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gtsam/cpp/testGaussianFactor.cpp

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/**
* @file testGaussianFactor.cpp
* @brief Unit tests for Linear Factor
* @author Christian Potthast
* @author Frank Dellaert
**/
#include <iostream>
#include <boost/tuple/tuple.hpp>
#include <boost/assign/std/list.hpp> // for operator +=
#include <boost/assign/std/set.hpp>
#include <boost/assign/std/map.hpp> // for insert
using namespace boost::assign;
#include <CppUnitLite/TestHarness.h>
#define GTSAM_MAGIC_KEY
#include "Matrix.h"
#include "Ordering.h"
#include "GaussianConditional.h"
#include "smallExample.h"
using namespace std;
using namespace gtsam;
using namespace example;
using namespace boost;
static SharedDiagonal
sigma0_1 = sharedSigma(2,0.1), sigma_02 = sharedSigma(2,0.2),
constraintModel = noiseModel::Constrained::All(2);
/* ************************************************************************* */
TEST( GaussianFactor, linearFactor )
{
Matrix I = eye(2);
Vector b = Vector_(2, 2.0, -1.0);
GaussianFactor expected("x1", -10*I,"x2", 10*I, b, noiseModel::Unit::Create(2));
// create a small linear factor graph
GaussianFactorGraph fg = createGaussianFactorGraph();
// get the factor "f2" from the factor graph
GaussianFactor::shared_ptr lf = fg[1];
// check if the two factors are the same
CHECK(assert_equal(expected,*lf));
}
/* ************************************************************************* */
TEST( GaussianFactor, operators )
{
Matrix I = eye(2);
Vector b = Vector_(2,0.2,-0.1);
GaussianFactor lf("x1", -I, "x2", I, b, sigma0_1);
VectorConfig c;
c.insert("x1",Vector_(2,10.,20.));
c.insert("x2",Vector_(2,30.,60.));
// test A*x
Vector expectedE = Vector_(2,200.,400.), e = lf*c;
CHECK(assert_equal(expectedE,e));
// test A^e
VectorConfig expectedX;
expectedX.insert("x1",Vector_(2,-2000.,-4000.));
expectedX.insert("x2",Vector_(2, 2000., 4000.));
CHECK(assert_equal(expectedX,lf^e));
// test transposeMultiplyAdd
VectorConfig x;
x.insert("x1",Vector_(2, 1.,2.));
x.insert("x2",Vector_(2, 3.,4.));
VectorConfig expectedX2 = x + 0.1 * (lf^e);
lf.transposeMultiplyAdd(0.1,e,x);
CHECK(assert_equal(expectedX2,x));
}
/* ************************************************************************* */
TEST( GaussianFactor, keys )
{
// get the factor "f2" from the small linear factor graph
GaussianFactorGraph fg = createGaussianFactorGraph();
GaussianFactor::shared_ptr lf = fg[1];
list<Symbol> expected;
expected.push_back("x1");
expected.push_back("x2");
CHECK(lf->keys() == expected);
}
/* ************************************************************************* */
TEST( GaussianFactor, dimensions )
{
// get the factor "f2" from the small linear factor graph
GaussianFactorGraph fg = createGaussianFactorGraph();
// Check a single factor
Dimensions expected;
insert(expected)("x1", 2)("x2", 2);
Dimensions actual = fg[1]->dimensions();
CHECK(expected==actual);
}
/* ************************************************************************* */
TEST( GaussianFactor, getDim )
{
// get a factor
GaussianFactorGraph fg = createGaussianFactorGraph();
GaussianFactor::shared_ptr factor = fg[0];
// get the size of a variable
size_t actual = factor->getDim("x1");
// verify
size_t expected = 2;
CHECK(actual == expected);
}
/* ************************************************************************* */
TEST( GaussianFactor, combine )
{
// create a small linear factor graph
GaussianFactorGraph fg = createGaussianFactorGraph();
// get two factors from it and insert the factors into a vector
vector<GaussianFactor::shared_ptr> lfg;
lfg.push_back(fg[4 - 1]);
lfg.push_back(fg[2 - 1]);
// combine in a factor
GaussianFactor combined(lfg);
// sigmas
double sigma2 = 0.1;
double sigma4 = 0.2;
Vector sigmas = Vector_(4, sigma4, sigma4, sigma2, sigma2);
// the expected combined linear factor
Matrix Ax2 = Matrix_(4, 2, // x2
-5., 0.,
+0., -5.,
10., 0.,
+0., 10.);
Matrix Al1 = Matrix_(4, 2, // l1
5., 0.,
0., 5.,
0., 0.,
0., 0.);
Matrix Ax1 = Matrix_(4, 2, // x1
0.00, 0., // f4
0.00, 0., // f4
-10., 0., // f2
0.00, -10. // f2
);
// the RHS
Vector b2(4);
b2(0) = -1.0;
b2(1) = 1.5;
b2(2) = 2.0;
b2(3) = -1.0;
// use general constructor for making arbitrary factors
vector<pair<Symbol, Matrix> > meas;
meas.push_back(make_pair("x2", Ax2));
meas.push_back(make_pair("l1", Al1));
meas.push_back(make_pair("x1", Ax1));
GaussianFactor expected(meas, b2, noiseModel::Diagonal::Sigmas(ones(4)));
CHECK(assert_equal(expected,combined));
}
/* ************************************************************************* */
TEST( NonlinearFactorGraph, combine2){
double sigma1 = 0.0957;
Matrix A11(2,2);
A11(0,0) = 1; A11(0,1) = 0;
A11(1,0) = 0; A11(1,1) = 1;
Vector b(2);
b(0) = 2; b(1) = -1;
GaussianFactor::shared_ptr f1(new GaussianFactor("x1", A11, b*sigma1, sharedSigma(2,sigma1)));
double sigma2 = 0.5;
A11(0,0) = 1; A11(0,1) = 0;
A11(1,0) = 0; A11(1,1) = -1;
b(0) = 4 ; b(1) = -5;
GaussianFactor::shared_ptr f2(new GaussianFactor("x1", A11, b*sigma2, sharedSigma(2,sigma2)));
double sigma3 = 0.25;
A11(0,0) = 1; A11(0,1) = 0;
A11(1,0) = 0; A11(1,1) = -1;
b(0) = 3 ; b(1) = -88;
GaussianFactor::shared_ptr f3(new GaussianFactor("x1", A11, b*sigma3, sharedSigma(2,sigma3)));
// TODO: find a real sigma value for this example
double sigma4 = 0.1;
A11(0,0) = 6; A11(0,1) = 0;
A11(1,0) = 0; A11(1,1) = 7;
b(0) = 5 ; b(1) = -6;
GaussianFactor::shared_ptr f4(new GaussianFactor("x1", A11*sigma4, b*sigma4, sharedSigma(2,sigma4)));
vector<GaussianFactor::shared_ptr> lfg;
lfg.push_back(f1);
lfg.push_back(f2);
lfg.push_back(f3);
lfg.push_back(f4);
GaussianFactor combined(lfg);
Vector sigmas = Vector_(8, sigma1, sigma1, sigma2, sigma2, sigma3, sigma3, sigma4, sigma4);
Matrix A22(8,2);
A22(0,0) = 1; A22(0,1) = 0;
A22(1,0) = 0; A22(1,1) = 1;
A22(2,0) = 1; A22(2,1) = 0;
A22(3,0) = 0; A22(3,1) = -1;
A22(4,0) = 1; A22(4,1) = 0;
A22(5,0) = 0; A22(5,1) = -1;
A22(6,0) = 0.6; A22(6,1) = 0;
A22(7,0) = 0; A22(7,1) = 0.7;
Vector exb(8);
exb(0) = 2*sigma1 ; exb(1) = -1*sigma1; exb(2) = 4*sigma2 ; exb(3) = -5*sigma2;
exb(4) = 3*sigma3 ; exb(5) = -88*sigma3; exb(6) = 5*sigma4 ; exb(7) = -6*sigma4;
vector<pair<Symbol, Matrix> > meas;
meas.push_back(make_pair("x1", A22));
GaussianFactor expected(meas, exb, sigmas);
CHECK(assert_equal(expected,combined));
}
/* ************************************************************************* */
TEST( GaussianFactor, linearFactorN){
Matrix I = eye(2);
vector<GaussianFactor::shared_ptr> f;
SharedDiagonal model = sharedSigma(2,1.0);
f.push_back(GaussianFactor::shared_ptr(new GaussianFactor("x1", I, Vector_(2,
10.0, 5.0), model)));
f.push_back(GaussianFactor::shared_ptr(new GaussianFactor("x1", -10 * I,
"x2", 10 * I, Vector_(2, 1.0, -2.0), model)));
f.push_back(GaussianFactor::shared_ptr(new GaussianFactor("x2", -10 * I,
"x3", 10 * I, Vector_(2, 1.5, -1.5), model)));
f.push_back(GaussianFactor::shared_ptr(new GaussianFactor("x3", -10 * I,
"x4", 10 * I, Vector_(2, 2.0, -1.0), model)));
GaussianFactor combinedFactor(f);
vector<pair<Symbol, Matrix> > combinedMeasurement;
combinedMeasurement.push_back(make_pair("x1", Matrix_(8,2,
1.0, 0.0,
0.0, 1.0,
-10.0, 0.0,
0.0,-10.0,
0.0, 0.0,
0.0, 0.0,
0.0, 0.0,
0.0, 0.0)));
combinedMeasurement.push_back(make_pair("x2", Matrix_(8,2,
0.0, 0.0,
0.0, 0.0,
10.0, 0.0,
0.0, 10.0,
-10.0, 0.0,
0.0,-10.0,
0.0, 0.0,
0.0, 0.0)));
combinedMeasurement.push_back(make_pair("x3", Matrix_(8,2,
0.0, 0.0,
0.0, 0.0,
0.0, 0.0,
0.0, 0.0,
10.0, 0.0,
0.0, 10.0,
-10.0, 0.0,
0.0,-10.0)));
combinedMeasurement.push_back(make_pair("x4", Matrix_(8,2,
0.0, 0.0,
0.0, 0.0,
0.0, 0.0,
0.0, 0.0,
0.0, 0.0,
0.0, 0.0,
10.0, 0.0,
0.0,10.0)));
Vector b = Vector_(8,
10.0, 5.0, 1.0, -2.0, 1.5, -1.5, 2.0, -1.0);
Vector sigmas = repeat(8,1.0);
GaussianFactor expected(combinedMeasurement, b, sigmas);
CHECK(assert_equal(expected,combinedFactor));
}
/* ************************************************************************* */
TEST( GaussianFactor, error )
{
// create a small linear factor graph
GaussianFactorGraph fg = createGaussianFactorGraph();
// get the first factor from the factor graph
GaussianFactor::shared_ptr lf = fg[0];
// check the error of the first factor with noisy config
VectorConfig cfg = createZeroDelta();
// calculate the error from the factor "f1"
// note the error is the same as in testNonlinearFactor
double actual = lf->error(cfg);
DOUBLES_EQUAL( 1.0, actual, 0.00000001 );
}
/* ************************************************************************* */
TEST( GaussianFactor, eliminate )
{
// create a small linear factor graph
GaussianFactorGraph fg = createGaussianFactorGraph();
// get two factors from it and insert the factors into a vector
vector<GaussianFactor::shared_ptr> lfg;
lfg.push_back(fg[4 - 1]);
lfg.push_back(fg[2 - 1]);
// combine in a factor
GaussianFactor combined(lfg);
// eliminate the combined factor
GaussianConditional::shared_ptr actualCG;
GaussianFactor::shared_ptr actualLF;
boost::tie(actualCG,actualLF) = combined.eliminate("x2");
// create expected Conditional Gaussian
Matrix I = eye(2)*sqrt(125.0);
Matrix R11 = I, S12 = -0.2*I, S13 = -0.8*I;
Vector d = I*Vector_(2,0.2,-0.14);
// Check the conditional Gaussian
GaussianConditional
expectedCG("x2", d, R11, "l1", S12, "x1", S13, repeat(2, 1.0));
// the expected linear factor
I = eye(2)/0.2236;
Matrix Bl1 = I, Bx1 = -I;
Vector b1 = I*Vector_(2,0.0,0.2);
GaussianFactor expectedLF("l1", Bl1, "x1", Bx1, b1, repeat(2,1.0));
// check if the result matches
CHECK(assert_equal(expectedCG,*actualCG,1e-3));
CHECK(assert_equal(expectedLF,*actualLF,1e-3));
}
/* ************************************************************************* */
TEST( GaussianFactor, eliminate2 )
{
// sigmas
double sigma1 = 0.2;
double sigma2 = 0.1;
Vector sigmas = Vector_(4, sigma1, sigma1, sigma2, sigma2);
// the combined linear factor
Matrix Ax2 = Matrix_(4,2,
// x2
-1., 0.,
+0.,-1.,
1., 0.,
+0.,1.
);
Matrix Al1x1 = Matrix_(4,4,
// l1 x1
1., 0., 0.00, 0., // f4
0., 1., 0.00, 0., // f4
0., 0., -1., 0., // f2
0., 0., 0.00,-1. // f2
);
// the RHS
Vector b2(4);
b2(0) = -0.2;
b2(1) = 0.3;
b2(2) = 0.2;
b2(3) = -0.1;
vector<pair<Symbol, Matrix> > meas;
meas.push_back(make_pair("x2", Ax2));
meas.push_back(make_pair("l11", Al1x1));
GaussianFactor combined(meas, b2, sigmas);
// eliminate the combined factor
GaussianConditional::shared_ptr actualCG;
GaussianFactor::shared_ptr actualLF;
boost::tie(actualCG,actualLF) = combined.eliminate("x2");
// create expected Conditional Gaussian
double oldSigma = 0.0894427; // from when R was made unit
Matrix R11 = Matrix_(2,2,
1.00, 0.00,
0.00, 1.00
)/oldSigma;
Matrix S12 = Matrix_(2,4,
-0.20, 0.00,-0.80, 0.00,
+0.00,-0.20,+0.00,-0.80
)/oldSigma;
Vector d = Vector_(2,0.2,-0.14)/oldSigma;
GaussianConditional expectedCG("x2",d,R11,"l11",S12,ones(2));
CHECK(assert_equal(expectedCG,*actualCG,1e-4));
// the expected linear factor
double sigma = 0.2236;
Matrix Bl1x1 = Matrix_(2,4,
// l1 x1
1.00, 0.00, -1.00, 0.00,
0.00, 1.00, +0.00, -1.00
)/sigma;
Vector b1 =Vector_(2,0.0,0.894427);
GaussianFactor expectedLF("l11", Bl1x1, b1, repeat(2,1.0));
CHECK(assert_equal(expectedLF,*actualLF,1e-3));
}
/* ************************************************************************* */
TEST( GaussianFactor, default_error )
{
GaussianFactor f;
VectorConfig c;
double actual = f.error(c);
CHECK(actual==0.0);
}
//* ************************************************************************* */
TEST( GaussianFactor, eliminate_empty )
{
// create an empty factor
GaussianFactor f;
// eliminate the empty factor
GaussianConditional::shared_ptr actualCG;
GaussianFactor::shared_ptr actualLF;
boost::tie(actualCG,actualLF) = f.eliminate("x2");
// expected Conditional Gaussian is just a parent-less node with P(x)=1
GaussianConditional expectedCG("x2");
// expected remaining factor is still empty :-)
GaussianFactor expectedLF;
// check if the result matches
CHECK(actualCG->equals(expectedCG));
CHECK(actualLF->equals(expectedLF));
}
//* ************************************************************************* */
TEST( GaussianFactor, empty )
{
// create an empty factor
GaussianFactor f;
CHECK(f.empty()==true);
}
/* ************************************************************************* */
TEST( GaussianFactor, matrix )
{
// create a small linear factor graph
GaussianFactorGraph fg = createGaussianFactorGraph();
// get the factor "f2" from the factor graph
//GaussianFactor::shared_ptr lf = fg[1]; // NOTE: using the older version
Vector b2 = Vector_(2, 0.2, -0.1);
Matrix I = eye(2);
GaussianFactor::shared_ptr lf(new GaussianFactor("x1", -I, "x2", I, b2, sigma0_1));
// render with a given ordering
Ordering ord;
ord += "x1","x2";
// Test whitened version
Matrix A_act1; Vector b_act1;
boost::tie(A_act1,b_act1) = lf->matrix(ord, true);
Matrix A1 = Matrix_(2,4,
-10.0, 0.0, 10.0, 0.0,
000.0,-10.0, 0.0, 10.0 );
Vector b1 = Vector_(2, 2.0, -1.0);
EQUALITY(A_act1,A1);
EQUALITY(b_act1,b1);
// Test unwhitened version
Matrix A_act2; Vector b_act2;
boost::tie(A_act2,b_act2) = lf->matrix(ord, false);
Matrix A2 = Matrix_(2,4,
-1.0, 0.0, 1.0, 0.0,
000.0,-1.0, 0.0, 1.0 );
//Vector b2 = Vector_(2, 2.0, -1.0);
EQUALITY(A_act2,A2);
EQUALITY(b_act2,b2);
// Ensure that whitening is consistent
shared_ptr<noiseModel::Gaussian> model = lf->get_model();
model->WhitenSystem(A_act2, b_act2);
EQUALITY(A_act1, A_act2);
EQUALITY(b_act1, b_act2);
}
/* ************************************************************************* */
TEST( GaussianFactor, matrix_aug )
{
// create a small linear factor graph
GaussianFactorGraph fg = createGaussianFactorGraph();
// get the factor "f2" from the factor graph
//GaussianFactor::shared_ptr lf = fg[1];
Vector b2 = Vector_(2, 0.2, -0.1);
Matrix I = eye(2);
GaussianFactor::shared_ptr lf(new GaussianFactor("x1", -I, "x2", I, b2, sigma0_1));
// render with a given ordering
Ordering ord;
ord += "x1","x2";
// Test unwhitened version
Matrix Ab_act1;
Ab_act1 = lf->matrix_augmented(ord, false);
Matrix Ab1 = Matrix_(2,5,
-1.0, 0.0, 1.0, 0.0, 0.2,
00.0,- 1.0, 0.0, 1.0, -0.1 );
EQUALITY(Ab_act1,Ab1);
// Test whitened version
Matrix Ab_act2;
Ab_act2 = lf->matrix_augmented(ord, true);
Matrix Ab2 = Matrix_(2,5,
-10.0, 0.0, 10.0, 0.0, 2.0,
00.0, -10.0, 0.0, 10.0, -1.0 );
EQUALITY(Ab_act2,Ab2);
// Ensure that whitening is consistent
shared_ptr<noiseModel::Gaussian> model = lf->get_model();
model->WhitenInPlace(Ab_act1);
EQUALITY(Ab_act1, Ab_act2);
}
/* ************************************************************************* */
// small aux. function to print out lists of anything
template<class T>
void print(const list<T>& i) {
copy(i.begin(), i.end(), ostream_iterator<T> (cout, ","));
cout << endl;
}
/* ************************************************************************* */
TEST( GaussianFactor, sparse )
{
// create a small linear factor graph
GaussianFactorGraph fg = createGaussianFactorGraph();
// get the factor "f2" from the factor graph
GaussianFactor::shared_ptr lf = fg[1];
// render with a given ordering
Ordering ord;
ord += "x1","x2";
list<int> i,j;
list<double> s;
boost::tie(i,j,s) = lf->sparse(fg.columnIndices(ord));
list<int> i1,j1;
i1 += 1,2,1,2;
j1 += 1,2,3,4;
list<double> s1;
s1 += -10,-10,10,10;
CHECK(i==i1);
CHECK(j==j1);
CHECK(s==s1);
}
/* ************************************************************************* */
TEST( GaussianFactor, sparse2 )
{
// create a small linear factor graph
GaussianFactorGraph fg = createGaussianFactorGraph();
// get the factor "f2" from the factor graph
GaussianFactor::shared_ptr lf = fg[1];
// render with a given ordering
Ordering ord;
ord += "x2","l1","x1";
list<int> i,j;
list<double> s;
boost::tie(i,j,s) = lf->sparse(fg.columnIndices(ord));
list<int> i1,j1;
i1 += 1,2,1,2;
j1 += 5,6,1,2;
list<double> s1;
s1 += -10,-10,10,10;
CHECK(i==i1);
CHECK(j==j1);
CHECK(s==s1);
}
/* ************************************************************************* */
TEST( GaussianFactor, size )
{
// create a linear factor graph
GaussianFactorGraph fg = createGaussianFactorGraph();
// get some factors from the graph
boost::shared_ptr<GaussianFactor> factor1 = fg[0];
boost::shared_ptr<GaussianFactor> factor2 = fg[1];
boost::shared_ptr<GaussianFactor> factor3 = fg[2];
CHECK(factor1->size() == 1);
CHECK(factor2->size() == 2);
CHECK(factor3->size() == 2);
}
/* ************************************************************************* */
TEST( GaussianFactor, tally_separator )
{
GaussianFactor f("x1", eye(2), "x2", eye(2), "l1", eye(2), ones(2), sigma0_1);
std::set<Symbol> act1, act2, act3;
f.tally_separator("x1", act1);
f.tally_separator("x2", act2);
f.tally_separator("l1", act3);
CHECK(act1.size() == 2);
CHECK(act1.count("x2") == 1);
CHECK(act1.count("l1") == 1);
CHECK(act2.size() == 2);
CHECK(act2.count("x1") == 1);
CHECK(act2.count("l1") == 1);
CHECK(act3.size() == 2);
CHECK(act3.count("x1") == 1);
CHECK(act3.count("x2") == 1);
}
/* ************************************************************************* */
TEST( GaussianFactor, CONSTRUCTOR_GaussianConditional )
{
Matrix R11 = eye(2);
Matrix S12 = Matrix_(2,2,
-0.200001, 0.00,
+0.00,-0.200001
);
Vector d(2); d(0) = 2.23607; d(1) = -1.56525;
Vector sigmas =repeat(2,0.29907);
GaussianConditional::shared_ptr CG(new GaussianConditional("x2",d,R11,"l11",S12,sigmas));
// Call the constructor we are testing !
GaussianFactor actualLF(CG);
GaussianFactor expectedLF("x2",R11,"l11",S12,d, sigmas);
CHECK(assert_equal(expectedLF,actualLF,1e-5));
}
/* ************************************************************************* */
TEST ( GaussianFactor, constraint_eliminate1 )
{
// construct a linear constraint
Vector v(2); v(0)=1.2; v(1)=3.4;
string key = "x0";
GaussianFactor lc(key, eye(2), v, constraintModel);
// eliminate it
GaussianConditional::shared_ptr actualCG;
GaussianFactor::shared_ptr actualLF;
boost::tie(actualCG,actualLF) = lc.eliminate("x0");
// verify linear factor
CHECK(actualLF->size() == 0);
// verify conditional Gaussian
Vector sigmas = Vector_(2, 0.0, 0.0);
GaussianConditional expCG("x0", v, eye(2), sigmas);
CHECK(assert_equal(expCG, *actualCG));
}
/* ************************************************************************* */
TEST ( GaussianFactor, constraint_eliminate2 )
{
// Construct a linear constraint
// RHS
Vector b(2); b(0)=3.0; b(1)=4.0;
// A1 - invertible
Matrix A1(2,2);
A1(0,0) = 1.0 ; A1(0,1) = 2.0;
A1(1,0) = 2.0 ; A1(1,1) = 1.0;
// A2 - not invertible
Matrix A2(2,2);
A2(0,0) = 1.0 ; A2(0,1) = 2.0;
A2(1,0) = 2.0 ; A2(1,1) = 4.0;
GaussianFactor lc("x", A1, "y", A2, b, constraintModel);
// eliminate x and verify results
GaussianConditional::shared_ptr actualCG;
GaussianFactor::shared_ptr actualLF;
boost::tie(actualCG, actualLF) = lc.eliminate("x");
// LF should be null
GaussianFactor expectedLF;
CHECK(assert_equal(*actualLF, expectedLF));
// 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);
GaussianConditional expectedCG("x", d, R, "y", S, zero(2));
CHECK(assert_equal(expectedCG, *actualCG, 1e-4));
}
/* ************************************************************************* */
TEST ( GaussianFactor, combine_matrix ) {
// create a small linear factor graph
GaussianFactorGraph fg = createGaussianFactorGraph();
Dimensions dimensions = fg.dimensions();
// get two factors from it and insert the factors into a vector
vector<GaussianFactor::shared_ptr> lfg;
lfg.push_back(fg[4 - 1]);
lfg.push_back(fg[2 - 1]);
// combine in a factor
Matrix Ab; SharedDiagonal noise;
Ordering order; order += "x2", "l1", "x1";
boost::tie(Ab, noise) = GaussianFactor::combineFactorsAndCreateMatrix(lfg, order, dimensions);
// the expected augmented matrix
Matrix expAb = Matrix_(4, 7,
-5., 0., 5., 0., 0., 0.,-1.0,
+0., -5., 0., 5., 0., 0., 1.5,
10., 0., 0., 0.,-10., 0., 2.0,
+0., 10., 0., 0., 0.,-10.,-1.0);
// expected noise model
SharedDiagonal expModel = noiseModel::Unit::Create(4);
CHECK(assert_equal(expAb, Ab));
CHECK(assert_equal(*expModel, *noise));
}
///* ************************************************************************* *
//TEST ( GaussianFactor, constraint_eliminate3 )
//{
// // This test shows that ordering matters if there are non-invertible
// // blocks, as this example can be eliminated if x is first, but not
// // if y is first.
//
// // Construct a linear constraint
// // RHS
// Vector b(2); b(0)=3.0; b(1)=4.0;
//
// // A1 - invertible
// Matrix A1(2,2);
// A1(0,0) = 1.0 ; A1(0,1) = 2.0;
// A1(1,0) = 2.0 ; A1(1,1) = 1.0;
//
// // A2 - not invertible
// Matrix A2(2,2);
// A2(0,0) = 1.0 ; A2(0,1) = 2.0;
// A2(1,0) = 2.0 ; A2(1,1) = 4.0;
//
// GaussianFactor lc("x", A1, "y", A2, b, 0.0);
//
// // eliminate y from original graph
// // NOTE: this will throw an exception, as
// // the leading matrix is rank deficient
// GaussianConditional::shared_ptr actualCG;
// GaussianFactor::shared_ptr actualLF;
// try {
// boost::tie(actualCG, actualLF) = lc.eliminate("y");
// CHECK(false);
// } catch (domain_error) {
// CHECK(true);
// }
//}
/* ************************************************************************* */
int main() { TestResult tr; return TestRegistry::runAllTests(tr);}
/* ************************************************************************* */
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