99 lines
3.3 KiB
C++
99 lines
3.3 KiB
C++
/**
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* @file BayesNet-inl.h
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* @brief Bayes chain template definitions
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* @author Frank Dellaert
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*/
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#include <iostream>
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#include <boost/foreach.hpp>
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#include <boost/tuple/tuple.hpp>
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#include <boost/assign/std/vector.hpp> // for +=
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using namespace boost::assign;
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#include "Ordering.h"
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#include "BayesNet.h"
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#include "FactorGraph-inl.h"
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using namespace std;
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namespace gtsam {
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/* ************************************************************************* */
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template<class Conditional>
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void BayesNet<Conditional>::print(const string& s) const {
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cout << s << ":\n";
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std::string key;
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BOOST_FOREACH(sharedConditional conditional,conditionals_)
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conditional->print("Node[" + conditional->key() + "]");
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}
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/* ************************************************************************* */
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template<class Conditional>
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bool BayesNet<Conditional>::equals(const BayesNet& cbn, double tol) const {
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if(size() != cbn.size()) return false;
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return equal(conditionals_.begin(),conditionals_.end(),cbn.conditionals_.begin(),equals_star<Conditional>(tol));
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}
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/* ************************************************************************* */
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template<class Conditional>
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void BayesNet<Conditional>::push_back(const BayesNet<Conditional> bn) {
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BOOST_FOREACH(sharedConditional conditional,bn.conditionals_)
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push_back(conditional);
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}
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/* ************************************************************************* */
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template<class Conditional>
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void BayesNet<Conditional>::push_front(const BayesNet<Conditional> bn) {
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BOOST_FOREACH(sharedConditional conditional,bn.conditionals_)
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push_front(conditional);
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}
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/* ************************************************************************* */
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template<class Conditional>
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Ordering BayesNet<Conditional>::ordering() const {
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Ordering ord;
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BOOST_FOREACH(sharedConditional conditional,conditionals_)
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ord.push_back(conditional->key());
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return ord;
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}
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/* ************************************************************************* */
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template<class Conditional>
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typename BayesNet<Conditional>::sharedConditional
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BayesNet<Conditional>::operator[](const std::string& key) const {
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const_iterator it = find_if(conditionals_.begin(),conditionals_.end(),onKey<Conditional>(key));
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if (it == conditionals_.end()) throw(invalid_argument(
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"BayesNet::operator['"+key+"']: not found"));
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return *it;
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}
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/* ************************************************************************* */
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template<class Factor, class Conditional>
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BayesNet<Conditional> marginals(const BayesNet<Conditional>& bn, const Ordering& keys) {
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// Convert to factor graph
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FactorGraph<Factor> factorGraph(bn);
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// Get the keys of all variables and remove all keys we want the marginal for
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Ordering ord = bn.ordering();
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BOOST_FOREACH(string key, keys) ord.remove(key); // TODO: O(n*k), faster possible?
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// add marginal keys at end
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BOOST_FOREACH(string key, keys) ord.push_back(key);
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// eliminate to get joint
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BayesNet<Conditional> joint = _eliminate<Factor,Conditional>(factorGraph,ord);
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// remove all integrands, P(K) = \int_I P(I|K) P(K)
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size_t nrIntegrands = ord.size()-keys.size();
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for(int i=0;i<nrIntegrands;i++) joint.pop_front();
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// joint is now only on keys, return it
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return joint;
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}
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/* ************************************************************************* */
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} // namespace gtsam
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