gtsam/gtsam_unstable/linear/tests/testLinearEquality.cpp

231 lines
8.2 KiB
C++

/* ----------------------------------------------------------------------------
* 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 testLinearEquality.cpp
* @brief Unit tests for LinearEquality
* @author Duy-Nguyen Ta
**/
#include <CppUnitLite/TestHarness.h>
#include <gtsam/base/TestableAssertions.h>
#include <gtsam/linear/HessianFactor.h>
#include <gtsam/linear/VectorValues.h>
#include <gtsam_unstable/linear/LinearEquality.h>
using namespace std;
using namespace gtsam;
GTSAM_CONCEPT_TESTABLE_INST(LinearEquality)
namespace {
namespace simple {
// Terms we'll use
using Terms = vector<pair<Key, Matrix> >;
const Terms terms{make_pair(5, I_3x3), make_pair(10, 2 * I_3x3),
make_pair(15, 3 * I_3x3)};
// RHS and sigmas
const Vector b = (Vector(3) << 1., 2., 3.).finished();
const SharedDiagonal noise = noiseModel::Constrained::All(3);
} // namespace simple
} // namespace
/* ************************************************************************* */
TEST(LinearEquality, constructors_and_accessors) {
using namespace simple;
// Test for using different numbers of terms
{
// One term constructor
LinearEquality expected(Terms(terms.begin(), terms.begin() + 1), b, 0);
LinearEquality actual(terms[0].first, terms[0].second, b, 0);
EXPECT(assert_equal(expected, actual));
LONGS_EQUAL((long)terms[0].first, (long)actual.keys().back());
EXPECT(assert_equal(terms[0].second, actual.getA(actual.end() - 1)));
EXPECT(assert_equal(b, expected.getb()));
EXPECT(assert_equal(b, actual.getb()));
EXPECT(assert_equal(*noise, *actual.get_model()));
}
{
// Two term constructor
LinearEquality expected(Terms(terms.begin(), terms.begin() + 2), b, 0);
LinearEquality actual(terms[0].first, terms[0].second, terms[1].first,
terms[1].second, b, 0);
EXPECT(assert_equal(expected, actual));
LONGS_EQUAL((long)terms[1].first, (long)actual.keys().back());
EXPECT(assert_equal(terms[1].second, actual.getA(actual.end() - 1)));
EXPECT(assert_equal(b, expected.getb()));
EXPECT(assert_equal(b, actual.getb()));
EXPECT(assert_equal(*noise, *actual.get_model()));
}
{
// Three term constructor
LinearEquality expected(Terms(terms.begin(), terms.begin() + 3), b, 0);
LinearEquality actual(terms[0].first, terms[0].second, terms[1].first,
terms[1].second, terms[2].first, terms[2].second, b,
0);
EXPECT(assert_equal(expected, actual));
LONGS_EQUAL((long)terms[2].first, (long)actual.keys().back());
EXPECT(assert_equal(terms[2].second, actual.getA(actual.end() - 1)));
EXPECT(assert_equal(b, expected.getb()));
EXPECT(assert_equal(b, actual.getb()));
EXPECT(assert_equal(*noise, *actual.get_model()));
}
}
/* ************************************************************************* */
TEST(LinearEquality, Hessian_conversion) {
HessianFactor hessian(
0,
(Matrix(4, 4) << 1.57, 2.695, -1.1, -2.35, 2.695, 11.3125, -0.65, -10.225,
-1.1, -0.65, 1, 0.5, -2.35, -10.225, 0.5, 9.25)
.finished(),
(Vector(4) << -7.885, -28.5175, 2.75, 25.675).finished(), 73.1725);
try {
LinearEquality actual(hessian);
EXPECT(false);
} catch (const std::runtime_error& exception) {
EXPECT(true);
}
}
/* ************************************************************************* */
TEST(LinearEquality, error) {
LinearEquality factor(simple::terms, simple::b, 0);
VectorValues values;
values.insert(5, Vector::Constant(3, 1.0));
values.insert(10, Vector::Constant(3, 0.5));
values.insert(15, Vector::Constant(3, 1.0 / 3.0));
Vector expected_unwhitened(3);
expected_unwhitened << 2.0, 1.0, 0.0;
Vector actual_unwhitened = factor.unweighted_error(values);
EXPECT(assert_equal(expected_unwhitened, actual_unwhitened));
// whitened is meaningless in constraints
Vector expected_whitened(3);
expected_whitened = expected_unwhitened;
Vector actual_whitened = factor.error_vector(values);
EXPECT(assert_equal(expected_whitened, actual_whitened));
double expected_error = 0.0;
double actual_error = factor.error(values);
DOUBLES_EQUAL(expected_error, actual_error, 1e-10);
}
/* ************************************************************************* */
TEST(LinearEquality, matrices_NULL) {
// Make sure everything works with nullptr noise model
LinearEquality factor(simple::terms, simple::b, 0);
Matrix AExpected(3, 9);
AExpected << simple::terms[0].second, simple::terms[1].second,
simple::terms[2].second;
Vector rhsExpected = simple::b;
Matrix augmentedJacobianExpected(3, 10);
augmentedJacobianExpected << AExpected, rhsExpected;
// Whitened Jacobian
EXPECT(assert_equal(AExpected, factor.jacobian().first));
EXPECT(assert_equal(rhsExpected, factor.jacobian().second));
EXPECT(assert_equal(augmentedJacobianExpected, factor.augmentedJacobian()));
// Unwhitened Jacobian
EXPECT(assert_equal(AExpected, factor.jacobianUnweighted().first));
EXPECT(assert_equal(rhsExpected, factor.jacobianUnweighted().second));
EXPECT(assert_equal(augmentedJacobianExpected,
factor.augmentedJacobianUnweighted()));
}
/* ************************************************************************* */
TEST(LinearEquality, matrices) {
// And now witgh a non-unit noise model
LinearEquality factor(simple::terms, simple::b, 0);
Matrix jacobianExpected(3, 9);
jacobianExpected << simple::terms[0].second, simple::terms[1].second,
simple::terms[2].second;
Vector rhsExpected = simple::b;
Matrix augmentedJacobianExpected(3, 10);
augmentedJacobianExpected << jacobianExpected, rhsExpected;
Matrix augmentedHessianExpected =
augmentedJacobianExpected.transpose() * simple::noise->R().transpose() *
simple::noise->R() * augmentedJacobianExpected;
// Whitened Jacobian
EXPECT(assert_equal(jacobianExpected, factor.jacobian().first));
EXPECT(assert_equal(rhsExpected, factor.jacobian().second));
EXPECT(assert_equal(augmentedJacobianExpected, factor.augmentedJacobian()));
// Unwhitened Jacobian
EXPECT(assert_equal(jacobianExpected, factor.jacobianUnweighted().first));
EXPECT(assert_equal(rhsExpected, factor.jacobianUnweighted().second));
EXPECT(assert_equal(augmentedJacobianExpected,
factor.augmentedJacobianUnweighted()));
}
/* ************************************************************************* */
TEST(LinearEquality, operators) {
Matrix I = I_2x2;
Vector b = (Vector(2) << 0.2, -0.1).finished();
LinearEquality lf(1, -I, 2, I, b, 0);
VectorValues c;
c.insert(1, (Vector(2) << 10., 20.).finished());
c.insert(2, (Vector(2) << 30., 60.).finished());
// test A*x
Vector expectedE = (Vector(2) << 20., 40.).finished();
Vector actualE = lf * c;
EXPECT(assert_equal(expectedE, actualE));
// test A^e
VectorValues expectedX;
expectedX.insert(1, (Vector(2) << -20., -40.).finished());
expectedX.insert(2, (Vector(2) << 20., 40.).finished());
VectorValues actualX = VectorValues::Zero(expectedX);
lf.transposeMultiplyAdd(1.0, actualE, actualX);
EXPECT(assert_equal(expectedX, actualX));
// test gradient at zero
const auto [A, b2] = lf.jacobian();
VectorValues expectedG;
expectedG.insert(1, (Vector(2) << 0.2, -0.1).finished());
expectedG.insert(2, (Vector(2) << -0.2, 0.1).finished());
VectorValues actualG = lf.gradientAtZero();
EXPECT(assert_equal(expectedG, actualG));
}
/* ************************************************************************* */
TEST(LinearEquality, default_error) {
LinearEquality f;
double actual = f.error(VectorValues());
DOUBLES_EQUAL(0.0, actual, 1e-15);
}
//* ************************************************************************* */
TEST(LinearEquality, empty) {
// create an empty factor
LinearEquality f;
EXPECT(f.empty());
}
/* ************************************************************************* */
int main() {
TestResult tr;
return TestRegistry::runAllTests(tr);
}
/* ************************************************************************* */