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Made all Bayesnets derive from BayesNet

release/4.3a0
Frank Dellaert 2022-01-26 12:03:25 -05:00
parent 2fda2a1c00
commit a07f1497c7
6 changed files with 64 additions and 43 deletions
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21
gtsam/discrete/DiscreteBayesNet.h
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@ -31,11 +31,12 @@
namespace gtsam {
/** A Bayes net made from discrete conditional distributions. */
class GTSAM_EXPORT DiscreteBayesNet: public BayesNet<DiscreteConditional>
{
/**
* A Bayes net made from discrete conditional distributions.
* @addtogroup discrete
*/
class GTSAM_EXPORT DiscreteBayesNet: public BayesNet<DiscreteConditional> {
public:
typedef BayesNet<DiscreteConditional> Base;
typedef DiscreteBayesNet This;
typedef DiscreteConditional ConditionalType;
@ -50,15 +51,19 @@ namespace gtsam {
/** Construct from iterator over conditionals */
template <typename ITERATOR>
DiscreteBayesNet(ITERATOR firstConditional, ITERATOR lastConditional) : Base(firstConditional, lastConditional) {}
DiscreteBayesNet(ITERATOR firstConditional, ITERATOR lastConditional)
: Base(firstConditional, lastConditional) {}
/** Construct from container of factors (shared_ptr or plain objects) */
template <class CONTAINER>
explicit DiscreteBayesNet(const CONTAINER& conditionals) : Base(conditionals) {}
explicit DiscreteBayesNet(const CONTAINER& conditionals)
: Base(conditionals) {}
/** Implicit copy/downcast constructor to override explicit template container constructor */
/** Implicit copy/downcast constructor to override explicit template
* container constructor */
template <class DERIVEDCONDITIONAL>
DiscreteBayesNet(const FactorGraph<DERIVEDCONDITIONAL>& graph) : Base(graph) {}
DiscreteBayesNet(const FactorGraph<DERIVEDCONDITIONAL>& graph)
: Base(graph) {}
/// Destructor
virtual ~DiscreteBayesNet() {}

5
gtsam/discrete/tests/testDiscreteBayesNet.cpp
View File

@ -150,12 +150,13 @@ TEST(DiscreteBayesNet, Dot) {
fragment.add((Either | Tuberculosis, LungCancer) = "F T T T");
string actual = fragment.dot();
cout << actual << endl;
EXPECT(actual ==
"digraph G{\n"
"0->3\n"
"4->6\n"
"3->5\n"
"6->5\n"
"4->6\n"
"0->3\n"
"}");
}

3
gtsam/inference/BayesNet-inst.h
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@ -23,6 +23,7 @@
#include <boost/range/adaptor/reversed.hpp>
#include <fstream>
#include <string>
namespace gtsam {
@ -39,7 +40,7 @@ void BayesNet<CONDITIONAL>::dot(std::ostream& os,
const KeyFormatter& keyFormatter) const {
os << "digraph G{\n";
for (auto conditional : *this) {
for (auto conditional : boost::adaptors::reverse(*this)) {
auto frontals = conditional->frontals();
const Key me = frontals.front();
auto parents = conditional->parents();

8
gtsam/inference/BayesNet.h
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@ -18,17 +18,17 @@
#pragma once
#include <gtsam/inference/FactorGraph.h>
#include <boost/shared_ptr.hpp>
#include <gtsam/inference/FactorGraph.h>
#include <string>
namespace gtsam {
/**
* A BayesNet is a tree of conditionals, stored in elimination order.
*
* todo: how to handle Bayes nets with an optimize function? Currently using global functions.
* \nosubgrouping
* @addtogroup inference
*/
template<class CONDITIONAL>
class BayesNet : public FactorGraph<CONDITIONAL> {

23
gtsam/linear/GaussianBayesNet.h
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@ -21,17 +21,21 @@
#pragma once
#include <gtsam/linear/GaussianConditional.h>
#include <gtsam/inference/BayesNet.h>
#include <gtsam/inference/FactorGraph.h>
#include <gtsam/global_includes.h>
namespace gtsam {
/** A Bayes net made from linear-Gaussian densities */
class GTSAM_EXPORT GaussianBayesNet: public FactorGraph<GaussianConditional>
/**
* GaussianBayesNet is a Bayes net made from linear-Gaussian conditionals.
* @addtogroup linear
*/
class GTSAM_EXPORT GaussianBayesNet: public BayesNet<GaussianConditional>
{
public:
typedef FactorGraph<GaussianConditional> Base;
typedef BayesNet<GaussianConditional> Base;
typedef GaussianBayesNet This;
typedef GaussianConditional ConditionalType;
typedef boost::shared_ptr<This> shared_ptr;
@ -45,15 +49,20 @@ namespace gtsam {
/** Construct from iterator over conditionals */
template <typename ITERATOR>
GaussianBayesNet(ITERATOR firstConditional, ITERATOR lastConditional) : Base(firstConditional, lastConditional) {}
GaussianBayesNet(ITERATOR firstConditional, ITERATOR lastConditional)
: Base(firstConditional, lastConditional) {}
/** Construct from container of factors (shared_ptr or plain objects) */
template <class CONTAINER>
explicit GaussianBayesNet(const CONTAINER& conditionals) : Base(conditionals) {}
explicit GaussianBayesNet(const CONTAINER& conditionals) {
push_back(conditionals);
}
/** Implicit copy/downcast constructor to override explicit template container constructor */
/** Implicit copy/downcast constructor to override explicit template
* container constructor */
template <class DERIVEDCONDITIONAL>
GaussianBayesNet(const FactorGraph<DERIVEDCONDITIONAL>& graph) : Base(graph) {}
explicit GaussianBayesNet(const FactorGraph<DERIVEDCONDITIONAL>& graph)
: Base(graph) {}
/// Destructor
virtual ~GaussianBayesNet() {}

25
gtsam/symbolic/SymbolicBayesNet.h
View File

@ -19,19 +19,19 @@
#pragma once
#include <gtsam/symbolic/SymbolicConditional.h>
#include <gtsam/inference/BayesNet.h>
#include <gtsam/inference/FactorGraph.h>
#include <gtsam/base/types.h>
namespace gtsam {
/** Symbolic Bayes Net
* \nosubgrouping
/**
* A SymbolicBayesNet is a Bayes Net of purely symbolic conditionals.
* @addtogroup symbolic
*/
class SymbolicBayesNet : public FactorGraph<SymbolicConditional> {
class SymbolicBayesNet : public BayesNet<SymbolicConditional> {
public:
typedef FactorGraph<SymbolicConditional> Base;
typedef BayesNet<SymbolicConditional> Base;
typedef SymbolicBayesNet This;
typedef SymbolicConditional ConditionalType;
typedef boost::shared_ptr<This> shared_ptr;
@ -45,15 +45,20 @@ namespace gtsam {
/** Construct from iterator over conditionals */
template <typename ITERATOR>
SymbolicBayesNet(ITERATOR firstConditional, ITERATOR lastConditional) : Base(firstConditional, lastConditional) {}
SymbolicBayesNet(ITERATOR firstConditional, ITERATOR lastConditional)
: Base(firstConditional, lastConditional) {}
/** Construct from container of factors (shared_ptr or plain objects) */
template <class CONTAINER>
explicit SymbolicBayesNet(const CONTAINER& conditionals) : Base(conditionals) {}
explicit SymbolicBayesNet(const CONTAINER& conditionals) {
push_back(conditionals);
}
/** Implicit copy/downcast constructor to override explicit template container constructor */
/** Implicit copy/downcast constructor to override explicit template
* container constructor */
template <class DERIVEDCONDITIONAL>
SymbolicBayesNet(const FactorGraph<DERIVEDCONDITIONAL>& graph) : Base(graph) {}
explicit SymbolicBayesNet(const FactorGraph<DERIVEDCONDITIONAL>& graph)
: Base(graph) {}
/// Destructor
virtual ~SymbolicBayesNet() {}