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Added and wrapped determinant functions for BayesTree

release/4.3a0
Alex Cunningham 2013-02-04 21:00:43 +00:00
parent 059a2c1b97
commit 40929e9cc3
4 changed files with 62 additions and 16 deletions
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30
gtsam.h
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@ -871,7 +871,6 @@ virtual class SymbolicBayesNet : gtsam::SymbolicBayesNetBase {
bool permuteSeparatorWithInverse(const gtsam::Permutation& inversePermutation);
};
#include <gtsam/inference/SymbolicFactorGraph.h>
typedef gtsam::BayesTreeClique<gtsam::IndexConditional> SymbolicBayesTreeClique;
typedef gtsam::BayesTree<gtsam::IndexConditional, gtsam::SymbolicBayesTreeClique> SymbolicBayesTreeBase;
virtual class SymbolicBayesTree : gtsam::SymbolicBayesTreeBase {
@ -1114,21 +1113,21 @@ virtual class GaussianBayesNet : gtsam::GaussianBayesNetBase {
//Non-Class methods found in GaussianBayesNet.h
//FIXME: No MATLAB documentation for these functions!
/*gtsam::GaussianBayesNet scalarGaussian(size_t key, double mu, double sigma);
gtsam::GaussianBayesNet simpleGaussian(size_t key, const Vector& mu, double sigma);
void push_front(gtsam::GaussianBayesNet& bn, size_t key, Vector d, Matrix R, size_t name1, Matrix S, Vector sigmas);
void push_front(gtsam::GaussianBayesNet& bn, size_t key, Vector d, Matrix R, size_t name1, Matrix S, size_t name2, Matrix T, Vector sigmas);
gtsam::VectorValues* allocateVectorValues(const gtsam::GaussianBayesNet& bn);
gtsam::VectorValues optimize(const gtsam::GaussianBayesNet& bn);
void optimizeInPlace(const gtsam::GaussianBayesNet& bn, gtsam::VectorValues& x);
gtsam::VectorValues optimizeGradientSearch(const gtsam::GaussianBayesNet& bn);
void optimizeGradientSearchInPlace(const gtsam::GaussianBayesNet& bn, gtsam::VectorValues& grad);
gtsam::VectorValues backSubstitute(const gtsam::GaussianBayesNet& bn, const gtsam::VectorValues& gx);
gtsam::VectorValues backSubstituteTranspose(const gtsam::GaussianBayesNet& bn, const gtsam::VectorValues& gx);
pair<Matrix, Vector> matrix(const gtsam::GaussianBayesNet& bn);
//gtsam::GaussianBayesNet scalarGaussian(size_t key, double mu, double sigma);
//gtsam::GaussianBayesNet simpleGaussian(size_t key, const Vector& mu, double sigma);
//void push_front(gtsam::GaussianBayesNet& bn, size_t key, Vector d, Matrix R, size_t name1, Matrix S, Vector sigmas);
//void push_front(gtsam::GaussianBayesNet& bn, size_t key, Vector d, Matrix R, size_t name1, Matrix S, size_t name2, Matrix T, Vector sigmas);
//gtsam::VectorValues* allocateVectorValues(const gtsam::GaussianBayesNet& bn);
//gtsam::VectorValues optimize(const gtsam::GaussianBayesNet& bn);
//void optimizeInPlace(const gtsam::GaussianBayesNet& bn, gtsam::VectorValues& x);
//gtsam::VectorValues optimizeGradientSearch(const gtsam::GaussianBayesNet& bn);
//void optimizeGradientSearchInPlace(const gtsam::GaussianBayesNet& bn, gtsam::VectorValues& grad);
//gtsam::VectorValues backSubstitute(const gtsam::GaussianBayesNet& bn, const gtsam::VectorValues& gx);
//gtsam::VectorValues backSubstituteTranspose(const gtsam::GaussianBayesNet& bn, const gtsam::VectorValues& gx);
//pair<Matrix, Vector> matrix(const gtsam::GaussianBayesNet& bn);
double determinant(const gtsam::GaussianBayesNet& bayesNet);
gtsam::VectorValues gradient(const gtsam::GaussianBayesNet& bayesNet, const gtsam::VectorValues& x0);
void gradientAtZero(const gtsam::GaussianBayesNet& bayesNet, const gtsam::VectorValues& g);*/
//gtsam::VectorValues gradient(const gtsam::GaussianBayesNet& bayesNet, const gtsam::VectorValues& x0);
//void gradientAtZero(const gtsam::GaussianBayesNet& bayesNet, const gtsam::VectorValues& g);
#include <gtsam/linear/GaussianBayesTree.h>
typedef gtsam::BayesTreeClique<gtsam::GaussianConditional> GaussianBayesTreeClique;
@ -1145,6 +1144,7 @@ gtsam::VectorValues optimize(const gtsam::GaussianBayesTree& bayesTree);
gtsam::VectorValues optimizeGradientSearch(const gtsam::GaussianBayesTree& bayesTree);
gtsam::VectorValues gradient(const gtsam::GaussianBayesTree& bayesTree, const gtsam::VectorValues& x0);
gtsam::VectorValues* allocateVectorValues(const gtsam::GaussianBayesTree& bt);
double determinant(const gtsam::GaussianBayesTree& bayesTree);
virtual class GaussianFactor {
void print(string s) const;

22
gtsam/linear/GaussianBayesTree-inl.h
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@ -23,6 +23,10 @@
#include <gtsam/linear/GaussianBayesTree.h> // Only to help Eclipse
#include <stdarg.h>
using namespace std;
namespace gtsam {
/* ************************************************************************* */
@ -36,6 +40,22 @@ void optimizeInPlace(const typename BAYESTREE::sharedClique& clique, VectorValue
BOOST_FOREACH(const typename BAYESTREE::sharedClique& child, clique->children_)
optimizeInPlace<BAYESTREE>(child, result);
}
/* ************************************************************************* */
template<class BAYESTREE>
double logDeterminant(const typename BAYESTREE::sharedClique& clique) {
double result = 0.0;
// this clique
result += clique->conditional()->get_R().diagonal().unaryExpr(ptr_fun<double,double>(log)).sum();
// sum of children
BOOST_FOREACH(const typename BAYESTREE::sharedClique& child, clique->children_)
result += logDeterminant<BAYESTREE>(child);
return result;
}
}
/* ************************************************************************* */
} // \namespace internal
} // \namespace gtsam

9
gtsam/linear/GaussianBayesTree.cpp
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@ -80,7 +80,16 @@ void gradientAtZero(const GaussianBayesTree& bayesTree, VectorValues& g) {
gradientAtZero(FactorGraph<JacobianFactor>(bayesTree), g);
}
/* ************************************************************************* */
double determinant(const GaussianBayesTree& bayesTree) {
if (!bayesTree.root())
return 0.0;
return exp(internal::logDeterminant<GaussianBayesTree>(bayesTree.root()));
}
/* ************************************************************************* */
} // \namespace gtsam

17
gtsam/linear/GaussianBayesTree.h
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@ -91,6 +91,23 @@ VectorValues gradient(const GaussianBayesTree& bayesTree, const VectorValues& x0
*/
void gradientAtZero(const GaussianBayesTree& bayesTree, VectorValues& g);
/**
* Computes the determinant of a GassianBayesTree
* A GassianBayesTree is an upper triangular matrix and for an upper triangular matrix
* determinant is the product of the diagonal elements. Instead of actually multiplying
* we add the logarithms of the diagonal elements and take the exponent at the end
* because this is more numerically stable.
* @param bayesTree The input GassianBayesTree
* @return The determinant
*/
double determinant(const GaussianBayesTree& bayesTree);
namespace internal {
template<class BAYESTREE>
double logDeterminant(const typename BAYESTREE::sharedClique& clique);
}
}
#include <gtsam/linear/GaussianBayesTree-inl.h>