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undo HybridBayesNet changes

release/4.3a0
Varun Agrawal 2025-01-01 19:51:04 -05:00
parent 26e1f088e4
commit c7c42afbaf
1 changed files with 1 additions and 20 deletions
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21
gtsam/hybrid/HybridBayesNet.cpp
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@ -19,7 +19,6 @@
#include <gtsam/discrete/DiscreteBayesNet.h>
#include <gtsam/discrete/DiscreteConditional.h>
#include <gtsam/discrete/DiscreteFactorGraph.h>
#include <gtsam/discrete/DiscreteTableConditional.h>
#include <gtsam/hybrid/HybridBayesNet.h>
#include <gtsam/hybrid/HybridGaussianFactorGraph.h>
#include <gtsam/hybrid/HybridValues.h>
@ -121,24 +120,6 @@ GaussianBayesNet HybridBayesNet::choose(
return gbn;
}
DiscreteValues HybridBayesNet::discreteMaxProduct(
const DiscreteFactorGraph &dfg) const {
TableFactor product = TableProductAndNormalize(dfg);
uint64_t maxIdx = 0;
double maxValue = 0.0;
Eigen::SparseVector<double> sparseTable = product.sparseTable();
for (TableFactor::SparseIt it(sparseTable); it; ++it) {
if (it.value() > maxValue) {
maxIdx = it.index();
maxValue = it.value();
}
}
DiscreteValues assignment = product.findAssignments(maxIdx);
return assignment;
}
/* ************************************************************************* */
HybridValues HybridBayesNet::optimize() const {
// Collect all the discrete factors to compute MPE
@ -151,7 +132,7 @@ HybridValues HybridBayesNet::optimize() const {
}
// Solve for the MPE
DiscreteValues mpe = this->discreteMaxProduct(discrete_fg);
DiscreteValues mpe = discrete_fg.optimize();
// Given the MPE, compute the optimal continuous values.
return HybridValues(optimize(mpe), mpe);