282 lines
9.4 KiB
C++
282 lines
9.4 KiB
C++
/* ----------------------------------------------------------------------------
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* GTSAM Copyright 2010, Georgia Tech Research Corporation,
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* Atlanta, Georgia 30332-0415
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* All Rights Reserved
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* Authors: Frank Dellaert, et al. (see THANKS for the full author list)
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* See LICENSE for the license information
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* -------------------------------------------------------------------------- */
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/**
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* @file LinearizedFactor.cpp
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* @brief A dummy factor that allows a linear factor to act as a nonlinear factor
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* @author Alex Cunningham
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*/
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#include <gtsam_unstable/nonlinear/LinearizedFactor.h>
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#include <boost/foreach.hpp>
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#include <iostream>
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namespace gtsam {
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/* ************************************************************************* */
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LinearizedGaussianFactor::LinearizedGaussianFactor(const GaussianFactor::shared_ptr& gaussian, const Ordering& ordering, const Values& lin_points) {
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// Extract the keys and linearization points
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BOOST_FOREACH(const Index& idx, gaussian->keys()) {
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// find full symbol
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if (idx < ordering.size()) {
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Key key = ordering.key(idx);
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// extract linearization point
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assert(lin_points.exists(key));
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this->lin_points_.insert(key, lin_points.at(key));
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// store keys
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this->keys_.push_back(key);
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} else {
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throw std::runtime_error("LinearizedGaussianFactor: could not find index in decoder!");
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}
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}
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}
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/* ************************************************************************* */
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LinearizedJacobianFactor::LinearizedJacobianFactor() : Ab_(matrix_) {
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}
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/* ************************************************************************* */
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LinearizedJacobianFactor::LinearizedJacobianFactor(const JacobianFactor::shared_ptr& jacobian,
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const Ordering& ordering, const Values& lin_points)
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: Base(jacobian, ordering, lin_points), Ab_(matrix_) {
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// Get the Ab matrix from the Jacobian factor, with any covariance baked in
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AbMatrix fullMatrix = jacobian->matrix_augmented(true);
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// Create the dims array
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size_t dims[jacobian->size() + 1];
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size_t index = 0;
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for(JacobianFactor::const_iterator iter = jacobian->begin(); iter != jacobian->end(); ++iter) {
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dims[index++] = jacobian->getDim(iter);
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}
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dims[index] = 1;
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// Update the BlockInfo accessor
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BlockAb Ab(fullMatrix, dims, dims+jacobian->size()+1);
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Ab.swap(Ab_);
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}
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/* ************************************************************************* */
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void LinearizedJacobianFactor::print(const std::string& s, const KeyFormatter& keyFormatter) const {
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std::cout << s << std::endl;
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std::cout << "Nonlinear Keys: ";
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BOOST_FOREACH(const Key& key, this->keys())
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std::cout << keyFormatter(key) << " ";
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std::cout << std::endl;
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for(const_iterator key=begin(); key!=end(); ++key)
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std::cout << boost::format("A[%1%]=\n")%keyFormatter(*key) << A(*key) << std::endl;
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std::cout << "b=\n" << b() << std::endl;
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lin_points_.print("Linearization Point: ");
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}
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/* ************************************************************************* */
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bool LinearizedJacobianFactor::equals(const NonlinearFactor& expected, double tol) const {
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const This *e = dynamic_cast<const This*> (&expected);
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if (e) {
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Matrix thisMatrix = this->Ab_.range(0, Ab_.nBlocks());
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Matrix rhsMatrix = e->Ab_.range(0, Ab_.nBlocks());
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return Base::equals(expected, tol)
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&& lin_points_.equals(e->lin_points_, tol)
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&& equal_with_abs_tol(thisMatrix, rhsMatrix, tol);
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} else {
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return false;
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}
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}
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/* ************************************************************************* */
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double LinearizedJacobianFactor::error(const Values& c) const {
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Vector errorVector = error_vector(c);
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return 0.5 * errorVector.dot(errorVector);
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}
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/* ************************************************************************* */
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boost::shared_ptr<GaussianFactor>
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LinearizedJacobianFactor::linearize(const Values& c, const Ordering& ordering) const {
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// Create the 'terms' data structure for the Jacobian constructor
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std::vector<std::pair<Index, Matrix> > terms;
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BOOST_FOREACH(Key key, keys()) {
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terms.push_back(std::make_pair(ordering[key], this->A(key)));
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}
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// compute rhs
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Vector b = -error_vector(c);
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return boost::shared_ptr<GaussianFactor>(new JacobianFactor(terms, b, noiseModel::Unit::Create(dim())));
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}
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/* ************************************************************************* */
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Vector LinearizedJacobianFactor::error_vector(const Values& c) const {
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Vector errorVector = -b();
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BOOST_FOREACH(Key key, this->keys()) {
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const Value& newPt = c.at(key);
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const Value& linPt = lin_points_.at(key);
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Vector d = linPt.localCoordinates_(newPt);
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const constABlock A = this->A(key);
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errorVector += A * d;
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}
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return errorVector;
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}
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/* ************************************************************************* */
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LinearizedHessianFactor::LinearizedHessianFactor() : info_(matrix_) {
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}
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/* ************************************************************************* */
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LinearizedHessianFactor::LinearizedHessianFactor(const HessianFactor::shared_ptr& hessian,
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const Ordering& ordering, const Values& lin_points)
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: Base(hessian, ordering, lin_points), info_(matrix_) {
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// Copy the augmented matrix holding G, g, and f
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Matrix fullMatrix = hessian->info();
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// Create the dims array
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size_t dims[hessian->size() + 1];
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size_t index = 0;
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for(HessianFactor::const_iterator iter = hessian->begin(); iter != hessian->end(); ++iter) {
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dims[index++] = hessian->getDim(iter);
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}
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dims[index] = 1;
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// Update the BlockInfo accessor
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BlockInfo infoMatrix(fullMatrix, dims, dims+hessian->size()+1);
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infoMatrix.swap(info_);
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}
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/* ************************************************************************* */
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void LinearizedHessianFactor::print(const std::string& s, const KeyFormatter& keyFormatter) const {
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std::cout << s << std::endl;
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std::cout << "Nonlinear Keys: ";
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BOOST_FOREACH(const Key& key, this->keys())
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std::cout << keyFormatter(key) << " ";
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std::cout << std::endl;
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gtsam::print(Matrix(info_.range(0,info_.nBlocks(), 0,info_.nBlocks()).selfadjointView<Eigen::Upper>()), "Ab^T * Ab: ");
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lin_points_.print("Linearization Point: ");
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}
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/* ************************************************************************* */
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bool LinearizedHessianFactor::equals(const NonlinearFactor& expected, double tol) const {
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const This *e = dynamic_cast<const This*> (&expected);
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if (e) {
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Matrix thisMatrix = this->info_.full().selfadjointView<Eigen::Upper>();
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thisMatrix(thisMatrix.rows()-1, thisMatrix.cols()-1) = 0.0;
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Matrix rhsMatrix = e->info_.full().selfadjointView<Eigen::Upper>();
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rhsMatrix(rhsMatrix.rows()-1, rhsMatrix.cols()-1) = 0.0;
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return Base::equals(expected, tol)
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&& lin_points_.equals(e->lin_points_, tol)
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&& equal_with_abs_tol(thisMatrix, rhsMatrix, tol);
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} else {
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return false;
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}
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}
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/* ************************************************************************* */
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double LinearizedHessianFactor::error(const Values& c) const {
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// Construct an error vector in key-order from the Values
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Vector dx = zero(dim());
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size_t index = 0;
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for(unsigned int i = 0; i < this->size(); ++i){
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Key key = this->keys()[i];
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const Value& newPt = c.at(key);
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const Value& linPt = lin_points_.at(key);
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dx.segment(index, linPt.dim()) = linPt.localCoordinates_(newPt);
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index += linPt.dim();
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}
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// error 0.5*(f - 2*x'*g + x'*G*x)
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double f = constantTerm();
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double xtg = dx.dot(linearTerm());
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double xGx = dx.transpose() * squaredTerm().selfadjointView<Eigen::Upper>() * dx;
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return 0.5 * (f - 2.0 * xtg + xGx);
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}
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/* ************************************************************************* */
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boost::shared_ptr<GaussianFactor>
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LinearizedHessianFactor::linearize(const Values& c, const Ordering& ordering) const {
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// Use the ordering to convert the keys into indices;
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std::vector<Index> js;
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BOOST_FOREACH(Key key, this->keys()){
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js.push_back(ordering.at(key));
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}
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// Make a copy of the info matrix
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Matrix newMatrix;
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SymmetricBlockView<Matrix> newInfo(newMatrix);
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newInfo.assignNoalias(info_);
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// Construct an error vector in key-order from the Values
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Vector dx = zero(dim());
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size_t index = 0;
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for(unsigned int i = 0; i < this->size(); ++i){
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Key key = this->keys()[i];
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const Value& newPt = c.at(key);
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const Value& linPt = lin_points_.at(key);
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dx.segment(index, linPt.dim()) = linPt.localCoordinates_(newPt);
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index += linPt.dim();
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}
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// f2 = f1 - 2*dx'*g1 + dx'*G1*dx
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//newInfo(this->size(), this->size())(0,0) += -2*dx.dot(linearTerm()) + dx.transpose() * squaredTerm().selfadjointView<Eigen::Upper>() * dx;
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double f = constantTerm() - 2*dx.dot(linearTerm()) + dx.transpose() * squaredTerm().selfadjointView<Eigen::Upper>() * dx;
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// g2 = g1 - G1*dx
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//newInfo.rangeColumn(0, this->size(), this->size(), 0) -= squaredTerm().selfadjointView<Eigen::Upper>() * dx;
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Vector g = linearTerm() - squaredTerm().selfadjointView<Eigen::Upper>() * dx;
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std::vector<Vector> gs;
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for(size_t i = 0; i < info_.nBlocks()-1; ++i) {
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gs.push_back(g.segment(info_.offset(i), info_.offset(i+1) - info_.offset(i)));
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}
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// G2 = G1
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// Do Nothing
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std::vector<Matrix> Gs;
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for(size_t i = 0; i < info_.nBlocks()-1; ++i) {
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for(size_t j = i; j < info_.nBlocks()-1; ++j) {
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Gs.push_back(info_(i,j));
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}
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}
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// Create a Hessian Factor from the modified info matrix
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//return boost::shared_ptr<GaussianFactor>(new HessianFactor(js, newInfo));
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return boost::shared_ptr<GaussianFactor>(new HessianFactor(js, Gs, gs, f));
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}
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} // \namespace aspn
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