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include missing headers for msvc and fix warning

release/4.3a0
Varun Agrawal 2022-05-23 20:37:57 -04:00
parent b215d3a377
commit e36583e6d5
2 changed files with 7 additions and 4 deletions
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2
gtsam/hybrid/HybridConditional.cpp
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@ -66,7 +66,7 @@ void HybridConditional::print(const std::string &s,
if (isDiscrete_) std::cout << "Disc. "; if (isDiscrete_) std::cout << "Disc. ";
if (isHybrid_) std::cout << "Hybr. "; if (isHybrid_) std::cout << "Hybr. ";
std::cout << "P("; std::cout << "P(";
int index = 0; size_t index = 0;
const size_t N = keys().size(); const size_t N = keys().size();
const size_t contN = N - discreteKeys_.size(); const size_t contN = N - discreteKeys_.size();
while (index < N) { while (index < N) {

9
gtsam/hybrid/HybridFactorGraph.cpp
View File

@ -22,6 +22,7 @@
#include <gtsam/discrete/Assignment.h> #include <gtsam/discrete/Assignment.h>
#include <gtsam/discrete/DiscreteEliminationTree.h> #include <gtsam/discrete/DiscreteEliminationTree.h>
#include <gtsam/discrete/DiscreteFactorGraph.h> #include <gtsam/discrete/DiscreteFactorGraph.h>
#include <gtsam/discrete/DiscreteJunctionTree.h>
#include <gtsam/hybrid/GaussianMixtureConditional.h> #include <gtsam/hybrid/GaussianMixtureConditional.h>
#include <gtsam/hybrid/GaussianMixtureFactor.h> #include <gtsam/hybrid/GaussianMixtureFactor.h>
#include <gtsam/hybrid/HybridConditional.h> #include <gtsam/hybrid/HybridConditional.h>
@ -34,6 +35,7 @@
#include <gtsam/inference/EliminateableFactorGraph-inst.h> #include <gtsam/inference/EliminateableFactorGraph-inst.h>
#include <gtsam/inference/Key.h> #include <gtsam/inference/Key.h>
#include <gtsam/linear/GaussianConditional.h> #include <gtsam/linear/GaussianConditional.h>
#include <gtsam/linear/GaussianEliminationTree.h>
#include <gtsam/linear/GaussianFactorGraph.h> #include <gtsam/linear/GaussianFactorGraph.h>
#include <gtsam/linear/HessianFactor.h> #include <gtsam/linear/HessianFactor.h>
#include <gtsam/linear/JacobianFactor.h> #include <gtsam/linear/JacobianFactor.h>
@ -104,9 +106,10 @@ EliminateHybrid(const HybridFactorGraph &factors, const Ordering &frontalKeys) {
// Because of all these reasons, we need to think very carefully about how to // Because of all these reasons, we need to think very carefully about how to
// implement the hybrid factors so that we do not get poor performance. // implement the hybrid factors so that we do not get poor performance.
// //
// The first thing is how to represent the GaussianMixtureConditional. A very possible // The first thing is how to represent the GaussianMixtureConditional. A very
// scenario is that the incoming factors will have different levels of // possible scenario is that the incoming factors will have different levels
// discrete keys. For example, imagine we are going to eliminate the fragment: // of discrete keys. For example, imagine we are going to eliminate the
// fragment:
// $\phi(x1,c1,c2)$, $\phi(x1,c2,c3)$, which is perfectly valid. Now we will // $\phi(x1,c1,c2)$, $\phi(x1,c2,c3)$, which is perfectly valid. Now we will
// need to know how to retrieve the corresponding continuous densities for the // need to know how to retrieve the corresponding continuous densities for the
// assi- -gnment (c1,c2,c3) (OR (c2,c3,c1)! note there is NO defined order!). // assi- -gnment (c1,c2,c3) (OR (c2,c3,c1)! note there is NO defined order!).