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Inference::Marginal returns a factor graph

release/4.3a0
Richard Roberts 2010-10-13 20:41:26 +00:00
parent cbda1ac6f6
commit e545f59fb2
4 changed files with 12 additions and 10 deletions
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2
inference/BayesTree-inl.h
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@ -348,7 +348,7 @@ namespace gtsam {
p_FSR.push_back(*R);
// Find marginal on the keys we are interested in
return FactorGraph(*Inference::Marginal(FactorGraph(p_FSR), keys()));
return Inference::Marginal(FactorGraph(p_FSR), keys());
}
// /* ************************************************************************* */

12
inference/inference-inl.h
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@ -278,7 +278,7 @@ Inference::EliminateOne(FactorGraph& factorGraph, typename FactorGraph::variable
/* ************************************************************************* */
template<class FactorGraph, class VarContainer>
typename FactorGraph::bayesnet_type::shared_ptr Inference::Marginal(const FactorGraph& factorGraph, const VarContainer& variables) {
FactorGraph Inference::Marginal(const FactorGraph& factorGraph, const VarContainer& variables) {
// Compute a COLAMD permutation with the marginal variables constrained to the end
typename FactorGraph::variableindex_type varIndex(factorGraph);
@ -298,16 +298,18 @@ typename FactorGraph::bayesnet_type::shared_ptr Inference::Marginal(const Factor
typename FactorGraph::bayesnet_type::shared_ptr bn(Inference::Eliminate(eliminationGraph, varIndex));
// The last conditionals in the eliminated BayesNet contain the marginal for
// the variables we want.
typename FactorGraph::bayesnet_type::shared_ptr marginal(new typename FactorGraph::bayesnet_type());
// the variables we want. Undo the permutation as we add the marginal
// factors.
FactorGraph marginal; marginal.reserve(variables.size());
typename FactorGraph::bayesnet_type::const_reverse_iterator conditional = bn->rbegin();
for(Index j=0; j<variables.size(); ++j, ++conditional) {
marginal->push_front(*conditional);
typename FactorGraph::sharedFactor factor(new typename FactorGraph::factor_type(**conditional));
factor->permuteWithInverse(*permutation);
marginal.push_back(factor);
assert(std::find(variables.begin(), variables.end(), (*permutation)[(*conditional)->key()]) != variables.end());
}
// Undo the permutation
marginal->permuteWithInverse(*permutation);
return marginal;
}

2
inference/inference.h
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@ -94,7 +94,7 @@ class Conditional;
* variables.
*/
template<class FactorGraph, class VarContainer>
static typename FactorGraph::bayesnet_type::shared_ptr Marginal(const FactorGraph& factorGraph, const VarContainer& variables);
static FactorGraph Marginal(const FactorGraph& factorGraph, const VarContainer& variables);
/**
* Compute a permutation (variable ordering) using colamd

6
tests/testInference.cpp
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@ -31,8 +31,8 @@ TEST(GaussianFactorGraph, createSmoother)
// eliminate
list<Index> x3var; x3var.push_back(ordering["x3"]);
list<Index> x1var; x1var.push_back(ordering["x1"]);
GaussianBayesNet p_x3 = *Inference::Marginal(fg2, x3var);
GaussianBayesNet p_x1 = *Inference::Marginal(fg2, x1var);
GaussianBayesNet p_x3 = *Inference::Eliminate(Inference::Marginal(fg2, x3var));
GaussianBayesNet p_x1 = *Inference::Eliminate(Inference::Marginal(fg2, x1var));
CHECK(assert_equal(*p_x1.back(),*p_x3.front())); // should be the same because of symmetry
}
@ -42,7 +42,7 @@ TEST( Inference, marginals )
// create and marginalize a small Bayes net on "x"
GaussianBayesNet cbn = createSmallGaussianBayesNet();
list<Index> xvar; xvar.push_back(0);
GaussianBayesNet actual = *Inference::Marginal(GaussianFactorGraph(cbn), xvar);
GaussianBayesNet actual = *Inference::Eliminate(Inference::Marginal(GaussianFactorGraph(cbn), xvar));
// expected is just scalar Gaussian on x
GaussianBayesNet expected = scalarGaussian(0, 4, sqrt(2));