238 lines
8.4 KiB
C++
238 lines
8.4 KiB
C++
/* ----------------------------------------------------------------------------
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* GTSAM Copyright 2010, Georgia Tech Research Corporation,
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* Atlanta, Georgia 30332-0415
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* All Rights Reserved
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* Authors: Frank Dellaert, et al. (see THANKS for the full author list)
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* See LICENSE for the license information
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* -------------------------------------------------------------------------- */
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/**
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* @file testLinearEquality.cpp
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* @brief Unit tests for LinearEquality
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* @author thduynguyen
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**/
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#include <gtsam_unstable/linear/LinearEquality.h>
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#include <gtsam/base/TestableAssertions.h>
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#include <gtsam/linear/HessianFactor.h>
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#include <gtsam/linear/VectorValues.h>
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#include <CppUnitLite/TestHarness.h>
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#include <boost/assign/std/vector.hpp>
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#include <boost/assign/list_of.hpp>
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using namespace std;
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using namespace gtsam;
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using namespace boost::assign;
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GTSAM_CONCEPT_TESTABLE_INST(LinearEquality)
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namespace {
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namespace simple {
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// Terms we'll use
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const vector<pair<Key, Matrix> > terms = list_of<pair<Key,Matrix> >
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(make_pair(5, Matrix3::Identity()))
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(make_pair(10, 2*Matrix3::Identity()))
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(make_pair(15, 3*Matrix3::Identity()));
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// RHS and sigmas
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const Vector b = (Vector(3) << 1., 2., 3.).finished();
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const SharedDiagonal noise = noiseModel::Constrained::All(3);
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}
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}
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/* ************************************************************************* */
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TEST(LinearEquality, constructors_and_accessors)
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{
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using namespace simple;
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// Test for using different numbers of terms
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{
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// One term constructor
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LinearEquality expected(
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boost::make_iterator_range(terms.begin(), terms.begin() + 1), b, 0);
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LinearEquality actual(terms[0].first, terms[0].second, b, 0);
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EXPECT(assert_equal(expected, actual));
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LONGS_EQUAL((long)terms[0].first, (long)actual.keys().back());
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EXPECT(assert_equal(terms[0].second, actual.getA(actual.end() - 1)));
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EXPECT(assert_equal(b, expected.getb()));
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EXPECT(assert_equal(b, actual.getb()));
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EXPECT(assert_equal(*noise, *actual.get_model()));
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}
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{
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// Two term constructor
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LinearEquality expected(
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boost::make_iterator_range(terms.begin(), terms.begin() + 2), b, 0);
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LinearEquality actual(terms[0].first, terms[0].second,
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terms[1].first, terms[1].second, b, 0);
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EXPECT(assert_equal(expected, actual));
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LONGS_EQUAL((long)terms[1].first, (long)actual.keys().back());
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EXPECT(assert_equal(terms[1].second, actual.getA(actual.end() - 1)));
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EXPECT(assert_equal(b, expected.getb()));
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EXPECT(assert_equal(b, actual.getb()));
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EXPECT(assert_equal(*noise, *actual.get_model()));
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}
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{
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// Three term constructor
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LinearEquality expected(
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boost::make_iterator_range(terms.begin(), terms.begin() + 3), b, 0);
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LinearEquality actual(terms[0].first, terms[0].second,
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terms[1].first, terms[1].second, terms[2].first, terms[2].second, b, 0);
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EXPECT(assert_equal(expected, actual));
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LONGS_EQUAL((long)terms[2].first, (long)actual.keys().back());
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EXPECT(assert_equal(terms[2].second, actual.getA(actual.end() - 1)));
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EXPECT(assert_equal(b, expected.getb()));
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EXPECT(assert_equal(b, actual.getb()));
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EXPECT(assert_equal(*noise, *actual.get_model()));
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}
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}
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/* ************************************************************************* */
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TEST(LinearEquality, Hessian_conversion) {
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HessianFactor hessian(0, (Matrix(4,4) <<
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1.57, 2.695, -1.1, -2.35,
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2.695, 11.3125, -0.65, -10.225,
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-1.1, -0.65, 1, 0.5,
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-2.35, -10.225, 0.5, 9.25).finished(),
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(Vector(4) << -7.885, -28.5175, 2.75, 25.675).finished(),
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73.1725);
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try {
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LinearEquality actual(hessian);
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EXPECT(false);
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}
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catch (const std::runtime_error& exception) {
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EXPECT(true);
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}
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}
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/* ************************************************************************* */
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TEST(LinearEquality, error)
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{
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LinearEquality factor(simple::terms, simple::b, 0);
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VectorValues values;
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values.insert(5, Vector::Constant(3, 1.0));
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values.insert(10, Vector::Constant(3, 0.5));
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values.insert(15, Vector::Constant(3, 1.0/3.0));
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Vector expected_unwhitened(3); expected_unwhitened << 2.0, 1.0, 0.0;
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Vector actual_unwhitened = factor.unweighted_error(values);
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EXPECT(assert_equal(expected_unwhitened, actual_unwhitened));
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// whitened is meaningless in constraints
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Vector expected_whitened(3); expected_whitened = expected_unwhitened;
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Vector actual_whitened = factor.error_vector(values);
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EXPECT(assert_equal(expected_whitened, actual_whitened));
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double expected_error = 0.0;
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double actual_error = factor.error(values);
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DOUBLES_EQUAL(expected_error, actual_error, 1e-10);
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}
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/* ************************************************************************* */
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TEST(LinearEquality, matrices_NULL)
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{
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// Make sure everything works with NULL noise model
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LinearEquality factor(simple::terms, simple::b, 0);
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Matrix AExpected(3, 9);
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AExpected << simple::terms[0].second, simple::terms[1].second, simple::terms[2].second;
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Vector rhsExpected = simple::b;
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Matrix augmentedJacobianExpected(3, 10);
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augmentedJacobianExpected << AExpected, rhsExpected;
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// Whitened Jacobian
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EXPECT(assert_equal(AExpected, factor.jacobian().first));
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EXPECT(assert_equal(rhsExpected, factor.jacobian().second));
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EXPECT(assert_equal(augmentedJacobianExpected, factor.augmentedJacobian()));
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// Unwhitened Jacobian
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EXPECT(assert_equal(AExpected, factor.jacobianUnweighted().first));
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EXPECT(assert_equal(rhsExpected, factor.jacobianUnweighted().second));
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EXPECT(assert_equal(augmentedJacobianExpected, factor.augmentedJacobianUnweighted()));
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}
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/* ************************************************************************* */
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TEST(LinearEquality, matrices)
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{
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// And now witgh a non-unit noise model
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LinearEquality factor(simple::terms, simple::b, 0);
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Matrix jacobianExpected(3, 9);
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jacobianExpected << simple::terms[0].second, simple::terms[1].second, simple::terms[2].second;
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Vector rhsExpected = simple::b;
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Matrix augmentedJacobianExpected(3, 10);
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augmentedJacobianExpected << jacobianExpected, rhsExpected;
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Matrix augmentedHessianExpected =
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augmentedJacobianExpected.transpose() * simple::noise->R().transpose()
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* simple::noise->R() * augmentedJacobianExpected;
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// Whitened Jacobian
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EXPECT(assert_equal(jacobianExpected, factor.jacobian().first));
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EXPECT(assert_equal(rhsExpected, factor.jacobian().second));
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EXPECT(assert_equal(augmentedJacobianExpected, factor.augmentedJacobian()));
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// Unwhitened Jacobian
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EXPECT(assert_equal(jacobianExpected, factor.jacobianUnweighted().first));
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EXPECT(assert_equal(rhsExpected, factor.jacobianUnweighted().second));
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EXPECT(assert_equal(augmentedJacobianExpected, factor.augmentedJacobianUnweighted()));
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}
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/* ************************************************************************* */
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TEST(LinearEquality, operators )
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{
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Matrix I = eye(2);
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Vector b = (Vector(2) << 0.2,-0.1).finished();
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LinearEquality lf(1, -I, 2, I, b, 0);
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VectorValues c;
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c.insert(1, (Vector(2) << 10.,20.).finished());
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c.insert(2, (Vector(2) << 30.,60.).finished());
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// test A*x
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Vector expectedE = (Vector(2) << 20.,40.).finished();
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Vector actualE = lf * c;
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EXPECT(assert_equal(expectedE, actualE));
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// test A^e
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VectorValues expectedX;
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expectedX.insert(1, (Vector(2) << -20.,-40.).finished());
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expectedX.insert(2, (Vector(2) << 20., 40.).finished());
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VectorValues actualX = VectorValues::Zero(expectedX);
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lf.transposeMultiplyAdd(1.0, actualE, actualX);
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EXPECT(assert_equal(expectedX, actualX));
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// test gradient at zero
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Matrix A; Vector b2; boost::tie(A,b2) = lf.jacobian();
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VectorValues expectedG;
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expectedG.insert(1, (Vector(2) << 0.2, -0.1).finished());
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expectedG.insert(2, (Vector(2) << -0.2, 0.1).finished());
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VectorValues actualG = lf.gradientAtZero();
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EXPECT(assert_equal(expectedG, actualG));
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}
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/* ************************************************************************* */
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TEST(LinearEquality, default_error )
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{
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LinearEquality f;
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double actual = f.error(VectorValues());
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DOUBLES_EQUAL(0.0, actual, 1e-15);
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}
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//* ************************************************************************* */
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TEST(LinearEquality, empty )
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{
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// create an empty factor
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LinearEquality f;
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EXPECT(f.empty());
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}
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/* ************************************************************************* */
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int main() { TestResult tr; return TestRegistry::runAllTests(tr);}
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/* ************************************************************************* */
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