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address some PR comments

release/4.3a0
Varun Agrawal 2024-09-15 09:16:31 -04:00
parent e2f1ad78a0
commit 3eb91a4ba0
2 changed files with 5 additions and 5 deletions
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2
gtsam/hybrid/HybridNonlinearFactor.h
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@ -110,7 +110,7 @@ class HybridNonlinearFactor : public HybridFactor {
std::inserter(factor_keys_set, factor_keys_set.end())); std::inserter(factor_keys_set, factor_keys_set.end()));
if (auto nf = std::dynamic_pointer_cast<NonlinearFactor>(f)) { if (auto nf = std::dynamic_pointer_cast<NonlinearFactor>(f)) {
nonlinear_factors.push_back(std::make_pair(nf, val)); nonlinear_factors.emplace_back(nf, val);
} else { } else {
throw std::runtime_error( throw std::runtime_error(
"Factors passed into HybridNonlinearFactor need to be nonlinear!"); "Factors passed into HybridNonlinearFactor need to be nonlinear!");

8
gtsam/hybrid/tests/testHybridGaussianFactor.cpp
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@ -616,15 +616,15 @@ TEST(HybridGaussianFactor, TwoStateModel2) {
/* ************************************************************************* */ /* ************************************************************************* */
/** /**
* Test a model P(z0|x0)P(x1|x0,m1)P(z1|x1)P(m1). * Test a model p(z0|x0)p(x1|x0,m1)p(z1|x1)p(m1).
* *
* P(x1|x0,m1) has the same means but different covariances. * p(x1|x0,m1) has the same means but different covariances.
* *
* Converting to a factor graph gives us * Converting to a factor graph gives us
* ϕ(x0)ϕ(x1,x0,m1)ϕ(x1)P(m1) * ϕ(x0)ϕ(x1,x0,m1)ϕ(x1)p(m1)
* *
* If we only have a measurement on z0, then * If we only have a measurement on z0, then
* the P(m1) should be 0.5/0.5. * the p(m1) should be 0.5/0.5.
* Getting a measurement on z1 gives use more information. * Getting a measurement on z1 gives use more information.
*/ */
TEST(HybridGaussianFactor, TwoStateModel3) { TEST(HybridGaussianFactor, TwoStateModel3) {