Adapted and cleaned up unit tests for JacobianFactorUnordered
Richard Roberts
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/* ----------------------------------------------------------------------------
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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 testJacobianFactor.cpp
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* @brief Unit tests for Linear Factor
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* @author Christian Potthast
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* @author Frank Dellaert
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**/
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#include <gtsam/base/TestableAssertions.h>
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#include <CppUnitLite/TestHarness.h>
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#include <gtsam/linear/JacobianFactorUnordered.h>
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#include <gtsam/linear/GaussianFactorGraphUnordered.h>
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#include <gtsam/linear/GaussianConditionalUnordered.h>
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#include <gtsam/linear/VectorValuesUnordered.h>
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#include <boost/assign/list_of.hpp>
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#include <boost/range/iterator_range.hpp>
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#include <boost/range/adaptor/map.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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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.);
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const SharedDiagonal noise = noiseModel::Diagonal::Sigmas(Vector_(3, 0.5, 0.5, 0.5));
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}
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}
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/* ************************************************************************* */
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TEST(JacobianFactorUnordered, 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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// b vector only constructor
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JacobianFactorUnordered expected(
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boost::make_iterator_range(terms.begin(), terms.begin()), b);
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JacobianFactorUnordered actual(b);
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EXPECT(assert_equal(expected, actual));
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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(!expected.get_model());
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EXPECT(!actual.get_model());
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}
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{
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// One term constructor
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JacobianFactorUnordered expected(
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boost::make_iterator_range(terms.begin(), terms.begin() + 1), b, noise);
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JacobianFactorUnordered actual(terms[0].first, terms[0].second, b, noise);
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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(noise == expected.get_model());
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EXPECT(noise == actual.get_model());
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}
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{
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// Two term constructor
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JacobianFactorUnordered expected(
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boost::make_iterator_range(terms.begin(), terms.begin() + 2), b, noise);
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JacobianFactorUnordered actual(terms[0].first, terms[0].second,
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terms[1].first, terms[1].second, b, noise);
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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(noise == expected.get_model());
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EXPECT(noise == actual.get_model());
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}
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{
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// Three term constructor
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JacobianFactorUnordered expected(
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boost::make_iterator_range(terms.begin(), terms.begin() + 3), b, noise);
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JacobianFactorUnordered actual(terms[0].first, terms[0].second,
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terms[1].first, terms[1].second, terms[2].first, terms[2].second, b, noise);
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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(noise == expected.get_model());
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EXPECT(noise == actual.get_model());
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}
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{
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// VerticalBlockMatrix constructor
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JacobianFactorUnordered expected(
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boost::make_iterator_range(terms.begin(), terms.begin() + 3), b, noise);
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VerticalBlockMatrix blockMatrix(list_of(3)(3)(3)(1), 3);
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blockMatrix(0) = terms[0].second;
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blockMatrix(1) = terms[1].second;
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blockMatrix(2) = terms[2].second;
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blockMatrix(3) = b;
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JacobianFactorUnordered actual(terms | boost::adaptors::map_keys, blockMatrix, noise);
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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(noise == expected.get_model());
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EXPECT(noise == actual.get_model());
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}
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}
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/* ************************************************************************* */
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//TEST(JabobianFactor, 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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//
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// JacobianFactor expected(0, (Matrix(2,4) <<
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// 1.2530, 2.1508, -0.8779, -1.8755,
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// 0, 2.5858, 0.4789, -2.3943).finished(),
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// (Vector(2) << -6.2929, -5.7941).finished(),
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// noiseModel::Unit::Create(2));
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//
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// JacobianFactor actual(hessian);
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//
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// EXPECT(assert_equal(expected, actual, 1e-3));
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//}
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/* ************************************************************************* */
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TEST( JacobianFactorUnordered, construct_from_graph)
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{
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GaussianFactorGraphUnordered factors;
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double sigma1 = 0.1;
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Matrix A11 = Matrix::Identity(2,2);
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Vector b1(2); b1 << 2, -1;
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factors.add(JacobianFactorUnordered(10, A11, b1, noiseModel::Isotropic::Sigma(2, sigma1)));
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double sigma2 = 0.5;
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Matrix A21 = -10 * Matrix::Identity(2,2);
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Matrix A22 = 10 * Matrix::Identity(2,2);
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Vector b2(2); b2 << 4, -5;
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factors.add(JacobianFactorUnordered(10, A21, 8, A22, b2, noiseModel::Isotropic::Sigma(2, sigma2)));
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double sigma3 = 1.0;
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Matrix A32 = -10 * Matrix::Identity(2,2);
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Matrix A33 = 10 * Matrix::Identity(2,2);
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Vector b3(2); b3 << 4, -5;
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factors.add(JacobianFactorUnordered(8, A32, 12, A33, b3, noiseModel::Isotropic::Sigma(2, sigma3)));
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Matrix A1(6,2); A1 << A11, A21, Matrix::Zero(2,2);
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Matrix A2(6,2); A2 << Matrix::Zero(2,2), A22, A32;
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Matrix A3(6,2); A3 << Matrix::Zero(4,2), A33;
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Vector b(6); b << b1, b2, b3;
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Vector sigmas(6); sigmas << sigma1, sigma1, sigma2, sigma2, sigma3, sigma3;
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JacobianFactorUnordered expected(10, A1, 8, A2, 12, A3, b, noiseModel::Diagonal::Sigmas(sigmas));
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// The ordering here specifies the order in which the variables will appear in the combined factor
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JacobianFactorUnordered actual(factors, OrderingUnordered(list_of(10)(8)(12)));
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EXPECT(assert_equal(expected, actual));
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}
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/* ************************************************************************* */
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TEST(JacobianFactorUnordered, error)
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{
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JacobianFactorUnordered factor(simple::terms, simple::b, simple::noise);
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VectorValuesUnordered 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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Vector expected_whitened(3); expected_whitened << 4.0, 2.0, 0.0;
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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 = 0.5 * expected_whitened.squaredNorm();
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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(JacobianFactorUnordered, matrices)
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{
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JacobianFactorUnordered factor(simple::terms, simple::b, simple::noise);
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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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// Hessian
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EXPECT(assert_equal(Matrix(augmentedHessianExpected.topLeftCorner(9,9)), factor.information()));
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EXPECT(assert_equal(augmentedHessianExpected, factor.augmentedInformation()));
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// Whitened Jacobian
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EXPECT(assert_equal(simple::noise->R() * jacobianExpected, factor.jacobian().first));
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EXPECT(assert_equal(simple::noise->R() * rhsExpected, factor.jacobian().second));
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EXPECT(assert_equal(simple::noise->R() * augmentedJacobianExpected, factor.augmentedJacobian()));
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// Unwhitened Jacobian
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EXPECT(assert_equal(jacobianExpected, factor.jacobian(false).first));
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EXPECT(assert_equal(rhsExpected, factor.jacobian(false).second));
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EXPECT(assert_equal(augmentedJacobianExpected, factor.augmentedJacobian(false)));
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}
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/* ************************************************************************* */
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TEST(JacobianFactorUnordered, operators )
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{
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SharedDiagonal sigma0_1 = noiseModel::Isotropic::Sigma(2,0.1);
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Matrix I = eye(2);
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Vector b = Vector_(2,0.2,-0.1);
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JacobianFactorUnordered lf(1, -I, 2, I, b, sigma0_1);
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VectorValuesUnordered c;
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c.insert(1, Vector_(2,10.,20.));
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c.insert(2, Vector_(2,30.,60.));
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// test A*x
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Vector expectedE = Vector_(2,200.,400.);
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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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VectorValuesUnordered expectedX;
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expectedX.insert(1, Vector_(2,-2000.,-4000.));
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expectedX.insert(2, Vector_(2, 2000., 4000.));
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VectorValuesUnordered actualX = VectorValuesUnordered::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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}
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/* ************************************************************************* */
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TEST(JacobianFactorUnordered, default_error )
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{
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JacobianFactorUnordered f;
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double actual = f.error(VectorValuesUnordered());
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DOUBLES_EQUAL(0.0, actual, 1e-15);
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}
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//* ************************************************************************* */
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TEST(JacobianFactorUnordered, empty )
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{
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// create an empty factor
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JacobianFactorUnordered f;
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EXPECT(f.empty());
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}
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/* ************************************************************************* */
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TEST(JacobianFactorUnordered, eliminate2 )
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{
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// sigmas
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double sigma1 = 0.2;
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double sigma2 = 0.1;
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Vector sigmas = Vector_(4, sigma1, sigma1, sigma2, sigma2);
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// the combined linear factor
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Matrix Ax2 = Matrix_(4,2,
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// x2
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-1., 0.,
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+0.,-1.,
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1., 0.,
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+0.,1.
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);
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Matrix Al1x1 = Matrix_(4,4,
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// l1 x1
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1., 0., 0.00, 0., // f4
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0., 1., 0.00, 0., // f4
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0., 0., -1., 0., // f2
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0., 0., 0.00,-1. // f2
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);
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// the RHS
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Vector b2(4);
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b2(0) = -0.2;
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b2(1) = 0.3;
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b2(2) = 0.2;
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b2(3) = -0.1;
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vector<pair<Index, Matrix> > meas;
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meas.push_back(make_pair(2, Ax2));
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meas.push_back(make_pair(11, Al1x1));
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JacobianFactorUnordered combined(meas, b2, noiseModel::Diagonal::Sigmas(sigmas));
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// eliminate the combined factor
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pair<GaussianConditionalUnordered::shared_ptr, JacobianFactorUnordered::shared_ptr>
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actual = combined.eliminate(OrderingUnordered(list_of(2)));
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// create expected Conditional Gaussian
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double oldSigma = 0.0894427; // from when R was made unit
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Matrix R11 = Matrix_(2,2,
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1.00, 0.00,
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0.00, 1.00
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)/oldSigma;
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Matrix S12 = Matrix_(2,4,
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-0.20, 0.00,-0.80, 0.00,
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+0.00,-0.20,+0.00,-0.80
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)/oldSigma;
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Vector d = Vector_(2,0.2,-0.14)/oldSigma;
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GaussianConditionalUnordered expectedCG(2, d, R11, 11, S12);
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EXPECT(assert_equal(expectedCG, *actual.first, 1e-4));
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// the expected linear factor
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double sigma = 0.2236;
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Matrix Bl1x1 = Matrix_(2,4,
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// l1 x1
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1.00, 0.00, -1.00, 0.00,
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0.00, 1.00, +0.00, -1.00
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)/sigma;
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Vector b1 = Vector_(2, 0.0, 0.894427);
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JacobianFactorUnordered expectedLF(11, Bl1x1, b1);
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EXPECT(assert_equal(expectedLF, *actual.second,1e-3));
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}
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/* ************************************************************************* */
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TEST ( JacobianFactorUnordered, constraint_eliminate1 )
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{
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// construct a linear constraint
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Vector v(2); v(0)=1.2; v(1)=3.4;
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JacobianFactorUnordered lc(1, eye(2), v, noiseModel::Constrained::All(2));
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// eliminate it
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pair<GaussianConditionalUnordered::shared_ptr, JacobianFactorUnordered::shared_ptr>
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actual = lc.eliminate(list_of(1));
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// verify linear factor
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EXPECT(actual.second->size() == 0);
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|
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||||||
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// verify conditional Gaussian
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Vector sigmas = Vector_(2, 0.0, 0.0);
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GaussianConditionalUnordered expCG(1, v, eye(2), noiseModel::Diagonal::Sigmas(sigmas));
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|
EXPECT(assert_equal(expCG, *actual.first));
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||||||
|
}
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||||||
|
|
||||||
|
/* ************************************************************************* */
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||||||
|
TEST ( JacobianFactorUnordered, constraint_eliminate2 )
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||||||
|
{
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||||||
|
// Construct a linear constraint
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||||||
|
// RHS
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||||||
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Vector b(2); b(0)=3.0; b(1)=4.0;
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||||||
|
|
||||||
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// A1 - invertible
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Matrix A1(2,2);
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||||||
|
A1(0,0) = 1.0 ; A1(0,1) = 2.0;
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||||||
|
A1(1,0) = 2.0 ; A1(1,1) = 1.0;
|
||||||
|
|
||||||
|
// A2 - not invertible
|
||||||
|
Matrix A2(2,2);
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||||||
|
A2(0,0) = 1.0 ; A2(0,1) = 2.0;
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||||||
|
A2(1,0) = 2.0 ; A2(1,1) = 4.0;
|
||||||
|
|
||||||
|
JacobianFactorUnordered lc(1, A1, 2, A2, b, noiseModel::Constrained::All(2));
|
||||||
|
|
||||||
|
// eliminate x and verify results
|
||||||
|
pair<GaussianConditionalUnordered::shared_ptr, JacobianFactorUnordered::shared_ptr>
|
||||||
|
actual = lc.eliminate(list_of(1));
|
||||||
|
|
||||||
|
// LF should be empty
|
||||||
|
// It's tricky to create Eigen matrices that are only zero along one dimension
|
||||||
|
Matrix m(1,2);
|
||||||
|
Matrix Aempty = m.topRows(0);
|
||||||
|
Vector bempty = m.block(0,0,0,1);
|
||||||
|
JacobianFactorUnordered expectedLF(2, Aempty, bempty, noiseModel::Constrained::All(0));
|
||||||
|
EXPECT(assert_equal(expectedLF, *actual.second));
|
||||||
|
|
||||||
|
// verify CG
|
||||||
|
Matrix R = Matrix_(2, 2,
|
||||||
|
1.0, 2.0,
|
||||||
|
0.0, 1.0);
|
||||||
|
Matrix S = Matrix_(2,2,
|
||||||
|
1.0, 2.0,
|
||||||
|
0.0, 0.0);
|
||||||
|
Vector d = Vector_(2, 3.0, 0.6666);
|
||||||
|
Vector sigmas = Vector_(2, 0.0, 0.0);
|
||||||
|
GaussianConditionalUnordered expectedCG(1, d, R, 2, S, noiseModel::Diagonal::Sigmas(sigmas));
|
||||||
|
EXPECT(assert_equal(expectedCG, *actual.first, 1e-4));
|
||||||
|
}
|
||||||
|
|
||||||
|
/* ************************************************************************* */
|
||||||
|
int main() { TestResult tr; return TestRegistry::runAllTests(tr);}
|
||||||
|
/* ************************************************************************* */
|
||||||
Loading…
Reference in New Issue