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Added tests for R/covariance/information

release/4.3a0
Frank Dellaert 2015-07-11 14:14:04 -07:00
parent d45f6336d8
commit 5b4daf7527
1 changed files with 22 additions and 31 deletions
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53
gtsam/linear/tests/testNoiseModel.cpp
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@ -33,19 +33,12 @@ using namespace gtsam;
using namespace noiseModel; using namespace noiseModel;
using namespace boost::assign; using namespace boost::assign;
static const double kSigma = 2, s_1=1.0/kSigma, kVariance = kSigma*kSigma, prc = 1.0/kVariance; static const double kSigma = 2, kInverseSigma = 1.0 / kSigma,
static const Matrix R = (Matrix(3, 3) << kVariance = kSigma * kSigma, prc = 1.0 / kVariance;
s_1, 0.0, 0.0, static const Matrix R = Matrix3::Identity() * kInverseSigma;
0.0, s_1, 0.0, static const Matrix kCovariance = Matrix3::Identity() * kVariance;
0.0, 0.0, s_1).finished();
static const Matrix kCovariance = (Matrix(3, 3) <<
kVariance, 0.0, 0.0,
0.0, kVariance, 0.0,
0.0, 0.0, kVariance).finished();
static const Vector3 kSigmas(kSigma, kSigma, kSigma); static const Vector3 kSigmas(kSigma, kSigma, kSigma);
//static double inf = numeric_limits<double>::infinity();
/* ************************************************************************* */ /* ************************************************************************* */
TEST(NoiseModel, constructors) TEST(NoiseModel, constructors)
{ {
@ -58,8 +51,8 @@ TEST(NoiseModel, constructors)
m.push_back(Gaussian::Covariance(kCovariance,false)); m.push_back(Gaussian::Covariance(kCovariance,false));
m.push_back(Gaussian::Information(kCovariance.inverse(),false)); m.push_back(Gaussian::Information(kCovariance.inverse(),false));
m.push_back(Diagonal::Sigmas(kSigmas,false)); m.push_back(Diagonal::Sigmas(kSigmas,false));
m.push_back(Diagonal::Variances((Vector(3) << kVariance, kVariance, kVariance).finished(),false)); m.push_back(Diagonal::Variances((Vector3(kVariance, kVariance, kVariance)),false));
m.push_back(Diagonal::Precisions((Vector(3) << prc, prc, prc).finished(),false)); m.push_back(Diagonal::Precisions(Vector3(prc, prc, prc),false));
m.push_back(Isotropic::Sigma(3, kSigma,false)); m.push_back(Isotropic::Sigma(3, kSigma,false));
m.push_back(Isotropic::Variance(3, kVariance,false)); m.push_back(Isotropic::Variance(3, kVariance,false));
m.push_back(Isotropic::Precision(3, prc,false)); m.push_back(Isotropic::Precision(3, prc,false));
@ -82,25 +75,23 @@ TEST(NoiseModel, constructors)
DOUBLES_EQUAL(distance,mi->Mahalanobis(unwhitened),1e-9); DOUBLES_EQUAL(distance,mi->Mahalanobis(unwhitened),1e-9);
// test R matrix // test R matrix
Matrix expectedR((Matrix(3, 3) <<
s_1, 0.0, 0.0,
0.0, s_1, 0.0,
0.0, 0.0, s_1).finished());
BOOST_FOREACH(Gaussian::shared_ptr mi, m) BOOST_FOREACH(Gaussian::shared_ptr mi, m)
EXPECT(assert_equal(expectedR,mi->R())); EXPECT(assert_equal(R,mi->R()));
// test covariance
BOOST_FOREACH(Gaussian::shared_ptr mi, m)
EXPECT(assert_equal(kCovariance,mi->covariance()));
// test covariance
BOOST_FOREACH(Gaussian::shared_ptr mi, m)
EXPECT(assert_equal(kCovariance.inverse(),mi->information()));
// test Whiten operator // test Whiten operator
Matrix H((Matrix(3, 4) << Matrix H((Matrix(3, 4) <<
0.0, 0.0, 1.0, 1.0, 0.0, 0.0, 1.0, 1.0,
0.0, 1.0, 0.0, 1.0, 0.0, 1.0, 0.0, 1.0,
1.0, 0.0, 0.0, 1.0).finished()); 1.0, 0.0, 0.0, 1.0).finished());
Matrix expected = kInverseSigma * H;
Matrix expected((Matrix(3, 4) <<
0.0, 0.0, s_1, s_1,
0.0, s_1, 0.0, s_1,
s_1, 0.0, 0.0, s_1).finished());
BOOST_FOREACH(Gaussian::shared_ptr mi, m) BOOST_FOREACH(Gaussian::shared_ptr mi, m)
EXPECT(assert_equal(expected,mi->Whiten(H))); EXPECT(assert_equal(expected,mi->Whiten(H)));
@ -122,7 +113,7 @@ TEST(NoiseModel, equals)
{ {
Gaussian::shared_ptr g1 = Gaussian::SqrtInformation(R), Gaussian::shared_ptr g1 = Gaussian::SqrtInformation(R),
g2 = Gaussian::SqrtInformation(eye(3,3)); g2 = Gaussian::SqrtInformation(eye(3,3));
Diagonal::shared_ptr d1 = Diagonal::Sigmas((Vector(3) << kSigma, kSigma, kSigma).finished()), Diagonal::shared_ptr d1 = Diagonal::Sigmas(Vector3(kSigma, kSigma, kSigma)),
d2 = Diagonal::Sigmas(Vector3(0.1, 0.2, 0.3)); d2 = Diagonal::Sigmas(Vector3(0.1, 0.2, 0.3));
Isotropic::shared_ptr i1 = Isotropic::Sigma(3, kSigma), Isotropic::shared_ptr i1 = Isotropic::Sigma(3, kSigma),
i2 = Isotropic::Sigma(3, 0.7); i2 = Isotropic::Sigma(3, 0.7);
@ -141,7 +132,7 @@ TEST(NoiseModel, equals)
///* ************************************************************************* */ ///* ************************************************************************* */
//TEST(NoiseModel, ConstrainedSmart ) //TEST(NoiseModel, ConstrainedSmart )
//{ //{
// Gaussian::shared_ptr nonconstrained = Constrained::MixedSigmas((Vector(3) << sigma, 0.0, sigma), true); // Gaussian::shared_ptr nonconstrained = Constrained::MixedSigmas((Vector3(sigma, 0.0, sigma), true);
// Diagonal::shared_ptr n1 = boost::dynamic_pointer_cast<Diagonal>(nonconstrained); // Diagonal::shared_ptr n1 = boost::dynamic_pointer_cast<Diagonal>(nonconstrained);
// Constrained::shared_ptr n2 = boost::dynamic_pointer_cast<Constrained>(nonconstrained); // Constrained::shared_ptr n2 = boost::dynamic_pointer_cast<Constrained>(nonconstrained);
// EXPECT(n1); // EXPECT(n1);
@ -160,8 +151,8 @@ TEST(NoiseModel, ConstrainedConstructors )
Constrained::shared_ptr actual; Constrained::shared_ptr actual;
size_t d = 3; size_t d = 3;
double m = 100.0; double m = 100.0;
Vector sigmas = (Vector(3) << kSigma, 0.0, 0.0).finished(); Vector3 sigmas(kSigma, 0.0, 0.0);
Vector mu = Vector3(200.0, 300.0, 400.0); Vector3 mu(200.0, 300.0, 400.0);
actual = Constrained::All(d); actual = Constrained::All(d);
// TODO: why should this be a thousand ??? Dummy variable? // TODO: why should this be a thousand ??? Dummy variable?
EXPECT(assert_equal(gtsam::repeat(d, 1000.0), actual->mu())); EXPECT(assert_equal(gtsam::repeat(d, 1000.0), actual->mu()));
@ -187,7 +178,7 @@ TEST(NoiseModel, ConstrainedMixed )
{ {
Vector feasible = Vector3(1.0, 0.0, 1.0), Vector feasible = Vector3(1.0, 0.0, 1.0),
infeasible = Vector3(1.0, 1.0, 1.0); infeasible = Vector3(1.0, 1.0, 1.0);
Diagonal::shared_ptr d = Constrained::MixedSigmas((Vector(3) << kSigma, 0.0, kSigma).finished()); Diagonal::shared_ptr d = Constrained::MixedSigmas(Vector3(kSigma, 0.0, kSigma));
// NOTE: we catch constrained variables elsewhere, so whitening does nothing // NOTE: we catch constrained variables elsewhere, so whitening does nothing
EXPECT(assert_equal(Vector3(0.5, 1.0, 0.5),d->whiten(infeasible))); EXPECT(assert_equal(Vector3(0.5, 1.0, 0.5),d->whiten(infeasible)));
EXPECT(assert_equal(Vector3(0.5, 0.0, 0.5),d->whiten(feasible))); EXPECT(assert_equal(Vector3(0.5, 0.0, 0.5),d->whiten(feasible)));
@ -346,7 +337,7 @@ TEST(NoiseModel, robustNoise)
{ {
const double k = 10.0, error1 = 1.0, error2 = 100.0; const double k = 10.0, error1 = 1.0, error2 = 100.0;
Matrix A = (Matrix(2, 2) << 1.0, 10.0, 100.0, 1000.0).finished(); Matrix A = (Matrix(2, 2) << 1.0, 10.0, 100.0, 1000.0).finished();
Vector b = (Vector(2) << error1, error2).finished(); Vector b = Vector2(error1, error2);
const Robust::shared_ptr robust = Robust::Create( const Robust::shared_ptr robust = Robust::Create(
mEstimator::Huber::Create(k, mEstimator::Huber::Scalar), mEstimator::Huber::Create(k, mEstimator::Huber::Scalar),
Unit::Create(2)); Unit::Create(2));