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Moved mixture model tests to its own file

release/4.3a0
Frank Dellaert 2024-09-25 16:10:24 -07:00
parent 8f474600e0
commit 06887b702a
2 changed files with 239 additions and 187 deletions
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239
gtsam/hybrid/tests/testGaussianMixture.cpp Normal file
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@ -0,0 +1,239 @@
/* ----------------------------------------------------------------------------
* GTSAM Copyright 2010, Georgia Tech Research Corporation,
* Atlanta, Georgia 30332-0415
* All Rights Reserved
* Authors: Frank Dellaert, et al. (see THANKS for the full author list)
* See LICENSE for the license information
* -------------------------------------------------------------------------- */
/**
* @file testHybridGaussianFactor.cpp
* @brief Unit tests for HybridGaussianFactor
* @author Varun Agrawal
* @author Fan Jiang
* @author Frank Dellaert
* @date December 2021
*/
#include <gtsam/base/Testable.h>
#include <gtsam/base/TestableAssertions.h>
#include <gtsam/discrete/DiscreteConditional.h>
#include <gtsam/discrete/DiscreteValues.h>
#include <gtsam/hybrid/HybridBayesNet.h>
#include <gtsam/hybrid/HybridGaussianConditional.h>
#include <gtsam/hybrid/HybridGaussianFactor.h>
#include <gtsam/hybrid/HybridGaussianFactorGraph.h>
#include <gtsam/hybrid/HybridValues.h>
#include <gtsam/inference/Symbol.h>
#include <gtsam/linear/GaussianFactorGraph.h>
#include <gtsam/linear/VectorValues.h>
#include <gtsam/nonlinear/PriorFactor.h>
#include <gtsam/slam/BetweenFactor.h>
// Include for test suite
#include <CppUnitLite/TestHarness.h>
#include <memory>
using namespace std;
using namespace gtsam;
using symbol_shorthand::M;
using symbol_shorthand::X;
using symbol_shorthand::Z;
namespace test_gmm {
/**
* Function to compute P(m=1|z). For P(m=0|z), swap mus and sigmas.
* If sigma0 == sigma1, it simplifies to a sigmoid function.
*
* Follows equation 7.108 since it is more generic.
*/
double prob_m_z(double mu0, double mu1, double sigma0, double sigma1,
double z) {
double x1 = ((z - mu0) / sigma0), x2 = ((z - mu1) / sigma1);
double d = sigma1 / sigma0;
double e = d * std::exp(-0.5 * (x1 * x1 - x2 * x2));
return 1 / (1 + e);
};
static HybridBayesNet GetGaussianMixtureModel(double mu0, double mu1,
double sigma0, double sigma1) {
DiscreteKey m(M(0), 2);
Key z = Z(0);
auto model0 = noiseModel::Isotropic::Sigma(1, sigma0);
auto model1 = noiseModel::Isotropic::Sigma(1, sigma1);
auto c0 = make_shared<GaussianConditional>(z, Vector1(mu0), I_1x1, model0),
c1 = make_shared<GaussianConditional>(z, Vector1(mu1), I_1x1, model1);
HybridBayesNet hbn;
DiscreteKeys discreteParents{m};
hbn.emplace_shared<HybridGaussianConditional>(
KeyVector{z}, KeyVector{}, discreteParents,
HybridGaussianConditional::Conditionals(discreteParents,
std::vector{c0, c1}));
auto mixing = make_shared<DiscreteConditional>(m, "50/50");
hbn.push_back(mixing);
return hbn;
}
} // namespace test_gmm
/* ************************************************************************* */
/**
* Test a simple Gaussian Mixture Model represented as P(m)P(z|m)
* where m is a discrete variable and z is a continuous variable.
* m is binary and depending on m, we have 2 different means
* μ1 and μ2 for the Gaussian distribution around which we sample z.
*
* The resulting factor graph should eliminate to a Bayes net
* which represents a sigmoid function.
*/
TEST(HybridGaussianFactor, GaussianMixtureModel) {
using namespace test_gmm;
double mu0 = 1.0, mu1 = 3.0;
double sigma = 2.0;
DiscreteKey m(M(0), 2);
Key z = Z(0);
auto hbn = GetGaussianMixtureModel(mu0, mu1, sigma, sigma);
// The result should be a sigmoid.
// So should be P(m=1|z) = 0.5 at z=3.0 - 1.0=2.0
double midway = mu1 - mu0, lambda = 4;
{
VectorValues given;
given.insert(z, Vector1(midway));
HybridGaussianFactorGraph gfg = hbn.toFactorGraph(given);
HybridBayesNet::shared_ptr bn = gfg.eliminateSequential();
EXPECT_DOUBLES_EQUAL(
prob_m_z(mu0, mu1, sigma, sigma, midway),
bn->at(0)->asDiscrete()->operator()(DiscreteValues{{m.first, 1}}),
1e-8);
// At the halfway point between the means, we should get P(m|z)=0.5
HybridBayesNet expected;
expected.emplace_shared<DiscreteConditional>(m, "50/50");
EXPECT(assert_equal(expected, *bn));
}
{
// Shift by -lambda
VectorValues given;
given.insert(z, Vector1(midway - lambda));
HybridGaussianFactorGraph gfg = hbn.toFactorGraph(given);
HybridBayesNet::shared_ptr bn = gfg.eliminateSequential();
EXPECT_DOUBLES_EQUAL(
prob_m_z(mu0, mu1, sigma, sigma, midway - lambda),
bn->at(0)->asDiscrete()->operator()(DiscreteValues{{m.first, 1}}),
1e-8);
}
{
// Shift by lambda
VectorValues given;
given.insert(z, Vector1(midway + lambda));
HybridGaussianFactorGraph gfg = hbn.toFactorGraph(given);
HybridBayesNet::shared_ptr bn = gfg.eliminateSequential();
EXPECT_DOUBLES_EQUAL(
prob_m_z(mu0, mu1, sigma, sigma, midway + lambda),
bn->at(0)->asDiscrete()->operator()(DiscreteValues{{m.first, 1}}),
1e-8);
}
}
/* ************************************************************************* */
/**
* Test a simple Gaussian Mixture Model represented as P(m)P(z|m)
* where m is a discrete variable and z is a continuous variable.
* m is binary and depending on m, we have 2 different means
* and covariances each for the
* Gaussian distribution around which we sample z.
*
* The resulting factor graph should eliminate to a Bayes net
* which represents a Gaussian-like function
* where m1>m0 close to 3.1333.
*/
TEST(HybridGaussianFactor, GaussianMixtureModel2) {
using namespace test_gmm;
double mu0 = 1.0, mu1 = 3.0;
double sigma0 = 8.0, sigma1 = 4.0;
DiscreteKey m(M(0), 2);
Key z = Z(0);
auto hbn = GetGaussianMixtureModel(mu0, mu1, sigma0, sigma1);
double m1_high = 3.133, lambda = 4;
{
// The result should be a bell curve like function
// with m1 > m0 close to 3.1333.
// We get 3.1333 by finding the maximum value of the function.
VectorValues given;
given.insert(z, Vector1(3.133));
HybridGaussianFactorGraph gfg = hbn.toFactorGraph(given);
HybridBayesNet::shared_ptr bn = gfg.eliminateSequential();
EXPECT_DOUBLES_EQUAL(
prob_m_z(mu0, mu1, sigma0, sigma1, m1_high),
bn->at(0)->asDiscrete()->operator()(DiscreteValues{{M(0), 1}}), 1e-8);
// At the halfway point between the means
HybridBayesNet expected;
expected.emplace_shared<DiscreteConditional>(
m, DiscreteKeys{},
vector<double>{prob_m_z(mu1, mu0, sigma1, sigma0, m1_high),
prob_m_z(mu0, mu1, sigma0, sigma1, m1_high)});
EXPECT(assert_equal(expected, *bn));
}
{
// Shift by -lambda
VectorValues given;
given.insert(z, Vector1(m1_high - lambda));
HybridGaussianFactorGraph gfg = hbn.toFactorGraph(given);
HybridBayesNet::shared_ptr bn = gfg.eliminateSequential();
EXPECT_DOUBLES_EQUAL(
prob_m_z(mu0, mu1, sigma0, sigma1, m1_high - lambda),
bn->at(0)->asDiscrete()->operator()(DiscreteValues{{m.first, 1}}),
1e-8);
}
{
// Shift by lambda
VectorValues given;
given.insert(z, Vector1(m1_high + lambda));
HybridGaussianFactorGraph gfg = hbn.toFactorGraph(given);
HybridBayesNet::shared_ptr bn = gfg.eliminateSequential();
EXPECT_DOUBLES_EQUAL(
prob_m_z(mu0, mu1, sigma0, sigma1, m1_high + lambda),
bn->at(0)->asDiscrete()->operator()(DiscreteValues{{m.first, 1}}),
1e-8);
}
}
/* ************************************************************************* */
int main() {
TestResult tr;
return TestRegistry::runAllTests(tr);
}
/* ************************************************************************* */

187
gtsam/hybrid/tests/testHybridGaussianFactor.cpp
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@ -213,193 +213,6 @@ TEST(HybridGaussianFactor, Error) {
4.0, hybridFactor.error({continuousValues, discreteValues}), 1e-9);
}
namespace test_gmm {
/**
* Function to compute P(m=1|z). For P(m=0|z), swap mus and sigmas.
* If sigma0 == sigma1, it simplifies to a sigmoid function.
*
* Follows equation 7.108 since it is more generic.
*/
double prob_m_z(double mu0, double mu1, double sigma0, double sigma1,
double z) {
double x1 = ((z - mu0) / sigma0), x2 = ((z - mu1) / sigma1);
double d = sigma1 / sigma0;
double e = d * std::exp(-0.5 * (x1 * x1 - x2 * x2));
return 1 / (1 + e);
};
static HybridBayesNet GetGaussianMixtureModel(double mu0, double mu1,
double sigma0, double sigma1) {
DiscreteKey m(M(0), 2);
Key z = Z(0);
auto model0 = noiseModel::Isotropic::Sigma(1, sigma0);
auto model1 = noiseModel::Isotropic::Sigma(1, sigma1);
auto c0 = make_shared<GaussianConditional>(z, Vector1(mu0), I_1x1, model0),
c1 = make_shared<GaussianConditional>(z, Vector1(mu1), I_1x1, model1);
HybridBayesNet hbn;
DiscreteKeys discreteParents{m};
hbn.emplace_shared<HybridGaussianConditional>(
KeyVector{z}, KeyVector{}, discreteParents,
HybridGaussianConditional::Conditionals(discreteParents,
std::vector{c0, c1}));
auto mixing = make_shared<DiscreteConditional>(m, "50/50");
hbn.push_back(mixing);
return hbn;
}
} // namespace test_gmm
/* ************************************************************************* */
/**
* Test a simple Gaussian Mixture Model represented as P(m)P(z|m)
* where m is a discrete variable and z is a continuous variable.
* m is binary and depending on m, we have 2 different means
* μ1 and μ2 for the Gaussian distribution around which we sample z.
*
* The resulting factor graph should eliminate to a Bayes net
* which represents a sigmoid function.
*/
TEST(HybridGaussianFactor, GaussianMixtureModel) {
using namespace test_gmm;
double mu0 = 1.0, mu1 = 3.0;
double sigma = 2.0;
DiscreteKey m(M(0), 2);
Key z = Z(0);
auto hbn = GetGaussianMixtureModel(mu0, mu1, sigma, sigma);
// The result should be a sigmoid.
// So should be P(m=1|z) = 0.5 at z=3.0 - 1.0=2.0
double midway = mu1 - mu0, lambda = 4;
{
VectorValues given;
given.insert(z, Vector1(midway));
HybridGaussianFactorGraph gfg = hbn.toFactorGraph(given);
HybridBayesNet::shared_ptr bn = gfg.eliminateSequential();
EXPECT_DOUBLES_EQUAL(
prob_m_z(mu0, mu1, sigma, sigma, midway),
bn->at(0)->asDiscrete()->operator()(DiscreteValues{{m.first, 1}}),
1e-8);
// At the halfway point between the means, we should get P(m|z)=0.5
HybridBayesNet expected;
expected.emplace_shared<DiscreteConditional>(m, "50/50");
EXPECT(assert_equal(expected, *bn));
}
{
// Shift by -lambda
VectorValues given;
given.insert(z, Vector1(midway - lambda));
HybridGaussianFactorGraph gfg = hbn.toFactorGraph(given);
HybridBayesNet::shared_ptr bn = gfg.eliminateSequential();
EXPECT_DOUBLES_EQUAL(
prob_m_z(mu0, mu1, sigma, sigma, midway - lambda),
bn->at(0)->asDiscrete()->operator()(DiscreteValues{{m.first, 1}}),
1e-8);
}
{
// Shift by lambda
VectorValues given;
given.insert(z, Vector1(midway + lambda));
HybridGaussianFactorGraph gfg = hbn.toFactorGraph(given);
HybridBayesNet::shared_ptr bn = gfg.eliminateSequential();
EXPECT_DOUBLES_EQUAL(
prob_m_z(mu0, mu1, sigma, sigma, midway + lambda),
bn->at(0)->asDiscrete()->operator()(DiscreteValues{{m.first, 1}}),
1e-8);
}
}
/* ************************************************************************* */
/**
* Test a simple Gaussian Mixture Model represented as P(m)P(z|m)
* where m is a discrete variable and z is a continuous variable.
* m is binary and depending on m, we have 2 different means
* and covariances each for the
* Gaussian distribution around which we sample z.
*
* The resulting factor graph should eliminate to a Bayes net
* which represents a Gaussian-like function
* where m1>m0 close to 3.1333.
*/
TEST(HybridGaussianFactor, GaussianMixtureModel2) {
using namespace test_gmm;
double mu0 = 1.0, mu1 = 3.0;
double sigma0 = 8.0, sigma1 = 4.0;
DiscreteKey m(M(0), 2);
Key z = Z(0);
auto hbn = GetGaussianMixtureModel(mu0, mu1, sigma0, sigma1);
double m1_high = 3.133, lambda = 4;
{
// The result should be a bell curve like function
// with m1 > m0 close to 3.1333.
// We get 3.1333 by finding the maximum value of the function.
VectorValues given;
given.insert(z, Vector1(3.133));
HybridGaussianFactorGraph gfg = hbn.toFactorGraph(given);
HybridBayesNet::shared_ptr bn = gfg.eliminateSequential();
EXPECT_DOUBLES_EQUAL(
prob_m_z(mu0, mu1, sigma0, sigma1, m1_high),
bn->at(0)->asDiscrete()->operator()(DiscreteValues{{M(0), 1}}), 1e-8);
// At the halfway point between the means
HybridBayesNet expected;
expected.emplace_shared<DiscreteConditional>(
m, DiscreteKeys{},
vector<double>{prob_m_z(mu1, mu0, sigma1, sigma0, m1_high),
prob_m_z(mu0, mu1, sigma0, sigma1, m1_high)});
EXPECT(assert_equal(expected, *bn));
}
{
// Shift by -lambda
VectorValues given;
given.insert(z, Vector1(m1_high - lambda));
HybridGaussianFactorGraph gfg = hbn.toFactorGraph(given);
HybridBayesNet::shared_ptr bn = gfg.eliminateSequential();
EXPECT_DOUBLES_EQUAL(
prob_m_z(mu0, mu1, sigma0, sigma1, m1_high - lambda),
bn->at(0)->asDiscrete()->operator()(DiscreteValues{{m.first, 1}}),
1e-8);
}
{
// Shift by lambda
VectorValues given;
given.insert(z, Vector1(m1_high + lambda));
HybridGaussianFactorGraph gfg = hbn.toFactorGraph(given);
HybridBayesNet::shared_ptr bn = gfg.eliminateSequential();
EXPECT_DOUBLES_EQUAL(
prob_m_z(mu0, mu1, sigma0, sigma1, m1_high + lambda),
bn->at(0)->asDiscrete()->operator()(DiscreteValues{{m.first, 1}}),
1e-8);
}
}
namespace test_two_state_estimation {
DiscreteKey m1(M(1), 2);