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

All tests still work with zero constant!

release/4.3a0
Frank Dellaert 2023-01-12 10:59:16 -08:00
parent 34a9aef6f3
commit 1dcc6ddde9
4 changed files with 12 additions and 12 deletions
Show Stats Download Patch File Download Diff File Expand all files Collapse all files

3
gtsam/hybrid/GaussianMixture.cpp
View File

@ -204,8 +204,7 @@ boost::shared_ptr<GaussianMixtureFactor> GaussianMixture::likelihood(
const GaussianMixtureFactor::Factors likelihoods( const GaussianMixtureFactor::Factors likelihoods(
conditionals_, [&](const GaussianConditional::shared_ptr &conditional) { conditionals_, [&](const GaussianConditional::shared_ptr &conditional) {
return GaussianMixtureFactor::FactorAndConstant{ return GaussianMixtureFactor::FactorAndConstant{
conditional->likelihood(given), conditional->likelihood(given), 0.0};
conditional->logNormalizationConstant()};
}); });
return boost::make_shared<GaussianMixtureFactor>( return boost::make_shared<GaussianMixtureFactor>(
continuousParentKeys, discreteParentKeys, likelihoods); continuousParentKeys, discreteParentKeys, likelihoods);

8
gtsam/hybrid/HybridBayesNet.cpp
View File

@ -341,11 +341,13 @@ HybridGaussianFactorGraph HybridBayesNet::toFactorGraph(
// replace it by a likelihood factor: // replace it by a likelihood factor:
for (auto &&conditional : *this) { for (auto &&conditional : *this) {
if (conditional->frontalsIn(measurements)) { if (conditional->frontalsIn(measurements)) {
if (auto gc = conditional->asGaussian()) if (auto gc = conditional->asGaussian()) {
fg.push_back(gc->likelihood(measurements)); fg.push_back(gc->likelihood(measurements));
else if (auto gm = conditional->asMixture()) } else if (auto gm = conditional->asMixture()) {
fg.push_back(gm->likelihood(measurements)); fg.push_back(gm->likelihood(measurements));
else { const auto constantsFactor = gm->normalizationConstants();
if (constantsFactor) fg.push_back(constantsFactor);
} else {
throw std::runtime_error("Unknown conditional type"); throw std::runtime_error("Unknown conditional type");
} }
} else { } else {

3
gtsam/hybrid/tests/testGaussianMixture.cpp
View File

@ -198,8 +198,7 @@ TEST(GaussianMixture, Likelihood) {
gm.conditionals(), gm.conditionals(),
[measurements](const GaussianConditional::shared_ptr& conditional) { [measurements](const GaussianConditional::shared_ptr& conditional) {
return GaussianMixtureFactor::FactorAndConstant{ return GaussianMixtureFactor::FactorAndConstant{
conditional->likelihood(measurements), conditional->likelihood(measurements), 0.0};
conditional->logNormalizationConstant()};
}); });
const GaussianMixtureFactor expected({X(0)}, {mode}, factors); const GaussianMixtureFactor expected({X(0)}, {mode}, factors);
EXPECT(assert_equal(expected, *factor)); EXPECT(assert_equal(expected, *factor));

10
gtsam/hybrid/tests/testHybridGaussianFactorGraph.cpp
View File

@ -613,7 +613,7 @@ TEST(HybridGaussianFactorGraph, assembleGraphTree) {
const int num_measurements = 1; const int num_measurements = 1;
auto fg = tiny::createHybridGaussianFactorGraph( auto fg = tiny::createHybridGaussianFactorGraph(
num_measurements, VectorValues{{Z(0), Vector1(5.0)}}); num_measurements, VectorValues{{Z(0), Vector1(5.0)}});
EXPECT_LONGS_EQUAL(3, fg.size()); EXPECT_LONGS_EQUAL(4, fg.size());
// Assemble graph tree: // Assemble graph tree:
auto actual = fg.assembleGraphTree(); auto actual = fg.assembleGraphTree();
@ -625,7 +625,7 @@ TEST(HybridGaussianFactorGraph, assembleGraphTree) {
CHECK(mixture); CHECK(mixture);
// Get prior factor: // Get prior factor:
const auto gf = boost::dynamic_pointer_cast<HybridConditional>(fg.at(1)); const auto gf = boost::dynamic_pointer_cast<HybridConditional>(fg.at(2));
CHECK(gf); CHECK(gf);
using GF = GaussianFactor::shared_ptr; using GF = GaussianFactor::shared_ptr;
const GF prior = gf->asGaussian(); const GF prior = gf->asGaussian();
@ -654,7 +654,7 @@ TEST(HybridGaussianFactorGraph, EliminateTiny1) {
const int num_measurements = 1; const int num_measurements = 1;
auto fg = tiny::createHybridGaussianFactorGraph( auto fg = tiny::createHybridGaussianFactorGraph(
num_measurements, VectorValues{{Z(0), Vector1(5.0)}}); num_measurements, VectorValues{{Z(0), Vector1(5.0)}});
EXPECT_LONGS_EQUAL(3, fg.size()); EXPECT_LONGS_EQUAL(4, fg.size());
// Create expected Bayes Net: // Create expected Bayes Net:
HybridBayesNet expectedBayesNet; HybridBayesNet expectedBayesNet;
@ -686,7 +686,7 @@ TEST(HybridGaussianFactorGraph, EliminateTiny2) {
auto fg = tiny::createHybridGaussianFactorGraph( auto fg = tiny::createHybridGaussianFactorGraph(
num_measurements, num_measurements,
VectorValues{{Z(0), Vector1(4.0)}, {Z(1), Vector1(6.0)}}); VectorValues{{Z(0), Vector1(4.0)}, {Z(1), Vector1(6.0)}});
EXPECT_LONGS_EQUAL(4, fg.size()); EXPECT_LONGS_EQUAL(6, fg.size());
// Create expected Bayes Net: // Create expected Bayes Net:
HybridBayesNet expectedBayesNet; HybridBayesNet expectedBayesNet;
@ -721,7 +721,7 @@ TEST(HybridGaussianFactorGraph, EliminateTiny22) {
auto bn = tiny::createHybridBayesNet(num_measurements, manyModes); auto bn = tiny::createHybridBayesNet(num_measurements, manyModes);
const VectorValues measurements{{Z(0), Vector1(4.0)}, {Z(1), Vector1(6.0)}}; const VectorValues measurements{{Z(0), Vector1(4.0)}, {Z(1), Vector1(6.0)}};
auto fg = bn.toFactorGraph(measurements); auto fg = bn.toFactorGraph(measurements);
EXPECT_LONGS_EQUAL(5, fg.size()); EXPECT_LONGS_EQUAL(7, fg.size());
// Test elimination // Test elimination
const auto posterior = fg.eliminateSequential(); const auto posterior = fg.eliminateSequential();