227 lines
7.3 KiB
C++
227 lines
7.3 KiB
C++
/*
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* testNoiseModel.cpp
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*
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* Created on: Jan 13, 2010
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* Author: Richard Roberts
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* Author: Frank Dellaert
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*/
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#include <CppUnitLite/TestHarness.h>
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#include <boost/foreach.hpp>
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#include <iostream>
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#include "NoiseModel.h"
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#include "SharedGaussian.h"
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#include "SharedDiagonal.h"
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using namespace std;
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using namespace gtsam;
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using namespace noiseModel;
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static double sigma = 2, s_1=1.0/sigma, var = sigma*sigma, prc = 1.0/var;
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static Matrix R = Matrix_(3, 3,
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s_1, 0.0, 0.0,
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0.0, s_1, 0.0,
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0.0, 0.0, s_1);
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static Matrix Sigma = Matrix_(3, 3,
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var, 0.0, 0.0,
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0.0, var, 0.0,
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0.0, 0.0, var);
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static Matrix Q = Matrix_(3, 3,
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prc, 0.0, 0.0,
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0.0, prc, 0.0,
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0.0, 0.0, prc);
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static double inf = std::numeric_limits<double>::infinity();
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/* ************************************************************************* */
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TEST(NoiseModel, constructors)
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{
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Vector whitened = Vector_(3,5.0,10.0,15.0);
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Vector unwhitened = Vector_(3,10.0,20.0,30.0);
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// Construct noise models
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vector<Gaussian::shared_ptr> m;
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m.push_back(Gaussian::SqrtInformation(R));
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m.push_back(Gaussian::Covariance(Sigma));
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m.push_back(Gaussian::Information(Q));
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m.push_back(Diagonal::Sigmas(Vector_(3, sigma, sigma, sigma)));
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m.push_back(Diagonal::Variances(Vector_(3, var, var, var)));
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m.push_back(Diagonal::Precisions(Vector_(3, prc, prc, prc)));
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m.push_back(Isotropic::Sigma(3, sigma));
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m.push_back(Isotropic::Variance(3, var));
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m.push_back(Isotropic::Precision(3, prc));
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// test whiten
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BOOST_FOREACH(Gaussian::shared_ptr mi, m)
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CHECK(assert_equal(whitened,mi->whiten(unwhitened)));
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// test unwhiten
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BOOST_FOREACH(Gaussian::shared_ptr mi, m)
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CHECK(assert_equal(unwhitened,mi->unwhiten(whitened)));
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// test Mahalanobis distance
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double distance = 5*5+10*10+15*15;
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BOOST_FOREACH(Gaussian::shared_ptr mi, m)
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DOUBLES_EQUAL(distance,mi->Mahalanobis(unwhitened),1e-9);
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// test R matrix
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Matrix expectedR(Matrix_(3, 3,
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s_1, 0.0, 0.0,
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0.0, s_1, 0.0,
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0.0, 0.0, s_1));
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BOOST_FOREACH(Gaussian::shared_ptr mi, m)
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CHECK(assert_equal(expectedR,mi->R()));
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// test Whiten operator
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Matrix H(Matrix_(3, 4,
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0.0, 0.0, 1.0, 1.0,
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0.0, 1.0, 0.0, 1.0,
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1.0, 0.0, 0.0, 1.0));
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Matrix expected(Matrix_(3, 4,
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0.0, 0.0, s_1, s_1,
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0.0, s_1, 0.0, s_1,
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s_1, 0.0, 0.0, s_1));
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BOOST_FOREACH(Gaussian::shared_ptr mi, m)
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CHECK(assert_equal(expected,mi->Whiten(H)));
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// can only test inplace version once :-)
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m[0]->WhitenInPlace(H);
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CHECK(assert_equal(expected,H));
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}
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/* ************************************************************************* */
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TEST(NoiseModel, Unit) {
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Vector v = Vector_(3,5.0,10.0,15.0);
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Gaussian::shared_ptr u(Unit::Create(3));
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CHECK(assert_equal(v,u->whiten(v)));
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}
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/* ************************************************************************* */
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TEST(NoiseModel, equals)
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{
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Gaussian::shared_ptr g = Gaussian::SqrtInformation(R);
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Diagonal::shared_ptr d = Diagonal::Sigmas(Vector_(3, sigma, sigma, sigma));
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Isotropic::shared_ptr i = Isotropic::Sigma(3, sigma);
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CHECK(assert_equal(*g,*g));
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}
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/* ************************************************************************* */
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TEST(NoiseModel, ConstrainedMixed )
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{
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Vector feasible = Vector_(3, 1.0, 0.0, 1.0),
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infeasible = Vector_(3, 1.0, 1.0, 1.0);
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Constrained::shared_ptr d = Constrained::MixedSigmas(Vector_(3, sigma, 0.0, sigma));
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CHECK(assert_equal(Vector_(3, 0.5, inf, 0.5),d->whiten(infeasible)));
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CHECK(assert_equal(Vector_(3, 0.5, 0.0, 0.5),d->whiten(feasible)));
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DOUBLES_EQUAL(inf,d->Mahalanobis(infeasible),1e-9);
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DOUBLES_EQUAL(0.5,d->Mahalanobis(feasible),1e-9);
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}
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/* ************************************************************************* */
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TEST(NoiseModel, ConstrainedAll )
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{
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Vector feasible = Vector_(3, 0.0, 0.0, 0.0),
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infeasible = Vector_(3, 1.0, 1.0, 1.0);
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Constrained::shared_ptr i = Constrained::All(3);
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CHECK(assert_equal(Vector_(3, inf, inf, inf),i->whiten(infeasible)));
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CHECK(assert_equal(Vector_(3, 0.0, 0.0, 0.0),i->whiten(feasible)));
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DOUBLES_EQUAL(inf,i->Mahalanobis(infeasible),1e-9);
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DOUBLES_EQUAL(0.0,i->Mahalanobis(feasible),1e-9);
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}
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/* ************************************************************************* */
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TEST( NoiseModel, QR )
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{
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// create a matrix to eliminate
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Matrix Ab1 = Matrix_(4, 6+1,
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-1., 0., 1., 0., 0., 0., -0.2,
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0., -1., 0., 1., 0., 0., 0.3,
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1., 0., 0., 0., -1., 0., 0.2,
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0., 1., 0., 0., 0., -1., -0.1);
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Matrix Ab2 = Ab1; // otherwise overwritten !
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Vector sigmas = Vector_(4, 0.2, 0.2, 0.1, 0.1);
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// Expected result
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Vector expectedSigmas = Vector_(4, 0.0894427, 0.0894427, 0.223607, 0.223607);
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SharedDiagonal expectedModel = noiseModel::Diagonal::Sigmas(expectedSigmas);
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// Call Gaussian version
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SharedDiagonal diagonal = noiseModel::Diagonal::Sigmas(sigmas);
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SharedDiagonal actual1 = diagonal->QR(Ab1);
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SharedDiagonal expected = noiseModel::Unit::Create(4);
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CHECK(assert_equal(*expected,*actual1));
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Matrix expectedRd1 = Matrix_(4, 6+1,
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11.1803, 0.0, -2.23607, 0.0, -8.94427, 0.0, 2.23607,
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0.0, 11.1803, 0.0, -2.23607, 0.0, -8.94427,-1.56525,
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0.0, 0.0, 4.47214, 0.0, -4.47214, 0.0, 0.0,
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0.0, 0.0, 0.0, 4.47214, 0.0, -4.47214, 0.894427);
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CHECK(linear_dependent(expectedRd1,Ab1,1e-4)); // Ab was modified in place !!!
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// Call Constrained version
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SharedDiagonal constrained = noiseModel::Constrained::MixedSigmas(sigmas);
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SharedDiagonal actual2 = constrained->QR(Ab2);
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SharedDiagonal expectedModel2 = noiseModel::Diagonal::Sigmas(expectedSigmas);
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CHECK(assert_equal(*expectedModel2,*actual2));
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Matrix expectedRd2 = Matrix_(4, 6+1,
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1., 0., -0.2, 0., -0.8, 0., 0.2,
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0., 1., 0.,-0.2, 0., -0.8,-0.14,
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0., 0., 1., 0., -1., 0., 0.0,
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0., 0., 0., 1., 0., -1., 0.2);
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CHECK(linear_dependent(expectedRd2,Ab2,1e-6)); // Ab was modified in place !!!
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}
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/* ************************************************************************* */
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TEST(NoiseModel, QRNan )
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{
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SharedDiagonal constrained = noiseModel::Constrained::All(2);
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Matrix Ab = Matrix_(2, 5, 1., 2., 1., 2., 3., 2., 1., 2., 4., 4.);
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SharedDiagonal expected = noiseModel::Constrained::All(2);
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Matrix expectedAb = Matrix_(2, 5, 1., 2., 1., 2., 3., 0., 1., 0., 0., 2.0/3);
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SharedDiagonal actual = constrained->QR(Ab);
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CHECK(assert_equal(*expected,*actual));
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CHECK(assert_equal(expectedAb,Ab));
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}
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/* ************************************************************************* */
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TEST(NoiseModel, SmartCovariance )
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{
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bool smart = true;
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SharedGaussian expected = Unit::Create(3);
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SharedGaussian actual = Gaussian::Covariance(eye(3), smart);
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CHECK(assert_equal(*expected,*actual));
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}
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/* ************************************************************************* */
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TEST(NoiseModel, ScalarOrVector )
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{
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bool smart = true;
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SharedGaussian expected = Unit::Create(3);
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SharedGaussian actual = Gaussian::Covariance(eye(3), smart);
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CHECK(assert_equal(*expected,*actual));
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}
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/* ************************************************************************* */
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TEST(NoiseModel, WhitenInPlace)
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{
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Vector sigmas = Vector_(3, 0.1, 0.1, 0.1);
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SharedDiagonal model(sigmas);
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Matrix A = eye(3);
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model->WhitenInPlace(A);
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Matrix expected = eye(3) * 10;
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CHECK(assert_equal(expected, A));
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}
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/* ************************************************************************* */
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int main() {
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TestResult tr;
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return TestRegistry::runAllTests(tr);
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}
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/* ************************************************************************* */
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