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gtsam/linear/GaussianFactor.cpp

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/* ----------------------------------------------------------------------------
* GTSAM Copyright 2010, Georgia Tech Research Corporation,
* Atlanta, Georgia 30332-0415
* All Rights Reserved
* Authors: Frank Dellaert, et al. (see THANKS for the full author list)
* See LICENSE for the license information
* -------------------------------------------------------------------------- */
/**
* @file GaussianFactor.cpp
* @brief Linear Factor....A Gaussian
* @brief linearFactor
* @author Christian Potthast
*/
#include <boost/foreach.hpp>
#include <boost/format.hpp>
#include <boost/make_shared.hpp>
#include <boost/pool/pool_alloc.hpp>
#include <boost/lambda/bind.hpp>
#include <boost/lambda/lambda.hpp>
#include <boost/tuple/tuple.hpp>
#include <boost/numeric/ublas/triangular.hpp>
#include <boost/numeric/ublas/io.hpp>
#include <boost/numeric/ublas/matrix_proxy.hpp>
#include <boost/numeric/ublas/vector_proxy.hpp>
#include <gtsam/base/timing.h>
#include <gtsam/base/Matrix.h>
#include <gtsam/linear/GaussianConditional.h>
#include <gtsam/linear/GaussianFactor.h>
#include <gtsam/linear/GaussianFactorGraph.h>
using namespace std;
//using namespace boost::assign;
namespace ublas = boost::numeric::ublas;
using namespace boost::lambda;
namespace gtsam {
/* ************************************************************************* */
inline void GaussianFactor::assertInvariants() const {
#ifndef NDEBUG
IndexFactor::assertInvariants();
assert((keys_.size() == 0 && Ab_.size1() == 0 && Ab_.nBlocks() == 0) || keys_.size()+1 == Ab_.nBlocks());
#endif
}
/* ************************************************************************* */
GaussianFactor::GaussianFactor(const GaussianFactor& gf) :
IndexFactor(gf), model_(gf.model_), firstNonzeroBlocks_(gf.firstNonzeroBlocks_), Ab_(matrix_) {
Ab_.assignNoalias(gf.Ab_);
assertInvariants();
}
/* ************************************************************************* */
GaussianFactor::GaussianFactor() : Ab_(matrix_) { assertInvariants(); }
/* ************************************************************************* */
GaussianFactor::GaussianFactor(const Vector& b_in) : firstNonzeroBlocks_(b_in.size(), 0), Ab_(matrix_) {
size_t dims[] = { 1 };
Ab_.copyStructureFrom(ab_type(matrix_, dims, dims+1, b_in.size()));
getb() = b_in;
assertInvariants();
}
/* ************************************************************************* */
GaussianFactor::GaussianFactor(Index i1, const Matrix& A1,
const Vector& b, const SharedDiagonal& model) :
IndexFactor(i1), model_(model), firstNonzeroBlocks_(b.size(), 0), Ab_(matrix_) {
size_t dims[] = { A1.size2(), 1};
Ab_.copyStructureFrom(ab_type(matrix_, dims, dims+2, b.size()));
Ab_(0) = A1;
getb() = b;
assertInvariants();
}
/* ************************************************************************* */
GaussianFactor::GaussianFactor(Index i1, const Matrix& A1, Index i2, const Matrix& A2,
const Vector& b, const SharedDiagonal& model) :
IndexFactor(i1,i2), model_(model), firstNonzeroBlocks_(b.size(), 0), Ab_(matrix_) {
size_t dims[] = { A1.size2(), A2.size2(), 1};
Ab_.copyStructureFrom(ab_type(matrix_, dims, dims+3, b.size()));
Ab_(0) = A1;
Ab_(1) = A2;
getb() = b;
assertInvariants();
}
/* ************************************************************************* */
GaussianFactor::GaussianFactor(Index i1, const Matrix& A1, Index i2, const Matrix& A2,
Index i3, const Matrix& A3, const Vector& b, const SharedDiagonal& model) :
IndexFactor(i1,i2,i3), model_(model), firstNonzeroBlocks_(b.size(), 0), Ab_(matrix_) {
size_t dims[] = { A1.size2(), A2.size2(), A3.size2(), 1};
Ab_.copyStructureFrom(ab_type(matrix_, dims, dims+4, b.size()));
Ab_(0) = A1;
Ab_(1) = A2;
Ab_(2) = A3;
getb() = b;
assertInvariants();
}
/* ************************************************************************* */
GaussianFactor::GaussianFactor(const std::vector<std::pair<Index, Matrix> > &terms,
const Vector &b, const SharedDiagonal& model) :
model_(model), firstNonzeroBlocks_(b.size(), 0), Ab_(matrix_) {
keys_.resize(terms.size());
size_t dims[terms.size()+1];
for(size_t j=0; j<terms.size(); ++j) {
keys_[j] = terms[j].first;
dims[j] = terms[j].second.size2();
}
dims[terms.size()] = 1;
Ab_.copyStructureFrom(ab_type(matrix_, dims, dims+terms.size()+1, b.size()));
for(size_t j=0; j<terms.size(); ++j)
Ab_(j) = terms[j].second;
getb() = b;
assertInvariants();
}
/* ************************************************************************* */
GaussianFactor::GaussianFactor(const std::list<std::pair<Index, Matrix> > &terms,
const Vector &b, const SharedDiagonal& model) :
model_(model), firstNonzeroBlocks_(b.size(), 0), Ab_(matrix_) {
keys_.resize(terms.size());
size_t dims[terms.size()+1];
size_t j=0;
for(std::list<std::pair<Index, Matrix> >::const_iterator term=terms.begin(); term!=terms.end(); ++term) {
keys_[j] = term->first;
dims[j] = term->second.size2();
++ j;
}
dims[j] = 1;
firstNonzeroBlocks_.resize(b.size(), 0);
Ab_.copyStructureFrom(ab_type(matrix_, dims, dims+terms.size()+1, b.size()));
j = 0;
for(std::list<std::pair<Index, Matrix> >::const_iterator term=terms.begin(); term!=terms.end(); ++term) {
Ab_(j) = term->second;
++ j;
}
getb() = b;
assertInvariants();
}
/* ************************************************************************* */
GaussianFactor::GaussianFactor(const GaussianConditional& cg) : IndexFactor(cg), model_(noiseModel::Diagonal::Sigmas(cg.get_sigmas(), true)), Ab_(matrix_) {
Ab_.assignNoalias(cg.rsd_);
// todo SL: make firstNonzeroCols triangular?
firstNonzeroBlocks_.resize(cg.get_d().size(), 0); // set sigmas from precisions
assertInvariants();
}
/* ************************************************************************* */
void GaussianFactor::print(const string& s) const {
cout << s << "\n";
if (empty()) {
cout << " empty, keys: ";
BOOST_FOREACH(const Index key, keys_) { cout << key << " "; }
cout << endl;
} else {
for(const_iterator key=begin(); key!=end(); ++key)
gtsam::print(getA(key), (boost::format("A[%1%]=\n")%*key).str());
gtsam::print(getb(),"b=");
model_->print("model");
}
}
/* ************************************************************************* */
// Check if two linear factors are equal
bool GaussianFactor::equals(const GaussianFactor& f, double tol) const {
if (empty()) return (f.empty());
if(keys_!=f.keys_ /*|| !model_->equals(lf->model_, tol)*/)
return false;
assert(Ab_.size1() == f.Ab_.size1() && Ab_.size2() == f.Ab_.size2());
ab_type::const_block_type Ab1(Ab_.range(0, Ab_.nBlocks()));
ab_type::const_block_type Ab2(f.Ab_.range(0, f.Ab_.nBlocks()));
for(size_t row=0; row<Ab1.size1(); ++row)
if(!equal_with_abs_tol(ublas::row(Ab1, row), ublas::row(Ab2, row), tol) &&
!equal_with_abs_tol(-ublas::row(Ab1, row), ublas::row(Ab2, row), tol))
return false;
return true;
}
/* ************************************************************************* */
void GaussianFactor::permuteWithInverse(const Permutation& inversePermutation) {
// Build a map from the new variable indices to the old slot positions.
typedef map<size_t, size_t, std::less<size_t>, boost::fast_pool_allocator<std::pair<const size_t, size_t> > > SourceSlots;
SourceSlots sourceSlots;
for(size_t j=0; j<keys_.size(); ++j)
sourceSlots.insert(make_pair(inversePermutation[keys_[j]], j));
// Build a vector of variable dimensions in the new order
vector<size_t> dimensions(keys_.size() + 1);
size_t j = 0;
BOOST_FOREACH(const SourceSlots::value_type& sourceSlot, sourceSlots) {
dimensions[j++] = Ab_(sourceSlot.second).size2();
}
assert(j == keys_.size());
dimensions.back() = 1;
// Copy the variables and matrix into the new order
vector<Index> oldKeys(keys_.size());
keys_.swap(oldKeys);
matrix_type oldMatrix;
ab_type oldAb(oldMatrix, dimensions.begin(), dimensions.end(), Ab_.size1());
Ab_.swap(oldAb);
j = 0;
BOOST_FOREACH(const SourceSlots::value_type& sourceSlot, sourceSlots) {
keys_[j] = sourceSlot.first;
ublas::noalias(Ab_(j++)) = oldAb(sourceSlot.second);
}
ublas::noalias(Ab_(j)) = oldAb(j);
// Since we're permuting the variables, ensure that entire rows from this
// factor are copied when Combine is called
BOOST_FOREACH(size_t& varpos, firstNonzeroBlocks_) { varpos = 0; }
assertInvariants();
}
/* ************************************************************************* */
Vector GaussianFactor::unweighted_error(const VectorValues& c) const {
Vector e = -getb();
if (empty()) return e;
for(size_t pos=0; pos<keys_.size(); ++pos)
e += ublas::prod(Ab_(pos), c[keys_[pos]]);
return e;
}
/* ************************************************************************* */
Vector GaussianFactor::error_vector(const VectorValues& c) const {
if (empty()) return model_->whiten(-getb());
return model_->whiten(unweighted_error(c));
}
/* ************************************************************************* */
double GaussianFactor::error(const VectorValues& c) const {
if (empty()) return 0;
Vector weighted = error_vector(c);
return 0.5 * inner_prod(weighted,weighted);
}
///* ************************************************************************* */
//Dimensions GaussianFactor::dimensions() const {
// Dimensions result;
// BOOST_FOREACH(const NamedMatrix& jA, As_)
// result.insert(std::pair<Index,size_t>(jA.first,jA.second.size2()));
// return result;
//}
//
///* ************************************************************************* */
//void GaussianFactor::tally_separator(Index key, set<Index>& separator) const {
// if(involves(key)) {
// BOOST_FOREACH(const NamedMatrix& jA, As_)
// if(jA.first != key) separator.insert(jA.first);
// }
//}
/* ************************************************************************* */
Vector GaussianFactor::operator*(const VectorValues& x) const {
Vector Ax = zero(Ab_.size1());
if (empty()) return Ax;
// Just iterate over all A matrices and multiply in correct config part
for(size_t pos=0; pos<keys_.size(); ++pos)
Ax += ublas::prod(Ab_(pos), x[keys_[pos]]);
// BOOST_FOREACH(const NamedMatrix& jA, As_)
// Ax += (jA.second * x[jA.first]);
return model_->whiten(Ax);
}
///* ************************************************************************* */
//VectorValues GaussianFactor::operator^(const Vector& e) const {
// Vector E = model_->whiten(e);
// VectorValues x;
// // Just iterate over all A matrices and insert Ai^e into VectorValues
// for(size_t pos=0; pos<keys_.size(); ++pos)
// x.insert(keys_[pos], ARange(pos)^E);
//// BOOST_FOREACH(const NamedMatrix& jA, As_)
//// x.insert(jA.first,jA.second^E);
// return x;
//}
/* ************************************************************************* */
void GaussianFactor::transposeMultiplyAdd(double alpha, const Vector& e,
VectorValues& x) const {
Vector E = alpha * model_->whiten(e);
// Just iterate over all A matrices and insert Ai^e into VectorValues
for(size_t pos=0; pos<keys_.size(); ++pos)
gtsam::transposeMultiplyAdd(1.0, Ab_(pos), E, x[keys_[pos]]);
// BOOST_FOREACH(const NamedMatrix& jA, As_)
// gtsam::transposeMultiplyAdd(1.0, jA.second, E, x[jA.first]);
}
/* ************************************************************************* */
pair<Matrix,Vector> GaussianFactor::matrix(bool weight) const {
Matrix A(Ab_.range(0, keys_.size()));
Vector b(getb());
// divide in sigma so error is indeed 0.5*|Ax-b|
if (weight) model_->WhitenSystem(A,b);
return make_pair(A, b);
}
/* ************************************************************************* */
Matrix GaussianFactor::matrix_augmented(bool weight) const {
if (weight) { Matrix Ab(Ab_.range(0,Ab_.nBlocks())); model_->WhitenInPlace(Ab); return Ab; }
else return Ab_.range(0, Ab_.nBlocks());
}
/* ************************************************************************* */
boost::tuple<list<int>, list<int>, list<double> >
GaussianFactor::sparse(const Dimensions& columnIndices) const {
// declare return values
list<int> I,J;
list<double> S;
// iterate over all matrices in the factor
for(size_t pos=0; pos<keys_.size(); ++pos) {
ab_type::const_block_type A(Ab_(pos));
// find first column index for this key
int column_start = columnIndices.at(keys_[pos]);
for (size_t i = 0; i < A.size1(); i++) {
double sigma_i = model_->sigma(i);
for (size_t j = 0; j < A.size2(); j++)
if (A(i, j) != 0.0) {
I.push_back(i + 1);
J.push_back(j + column_start);
S.push_back(A(i, j) / sigma_i);
}
}
}
// return the result
return boost::tuple<list<int>, list<int>, list<double> >(I,J,S);
}
/* ************************************************************************* */
GaussianConditional::shared_ptr GaussianFactor::eliminateFirst() {
assert(Ab_.rowStart() == 0 && Ab_.rowEnd() == matrix_.size1() && Ab_.firstBlock() == 0);
assert(!keys_.empty());
assertInvariants();
static const bool debug = false;
tic("eliminateFirst");
if(debug) print("Eliminating GaussianFactor: ");
tic("eliminateFirst: stairs");
// Translate the left-most nonzero column indices into top-most zero row indices
vector<long> firstZeroRows(Ab_.size2());
{
size_t lastNonzeroRow = 0;
vector<long>::iterator firstZeroRowsIt = firstZeroRows.begin();
for(size_t var=0; var<keys().size(); ++var) {
while(lastNonzeroRow < this->numberOfRows() && firstNonzeroBlocks_[lastNonzeroRow] <= var)
++ lastNonzeroRow;
fill(firstZeroRowsIt, firstZeroRowsIt+Ab_(var).size2(), lastNonzeroRow);
firstZeroRowsIt += Ab_(var).size2();
}
assert(firstZeroRowsIt+1 == firstZeroRows.end());
*firstZeroRowsIt = this->numberOfRows();
}
toc("eliminateFirst: stairs");
#ifndef NDEBUG
for(size_t col=0; col<Ab_.size2(); ++col) {
if(debug) cout << "Staircase[" << col << "] = " << firstZeroRows[col] << endl;
if(col != 0) assert(firstZeroRows[col] >= firstZeroRows[col-1]);
assert(firstZeroRows[col] <= (long)this->numberOfRows());
}
#endif
if(debug) gtsam::print(matrix_, "Augmented Ab: ");
// Use in-place QR on dense Ab appropriate to NoiseModel
tic("eliminateFirst: QR");
SharedDiagonal noiseModel = model_->QRColumnWise(matrix_, firstZeroRows);
toc("eliminateFirst: QR");
if(matrix_.size1() > 0) {
for(size_t j=0; j<matrix_.size2(); ++j)
for(size_t i=j+1; i<noiseModel->dim(); ++i)
matrix_(i,j) = 0.0;
// ublas::triangular_adaptor<matrix_type, ublas::strict_lower> AbLower(Ab_);
// fill(AbLower.begin1(), AbLower.end1(), 0.0);
}
if(debug) gtsam::print(matrix_, "QR result: ");
size_t firstVarDim = Ab_(0).size2();
// Check for singular factor
if(noiseModel->dim() < firstVarDim) {
throw domain_error((boost::format(
"GaussianFactor is singular in variable %1%, discovered while attempting\n"
"to eliminate this variable.") % keys_.front()).str());
}
// Extract conditional
// todo SL: are we pulling Householder vectors into the conditional and does it matter?
tic("eliminateFirst: cond Rd");
// Temporarily restrict the matrix view to the conditional blocks of the
// eliminated Ab matrix to create the GaussianConditional from it.
Ab_.rowStart() = 0;
Ab_.rowEnd() = firstVarDim;
Ab_.firstBlock() = 0;
const ublas::vector_range<const Vector> sigmas(noiseModel->sigmas(), ublas::range(0, firstVarDim));
GaussianConditional::shared_ptr conditional(new GaussianConditional(
keys_.begin(), keys_.end(), 1, Ab_, sigmas));
toc("eliminateFirst: cond Rd");
if(debug) conditional->print("Extracted conditional: ");
tic("eliminateFirst: remaining factor");
// Take lower-right block of Ab to get the new factor
Ab_.rowStart() = firstVarDim;
Ab_.rowEnd() = noiseModel->dim();
Ab_.firstBlock() = 1;
keys_.erase(keys_.begin());
// Set sigmas with the right model
if (noiseModel->isConstrained())
model_ = noiseModel::Constrained::MixedSigmas(sub(noiseModel->sigmas(), firstVarDim, noiseModel->dim()));
else
model_ = noiseModel::Diagonal::Sigmas(sub(noiseModel->sigmas(), firstVarDim, noiseModel->dim()));
assert(Ab_.size1() <= Ab_.size2()-1);
toc("eliminateFirst: remaining factor");
// todo SL: deal with "dead" pivot columns!!!
tic("eliminateFirst: rowstarts");
size_t varpos = 0;
firstNonzeroBlocks_.resize(this->numberOfRows());
for(size_t row=0; row<numberOfRows(); ++row) {
while(varpos < this->keys_.size() && Ab_.offset(varpos+1) <= row)
++ varpos;
firstNonzeroBlocks_[row] = varpos;
if(debug) cout << "firstNonzeroVars_[" << row << "] = " << firstNonzeroBlocks_[row] << endl;
}
toc("eliminateFirst: rowstarts");
if(debug) print("Eliminated factor: ");
toc("eliminateFirst");
assertInvariants();
return conditional;
}
/* ************************************************************************* */
GaussianBayesNet::shared_ptr GaussianFactor::eliminate(size_t nrFrontals) {
assert(Ab_.rowStart() == 0 && Ab_.rowEnd() == matrix_.size1() && Ab_.firstBlock() == 0);
assert(keys_.size() >= nrFrontals);
assertInvariants();
static const bool debug = false;
tic("eliminate");
if(debug) this->print("Eliminating GaussianFactor: ");
tic("eliminate: stairs");
// Translate the left-most nonzero column indices into top-most zero row indices
vector<long> firstZeroRows(Ab_.size2());
{
size_t lastNonzeroRow = 0;
vector<long>::iterator firstZeroRowsIt = firstZeroRows.begin();
for(size_t var=0; var<keys().size(); ++var) {
while(lastNonzeroRow < this->numberOfRows() && firstNonzeroBlocks_[lastNonzeroRow] <= var)
++ lastNonzeroRow;
fill(firstZeroRowsIt, firstZeroRowsIt+Ab_(var).size2(), lastNonzeroRow);
firstZeroRowsIt += Ab_(var).size2();
}
assert(firstZeroRowsIt+1 == firstZeroRows.end());
*firstZeroRowsIt = this->numberOfRows();
}
toc("eliminate: stairs");
#ifndef NDEBUG
for(size_t col=0; col<Ab_.size2(); ++col) {
if(debug) cout << "Staircase[" << col << "] = " << firstZeroRows[col] << endl;
if(col != 0) assert(firstZeroRows[col] >= firstZeroRows[col-1]);
assert(firstZeroRows[col] <= (long)this->numberOfRows());
}
#endif
if(debug) gtsam::print(matrix_, "Augmented Ab: ");
// Use in-place QR on dense Ab appropriate to NoiseModel
tic("eliminate: QR");
SharedDiagonal noiseModel = model_->QRColumnWise(matrix_, firstZeroRows);
toc("eliminate: QR");
// Zero the lower-left triangle. todo: not all of these entries actually
// need to be zeroed if we are careful to start copying rows after the last
// structural zero.
if(matrix_.size1() > 0) {
for(size_t j=0; j<matrix_.size2(); ++j)
for(size_t i=j+1; i<noiseModel->dim(); ++i)
matrix_(i,j) = 0.0;
// ublas::triangular_adaptor<matrix_type, ublas::strict_lower> AbLower(Ab_);
// fill(AbLower.begin1(), AbLower.end1(), 0.0);
}
if(debug) gtsam::print(matrix_, "QR result: ");
size_t frontalDim = Ab_.range(0,nrFrontals).size2();
// Check for singular factor
if(noiseModel->dim() < frontalDim) {
throw domain_error((boost::format(
"GaussianFactor is singular in variable %1%, discovered while attempting\n"
"to eliminate this variable.") % keys_.front()).str());
}
// Extract conditionals
tic("eliminate: cond Rd");
GaussianBayesNet::shared_ptr conditionals(new GaussianBayesNet());
for(size_t j=0; j<nrFrontals; ++j) {
// Temporarily restrict the matrix view to the conditional blocks of the
// eliminated Ab matrix to create the GaussianConditional from it.
size_t varDim = Ab_(0).size2();
Ab_.rowEnd() = Ab_.rowStart() + varDim;
const ublas::vector_range<const Vector> sigmas(noiseModel->sigmas(), ublas::range(Ab_.rowStart(), Ab_.rowEnd()));
conditionals->push_back(boost::make_shared<Conditional>(keys_.begin()+j, keys_.end(), 1, Ab_, sigmas));
if(debug) conditionals->back()->print("Extracted conditional: ");
Ab_.rowStart() += varDim;
Ab_.firstBlock() += 1;
}
toc("eliminate: cond Rd");
if(debug) conditionals->print("Extracted conditionals: ");
tic("eliminate: remaining factor");
// Take lower-right block of Ab to get the new factor
Ab_.rowEnd() = noiseModel->dim();
keys_.assign(keys_.begin() + nrFrontals, keys_.end());
// Set sigmas with the right model
if (noiseModel->isConstrained())
model_ = noiseModel::Constrained::MixedSigmas(sub(noiseModel->sigmas(), frontalDim, noiseModel->dim()));
else
model_ = noiseModel::Diagonal::Sigmas(sub(noiseModel->sigmas(), frontalDim, noiseModel->dim()));
assert(Ab_.size1() <= Ab_.size2()-1);
if(debug) this->print("Eliminated factor: ");
toc("eliminate: remaining factor");
// todo SL: deal with "dead" pivot columns!!!
tic("eliminate: rowstarts");
size_t varpos = 0;
firstNonzeroBlocks_.resize(this->numberOfRows());
for(size_t row=0; row<numberOfRows(); ++row) {
if(debug) cout << "row " << row << " varpos " << varpos << " Ab_.offset(varpos)=" << Ab_.offset(varpos) << " Ab_.offset(varpos+1)=" << Ab_.offset(varpos+1) << endl;
while(varpos < this->keys_.size() && Ab_.offset(varpos+1) <= row)
++ varpos;
firstNonzeroBlocks_[row] = varpos;
if(debug) cout << "firstNonzeroVars_[" << row << "] = " << firstNonzeroBlocks_[row] << endl;
}
toc("eliminate: rowstarts");
if(debug) print("Eliminated factor: ");
toc("eliminate");
assertInvariants();
return conditionals;
}
/* ************************************************************************* */
/* Used internally by GaussianFactor::Combine for sorting */
struct _RowSource {
size_t firstNonzeroVar;
size_t factorI;
size_t factorRowI;
_RowSource(size_t _firstNonzeroVar, size_t _factorI, size_t _factorRowI) :
firstNonzeroVar(_firstNonzeroVar), factorI(_factorI), factorRowI(_factorRowI) {}
bool operator<(const _RowSource& o) const { return firstNonzeroVar < o.firstNonzeroVar; }
};
/* Explicit instantiations for storage types */
template GaussianFactor::shared_ptr GaussianFactor::Combine(const FactorGraph<GaussianFactor>&, const GaussianVariableIndex<VariableIndexStorage_vector>&, const vector<size_t>&, const vector<Index>&, const std::vector<std::vector<size_t> >&);
template GaussianFactor::shared_ptr GaussianFactor::Combine(const FactorGraph<GaussianFactor>&, const GaussianVariableIndex<VariableIndexStorage_deque>&, const vector<size_t>&, const vector<Index>&, const std::vector<std::vector<size_t> >&);
// Utility function to determine row count and check if any noise models are constrained
// TODO: would be nicer if this function was split and are factorgraph methods
static std::pair<size_t,bool> rowCount(const FactorGraph<GaussianFactor>& factorGraph,
const vector<size_t>& factorIndices)
{
static const bool debug = false;
tic("Combine: count sizes");
size_t m = 0;
bool constrained = false;
BOOST_FOREACH(const size_t i, factorIndices)
{
assert(factorGraph[i] != NULL);
assert(!factorGraph[i]->keys().empty());
m += factorGraph[i]->numberOfRows();
if (debug) cout << "Combining factor " << i << endl;
if (debug) factorGraph[i]->print(" :");
if (!constrained && factorGraph[i]->isConstrained()) {
constrained = true;
if (debug) std::cout << "Found a constraint!" << std::endl;
}
}
toc("Combine: count sizes");
return make_pair(m,constrained);
}
// Determine row and column counts and check if any noise models are constrained
template<class STORAGE>
static vector<size_t> columnDimensions(
const GaussianVariableIndex<STORAGE>& variableIndex,
const vector<Index>& variables)
{
tic("Combine: count dims");
static const bool debug = false;
vector<size_t> dims(variables.size() + 1);
size_t n = 0;
{
size_t j = 0;
BOOST_FOREACH(const Index& var, variables)
{
if (debug) cout << "Have variable " << var << endl;
dims[j] = variableIndex.dim(var);
n += dims[j];
++j;
}
dims[j] = 1;
}
toc("Combine: count dims");
return dims;
}
// To do this, we merge-sort the rows so that the column indices of the first structural
// non-zero in each row increase monotonically.
vector<_RowSource> computeRowPermutation(size_t m, const vector<size_t>& factorIndices,
const FactorGraph<GaussianFactor>& factorGraph) {
vector<_RowSource> rowSources;
rowSources.reserve(m);
size_t i1 = 0;
BOOST_FOREACH(const size_t i2, factorIndices) {
const GaussianFactor& factor(*factorGraph[i2]);
size_t factorRowI = 0;
assert(factor.get_firstNonzeroBlocks().size() == factor.numberOfRows());
BOOST_FOREACH(const size_t factorFirstNonzeroVarpos, factor.get_firstNonzeroBlocks()) {
Index firstNonzeroVar;
firstNonzeroVar = factor.keys()[factorFirstNonzeroVarpos];
rowSources.push_back(_RowSource(firstNonzeroVar, i1, factorRowI));
++ factorRowI;
}
assert(factorRowI == factor.numberOfRows());
++ i1;
}
assert(rowSources.size() == m);
assert(i1 == factorIndices.size());
sort(rowSources.begin(), rowSources.end());
return rowSources;
}
void copyMatrices(boost::shared_ptr<GaussianFactor> combinedFactor, size_t row,
const GaussianFactor& factor, const std::vector<std::vector<size_t> >& variablePositions,
const size_t factorRow, const size_t factorI, const vector<Index>& variables) {
const static bool debug = false;
const size_t factorFirstNonzeroVarpos = factor.get_firstNonzeroBlocks()[factorRow];
std::vector<Index>::const_iterator keyit = factor.keys().begin() + factorFirstNonzeroVarpos;
std::vector<size_t>::const_iterator varposIt = variablePositions[factorI].begin() + factorFirstNonzeroVarpos;
combinedFactor->set_firstNonzeroBlocks(row, *varposIt);
if(debug) cout << " copying starting at varpos " << *varposIt << " (variable " << variables[*varposIt] << ")" << endl;
std::vector<Index>::const_iterator keyitend = factor.keys().end();
while(keyit != keyitend) {
const size_t varpos = *varposIt;
assert(variables[varpos] == *keyit);
GaussianFactor::ab_type::block_type retBlock(combinedFactor->getAb(varpos));
const GaussianFactor::ab_type::const_block_type factorBlock(factor.getA(keyit));
ublas::noalias(ublas::row(retBlock, row)) = ublas::row(factorBlock, factorRow);
++ keyit;
++ varposIt;
}
}
template<class STORAGE>
GaussianFactor::shared_ptr GaussianFactor::Combine(const FactorGraph<GaussianFactor>& factorGraph,
const GaussianVariableIndex<STORAGE>& variableIndex, const vector<size_t>& factorIndices,
const vector<Index>& variables, const std::vector<std::vector<size_t> >& variablePositions) {
// Debugging flags
static const bool verbose = false;
static const bool debug = false;
if (verbose) std::cout << "GaussianFactor::GaussianFactor (factors)" << std::endl;
assert(factorIndices.size() == variablePositions.size());
// Determine row count and check if any noise models are constrained
size_t m; bool constrained;
boost::tie(m,constrained) = rowCount(factorGraph,factorIndices);
// Determine column dimensions
vector<size_t> dims = columnDimensions(variableIndex,variables);
// Allocate return value, the combined factor, the augmented Ab matrix and other arrays
tic("Combine: set up empty");
shared_ptr combinedFactor(boost::make_shared<GaussianFactor>());
combinedFactor->Ab_.copyStructureFrom(ab_type(combinedFactor->matrix_, dims.begin(), dims.end(), m));
ublas::noalias(combinedFactor->matrix_) = ublas::zero_matrix<double>(combinedFactor->matrix_.size1(), combinedFactor->matrix_.size2());
combinedFactor->firstNonzeroBlocks_.resize(m);
Vector sigmas(m);
// Copy keys
combinedFactor->keys_.reserve(variables.size());
combinedFactor->keys_.insert(combinedFactor->keys_.end(), variables.begin(), variables.end());
toc("Combine: set up empty");
// Compute a row permutation that maintains a staircase pattern in the new combined factor.
tic("Combine: sort rows");
vector<_RowSource> rowSources = computeRowPermutation(m, factorIndices, factorGraph);
toc("Combine: sort rows");
// Fill in the rows of the new factor in sorted order. Fill in the array of
// the left-most nonzero for each row and the first structural zero in each column.
// todo SL: smarter ignoring of zero factor variables (store first possible like above)
if(debug) gtsam::print(combinedFactor->matrix_, "matrix_ before copying rows: ");
tic("Combine: copy rows");
#ifndef NDEBUG
size_t lastRowFirstVarpos = 0;
#endif
for(size_t row=0; row<m; ++row) {
const _RowSource& rowSource = rowSources[row];
assert(rowSource.factorI < factorGraph.size());
const size_t factorI = rowSource.factorI;
const GaussianFactor& factor(*factorGraph[factorIndices[factorI]]);
const size_t factorRow = rowSource.factorRowI;
if(debug)
cout << "Combined row " << row << " is from row " << factorRow << " of factor " << factorIndices[factorI] << endl;
// Copy rhs b and sigma
combinedFactor->getb()(row) = factor.getb()(factorRow);
sigmas(row) = factor.get_sigmas()(factorRow);
// Copy the row of A variable by variable, starting at the first nonzero variable.
copyMatrices(combinedFactor, row, factor, variablePositions, factorRow, factorI, variables);
#ifndef NDEBUG
// Debug check, make sure the first column of nonzeros increases monotonically
if(row != 0)
assert(combinedFactor->firstNonzeroBlocks_[row] >= lastRowFirstVarpos);
lastRowFirstVarpos = combinedFactor->firstNonzeroBlocks_[row];
#endif
}
toc("Combine: copy rows");
if (verbose) std::cout << "GaussianFactor::GaussianFactor done" << std::endl;
if (constrained) {
combinedFactor->model_ = noiseModel::Constrained::MixedSigmas(sigmas);
if (verbose) combinedFactor->model_->print("Just created Constraint ^");
} else {
combinedFactor->model_ = noiseModel::Diagonal::Sigmas(sigmas);
if (verbose) combinedFactor->model_->print("Just created Diagonal");
}
if(debug) combinedFactor->print("Combined factor: ");
combinedFactor->assertInvariants();
return combinedFactor;
}
/* ************************************************************************* */
// Helper functions for Combine
boost::tuple<vector<size_t>, size_t, size_t> countDims(const std::vector<GaussianFactor::shared_ptr>& factors, const VariableSlots& variableSlots) {
#ifndef NDEBUG
vector<size_t> varDims(variableSlots.size(), numeric_limits<size_t>::max());
#else
vector<size_t> varDims(variableSlots.size());
#endif
size_t m = 0;
size_t n = 0;
{
Index jointVarpos = 0;
BOOST_FOREACH(const VariableSlots::value_type& slots, variableSlots) {
assert(slots.second.size() == factors.size());
Index sourceFactorI = 0;
BOOST_FOREACH(const Index sourceVarpos, slots.second) {
if(sourceVarpos < numeric_limits<Index>::max()) {
const GaussianFactor& sourceFactor = *factors[sourceFactorI];
size_t vardim = sourceFactor.getDim(sourceFactor.begin() + sourceVarpos);
#ifndef NDEBUG
if(varDims[jointVarpos] == numeric_limits<size_t>::max()) {
varDims[jointVarpos] = vardim;
n += vardim;
} else
assert(varDims[jointVarpos] == vardim);
#else
varDims[jointVarpos] = vardim;
n += vardim;
break;
#endif
}
++ sourceFactorI;
}
++ jointVarpos;
}
BOOST_FOREACH(const GaussianFactor::shared_ptr& factor, factors) {
m += factor->numberOfRows();
}
}
return boost::make_tuple(varDims, m, n);
}
GaussianFactor::shared_ptr GaussianFactor::Combine(const FactorGraph<GaussianFactor>& factors, const VariableSlots& variableSlots) {
static const bool verbose = false;
static const bool debug = false;
if (verbose) std::cout << "GaussianFactor::GaussianFactor (factors)" << std::endl;
if(debug) variableSlots.print();
// Determine dimensions
tic("Combine 1: countDims");
vector<size_t> varDims;
size_t m;
size_t n;
boost::tie(varDims, m, n) = countDims(factors, variableSlots);
if(debug) {
cout << "Dims: " << m << " x " << n << "\n";
BOOST_FOREACH(const size_t dim, varDims) { cout << dim << " "; }
cout << endl;
}
toc("Combine 1: countDims");
// Sort rows
tic("Combine 2: sort rows");
vector<_RowSource> rowSources; rowSources.reserve(m);
bool anyConstrained = false;
for(size_t sourceFactorI = 0; sourceFactorI < factors.size(); ++sourceFactorI) {
const GaussianFactor& sourceFactor(*factors[sourceFactorI]);
for(size_t sourceFactorRow = 0; sourceFactorRow < sourceFactor.numberOfRows(); ++sourceFactorRow) {
Index firstNonzeroVar;
firstNonzeroVar = sourceFactor.keys_[sourceFactor.firstNonzeroBlocks_[sourceFactorRow]];
rowSources.push_back(_RowSource(firstNonzeroVar, sourceFactorI, sourceFactorRow));
}
if(sourceFactor.model_->isConstrained()) anyConstrained = true;
}
assert(rowSources.size() == m);
std::sort(rowSources.begin(), rowSources.end());
toc("Combine 2: sort rows");
// Allocate new factor
tic("Combine 3: allocate");
shared_ptr combined(new GaussianFactor());
combined->keys_.resize(variableSlots.size());
std::transform(variableSlots.begin(), variableSlots.end(), combined->keys_.begin(), bind(&VariableSlots::const_iterator::value_type::first, boost::lambda::_1));
varDims.push_back(1);
combined->Ab_.copyStructureFrom(ab_type(combined->matrix_, varDims.begin(), varDims.end(), m));
combined->firstNonzeroBlocks_.resize(m);
Vector sigmas(m);
toc("Combine 3: allocate");
// Copy rows
tic("Combine 4: copy rows");
Index combinedSlot = 0;
BOOST_FOREACH(const VariableSlots::value_type& varslot, variableSlots) {
for(size_t row = 0; row < m; ++row) {
const Index sourceSlot = varslot.second[rowSources[row].factorI];
ab_type::block_type combinedBlock(combined->Ab_(combinedSlot));
if(sourceSlot != numeric_limits<Index>::max()) {
const GaussianFactor& source(*factors[rowSources[row].factorI]);
const size_t sourceRow = rowSources[row].factorRowI;
if(source.firstNonzeroBlocks_[sourceRow] <= sourceSlot) {
const ab_type::const_block_type sourceBlock(source.Ab_(sourceSlot));
ublas::noalias(ublas::row(combinedBlock, row)) = ublas::row(sourceBlock, sourceRow);
} else
ublas::noalias(ublas::row(combinedBlock, row)) = ublas::zero_vector<double>(combinedBlock.size2());
} else
ublas::noalias(ublas::row(combinedBlock, row)) = ublas::zero_vector<double>(combinedBlock.size2());
}
++ combinedSlot;
}
toc("Combine 4: copy rows");
// Copy rhs (b), sigma, and firstNonzeroBlocks
tic("Combine 5: copy vectors");
Index firstNonzeroSlot = 0;
for(size_t row = 0; row < m; ++row) {
const GaussianFactor& source(*factors[rowSources[row].factorI]);
const size_t sourceRow = rowSources[row].factorRowI;
combined->getb()(row) = source.getb()(sourceRow);
sigmas(row) = source.get_sigmas()(sourceRow);
while(firstNonzeroSlot < variableSlots.size() && rowSources[row].firstNonzeroVar > combined->keys_[firstNonzeroSlot])
++ firstNonzeroSlot;
combined->firstNonzeroBlocks_[row] = firstNonzeroSlot;
}
toc("Combine 5: copy vectors");
// Create noise model from sigmas
tic("Combine 6: noise model");
if(anyConstrained) combined->model_ = noiseModel::Constrained::MixedSigmas(sigmas);
else combined->model_ = noiseModel::Diagonal::Sigmas(sigmas);
toc("Combine 6: noise model");
combined->assertInvariants();
return combined;
}
}
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