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comprehensive unit testing

release/4.3a0
Varun Agrawal 2024-09-20 04:35:37 -04:00
parent 364b4b4a75
commit 67a8b8fea0
1 changed files with 69 additions and 11 deletions
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80
gtsam/linear/tests/testNoiseModel.cpp
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@ -807,23 +807,81 @@ TEST(NoiseModel, NonDiagonalGaussian)
}
}
TEST(NoiseModel, ComputeLogNormalizerConstant) {
TEST(NoiseModel, LogNormalizationConstant1D) {
// Very simple 1D noise model, which we can compute by hand.
double sigma = 0.1;
auto noise_model = Isotropic::Sigma(1, sigma);
double actual_value = noise_model->logNormalizationConstant();
// Compute 1/log(sqrt(|2πΣ|)) by hand.
// For expected values, we compute 1/log(sqrt(|2πΣ|)) by hand.
// = -0.5*(log(2π) + log(Σ)) (since it is 1D)
double expected_value = -0.5 * log(2 * M_PI * sigma * sigma);
EXPECT_DOUBLES_EQUAL(expected_value, actual_value, 1e-9);
// Similar situation in the 3D case
// Gaussian
{
Matrix11 R;
R << 1 / sigma;
auto noise_model = Gaussian::SqrtInformation(R);
double actual_value = noise_model->logNormalizationConstant();
EXPECT_DOUBLES_EQUAL(expected_value, actual_value, 1e-9);
}
// Diagonal
{
auto noise_model = Diagonal::Sigmas(Vector1(sigma));
double actual_value = noise_model->logNormalizationConstant();
EXPECT_DOUBLES_EQUAL(expected_value, actual_value, 1e-9);
}
// Isotropic
{
auto noise_model = Isotropic::Sigma(1, sigma);
double actual_value = noise_model->logNormalizationConstant();
EXPECT_DOUBLES_EQUAL(expected_value, actual_value, 1e-9);
}
// Unit
{
auto noise_model = Unit::Create(1);
double actual_value = noise_model->logNormalizationConstant();
double sigma = 1.0;
expected_value = -0.5 * log(2 * M_PI * sigma * sigma);
EXPECT_DOUBLES_EQUAL(expected_value, actual_value, 1e-9);
}
}
TEST(NoiseModel, LogNormalizationConstant3D) {
// Simple 3D noise model, which we can compute by hand.
double sigma = 0.1;
size_t n = 3;
auto noise_model2 = Isotropic::Sigma(n, sigma);
double actual_value2 = noise_model2->logNormalizationConstant();
// We multiply by 3 due to the determinant
double expected_value2 = -0.5 * n * log(2 * M_PI * sigma * sigma);
EXPECT_DOUBLES_EQUAL(expected_value2, actual_value2, 1e-9);
// We compute the expected values just like in the LogNormalizationConstant1D
// test, but we multiply by 3 due to the determinant.
double expected_value = -0.5 * n * log(2 * M_PI * sigma * sigma);
// Gaussian
{
Matrix33 R;
R << 1 / sigma, 2, 3, //
0, 1 / sigma, 4, //
0, 0, 1 / sigma;
auto noise_model = Gaussian::SqrtInformation(R);
double actual_value = noise_model->logNormalizationConstant();
EXPECT_DOUBLES_EQUAL(expected_value, actual_value, 1e-9);
}
// Diagonal
{
auto noise_model = Diagonal::Sigmas(Vector3(sigma, sigma, sigma));
double actual_value = noise_model->logNormalizationConstant();
EXPECT_DOUBLES_EQUAL(expected_value, actual_value, 1e-9);
}
// Isotropic
{
auto noise_model = Isotropic::Sigma(n, sigma);
double actual_value = noise_model->logNormalizationConstant();
EXPECT_DOUBLES_EQUAL(expected_value, actual_value, 1e-9);
}
// Unit
{
auto noise_model = Unit::Create(3);
double actual_value = noise_model->logNormalizationConstant();
double sigma = 1.0;
expected_value = -0.5 * n * log(2 * M_PI * sigma * sigma);
EXPECT_DOUBLES_EQUAL(expected_value, actual_value, 1e-9);
}
}
/* ************************************************************************* */