
|
|
||||||
|
// Check the conditional P(Root|Root)
|
||||||
|
GaussianBayesNet empty;
|
||||||
|
GaussianBayesTree::sharedClique R = bayesTree.root();
|
||||||
|
GaussianBayesNet actual1 = R->shortcut(R, EliminateCholesky);
|
||||||
|
EXPECT(assert_equal(empty,actual1,tol));
|
||||||
|
|
||||||
|
// Check the conditional P(C2|Root)
|
||||||
|
GaussianBayesTree::sharedClique C2 = bayesTree[ordering[X(3)]];
|
||||||
|
GaussianBayesNet actual2 = C2->shortcut(R, EliminateCholesky);
|
||||||
|
EXPECT(assert_equal(empty,actual2,tol));
|
||||||
|
|
||||||
|
// Check the conditional P(C3|Root), which should be equal to P(x2|x4)
|
||||||
|
/** TODO: Note for multifrontal conditional:
|
||||||
|
* p_x2_x4 is now an element conditional of the multifrontal conditional bayesTree[ordering[X(2)]]->conditional()
|
||||||
|
* We don't know yet how to take it out.
|
||||||
|
*/
|
||||||
|
// GaussianConditional::shared_ptr p_x2_x4 = bayesTree[ordering[X(2)]]->conditional();
|
||||||
|
// p_x2_x4->print("Conditional p_x2_x4: ");
|
||||||
|
// GaussianBayesNet expected3(p_x2_x4);
|
||||||
|
// GaussianISAM::sharedClique C3 = isamTree[ordering[X(1)]];
|
||||||
|
// GaussianBayesNet actual3 = GaussianISAM::shortcut(C3,R);
|
||||||
|
// EXPECT(assert_equal(expected3,actual3,tol));
|
||||||
|
}
|
||||||
|
|
||||||
|
///* ************************************************************************* */
|
||||||
|
//TEST( BayesTree, balanced_smoother_clique_marginals )
|
||||||
|
//{
|
||||||
|
// // Create smoother with 7 nodes
|
||||||
|
// Ordering ordering;
|
||||||
|
// ordering += X(1),X(3),X(5),X(7),X(2),X(6),X(4);
|
||||||
|
// GaussianFactorGraph smoother = createSmoother(7, ordering).first;
|
||||||
|
//
|
||||||
|
// // Create the Bayes tree
|
||||||
|
// GaussianBayesNet chordalBayesNet = *GaussianSequentialSolver(smoother).eliminate();
|
||||||
|
// GaussianISAM bayesTree(chordalBayesNet);
|
||||||
|
//
|
||||||
|
// // Check the clique marginal P(C3)
|
||||||
|
// double sigmax2_alt = 1/1.45533; // THIS NEEDS TO BE CHECKED!
|
||||||
|
// GaussianBayesNet expected = simpleGaussian(ordering[X(2)],zero(2),sigmax2_alt);
|
||||||
|
// push_front(expected,ordering[X(1)], zero(2), eye(2)*sqrt(2), ordering[X(2)], -eye(2)*sqrt(2)/2, ones(2));
|
||||||
|
// GaussianISAM::sharedClique R = bayesTree.root(), C3 = bayesTree[ordering[X(1)]];
|
||||||
|
// GaussianFactorGraph marginal = C3->marginal(R);
|
||||||
|
// GaussianVariableIndex varIndex(marginal);
|
||||||
|
// Permutation toFront(Permutation::PullToFront(C3->keys(), varIndex.size()));
|
||||||
|
// Permutation toFrontInverse(*toFront.inverse());
|
||||||
|
// varIndex.permute(toFront);
|
||||||
|
// BOOST_FOREACH(const GaussianFactor::shared_ptr& factor, marginal) {
|
||||||
|
// factor->permuteWithInverse(toFrontInverse); }
|
||||||
|
// GaussianBayesNet actual = *inference::EliminateUntil(marginal, C3->keys().size(), varIndex);
|
||||||
|
// actual.permuteWithInverse(toFront);
|
||||||
|
// EXPECT(assert_equal(expected,actual,tol));
|
||||||
|
//}
|
||||||
|
|
||||||
|
/* ************************************************************************* */
|
||||||
|
TEST_UNSAFE( BayesTree, balanced_smoother_joint )
|
||||||
|
{
|
||||||
|
// Create smoother with 7 nodes
|
||||||
|
Ordering ordering;
|
||||||
|
ordering += X(1),X(3),X(5),X(7),X(2),X(6),X(4);
|
||||||
|
GaussianFactorGraph smoother = createSmoother(7, ordering).first;
|
||||||
|
|
||||||
|
// Create the Bayes tree, expected to look like:
|
||||||
|
// x5 x6 x4
|
||||||
|
// x3 x2 : x4
|
||||||
|
// x1 : x2
|
||||||
|
// x7 : x6
|
||||||
|
GaussianBayesTree bayesTree = *GaussianMultifrontalSolver(smoother).eliminate();
|
||||||
|
|
||||||
|
// Conditional density elements reused by both tests
|
||||||
|
const Vector sigma = ones(2);
|
||||||
|
const Matrix I = eye(2), A = -0.00429185*I;
|
||||||
|
|
||||||
|
// Check the joint density P(x1,x7) factored as P(x1|x7)P(x7)
|
||||||
|
GaussianBayesNet expected1;
|
||||||
|
// Why does the sign get flipped on the prior?
|
||||||
|
GaussianConditional::shared_ptr
|
||||||
|
parent1(new GaussianConditional(ordering[X(7)], zero(2), -1*I/sigmax7, ones(2)));
|
||||||
|
expected1.push_front(parent1);
|
||||||
|
push_front(expected1,ordering[X(1)], zero(2), I/sigmax7, ordering[X(7)], A/sigmax7, sigma);
|
||||||
|
GaussianBayesNet actual1 = *bayesTree.jointBayesNet(ordering[X(1)],ordering[X(7)], EliminateCholesky);
|
||||||
|
EXPECT(assert_equal(expected1,actual1,tol));
|
||||||
|
|
||||||
|
// // Check the joint density P(x7,x1) factored as P(x7|x1)P(x1)
|
||||||
|
// GaussianBayesNet expected2;
|
||||||
|
// GaussianConditional::shared_ptr
|
||||||
|
// parent2(new GaussianConditional(ordering[X(1)], zero(2), -1*I/sigmax1, ones(2)));
|
||||||
|
// expected2.push_front(parent2);
|
||||||
|
// push_front(expected2,ordering[X(7)], zero(2), I/sigmax1, ordering[X(1)], A/sigmax1, sigma);
|
||||||
|
// GaussianBayesNet actual2 = *bayesTree.jointBayesNet(ordering[X(7)],ordering[X(1)]);
|
||||||
|
// EXPECT(assert_equal(expected2,actual2,tol));
|
||||||
|
|
||||||
|
// Check the joint density P(x1,x4), i.e. with a root variable
|
||||||
|
GaussianBayesNet expected3;
|
||||||
|
GaussianConditional::shared_ptr
|
||||||
|
parent3(new GaussianConditional(ordering[X(4)], zero(2), I/sigmax4, ones(2)));
|
||||||
|
expected3.push_front(parent3);
|
||||||
|
double sig14 = 0.784465;
|
||||||
|
Matrix A14 = -0.0769231*I;
|
||||||
|
push_front(expected3,ordering[X(1)], zero(2), I/sig14, ordering[X(4)], A14/sig14, sigma);
|
||||||
|
GaussianBayesNet actual3 = *bayesTree.jointBayesNet(ordering[X(1)],ordering[X(4)], EliminateCholesky);
|
||||||
|
EXPECT(assert_equal(expected3,actual3,tol));
|
||||||
|
|
||||||
|
// // Check the joint density P(x4,x1), i.e. with a root variable, factored the other way
|
||||||
|
// GaussianBayesNet expected4;
|
||||||
|
// GaussianConditional::shared_ptr
|
||||||
|
// parent4(new GaussianConditional(ordering[X(1)], zero(2), -1.0*I/sigmax1, ones(2)));
|
||||||
|
// expected4.push_front(parent4);
|
||||||
|
// double sig41 = 0.668096;
|
||||||
|
// Matrix A41 = -0.055794*I;
|
||||||
|
// push_front(expected4,ordering[X(4)], zero(2), I/sig41, ordering[X(1)], A41/sig41, sigma);
|
||||||
|
// GaussianBayesNet actual4 = *bayesTree.jointBayesNet(ordering[X(4)],ordering[X(1)]);
|
||||||
|
// EXPECT(assert_equal(expected4,actual4,tol));
|
||||||
|
}
|
||||||
|
|
||||||
|
/* ************************************************************************* */
|
||||||
|
TEST_UNSAFE(BayesTree, simpleMarginal)
|
||||||
|
{
|
||||||
|
GaussianFactorGraph gfg;
|
||||||
|
|
||||||
|
Matrix A12 = Rot2::fromDegrees(45.0).matrix();
|
||||||
|
|
||||||
|
gfg.add(0, eye(2), zero(2), noiseModel::Isotropic::Sigma(2, 1.0));
|
||||||
|
gfg.add(0, -eye(2), 1, eye(2), ones(2), noiseModel::Isotropic::Sigma(2, 1.0));
|
||||||
|
gfg.add(1, -eye(2), 2, A12, ones(2), noiseModel::Isotropic::Sigma(2, 1.0));
|
||||||
|
|
||||||
|
Matrix expected(GaussianSequentialSolver(gfg).marginalCovariance(2));
|
||||||
|
Matrix actual(GaussianMultifrontalSolver(gfg).marginalCovariance(2));
|
||||||
|
|
||||||
|
EXPECT(assert_equal(expected, actual));
|
||||||
|
}
|
||||||
|
|
@ -83,288 +83,6 @@ TEST( ISAM, iSAM_smoother )
|
||||||
EXPECT(assert_equal(e, optimized));
|
EXPECT(assert_equal(e, optimized));
|
||||||
}
|
}
|
||||||
|
|
||||||
/* ************************************************************************* *
|
|
||||||
Bayes tree for smoother with "natural" ordering:
|
|
||||||
C1 x6 x7
|
|
||||||
C2 x5 : x6
|
|
||||||
C3 x4 : x5
|
|
||||||
C4 x3 : x4
|
|
||||||
C5 x2 : x3
|
|
||||||
C6 x1 : x2
|
|
||||||
**************************************************************************** */
|
|
||||||
TEST_UNSAFE( BayesTree, linear_smoother_shortcuts )
|
|
||||||
{
|
|
||||||
// Create smoother with 7 nodes
|
|
||||||
Ordering ordering;
|
|
||||||
GaussianFactorGraph smoother;
|
|
||||||
boost::tie(smoother, ordering) = createSmoother(7);
|
|
||||||
|
|
||||||
BayesTree<GaussianConditional> bayesTree = *GaussianMultifrontalSolver(smoother).eliminate();
|
|
||||||
|
|
||||||
// Create the Bayes tree
|
|
||||||
GaussianISAM isamTree(bayesTree);
|
|
||||||
LONGS_EQUAL(6,isamTree.size());
|
|
||||||
|
|
||||||
// Check the conditional P(Root|Root)
|
|
||||||
GaussianBayesNet empty;
|
|
||||||
GaussianISAM::sharedClique R = isamTree.root();
|
|
||||||
GaussianBayesNet actual1 = GaussianISAM::shortcut(R,R);
|
|
||||||
EXPECT(assert_equal(empty,actual1,tol));
|
|
||||||
|
|
||||||
// Check the conditional P(C2|Root)
|
|
||||||
GaussianISAM::sharedClique C2 = isamTree[ordering[X(5)]];
|
|
||||||
GaussianBayesNet actual2 = GaussianISAM::shortcut(C2,R);
|
|
||||||
EXPECT(assert_equal(empty,actual2,tol));
|
|
||||||
|
|
||||||
// Check the conditional P(C3|Root)
|
|
||||||
double sigma3 = 0.61808;
|
|
||||||
Matrix A56 = Matrix_(2,2,-0.382022,0.,0.,-0.382022);
|
|
||||||
GaussianBayesNet expected3;
|
|
||||||
push_front(expected3,ordering[X(5)], zero(2), eye(2)/sigma3, ordering[X(6)], A56/sigma3, ones(2));
|
|
||||||
GaussianISAM::sharedClique C3 = isamTree[ordering[X(4)]];
|
|
||||||
GaussianBayesNet actual3 = GaussianISAM::shortcut(C3,R);
|
|
||||||
EXPECT(assert_equal(expected3,actual3,tol));
|
|
||||||
|
|
||||||
// Check the conditional P(C4|Root)
|
|
||||||
double sigma4 = 0.661968;
|
|
||||||
Matrix A46 = Matrix_(2,2,-0.146067,0.,0.,-0.146067);
|
|
||||||
GaussianBayesNet expected4;
|
|
||||||
push_front(expected4, ordering[X(4)], zero(2), eye(2)/sigma4, ordering[X(6)], A46/sigma4, ones(2));
|
|
||||||
GaussianISAM::sharedClique C4 = isamTree[ordering[X(3)]];
|
|
||||||
GaussianBayesNet actual4 = GaussianISAM::shortcut(C4,R);
|
|
||||||
EXPECT(assert_equal(expected4,actual4,tol));
|
|
||||||
}
|
|
||||||
|
|
||||||
/* ************************************************************************* *
|
|
||||||
Bayes tree for smoother with "nested dissection" ordering:
|
|
||||||
|
|
||||||
Node[x1] P(x1 | x2)
|
|
||||||
Node[x3] P(x3 | x2 x4)
|
|
||||||
Node[x5] P(x5 | x4 x6)
|
|
||||||
Node[x7] P(x7 | x6)
|
|
||||||
Node[x2] P(x2 | x4)
|
|
||||||
Node[x6] P(x6 | x4)
|
|
||||||
Node[x4] P(x4)
|
|
||||||
|
|
||||||
becomes
|
|
||||||
|
|
||||||
C1 x5 x6 x4
|
|
||||||
C2 x3 x2 : x4
|
|
||||||
C3 x1 : x2
|
|
||||||
C4 x7 : x6
|
|
||||||
|
|
||||||
************************************************************************* */
|
|
||||||
TEST_UNSAFE( BayesTree, balanced_smoother_marginals )
|
|
||||||
{
|
|
||||||
// Create smoother with 7 nodes
|
|
||||||
Ordering ordering;
|
|
||||||
ordering += X(1),X(3),X(5),X(7),X(2),X(6),X(4);
|
|
||||||
GaussianFactorGraph smoother = createSmoother(7, ordering).first;
|
|
||||||
|
|
||||||
// Create the Bayes tree
|
|
||||||
BayesTree<GaussianConditional> chordalBayesNet = *GaussianMultifrontalSolver(smoother).eliminate();
|
|
||||||
|
|
||||||
VectorValues expectedSolution(VectorValues::Zero(7,2));
|
|
||||||
VectorValues actualSolution = optimize(chordalBayesNet);
|
|
||||||
EXPECT(assert_equal(expectedSolution,actualSolution,tol));
|
|
||||||
|
|
||||||
// Create the Bayes tree
|
|
||||||
GaussianISAM bayesTree(chordalBayesNet);
|
|
||||||
LONGS_EQUAL(4,bayesTree.size());
|
|
||||||
|
|
||||||
double tol=1e-5;
|
|
||||||
|
|
||||||
// Check marginal on x1
|
|
||||||
GaussianBayesNet expected1 = simpleGaussian(ordering[X(1)], zero(2), sigmax1);
|
|
||||||
GaussianBayesNet actual1 = *bayesTree.marginalBayesNet(ordering[X(1)]);
|
|
||||||
Matrix expectedCovarianceX1 = eye(2,2) * (sigmax1 * sigmax1);
|
|
||||||
Matrix actualCovarianceX1;
|
|
||||||
actualCovarianceX1 = bayesTree.marginalCovariance(ordering[X(1)]);
|
|
||||||
EXPECT(assert_equal(expectedCovarianceX1, actualCovarianceX1, tol));
|
|
||||||
EXPECT(assert_equal(expected1,actual1,tol));
|
|
||||||
|
|
||||||
// Check marginal on x2
|
|
||||||
double sigx2 = 0.68712938; // FIXME: this should be corrected analytically
|
|
||||||
GaussianBayesNet expected2 = simpleGaussian(ordering[X(2)], zero(2), sigx2);
|
|
||||||
GaussianBayesNet actual2 = *bayesTree.marginalBayesNet(ordering[X(2)]);
|
|
||||||
Matrix expectedCovarianceX2 = eye(2,2) * (sigx2 * sigx2);
|
|
||||||
Matrix actualCovarianceX2;
|
|
||||||
actualCovarianceX2 = bayesTree.marginalCovariance(ordering[X(2)]);
|
|
||||||
EXPECT(assert_equal(expectedCovarianceX2, actualCovarianceX2, tol));
|
|
||||||
EXPECT(assert_equal(expected2,actual2,tol));
|
|
||||||
|
|
||||||
// Check marginal on x3
|
|
||||||
GaussianBayesNet expected3 = simpleGaussian(ordering[X(3)], zero(2), sigmax3);
|
|
||||||
GaussianBayesNet actual3 = *bayesTree.marginalBayesNet(ordering[X(3)]);
|
|
||||||
Matrix expectedCovarianceX3 = eye(2,2) * (sigmax3 * sigmax3);
|
|
||||||
Matrix actualCovarianceX3;
|
|
||||||
actualCovarianceX3 = bayesTree.marginalCovariance(ordering[X(3)]);
|
|
||||||
EXPECT(assert_equal(expectedCovarianceX3, actualCovarianceX3, tol));
|
|
||||||
EXPECT(assert_equal(expected3,actual3,tol));
|
|
||||||
|
|
||||||
// Check marginal on x4
|
|
||||||
GaussianBayesNet expected4 = simpleGaussian(ordering[X(4)], zero(2), sigmax4);
|
|
||||||
GaussianBayesNet actual4 = *bayesTree.marginalBayesNet(ordering[X(4)]);
|
|
||||||
Matrix expectedCovarianceX4 = eye(2,2) * (sigmax4 * sigmax4);
|
|
||||||
Matrix actualCovarianceX4;
|
|
||||||
actualCovarianceX4 = bayesTree.marginalCovariance(ordering[X(4)]);
|
|
||||||
EXPECT(assert_equal(expectedCovarianceX4, actualCovarianceX4, tol));
|
|
||||||
EXPECT(assert_equal(expected4,actual4,tol));
|
|
||||||
|
|
||||||
// Check marginal on x7 (should be equal to x1)
|
|
||||||
GaussianBayesNet expected7 = simpleGaussian(ordering[X(7)], zero(2), sigmax7);
|
|
||||||
GaussianBayesNet actual7 = *bayesTree.marginalBayesNet(ordering[X(7)]);
|
|
||||||
Matrix expectedCovarianceX7 = eye(2,2) * (sigmax7 * sigmax7);
|
|
||||||
Matrix actualCovarianceX7;
|
|
||||||
actualCovarianceX7 = bayesTree.marginalCovariance(ordering[X(7)]);
|
|
||||||
EXPECT(assert_equal(expectedCovarianceX7, actualCovarianceX7, tol));
|
|
||||||
EXPECT(assert_equal(expected7,actual7,tol));
|
|
||||||
}
|
|
||||||
|
|
||||||
/* ************************************************************************* */
|
|
||||||
TEST_UNSAFE( BayesTree, balanced_smoother_shortcuts )
|
|
||||||
{
|
|
||||||
// Create smoother with 7 nodes
|
|
||||||
Ordering ordering;
|
|
||||||
ordering += X(1),X(3),X(5),X(7),X(2),X(6),X(4);
|
|
||||||
GaussianFactorGraph smoother = createSmoother(7, ordering).first;
|
|
||||||
|
|
||||||
// Create the Bayes tree
|
|
||||||
BayesTree<GaussianConditional> bayesTree = *GaussianMultifrontalSolver(smoother).eliminate();
|
|
||||||
GaussianISAM isamTree(bayesTree);
|
|
||||||
|
|
||||||
// Check the conditional P(Root|Root)
|
|
||||||
GaussianBayesNet empty;
|
|
||||||
GaussianISAM::sharedClique R = isamTree.root();
|
|
||||||
GaussianBayesNet actual1 = GaussianISAM::shortcut(R,R);
|
|
||||||
EXPECT(assert_equal(empty,actual1,tol));
|
|
||||||
|
|
||||||
// Check the conditional P(C2|Root)
|
|
||||||
GaussianISAM::sharedClique C2 = isamTree[ordering[X(3)]];
|
|
||||||
GaussianBayesNet actual2 = GaussianISAM::shortcut(C2,R);
|
|
||||||
EXPECT(assert_equal(empty,actual2,tol));
|
|
||||||
|
|
||||||
// Check the conditional P(C3|Root), which should be equal to P(x2|x4)
|
|
||||||
/** TODO: Note for multifrontal conditional:
|
|
||||||
* p_x2_x4 is now an element conditional of the multifrontal conditional bayesTree[ordering[X(2)]]->conditional()
|
|
||||||
* We don't know yet how to take it out.
|
|
||||||
*/
|
|
||||||
// GaussianConditional::shared_ptr p_x2_x4 = bayesTree[ordering[X(2)]]->conditional();
|
|
||||||
// p_x2_x4->print("Conditional p_x2_x4: ");
|
|
||||||
// GaussianBayesNet expected3(p_x2_x4);
|
|
||||||
// GaussianISAM::sharedClique C3 = isamTree[ordering[X(1)]];
|
|
||||||
// GaussianBayesNet actual3 = GaussianISAM::shortcut(C3,R);
|
|
||||||
// EXPECT(assert_equal(expected3,actual3,tol));
|
|
||||||
}
|
|
||||||
|
|
||||||
///* ************************************************************************* */
|
|
||||||
//TEST( BayesTree, balanced_smoother_clique_marginals )
|
|
||||||
//{
|
|
||||||
// // Create smoother with 7 nodes
|
|
||||||
// Ordering ordering;
|
|
||||||
// ordering += X(1),X(3),X(5),X(7),X(2),X(6),X(4);
|
|
||||||
// GaussianFactorGraph smoother = createSmoother(7, ordering).first;
|
|
||||||
//
|
|
||||||
// // Create the Bayes tree
|
|
||||||
// GaussianBayesNet chordalBayesNet = *GaussianSequentialSolver(smoother).eliminate();
|
|
||||||
// GaussianISAM bayesTree(chordalBayesNet);
|
|
||||||
//
|
|
||||||
// // Check the clique marginal P(C3)
|
|
||||||
// double sigmax2_alt = 1/1.45533; // THIS NEEDS TO BE CHECKED!
|
|
||||||
// GaussianBayesNet expected = simpleGaussian(ordering[X(2)],zero(2),sigmax2_alt);
|
|
||||||
// push_front(expected,ordering[X(1)], zero(2), eye(2)*sqrt(2), ordering[X(2)], -eye(2)*sqrt(2)/2, ones(2));
|
|
||||||
// GaussianISAM::sharedClique R = bayesTree.root(), C3 = bayesTree[ordering[X(1)]];
|
|
||||||
// GaussianFactorGraph marginal = C3->marginal(R);
|
|
||||||
// GaussianVariableIndex varIndex(marginal);
|
|
||||||
// Permutation toFront(Permutation::PullToFront(C3->keys(), varIndex.size()));
|
|
||||||
// Permutation toFrontInverse(*toFront.inverse());
|
|
||||||
// varIndex.permute(toFront);
|
|
||||||
// BOOST_FOREACH(const GaussianFactor::shared_ptr& factor, marginal) {
|
|
||||||
// factor->permuteWithInverse(toFrontInverse); }
|
|
||||||
// GaussianBayesNet actual = *inference::EliminateUntil(marginal, C3->keys().size(), varIndex);
|
|
||||||
// actual.permuteWithInverse(toFront);
|
|
||||||
// EXPECT(assert_equal(expected,actual,tol));
|
|
||||||
//}
|
|
||||||
|
|
||||||
/* ************************************************************************* */
|
|
||||||
TEST_UNSAFE( BayesTree, balanced_smoother_joint )
|
|
||||||
{
|
|
||||||
// Create smoother with 7 nodes
|
|
||||||
Ordering ordering;
|
|
||||||
ordering += X(1),X(3),X(5),X(7),X(2),X(6),X(4);
|
|
||||||
GaussianFactorGraph smoother = createSmoother(7, ordering).first;
|
|
||||||
|
|
||||||
// Create the Bayes tree, expected to look like:
|
|
||||||
// x5 x6 x4
|
|
||||||
// x3 x2 : x4
|
|
||||||
// x1 : x2
|
|
||||||
// x7 : x6
|
|
||||||
BayesTree<GaussianConditional> chordalBayesNet = *GaussianMultifrontalSolver(smoother).eliminate();
|
|
||||||
GaussianISAM bayesTree(chordalBayesNet);
|
|
||||||
|
|
||||||
// Conditional density elements reused by both tests
|
|
||||||
const Vector sigma = ones(2);
|
|
||||||
const Matrix I = eye(2), A = -0.00429185*I;
|
|
||||||
|
|
||||||
// Check the joint density P(x1,x7) factored as P(x1|x7)P(x7)
|
|
||||||
GaussianBayesNet expected1;
|
|
||||||
// Why does the sign get flipped on the prior?
|
|
||||||
GaussianConditional::shared_ptr
|
|
||||||
parent1(new GaussianConditional(ordering[X(7)], zero(2), -1*I/sigmax7, ones(2)));
|
|
||||||
expected1.push_front(parent1);
|
|
||||||
push_front(expected1,ordering[X(1)], zero(2), I/sigmax7, ordering[X(7)], A/sigmax7, sigma);
|
|
||||||
GaussianBayesNet actual1 = *bayesTree.jointBayesNet(ordering[X(1)],ordering[X(7)]);
|
|
||||||
EXPECT(assert_equal(expected1,actual1,tol));
|
|
||||||
|
|
||||||
// // Check the joint density P(x7,x1) factored as P(x7|x1)P(x1)
|
|
||||||
// GaussianBayesNet expected2;
|
|
||||||
// GaussianConditional::shared_ptr
|
|
||||||
// parent2(new GaussianConditional(ordering[X(1)], zero(2), -1*I/sigmax1, ones(2)));
|
|
||||||
// expected2.push_front(parent2);
|
|
||||||
// push_front(expected2,ordering[X(7)], zero(2), I/sigmax1, ordering[X(1)], A/sigmax1, sigma);
|
|
||||||
// GaussianBayesNet actual2 = *bayesTree.jointBayesNet(ordering[X(7)],ordering[X(1)]);
|
|
||||||
// EXPECT(assert_equal(expected2,actual2,tol));
|
|
||||||
|
|
||||||
// Check the joint density P(x1,x4), i.e. with a root variable
|
|
||||||
GaussianBayesNet expected3;
|
|
||||||
GaussianConditional::shared_ptr
|
|
||||||
parent3(new GaussianConditional(ordering[X(4)], zero(2), I/sigmax4, ones(2)));
|
|
||||||
expected3.push_front(parent3);
|
|
||||||
double sig14 = 0.784465;
|
|
||||||
Matrix A14 = -0.0769231*I;
|
|
||||||
push_front(expected3,ordering[X(1)], zero(2), I/sig14, ordering[X(4)], A14/sig14, sigma);
|
|
||||||
GaussianBayesNet actual3 = *bayesTree.jointBayesNet(ordering[X(1)],ordering[X(4)]);
|
|
||||||
EXPECT(assert_equal(expected3,actual3,tol));
|
|
||||||
|
|
||||||
// // Check the joint density P(x4,x1), i.e. with a root variable, factored the other way
|
|
||||||
// GaussianBayesNet expected4;
|
|
||||||
// GaussianConditional::shared_ptr
|
|
||||||
// parent4(new GaussianConditional(ordering[X(1)], zero(2), -1.0*I/sigmax1, ones(2)));
|
|
||||||
// expected4.push_front(parent4);
|
|
||||||
// double sig41 = 0.668096;
|
|
||||||
// Matrix A41 = -0.055794*I;
|
|
||||||
// push_front(expected4,ordering[X(4)], zero(2), I/sig41, ordering[X(1)], A41/sig41, sigma);
|
|
||||||
// GaussianBayesNet actual4 = *bayesTree.jointBayesNet(ordering[X(4)],ordering[X(1)]);
|
|
||||||
// EXPECT(assert_equal(expected4,actual4,tol));
|
|
||||||
}
|
|
||||||
|
|
||||||
/* ************************************************************************* */
|
|
||||||
TEST_UNSAFE(BayesTree, simpleMarginal)
|
|
||||||
{
|
|
||||||
GaussianFactorGraph gfg;
|
|
||||||
|
|
||||||
Matrix A12 = Rot2::fromDegrees(45.0).matrix();
|
|
||||||
|
|
||||||
gfg.add(0, eye(2), zero(2), noiseModel::Isotropic::Sigma(2, 1.0));
|
|
||||||
gfg.add(0, -eye(2), 1, eye(2), ones(2), noiseModel::Isotropic::Sigma(2, 1.0));
|
|
||||||
gfg.add(1, -eye(2), 2, A12, ones(2), noiseModel::Isotropic::Sigma(2, 1.0));
|
|
||||||
|
|
||||||
Matrix expected(GaussianSequentialSolver(gfg).marginalCovariance(2));
|
|
||||||
Matrix actual(GaussianMultifrontalSolver(gfg).marginalCovariance(2));
|
|
||||||
|
|
||||||
EXPECT(assert_equal(expected, actual));
|
|
||||||
}
|
|
||||||
|
|
||||||
/* ************************************************************************* */
|
/* ************************************************************************* */
|
||||||
int main() { TestResult tr; return TestRegistry::runAllTests(tr);}
|
int main() { TestResult tr; return TestRegistry::runAllTests(tr);}
|
||||||
/* ************************************************************************* */
|
/* ************************************************************************* */
|
||||||
|
|
|
||||||
Loading…
Reference in New Issue