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Added tests for matrix/vector conversion

release/4.3a0
Frank Dellaert 2019-04-07 12:03:33 -04:00
parent 42284355f4
commit 1c2646000b
1 changed files with 28 additions and 6 deletions
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34
gtsam/linear/tests/testGaussianBayesNet.cpp
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@ -21,6 +21,8 @@
#include <gtsam/base/Testable.h>
#include <gtsam/base/numericalDerivative.h>
#include <CppUnitLite/TestHarness.h>
#include <boost/tuple/tuple.hpp>
#include <boost/assign/list_of.hpp>
#include <boost/assign/std/list.hpp> // for operator +=
using namespace boost::assign;
@ -28,13 +30,11 @@ using namespace boost::assign;
// STL/C++
#include <iostream>
#include <sstream>
#include <CppUnitLite/TestHarness.h>
#include <boost/tuple/tuple.hpp>
using namespace std;
using namespace gtsam;
static const Key _x_=0, _y_=1;
static const Key _x_ = 11, _y_ = 22, _z_ = 33;
static GaussianBayesNet smallBayesNet =
list_of(GaussianConditional(_x_, Vector1::Constant(9), I_1x1, _y_, I_1x1))(
@ -42,9 +42,9 @@ static GaussianBayesNet smallBayesNet =
static GaussianBayesNet noisyBayesNet =
list_of(GaussianConditional(_x_, Vector1::Constant(9), I_1x1, _y_, I_1x1,
noiseModel::Diagonal::Sigmas(Vector1::Constant(2))))(
noiseModel::Isotropic::Sigma(1, 2.0)))(
GaussianConditional(_y_, Vector1::Constant(5), I_1x1,
noiseModel::Diagonal::Sigmas(Vector1::Constant(3))));
noiseModel::Isotropic::Sigma(1, 3.0)));
/* ************************************************************************* */
TEST( GaussianBayesNet, Matrix )
@ -140,11 +140,33 @@ TEST( GaussianBayesNet, optimize3 )
TEST(GaussianBayesNet, ordering)
{
Ordering expected;
expected += 0, 1;
expected += _x_, _y_;
const auto actual = noisyBayesNet.ordering();
EXPECT(assert_equal(expected, actual));
}
/* ************************************************************************* */
TEST( GaussianBayesNet, MatrixStress )
{
GaussianBayesNet bn;
using GC = GaussianConditional;
bn.emplace_shared<GC>(_x_, Vector2(1, 2), 1 * I_2x2, _y_, 2 * I_2x2, _z_, 3 * I_2x2);
bn.emplace_shared<GC>(_y_, Vector2(3, 4), 4 * I_2x2, _z_, 5 * I_2x2);
bn.emplace_shared<GC>(_z_, Vector2(5, 6), 6 * I_2x2);
const VectorValues expected = bn.optimize();
for (const auto keys :
{KeyVector({_x_, _y_, _z_}), KeyVector({_x_, _z_, _y_}),
KeyVector({_y_, _x_, _z_}), KeyVector({_y_, _z_, _x_}),
KeyVector({_z_, _x_, _y_}), KeyVector({_z_, _y_, _x_})}) {
const Ordering ordering(keys);
Matrix R;
Vector d;
boost::tie(R, d) = bn.matrix(ordering);
EXPECT(assert_equal(expected.vector(ordering), R.inverse() * d));
}
}
/* ************************************************************************* */
TEST( GaussianBayesNet, backSubstituteTranspose )
{