12345qiupeng
/
gtsam
1
0
Fork 0
Code Issues Pull Requests Packages Projects Releases Wiki Activity

Formatting to default BORG format

release/4.3a0
dellaert 2015-02-13 17:16:38 +01:00
parent 674794d387
commit 56456a2396
3 changed files with 469 additions and 455 deletions
Show Stats Download Patch File Download Diff File Expand all files Collapse all files

424
gtsam/inference/Ordering.cpp
View File

@ -29,242 +29,236 @@ using namespace std;
namespace gtsam { namespace gtsam {
/* ************************************************************************* */ /* ************************************************************************* */
FastMap<Key, size_t> Ordering::invert() const FastMap<Key, size_t> Ordering::invert() const {
{ FastMap<Key, size_t> inverted;
FastMap<Key, size_t> inverted; for (size_t pos = 0; pos < this->size(); ++pos)
for(size_t pos = 0; pos < this->size(); ++pos) inverted.insert(make_pair((*this)[pos], pos));
inverted.insert(make_pair((*this)[pos], pos)); return inverted;
return inverted; }
/* ************************************************************************* */
Ordering Ordering::colamd(const VariableIndex& variableIndex) {
// Call constrained version with all groups set to zero
vector<int> dummy_groups(variableIndex.size(), 0);
return Ordering::colamdConstrained(variableIndex, dummy_groups);
}
/* ************************************************************************* */
Ordering Ordering::colamdConstrained(const VariableIndex& variableIndex,
std::vector<int>& cmember) {
gttic(Ordering_COLAMDConstrained);
gttic(Prepare);
size_t nEntries = variableIndex.nEntries(), nFactors =
variableIndex.nFactors(), nVars = variableIndex.size();
// Convert to compressed column major format colamd wants it in (== MATLAB format!)
size_t Alen = ccolamd_recommended((int) nEntries, (int) nFactors,
(int) nVars); /* colamd arg 3: size of the array A */
vector<int> A = vector<int>(Alen); /* colamd arg 4: row indices of A, of size Alen */
vector<int> p = vector<int>(nVars + 1); /* colamd arg 5: column pointers of A, of size n_col+1 */
// Fill in input data for COLAMD
p[0] = 0;
int count = 0;
vector<Key> keys(nVars); // Array to store the keys in the order we add them so we can retrieve them in permuted order
size_t index = 0;
BOOST_FOREACH(const VariableIndex::value_type key_factors, variableIndex) {
// Arrange factor indices into COLAMD format
const VariableIndex::Factors& column = key_factors.second;
size_t lastFactorId = numeric_limits<size_t>::max();
BOOST_FOREACH(size_t factorIndex, column) {
if (lastFactorId != numeric_limits<size_t>::max())
assert(factorIndex > lastFactorId);
A[count++] = (int) factorIndex; // copy sparse column
}
p[index + 1] = count; // column j (base 1) goes from A[j-1] to A[j]-1
// Store key in array and increment index
keys[index] = key_factors.first;
++index;
} }
/* ************************************************************************* */ assert((size_t)count == variableIndex.nEntries());
Ordering Ordering::colamd(const VariableIndex& variableIndex)
{ //double* knobs = NULL; /* colamd arg 6: parameters (uses defaults if NULL) */
// Call constrained version with all groups set to zero double knobs[CCOLAMD_KNOBS];
vector<int> dummy_groups(variableIndex.size(), 0); ccolamd_set_defaults(knobs);
return Ordering::colamdConstrained(variableIndex, dummy_groups); knobs[CCOLAMD_DENSE_ROW] = -1;
knobs[CCOLAMD_DENSE_COL] = -1;
int stats[CCOLAMD_STATS]; /* colamd arg 7: colamd output statistics and error codes */
gttoc(Prepare);
// call colamd, result will be in p
/* returns (1) if successful, (0) otherwise*/
if (nVars > 0) {
gttic(ccolamd);
int rv = ccolamd((int) nFactors, (int) nVars, (int) Alen, &A[0], &p[0],
knobs, stats, &cmember[0]);
if (rv != 1)
throw runtime_error(
(boost::format("ccolamd failed with return value %1%") % rv).str());
} }
/* ************************************************************************* */ // ccolamd_report(stats);
Ordering Ordering::colamdConstrained(
const VariableIndex& variableIndex, std::vector<int>& cmember)
{
gttic(Ordering_COLAMDConstrained);
gttic(Prepare); gttic(Fill_Ordering);
size_t nEntries = variableIndex.nEntries(), nFactors = variableIndex.nFactors(), nVars = variableIndex.size(); // Convert elimination ordering in p to an ordering
// Convert to compressed column major format colamd wants it in (== MATLAB format!) Ordering result;
size_t Alen = ccolamd_recommended((int)nEntries, (int)nFactors, (int)nVars); /* colamd arg 3: size of the array A */ result.resize(nVars);
vector<int> A = vector<int>(Alen); /* colamd arg 4: row indices of A, of size Alen */ for (size_t j = 0; j < nVars; ++j)
vector<int> p = vector<int>(nVars + 1); /* colamd arg 5: column pointers of A, of size n_col+1 */ result[j] = keys[p[j]];
gttoc(Fill_Ordering);
// Fill in input data for COLAMD return result;
p[0] = 0; }
int count = 0;
vector<Key> keys(nVars); // Array to store the keys in the order we add them so we can retrieve them in permuted order
size_t index = 0;
BOOST_FOREACH(const VariableIndex::value_type key_factors, variableIndex) {
// Arrange factor indices into COLAMD format
const VariableIndex::Factors& column = key_factors.second;
size_t lastFactorId = numeric_limits<size_t>::max();
BOOST_FOREACH(size_t factorIndex, column) {
if(lastFactorId != numeric_limits<size_t>::max())
assert(factorIndex > lastFactorId);
A[count++] = (int)factorIndex; // copy sparse column
}
p[index+1] = count; // column j (base 1) goes from A[j-1] to A[j]-1
// Store key in array and increment index
keys[index] = key_factors.first;
++ index;
}
assert((size_t)count == variableIndex.nEntries()); /* ************************************************************************* */
Ordering Ordering::colamdConstrainedLast(const VariableIndex& variableIndex,
const std::vector<Key>& constrainLast, bool forceOrder) {
gttic(Ordering_COLAMDConstrainedLast);
//double* knobs = NULL; /* colamd arg 6: parameters (uses defaults if NULL) */ size_t n = variableIndex.size();
double knobs[CCOLAMD_KNOBS]; std::vector<int> cmember(n, 0);
ccolamd_set_defaults(knobs);
knobs[CCOLAMD_DENSE_ROW]=-1;
knobs[CCOLAMD_DENSE_COL]=-1;
int stats[CCOLAMD_STATS]; /* colamd arg 7: colamd output statistics and error codes */ // Build a mapping to look up sorted Key indices by Key
FastMap<Key, size_t> keyIndices;
size_t j = 0;
BOOST_FOREACH(const VariableIndex::value_type key_factors, variableIndex)
keyIndices.insert(keyIndices.end(), make_pair(key_factors.first, j++));
gttoc(Prepare); // If at least some variables are not constrained to be last, constrain the
// ones that should be constrained.
int group = (constrainLast.size() != n ? 1 : 0);
BOOST_FOREACH(Key key, constrainLast) {
cmember[keyIndices.at(key)] = group;
if (forceOrder)
++group;
}
// call colamd, result will be in p return Ordering::colamdConstrained(variableIndex, cmember);
/* returns (1) if successful, (0) otherwise*/ }
if(nVars > 0) {
gttic(ccolamd);
int rv = ccolamd((int)nFactors, (int)nVars, (int)Alen, &A[0], &p[0], knobs, stats, &cmember[0]);
if(rv != 1)
throw runtime_error((boost::format("ccolamd failed with return value %1%")%rv).str());
}
// ccolamd_report(stats); /* ************************************************************************* */
Ordering Ordering::colamdConstrainedFirst(const VariableIndex& variableIndex,
const std::vector<Key>& constrainFirst, bool forceOrder) {
gttic(Ordering_COLAMDConstrainedFirst);
gttic(Fill_Ordering); const int none = -1;
// Convert elimination ordering in p to an ordering size_t n = variableIndex.size();
Ordering result; std::vector<int> cmember(n, none);
result.resize(nVars);
for(size_t j = 0; j < nVars; ++j)
result[j] = keys[p[j]];
gttoc(Fill_Ordering);
// Build a mapping to look up sorted Key indices by Key
FastMap<Key, size_t> keyIndices;
size_t j = 0;
BOOST_FOREACH(const VariableIndex::value_type key_factors, variableIndex)
keyIndices.insert(keyIndices.end(), make_pair(key_factors.first, j++));
// If at least some variables are not constrained to be last, constrain the
// ones that should be constrained.
int group = 0;
BOOST_FOREACH(Key key, constrainFirst) {
cmember[keyIndices.at(key)] = group;
if (forceOrder)
++group;
}
if (!forceOrder && !constrainFirst.empty())
++group;
BOOST_FOREACH(int& c, cmember)
if (c == none)
c = group;
return Ordering::colamdConstrained(variableIndex, cmember);
}
/* ************************************************************************* */
Ordering Ordering::colamdConstrained(const VariableIndex& variableIndex,
const FastMap<Key, int>& groups) {
gttic(Ordering_COLAMDConstrained);
size_t n = variableIndex.size();
std::vector<int> cmember(n, 0);
// Build a mapping to look up sorted Key indices by Key
FastMap<Key, size_t> keyIndices;
size_t j = 0;
BOOST_FOREACH(const VariableIndex::value_type key_factors, variableIndex)
keyIndices.insert(keyIndices.end(), make_pair(key_factors.first, j++));
// Assign groups
typedef FastMap<Key, int>::value_type key_group;
BOOST_FOREACH(const key_group& p, groups) {
// FIXME: check that no groups are skipped
cmember[keyIndices.at(p.first)] = p.second;
}
return Ordering::colamdConstrained(variableIndex, cmember);
}
/* ************************************************************************* */
Ordering Ordering::metis(const MetisIndex& met) {
gttic(Ordering_METIS);
vector<idx_t> xadj = met.xadj();
vector<idx_t> adj = met.adj();
vector<idx_t> perm, iperm;
idx_t size = met.nValues();
for (idx_t i = 0; i < size; i++) {
perm.push_back(0);
iperm.push_back(0);
}
int outputError;
outputError = METIS_NodeND(&size, &xadj[0], &adj[0], NULL, NULL, &perm[0],
&iperm[0]);
Ordering result;
if (outputError != METIS_OK) {
std::cout << "METIS failed during Nested Dissection ordering!\n";
return result; return result;
} }
/* ************************************************************************* */ result.resize(size);
Ordering Ordering::colamdConstrainedLast( for (size_t j = 0; j < (size_t) size; ++j) {
const VariableIndex& variableIndex, const std::vector<Key>& constrainLast, bool forceOrder) // We have to add the minKey value back to obtain the original key in the Values
{ result[j] = met.intToKey(perm[j]);
gttic(Ordering_COLAMDConstrainedLast);
size_t n = variableIndex.size();
std::vector<int> cmember(n, 0);
// Build a mapping to look up sorted Key indices by Key
FastMap<Key, size_t> keyIndices;
size_t j = 0;
BOOST_FOREACH(const VariableIndex::value_type key_factors, variableIndex)
keyIndices.insert(keyIndices.end(), make_pair(key_factors.first, j++));
// If at least some variables are not constrained to be last, constrain the
// ones that should be constrained.
int group = (constrainLast.size() != n ? 1 : 0);
BOOST_FOREACH(Key key, constrainLast) {
cmember[keyIndices.at(key)] = group;
if(forceOrder)
++ group;
}
return Ordering::colamdConstrained(variableIndex, cmember);
} }
return result;
}
/* ************************************************************************* */ /* ************************************************************************* */
Ordering Ordering::colamdConstrainedFirst( void Ordering::print(const std::string& str,
const VariableIndex& variableIndex, const std::vector<Key>& constrainFirst, bool forceOrder) const KeyFormatter& keyFormatter) const {
{ cout << str;
gttic(Ordering_COLAMDConstrainedFirst); // Print ordering in index order
// Print the ordering with varsPerLine ordering entries printed on each line,
const int none = -1; // for compactness.
size_t n = variableIndex.size(); static const size_t varsPerLine = 10;
std::vector<int> cmember(n, none); bool endedOnNewline = false;
for (size_t i = 0; i < size(); ++i) {
// Build a mapping to look up sorted Key indices by Key if (i % varsPerLine == 0)
FastMap<Key, size_t> keyIndices; cout << "Position " << i << ": ";
size_t j = 0; if (i % varsPerLine != 0)
BOOST_FOREACH(const VariableIndex::value_type key_factors, variableIndex) cout << ", ";
keyIndices.insert(keyIndices.end(), make_pair(key_factors.first, j++)); cout << keyFormatter(at(i));
if (i % varsPerLine == varsPerLine - 1) {
// If at least some variables are not constrained to be last, constrain the
// ones that should be constrained.
int group = 0;
BOOST_FOREACH(Key key, constrainFirst) {
cmember[keyIndices.at(key)] = group;
if(forceOrder)
++ group;
}
if(!forceOrder && !constrainFirst.empty())
++ group;
BOOST_FOREACH(int& c, cmember)
if(c == none)
c = group;
return Ordering::colamdConstrained(variableIndex, cmember);
}
/* ************************************************************************* */
Ordering Ordering::colamdConstrained(const VariableIndex& variableIndex,
const FastMap<Key, int>& groups)
{
gttic(Ordering_COLAMDConstrained);
size_t n = variableIndex.size();
std::vector<int> cmember(n, 0);
// Build a mapping to look up sorted Key indices by Key
FastMap<Key, size_t> keyIndices;
size_t j = 0;
BOOST_FOREACH(const VariableIndex::value_type key_factors, variableIndex)
keyIndices.insert(keyIndices.end(), make_pair(key_factors.first, j++));
// Assign groups
typedef FastMap<Key, int>::value_type key_group;
BOOST_FOREACH(const key_group& p, groups) {
// FIXME: check that no groups are skipped
cmember[keyIndices.at(p.first)] = p.second;
}
return Ordering::colamdConstrained(variableIndex, cmember);
}
/* ************************************************************************* */
Ordering Ordering::metis(const MetisIndex& met)
{
gttic(Ordering_METIS);
vector<idx_t> xadj = met.xadj();
vector<idx_t> adj = met.adj();
vector<idx_t> perm, iperm;
idx_t size = met.nValues();
for (idx_t i = 0; i < size; i++)
{
perm.push_back(0);
iperm.push_back(0);
}
int outputError;
outputError = METIS_NodeND(&size, &xadj[0], &adj[0], NULL, NULL, &perm[0], &iperm[0]);
Ordering result;
if (outputError != METIS_OK)
{
std::cout << "METIS failed during Nested Dissection ordering!\n";
return result;
}
result.resize(size);
for (size_t j = 0; j < (size_t)size; ++j){
// We have to add the minKey value back to obtain the original key in the Values
result[j] = met.intToKey(perm[j]);
}
return result;
}
/* ************************************************************************* */
void Ordering::print(const std::string& str, const KeyFormatter& keyFormatter) const
{
cout << str;
// Print ordering in index order
// Print the ordering with varsPerLine ordering entries printed on each line,
// for compactness.
static const size_t varsPerLine = 10;
bool endedOnNewline = false;
for(size_t i = 0; i < size(); ++i) {
if(i % varsPerLine == 0)
cout << "Position " << i << ": ";
if(i % varsPerLine != 0)
cout << ", ";
cout << keyFormatter(at(i));
if(i % varsPerLine == varsPerLine - 1) {
cout << "\n";
endedOnNewline = true;
} else {
endedOnNewline = false;
}
}
if(!endedOnNewline)
cout << "\n"; cout << "\n";
cout.flush(); endedOnNewline = true;
} else {
endedOnNewline = false;
}
} }
if (!endedOnNewline)
cout << "\n";
cout.flush();
}
/* ************************************************************************* */ /* ************************************************************************* */
bool Ordering::equals(const Ordering& other, double tol) const bool Ordering::equals(const Ordering& other, double tol) const {
{ return (*this) == other;
return (*this) == other; }
}
} }

382
gtsam/inference/Ordering.h
View File

@ -30,192 +30,210 @@
namespace gtsam { namespace gtsam {
class Ordering : public std::vector<Key> { class Ordering: public std::vector<Key> {
protected: protected:
typedef std::vector<Key> Base; typedef std::vector<Key> Base;
public: public:
/// Type of ordering to use /// Type of ordering to use
enum OrderingType { enum OrderingType {
COLAMD, METIS, CUSTOM COLAMD, METIS, CUSTOM
};
typedef Ordering This; ///< Typedef to this class
typedef boost::shared_ptr<This> shared_ptr; ///< shared_ptr to this class
/// Create an empty ordering
GTSAM_EXPORT Ordering() {}
/// Create from a container
template<typename KEYS>
explicit Ordering(const KEYS& keys) : Base(keys.begin(), keys.end()) {}
/// Create an ordering using iterators over keys
template<typename ITERATOR>
Ordering(ITERATOR firstKey, ITERATOR lastKey) : Base(firstKey, lastKey) {}
/// Add new variables to the ordering as ordering += key1, key2, ... Equivalent to calling
/// push_back.
boost::assign::list_inserter<boost::assign_detail::call_push_back<This> >
operator+=(Key key) {
return boost::assign::make_list_inserter(boost::assign_detail::call_push_back<This>(*this))(key);
}
/// Invert (not reverse) the ordering - returns a map from key to order position
FastMap<Key, size_t> invert() const;
/// @name Fill-reducing Orderings @{
/// Compute a fill-reducing ordering using COLAMD from a factor graph (see details for note on
/// performance). This internally builds a VariableIndex so if you already have a VariableIndex,
/// it is faster to use COLAMD(const VariableIndex&)
template<class FACTOR>
static Ordering colamd(const FactorGraph<FACTOR>& graph) {
return colamd(VariableIndex(graph)); }
/// Compute a fill-reducing ordering using COLAMD from a VariableIndex.
static GTSAM_EXPORT Ordering colamd(const VariableIndex& variableIndex);
/// Compute a fill-reducing ordering using constrained COLAMD from a factor graph (see details
/// for note on performance). This internally builds a VariableIndex so if you already have a
/// VariableIndex, it is faster to use COLAMD(const VariableIndex&). This function constrains
/// the variables in \c constrainLast to the end of the ordering, and orders all other variables
/// before in a fill-reducing ordering. If \c forceOrder is true, the variables in \c
/// constrainLast will be ordered in the same order specified in the vector<Key> \c
/// constrainLast. If \c forceOrder is false, the variables in \c constrainLast will be
/// ordered after all the others, but will be rearranged by CCOLAMD to reduce fill-in as well.
template<class FACTOR>
static Ordering colamdConstrainedLast(const FactorGraph<FACTOR>& graph,
const std::vector<Key>& constrainLast, bool forceOrder = false) {
return colamdConstrainedLast(VariableIndex(graph), constrainLast, forceOrder); }
/// Compute a fill-reducing ordering using constrained COLAMD from a VariableIndex. This
/// function constrains the variables in \c constrainLast to the end of the ordering, and orders
/// all other variables before in a fill-reducing ordering. If \c forceOrder is true, the
/// variables in \c constrainLast will be ordered in the same order specified in the vector<Key>
/// \c constrainLast. If \c forceOrder is false, the variables in \c constrainLast will be
/// ordered after all the others, but will be rearranged by CCOLAMD to reduce fill-in as well.
static GTSAM_EXPORT Ordering colamdConstrainedLast(const VariableIndex& variableIndex,
const std::vector<Key>& constrainLast, bool forceOrder = false);
/// Compute a fill-reducing ordering using constrained COLAMD from a factor graph (see details
/// for note on performance). This internally builds a VariableIndex so if you already have a
/// VariableIndex, it is faster to use COLAMD(const VariableIndex&). This function constrains
/// the variables in \c constrainLast to the end of the ordering, and orders all other variables
/// before in a fill-reducing ordering. If \c forceOrder is true, the variables in \c
/// constrainLast will be ordered in the same order specified in the vector<Key> \c
/// constrainLast. If \c forceOrder is false, the variables in \c constrainFirst will be
/// ordered after all the others, but will be rearranged by CCOLAMD to reduce fill-in as well.
template<class FACTOR>
static Ordering colamdConstrainedFirst(const FactorGraph<FACTOR>& graph,
const std::vector<Key>& constrainFirst, bool forceOrder = false) {
return colamdConstrainedFirst(VariableIndex(graph), constrainFirst, forceOrder); }
/// Compute a fill-reducing ordering using constrained COLAMD from a VariableIndex. This
/// function constrains the variables in \c constrainFirst to the front of the ordering, and
/// orders all other variables after in a fill-reducing ordering. If \c forceOrder is true, the
/// variables in \c constrainFirst will be ordered in the same order specified in the
/// vector<Key> \c constrainFirst. If \c forceOrder is false, the variables in \c
/// constrainFirst will be ordered after all the others, but will be rearranged by CCOLAMD to
/// reduce fill-in as well.
static GTSAM_EXPORT Ordering colamdConstrainedFirst(const VariableIndex& variableIndex,
const std::vector<Key>& constrainFirst, bool forceOrder = false);
/// Compute a fill-reducing ordering using constrained COLAMD from a factor graph (see details
/// for note on performance). This internally builds a VariableIndex so if you already have a
/// VariableIndex, it is faster to use COLAMD(const VariableIndex&). In this function, a group
/// for each variable should be specified in \c groups, and each group of variables will appear
/// in the ordering in group index order. \c groups should be a map from Key to group index.
/// The group indices used should be consecutive starting at 0, but may appear in \c groups in
/// arbitrary order. Any variables not present in \c groups will be assigned to group 0. This
/// function simply fills the \c cmember argument to CCOLAMD with the supplied indices, see the
/// CCOLAMD documentation for more information.
template<class FACTOR>
static Ordering colamdConstrained(const FactorGraph<FACTOR>& graph,
const FastMap<Key, int>& groups) {
return colamdConstrained(VariableIndex(graph), groups); }
/// Compute a fill-reducing ordering using constrained COLAMD from a VariableIndex. In this
/// function, a group for each variable should be specified in \c groups, and each group of
/// variables will appear in the ordering in group index order. \c groups should be a map from
/// Key to group index. The group indices used should be consecutive starting at 0, but may
/// appear in \c groups in arbitrary order. Any variables not present in \c groups will be
/// assigned to group 0. This function simply fills the \c cmember argument to CCOLAMD with the
/// supplied indices, see the CCOLAMD documentation for more information.
static GTSAM_EXPORT Ordering colamdConstrained(const VariableIndex& variableIndex,
const FastMap<Key, int>& groups);
/// Return a natural Ordering. Typically used by iterative solvers
template <class FACTOR>
static Ordering Natural(const FactorGraph<FACTOR> &fg) {
FastSet<Key> src = fg.keys();
std::vector<Key> keys(src.begin(), src.end());
std::stable_sort(keys.begin(), keys.end());
return Ordering(keys);
}
/// METIS Formatting function
template<class FACTOR>
static GTSAM_EXPORT void CSRFormat(std::vector<int>& xadj, std::vector<int>& adj, const FactorGraph<FACTOR>& graph);
/// Compute an ordering determined by METIS from a VariableIndex
static GTSAM_EXPORT Ordering metis(const MetisIndex& met);
template<class FACTOR>
static Ordering metis(const FactorGraph<FACTOR>& graph)
{
return metis(MetisIndex(graph));
}
/// @}
/// @name Named Constructors @{
template<class FACTOR>
static Ordering Create(OrderingType orderingType,
const FactorGraph<FACTOR>& graph) {
switch (orderingType) {
case COLAMD:
return colamd(graph);
case METIS:
return metis(graph);
case CUSTOM:
throw std::runtime_error(
"Ordering::Create error: called with CUSTOM ordering type.");
default:
throw std::runtime_error(
"Ordering::Create error: called with unknown ordering type.");
}
}
/// @}
/// @name Testable @{
GTSAM_EXPORT void print(const std::string& str = "", const KeyFormatter& keyFormatter = DefaultKeyFormatter) const;
GTSAM_EXPORT bool equals(const Ordering& other, double tol = 1e-9) const;
/// @}
private:
/// Internal COLAMD function
static GTSAM_EXPORT Ordering colamdConstrained(
const VariableIndex& variableIndex, std::vector<int>& cmember);
/** Serialization function */
friend class boost::serialization::access;
template<class ARCHIVE>
void serialize(ARCHIVE & ar, const unsigned int version) {
ar & BOOST_SERIALIZATION_BASE_OBJECT_NVP(Base);
}
}; };
/// traits typedef Ordering This; ///< Typedef to this class
template<> struct traits<Ordering> : public Testable<Ordering> {}; typedef boost::shared_ptr<This> shared_ptr; ///< shared_ptr to this class
/// Create an empty ordering
GTSAM_EXPORT
Ordering() {
}
/// Create from a container
template<typename KEYS>
explicit Ordering(const KEYS& keys) :
Base(keys.begin(), keys.end()) {
}
/// Create an ordering using iterators over keys
template<typename ITERATOR>
Ordering(ITERATOR firstKey, ITERATOR lastKey) :
Base(firstKey, lastKey) {
}
/// Add new variables to the ordering as ordering += key1, key2, ... Equivalent to calling
/// push_back.
boost::assign::list_inserter<boost::assign_detail::call_push_back<This> > operator+=(
Key key) {
return boost::assign::make_list_inserter(
boost::assign_detail::call_push_back<This>(*this))(key);
}
/// Invert (not reverse) the ordering - returns a map from key to order position
FastMap<Key, size_t> invert() const;
/// @name Fill-reducing Orderings @{
/// Compute a fill-reducing ordering using COLAMD from a factor graph (see details for note on
/// performance). This internally builds a VariableIndex so if you already have a VariableIndex,
/// it is faster to use COLAMD(const VariableIndex&)
template<class FACTOR>
static Ordering colamd(const FactorGraph<FACTOR>& graph) {
return colamd(VariableIndex(graph));
}
/// Compute a fill-reducing ordering using COLAMD from a VariableIndex.
static GTSAM_EXPORT Ordering colamd(const VariableIndex& variableIndex);
/// Compute a fill-reducing ordering using constrained COLAMD from a factor graph (see details
/// for note on performance). This internally builds a VariableIndex so if you already have a
/// VariableIndex, it is faster to use COLAMD(const VariableIndex&). This function constrains
/// the variables in \c constrainLast to the end of the ordering, and orders all other variables
/// before in a fill-reducing ordering. If \c forceOrder is true, the variables in \c
/// constrainLast will be ordered in the same order specified in the vector<Key> \c
/// constrainLast. If \c forceOrder is false, the variables in \c constrainLast will be
/// ordered after all the others, but will be rearranged by CCOLAMD to reduce fill-in as well.
template<class FACTOR>
static Ordering colamdConstrainedLast(const FactorGraph<FACTOR>& graph,
const std::vector<Key>& constrainLast, bool forceOrder = false) {
return colamdConstrainedLast(VariableIndex(graph), constrainLast,
forceOrder);
}
/// Compute a fill-reducing ordering using constrained COLAMD from a VariableIndex. This
/// function constrains the variables in \c constrainLast to the end of the ordering, and orders
/// all other variables before in a fill-reducing ordering. If \c forceOrder is true, the
/// variables in \c constrainLast will be ordered in the same order specified in the vector<Key>
/// \c constrainLast. If \c forceOrder is false, the variables in \c constrainLast will be
/// ordered after all the others, but will be rearranged by CCOLAMD to reduce fill-in as well.
static GTSAM_EXPORT Ordering colamdConstrainedLast(
const VariableIndex& variableIndex, const std::vector<Key>& constrainLast,
bool forceOrder = false);
/// Compute a fill-reducing ordering using constrained COLAMD from a factor graph (see details
/// for note on performance). This internally builds a VariableIndex so if you already have a
/// VariableIndex, it is faster to use COLAMD(const VariableIndex&). This function constrains
/// the variables in \c constrainLast to the end of the ordering, and orders all other variables
/// before in a fill-reducing ordering. If \c forceOrder is true, the variables in \c
/// constrainLast will be ordered in the same order specified in the vector<Key> \c
/// constrainLast. If \c forceOrder is false, the variables in \c constrainFirst will be
/// ordered after all the others, but will be rearranged by CCOLAMD to reduce fill-in as well.
template<class FACTOR>
static Ordering colamdConstrainedFirst(const FactorGraph<FACTOR>& graph,
const std::vector<Key>& constrainFirst, bool forceOrder = false) {
return colamdConstrainedFirst(VariableIndex(graph), constrainFirst,
forceOrder);
}
/// Compute a fill-reducing ordering using constrained COLAMD from a VariableIndex. This
/// function constrains the variables in \c constrainFirst to the front of the ordering, and
/// orders all other variables after in a fill-reducing ordering. If \c forceOrder is true, the
/// variables in \c constrainFirst will be ordered in the same order specified in the
/// vector<Key> \c constrainFirst. If \c forceOrder is false, the variables in \c
/// constrainFirst will be ordered after all the others, but will be rearranged by CCOLAMD to
/// reduce fill-in as well.
static GTSAM_EXPORT Ordering colamdConstrainedFirst(
const VariableIndex& variableIndex,
const std::vector<Key>& constrainFirst, bool forceOrder = false);
/// Compute a fill-reducing ordering using constrained COLAMD from a factor graph (see details
/// for note on performance). This internally builds a VariableIndex so if you already have a
/// VariableIndex, it is faster to use COLAMD(const VariableIndex&). In this function, a group
/// for each variable should be specified in \c groups, and each group of variables will appear
/// in the ordering in group index order. \c groups should be a map from Key to group index.
/// The group indices used should be consecutive starting at 0, but may appear in \c groups in
/// arbitrary order. Any variables not present in \c groups will be assigned to group 0. This
/// function simply fills the \c cmember argument to CCOLAMD with the supplied indices, see the
/// CCOLAMD documentation for more information.
template<class FACTOR>
static Ordering colamdConstrained(const FactorGraph<FACTOR>& graph,
const FastMap<Key, int>& groups) {
return colamdConstrained(VariableIndex(graph), groups);
}
/// Compute a fill-reducing ordering using constrained COLAMD from a VariableIndex. In this
/// function, a group for each variable should be specified in \c groups, and each group of
/// variables will appear in the ordering in group index order. \c groups should be a map from
/// Key to group index. The group indices used should be consecutive starting at 0, but may
/// appear in \c groups in arbitrary order. Any variables not present in \c groups will be
/// assigned to group 0. This function simply fills the \c cmember argument to CCOLAMD with the
/// supplied indices, see the CCOLAMD documentation for more information.
static GTSAM_EXPORT Ordering colamdConstrained(
const VariableIndex& variableIndex, const FastMap<Key, int>& groups);
/// Return a natural Ordering. Typically used by iterative solvers
template<class FACTOR>
static Ordering Natural(const FactorGraph<FACTOR> &fg) {
FastSet<Key> src = fg.keys();
std::vector<Key> keys(src.begin(), src.end());
std::stable_sort(keys.begin(), keys.end());
return Ordering(keys);
}
/// METIS Formatting function
template<class FACTOR>
static GTSAM_EXPORT void CSRFormat(std::vector<int>& xadj,
std::vector<int>& adj, const FactorGraph<FACTOR>& graph);
/// Compute an ordering determined by METIS from a VariableIndex
static GTSAM_EXPORT Ordering metis(const MetisIndex& met);
template<class FACTOR>
static Ordering metis(const FactorGraph<FACTOR>& graph) {
return metis(MetisIndex(graph));
}
/// @}
/// @name Named Constructors @{
template<class FACTOR>
static Ordering Create(OrderingType orderingType,
const FactorGraph<FACTOR>& graph) {
switch (orderingType) {
case COLAMD:
return colamd(graph);
case METIS:
return metis(graph);
case CUSTOM:
throw std::runtime_error(
"Ordering::Create error: called with CUSTOM ordering type.");
default:
throw std::runtime_error(
"Ordering::Create error: called with unknown ordering type.");
}
}
/// @}
/// @name Testable @{
GTSAM_EXPORT
void print(const std::string& str = "", const KeyFormatter& keyFormatter =
DefaultKeyFormatter) const;
GTSAM_EXPORT
bool equals(const Ordering& other, double tol = 1e-9) const;
/// @}
private:
/// Internal COLAMD function
static GTSAM_EXPORT Ordering colamdConstrained(
const VariableIndex& variableIndex, std::vector<int>& cmember);
/** Serialization function */
friend class boost::serialization::access;
template<class ARCHIVE>
void serialize(ARCHIVE & ar, const unsigned int version) {
ar & BOOST_SERIALIZATION_BASE_OBJECT_NVP(Base);
}
};
/// traits
template<> struct traits<Ordering> : public Testable<Ordering> {
};
} }

118
gtsam/inference/tests/testOrdering.cpp
View File

@ -56,7 +56,8 @@ TEST(Ordering, constrained_ordering) {
EXPECT(assert_equal(expConstrained, actConstrained)); EXPECT(assert_equal(expConstrained, actConstrained));
// constrained version - push one set to the start // constrained version - push one set to the start
Ordering actConstrained2 = Ordering::colamdConstrainedFirst(sfg, list_of(2)(4)); Ordering actConstrained2 = Ordering::colamdConstrainedFirst(sfg,
list_of(2)(4));
Ordering expConstrained2 = Ordering(list_of(2)(4)(0)(1)(3)(5)); Ordering expConstrained2 = Ordering(list_of(2)(4)(0)(1)(3)(5));
EXPECT(assert_equal(expConstrained2, actConstrained2)); EXPECT(assert_equal(expConstrained2, actConstrained2));
} }
@ -82,43 +83,41 @@ TEST(Ordering, grouped_constrained_ordering) {
/* ************************************************************************* */ /* ************************************************************************* */
TEST(Ordering, csr_format) { TEST(Ordering, csr_format) {
// Example in METIS manual // Example in METIS manual
SymbolicFactorGraph sfg; SymbolicFactorGraph sfg;
sfg.push_factor(0, 1); sfg.push_factor(0, 1);
sfg.push_factor(1, 2); sfg.push_factor(1, 2);
sfg.push_factor(2, 3); sfg.push_factor(2, 3);
sfg.push_factor(3, 4); sfg.push_factor(3, 4);
sfg.push_factor(5, 6); sfg.push_factor(5, 6);
sfg.push_factor(6, 7); sfg.push_factor(6, 7);
sfg.push_factor(7, 8); sfg.push_factor(7, 8);
sfg.push_factor(8, 9); sfg.push_factor(8, 9);
sfg.push_factor(10, 11); sfg.push_factor(10, 11);
sfg.push_factor(11, 12); sfg.push_factor(11, 12);
sfg.push_factor(12, 13); sfg.push_factor(12, 13);
sfg.push_factor(13, 14); sfg.push_factor(13, 14);
sfg.push_factor(0, 5); sfg.push_factor(0, 5);
sfg.push_factor(5, 10); sfg.push_factor(5, 10);
sfg.push_factor(1, 6); sfg.push_factor(1, 6);
sfg.push_factor(6, 11); sfg.push_factor(6, 11);
sfg.push_factor(2, 7); sfg.push_factor(2, 7);
sfg.push_factor(7, 12); sfg.push_factor(7, 12);
sfg.push_factor(3, 8); sfg.push_factor(3, 8);
sfg.push_factor(8, 13); sfg.push_factor(8, 13);
sfg.push_factor(4, 9); sfg.push_factor(4, 9);
sfg.push_factor(9, 14); sfg.push_factor(9, 14);
MetisIndex mi(sfg); MetisIndex mi(sfg);
vector<int> xadjExpected, adjExpected; vector<int> xadjExpected, adjExpected;
xadjExpected += 0, 2, 5, 8, 11, 13, 16, 20, 24, 28, 31, 33, 36, 39, 42, 44; xadjExpected += 0, 2, 5, 8, 11, 13, 16, 20, 24, 28, 31, 33, 36, 39, 42, 44;
adjExpected += 1, 5, 0, 2, 6, 1, 3, 7, 2, 4, 8, 3, 9, 0, 6, 10, 1, 5, 7, 11, adjExpected += 1, 5, 0, 2, 6, 1, 3, 7, 2, 4, 8, 3, 9, 0, 6, 10, 1, 5, 7, 11, 2, 6, 8, 12, 3, 7, 9, 13, 4, 8, 14, 5, 11, 6, 10, 12, 7, 11, 13, 8, 12, 14, 9, 13;
2, 6, 8, 12, 3, 7, 9, 13, 4, 8, 14, 5, 11, 6, 10, 12, 7, 11,
13, 8, 12, 14, 9, 13 ;
EXPECT(xadjExpected == mi.xadj()); EXPECT(xadjExpected == mi.xadj());
EXPECT(adjExpected.size() == mi.adj().size()); EXPECT(adjExpected.size() == mi.adj().size());
EXPECT(adjExpected == mi.adj()); EXPECT(adjExpected == mi.adj());
} }
/* ************************************************************************* */ /* ************************************************************************* */
@ -136,7 +135,7 @@ TEST(Ordering, csr_format_2) {
vector<int> xadjExpected, adjExpected; vector<int> xadjExpected, adjExpected;
xadjExpected += 0, 1, 4, 6, 8, 10; xadjExpected += 0, 1, 4, 6, 8, 10;
adjExpected += 1, 0, 2, 4, 1, 3, 2, 4, 1, 3; adjExpected += 1, 0, 2, 4, 1, 3, 2, 4, 1, 3;
EXPECT(xadjExpected == mi.xadj()); EXPECT(xadjExpected == mi.xadj());
EXPECT(adjExpected.size() == mi.adj().size()); EXPECT(adjExpected.size() == mi.adj().size());
@ -237,18 +236,18 @@ TEST(Ordering, MetisLoop) {
SymbolicFactorGraph sfg = example::symbolicChain(); SymbolicFactorGraph sfg = example::symbolicChain();
// add loop closure // add loop closure
sfg.push_factor(0,5); sfg.push_factor(0, 5);
// METIS // METIS
{ {
Ordering actual = Ordering::Create(Ordering::METIS,sfg); Ordering actual = Ordering::Create(Ordering::METIS, sfg);
// 0,3 // 0,3
// 1 // 1,3
// 2 // 2
// 4 // 4,0
// 5 // 5
Ordering expected = Ordering(list_of(5)(4)(2)(1)(0)(3)); Ordering expected = Ordering(list_of(5)(4)(2)(1)(0)(3));
EXPECT(assert_equal(expected, actual)); EXPECT(assert_equal(expected, actual));
} }
} }
@ -260,28 +259,31 @@ TEST(Ordering, Create) {
// COLAMD // COLAMD
{ {
Ordering actual = Ordering::Create(Ordering::COLAMD,sfg); Ordering actual = Ordering::Create(Ordering::COLAMD, sfg);
Ordering expected = Ordering(list_of(0)(1)(2)(3)(4)(5)); Ordering expected = Ordering(list_of(0)(1)(2)(3)(4)(5));
EXPECT(assert_equal(expected, actual)); EXPECT(assert_equal(expected, actual));
} }
// METIS // METIS
{ {
Ordering actual = Ordering::Create(Ordering::METIS,sfg); Ordering actual = Ordering::Create(Ordering::METIS, sfg);
// 2 // 2
// 0 // 0
// 1 // 1
// 4 // 4
// 3 // 3
// 5 // 5
Ordering expected = Ordering(list_of(5)(3)(4)(1)(0)(2)); Ordering expected = Ordering(list_of(5)(3)(4)(1)(0)(2));
EXPECT(assert_equal(expected, actual)); EXPECT(assert_equal(expected, actual));
} }
// CUSTOM // CUSTOM
CHECK_EXCEPTION(Ordering::Create(Ordering::CUSTOM,sfg),runtime_error); CHECK_EXCEPTION(Ordering::Create(Ordering::CUSTOM, sfg), runtime_error);
} }
/* ************************************************************************* */ /* ************************************************************************* */
int main() { TestResult tr; return TestRegistry::runAllTests(tr); } int main() {
TestResult tr;
return TestRegistry::runAllTests(tr);
}
/* ************************************************************************* */ /* ************************************************************************* */