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cleanup

release/4.3a0
Rahul Sawhney 2010-10-21 23:20:18 +00:00
parent 50547af231
commit 7b414ce6f7
9 changed files with 2 additions and 121 deletions
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13
linear/GaussianFactorGraph.cpp
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@ -26,7 +26,7 @@
#include <gtsam/inference/FactorGraph-inl.h>
#include <gtsam/inference/inference-inl.h>
#include <gtsam/linear/iterative.h>
//#include <gtsam/linear/GaussianJunctionTree.h>
using namespace std;
using namespace gtsam;
@ -104,17 +104,6 @@ void GaussianFactorGraph::multiplyInPlace(const VectorValues& x,
}
}
///* ************************************************************************* */
//VectorValues GaussianFactorGraph::operator^(const Errors& e) const {
// VectorValues x;
// // For each factor add the gradient contribution
// Errors::const_iterator it = e.begin();
// BOOST_FOREACH(const sharedFactor& Ai,factors_) {
// VectorValues xi = (*Ai)^(*(it++));
// x.insertAdd(xi);
// }
// return x;
//}
/* ************************************************************************* */
// x += alpha*A'*e

24
linear/GaussianFactorGraph.h
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@ -117,9 +117,6 @@ namespace gtsam {
/* In-place version e <- A*x that takes an iterator. */
void multiplyInPlace(const VectorValues& x, const Errors::iterator& e) const;
// /** return A^e */
// VectorValues operator^(const Errors& e) const;
/** x += alpha*A'*e */
void transposeMultiplyAdd(double alpha, const Errors& e, VectorValues& x) const;
@ -270,28 +267,7 @@ namespace gtsam {
dims_[*var] = factor->getDim(var);
}
}
// for(Index var=0; var<dims_.size(); ++var) {
//#ifndef NDEBUG
// if(var >= varIndex.dims_.size() || varIndex.dims_[var] == 0)
// assert(dims_[var] != 0);
// else if(varIndex.dims_[var] != 0 && dims_[var] != 0)
// assert(dims_[var] == varIndex.dims_[var]);
//#endif
// if(dims_[var] == 0)
// dims_[var] = varIndex.dims_[var];
// }
}
// /**
// * Returns the augmented matrix version of a set of factors
// * with the corresponding noiseModel
// * @param factors is the set of factors to combine
// * @param ordering of variables needed for matrix column order
// * @return the augmented matrix and a noise model
// */
// template <class FACTORS>
// std::pair<Matrix, SharedDiagonal> combineFactorsAndCreateMatrix(
// const FACTORS& factors,
// const Ordering& order, const Dimensions& dimensions);
} // namespace gtsam

2
linear/GaussianISAM.cpp
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@ -36,8 +36,6 @@ void optimize(const GaussianISAM::sharedClique& clique, VectorValues& result) {
result[cg->key()] = x; // store result in partial solution
}
BOOST_FOREACH(const GaussianISAM::sharedClique& child, clique->children_) {
// list<GaussianISAM::Clique::shared_ptr>::const_iterator child;
// for (child = clique->children_.begin(); child != clique->children_.end(); child++) {
optimize(child, result);
}
}

3
linear/NoiseModel.h
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@ -190,9 +190,6 @@ namespace gtsam {
}; // Gaussian
// FD: does not work, ambiguous overload :-(
// inline Vector operator*(const Gaussian& R, const Vector& v) {return R.whiten(v);}
/**
* A diagonal noise model implements a diagonal covariance matrix, with the
* elements of the diagonal specified in a Vector. This class has no public

57
linear/SubgraphPreconditioner.cpp
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@ -50,17 +50,6 @@ namespace gtsam {
/* ************************************************************************* */
double SubgraphPreconditioner::error(const VectorValues& y) const {
// Errors e;
// // Use BayesNet order to add y contributions in order
// BOOST_FOREACH(GaussianConditional::shared_ptr cg, *Rc1_) {
// const Symbol& j = cg->key();
// e.push_back(y[j]); // append y
// }
// // Add A2 contribution
// VectorValues x = this->x(y);
// Errors e2 = Ab2_->errors(x);
// e.splice(e.end(), e2);
// return 0.5 * dot(e, e);
Errors e(y);
@ -84,14 +73,6 @@ namespace gtsam {
// Apply operator A, A*y = [I;A2*inv(R1)]*y = [y; A2*inv(R1)*y]
Errors SubgraphPreconditioner::operator*(const VectorValues& y) const {
// Errors e;
//
// // Use BayesNet order to add y contributions in order
// BOOST_FOREACH(GaussianConditional::shared_ptr cg, *Rc1_) {
// const Symbol& j = cg->key();
// e.push_back(y[j]); // append y
// }
Errors e(y);
// Add A2 contribution
@ -107,17 +88,6 @@ namespace gtsam {
// In-place version that overwrites e
void SubgraphPreconditioner::multiplyInPlace(const VectorValues& y, Errors& e) const {
// Errors::iterator ei = e.begin();
// // Use BayesNet order to add y contributions in order
// BOOST_FOREACH(GaussianConditional::shared_ptr cg, *Rc1_) {
// const Symbol& j = cg->key();
// *ei = y[j]; // append y
// ei++;
// }
// // Add A2 contribution
// VectorValues x = y; // TODO avoid ?
// gtsam::backSubstituteInPlace(*Rc1_, x); // x=inv(R1)*y
// Ab2_->multiplyInPlace(x,ei); // use iterator version
Errors::iterator ei = e.begin();
for ( Index i = 0 ; i < y.size() ; ++i, ++ei ) {
@ -134,20 +104,6 @@ namespace gtsam {
// Apply operator A', A'*e = [I inv(R1')*A2']*e = e1 + inv(R1')*A2'*e2
VectorValues SubgraphPreconditioner::operator^(const Errors& e) const {
// VectorValues y;
//
// // Use BayesNet order to remove y contributions in order
// Errors::const_iterator it = e.begin();
// BOOST_FOREACH(GaussianConditional::shared_ptr cg, *Rc1_) {
// const Symbol& j = cg->key();
// const Vector& ej = *(it++);
// y.insert(j,ej);
// }
//
// // get A2 part
// transposeMultiplyAdd2(1.0,it,e.end(),y);
//
// return y;
Errors::const_iterator it = e.begin();
VectorValues y = zero();
@ -162,15 +118,6 @@ namespace gtsam {
void SubgraphPreconditioner::transposeMultiplyAdd
(double alpha, const Errors& e, VectorValues& y) const {
// // Use BayesNet order to remove y contributions in order
// Errors::const_iterator it = e.begin();
// BOOST_FOREACH(GaussianConditional::shared_ptr cg, *Rc1_) {
// const Symbol& j = cg->key();
// const Vector& ej = *(it++);
// axpy(alpha,ej,y[j]);
// }
// // get A2 part
// transposeMultiplyAdd2(alpha,it,e.end(),y);
Errors::const_iterator it = e.begin();
for ( Index i = 0 ; i < y.size() ; ++i, ++it ) {
@ -191,10 +138,6 @@ namespace gtsam {
while (it != end)
e2.push_back(*(it++));
// Old code:
// VectorValues x = *Ab2_ ^ e2; // x = A2'*e2
// y += alpha * gtsam::backSubstituteTranspose(*Rc1_, x); // inv(R1')*x;
// New Code:
VectorValues x = VectorValues::zero(y); // x = 0
Ab2_->transposeMultiplyAdd(1.0,e2,x); // x += A2'*e2
axpy(alpha, gtsam::backSubstituteTranspose(*Rc1_, x), y); // y += alpha*inv(R1')*x

7
linear/SubgraphPreconditioner.h
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@ -55,13 +55,6 @@ namespace gtsam {
*/
SubgraphPreconditioner(sharedFG& Ab1, sharedFG& Ab2, sharedBayesNet& Rc1, sharedValues& xbar);
// std::pair<Matrix,Vector> Ab1(const Ordering& ordering) const { return Ab1_->matrix(ordering); }
// std::pair<Matrix,Vector> Ab2(const Ordering& ordering) const { return Ab2_->matrix(ordering); }
// Matrix A1(const Ordering& ordering) const { return Ab1_->sparse(ordering); }
// Matrix A2(const Ordering& ordering) const { return Ab2_->sparse(Ab1_->columnIndices(ordering)); }
// Vector b1() const { return Ab1_->rhsVector(); }
// Vector b2() const { return Ab2_->rhsVector(); }
// VectorValues assembleValues(const Vector& v, const Ordering& ordering) const { return Ab1_->assembleValues(v, ordering); }
/**
* Add zero-mean i.i.d. Gaussian prior terms to each variable

1
linear/VectorBTree.h
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@ -20,7 +20,6 @@
#pragma once
#include <map>
//#include <boost/serialization/map.hpp>
#include <boost/numeric/ublas/storage.hpp>
#include <gtsam/base/Testable.h>

14
linear/VectorValues.h
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@ -45,11 +45,6 @@ public:
typedef boost::numeric::ublas::vector_range<Vector> mapped_type;
typedef boost::numeric::ublas::vector_range<const Vector> const_mapped_type;
// /**
// * Constructor requires an existing GaussianVariableIndex to get variable
// * dimensions.
// */
// VectorValues(const GaussianVariableIndex& variableIndex);
/**
* Default constructor creates an empty VectorValues. reserve(...) must be
@ -203,15 +198,6 @@ public:
};
//inline VectorValues::VectorValues(const GaussianVariableIndex& variableIndex) : varStarts_(variableIndex.size()+1) {
// size_t varStart = 0;
// varStarts_[0] = 0;
// for(Index var=0; var<variableIndex.size(); ++var) {
// varStart += variableIndex.dim(var);
// varStarts_[var+1] = varStart;
// }
// values_.resize(varStarts_.back(), false);
//}
template<class CONTAINER>
inline VectorValues::VectorValues(const CONTAINER& dimensions) : varStarts_(dimensions.size()+1) {

2
myconfigure
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@ -1,4 +1,4 @@
../configure --prefix=$HOME --with-toolbox=$HOME/toolbox/ CPP="/opt/local/bin/cpp-mp-4.5" CC="/opt/local/bin/gcc-mp-4.5" CXX="/usr/local/bin/gfilt" CFLAGS="-fno-inline -g -Wall" CXXFLAGS="-fno-inline -g -Wall" CPPFLAGS="-D_GLIBCXX_DEBUG" LDFLAGS="-fno-inline -g -Wall" --disable-static --enable-blas --enable-lapack --disable-fast-install
../configure --prefix=$HOME --with-toolbox=$HOME/toolbox/ CFLAGS="-fno-inline -g -Wall" CXXFLAGS="-fno-inline -g -Wall" CPPFLAGS="-D_GLIBCXX_DEBUG" LDFLAGS="-fno-inline -g -Wall" --disable-static --enable-blas --enable-lapack --disable-fast-install
#../configure --prefix=$HOME/borg-simplelinear --with-toolbox=$HOME/toolbox/ --with-boost=/opt/local/include/ CPP="/opt/local/bin/cpp-mp-4.5" CC="/opt/local/bin/gcc-mp-4.5" CXX="/usr/local/bin/gfilt" CXXFLAGS="-fno-inline -g -Wall -D_GLIBCXX_DEBUG -DNDEBUG" CFLAGS="-fno-inline -g -Wall -D_GLIBCXX_DEBUG -DNDEBUG" LDFLAGS="-fno-inline -g -Wall" --disable-static --enable-blas --enable-lapack --disable-fast-install
#cd build && ../configure --prefix=$HOME/borg-simplelinear --with-toolbox=$HOME/toolbox/ --with-boost=/opt/local/include/ CXXFLAGS="-fno-inline -g -DNDEBUG -O3" CFLAGS="-fno-inline -g -DNDEBUG -O3" LDFLAGS="-fno-inline -g -DNDEBUG -O3" --disable-static --enable-blas --enable-lapack
#cd build && ../configure --prefix=$HOME/borg-simplelinear --with-toolbox=$HOME/toolbox/ --with-boost=/opt/local/include/ CXXFLAGS="-DNDEBUG -O3" CFLAGS="-g -DNDEBUG -O3" LDFLAGS="-DNDEBUG -O3" --disable-static --enable-blas --enable-lapack