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More consistent order of private/protected/public - typedefs, private/protected variables, public interface, private/protected functions

release/4.3a0
Richard Roberts 2012-08-06 21:42:26 +00:00
parent 900f30547f
commit 1565833c2c
11 changed files with 139 additions and 157 deletions
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7
gtsam/base/DerivedValue.h
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@ -24,9 +24,6 @@ namespace gtsam {
template<class DERIVED>
class DerivedValue : public Value {
private:
/// Fake Tag struct for singleton pool allocator. In fact, it is never used!
struct PoolTag { };
protected:
DerivedValue() {}
@ -119,6 +116,10 @@ protected:
return *this;
}
private:
/// Fake Tag struct for singleton pool allocator. In fact, it is never used!
struct PoolTag { };
};
} /* namespace gtsam */

92
gtsam/inference/BayesTree.h
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@ -94,51 +94,6 @@ namespace gtsam {
/** Map from keys to Clique */
typedef std::deque<sharedClique> Nodes;
protected:
/** Map from keys to Clique */
Nodes nodes_;
/** private helper method for saving the Tree to a text file in GraphViz format */
void saveGraph(std::ostream &s, sharedClique clique, const IndexFormatter& indexFormatter,
int parentnum = 0) const;
/** Gather data on a single clique */
void getCliqueData(CliqueData& stats, sharedClique clique) const;
/** Root clique */
sharedClique root_;
/** remove a clique: warning, can result in a forest */
void removeClique(sharedClique clique);
/** add a clique (top down) */
sharedClique addClique(const sharedConditional& conditional, const sharedClique& parent_clique = sharedClique());
/** add a clique (top down) */
void addClique(const sharedClique& clique, const sharedClique& parent_clique = sharedClique());
/** add a clique (bottom up) */
sharedClique addClique(const sharedConditional& conditional, std::list<sharedClique>& child_cliques);
/**
* Add a conditional to the front of a clique, i.e. a conditional whose
* parents are already in the clique or its separators. This function does
* not check for this condition, it just updates the data structures.
*/
static void addToCliqueFront(BayesTree<CONDITIONAL,CLIQUE>& bayesTree,
const sharedConditional& conditional, const sharedClique& clique);
/** Fill the nodes index for a subtree */
void fillNodesIndex(const sharedClique& subtree);
/** Helper function to build a non-symbolic tree (e.g. Gaussian) using a
* symbolic tree, used in the BT(BN) constructor.
*/
void recursiveTreeBuild(const boost::shared_ptr<BayesTreeClique<IndexConditional> >& symbolic,
const std::vector<boost::shared_ptr<CONDITIONAL> >& conditionals,
const typename BayesTree<CONDITIONAL,CLIQUE>::sharedClique& parent);
public:
/// @name Standard Constructors
@ -184,8 +139,6 @@ namespace gtsam {
/// @name Standard Interface
/// @{
public:
/**
* Find parent clique of a conditional. It will look at all parents and
* return the one with the lowest index in the ordering.
@ -285,6 +238,51 @@ namespace gtsam {
std::list<sharedClique>& children, bool isRootClique = false);
protected:
/** Map from keys to Clique */
Nodes nodes_;
/** private helper method for saving the Tree to a text file in GraphViz format */
void saveGraph(std::ostream &s, sharedClique clique, const IndexFormatter& indexFormatter,
int parentnum = 0) const;
/** Gather data on a single clique */
void getCliqueData(CliqueData& stats, sharedClique clique) const;
/** Root clique */
sharedClique root_;
/** remove a clique: warning, can result in a forest */
void removeClique(sharedClique clique);
/** add a clique (top down) */
sharedClique addClique(const sharedConditional& conditional, const sharedClique& parent_clique = sharedClique());
/** add a clique (top down) */
void addClique(const sharedClique& clique, const sharedClique& parent_clique = sharedClique());
/** add a clique (bottom up) */
sharedClique addClique(const sharedConditional& conditional, std::list<sharedClique>& child_cliques);
/**
* Add a conditional to the front of a clique, i.e. a conditional whose
* parents are already in the clique or its separators. This function does
* not check for this condition, it just updates the data structures.
*/
static void addToCliqueFront(BayesTree<CONDITIONAL,CLIQUE>& bayesTree,
const sharedConditional& conditional, const sharedClique& clique);
/** Fill the nodes index for a subtree */
void fillNodesIndex(const sharedClique& subtree);
/** Helper function to build a non-symbolic tree (e.g. Gaussian) using a
* symbolic tree, used in the BT(BN) constructor.
*/
void recursiveTreeBuild(const boost::shared_ptr<BayesTreeClique<IndexConditional> >& symbolic,
const std::vector<boost::shared_ptr<CONDITIONAL> >& conditionals,
const typename BayesTree<CONDITIONAL,CLIQUE>::sharedClique& parent);
private:
/** deep copy to another tree */

58
gtsam/inference/EliminationTree.h
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@ -73,39 +73,11 @@ private:
Factors factors_; ///< factors associated with root
SubTrees subTrees_; ///< sub-trees
public:
/// @name Standard Constructors
/// @{
/** default constructor, private, as you should use Create below */
EliminationTree(Index key = 0) : key_(key) {}
/// @}
/// @name Advanced Interface
/// @{
/**
* Static internal function to build a vector of parent pointers using the
* algorithm of Gilbert et al., 2001, BIT.
*/
static std::vector<Index> ComputeParents(const VariableIndex& structure);
/** add a factor, for Create use only */
void add(const sharedFactor& factor) { factors_.push_back(factor); }
/** add a subtree, for Create use only */
void add(const shared_ptr& child) { subTrees_.push_back(child); }
/**
* Recursive routine that eliminates the factors arranged in an elimination tree
* @param Conditionals is a vector of shared pointers that will be modified in place
*/
sharedFactor eliminate_(Eliminate function, Conditionals& conditionals) const;
/// Allow access to constructor and add methods for testing purposes
friend class ::EliminationTreeTester;
public:
/**
* Named constructor to build the elimination tree of a factor graph using
* pre-computed column structure.
@ -151,6 +123,32 @@ public:
/// @}
private:
/** default constructor, private, as you should use Create below */
EliminationTree(Index key = 0) : key_(key) {}
/**
* Static internal function to build a vector of parent pointers using the
* algorithm of Gilbert et al., 2001, BIT.
*/
static std::vector<Index> ComputeParents(const VariableIndex& structure);
/** add a factor, for Create use only */
void add(const sharedFactor& factor) { factors_.push_back(factor); }
/** add a subtree, for Create use only */
void add(const shared_ptr& child) { subTrees_.push_back(child); }
/**
* Recursive routine that eliminates the factors arranged in an elimination tree
* @param Conditionals is a vector of shared pointers that will be modified in place
*/
sharedFactor eliminate_(Eliminate function, Conditionals& conditionals) const;
/// Allow access to constructor and add methods for testing purposes
friend class ::EliminationTreeTester;
};

17
gtsam/inference/Factor.h
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@ -81,15 +81,6 @@ protected:
/// The keys involved in this factor
std::vector<KeyType> keys_;
friend class JacobianFactor;
friend class HessianFactor;
protected:
/// Internal consistency check that is run frequently when in debug mode.
/// If NDEBUG is defined, this is empty and optimized out.
void assertInvariants() const;
public:
/// @name Standard Constructors
@ -212,9 +203,15 @@ public:
iterator begin() { return keys_.begin(); } ///TODO: comment
iterator end() { return keys_.end(); } ///TODO: comment
protected:
friend class JacobianFactor;
friend class HessianFactor;
/// Internal consistency check that is run frequently when in debug mode.
/// If NDEBUG is defined, this is empty and optimized out.
void assertInvariants() const;
private:
/** Serialization function */
friend class boost::serialization::access;
template<class Archive>

6
gtsam/linear/JacobianFactor.h
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@ -78,12 +78,10 @@ namespace gtsam {
* \f[ E(x) = \frac{1}{2} (A_1 x_{j1} + A_2 x_{j2} - b)^T \Sigma^{-1} (A_1 x_{j1} + A_2 x_{j2} - b) . \f]
*/
class JacobianFactor : public GaussianFactor {
public:
protected:
typedef Matrix AbMatrix;
typedef VerticalBlockView<AbMatrix> BlockAb;
protected:
noiseModel::Diagonal::shared_ptr model_; // Gaussian noise model with diagonal covariance matrix
std::vector<size_t> firstNonzeroBlocks_;
AbMatrix matrix_; // the full matrix corresponding to the factor
@ -97,8 +95,6 @@ namespace gtsam {
typedef BlockAb::Column BVector;
typedef BlockAb::constColumn constBVector;
public:
/** Copy constructor */
JacobianFactor(const JacobianFactor& gf);

9
gtsam/nonlinear/DoglegOptimizer.h
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@ -67,7 +67,6 @@ private:
*/
class DoglegState : public NonlinearOptimizerState {
public:
double Delta;
DoglegState() {}
@ -86,8 +85,11 @@ protected:
*/
class DoglegOptimizer : public NonlinearOptimizer {
public:
protected:
DoglegParams params_;
DoglegState state_;
public:
typedef boost::shared_ptr<DoglegOptimizer> shared_ptr;
/// @name Standard interface
@ -143,9 +145,6 @@ public:
/// @}
protected:
DoglegParams params_;
DoglegState state_;
/** Access the parameters (base class version) */
virtual const NonlinearOptimizerParams& _params() const { return params_; }

9
gtsam/nonlinear/GaussNewtonOptimizer.h
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@ -43,8 +43,11 @@ protected:
*/
class GaussNewtonOptimizer : public NonlinearOptimizer {
public:
protected:
GaussNewtonParams params_;
GaussNewtonState state_;
public:
/// @name Standard interface
/// @{
@ -94,10 +97,6 @@ public:
/// @}
protected:
GaussNewtonParams params_;
GaussNewtonState state_;
/** Access the parameters (base class version) */
virtual const NonlinearOptimizerParams& _params() const { return params_; }

53
gtsam/nonlinear/LevenbergMarquardtOptimizer.h
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@ -42,12 +42,6 @@ public:
DAMPED
};
private:
VerbosityLM verbosityLMTranslator(const std::string &s) const;
std::string verbosityLMTranslator(VerbosityLM value) const;
public:
double lambdaInitial; ///< The initial Levenberg-Marquardt damping term (default: 1e-5)
double lambdaFactor; ///< The amount by which to multiply or divide lambda when adjusting lambda (default: 10.0)
double lambdaUpperBound; ///< The maximum lambda to try before assuming the optimization has failed (default: 1e5)
@ -67,6 +61,10 @@ public:
inline void setlambdaFactor(double value) { lambdaFactor = value; }
inline void setlambdaUpperBound(double value) { lambdaUpperBound = value; }
inline void setVerbosityLM(const std::string &s) { verbosityLM = verbosityLMTranslator(s); }
private:
VerbosityLM verbosityLMTranslator(const std::string &s) const;
std::string verbosityLMTranslator(VerbosityLM value) const;
};
/**
@ -74,20 +72,18 @@ public:
*/
class LevenbergMarquardtState : public NonlinearOptimizerState {
protected:
LevenbergMarquardtState(const NonlinearFactorGraph& graph, const Values& initialValues, const LevenbergMarquardtParams& params, unsigned int iterations = 0) :
NonlinearOptimizerState(graph, initialValues, iterations), lambda(params.lambdaInitial) {}
friend class LevenbergMarquardtOptimizer;
public:
double lambda;
LevenbergMarquardtState() {}
virtual ~LevenbergMarquardtState() {}
protected:
LevenbergMarquardtState(const NonlinearFactorGraph& graph, const Values& initialValues, const LevenbergMarquardtParams& params, unsigned int iterations = 0) :
NonlinearOptimizerState(graph, initialValues, iterations), lambda(params.lambdaInitial) {}
friend class LevenbergMarquardtOptimizer;
};
/**
@ -96,26 +92,11 @@ public:
class LevenbergMarquardtOptimizer : public NonlinearOptimizer {
protected:
LevenbergMarquardtParams params_; ///< LM parameters
LevenbergMarquardtState state_; ///< optimization state
std::vector<size_t> dimensions_; ///< undocumented
/** Access the parameters (base class version) */
virtual const NonlinearOptimizerParams& _params() const { return params_; }
/** Access the state (base class version) */
virtual const NonlinearOptimizerState& _state() const { return state_; }
/** Internal function for computing a COLAMD ordering if no ordering is specified */
LevenbergMarquardtParams ensureHasOrdering(LevenbergMarquardtParams params, const NonlinearFactorGraph& graph, const Values& values) const {
if(!params.ordering)
params.ordering = *graph.orderingCOLAMD(values);
return params;
}
public:
typedef boost::shared_ptr<LevenbergMarquardtOptimizer> shared_ptr;
/// @name Standard interface
@ -176,6 +157,20 @@ public:
const LevenbergMarquardtState& state() const { return state_; }
/// @}
protected:
/** Access the parameters (base class version) */
virtual const NonlinearOptimizerParams& _params() const { return params_; }
/** Access the state (base class version) */
virtual const NonlinearOptimizerState& _state() const { return state_; }
/** Internal function for computing a COLAMD ordering if no ordering is specified */
LevenbergMarquardtParams ensureHasOrdering(LevenbergMarquardtParams params, const NonlinearFactorGraph& graph, const Values& values) const {
if(!params.ordering)
params.ordering = *graph.orderingCOLAMD(values);
return params;
}
};
}

12
gtsam/nonlinear/Marginals.h
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@ -88,11 +88,6 @@ protected:
BlockView blockView_;
Ordering indices_;
JointMarginal(const Matrix& fullMatrix, const std::vector<size_t>& dims, const Ordering& indices) :
fullMatrix_(fullMatrix), blockView_(fullMatrix_, dims.begin(), dims.end()), indices_(indices) {}
friend class Marginals;
public:
/** A block view of the joint marginal - this stores a reference to the
* JointMarginal object, so the JointMarginal object must be kept in scope
@ -129,6 +124,13 @@ public:
/** Print */
void print(const std::string& s = "", const KeyFormatter& formatter = DefaultKeyFormatter) const;
protected:
JointMarginal(const Matrix& fullMatrix, const std::vector<size_t>& dims, const Ordering& indices) :
fullMatrix_(fullMatrix), blockView_(fullMatrix_, dims.begin(), dims.end()), indices_(indices) {}
friend class Marginals;
};
} /* namespace gtsam */

28
gtsam/nonlinear/NonlinearOptimizer.h
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@ -177,23 +177,9 @@ Values::const_shared_ptr result = DoglegOptimizer(graph, initialValues, params).
class NonlinearOptimizer {
protected:
NonlinearFactorGraph graph_;
/** A default implementation of the optimization loop, which calls iterate()
* until checkConvergence returns true.
*/
void defaultOptimize();
virtual const NonlinearOptimizerState& _state() const = 0;
virtual const NonlinearOptimizerParams& _params() const = 0;
/** Constructor for initial construction of base classes. */
NonlinearOptimizer(const NonlinearFactorGraph& graph) : graph_(graph) {}
public:
/** A shared pointer to this class */
typedef boost::shared_ptr<const NonlinearOptimizer> shared_ptr;
@ -243,6 +229,20 @@ public:
virtual void iterate() = 0;
/// @}
protected:
/** A default implementation of the optimization loop, which calls iterate()
* until checkConvergence returns true.
*/
void defaultOptimize();
virtual const NonlinearOptimizerState& _state() const = 0;
virtual const NonlinearOptimizerParams& _params() const = 0;
/** Constructor for initial construction of base classes. */
NonlinearOptimizer(const NonlinearFactorGraph& graph) : graph_(graph) {}
};
/** Check whether the relative error decrease is less than relativeErrorTreshold,

3
gtsam/nonlinear/Values.h
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@ -123,8 +123,6 @@ namespace gtsam {
typedef KeyValuePair value_type;
public:
/** A filtered view of a Values, returned from Values::filter. */
template<class ValueType = Value>
class Filtered;
@ -196,7 +194,6 @@ namespace gtsam {
/** Get a zero VectorValues of the correct structure */
VectorValues zeroVectors(const Ordering& ordering) const;
public:
const_iterator begin() const { return boost::make_transform_iterator(values_.begin(), &make_const_deref_pair); }
const_iterator end() const { return boost::make_transform_iterator(values_.end(), &make_const_deref_pair); }
iterator begin() { return boost::make_transform_iterator(values_.begin(), &make_deref_pair); }