12345qiupeng
/
gtsam
1
0
Fork 0
Code Issues Pull Requests Packages Projects Releases Wiki Activity

Inline q, use 100k

release/4.3a0
Frank Dellaert 2024-09-12 23:45:32 -07:00
parent 07b4c236eb
commit 70651e2cc5
1 changed files with 43 additions and 70 deletions
Show Stats Download Patch File Download Diff File Expand all files Collapse all files

113
gtsam/hybrid/tests/testGaussianMixtureFactor.cpp
View File

@ -235,7 +235,7 @@ static HybridBayesNet GetGaussianMixtureModel(double mu0, double mu1,
hbn.emplace_shared<GaussianMixture>(KeyVector{z}, KeyVector{},
DiscreteKeys{m}, std::vector{c0, c1});
auto mixing = make_shared<DiscreteConditional>(m, "0.5/0.5");
auto mixing = make_shared<DiscreteConditional>(m, "50/50");
hbn.push_back(mixing);
return hbn;
@ -281,7 +281,7 @@ TEST(GaussianMixtureFactor, GaussianMixtureModel) {
// At the halfway point between the means, we should get P(m|z)=0.5
HybridBayesNet expected;
expected.emplace_shared<DiscreteConditional>(m, "0.5/0.5");
expected.emplace_shared<DiscreteConditional>(m, "50/50");
EXPECT(assert_equal(expected, *bn));
}
@ -429,50 +429,37 @@ HybridBayesNet CreateBayesNet(
hbn.push_back(hybridMotionModel);
// Discrete uniform prior.
hbn.emplace_shared<DiscreteConditional>(m1, "0.5/0.5");
return hbn;
}
/// 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, const Vector1& z0,
double sigma_Q) {
HybridBayesNet hbn;
// Add hybrid motion model
hbn.push_back(hybridMotionModel);
// Add proposal q(x0) for x0
auto measurement_model = noiseModel::Isotropic::Sigma(1, sigma_Q);
hbn.emplace_shared<GaussianConditional>(
GaussianConditional::FromMeanAndStddev(X(0), z0, sigma_Q));
// Discrete uniform prior.
hbn.emplace_shared<DiscreteConditional>(m1, "0.5/0.5");
hbn.emplace_shared<DiscreteConditional>(m1, "50/50");
return hbn;
}
/// Approximate the discrete marginal P(m1) using importance sampling
/// Not typically called as expensive, but values are used in the tests.
void approximateDiscreteMarginal(const HybridBayesNet& hbn,
const HybridBayesNet& proposalNet,
const VectorValues& given) {
void approximateDiscreteMarginal(
const HybridBayesNet& hbn,
const GaussianMixture::shared_ptr& hybridMotionModel,
const VectorValues& given, size_t N = 100000) {
/// 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 q;
q.push_back(hybridMotionModel); // Add hybrid motion model
q.emplace_shared<GaussianConditional>(GaussianConditional::FromMeanAndStddev(
X(0), given.at(Z(0)), /* sigma_Q = */ 3.0)); // Add proposal q(x0) for x0
q.emplace_shared<DiscreteConditional>(m1, "50/50"); // Discrete prior.
// Do importance sampling
double w0 = 0.0, w1 = 0.0;
std::mt19937_64 rng(44);
for (int i = 0; i < 50000; i++) {
HybridValues sample = proposalNet.sample(&rng);
std::mt19937_64 rng(42);
for (int i = 0; i < N; i++) {
HybridValues sample = q.sample(&rng);
sample.insert(given);
double weight = hbn.evaluate(sample) / proposalNet.evaluate(sample);
double weight = hbn.evaluate(sample) / q.evaluate(sample);
(sample.atDiscrete(M(1)) == 0) ? w0 += weight : w1 += weight;
}
double sumWeights = w0 + w1;
double pm1 = w1 / sumWeights;
std::cout << "p(m0) ~ " << 1.0 - pm1 << std::endl;
std::cout << "p(m1) ~ " << pm1 << std::endl;
double pm1 = w1 / (w0 + w1);
std::cout << "p(m0) = " << 100 * (1.0 - pm1) << std::endl;
std::cout << "p(m1) = " << 100 * pm1 << std::endl;
}
} // namespace test_two_state_estimation
@ -502,38 +489,32 @@ TEST(GaussianMixtureFactor, TwoStateModel) {
VectorValues given;
given.insert(Z(0), z0);
// Create proposal network for importance sampling
auto proposalNet = CreateProposalNet(hybridMotionModel, z0, 3.0);
EXPECT_LONGS_EQUAL(3, proposalNet.size());
{
HybridBayesNet hbn = CreateBayesNet(hybridMotionModel);
HybridGaussianFactorGraph gfg = hbn.toFactorGraph(given);
HybridBayesNet::shared_ptr bn = gfg.eliminateSequential();
// Since no measurement on x1, we hedge our bets
// Importance sampling run with 50k samples gives 0.49934/0.50066
// approximateDiscreteMarginal(hbn, proposalNet, given);
DiscreteConditional expected(m1, "0.5/0.5");
// Importance sampling run with 100k samples gives 50.051/49.949
// approximateDiscreteMarginal(hbn, hybridMotionModel, given);
DiscreteConditional expected(m1, "50/50");
EXPECT(assert_equal(expected, *(bn->at(2)->asDiscrete())));
}
{
// Now we add a measurement z1 on x1
HybridBayesNet hbn = CreateBayesNet(hybridMotionModel, true);
// 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);
HybridBayesNet hbn = CreateBayesNet(hybridMotionModel, true);
HybridGaussianFactorGraph gfg = hbn.toFactorGraph(given);
HybridBayesNet::shared_ptr bn = gfg.eliminateSequential();
// Since we have a measurement on x1, we get a definite result
// Values taken from an importance sampling run with 50k samples:
// approximateDiscreteMarginal(hbn, proposalNet, given);
DiscreteConditional expected(m1, "0.446629/0.553371");
// Values taken from an importance sampling run with 100k samples:
// approximateDiscreteMarginal(hbn, hybridMotionModel, given);
DiscreteConditional expected(m1, "44.3854/55.6146");
EXPECT(assert_equal(expected, *(bn->at(2)->asDiscrete()), 0.002));
}
}
@ -563,10 +544,6 @@ TEST(GaussianMixtureFactor, TwoStateModel2) {
VectorValues given;
given.insert(Z(0), z0);
// Create proposal network for importance sampling
// uncomment this and the approximateDiscreteMarginal calls to run
// auto proposalNet = CreateProposalNet(hybridMotionModel, z0, 3.0);
{
HybridBayesNet hbn = CreateBayesNet(hybridMotionModel);
HybridGaussianFactorGraph gfg = hbn.toFactorGraph(given);
@ -584,22 +561,21 @@ TEST(GaussianMixtureFactor, TwoStateModel2) {
HybridBayesNet::shared_ptr bn = gfg.eliminateSequential();
// Importance sampling run with 50k samples gives 0.49934/0.50066
// approximateDiscreteMarginal(hbn, proposalNet, given);
// Importance sampling run with 100k samples gives 50.095/49.905
// approximateDiscreteMarginal(hbn, hybridMotionModel, given);
// 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{{M(1), 0}}), 1e-9);
EXPECT_DOUBLES_EQUAL(0.5, p_m->operator()(DiscreteValues{{M(1), 1}}), 1e-9);
EXPECT_DOUBLES_EQUAL(0.5, p_m->operator()({{M(1), 0}}), 1e-9);
EXPECT_DOUBLES_EQUAL(0.5, p_m->operator()({{M(1), 1}}), 1e-9);
}
{
// Now we add a measurement z1 on x1
HybridBayesNet hbn = CreateBayesNet(hybridMotionModel, true);
const Vector1 z1(4.0); // favors m==1
given.insert(Z(1), z1);
HybridBayesNet hbn = CreateBayesNet(hybridMotionModel, true);
HybridGaussianFactorGraph gfg = hbn.toFactorGraph(given);
// Check that ratio of Bayes net and factor graph for different modes is
@ -615,28 +591,25 @@ TEST(GaussianMixtureFactor, TwoStateModel2) {
HybridBayesNet::shared_ptr bn = gfg.eliminateSequential();
// 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()), 0.001));
// Values taken from an importance sampling run with 100k samples:
// approximateDiscreteMarginal(hbn, hybridMotionModel, given);
DiscreteConditional expected(m1, "48.3158/51.6842");
EXPECT(assert_equal(expected, *(bn->at(2)->asDiscrete()), 0.002));
}
{
// Add a different measurement z1 on x1 that favors m==0
HybridBayesNet hbn = CreateBayesNet(hybridMotionModel, true);
const Vector1 z1(1.1);
given.insert_or_assign(Z(1), z1);
HybridBayesNet hbn = CreateBayesNet(hybridMotionModel, true);
HybridGaussianFactorGraph gfg = hbn.toFactorGraph(given);
HybridBayesNet::shared_ptr bn = gfg.eliminateSequential();
// 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()), 0.001));
// Values taken from an importance sampling run with 100k samples:
// approximateDiscreteMarginal(hbn, hybridMotionModel, given);
DiscreteConditional expected(m1, "55.396/44.604");
EXPECT(assert_equal(expected, *(bn->at(2)->asDiscrete()), 0.002));
}
}