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deprecated functions for backwards compatibility 

also removed some edits that were tangential to the PR.
release/4.3a0
Gerry Chen 2019-10-28 17:41:16 -04:00
parent 4877bdb4f3
commit 8e62a1405e
14 changed files with 172 additions and 78 deletions
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8
gtsam/base/TestableAssertions.h
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@ -71,8 +71,12 @@ bool assert_equal(const V& expected, const boost::optional<V>& actual, double to
}
template<class V>
bool assert_equal(const V& expected, double tol = 1e-9) {
return false;
bool assert_equal(const V& expected, const boost::optional<const V&>& actual, double tol = 1e-9) {
if (!actual) {
std::cout << "actual is boost::none" << std::endl;
return false;
}
return assert_equal(expected, *actual, tol);
}
/**

7
gtsam/discrete/DiscreteConditional.cpp
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@ -57,12 +57,7 @@ DiscreteConditional::DiscreteConditional(const DecisionTreeFactor& joint,
/* ******************************************************************************** */
DiscreteConditional::DiscreteConditional(const DecisionTreeFactor& joint,
const DecisionTreeFactor& marginal, const Ordering& orderedKeys) :
BaseFactor(
ISDEBUG("DiscreteConditional::COUNT") ? joint : joint / marginal), BaseConditional(
joint.size()-marginal.size()) {
if (ISDEBUG("DiscreteConditional::DiscreteConditional"))
cout << (firstFrontalKey()) << endl; //TODO Print all keys
DiscreteConditional(joint, marginal) {
keys_.clear();
keys_.insert(keys_.end(), orderedKeys.begin(), orderedKeys.end());
}

16
gtsam/inference/EliminateableFactorGraph-inst.h
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@ -28,8 +28,8 @@ namespace gtsam {
template<class FACTORGRAPH>
boost::shared_ptr<typename EliminateableFactorGraph<FACTORGRAPH>::BayesNetType>
EliminateableFactorGraph<FACTORGRAPH>::eliminateSequential(
const Eliminate& function, OptionalVariableIndex variableIndex,
OptionalOrderingType orderingType) const {
OptionalOrderingType orderingType, const Eliminate& function,
OptionalVariableIndex variableIndex) const {
if(!variableIndex) {
// If no VariableIndex provided, compute one and call this function again IMPORTANT: we check
// for no variable index first so that it's always computed if we need to call COLAMD because
@ -56,12 +56,12 @@ namespace gtsam {
boost::shared_ptr<typename EliminateableFactorGraph<FACTORGRAPH>::BayesNetType>
EliminateableFactorGraph<FACTORGRAPH>::eliminateSequential(
const Ordering& ordering, const Eliminate& function,
OptionalVariableIndex variableIndex, OptionalOrderingType orderingType) const
OptionalVariableIndex variableIndex) const
{
if(!variableIndex) {
// If no VariableIndex provided, compute one and call this function again
VariableIndex computedVariableIndex(asDerived());
return eliminateSequential(ordering, function, computedVariableIndex, orderingType);
return eliminateSequential(ordering, function, computedVariableIndex);
} else {
gttic(eliminateSequential);
// Do elimination
@ -81,8 +81,8 @@ namespace gtsam {
template<class FACTORGRAPH>
boost::shared_ptr<typename EliminateableFactorGraph<FACTORGRAPH>::BayesTreeType>
EliminateableFactorGraph<FACTORGRAPH>::eliminateMultifrontal(
const Eliminate& function, OptionalVariableIndex variableIndex,
OptionalOrderingType orderingType) const
OptionalOrderingType orderingType, const Eliminate& function,
OptionalVariableIndex variableIndex) const
{
if(!variableIndex) {
// If no VariableIndex provided, compute one and call this function again IMPORTANT: we check
@ -110,12 +110,12 @@ namespace gtsam {
boost::shared_ptr<typename EliminateableFactorGraph<FACTORGRAPH>::BayesTreeType>
EliminateableFactorGraph<FACTORGRAPH>::eliminateMultifrontal(
const Ordering& ordering, const Eliminate& function,
OptionalVariableIndex variableIndex, OptionalOrderingType orderingType) const
OptionalVariableIndex variableIndex) const
{
if(!variableIndex) {
// If no VariableIndex provided, compute one and call this function again
VariableIndex computedVariableIndex(asDerived());
return eliminateMultifrontal(ordering, function, computedVariableIndex, orderingType);
return eliminateMultifrontal(ordering, function, computedVariableIndex);
} else {
gttic(eliminateMultifrontal);
// Do elimination with given ordering

73
gtsam/inference/EliminateableFactorGraph.h
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@ -89,7 +89,7 @@ namespace gtsam {
typedef boost::function<EliminationResult(const FactorGraphType&, const Ordering&)> Eliminate;
/// Typedef for an optional variable index as an argument to elimination functions
typedef const boost::optional<VariableIndex>& OptionalVariableIndex;
typedef boost::optional<const VariableIndex&> OptionalVariableIndex;
/// Typedef for an optional ordering type
typedef boost::optional<Ordering::OrderingType> OptionalOrderingType;
@ -115,9 +115,9 @@ namespace gtsam {
* \endcode
* */
boost::shared_ptr<BayesNetType> eliminateSequential(
OptionalOrderingType orderingType = boost::none,
const Eliminate& function = EliminationTraitsType::DefaultEliminate,
OptionalVariableIndex variableIndex = boost::none,
OptionalOrderingType orderingType = boost::none) const;
OptionalVariableIndex variableIndex = boost::none) const;
/** Do sequential elimination of all variables to produce a Bayes net.
*
@ -136,8 +136,7 @@ namespace gtsam {
boost::shared_ptr<BayesNetType> eliminateSequential(
const Ordering& ordering,
const Eliminate& function = EliminationTraitsType::DefaultEliminate,
OptionalVariableIndex variableIndex = boost::none,
OptionalOrderingType orderingType = boost::none) const; // orderingType is not necessary anymore, kept for backwards compatibility
OptionalVariableIndex variableIndex = boost::none) const;
/** Do multifrontal elimination of all variables to produce a Bayes tree. If an ordering is not
* provided, the ordering will be computed using either COLAMD or METIS, dependeing on
@ -156,9 +155,9 @@ namespace gtsam {
* \endcode
* */
boost::shared_ptr<BayesTreeType> eliminateMultifrontal(
OptionalOrderingType orderingType = boost::none,
const Eliminate& function = EliminationTraitsType::DefaultEliminate,
OptionalVariableIndex variableIndex = boost::none,
OptionalOrderingType orderingType = boost::none) const;
OptionalVariableIndex variableIndex = boost::none) const;
/** Do multifrontal elimination of all variables to produce a Bayes tree. If an ordering is not
* provided, the ordering will be computed using either COLAMD or METIS, dependeing on
@ -172,8 +171,7 @@ namespace gtsam {
boost::shared_ptr<BayesTreeType> eliminateMultifrontal(
const Ordering& ordering,
const Eliminate& function = EliminationTraitsType::DefaultEliminate,
OptionalVariableIndex variableIndex = boost::none,
OptionalOrderingType orderingType = boost::none) const; // orderingType no longer needed
OptionalVariableIndex variableIndex = boost::none) const;
/** Do sequential elimination of some variables, in \c ordering provided, to produce a Bayes net
* and a remaining factor graph. This computes the factorization \f$ p(X) = p(A|B) p(B) \f$,
@ -241,7 +239,7 @@ namespace gtsam {
* provided one will be computed. */
boost::shared_ptr<BayesNetType> marginalMultifrontalBayesNet(
boost::variant<const Ordering&, const KeyVector&> variables,
const Ordering& marginalizedVariableOrdering, // this no longer takes boost::none - potentially code breaking
const Ordering& marginalizedVariableOrdering,
const Eliminate& function = EliminationTraitsType::DefaultEliminate,
OptionalVariableIndex variableIndex = boost::none) const;
@ -271,7 +269,7 @@ namespace gtsam {
* provided one will be computed. */
boost::shared_ptr<BayesTreeType> marginalMultifrontalBayesTree(
boost::variant<const Ordering&, const KeyVector&> variables,
const Ordering& marginalizedVariableOrdering, // this no longer takes boost::none - potentially code breaking
const Ordering& marginalizedVariableOrdering,
const Eliminate& function = EliminationTraitsType::DefaultEliminate,
OptionalVariableIndex variableIndex = boost::none) const;
@ -288,6 +286,59 @@ namespace gtsam {
// Access the derived factor graph class
FactorGraphType& asDerived() { return static_cast<FactorGraphType&>(*this); }
public:
/** \deprecated ordering and orderingType shouldn't both be specified */
boost::shared_ptr<BayesNetType> eliminateSequential(
const Ordering& ordering,
const Eliminate& function,
OptionalVariableIndex variableIndex,
OptionalOrderingType orderingType) const {
return eliminateSequential(ordering, function, variableIndex);
}
/** \deprecated orderingType specified first for consistency */
boost::shared_ptr<BayesNetType> eliminateSequential(
const Eliminate& function,
OptionalVariableIndex variableIndex = boost::none,
OptionalOrderingType orderingType = boost::none) const {
return eliminateSequential(orderingType, function, variableIndex);
}
/** \deprecated ordering and orderingType shouldn't both be specified */
boost::shared_ptr<BayesTreeType> eliminateMultifrontal(
const Ordering& ordering,
const Eliminate& function,
OptionalVariableIndex variableIndex,
OptionalOrderingType orderingType) const {
return eliminateMultifrontal(ordering, function, variableIndex);
}
/** \deprecated orderingType specified first for consistency */
boost::shared_ptr<BayesTreeType> eliminateMultifrontal(
const Eliminate& function,
OptionalVariableIndex variableIndex = boost::none,
OptionalOrderingType orderingType = boost::none) const {
return eliminateMultifrontal(orderingType, function, variableIndex);
}
/** \deprecated */
boost::shared_ptr<BayesNetType> marginalMultifrontalBayesNet(
boost::variant<const Ordering&, const KeyVector&> variables,
boost::none_t,
const Eliminate& function = EliminationTraitsType::DefaultEliminate,
OptionalVariableIndex variableIndex = boost::none) const {
return marginalMultifrontalBayesNet(variables, function, variableIndex);
}
/** \deprecated */
boost::shared_ptr<BayesTreeType> marginalMultifrontalBayesTree(
boost::variant<const Ordering&, const KeyVector&> variables,
boost::none_t,
const Eliminate& function = EliminationTraitsType::DefaultEliminate,
OptionalVariableIndex variableIndex = boost::none) const {
return marginalMultifrontalBayesTree(variables, function, variableIndex);
}
};
}

7
gtsam/linear/GaussianFactorGraph.cpp
View File

@ -494,4 +494,11 @@ namespace gtsam {
return e;
}
/* ************************************************************************* */
/** \deprecated */
VectorValues GaussianFactorGraph::optimize(boost::none_t,
const Eliminate& function) const {
return optimize(function);
}
} // namespace gtsam

6
gtsam/linear/GaussianFactorGraph.h
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@ -375,6 +375,12 @@ namespace gtsam {
ar & BOOST_SERIALIZATION_BASE_OBJECT_NVP(Base);
}
public:
/** \deprecated */
VectorValues optimize(boost::none_t,
const Eliminate& function = EliminationTraitsType::DefaultEliminate) const;
};
/**

35
gtsam/linear/Scatter.cpp
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@ -33,8 +33,19 @@ string SlotEntry::toString() const {
return oss.str();
}
Scatter::Scatter(const GaussianFactorGraph& gfg) : Scatter(gfg, Ordering()) {}
/* ************************************************************************* */
void Scatter::setDimensions(const GaussianFactorGraph& gfg, size_t sortStart) {
Scatter::Scatter(const GaussianFactorGraph& gfg,
const Ordering& ordering) {
gttic(Scatter_Constructor);
// If we have an ordering, pre-fill the ordered variables first
for (Key key : ordering) {
add(key, 0);
}
// Now, find dimensions of variables and/or extend
for (const auto& factor : gfg) {
if (!factor)
continue;
@ -57,30 +68,10 @@ void Scatter::setDimensions(const GaussianFactorGraph& gfg, size_t sortStart) {
// To keep the same behavior as before, sort the keys after the ordering
iterator first = begin();
first += sortStart;
first += ordering.size();
if (first != end()) std::sort(first, end());
}
/* ************************************************************************* */
Scatter::Scatter(const GaussianFactorGraph& gfg) {
gttic(Scatter_Constructor);
setDimensions(gfg, 0);
}
/* ************************************************************************* */
Scatter::Scatter(const GaussianFactorGraph& gfg,
const Ordering& ordering) {
gttic(Scatter_Constructor);
// pre-fill the ordered variables first
for (Key key : ordering) {
add(key, 0);
}
setDimensions(gfg, ordering.size());
}
/* ************************************************************************* */
void Scatter::add(Key key, size_t dim) {
emplace_back(SlotEntry(key, dim));

5
gtsam/linear/Scatter.h
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@ -61,11 +61,6 @@ class Scatter : public FastVector<SlotEntry> {
void add(Key key, size_t dim);
private:
/// Helper function for constructors, adds/finds dimensions of variables and
// sorts starting from sortStart
void setDimensions(const GaussianFactorGraph& gfg, size_t sortStart);
/// Find the SlotEntry with the right key (linear time worst case)
iterator find(Key key);
};

21
gtsam/nonlinear/Marginals.h
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@ -64,7 +64,7 @@ public:
* @param factorization The linear decomposition mode - either Marginals::CHOLESKY (faster and suitable for most problems) or Marginals::QR (slower but more numerically stable for poorly-conditioned problems).
* @param ordering The ordering for elimination.
*/
Marginals(const NonlinearFactorGraph& graph, const Values& solution, const Ordering& ordering, // argument order switch due to default value of factorization, potentially code breaking
Marginals(const NonlinearFactorGraph& graph, const Values& solution, const Ordering& ordering,
Factorization factorization = CHOLESKY);
/** Construct a marginals class from a linear factor graph.
@ -80,7 +80,7 @@ public:
* @param factorization The linear decomposition mode - either Marginals::CHOLESKY (faster and suitable for most problems) or Marginals::QR (slower but more numerically stable for poorly-conditioned problems).
* @param ordering The ordering for elimination.
*/
Marginals(const GaussianFactorGraph& graph, const Values& solution, const Ordering& ordering, // argument order switch due to default value of factorization, potentially code breaking
Marginals(const GaussianFactorGraph& graph, const Values& solution, const Ordering& ordering,
Factorization factorization = CHOLESKY);
/** Construct a marginals class from a linear factor graph.
@ -97,7 +97,7 @@ public:
* @param factorization The linear decomposition mode - either Marginals::CHOLESKY (faster and suitable for most problems) or Marginals::QR (slower but more numerically stable for poorly-conditioned problems).
* @param ordering An optional variable ordering for elimination.
*/
Marginals(const GaussianFactorGraph& graph, const VectorValues& solution, const Ordering& ordering, // argument order switch due to default value of factorization, potentially code breaking
Marginals(const GaussianFactorGraph& graph, const VectorValues& solution, const Ordering& ordering,
Factorization factorization = CHOLESKY);
/** print */
@ -121,7 +121,7 @@ public:
/** Optimize the bayes tree */
VectorValues optimize() const;
protected:
/** Compute the Bayes Tree as a helper function to the constructor */
@ -130,6 +130,19 @@ protected:
/** Compute the Bayes Tree as a helper function to the constructor */
void computeBayesTree(const Ordering& ordering);
public:
/** \deprecated argument order changed due to removing boost::optional<Ordering> */
Marginals(const NonlinearFactorGraph& graph, const Values& solution, Factorization factorization,
const Ordering& ordering) : Marginals(graph, solution, ordering, factorization) {}
/** \deprecated argument order changed due to removing boost::optional<Ordering> */
Marginals(const GaussianFactorGraph& graph, const Values& solution, Factorization factorization,
const Ordering& ordering) : Marginals(graph, solution, ordering, factorization) {}
/** \deprecated argument order changed due to removing boost::optional<Ordering> */
Marginals(const GaussianFactorGraph& graph, const VectorValues& solution, Factorization factorization,
const Ordering& ordering) : Marginals(graph, solution, ordering, factorization) {}
};
/**

32
gtsam/nonlinear/NonlinearFactorGraph.cpp
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@ -376,19 +376,7 @@ static Scatter scatterFromValues(const Values& values, const Ordering& ordering)
/* ************************************************************************* */
HessianFactor::shared_ptr NonlinearFactorGraph::linearizeToHessianFactor(
const Values& values, const Dampen& dampen) const {
KeyVector keys = values.keys();
Ordering defaultOrdering(keys);
return linearizeToHessianFactor(values, defaultOrdering, dampen);
}
/* ************************************************************************* */
HessianFactor::shared_ptr NonlinearFactorGraph::linearizeToHessianFactor(
const Values& values, const Ordering& ordering, const Dampen& dampen) const {
gttic(NonlinearFactorGraph_linearizeToHessianFactor);
Scatter scatter = scatterFromValues(values, ordering);
const Values& values, const Scatter& scatter, const Dampen& dampen) const {
// NOTE(frank): we are heavily leaning on friendship below
HessianFactor::shared_ptr hessianFactor(new HessianFactor(scatter));
@ -409,6 +397,24 @@ HessianFactor::shared_ptr NonlinearFactorGraph::linearizeToHessianFactor(
return hessianFactor;
}
/* ************************************************************************* */
HessianFactor::shared_ptr NonlinearFactorGraph::linearizeToHessianFactor(
const Values& values, const Ordering& order, const Dampen& dampen) const {
gttic(NonlinearFactorGraph_linearizeToHessianFactor);
Scatter scatter = scatterFromValues(values, order);
return linearizeToHessianFactor(values, scatter, dampen);
}
/* ************************************************************************* */
HessianFactor::shared_ptr NonlinearFactorGraph::linearizeToHessianFactor(
const Values& values, const Dampen& dampen) const {
gttic(NonlinearFactorGraph_linearizeToHessianFactor);
Scatter scatter = scatterFromValues(values);
return linearizeToHessianFactor(values, scatter, dampen);
}
/* ************************************************************************* */
Values NonlinearFactorGraph::updateCholesky(const Values& values,
const Dampen& dampen) const {

20
gtsam/nonlinear/NonlinearFactorGraph.h
View File

@ -204,6 +204,13 @@ namespace gtsam {
private:
/**
* Linearize from Scatter rather than from Ordering. Made private because
* it doesn't include gttic.
*/
boost::shared_ptr<HessianFactor> linearizeToHessianFactor(
const Values& values, const Scatter& scatter, const Dampen& dampen = nullptr) const;
/** Serialization function */
friend class boost::serialization::access;
template<class ARCHIVE>
@ -211,6 +218,19 @@ namespace gtsam {
ar & boost::serialization::make_nvp("NonlinearFactorGraph",
boost::serialization::base_object<Base>(*this));
}
public:
/** \deprecated */
boost::shared_ptr<HessianFactor> linearizeToHessianFactor(
const Values& values, boost::none_t, const Dampen& dampen = nullptr) const
{return linearizeToHessianFactor(values, dampen);}
/** \deprecated */
Values updateCholesky(const Values& values, boost::none_t,
const Dampen& dampen = nullptr) const
{return updateCholesky(values, dampen);}
};
/// traits

16
gtsam/nonlinear/NonlinearOptimizer.cpp
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@ -128,18 +128,22 @@ VectorValues NonlinearOptimizer::solve(const GaussianFactorGraph& gfg,
const NonlinearOptimizerParams& params) const {
// solution of linear solver is an update to the linearization point
VectorValues delta;
Ordering ordering;
if (params.ordering)
ordering = *params.ordering;
// Check which solver we are using
if (params.isMultifrontal()) {
// Multifrontal QR or Cholesky (decided by params.getEliminationFunction())
delta = gfg.optimize(ordering, params.getEliminationFunction());
if (params.ordering)
delta = gfg.optimize(*params.ordering, params.getEliminationFunction());
else
delta = gfg.optimize(params.getEliminationFunction());
} else if (params.isSequential()) {
// Sequential QR or Cholesky (decided by params.getEliminationFunction())
delta = gfg.eliminateSequential(ordering, params.getEliminationFunction(), boost::none,
params.orderingType)->optimize();
if (params.ordering)
delta = gfg.eliminateSequential(*params.ordering, params.getEliminationFunction(),
boost::none, params.orderingType)->optimize();
else
delta = gfg.eliminateSequential(params.getEliminationFunction(), boost::none,
params.orderingType)->optimize();
} else if (params.isIterative()) {
// Conjugate Gradient -> needs params.iterativeParams
if (!params.iterativeParams)

2
tests/testGaussianFactorGraphB.cpp
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@ -206,6 +206,7 @@ TEST(GaussianFactorGraph, optimize_Cholesky) {
GaussianFactorGraph fg = createGaussianFactorGraph();
// optimize the graph
VectorValues actual1 = fg.optimize(boost::none, EliminateCholesky);
VectorValues actual = fg.optimize(EliminateCholesky);
// verify
@ -220,6 +221,7 @@ TEST( GaussianFactorGraph, optimize_QR )
GaussianFactorGraph fg = createGaussianFactorGraph();
// optimize the graph
VectorValues actual1 = fg.optimize(boost::none, EliminateQR);
VectorValues actual = fg.optimize(EliminateQR);
// verify

2
tests/testNonlinearFactorGraph.cpp
View File

@ -198,7 +198,7 @@ TEST(NonlinearFactorGraph, UpdateCholesky) {
}
}
};
EXPECT(assert_equal(initial, fg.updateCholesky(initial, dampen), 1e-6));
EXPECT(assert_equal(initial, fg.updateCholesky(initial, boost::none, dampen), 1e-6));
}
/* ************************************************************************* */