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gtsam/tests/testNonlinearFactor.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 testNonlinearFactor.cpp
* @brief Unit tests for Non-Linear Factor,
* create a non linear factor graph and a values structure for it and
* calculate the error for the factor.
* @author Christian Potthast
**/
/*STL/C++*/
#include <iostream>
#include <CppUnitLite/TestHarness.h>
// TODO: DANGEROUS, create shared pointers
#define GTSAM_MAGIC_GAUSSIAN 2
#include <gtsam/base/Testable.h>
#include <gtsam/base/Matrix.h>
#include <tests/smallExample.h>
#include <tests/simulated2D.h>
#include <gtsam/linear/GaussianFactor.h>
#include <gtsam/nonlinear/NonlinearFactorGraph.h>
#include <gtsam/inference/Symbol.h>
using namespace std;
using namespace gtsam;
using namespace example;
// Convenience for named keys
using symbol_shorthand::X;
using symbol_shorthand::L;
typedef std::shared_ptr<NonlinearFactor > shared_nlf;
/* ************************************************************************* */
TEST( NonlinearFactor, equals )
{
SharedNoiseModel sigma(noiseModel::Isotropic::Sigma(2,1.0));
// create two nonlinear2 factors
Point2 z3(0.,-1.);
simulated2D::Measurement f0(z3, sigma, X(1),L(1));
// measurement between x2 and l1
Point2 z4(-1.5, -1.);
simulated2D::Measurement f1(z4, sigma, X(2),L(1));
CHECK(assert_equal(f0,f0));
CHECK(f0.equals(f0));
CHECK(!f0.equals(f1));
CHECK(!f1.equals(f0));
}
/* ************************************************************************* */
TEST( NonlinearFactor, equals2 )
{
// create a non linear factor graph
NonlinearFactorGraph fg = createNonlinearFactorGraph();
// get two factors
NonlinearFactorGraph::sharedFactor f0 = fg[0], f1 = fg[1];
CHECK(f0->equals(*f0));
CHECK(!f0->equals(*f1));
CHECK(!f1->equals(*f0));
}
/* ************************************************************************* */
TEST( NonlinearFactor, NonlinearFactor )
{
// create a non linear factor graph
NonlinearFactorGraph fg = createNonlinearFactorGraph();
// create a values structure for the non linear factor graph
Values cfg = createNoisyValues();
// get the factor "f1" from the factor graph
NonlinearFactorGraph::sharedFactor factor = fg[0];
// calculate the error_vector from the factor "f1"
// error_vector = [0.1 0.1]
Vector actual_e = std::dynamic_pointer_cast<NoiseModelFactor>(factor)->unwhitenedError(cfg);
CHECK(assert_equal(0.1*Vector::Ones(2),actual_e));
// error = 0.5 * [1 1] * [1;1] = 1
double expected = 1.0;
// calculate the error from the factor "f1"
double actual = factor->error(cfg);
DOUBLES_EQUAL(expected,actual,0.00000001);
}
/* ************************************************************************* */
TEST(NonlinearFactor, Weight) {
// create a values structure for the non linear factor graph
Values values;
// Instantiate a concrete class version of a NoiseModelFactor
PriorFactor<Point2> factor1(X(1), Point2(0, 0));
values.insert(X(1), Point2(0.1, 0.1));
CHECK(assert_equal(1.0, factor1.weight(values)));
// Factor with noise model
auto noise = noiseModel::Isotropic::Sigma(2, 0.2);
PriorFactor<Point2> factor2(X(2), Point2(1, 1), noise);
values.insert(X(2), Point2(1.1, 1.1));
CHECK(assert_equal(1.0, factor2.weight(values)));
Point2 estimate(3, 3), prior(1, 1);
double distance = (estimate - prior).norm();
auto gaussian = noiseModel::Isotropic::Sigma(2, 0.2);
PriorFactor<Point2> factor;
// vector to store all the robust models in so we can test iteratively.
vector<noiseModel::Robust::shared_ptr> robust_models;
// Fair noise model
auto fair = noiseModel::Robust::Create(
noiseModel::mEstimator::Fair::Create(1.3998), gaussian);
robust_models.push_back(fair);
// Huber noise model
auto huber = noiseModel::Robust::Create(
noiseModel::mEstimator::Huber::Create(1.345), gaussian);
robust_models.push_back(huber);
// Cauchy noise model
auto cauchy = noiseModel::Robust::Create(
noiseModel::mEstimator::Cauchy::Create(0.1), gaussian);
robust_models.push_back(cauchy);
// Tukey noise model
auto tukey = noiseModel::Robust::Create(
noiseModel::mEstimator::Tukey::Create(4.6851), gaussian);
robust_models.push_back(tukey);
// Welsch noise model
auto welsch = noiseModel::Robust::Create(
noiseModel::mEstimator::Welsch::Create(2.9846), gaussian);
robust_models.push_back(welsch);
// Geman-McClure noise model
auto gm = noiseModel::Robust::Create(
noiseModel::mEstimator::GemanMcClure::Create(1.0), gaussian);
robust_models.push_back(gm);
// DCS noise model
auto dcs = noiseModel::Robust::Create(
noiseModel::mEstimator::DCS::Create(1.0), gaussian);
robust_models.push_back(dcs);
// L2WithDeadZone noise model
auto l2 = noiseModel::Robust::Create(
noiseModel::mEstimator::L2WithDeadZone::Create(1.0), gaussian);
robust_models.push_back(l2);
for(auto&& model: robust_models) {
factor = PriorFactor<Point2>(X(3), prior, model);
values.clear();
values.insert(X(3), estimate);
CHECK(assert_equal(model->robust()->weight(distance), factor.weight(values)));
}
}
/* ************************************************************************* */
TEST( NonlinearFactor, linearize_f1 )
{
Values c = createNoisyValues();
// Grab a non-linear factor
NonlinearFactorGraph nfg = createNonlinearFactorGraph();
NonlinearFactorGraph::sharedFactor nlf = nfg[0];
// We linearize at noisy config from SmallExample
GaussianFactor::shared_ptr actual = nlf->linearize(c);
GaussianFactorGraph lfg = createGaussianFactorGraph();
GaussianFactor::shared_ptr expected = lfg[0];
CHECK(assert_equal(*expected,*actual));
// The error |A*dx-b| approximates (h(x0+dx)-z) = -error_vector
// Hence i.e., b = approximates z-h(x0) = error_vector(x0)
//CHECK(assert_equal(nlf->error_vector(c),actual->get_b()));
}
/* ************************************************************************* */
TEST( NonlinearFactor, linearize_f2 )
{
Values c = createNoisyValues();
// Grab a non-linear factor
NonlinearFactorGraph nfg = createNonlinearFactorGraph();
NonlinearFactorGraph::sharedFactor nlf = nfg[1];
// We linearize at noisy config from SmallExample
GaussianFactor::shared_ptr actual = nlf->linearize(c);
GaussianFactorGraph lfg = createGaussianFactorGraph();
GaussianFactor::shared_ptr expected = lfg[1];
CHECK(assert_equal(*expected,*actual));
}
/* ************************************************************************* */
TEST( NonlinearFactor, linearize_f3 )
{
// Grab a non-linear factor
NonlinearFactorGraph nfg = createNonlinearFactorGraph();
NonlinearFactorGraph::sharedFactor nlf = nfg[2];
// We linearize at noisy config from SmallExample
Values c = createNoisyValues();
GaussianFactor::shared_ptr actual = nlf->linearize(c);
GaussianFactorGraph lfg = createGaussianFactorGraph();
GaussianFactor::shared_ptr expected = lfg[2];
CHECK(assert_equal(*expected,*actual));
}
/* ************************************************************************* */
TEST( NonlinearFactor, linearize_f4 )
{
// Grab a non-linear factor
NonlinearFactorGraph nfg = createNonlinearFactorGraph();
NonlinearFactorGraph::sharedFactor nlf = nfg[3];
// We linearize at noisy config from SmallExample
Values c = createNoisyValues();
GaussianFactor::shared_ptr actual = nlf->linearize(c);
GaussianFactorGraph lfg = createGaussianFactorGraph();
GaussianFactor::shared_ptr expected = lfg[3];
CHECK(assert_equal(*expected,*actual));
}
/* ************************************************************************* */
TEST( NonlinearFactor, size )
{
// create a non linear factor graph
NonlinearFactorGraph fg = createNonlinearFactorGraph();
// create a values structure for the non linear factor graph
Values cfg = createNoisyValues();
// get some factors from the graph
NonlinearFactorGraph::sharedFactor factor1 = fg[0], factor2 = fg[1],
factor3 = fg[2];
CHECK(factor1->size() == 1);
CHECK(factor2->size() == 2);
CHECK(factor3->size() == 2);
}
/* ************************************************************************* */
TEST( NonlinearFactor, linearize_constraint1 )
{
SharedDiagonal constraint = noiseModel::Constrained::MixedSigmas(Vector2(0.2,0));
Point2 mu(1., -1.);
NonlinearFactorGraph::sharedFactor f0(new simulated2D::Prior(mu, constraint, X(1)));
Values config;
config.insert(X(1), Point2(1.0, 2.0));
GaussianFactor::shared_ptr actual = f0->linearize(config);
// create expected
Vector2 b(0., -3.);
JacobianFactor expected(X(1), (Matrix(2, 2) << 5.0, 0.0, 0.0, 1.0).finished(), b,
noiseModel::Constrained::MixedSigmas(Vector2(1,0)));
CHECK(assert_equal((const GaussianFactor&)expected, *actual));
}
/* ************************************************************************* */
TEST( NonlinearFactor, linearize_constraint2 )
{
SharedDiagonal constraint = noiseModel::Constrained::MixedSigmas(Vector2(0.2,0));
Point2 z3(1.,-1.);
simulated2D::Measurement f0(z3, constraint, X(1),L(1));
Values config;
config.insert(X(1), Point2(1.0, 2.0));
config.insert(L(1), Point2(5.0, 4.0));
GaussianFactor::shared_ptr actual = f0.linearize(config);
// create expected
Matrix2 A; A << 5.0, 0.0, 0.0, 1.0;
Vector2 b(-15., -3.);
JacobianFactor expected(X(1), -1*A, L(1), A, b,
noiseModel::Constrained::MixedSigmas(Vector2(1,0)));
CHECK(assert_equal((const GaussianFactor&)expected, *actual));
}
/* ************************************************************************* */
TEST( NonlinearFactor, cloneWithNewNoiseModel )
{
// create original factor
double sigma1 = 0.1;
NonlinearFactorGraph nfg = example::nonlinearFactorGraphWithGivenSigma(sigma1);
// create expected
double sigma2 = 10;
NonlinearFactorGraph expected = example::nonlinearFactorGraphWithGivenSigma(sigma2);
// create actual
NonlinearFactorGraph actual;
SharedNoiseModel noise2 = noiseModel::Isotropic::Sigma(2,sigma2);
actual.push_back(nfg.at<NoiseModelFactor>(0)->cloneWithNewNoiseModel(noise2));
// check it's all good
CHECK(assert_equal(expected, actual));
}
/* ************************************************************************* */
class TestFactor1 : public NoiseModelFactor1<double> {
static_assert(std::is_same<Base, NoiseModelFactor>::value, "Base type wrong");
static_assert(std::is_same<This, NoiseModelFactor1<double>>::value,
"This type wrong");
public:
typedef NoiseModelFactor1<double> Base;
// Provide access to the Matrix& version of evaluateError:
using Base::evaluateError;
TestFactor1() : Base(noiseModel::Diagonal::Sigmas(Vector1(2.0)), L(1)) {}
// Provide access to the Matrix& version of evaluateError:
using Base::NoiseModelFactor1; // inherit constructors
Vector evaluateError(const double& x1, OptionalMatrixType H1) const override {
if (H1) *H1 = (Matrix(1, 1) << 1.0).finished();
return (Vector(1) << x1).finished();
}
gtsam::NonlinearFactor::shared_ptr clone() const override {
return std::static_pointer_cast<gtsam::NonlinearFactor>(
gtsam::NonlinearFactor::shared_ptr(new TestFactor1(*this)));
}
};
/* ************************************ */
TEST(NonlinearFactor, NoiseModelFactor1) {
TestFactor1 tf;
Values tv;
tv.insert(L(1), double((1.0)));
EXPECT(assert_equal((Vector(1) << 1.0).finished(), tf.unwhitenedError(tv)));
DOUBLES_EQUAL(0.25 / 2.0, tf.error(tv), 1e-9);
JacobianFactor jf(
*std::dynamic_pointer_cast<JacobianFactor>(tf.linearize(tv)));
LONGS_EQUAL((long)L(1), (long)jf.keys()[0]);
EXPECT(assert_equal((Matrix)(Matrix(1, 1) << 0.5).finished(),
jf.getA(jf.begin())));
EXPECT(assert_equal((Vector)(Vector(1) << -0.5).finished(), jf.getb()));
// Test all functions/types for backwards compatibility
static_assert(std::is_same<TestFactor1::X, double>::value,
"X type incorrect");
EXPECT(assert_equal(tf.key(), L(1)));
std::vector<Matrix> H = {Matrix()};
EXPECT(assert_equal(Vector1(1.0), tf.unwhitenedError(tv, H)));
// Test constructors
TestFactor1 tf2(noiseModel::Unit::Create(1), L(1));
TestFactor1 tf3(noiseModel::Unit::Create(1), {L(1)});
TestFactor1 tf4(noiseModel::Unit::Create(1), gtsam::Symbol('L', 1));
}
/* ************************************************************************* */
class TestFactor4 : public NoiseModelFactor4<double, double, double, double> {
static_assert(std::is_same<Base, NoiseModelFactor>::value, "Base type wrong");
static_assert(
std::is_same<This,
NoiseModelFactor4<double, double, double, double>>::value,
"This type wrong");
public:
typedef NoiseModelFactor4<double, double, double, double> Base;
// Provide access to the Matrix& version of evaluateError:
using Base::evaluateError;
TestFactor4() : Base(noiseModel::Diagonal::Sigmas((Vector(1) << 2.0).finished()), X(1), X(2), X(3), X(4)) {}
// Provide access to the Matrix& version of evaluateError:
using Base::NoiseModelFactor4; // inherit constructors
Vector
evaluateError(const double& x1, const double& x2, const double& x3, const double& x4,
OptionalMatrixType H1, OptionalMatrixType H2,
OptionalMatrixType H3, OptionalMatrixType H4) const override {
if(H1) {
*H1 = (Matrix(1, 1) << 1.0).finished();
*H2 = (Matrix(1, 1) << 2.0).finished();
*H3 = (Matrix(1, 1) << 3.0).finished();
*H4 = (Matrix(1, 1) << 4.0).finished();
}
return (Vector(1) << x1 + 2.0 * x2 + 3.0 * x3 + 4.0 * x4).finished();
}
gtsam::NonlinearFactor::shared_ptr clone() const override {
return std::static_pointer_cast<gtsam::NonlinearFactor>(
gtsam::NonlinearFactor::shared_ptr(new TestFactor4(*this))); }
};
/* ************************************ */
TEST(NonlinearFactor, NoiseModelFactor4) {
TestFactor4 tf;
Values tv;
tv.insert(X(1), double((1.0)));
tv.insert(X(2), double((2.0)));
tv.insert(X(3), double((3.0)));
tv.insert(X(4), double((4.0)));
EXPECT(assert_equal((Vector(1) << 30.0).finished(), tf.unwhitenedError(tv)));
DOUBLES_EQUAL(0.5 * 30.0 * 30.0 / 4.0, tf.error(tv), 1e-9);
JacobianFactor jf(*std::dynamic_pointer_cast<JacobianFactor>(tf.linearize(tv)));
LONGS_EQUAL((long)X(1), (long)jf.keys()[0]);
LONGS_EQUAL((long)X(2), (long)jf.keys()[1]);
LONGS_EQUAL((long)X(3), (long)jf.keys()[2]);
LONGS_EQUAL((long)X(4), (long)jf.keys()[3]);
EXPECT(assert_equal((Matrix)(Matrix(1, 1) << 0.5).finished(), jf.getA(jf.begin())));
EXPECT(assert_equal((Matrix)(Matrix(1, 1) << 1.0).finished(), jf.getA(jf.begin()+1)));
EXPECT(assert_equal((Matrix)(Matrix(1, 1) << 1.5).finished(), jf.getA(jf.begin()+2)));
EXPECT(assert_equal((Matrix)(Matrix(1, 1) << 2.0).finished(), jf.getA(jf.begin()+3)));
EXPECT(assert_equal((Vector)(Vector(1) << 0.5 * -30.).finished(), jf.getb()));
// Test all functions/types for backwards compatibility
static_assert(std::is_same<TestFactor4::X1, double>::value,
"X1 type incorrect");
static_assert(std::is_same<TestFactor4::X2, double>::value,
"X2 type incorrect");
static_assert(std::is_same<TestFactor4::X3, double>::value,
"X3 type incorrect");
static_assert(std::is_same<TestFactor4::X4, double>::value,
"X4 type incorrect");
EXPECT(assert_equal(tf.key1(), X(1)));
EXPECT(assert_equal(tf.key2(), X(2)));
EXPECT(assert_equal(tf.key3(), X(3)));
EXPECT(assert_equal(tf.key4(), X(4)));
std::vector<Matrix> H = {Matrix(), Matrix(), Matrix(), Matrix()};
EXPECT(assert_equal(Vector1(30.0), tf.unwhitenedError(tv, H)));
EXPECT(assert_equal((Matrix)(Matrix(1, 1) << 1.).finished(), H.at(0)));
EXPECT(assert_equal((Matrix)(Matrix(1, 1) << 2.).finished(), H.at(1)));
EXPECT(assert_equal((Matrix)(Matrix(1, 1) << 3.).finished(), H.at(2)));
EXPECT(assert_equal((Matrix)(Matrix(1, 1) << 4.).finished(), H.at(3)));
// And test "forward compatibility" using `key<N>` and `ValueType<N>` too
static_assert(std::is_same<TestFactor4::ValueType<1>, double>::value,
"ValueType<1> type incorrect");
static_assert(std::is_same<TestFactor4::ValueType<2>, double>::value,
"ValueType<2> type incorrect");
static_assert(std::is_same<TestFactor4::ValueType<3>, double>::value,
"ValueType<3> type incorrect");
static_assert(std::is_same<TestFactor4::ValueType<4>, double>::value,
"ValueType<4> type incorrect");
EXPECT(assert_equal(tf.key<1>(), X(1)));
EXPECT(assert_equal(tf.key<2>(), X(2)));
EXPECT(assert_equal(tf.key<3>(), X(3)));
EXPECT(assert_equal(tf.key<4>(), X(4)));
// Test constructors
TestFactor4 tf2(noiseModel::Unit::Create(1), L(1), L(2), L(3), L(4));
TestFactor4 tf3(noiseModel::Unit::Create(1), {L(1), L(2), L(3), L(4)});
TestFactor4 tf4(noiseModel::Unit::Create(1),
std::array<Key, 4>{L(1), L(2), L(3), L(4)});
std::vector<Key> keys = {L(1), L(2), L(3), L(4)};
TestFactor4 tf5(noiseModel::Unit::Create(1), keys);
}
/* ************************************************************************* */
class TestFactor5 : public NoiseModelFactor5<double, double, double, double, double> {
public:
typedef NoiseModelFactor5<double, double, double, double, double> Base;
// Provide access to the Matrix& version of evaluateError:
using Base::evaluateError;
TestFactor5() : Base(noiseModel::Diagonal::Sigmas((Vector(1) << 2.0).finished()), X(1), X(2), X(3), X(4), X(5)) {}
Vector
evaluateError(const X1& x1, const X2& x2, const X3& x3, const X4& x4, const X5& x5,
OptionalMatrixType H1, OptionalMatrixType H2, OptionalMatrixType H3,
OptionalMatrixType H4, OptionalMatrixType H5) const override {
if(H1) {
*H1 = (Matrix(1, 1) << 1.0).finished();
*H2 = (Matrix(1, 1) << 2.0).finished();
*H3 = (Matrix(1, 1) << 3.0).finished();
*H4 = (Matrix(1, 1) << 4.0).finished();
*H5 = (Matrix(1, 1) << 5.0).finished();
}
return (Vector(1) << x1 + 2.0 * x2 + 3.0 * x3 + 4.0 * x4 + 5.0 * x5)
.finished();
}
};
/* ************************************ */
TEST(NonlinearFactor, NoiseModelFactor5) {
TestFactor5 tf;
Values tv;
tv.insert(X(1), double((1.0)));
tv.insert(X(2), double((2.0)));
tv.insert(X(3), double((3.0)));
tv.insert(X(4), double((4.0)));
tv.insert(X(5), double((5.0)));
EXPECT(assert_equal((Vector(1) << 55.0).finished(), tf.unwhitenedError(tv)));
DOUBLES_EQUAL(0.5 * 55.0 * 55.0 / 4.0, tf.error(tv), 1e-9);
JacobianFactor jf(*std::dynamic_pointer_cast<JacobianFactor>(tf.linearize(tv)));
LONGS_EQUAL((long)X(1), (long)jf.keys()[0]);
LONGS_EQUAL((long)X(2), (long)jf.keys()[1]);
LONGS_EQUAL((long)X(3), (long)jf.keys()[2]);
LONGS_EQUAL((long)X(4), (long)jf.keys()[3]);
LONGS_EQUAL((long)X(5), (long)jf.keys()[4]);
EXPECT(assert_equal((Matrix)(Matrix(1, 1) << 0.5).finished(), jf.getA(jf.begin())));
EXPECT(assert_equal((Matrix)(Matrix(1, 1) << 1.0).finished(), jf.getA(jf.begin()+1)));
EXPECT(assert_equal((Matrix)(Matrix(1, 1) << 1.5).finished(), jf.getA(jf.begin()+2)));
EXPECT(assert_equal((Matrix)(Matrix(1, 1) << 2.0).finished(), jf.getA(jf.begin()+3)));
EXPECT(assert_equal((Matrix)(Matrix(1, 1) << 2.5).finished(), jf.getA(jf.begin()+4)));
EXPECT(assert_equal((Vector)(Vector(1) << 0.5 * -55.).finished(), jf.getb()));
}
/* ************************************************************************* */
class TestFactor6 : public NoiseModelFactor6<double, double, double, double, double, double> {
public:
typedef NoiseModelFactor6<double, double, double, double, double, double> Base;
// Provide access to the Matrix& version of evaluateError:
using Base::evaluateError;
TestFactor6() : Base(noiseModel::Diagonal::Sigmas((Vector(1) << 2.0).finished()), X(1), X(2), X(3), X(4), X(5), X(6)) {}
Vector
evaluateError(const X1& x1, const X2& x2, const X3& x3, const X4& x4, const X5& x5, const X6& x6,
OptionalMatrixType H1, OptionalMatrixType H2, OptionalMatrixType H3, OptionalMatrixType H4,
OptionalMatrixType H5, OptionalMatrixType H6) const override {
if(H1) {
*H1 = (Matrix(1, 1) << 1.0).finished();
*H2 = (Matrix(1, 1) << 2.0).finished();
*H3 = (Matrix(1, 1) << 3.0).finished();
*H4 = (Matrix(1, 1) << 4.0).finished();
*H5 = (Matrix(1, 1) << 5.0).finished();
*H6 = (Matrix(1, 1) << 6.0).finished();
}
return (Vector(1) << x1 + 2.0 * x2 + 3.0 * x3 + 4.0 * x4 + 5.0 * x5 +
6.0 * x6)
.finished();
}
};
/* ************************************ */
TEST(NonlinearFactor, NoiseModelFactor6) {
TestFactor6 tf;
Values tv;
tv.insert(X(1), double((1.0)));
tv.insert(X(2), double((2.0)));
tv.insert(X(3), double((3.0)));
tv.insert(X(4), double((4.0)));
tv.insert(X(5), double((5.0)));
tv.insert(X(6), double((6.0)));
EXPECT(assert_equal((Vector(1) << 91.0).finished(), tf.unwhitenedError(tv)));
DOUBLES_EQUAL(0.5 * 91.0 * 91.0 / 4.0, tf.error(tv), 1e-9);
JacobianFactor jf(*std::dynamic_pointer_cast<JacobianFactor>(tf.linearize(tv)));
LONGS_EQUAL((long)X(1), (long)jf.keys()[0]);
LONGS_EQUAL((long)X(2), (long)jf.keys()[1]);
LONGS_EQUAL((long)X(3), (long)jf.keys()[2]);
LONGS_EQUAL((long)X(4), (long)jf.keys()[3]);
LONGS_EQUAL((long)X(5), (long)jf.keys()[4]);
LONGS_EQUAL((long)X(6), (long)jf.keys()[5]);
EXPECT(assert_equal((Matrix)(Matrix(1, 1) << 0.5).finished(), jf.getA(jf.begin())));
EXPECT(assert_equal((Matrix)(Matrix(1, 1) << 1.0).finished(), jf.getA(jf.begin()+1)));
EXPECT(assert_equal((Matrix)(Matrix(1, 1) << 1.5).finished(), jf.getA(jf.begin()+2)));
EXPECT(assert_equal((Matrix)(Matrix(1, 1) << 2.0).finished(), jf.getA(jf.begin()+3)));
EXPECT(assert_equal((Matrix)(Matrix(1, 1) << 2.5).finished(), jf.getA(jf.begin()+4)));
EXPECT(assert_equal((Matrix)(Matrix(1, 1) << 3.0).finished(), jf.getA(jf.begin()+5)));
EXPECT(assert_equal((Vector)(Vector(1) << 0.5 * -91.).finished(), jf.getb()));
}
/* ************************************************************************* */
class TestFactorN : public NoiseModelFactorN<double, double, double, double> {
public:
typedef NoiseModelFactorN<double, double, double, double> Base;
// Provide access to the Matrix& version of evaluateError:
using Base::evaluateError;
using Type1 = ValueType<1>; // Test that we can use the ValueType<> template
TestFactorN() : Base(noiseModel::Diagonal::Sigmas((Vector(1) << 2.0).finished()), X(1), X(2), X(3), X(4)) {}
Vector
evaluateError(const double& x1, const double& x2, const double& x3, const double& x4,
OptionalMatrixType H1, OptionalMatrixType H2,
OptionalMatrixType H3, OptionalMatrixType H4) const override {
if (H1) *H1 = (Matrix(1, 1) << 1.0).finished();
if (H2) *H2 = (Matrix(1, 1) << 2.0).finished();
if (H3) *H3 = (Matrix(1, 1) << 3.0).finished();
if (H4) *H4 = (Matrix(1, 1) << 4.0).finished();
return (Vector(1) << x1 + 2.0 * x2 + 3.0 * x3 + 4.0 * x4).finished();
}
Key key1() const { return key<1>(); } // Test that we can use key<> template
};
/* ************************************ */
TEST(NonlinearFactor, NoiseModelFactorN) {
TestFactorN tf;
Values tv;
tv.insert(X(1), double((1.0)));
tv.insert(X(2), double((2.0)));
tv.insert(X(3), double((3.0)));
tv.insert(X(4), double((4.0)));
EXPECT(assert_equal((Vector(1) << 30.0).finished(), tf.unwhitenedError(tv)));
DOUBLES_EQUAL(0.5 * 30.0 * 30.0 / 4.0, tf.error(tv), 1e-9);
JacobianFactor jf(*std::dynamic_pointer_cast<JacobianFactor>(tf.linearize(tv)));
LONGS_EQUAL((long)X(1), (long)jf.keys()[0]);
LONGS_EQUAL((long)X(2), (long)jf.keys()[1]);
LONGS_EQUAL((long)X(3), (long)jf.keys()[2]);
LONGS_EQUAL((long)X(4), (long)jf.keys()[3]);
EXPECT(assert_equal((Matrix)(Matrix(1, 1) << 0.5).finished(), jf.getA(jf.begin())));
EXPECT(assert_equal((Matrix)(Matrix(1, 1) << 1.0).finished(), jf.getA(jf.begin()+1)));
EXPECT(assert_equal((Matrix)(Matrix(1, 1) << 1.5).finished(), jf.getA(jf.begin()+2)));
EXPECT(assert_equal((Matrix)(Matrix(1, 1) << 2.0).finished(), jf.getA(jf.begin()+3)));
EXPECT(assert_equal((Vector)(Vector(1) << -0.5 * 30.).finished(), jf.getb()));
// Test all evaluateError argument overloads to ensure backward compatibility
Matrix H1_expected, H2_expected, H3_expected, H4_expected;
Vector e_expected = tf.evaluateError(9, 8, 7, 6, H1_expected, H2_expected,
H3_expected, H4_expected);
std::unique_ptr<NoiseModelFactorN<double, double, double, double>> base_ptr(
new TestFactorN(tf));
Matrix H1, H2, H3, H4;
EXPECT(assert_equal(e_expected, base_ptr->evaluateError(9, 8, 7, 6)));
EXPECT(assert_equal(e_expected, base_ptr->evaluateError(9, 8, 7, 6, H1)));
EXPECT(assert_equal(H1_expected, H1));
EXPECT(assert_equal(e_expected, //
base_ptr->evaluateError(9, 8, 7, 6, H1, H2)));
EXPECT(assert_equal(H1_expected, H1));
EXPECT(assert_equal(H2_expected, H2));
EXPECT(assert_equal(e_expected,
base_ptr->evaluateError(9, 8, 7, 6, H1, H2, H3)));
EXPECT(assert_equal(H1_expected, H1));
EXPECT(assert_equal(H2_expected, H2));
EXPECT(assert_equal(H3_expected, H3));
EXPECT(assert_equal(e_expected,
base_ptr->evaluateError(9, 8, 7, 6, H1, H2, H3, H4)));
EXPECT(assert_equal(H1_expected, H1));
EXPECT(assert_equal(H2_expected, H2));
EXPECT(assert_equal(H3_expected, H3));
EXPECT(assert_equal(H4_expected, H4));
// Test all functions/types for backwards compatibility
static_assert(std::is_same<TestFactor4::X1, double>::value,
"X1 type incorrect");
EXPECT(assert_equal(tf.key3(), X(3)));
// Test using `key<N>` and `ValueType<N>`
static_assert(std::is_same<TestFactorN::ValueType<1>, double>::value,
"ValueType<1> type incorrect");
static_assert(std::is_same<TestFactorN::ValueType<2>, double>::value,
"ValueType<2> type incorrect");
static_assert(std::is_same<TestFactorN::ValueType<3>, double>::value,
"ValueType<3> type incorrect");
static_assert(std::is_same<TestFactorN::ValueType<4>, double>::value,
"ValueType<4> type incorrect");
static_assert(std::is_same<TestFactorN::Type1, double>::value,
"TestFactorN::Type1 type incorrect");
EXPECT(assert_equal(tf.key<1>(), X(1)));
EXPECT(assert_equal(tf.key<2>(), X(2)));
EXPECT(assert_equal(tf.key<3>(), X(3)));
EXPECT(assert_equal(tf.key<4>(), X(4)));
EXPECT(assert_equal(tf.key1(), X(1)));
}
/* ************************************************************************* */
TEST( NonlinearFactor, clone_rekey )
{
shared_nlf init(new TestFactor4());
EXPECT_LONGS_EQUAL((long)X(1), (long)init->keys()[0]);
EXPECT_LONGS_EQUAL((long)X(2), (long)init->keys()[1]);
EXPECT_LONGS_EQUAL((long)X(3), (long)init->keys()[2]);
EXPECT_LONGS_EQUAL((long)X(4), (long)init->keys()[3]);
// Standard clone
shared_nlf actClone = init->clone();
EXPECT(actClone.get() != init.get()); // Ensure different pointers
EXPECT(assert_equal(*init, *actClone));
// Re-key factor - clones with different keys
KeyVector new_keys {X(5),X(6),X(7),X(8)};
shared_nlf actRekey = init->rekey(new_keys);
EXPECT(actRekey.get() != init.get()); // Ensure different pointers
// Ensure init is unchanged
EXPECT_LONGS_EQUAL((long)X(1), (long)init->keys()[0]);
EXPECT_LONGS_EQUAL((long)X(2), (long)init->keys()[1]);
EXPECT_LONGS_EQUAL((long)X(3), (long)init->keys()[2]);
EXPECT_LONGS_EQUAL((long)X(4), (long)init->keys()[3]);
// Check new keys
EXPECT_LONGS_EQUAL((long)X(5), (long)actRekey->keys()[0]);
EXPECT_LONGS_EQUAL((long)X(6), (long)actRekey->keys()[1]);
EXPECT_LONGS_EQUAL((long)X(7), (long)actRekey->keys()[2]);
EXPECT_LONGS_EQUAL((long)X(8), (long)actRekey->keys()[3]);
}
/* ************************************************************************* */
int main() { TestResult tr; return TestRegistry::runAllTests(tr);}
/* ************************************************************************* */
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