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Finishing touches

release/4.3a0
Frank Dellaert 2024-09-12 23:20:05 -07:00
parent e59b3afc29
commit 07b4c236eb
1 changed files with 46 additions and 64 deletions
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110
gtsam/hybrid/tests/testGaussianMixtureFactor.cpp
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@ -29,6 +29,7 @@
#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>
@ -433,20 +434,20 @@ HybridBayesNet CreateBayesNet(
return hbn;
}
/// Create importance sampling network p(x1| x0, m1) p(x0) P(m1),
/// using Q(x0) = N(z0, sigma_Q) to sample from p(x0)
/// Create importance sampling network q(x0,x1,m) = p(x1|x0,m1) q(x0) P(m1),
/// using q(x0) = N(z0, sigma_Q) to sample x0.
HybridBayesNet CreateProposalNet(
const GaussianMixture::shared_ptr& hybridMotionModel, double z0,
const GaussianMixture::shared_ptr& hybridMotionModel, const Vector1& z0,
double sigma_Q) {
HybridBayesNet hbn;
// Add hybrid motion model
hbn.push_back(hybridMotionModel);
// Add proposal Q(x0) for x0
// Add proposal q(x0) for x0
auto measurement_model = noiseModel::Isotropic::Sigma(1, sigma_Q);
hbn.emplace_shared<GaussianConditional>(
GaussianConditional::FromMeanAndStddev(X(0), Vector1(z0), sigma_Q));
GaussianConditional::FromMeanAndStddev(X(0), z0, sigma_Q));
// Discrete uniform prior.
hbn.emplace_shared<DiscreteConditional>(m1, "0.5/0.5");
@ -466,7 +467,7 @@ void approximateDiscreteMarginal(const HybridBayesNet& hbn,
HybridValues sample = proposalNet.sample(&rng);
sample.insert(given);
double weight = hbn.evaluate(sample) / proposalNet.evaluate(sample);
(sample.atDiscrete(m1.first) == 0) ? w0 += weight : w1 += weight;
(sample.atDiscrete(M(1)) == 0) ? w0 += weight : w1 += weight;
}
double sumWeights = w0 + w1;
double pm1 = w1 / sumWeights;
@ -478,15 +479,14 @@ void approximateDiscreteMarginal(const HybridBayesNet& hbn,
/* ************************************************************************* */
/**
* Test a model P(z0|x0)P(x1|x0,m1)P(z1|x1)P(m1).
* Test a model p(z0|x0)p(z1|x1)p(x1|x0,m1)P(m1).
*
* P(f01|x1,x0,m1) has different means and same covariance.
* p(x1|x0,m1) has mode-dependent mean but same covariance.
*
* Converting to a factor graph gives us
* ϕ(x0)ϕ(x1,x0,m1)ϕ(x1)P(m1)
* Converting to a factor graph gives us ϕ(x0;z0)ϕ(x1;z1)ϕ(x1,x0,m1)P(m1)
*
* If we only have a measurement on z0, then
* the probability of m1 should be 0.5/0.5.
* If we only have a measurement on x0, then
* the posterior probability of m1 should be 0.5/0.5.
* Getting a measurement on z1 gives use more information.
*/
TEST(GaussianMixtureFactor, TwoStateModel) {
@ -497,10 +497,10 @@ TEST(GaussianMixtureFactor, TwoStateModel) {
auto hybridMotionModel = CreateHybridMotionModel(mu0, mu1, sigma, sigma);
// Start with no measurement on x1, only on x0
double z0 = 0.5;
const Vector1 z0(0.5);
VectorValues given;
given.insert(Z(0), Vector1(z0));
given.insert(Z(0), z0);
// Create proposal network for importance sampling
auto proposalNet = CreateProposalNet(hybridMotionModel, z0, 3.0);
@ -522,10 +522,10 @@ TEST(GaussianMixtureFactor, TwoStateModel) {
// Now we add a measurement z1 on x1
HybridBayesNet hbn = CreateBayesNet(hybridMotionModel, true);
// If we see z1=4.5 (>> 2.5 which is the halfway point),
// discrete mode should say m1=1
const double z1 = 4.5;
given.insert(Z(1), Vector1(z1));
// If we set z1=4.5 (>> 2.5 which is the halfway point),
// probability of discrete mode should be leaning to m1==1.
const Vector1 z1(4.5);
given.insert(Z(1), z1);
HybridGaussianFactorGraph gfg = hbn.toFactorGraph(given);
HybridBayesNet::shared_ptr bn = gfg.eliminateSequential();
@ -534,7 +534,7 @@ TEST(GaussianMixtureFactor, TwoStateModel) {
// Values taken from an importance sampling run with 50k samples:
// approximateDiscreteMarginal(hbn, proposalNet, given);
DiscreteConditional expected(m1, "0.446629/0.553371");
EXPECT(assert_equal(expected, *(bn->at(2)->asDiscrete()), 0.01));
EXPECT(assert_equal(expected, *(bn->at(2)->asDiscrete()), 0.002));
}
}
@ -559,9 +559,9 @@ TEST(GaussianMixtureFactor, TwoStateModel2) {
auto hybridMotionModel = CreateHybridMotionModel(mu0, mu1, sigma0, sigma1);
// Start with no measurement on x1, only on x0
double z0 = 0.5;
const Vector1 z0(0.5);
VectorValues given;
given.insert(Z(0), Vector1(z0));
given.insert(Z(0), z0);
// Create proposal network for importance sampling
// uncomment this and the approximateDiscreteMarginal calls to run
@ -571,19 +571,13 @@ TEST(GaussianMixtureFactor, TwoStateModel2) {
HybridBayesNet hbn = CreateBayesNet(hybridMotionModel);
HybridGaussianFactorGraph gfg = hbn.toFactorGraph(given);
{
VectorValues vv{
{X(0), Vector1(0.0)}, {X(1), Vector1(1.0)}, {Z(0), Vector1(z0)}};
HybridValues hv0(vv, DiscreteValues{{M(1), 0}}),
hv1(vv, DiscreteValues{{M(1), 1}});
EXPECT_DOUBLES_EQUAL(gfg.error(hv0) / hbn.error(hv0),
gfg.error(hv1) / hbn.error(hv1), 1e-9);
}
{
VectorValues vv{
{X(0), Vector1(0.5)}, {X(1), Vector1(3.0)}, {Z(0), Vector1(z0)}};
HybridValues hv0(vv, DiscreteValues{{M(1), 0}}),
hv1(vv, DiscreteValues{{M(1), 1}});
// Check that ratio of Bayes net and factor graph for different modes is
// equal for several values of {x0,x1}.
for (VectorValues vv :
{VectorValues{{X(0), Vector1(0.0)}, {X(1), Vector1(1.0)}},
VectorValues{{X(0), Vector1(0.5)}, {X(1), Vector1(3.0)}}}) {
vv.insert(given); // add measurements for HBN
HybridValues hv0(vv, {{M(1), 0}}), hv1(vv, {{M(1), 1}});
EXPECT_DOUBLES_EQUAL(gfg.error(hv0) / hbn.error(hv0),
gfg.error(hv1) / hbn.error(hv1), 1e-9);
}
@ -595,66 +589,54 @@ TEST(GaussianMixtureFactor, TwoStateModel2) {
// Since no measurement on x1, we a 50/50 probability
auto p_m = bn->at(2)->asDiscrete();
EXPECT_DOUBLES_EQUAL(0.5, p_m->operator()(DiscreteValues{{m1.first, 0}}),
1e-9);
EXPECT_DOUBLES_EQUAL(0.5, p_m->operator()(DiscreteValues{{m1.first, 1}}),
1e-9);
EXPECT_DOUBLES_EQUAL(0.5, p_m->operator()(DiscreteValues{{M(1), 0}}), 1e-9);
EXPECT_DOUBLES_EQUAL(0.5, p_m->operator()(DiscreteValues{{M(1), 1}}), 1e-9);
}
{
// Now we add a measurement z1 on x1
HybridBayesNet hbn = CreateBayesNet(hybridMotionModel, true);
double z1 = 4.0; // favors m==1
given.insert(Z(1), Vector1(z1));
const Vector1 z1(4.0); // favors m==1
given.insert(Z(1), z1);
HybridGaussianFactorGraph gfg = hbn.toFactorGraph(given);
{
VectorValues vv{{X(0), Vector1(0.0)},
{X(1), Vector1(1.0)},
{Z(0), Vector1(z0)},
{Z(1), Vector1(z1)}};
HybridValues hv0(vv, DiscreteValues{{M(1), 0}}),
hv1(vv, DiscreteValues{{M(1), 1}});
EXPECT_DOUBLES_EQUAL(gfg.error(hv0) / hbn.error(hv0),
gfg.error(hv1) / hbn.error(hv1), 1e-9);
}
{
VectorValues vv{{X(0), Vector1(0.5)},
{X(1), Vector1(3.0)},
{Z(0), Vector1(z0)},
{Z(1), Vector1(z1)}};
HybridValues hv0(vv, DiscreteValues{{M(1), 0}}),
hv1(vv, DiscreteValues{{M(1), 1}});
// Check that ratio of Bayes net and factor graph for different modes is
// equal for several values of {x0,x1}.
for (VectorValues vv :
{VectorValues{{X(0), Vector1(0.0)}, {X(1), Vector1(1.0)}},
VectorValues{{X(0), Vector1(0.5)}, {X(1), Vector1(3.0)}}}) {
vv.insert(given); // add measurements for HBN
HybridValues hv0(vv, {{M(1), 0}}), hv1(vv, {{M(1), 1}});
EXPECT_DOUBLES_EQUAL(gfg.error(hv0) / hbn.error(hv0),
gfg.error(hv1) / hbn.error(hv1), 1e-9);
}
HybridBayesNet::shared_ptr bn = gfg.eliminateSequential();
// Since we have a measurement on z2, we get a definite result
// Since we have a measurement z1 on x1, we get a definite result
// Values taken from an importance sampling run with 50k samples:
// approximateDiscreteMarginal(hbn, proposalNet, given);
DiscreteConditional expected(m1, "0.481793/0.518207");
EXPECT(assert_equal(expected, *(bn->at(2)->asDiscrete()), 0.01));
EXPECT(assert_equal(expected, *(bn->at(2)->asDiscrete()), 0.001));
}
{
// Add a different measurement z1 on that favors m==0
// Add a different measurement z1 on x1 that favors m==0
HybridBayesNet hbn = CreateBayesNet(hybridMotionModel, true);
double z1 = 1.1;
given.insert_or_assign(Z(1), Vector1(z1));
const Vector1 z1(1.1);
given.insert_or_assign(Z(1), z1);
HybridGaussianFactorGraph gfg = hbn.toFactorGraph(given);
HybridBayesNet::shared_ptr bn = gfg.eliminateSequential();
// Since we have a measurement on z2, we get a definite result
// Since we have a measurement z1 on x1, we get a definite result
// Values taken from an importance sampling run with 50k samples:
// approximateDiscreteMarginal(hbn, proposalNet, given);
DiscreteConditional expected(m1, "0.554485/0.445515");
EXPECT(assert_equal(expected, *(bn->at(2)->asDiscrete()), 0.01));
EXPECT(assert_equal(expected, *(bn->at(2)->asDiscrete()), 0.001));
}
}