266 lines
9.8 KiB
C++
266 lines
9.8 KiB
C++
/**
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* @file FactorGraph-inl.h
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* This is a template definition file, include it where needed (only!)
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* so that the appropriate code is generated and link errors avoided.
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* @brief Factor Graph Base Class
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* @author Carlos Nieto
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* @author Frank Dellaert
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* @author Alireza Fathi
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*/
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#pragma once
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#include <list>
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#include <sstream>
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#include <boost/foreach.hpp>
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#include <boost/tuple/tuple.hpp>
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#include <colamd/colamd.h>
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#include "Ordering.h"
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#include "FactorGraph.h"
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using namespace std;
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namespace gtsam {
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/* ************************************************************************* */
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template<class Factor>
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template<class Conditional>
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FactorGraph<Factor>::FactorGraph(const BayesNet<Conditional>& bayesNet)
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{
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typename BayesNet<Conditional>::const_iterator it = bayesNet.begin();
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for(; it != bayesNet.end(); it++) {
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typename boost::shared_ptr<Factor>::shared_ptr factor(new Factor(*it));
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push_back(factor);
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}
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}
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/* ************************************************************************* */
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template<class Factor>
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void FactorGraph<Factor>::print(const string& s) const {
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cout << s << endl;
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printf("size: %d\n", (int) size());
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for (int i = 0; i < factors_.size(); i++) {
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stringstream ss;
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ss << "factor " << i << ":";
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if (factors_[i] != NULL) factors_[i]->print(ss.str());
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}
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}
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/* ************************************************************************* */
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template<class Factor>
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bool FactorGraph<Factor>::equals
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(const FactorGraph<Factor>& fg, double tol) const {
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/** check whether the two factor graphs have the same number of factors_ */
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if (factors_.size() != fg.size()) return false;
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/** check whether the factors_ are the same */
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for (size_t i = 0; i < factors_.size(); i++) {
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// TODO: Doesn't this force order of factor insertion?
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shared_factor f1 = factors_[i], f2 = fg.factors_[i];
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if (f1 == NULL && f2 == NULL) continue;
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if (f1 == NULL || f2 == NULL) return false;
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if (!f1->equals(*f2, tol)) return false;
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}
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return true;
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}
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/* ************************************************************************* */
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template<class Factor>
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size_t FactorGraph<Factor>::nrFactors() const {
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int size_ = 0;
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for (const_iterator factor = factors_.begin(); factor != factors_.end(); factor++)
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if (*factor != NULL) size_++;
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return size_;
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}
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/* ************************************************************************* */
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template<class Factor>
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void FactorGraph<Factor>::push_back(shared_factor factor) {
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factors_.push_back(factor); // add the actual factor
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if (factor==NULL) return;
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int i = factors_.size() - 1; // index of factor
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list<string> keys = factor->keys(); // get keys for factor
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BOOST_FOREACH(string key, keys){ // for each key push i onto list
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Indices::iterator it = indices_.find(key); // old list for that key (if exists)
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if (it==indices_.end()){ // there's no list yet
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list<int> indices(1,i); // so make one
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indices_.insert(make_pair(key,indices)); // insert new indices into factorMap
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}
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else {
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list<int> *indices_ptr = &(it->second); // get the list
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indices_ptr->push_back(i); // add the index i to it
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}
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}
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}
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/* ************************************************************************* */
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/**
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* Call colamd given a column-major symbolic matrix A
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* @param n_col colamd arg 1: number of rows in A
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* @param n_row colamd arg 2: number of columns in A
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* @param nrNonZeros number of non-zero entries in A
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* @param columns map from keys to a sparse column of non-zero row indices
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*/
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template <class Key>
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Ordering colamd(int n_col, int n_row, int nrNonZeros, const map<Key, vector<int> >& columns) {
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// Convert to compressed column major format colamd wants it in (== MATLAB format!)
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vector<Key> initialOrder;
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int Alen = nrNonZeros*30; /* colamd arg 3: size of the array A TODO: use Tim's function ! */
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int * A = new int[Alen]; /* colamd arg 4: row indices of A, of size Alen */
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int * p = new int[n_col + 1]; /* colamd arg 5: column pointers of A, of size n_col+1 */
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p[0] = 0;
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int j = 1;
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int count = 0;
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typedef typename map<Key, vector<int> >::const_iterator iterator;
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for(iterator it = columns.begin(); it != columns.end(); it++) {
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const Key& key = it->first;
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const vector<int>& column = it->second;
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initialOrder.push_back(key);
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BOOST_FOREACH(int i, column) A[count++] = i; // copy sparse column
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p[j] = count; // column j (base 1) goes from A[j-1] to A[j]-1
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j+=1;
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}
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double* knobs = NULL; /* colamd arg 6: parameters (uses defaults if NULL) */
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int stats[COLAMD_STATS]; /* colamd arg 7: colamd output statistics and error codes */
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// call colamd, result will be in p *************************************************
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/* TODO: returns (1) if successful, (0) otherwise*/
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::colamd(n_row, n_col, Alen, A, p, knobs, stats);
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// **********************************************************************************
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delete [] A; // delete symbolic A
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// Convert elimination ordering in p to an ordering
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Ordering result;
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for(int j = 0; j < n_col; j++)
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result.push_back(initialOrder[j]);
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delete [] p; // delete colamd result vector
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return result;
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}
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/* ************************************************************************* */
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template<class Factor>
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Ordering FactorGraph<Factor>::getOrdering() const {
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// A factor graph is really laid out in row-major format, each factor a row
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// Below, we compute a symbolic matrix stored in sparse columns.
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typedef string Key; // default case with string keys
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map<Key, vector<int> > columns; // map from keys to a sparse column of non-zero row indices
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int nrNonZeros = 0; // number of non-zero entries
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int n_row = factors_.size(); /* colamd arg 1: number of rows in A */
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// loop over all factors = rows
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for (int i = 0; i < n_row; i++) {
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if (factors_[i]==NULL) continue;
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list<Key> keys = factors_[i]->keys();
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BOOST_FOREACH(Key key, keys) columns[key].push_back(i);
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nrNonZeros+= keys.size();
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}
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int n_col = (int)(columns.size()); /* colamd arg 2: number of columns in A */
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if(n_col == 0)
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return Ordering(); // empty ordering
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else
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return colamd(n_col, n_row, nrNonZeros, columns);
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}
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/* ************************************************************************* */
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/** O(1) */
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/* ************************************************************************* */
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template<class Factor>
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list<int> FactorGraph<Factor>::factors(const string& key) const {
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Indices::const_iterator it = indices_.find(key);
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return it->second;
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}
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/* ************************************************************************* */
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/** find all non-NULL factors for a variable, then set factors to NULL */
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/* ************************************************************************* */
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template<class Factor>
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vector<boost::shared_ptr<Factor> >
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FactorGraph<Factor>::findAndRemoveFactors(const string& key) {
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vector<boost::shared_ptr<Factor> > found;
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Indices::iterator it = indices_.find(key);
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if (it == indices_.end())
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throw(std::invalid_argument
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("FactorGraph::findAndRemoveFactors invalid key: " + key));
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list<int> *indices_ptr; // pointer to indices list in indices_ map
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indices_ptr = &(it->second);
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BOOST_FOREACH(int i, *indices_ptr) {
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if(factors_[i] == NULL) continue; // skip NULL factors
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found.push_back(factors_[i]); // add to found
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factors_[i].reset(); // set factor to NULL.
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}
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return found;
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}
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/* ************************************************************************* */
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/* find factors and remove them from the factor graph: O(n) */
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/* ************************************************************************* */
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template<class Factor>
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boost::shared_ptr<Factor>
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FactorGraph<Factor>::removeAndCombineFactors(const string& key)
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{
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bool verbose = false;
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if (verbose) cout << "FactorGraph::removeAndCombineFactors" << endl;
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typedef typename boost::shared_ptr<Factor> shared_factor;
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vector<shared_factor> found = findAndRemoveFactors(key);
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shared_factor new_factor(new Factor(found));
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if (verbose) cout << "FactorGraph::removeAndCombineFactors done" << endl;
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return new_factor;
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}
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/* ************************************************************************* */
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/* eliminate one node from the factor graph */
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/* ************************************************************************* */
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template<class Factor>
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template<class Conditional>
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boost::shared_ptr<Conditional> FactorGraph<Factor>::eliminateOne(const std::string& key) {
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// combine the factors of all nodes connected to the variable to be eliminated
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// if no factors are connected to key, returns an empty factor
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shared_factor joint_factor = removeAndCombineFactors(key);
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// eliminate that joint factor
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shared_factor factor;
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boost::shared_ptr<Conditional> conditional;
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boost::tie(conditional, factor) = joint_factor->eliminate(key);
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// add new factor on separator back into the graph
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if (!factor->empty()) push_back(factor);
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// return the conditional Gaussian
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return conditional;
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}
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/* ************************************************************************* */
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// This doubly templated function is generic. There is a LinearFactorGraph
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// version that returns a more specific GaussianBayesNet.
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// Note, you will need to include this file to instantiate the function.
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/* ************************************************************************* */
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template<class Factor>
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template<class Conditional>
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boost::shared_ptr<BayesNet<Conditional> >
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FactorGraph<Factor>::eliminate(boost::shared_ptr<BayesNet<Conditional> > xxx, const Ordering& ordering)
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{
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boost::shared_ptr<BayesNet<Conditional> > bayesNet (new BayesNet<Conditional>()); // empty
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BOOST_FOREACH(string key, ordering) {
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boost::shared_ptr<Conditional> cg = eliminateOne<Conditional>(key);
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bayesNet->push_back(cg);
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}
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return bayesNet;
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}
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/* ************************************************************************* */
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}
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