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gtsam/gtsam/linear/tests/testHessianFactor.cpp

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/* ----------------------------------------------------------------------------
* 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 testHessianFactor.cpp
* @author Richard Roberts
* @date Dec 15, 2010
*/
#include <gtsam/linear/HessianFactor.h>
#include <gtsam/linear/JacobianFactor.h>
#include <gtsam/linear/GaussianFactorGraph.h>
#include <gtsam/linear/GaussianConditional.h>
#include <gtsam/linear/VectorValues.h>
#include <gtsam/base/debug.h>
#include <gtsam/base/TestableAssertions.h>
#include <CppUnitLite/TestHarness.h>
#include <boost/assign/list_of.hpp>
#include <boost/assign/std/vector.hpp>
#include <boost/assign/std/map.hpp>
using namespace boost::assign;
#include <vector>
#include <utility>
using namespace std;
using namespace gtsam;
const double tol = 1e-5;
/* ************************************************************************* */
TEST(HessianFactor, Slot)
{
FastVector<Key> keys = list_of(2)(4)(1);
EXPECT_LONGS_EQUAL(0, GaussianFactor::Slot(keys,2));
EXPECT_LONGS_EQUAL(1, GaussianFactor::Slot(keys,4));
EXPECT_LONGS_EQUAL(2, GaussianFactor::Slot(keys,1));
EXPECT_LONGS_EQUAL(3, GaussianFactor::Slot(keys,5)); // does not exist
}
/* ************************************************************************* */
TEST(HessianFactor, emptyConstructor)
{
HessianFactor f;
DOUBLES_EQUAL(0.0, f.constantTerm(), 1e-9); // Constant term should be zero
EXPECT(assert_equal(Vector(), f.linearTerm())); // Linear term should be empty
EXPECT(assert_equal(zeros(1,1), f.info())); // Full matrix should be 1-by-1 zero matrix
DOUBLES_EQUAL(0.0, f.error(VectorValues()), 1e-9); // Should have zero error
}
/* ************************************************************************* */
TEST(HessianFactor, ConversionConstructor)
{
HessianFactor expected(list_of(0)(1),
SymmetricBlockMatrix(list_of(2)(4)(1), (Matrix(7,7) <<
125.0000, 0.0, -25.0000, 0.0, -100.0000, 0.0, 25.0000,
0.0, 125.0000, 0.0, -25.0000, 0.0, -100.0000, -17.5000,
-25.0000, 0.0, 25.0000, 0.0, 0.0, 0.0, -5.0000,
0.0, -25.0000, 0.0, 25.0000, 0.0, 0.0, 7.5000,
-100.0000, 0.0, 0.0, 0.0, 100.0000, 0.0, -20.0000,
0.0, -100.0000, 0.0, 0.0, 0.0, 100.0000, 10.0000,
25.0000, -17.5000, -5.0000, 7.5000, -20.0000, 10.0000, 8.2500).finished()));
JacobianFactor jacobian(
0, (Matrix(4,2) << -1., 0.,
+0.,-1.,
1., 0.,
+0.,1.).finished(),
1, (Matrix(4,4) << 1., 0., 0.00, 0., // f4
0., 1., 0.00, 0., // f4
0., 0., -1., 0., // f2
0., 0., 0.00, -1.).finished(), // f2
(Vector(4) << -0.2, 0.3, 0.2, -0.1).finished(),
noiseModel::Diagonal::Sigmas((Vector(4) << 0.2, 0.2, 0.1, 0.1).finished()));
HessianFactor actual(jacobian);
VectorValues values = pair_list_of<Key, Vector>
(0, Vector2(1.0, 2.0))
(1, (Vector(4) << 3.0, 4.0, 5.0, 6.0).finished());
EXPECT_LONGS_EQUAL(2, (long)actual.size());
EXPECT(assert_equal(expected, actual, 1e-9));
EXPECT_DOUBLES_EQUAL(jacobian.error(values), actual.error(values), 1e-9);
}
/* ************************************************************************* */
TEST(HessianFactor, Constructor1)
{
Matrix G = (Matrix(2,2) << 3.0, 5.0, 5.0, 6.0).finished();
Vector g = Vector2(-8.0, -9.0);
double f = 10.0;
HessianFactor factor(0, G, g, f);
// extract underlying parts
EXPECT(assert_equal(G, Matrix(factor.info(factor.begin(), factor.begin()))));
EXPECT_DOUBLES_EQUAL(f, factor.constantTerm(), 1e-10);
EXPECT(assert_equal(g, Vector(factor.linearTerm())));
EXPECT_LONGS_EQUAL(1, (long)factor.size());
VectorValues dx = pair_list_of<Key, Vector>(0, Vector2(1.5, 2.5));
// error 0.5*(f - 2*x'*g + x'*G*x)
double expected = 80.375;
double actual = factor.error(dx);
double expected_manual = 0.5 * (f - 2.0 * dx[0].dot(g) + dx[0].transpose() * G.selfadjointView<Eigen::Upper>() * dx[0]);
EXPECT_DOUBLES_EQUAL(expected, expected_manual, 1e-10);
EXPECT_DOUBLES_EQUAL(expected, actual, 1e-10);
}
/* ************************************************************************* */
TEST(HessianFactor, Constructor1b)
{
Vector mu = Vector2(1.0,2.0);
Matrix Sigma = eye(2,2);
HessianFactor factor(0, mu, Sigma);
Matrix G = eye(2,2);
Vector g = G*mu;
double f = dot(g,mu);
// Check
EXPECT(assert_equal(G, Matrix(factor.info(factor.begin(), factor.begin()))));
EXPECT_DOUBLES_EQUAL(f, factor.constantTerm(), 1e-10);
EXPECT(assert_equal(g, Vector(factor.linearTerm())));
EXPECT_LONGS_EQUAL(1, (long)factor.size());
}
/* ************************************************************************* */
TEST(HessianFactor, Constructor2)
{
Matrix G11 = (Matrix(1,1) << 1.0).finished();
Matrix G12 = (Matrix(1,2) << 2.0, 4.0).finished();
Matrix G22 = (Matrix(2,2) << 3.0, 5.0, 5.0, 6.0).finished();
Vector g1 = (Vector(1) << -7.0).finished();
Vector g2 = Vector2(-8.0, -9.0);
double f = 10.0;
Vector dx0 = (Vector(1) << 0.5).finished();
Vector dx1 = Vector2(1.5, 2.5);
VectorValues dx = pair_list_of
(0, dx0)
(1, dx1);
HessianFactor factor(0, 1, G11, G12, g1, G22, g2, f);
double expected = 90.5;
double actual = factor.error(dx);
DOUBLES_EQUAL(expected, actual, 1e-10);
LONGS_EQUAL(4, (long)factor.rows());
DOUBLES_EQUAL(10.0, factor.constantTerm(), 1e-10);
Vector linearExpected(3); linearExpected << g1, g2;
EXPECT(assert_equal(linearExpected, factor.linearTerm()));
EXPECT(assert_equal(G11, factor.info(factor.begin(), factor.begin())));
EXPECT(assert_equal(G12, factor.info(factor.begin(), factor.begin()+1)));
EXPECT(assert_equal(G22, factor.info(factor.begin()+1, factor.begin()+1)));
// Check case when vector values is larger than factor
VectorValues dxLarge = pair_list_of<Key, Vector>
(0, dx0)
(1, dx1)
(2, Vector2(0.1, 0.2));
EXPECT_DOUBLES_EQUAL(expected, factor.error(dxLarge), 1e-10);
}
/* ************************************************************************* */
TEST(HessianFactor, Constructor3)
{
Matrix G11 = (Matrix(1,1) << 1.0).finished();
Matrix G12 = (Matrix(1,2) << 2.0, 4.0).finished();
Matrix G13 = (Matrix(1,3) << 3.0, 6.0, 9.0).finished();
Matrix G22 = (Matrix(2,2) << 3.0, 5.0, 5.0, 6.0).finished();
Matrix G23 = (Matrix(2,3) << 4.0, 6.0, 8.0, 1.0, 2.0, 4.0).finished();
Matrix G33 = (Matrix(3,3) << 1.0, 2.0, 3.0, 2.0, 5.0, 6.0, 3.0, 6.0, 9.0).finished();
Vector g1 = (Vector(1) << -7.0).finished();
Vector g2 = Vector2(-8.0, -9.0);
Vector g3 = Vector3(1.0, 2.0, 3.0);
double f = 10.0;
Vector dx0 = (Vector(1) << 0.5).finished();
Vector dx1 = Vector2(1.5, 2.5);
Vector dx2 = Vector3(1.5, 2.5, 3.5);
VectorValues dx = pair_list_of
(0, dx0)
(1, dx1)
(2, dx2);
HessianFactor factor(0, 1, 2, G11, G12, G13, g1, G22, G23, g2, G33, g3, f);
double expected = 371.3750;
double actual = factor.error(dx);
DOUBLES_EQUAL(expected, actual, 1e-10);
LONGS_EQUAL(7, (long)factor.rows());
DOUBLES_EQUAL(10.0, factor.constantTerm(), 1e-10);
Vector linearExpected(6); linearExpected << g1, g2, g3;
EXPECT(assert_equal(linearExpected, factor.linearTerm()));
EXPECT(assert_equal(G11, factor.info(factor.begin()+0, factor.begin()+0)));
EXPECT(assert_equal(G12, factor.info(factor.begin()+0, factor.begin()+1)));
EXPECT(assert_equal(G13, factor.info(factor.begin()+0, factor.begin()+2)));
EXPECT(assert_equal(G22, factor.info(factor.begin()+1, factor.begin()+1)));
EXPECT(assert_equal(G23, factor.info(factor.begin()+1, factor.begin()+2)));
EXPECT(assert_equal(G33, factor.info(factor.begin()+2, factor.begin()+2)));
}
/* ************************************************************************* */
TEST(HessianFactor, ConstructorNWay)
{
Matrix G11 = (Matrix(1,1) << 1.0).finished();
Matrix G12 = (Matrix(1,2) << 2.0, 4.0).finished();
Matrix G13 = (Matrix(1,3) << 3.0, 6.0, 9.0).finished();
Matrix G22 = (Matrix(2,2) << 3.0, 5.0, 5.0, 6.0).finished();
Matrix G23 = (Matrix(2,3) << 4.0, 6.0, 8.0, 1.0, 2.0, 4.0).finished();
Matrix G33 = (Matrix(3,3) << 1.0, 2.0, 3.0, 2.0, 5.0, 6.0, 3.0, 6.0, 9.0).finished();
Vector g1 = (Vector(1) << -7.0).finished();
Vector g2 = Vector2(-8.0, -9.0);
Vector g3 = Vector3(1.0, 2.0, 3.0);
double f = 10.0;
Vector dx0 = (Vector(1) << 0.5).finished();
Vector dx1 = Vector2(1.5, 2.5);
Vector dx2 = Vector3(1.5, 2.5, 3.5);
VectorValues dx = pair_list_of
(0, dx0)
(1, dx1)
(2, dx2);
std::vector<Key> js;
js.push_back(0); js.push_back(1); js.push_back(2);
std::vector<Matrix> Gs;
Gs.push_back(G11); Gs.push_back(G12); Gs.push_back(G13); Gs.push_back(G22); Gs.push_back(G23); Gs.push_back(G33);
std::vector<Vector> gs;
gs.push_back(g1); gs.push_back(g2); gs.push_back(g3);
HessianFactor factor(js, Gs, gs, f);
double expected = 371.3750;
double actual = factor.error(dx);
DOUBLES_EQUAL(expected, actual, 1e-10);
LONGS_EQUAL(7, (long)factor.rows());
DOUBLES_EQUAL(10.0, factor.constantTerm(), 1e-10);
Vector linearExpected(6); linearExpected << g1, g2, g3;
EXPECT(assert_equal(linearExpected, factor.linearTerm()));
EXPECT(assert_equal(G11, factor.info(factor.begin()+0, factor.begin()+0)));
EXPECT(assert_equal(G12, factor.info(factor.begin()+0, factor.begin()+1)));
EXPECT(assert_equal(G13, factor.info(factor.begin()+0, factor.begin()+2)));
EXPECT(assert_equal(G22, factor.info(factor.begin()+1, factor.begin()+1)));
EXPECT(assert_equal(G23, factor.info(factor.begin()+1, factor.begin()+2)));
EXPECT(assert_equal(G33, factor.info(factor.begin()+2, factor.begin()+2)));
}
/* ************************************************************************* */
TEST(HessianFactor, CombineAndEliminate1) {
Matrix3 A01 = 3.0 * I_3x3;
Vector3 b0(1, 0, 0);
Matrix3 A21 = 4.0 * I_3x3;
Vector3 b2 = Vector3::Zero();
GaussianFactorGraph gfg;
gfg.add(1, A01, b0);
gfg.add(1, A21, b2);
Matrix63 A1;
A1 << A01, A21;
Vector6 b;
b << b0, b2;
// create a full, uneliminated version of the factor
JacobianFactor jacobian(1, A1, b);
// Make sure combining works
HessianFactor hessian(gfg);
VectorValues v;
v.insert(1, Vector3(1, 0, 0));
EXPECT_DOUBLES_EQUAL(jacobian.error(v), hessian.error(v), 1e-9);
EXPECT(assert_equal(HessianFactor(jacobian), hessian, 1e-6));
EXPECT(assert_equal(25.0 * I_3x3, hessian.information(), 1e-9));
EXPECT(
assert_equal(jacobian.augmentedInformation(),
hessian.augmentedInformation(), 1e-9));
// perform elimination on jacobian
Ordering ordering = list_of(1);
GaussianConditional::shared_ptr expectedConditional;
JacobianFactor::shared_ptr expectedFactor;
boost::tie(expectedConditional, expectedFactor) = jacobian.eliminate(ordering);
CHECK(expectedFactor);
// Eliminate
GaussianConditional::shared_ptr actualConditional;
HessianFactor::shared_ptr actualHessian;
boost::tie(actualConditional, actualHessian) = //
EliminateCholesky(gfg, ordering);
actualConditional->setModel(false,Vector3(1,1,1)); // add a unit model for comparison
EXPECT(assert_equal(*expectedConditional, *actualConditional, 1e-6));
#ifdef TEST_ERROR_EQUIVALENCE
// these tests disabled because QR cuts off the all zeros + error row
EXPECT_DOUBLES_EQUAL(expectedFactor->error(v), actualHessian->error(v), 1e-9);
EXPECT(
assert_equal(expectedFactor->augmentedInformation(),
actualHessian->augmentedInformation(), 1e-9));
EXPECT(assert_equal(HessianFactor(*expectedFactor), *actualHessian, 1e-6));
#endif
}
/* ************************************************************************* */
TEST(HessianFactor, CombineAndEliminate2) {
Matrix A01 = I_3x3;
Vector3 b0(1.5, 1.5, 1.5);
Vector3 s0(1.6, 1.6, 1.6);
Matrix A10 = 2.0 * I_3x3;
Matrix A11 = -2.0 * I_3x3;
Vector3 b1(2.5, 2.5, 2.5);
Vector3 s1(2.6, 2.6, 2.6);
Matrix A21 = 3.0 * I_3x3;
Vector3 b2(3.5, 3.5, 3.5);
Vector3 s2(3.6, 3.6, 3.6);
GaussianFactorGraph gfg;
gfg.add(1, A01, b0, noiseModel::Diagonal::Sigmas(s0, true));
gfg.add(0, A10, 1, A11, b1, noiseModel::Diagonal::Sigmas(s1, true));
gfg.add(1, A21, b2, noiseModel::Diagonal::Sigmas(s2, true));
Matrix93 A0, A1;
A0 << A10, Z_3x3, Z_3x3;
A1 << A11, A01, A21;
Vector9 b, sigmas;
b << b1, b0, b2;
sigmas << s1, s0, s2;
// create a full, uneliminated version of the factor
JacobianFactor jacobian(0, A0, 1, A1, b,
noiseModel::Diagonal::Sigmas(sigmas, true));
// Make sure combining works
HessianFactor hessian(gfg);
EXPECT(assert_equal(HessianFactor(jacobian), hessian, 1e-6));
EXPECT(
assert_equal(jacobian.augmentedInformation(),
hessian.augmentedInformation(), 1e-9));
// perform elimination on jacobian
Ordering ordering = list_of(0);
GaussianConditional::shared_ptr expectedConditional;
JacobianFactor::shared_ptr expectedFactor;
boost::tie(expectedConditional, expectedFactor) = //
jacobian.eliminate(ordering);
// Eliminate
GaussianConditional::shared_ptr actualConditional;
HessianFactor::shared_ptr actualHessian;
boost::tie(actualConditional, actualHessian) = //
EliminateCholesky(gfg, ordering);
actualConditional->setModel(false,Vector3(1,1,1)); // add a unit model for comparison
EXPECT(assert_equal(*expectedConditional, *actualConditional, 1e-6));
#ifdef TEST_ERROR_EQUIVALENCE
// these tests disabled because QR cuts off the all zeros + error row
VectorValues v;
v.insert(1, Vector3(1, 2, 3));
EXPECT_DOUBLES_EQUAL(expectedFactor->error(v), actualHessian->error(v), 1e-9);
EXPECT(
assert_equal(expectedFactor->augmentedInformation(),
actualHessian->augmentedInformation(), 1e-9));
EXPECT(assert_equal(HessianFactor(*expectedFactor), *actualHessian, 1e-6));
#endif
}
/* ************************************************************************* */
TEST(HessianFactor, eliminate2 )
{
// sigmas
double sigma1 = 0.2;
double sigma2 = 0.1;
Vector sigmas = (Vector(4) << sigma1, sigma1, sigma2, sigma2).finished();
// the combined linear factor
Matrix Ax2 = (Matrix(4,2) <<
// x2
-1., 0.,
+0.,-1.,
1., 0.,
+0.,1.
).finished();
Matrix Al1x1 = (Matrix(4,4) <<
// l1 x1
1., 0., 0.00, 0., // f4
0., 1., 0.00, 0., // f4
0., 0., -1., 0., // f2
0., 0., 0.00,-1. // f2
).finished();
// the RHS
Vector b2(4);
b2(0) = -0.2;
b2(1) = 0.3;
b2(2) = 0.2;
b2(3) = -0.1;
vector<pair<Key, Matrix> > meas;
meas.push_back(make_pair(0, Ax2));
meas.push_back(make_pair(1, Al1x1));
JacobianFactor combined(meas, b2, noiseModel::Diagonal::Sigmas(sigmas));
// eliminate the combined factor
HessianFactor::shared_ptr combinedLF_Chol(new HessianFactor(combined));
GaussianFactorGraph combinedLFG_Chol = list_of(combinedLF_Chol);
std::pair<GaussianConditional::shared_ptr, HessianFactor::shared_ptr> actual_Chol =
EliminateCholesky(combinedLFG_Chol, Ordering(list_of(0)));
// create expected Conditional Gaussian
double oldSigma = 0.0894427; // from when R was made unit
Matrix R11 = (Matrix(2,2) <<
1.00, 0.00,
0.00, 1.00
).finished()/oldSigma;
Matrix S12 = (Matrix(2,4) <<
-0.20, 0.00,-0.80, 0.00,
+0.00,-0.20,+0.00,-0.80
).finished()/oldSigma;
Vector d = Vector2(0.2,-0.14)/oldSigma;
GaussianConditional expectedCG(0, d, R11, 1, S12);
EXPECT(assert_equal(expectedCG, *actual_Chol.first, 1e-4));
// the expected linear factor
double sigma = 0.2236;
Matrix Bl1x1 = (Matrix(2,4) <<
// l1 x1
1.00, 0.00, -1.00, 0.00,
0.00, 1.00, +0.00, -1.00
).finished()/sigma;
Vector b1 = Vector2(0.0,0.894427);
JacobianFactor expectedLF(1, Bl1x1, b1, noiseModel::Isotropic::Sigma(2,1.0));
EXPECT(assert_equal(HessianFactor(expectedLF), *actual_Chol.second, 1.5e-3));
}
/* ************************************************************************* */
TEST(HessianFactor, combine) {
// update the information matrix with a single jacobian factor
Matrix A0 = (Matrix(2, 2) <<
11.1803399, 0.0,
0.0, 11.1803399).finished();
Matrix A1 = (Matrix(2, 2) <<
-2.23606798, 0.0,
0.0, -2.23606798).finished();
Matrix A2 = (Matrix(2, 2) <<
-8.94427191, 0.0,
0.0, -8.94427191).finished();
Vector b = Vector2(2.23606798,-1.56524758);
SharedDiagonal model = noiseModel::Diagonal::Sigmas(ones(2));
GaussianFactor::shared_ptr f(new JacobianFactor(0, A0, 1, A1, 2, A2, b, model));
GaussianFactorGraph factors = list_of(f);
// Form Ab' * Ab
HessianFactor actual(factors);
Matrix expected = (Matrix(7, 7) <<
125.0000, 0.0, -25.0000, 0.0, -100.0000, 0.0, 25.0000,
0.0, 125.0000, 0.0, -25.0000, 0.0, -100.0000, -17.5000,
-25.0000, 0.0, 5.0000, 0.0, 20.0000, 0.0, -5.0000,
0.0, -25.0000, 0.0, 5.0000, 0.0, 20.0000, 3.5000,
-100.0000, 0.0, 20.0000, 0.0, 80.0000, 0.0, -20.0000,
0.0, -100.0000, 0.0, 20.0000, 0.0, 80.0000, 14.0000,
25.0000, -17.5000, -5.0000, 3.5000, -20.0000, 14.0000, 7.4500).finished();
EXPECT(assert_equal(expected, Matrix(actual.matrixObject().full()), tol));
}
/* ************************************************************************* */
TEST(HessianFactor, gradientAtZero)
{
Matrix G11 = (Matrix(1, 1) << 1).finished();
Matrix G12 = (Matrix(1, 2) << 0, 0).finished();
Matrix G22 = (Matrix(2, 2) << 1, 0, 0, 1).finished();
Vector g1 = (Vector(1) << -7).finished();
Vector g2 = Vector2(-8, -9);
double f = 194;
HessianFactor factor(0, 1, G11, G12, g1, G22, g2, f);
// test gradient at zero
VectorValues expectedG = pair_list_of<Key, Vector>(0, -g1) (1, -g2);
Matrix A; Vector b; boost::tie(A,b) = factor.jacobian();
FastVector<Key> keys; keys += 0,1;
EXPECT(assert_equal(-A.transpose()*b, expectedG.vector(keys)));
VectorValues actualG = factor.gradientAtZero();
EXPECT(assert_equal(expectedG, actualG));
}
/* ************************************************************************* */
TEST(HessianFactor, gradient)
{
Matrix G11 = (Matrix(1, 1) << 1).finished();
Matrix G12 = (Matrix(1, 2) << 0, 0).finished();
Matrix G22 = (Matrix(2, 2) << 1, 0, 0, 1).finished();
Vector g1 = (Vector(1) << -7).finished();
Vector g2 = (Vector(2) << -8, -9).finished();
double f = 194;
HessianFactor factor(0, 1, G11, G12, g1, G22, g2, f);
// test gradient
Vector x0 = (Vector(1) << 3.0).finished();
Vector x1 = (Vector(2) << -3.5, 7.1).finished();
VectorValues x = pair_list_of<Key, Vector>(0, x0) (1, x1);
Vector expectedGrad0 = (Vector(1) << 10.0).finished();
Vector expectedGrad1 = (Vector(2) << 4.5, 16.1).finished();
Vector grad0 = factor.gradient(0, x);
Vector grad1 = factor.gradient(1, x);
EXPECT(assert_equal(expectedGrad0, grad0));
EXPECT(assert_equal(expectedGrad1, grad1));
}
/* ************************************************************************* */
TEST(HessianFactor, hessianDiagonal)
{
Matrix G11 = (Matrix(1, 1) << 1).finished();
Matrix G12 = (Matrix(1, 2) << 0, 0).finished();
Matrix G22 = (Matrix(2, 2) << 1, 0, 0, 1).finished();
Vector g1 = (Vector(1) << -7).finished();
Vector g2 = Vector2(-8, -9);
double f = 194;
HessianFactor factor(0, 1, G11, G12, g1, G22, g2, f);
// hessianDiagonal
VectorValues expected;
expected.insert(0, (Vector(1) << 1).finished());
expected.insert(1, Vector2(1,1));
EXPECT(assert_equal(expected, factor.hessianDiagonal()));
// hessianBlockDiagonal
map<Key,Matrix> actualBD = factor.hessianBlockDiagonal();
LONGS_EQUAL(2,actualBD.size());
EXPECT(assert_equal(G11,actualBD[0]));
EXPECT(assert_equal(G22,actualBD[1]));
}
/* ************************************************************************* */
int main() { TestResult tr; return TestRegistry::runAllTests(tr);}
/* ************************************************************************* */
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