403 lines
14 KiB
C++
403 lines
14 KiB
C++
/**
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* @file GaussianFactorGraph.h
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* @brief Linear Factor Graph where all factors are Gaussians
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* @author Kai Ni
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* @author Christian Potthast
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* @author Alireza Fathi
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*/
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// $Id: GaussianFactorGraph.h,v 1.24 2009/08/14 20:48:51 acunning Exp $
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// \callgraph
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#pragma once
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#include <boost/shared_ptr.hpp>
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#include <gtsam/inference/FactorGraph.h>
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#include <gtsam/linear/Errors.h>
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#include <gtsam/linear/GaussianFactor.h>
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#include <gtsam/linear/GaussianBayesNet.h>
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namespace gtsam {
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/**
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* A Linear Factor Graph is a factor graph where all factors are Gaussian, i.e.
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* Factor == GaussianFactor
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* VectorConfig = A configuration of vectors
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* Most of the time, linear factor graphs arise by linearizing a non-linear factor graph.
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*/
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class GaussianFactorGraph : public FactorGraph<GaussianFactor> {
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public:
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typedef boost::shared_ptr<GaussianFactorGraph> shared_ptr;
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typedef GaussianBayesNet bayesnet_type;
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typedef GaussianVariableIndex<> variableindex_type;
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/**
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* Default constructor
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*/
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GaussianFactorGraph() {}
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/**
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* Constructor that receives a Chordal Bayes Net and returns a GaussianFactorGraph
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*/
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GaussianFactorGraph(const GaussianBayesNet& CBN);
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/** Add a null factor */
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inline void add(const Vector& b) {
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push_back(sharedFactor(new GaussianFactor(b)));
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}
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/** Add a unary factor */
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inline void add(varid_t key1, const Matrix& A1,
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const Vector& b, const SharedDiagonal& model) {
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push_back(sharedFactor(new GaussianFactor(key1,A1,b,model)));
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}
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/** Add a binary factor */
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inline void add(varid_t key1, const Matrix& A1,
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varid_t key2, const Matrix& A2,
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const Vector& b, const SharedDiagonal& model) {
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push_back(sharedFactor(new GaussianFactor(key1,A1,key2,A2,b,model)));
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}
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/** Add a ternary factor */
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inline void add(varid_t key1, const Matrix& A1,
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varid_t key2, const Matrix& A2,
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varid_t key3, const Matrix& A3,
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const Vector& b, const SharedDiagonal& model) {
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push_back(sharedFactor(new GaussianFactor(key1,A1,key2,A2,key3,A3,b,model)));
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}
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/** Add an n-ary factor */
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inline void add(const std::vector<std::pair<varid_t, Matrix> > &terms,
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const Vector &b, const SharedDiagonal& model) {
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push_back(sharedFactor(new GaussianFactor(terms,b,model)));
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}
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/**
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* Return the set of variables involved in the factors (computes a set
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* union).
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*/
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std::set<varid_t, std::less<varid_t>, boost::fast_pool_allocator<varid_t> > keys() const;
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/** Permute the variables in the factors */
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void permuteWithInverse(const Permutation& inversePermutation);
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/** return A*x-b */
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Errors errors(const VectorConfig& x) const;
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/** shared pointer version */
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boost::shared_ptr<Errors> errors_(const VectorConfig& x) const;
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/** unnormalized error */
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double error(const VectorConfig& x) const;
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/** return A*x */
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Errors operator*(const VectorConfig& x) const;
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/* In-place version e <- A*x that overwrites e. */
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void multiplyInPlace(const VectorConfig& x, Errors& e) const;
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/* In-place version e <- A*x that takes an iterator. */
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void multiplyInPlace(const VectorConfig& x, const Errors::iterator& e) const;
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// /** return A^e */
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// VectorConfig operator^(const Errors& e) const;
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/** x += alpha*A'*e */
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void transposeMultiplyAdd(double alpha, const Errors& e, VectorConfig& x) const;
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// /**
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// * Calculate Gradient of A^(A*x-b) for a given config
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// * @param x: VectorConfig specifying where to calculate gradient
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// * @return gradient, as a VectorConfig as well
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// */
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// VectorConfig gradient(const VectorConfig& x) const;
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/** Unnormalized probability. O(n) */
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double probPrime(const VectorConfig& c) const {
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return exp(-0.5 * error(c));
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}
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// /**
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// * find the separator, i.e. all the nodes that have at least one
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// * common factor with the given node. FD: not used AFAIK.
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// */
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// std::set<varid_t> find_separator(varid_t key) const;
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// /**
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// * Peforms a supposedly-faster (fewer matrix copy) version of elimination
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// * CURRENTLY IN TESTING
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// */
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// GaussianConditional::shared_ptr eliminateOneMatrixJoin(varid_t key);
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//
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//
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// /**
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// * Eliminate multiple variables at once, mostly used to eliminate frontal variables
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// */
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// GaussianBayesNet eliminateFrontals(const Ordering& frontals);
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// /**
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// * optimize a linear factor graph
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// * @param ordering fg in order
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// * @param enableJoinFactor uses the older joint factor combine process when true,
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// * and when false uses the newer single matrix combine
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// */
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// VectorConfig optimize(const Ordering& ordering, bool enableJoinFactor = true);
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// /**
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// * optimize a linear factor graph using a multi-frontal solver
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// * @param ordering fg in order
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// */
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// VectorConfig optimizeMultiFrontals(const Ordering& ordering);
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// /**
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// * shared pointer versions for MATLAB
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// */
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// boost::shared_ptr<GaussianBayesNet> eliminate_(const Ordering&);
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// boost::shared_ptr<VectorConfig> optimize_(const Ordering&);
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/**
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* static function that combines two factor graphs
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* @param const &lfg1 Linear factor graph
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* @param const &lfg2 Linear factor graph
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* @return a new combined factor graph
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*/
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static GaussianFactorGraph combine2(const GaussianFactorGraph& lfg1,
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const GaussianFactorGraph& lfg2);
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/**
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* combine two factor graphs
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* @param *lfg Linear factor graph
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*/
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void combine(const GaussianFactorGraph &lfg);
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// /**
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// * Find all variables and their dimensions
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// * @return The set of all variable/dimension pairs
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// */
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// Dimensions dimensions() const;
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/**
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* Add zero-mean i.i.d. Gaussian prior terms to each variable
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* @param sigma Standard deviation of Gaussian
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*/
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GaussianFactorGraph add_priors(double sigma, const GaussianVariableIndex<>& variableIndex) const;
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// /**
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// * Return RHS (b./sigmas) as Errors class
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// */
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// Errors rhs() const;
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//
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// /**
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// * Return RHS (b./sigmas) as Vector
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// */
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// Vector rhsVector() const;
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//
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// /**
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// * Return (dense) matrix associated with factor graph
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// * @param ordering of variables needed for matrix column order
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// */
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// std::pair<Matrix,Vector> matrix (const Ordering& ordering) const;
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// /**
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// * split the source vector w.r.t. the given ordering and assemble a vector config
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// * @param v: the source vector
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// * @param ordeirng: the ordering corresponding to the vector
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// */
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// VectorConfig assembleConfig(const Vector& v, const Ordering& ordering) const;
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//
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// /**
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// * get the 1-based starting column indices for all variables
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// * @param ordering of variables needed for matrix column order
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// * @return The set of all variable/index pairs
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// */
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// std::pair<Dimensions, size_t> columnIndices_(const Ordering& ordering) const;
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// Dimensions columnIndices(const Ordering& ordering) const;
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//
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// /**
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// * return the size of corresponding A matrix
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// */
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// std::pair<std::size_t, std::size_t> sizeOfA() const;
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// /**
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// * Return 3*nzmax matrix where the rows correspond to the vectors i, j, and s
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// * to generate an m-by-n sparse matrix, which can be given to MATLAB's sparse function.
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// * The standard deviations are baked into A and b
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// * @param ordering of variables needed for matrix column order
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// */
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// Matrix sparse(const Ordering& ordering) const;
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//
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// /**
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// * Version that takes column indices rather than ordering
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// */
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// Matrix sparse(const Dimensions& indices) const;
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// /**
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// * Find solution using gradient descent
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// * @param x0: VectorConfig specifying initial estimate
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// * @return solution
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// */
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// VectorConfig steepestDescent(const VectorConfig& x0, bool verbose = false,
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// double epsilon = 1e-3, size_t maxIterations = 0) const;
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//
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// /**
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// * shared pointer versions for MATLAB
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// */
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// boost::shared_ptr<VectorConfig>
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// steepestDescent_(const VectorConfig& x0, bool verbose = false,
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// double epsilon = 1e-3, size_t maxIterations = 0) const;
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//
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// /**
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// * Find solution using conjugate gradient descent
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// * @param x0: VectorConfig specifying initial estimate
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// * @return solution
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// */
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// VectorConfig conjugateGradientDescent(const VectorConfig& x0, bool verbose =
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// false, double epsilon = 1e-3, size_t maxIterations = 0) const;
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//
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// /**
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// * shared pointer versions for MATLAB
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// */
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// boost::shared_ptr<VectorConfig> conjugateGradientDescent_(
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// const VectorConfig& x0, bool verbose = false, double epsilon = 1e-3,
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// size_t maxIterations = 0) const;
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};
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/* ************************************************************************* */
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template<class VariableIndexStorage>
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class GaussianVariableIndex : public VariableIndex<VariableIndexStorage> {
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public:
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typedef VariableIndex<VariableIndexStorage> Base;
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typedef typename VariableIndexStorage::template type_factory<size_t>::type storage_type;
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storage_type dims_;
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public:
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typedef boost::shared_ptr<GaussianVariableIndex> shared_ptr;
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/** Construct an empty GaussianVariableIndex */
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GaussianVariableIndex() {}
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/**
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* Constructor from a GaussianFactorGraph, lets the base class build the
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* column-wise index then fills the dims_ array.
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*/
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GaussianVariableIndex(const GaussianFactorGraph& factorGraph);
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/**
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* Constructor to "upgrade" from the base class without recomputing the
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* column index, i.e. just fills the dims_ array.
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*/
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GaussianVariableIndex(const VariableIndex<VariableIndexStorage>& variableIndex, const GaussianFactorGraph& factorGraph);
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/**
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* Another constructor to upgrade from the base class using an existing
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* array of variable dimensions.
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*/
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GaussianVariableIndex(const VariableIndex<VariableIndexStorage>& variableIndex, const storage_type& dimensions);
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const storage_type& dims() const { return dims_; }
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size_t dim(varid_t variable) const { Base::checkVar(variable); return dims_[variable]; }
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/** Permute */
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void permute(const Permutation& permutation);
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/** Augment this variable index with the contents of another one */
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void augment(const GaussianFactorGraph& factorGraph);
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protected:
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GaussianVariableIndex(size_t nVars) : Base(nVars), dims_(nVars) {}
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void fillDims(const GaussianFactorGraph& factorGraph);
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};
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/* ************************************************************************* */
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template<class Storage>
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GaussianVariableIndex<Storage>::GaussianVariableIndex(const GaussianFactorGraph& factorGraph) :
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Base(factorGraph), dims_(Base::index_.size()) {
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fillDims(factorGraph); }
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/* ************************************************************************* */
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template<class Storage>
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GaussianVariableIndex<Storage>::GaussianVariableIndex(
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const VariableIndex<Storage>& variableIndex, const GaussianFactorGraph& factorGraph) :
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Base(variableIndex), dims_(Base::index_.size()) {
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fillDims(factorGraph); }
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/* ************************************************************************* */
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template<class Storage>
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GaussianVariableIndex<Storage>::GaussianVariableIndex(
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const VariableIndex<Storage>& variableIndex, const storage_type& dimensions) :
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Base(variableIndex), dims_(dimensions) {
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assert(Base::index_.size() == dims_.size()); }
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/* ************************************************************************* */
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template<class Storage>
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void GaussianVariableIndex<Storage>::fillDims(const GaussianFactorGraph& factorGraph) {
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// Store dimensions of each variable
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assert(dims_.size() == Base::index_.size());
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for(varid_t var=0; var<Base::index_.size(); ++var)
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if(!Base::index_[var].empty()) {
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size_t factorIndex = Base::operator [](var).front().factorIndex;
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size_t variablePosition = Base::operator [](var).front().variablePosition;
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boost::shared_ptr<const GaussianFactor> factor(factorGraph[factorIndex]);
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dims_[var] = factor->getDim(factor->begin() + variablePosition);
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} else
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dims_[var] = 0;
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}
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/* ************************************************************************* */
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template<class Storage>
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void GaussianVariableIndex<Storage>::permute(const Permutation& permutation) {
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VariableIndex<Storage>::permute(permutation);
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storage_type original(this->dims_.size());
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this->dims_.swap(original);
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for(varid_t j=0; j<permutation.size(); ++j)
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this->dims_[j] = original[permutation[j]];
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}
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/* ************************************************************************* */
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template<class Storage>
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void GaussianVariableIndex<Storage>::augment(const GaussianFactorGraph& factorGraph) {
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Base::augment(factorGraph);
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dims_.resize(Base::index_.size(), 0);
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BOOST_FOREACH(boost::shared_ptr<const GaussianFactor> factor, factorGraph) {
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for(GaussianFactor::const_iterator var=factor->begin(); var!=factor->end(); ++var) {
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#ifndef NDEBUG
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if(dims_[*var] != 0)
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assert(dims_[*var] == factor->getDim(var));
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#endif
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if(dims_[*var] == 0)
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dims_[*var] = factor->getDim(var);
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}
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}
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// for(varid_t var=0; var<dims_.size(); ++var) {
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//#ifndef NDEBUG
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// if(var >= varIndex.dims_.size() || varIndex.dims_[var] == 0)
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// assert(dims_[var] != 0);
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// else if(varIndex.dims_[var] != 0 && dims_[var] != 0)
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// assert(dims_[var] == varIndex.dims_[var]);
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//#endif
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// if(dims_[var] == 0)
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// dims_[var] = varIndex.dims_[var];
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// }
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}
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// /**
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// * Returns the augmented matrix version of a set of factors
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// * with the corresponding noiseModel
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// * @param factors is the set of factors to combine
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// * @param ordering of variables needed for matrix column order
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// * @return the augmented matrix and a noise model
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// */
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// template <class Factors>
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// std::pair<Matrix, SharedDiagonal> combineFactorsAndCreateMatrix(
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// const Factors& factors,
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// const Ordering& order, const Dimensions& dimensions);
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} // namespace gtsam
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