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gtsam/gtsam/linear/JacobianFactor.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 JacobianFactor.cpp
* @brief
* @author Richard Roberts
* @created Dec 8, 2010
*/
#include <gtsam/base/timing.h>
#include <gtsam/base/Matrix.h>
#include <gtsam/base/FastMap.h>
#include <gtsam/base/cholesky.h>
#include <gtsam/inference/VariableSlots.h>
#include <gtsam/inference/FactorGraph-inl.h>
#include <gtsam/linear/GaussianConditional.h>
#include <gtsam/linear/JacobianFactor.h>
#include <gtsam/linear/HessianFactor.h>
#include <gtsam/linear/GaussianFactorGraph.h>
#include <boost/foreach.hpp>
#include <boost/format.hpp>
#include <boost/make_shared.hpp>
#include <boost/lambda/bind.hpp>
#include <boost/lambda/lambda.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 <boost/numeric/ublas/blas.hpp>
#include <sstream>
#include <stdexcept>
using namespace std;
namespace ublas = boost::numeric::ublas;
using namespace boost::lambda;
namespace gtsam {
/* ************************************************************************* */
inline void JacobianFactor::assertInvariants() const {
#ifndef NDEBUG
IndexFactor::assertInvariants();
assert((keys_.size() == 0 && Ab_.size1() == 0 && Ab_.nBlocks() == 0) || keys_.size()+1 == Ab_.nBlocks());
#endif
}
/* ************************************************************************* */
JacobianFactor::JacobianFactor(const JacobianFactor& gf) :
GaussianFactor(gf), model_(gf.model_), firstNonzeroBlocks_(gf.firstNonzeroBlocks_), Ab_(matrix_) {
Ab_.assignNoalias(gf.Ab_);
assertInvariants();
}
/* ************************************************************************* */
JacobianFactor::JacobianFactor() : Ab_(matrix_) { assertInvariants(); }
/* ************************************************************************* */
JacobianFactor::JacobianFactor(const Vector& b_in) : firstNonzeroBlocks_(b_in.size(), 0), Ab_(matrix_) {
size_t dims[] = { 1 };
Ab_.copyStructureFrom(BlockAb(matrix_, dims, dims+1, b_in.size()));
getb() = b_in;
assertInvariants();
}
/* ************************************************************************* */
JacobianFactor::JacobianFactor(Index i1, const Matrix& A1,
const Vector& b, const SharedDiagonal& model) :
GaussianFactor(i1), model_(model), firstNonzeroBlocks_(b.size(), 0), Ab_(matrix_) {
size_t dims[] = { A1.size2(), 1};
Ab_.copyStructureFrom(BlockAb(matrix_, dims, dims+2, b.size()));
Ab_(0) = A1;
getb() = b;
assertInvariants();
}
/* ************************************************************************* */
JacobianFactor::JacobianFactor(Index i1, const Matrix& A1, Index i2, const Matrix& A2,
const Vector& b, const SharedDiagonal& model) :
GaussianFactor(i1,i2), model_(model), firstNonzeroBlocks_(b.size(), 0), Ab_(matrix_) {
size_t dims[] = { A1.size2(), A2.size2(), 1};
Ab_.copyStructureFrom(BlockAb(matrix_, dims, dims+3, b.size()));
Ab_(0) = A1;
Ab_(1) = A2;
getb() = b;
assertInvariants();
}
/* ************************************************************************* */
JacobianFactor::JacobianFactor(Index i1, const Matrix& A1, Index i2, const Matrix& A2,
Index i3, const Matrix& A3, const Vector& b, const SharedDiagonal& model) :
GaussianFactor(i1,i2,i3), model_(model), firstNonzeroBlocks_(b.size(), 0), Ab_(matrix_) {
size_t dims[] = { A1.size2(), A2.size2(), A3.size2(), 1};
Ab_.copyStructureFrom(BlockAb(matrix_, dims, dims+4, b.size()));
Ab_(0) = A1;
Ab_(1) = A2;
Ab_(2) = A3;
getb() = b;
assertInvariants();
}
/* ************************************************************************* */
JacobianFactor::JacobianFactor(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(BlockAb(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();
}
/* ************************************************************************* */
JacobianFactor::JacobianFactor(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(BlockAb(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();
}
/* ************************************************************************* */
JacobianFactor::JacobianFactor(const GaussianConditional& cg) : GaussianFactor(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();
}
/* ************************************************************************* */
JacobianFactor::JacobianFactor(const HessianFactor& factor) : Ab_(matrix_) {
keys_ = factor.keys_;
Ab_.assignNoalias(factor.info_);
size_t maxrank = choleskyCareful(matrix_);
Ab_.rowEnd() = maxrank;
model_ = noiseModel::Unit::Create(maxrank);
size_t varpos = 0;
firstNonzeroBlocks_.resize(this->size1());
for(size_t row=0; row<this->size1(); ++row) {
while(varpos < this->keys_.size() && Ab_.offset(varpos+1) <= row)
++ varpos;
firstNonzeroBlocks_[row] = varpos;
}
}
/* ************************************************************************* */
void JacobianFactor::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 JacobianFactor::equals(const GaussianFactor& f_, double tol) const {
if(!dynamic_cast<const JacobianFactor*>(&f_))
return false;
else {
const JacobianFactor& f(static_cast<const JacobianFactor&>(f_));
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());
constABlock Ab1(Ab_.range(0, Ab_.nBlocks()));
constABlock 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 JacobianFactor::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);
AbMatrix oldMatrix;
BlockAb 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 JacobianFactor::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 JacobianFactor::error_vector(const VectorValues& c) const {
if (empty()) return model_->whiten(-getb());
return model_->whiten(unweighted_error(c));
}
/* ************************************************************************* */
double JacobianFactor::error(const VectorValues& c) const {
if (empty()) return 0;
Vector weighted = error_vector(c);
return 0.5 * inner_prod(weighted,weighted);
}
/* ************************************************************************* */
Vector JacobianFactor::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]]);
return model_->whiten(Ax);
}
/* ************************************************************************* */
void JacobianFactor::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]]);
}
/* ************************************************************************* */
pair<Matrix,Vector> JacobianFactor::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 JacobianFactor::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> >
JacobianFactor::sparse(const map<Index,size_t>& 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) {
constABlock 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);
}
/* ************************************************************************* */
JacobianFactor JacobianFactor::whiten() const {
JacobianFactor result(*this);
result.model_->WhitenInPlace(result.matrix_);
result.model_ = noiseModel::Unit::Create(result.model_->dim());
return result;
}
/* ************************************************************************* */
GaussianConditional::shared_ptr JacobianFactor::eliminateFirst() {
return this->eliminate(1)->front();
}
/* ************************************************************************* */
GaussianBayesNet::shared_ptr JacobianFactor::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) cout << "Eliminating " << nrFrontals << " frontal variables" << endl;
if(debug) this->print("Eliminating JacobianFactor: ");
tic("eliminate: stairs");
// Translate the left-most nonzero column indices into top-most zero row indices
vector<int> firstZeroRows(Ab_.size2());
{
size_t lastNonzeroRow = 0;
vector<int>::iterator firstZeroRowsIt = firstZeroRows.begin();
for(size_t var=0; var<keys().size(); ++var) {
while(lastNonzeroRow < this->size1() && firstNonzeroBlocks_[lastNonzeroRow] <= var)
++ lastNonzeroRow;
fill(firstZeroRowsIt, firstZeroRowsIt+Ab_(var).size2(), lastNonzeroRow);
firstZeroRowsIt += Ab_(var).size2();
}
assert(firstZeroRowsIt+1 == firstZeroRows.end());
*firstZeroRowsIt = this->size1();
}
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->size1());
}
#endif
if(debug) gtsam::print(matrix_, "Augmented Ab: ");
size_t frontalDim = Ab_.range(0,nrFrontals).size2();
if(debug) cout << "frontalDim = " << frontalDim << endl;
// Use in-place QR or Cholesky on dense Ab appropriate to NoiseModel
tic("eliminateFirst: QR");
SharedDiagonal noiseModel = model_->QRColumnWise(matrix_, firstZeroRows);
toc("eliminateFirst: 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;
}
if(debug) gtsam::print(matrix_, "QR result: ");
if(debug) noiseModel->print("QR result noise model: ");
// Check for singular factor
if(noiseModel->dim() < frontalDim) {
throw domain_error((boost::format(
"JacobianFactor 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()));
if(debug) this->print("Eliminated factor: ");
assert(Ab_.size1() <= Ab_.size2()-1);
toc("eliminate: remaining factor");
// todo SL: deal with "dead" pivot columns!!!
tic("eliminate: rowstarts");
size_t varpos = 0;
firstNonzeroBlocks_.resize(this->size1());
for(size_t row=0; row<size1(); ++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 JacobianFactor::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; }
};
/* ************************************************************************* */
// Helper functions for Combine
static boost::tuple<vector<size_t>, size_t, size_t> countDims(const std::vector<JacobianFactor::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 JacobianFactor& 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 JacobianFactor::shared_ptr& factor, factors) {
m += factor->size1();
}
}
return boost::make_tuple(varDims, m, n);
}
/* ************************************************************************* */
JacobianFactor::shared_ptr JacobianFactor::Combine(const FactorGraph<JacobianFactor>& factors, const VariableSlots& variableSlots) {
static const bool verbose = false;
static const bool debug = false;
if (verbose) std::cout << "JacobianFactor::JacobianFactor (factors)" << std::endl;
if(debug) factors.print("Combining factors: ");
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 JacobianFactor& sourceFactor(*factors[sourceFactorI]);
for(size_t sourceFactorRow = 0; sourceFactorRow < sourceFactor.size1(); ++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 JacobianFactor());
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(BlockAb(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];
ABlock combinedBlock(combined->Ab_(combinedSlot));
if(sourceSlot != numeric_limits<Index>::max()) {
const JacobianFactor& source(*factors[rowSources[row].factorI]);
const size_t sourceRow = rowSources[row].factorRowI;
if(source.firstNonzeroBlocks_[sourceRow] <= sourceSlot) {
const constABlock 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 JacobianFactor& source(*factors[rowSources[row].factorI]);
const size_t sourceRow = rowSources[row].factorRowI;
combined->getb()(row) = source.getb()(sourceRow);
sigmas(row) = source.get_model()->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;
}
/* ************************************************************************* */
pair<GaussianBayesNet::shared_ptr, JacobianFactor::shared_ptr> JacobianFactor::CombineAndEliminate(
const FactorGraph<JacobianFactor>& factors, size_t nrFrontals) {
shared_ptr jointFactor(Combine(factors, VariableSlots(factors)));
GaussianBayesNet::shared_ptr gbn(jointFactor->eliminate(nrFrontals));
return make_pair(gbn, jointFactor);
}
/* ************************************************************************* */
Errors operator*(const FactorGraph<JacobianFactor>& fg, const VectorValues& x) {
Errors e;
BOOST_FOREACH(const JacobianFactor::shared_ptr& Ai, fg) {
e.push_back((*Ai)*x);
}
return e;
}
/* ************************************************************************* */
void multiplyInPlace(const FactorGraph<JacobianFactor>& fg, const VectorValues& x, Errors& e) {
multiplyInPlace(fg,x,e.begin());
}
/* ************************************************************************* */
void multiplyInPlace(const FactorGraph<JacobianFactor>& fg, const VectorValues& x, const Errors::iterator& e) {
Errors::iterator ei = e;
BOOST_FOREACH(const JacobianFactor::shared_ptr& Ai, fg) {
*ei = (*Ai)*x;
ei++;
}
}
/* ************************************************************************* */
// x += alpha*A'*e
void transposeMultiplyAdd(const FactorGraph<JacobianFactor>& fg, double alpha, const Errors& e, VectorValues& x) {
// For each factor add the gradient contribution
Errors::const_iterator ei = e.begin();
BOOST_FOREACH(const JacobianFactor::shared_ptr& Ai, fg) {
Ai->transposeMultiplyAdd(alpha,*(ei++),x);
}
}
/* ************************************************************************* */
VectorValues gradient(const FactorGraph<JacobianFactor>& fg, const VectorValues& x) {
// It is crucial for performance to make a zero-valued clone of x
VectorValues g = VectorValues::zero(x);
Errors e;
BOOST_FOREACH(const JacobianFactor::shared_ptr& factor, fg) {
e.push_back(factor->error_vector(x));
}
transposeMultiplyAdd(fg, 1.0, e, g);
return g;
}
/* ************************************************************************* */
void residual(const FactorGraph<JacobianFactor>& fg, const VectorValues &x, VectorValues &r) {
Index i = 0 ;
BOOST_FOREACH(const JacobianFactor::shared_ptr& factor, fg) {
r[i] = factor->getb();
i++;
}
VectorValues Ax = VectorValues::SameStructure(r);
multiply(fg,x,Ax);
axpy(-1.0,Ax,r);
}
/* ************************************************************************* */
void multiply(const FactorGraph<JacobianFactor>& fg, const VectorValues &x, VectorValues &r) {
r.makeZero();
Index i = 0;
BOOST_FOREACH(const JacobianFactor::shared_ptr& factor, fg) {
for(JacobianFactor::const_iterator j = factor->begin(); j != factor->end(); ++j) {
r[i] += prod(factor->getA(j), x[*j]);
}
++i;
}
}
/* ************************************************************************* */
void transposeMultiply(const FactorGraph<JacobianFactor>& fg, const VectorValues &r, VectorValues &x) {
x.makeZero();
Index i = 0;
BOOST_FOREACH(const JacobianFactor::shared_ptr& factor, fg) {
for(JacobianFactor::const_iterator j = factor->begin(); j != factor->end(); ++j) {
x[*j] += prod(trans(factor->getA(j)), r[i]);
}
++i;
}
}
}
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