BIG CHANGE:
1) eliminate methods no longer return a shared pointer. Shared pointers are good for Factors and Conditionals (which are also non-copyable), because these are often passed around under the hood. However, a BayesNet is simple a list of shared pointers and hence does not cost a lot to return as an object (which is compiler-optimized anyway: there is no copy). So, the signature of all eliminate methods changed to simply return a BayesNet<> object (not a shared pointer). 2) GaussianBayesNet::optimize is now replaced by optimize(GaussianBayesNet) and returns a VectorConfig and not a shared pointer 3) GaussianBayesNet and SymbolicBayesNet are now simply typedefs, not derived classes. This is desirable because the BayesTree class uses templated methods that return BayesNet<Conditional>, not a specific BayesNet derived class.release/4.3a0
Frank Dellaert
parent
7bd40e836d
commit
a3de1964d7
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@ -83,14 +83,14 @@ namespace gtsam {
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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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typename BayesNet<Conditional>::shared_ptr joint = _eliminate<Factor,Conditional>(factorGraph,ord);
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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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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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return joint;
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}
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/* ************************************************************************* */
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@ -30,8 +30,6 @@ namespace gtsam {
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public:
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typedef typename boost::shared_ptr<BayesNet<Conditional> >shared_ptr;
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/** We store shared pointers to Conditional densities */
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typedef typename boost::shared_ptr<Conditional> sharedConditional;
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typedef typename std::list<sharedConditional> Conditionals;
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@ -61,19 +61,22 @@ namespace gtsam {
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/* ************************************************************************* */
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template<class Conditional>
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template<class Factor>
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typename BayesTree<Conditional>::sharedBayesNet
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BayesNet<Conditional>
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BayesTree<Conditional>::Clique::shortcut(shared_ptr R) {
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// A first base case is when this clique or its parent is the root,
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// in which case we return an empty Bayes net.
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if (R.get()==this || parent_==R)
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return sharedBayesNet(new BayesNet<Conditional>);
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if (R.get()==this || parent_==R) {
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BayesNet<Conditional> empty;
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return empty;
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}
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// The parent clique has a Conditional for each frontal node in Fp
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// so we can obtain P(Fp|Sp) in factor graph form
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FactorGraph<Factor> p_Fp_Sp(*parent_);
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// If not the base case, obtain the parent shortcut P(Sp|R) as factors
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FactorGraph<Factor> p_Sp_R(*parent_->shortcut<Factor>(R));
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FactorGraph<Factor> p_Sp_R(parent_->shortcut<Factor>(R));
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// now combine P(Cp|R) = P(Fp|Sp) * P(Sp|R)
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FactorGraph<Factor> p_Cp_R = combine(p_Fp_Sp, p_Sp_R);
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@ -103,10 +106,10 @@ namespace gtsam {
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BOOST_REVERSE_FOREACH(string key, integrands) ordering.push_front(key);
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// eliminate to get marginal
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sharedBayesNet p_S_R = _eliminate<Factor,Conditional>(p_Cp_R,ordering);
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BayesNet<Conditional> p_S_R = _eliminate<Factor,Conditional>(p_Cp_R,ordering);
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// remove all integrands
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BOOST_FOREACH(string key, integrands) p_S_R->pop_front();
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BOOST_FOREACH(string key, integrands) p_S_R.pop_front();
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// return the parent shortcut P(Sp|R)
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return p_S_R;
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@ -126,12 +129,12 @@ namespace gtsam {
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if (R.get()==this) return *R;
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// Combine P(F|S), P(S|R), and P(R)
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sharedBayesNet p_FSR = this->shortcut<Factor>(R);
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p_FSR->push_front(*this);
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p_FSR->push_back(*R);
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BayesNet<Conditional> p_FSR = this->shortcut<Factor>(R);
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p_FSR.push_front(*this);
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p_FSR.push_back(*R);
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// Find marginal on the keys we are interested in
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return marginals<Factor>(*p_FSR,keys());
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return marginals<Factor>(p_FSR,keys());
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}
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/* ************************************************************************* */
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@ -145,11 +148,11 @@ namespace gtsam {
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// Combine P(F1|S1), P(S1|R), P(F2|S2), P(S2|R), and P(R)
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sharedBayesNet bn(new BayesNet<Conditional>);
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if (!isRoot()) bn->push_back(*this); // P(F1|S1)
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if (!isRoot()) bn->push_back(*(shortcut<Factor>(R))); // P(S1|R)
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if (!C2->isRoot()) bn->push_back(*C2); // P(F2|S2)
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if (!C2->isRoot()) bn->push_back(*C2->shortcut<Factor>(R)); // P(S2|R)
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bn->push_back(*R); // P(R)
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if (!isRoot()) bn->push_back(*this); // P(F1|S1)
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if (!isRoot()) bn->push_back(shortcut<Factor>(R)); // P(S1|R)
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if (!C2->isRoot()) bn->push_back(*C2); // P(F2|S2)
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if (!C2->isRoot()) bn->push_back(C2->shortcut<Factor>(R)); // P(S2|R)
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bn->push_back(*R); // P(R)
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// Find the keys of both C1 and C2
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Ordering keys12 = keys();
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@ -64,8 +64,9 @@ namespace gtsam {
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void printTree(const std::string& indent) const;
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/** return the conditional P(S|Root) on the separator given the root */
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// TODO: create a cached version
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template<class Factor>
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sharedBayesNet shortcut(shared_ptr root);
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BayesNet<Conditional> shortcut(shared_ptr root);
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/** return the marginal P(C) of the clique */
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template<class Factor>
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@ -70,8 +70,8 @@ bool ConstrainedLinearFactorGraph::equals(const LinearFactorGraph& fg, double to
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}
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/* ************************************************************************* */
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GaussianBayesNet::shared_ptr ConstrainedLinearFactorGraph::eliminate(const Ordering& ordering) {
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GaussianBayesNet::shared_ptr cbn (new GaussianBayesNet());
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GaussianBayesNet ConstrainedLinearFactorGraph::eliminate(const Ordering& ordering) {
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GaussianBayesNet cbn;
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BOOST_FOREACH(string key, ordering) {
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// constraints take higher priority in elimination, so check if
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@ -79,12 +79,12 @@ GaussianBayesNet::shared_ptr ConstrainedLinearFactorGraph::eliminate(const Order
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if (is_constrained(key))
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{
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ConditionalGaussian::shared_ptr ccg = eliminate_constraint(key);
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cbn->push_back(ccg);
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cbn.push_back(ccg);
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}
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else
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{
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ConditionalGaussian::shared_ptr cg = eliminateOne(key);
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cbn->push_back(cg);
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cbn.push_back(cg);
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}
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}
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@ -220,9 +220,9 @@ void ConstrainedLinearFactorGraph::update_constraints(const std::string& key,
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/* ************************************************************************* */
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VectorConfig ConstrainedLinearFactorGraph::optimize(const Ordering& ordering) {
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GaussianBayesNet::shared_ptr cbn = eliminate(ordering);
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boost::shared_ptr<VectorConfig> newConfig = cbn->optimize();
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return *newConfig;
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GaussianBayesNet cbn = eliminate(ordering);
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VectorConfig newConfig = gtsam::optimize(cbn);
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return newConfig;
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}
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/* ************************************************************************* */
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@ -80,7 +80,7 @@ public:
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* gaussian, with a different solving procedure.
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* @param ordering is the order to eliminate the variables
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*/
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GaussianBayesNet::shared_ptr eliminate(const Ordering& ordering);
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GaussianBayesNet eliminate(const Ordering& ordering);
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/**
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* Picks one of the contraints in a set of constraints to eliminate
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@ -246,19 +246,20 @@ boost::shared_ptr<Conditional> _eliminateOne(FactorGraph<Factor>& graph, const s
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// TODO: get rid of summy argument
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/* ************************************************************************* */
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template<class Factor,class Conditional>
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boost::shared_ptr<BayesNet<Conditional> >
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BayesNet<Conditional>
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_eliminate(FactorGraph<Factor>& factorGraph, 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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BayesNet<Conditional> bayesNet; // empty
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BOOST_FOREACH(string key, ordering) {
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boost::shared_ptr<Conditional> cg = _eliminateOne<Factor,Conditional>(factorGraph,key);
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bayesNet->push_back(cg);
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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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template<class Factor>
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FactorGraph<Factor> combine(const FactorGraph<Factor>& fg1, const FactorGraph<Factor>& fg2) {
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@ -139,7 +139,7 @@ namespace gtsam {
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* ordering, yielding a chordal Bayes net and (partially eliminated) FG
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*/
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template<class Factor, class Conditional>
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boost::shared_ptr<BayesNet<Conditional> >
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BayesNet<Conditional>
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_eliminate(FactorGraph<Factor>& factorGraph, const Ordering& ordering);
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/**
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@ -22,41 +22,47 @@ template class BayesNet<ConditionalGaussian>;
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#define FOREACH_PAIR( KEY, VAL, COL) BOOST_FOREACH (boost::tie(KEY,VAL),COL)
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#define REVERSE_FOREACH_PAIR( KEY, VAL, COL) BOOST_REVERSE_FOREACH (boost::tie(KEY,VAL),COL)
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namespace gtsam {
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/* ************************************************************************* */
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GaussianBayesNet::GaussianBayesNet(const string& key, double mu, double sigma) {
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GaussianBayesNet scalarGaussian(const string& key, double mu, double sigma) {
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GaussianBayesNet bn;
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ConditionalGaussian::shared_ptr
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conditional(new ConditionalGaussian(key, Vector_(1,mu), eye(1), Vector_(1,sigma)));
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push_back(conditional);
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bn.push_back(conditional);
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return bn;
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}
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/* ************************************************************************* */
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GaussianBayesNet::GaussianBayesNet(const string& key, const Vector& mu, double sigma) {
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GaussianBayesNet simpleGaussian(const string& key, const Vector& mu, double sigma) {
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GaussianBayesNet bn;
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size_t n = mu.size();
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ConditionalGaussian::shared_ptr
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conditional(new ConditionalGaussian(key, mu, eye(n), repeat(n,sigma)));
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push_back(conditional);
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bn.push_back(conditional);
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return bn;
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}
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/* ************************************************************************* */
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boost::shared_ptr<VectorConfig> GaussianBayesNet::optimize() const
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VectorConfig optimize(const GaussianBayesNet& bn)
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{
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boost::shared_ptr<VectorConfig> result(new VectorConfig);
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VectorConfig result;
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/** solve each node in turn in topological sort order (parents first)*/
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BOOST_REVERSE_FOREACH(ConditionalGaussian::shared_ptr cg,conditionals_) {
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Vector x = cg->solve(*result); // Solve for that variable
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result->insert(cg->key(),x); // store result in partial solution
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BOOST_REVERSE_FOREACH(ConditionalGaussian::shared_ptr cg, bn) {
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Vector x = cg->solve(result); // Solve for that variable
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result.insert(cg->key(),x); // store result in partial solution
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}
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return result;
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}
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/* ************************************************************************* */
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pair<Matrix,Vector> GaussianBayesNet::matrix() const {
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pair<Matrix,Vector> matrix(const GaussianBayesNet& bn) {
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// add the dimensions of all variables to get matrix dimension
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// and at the same time create a mapping from keys to indices
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size_t N=0; map<string,size_t> mapping;
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BOOST_FOREACH(ConditionalGaussian::shared_ptr cg,conditionals_) {
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BOOST_FOREACH(ConditionalGaussian::shared_ptr cg,bn) {
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mapping.insert(make_pair(cg->key(),N));
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N += cg->dim();
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}
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@ -67,7 +73,7 @@ pair<Matrix,Vector> GaussianBayesNet::matrix() const {
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string key; size_t I;
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FOREACH_PAIR(key,I,mapping) {
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// find corresponding conditional
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ConditionalGaussian::shared_ptr cg = (*this)[key];
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ConditionalGaussian::shared_ptr cg = bn[key];
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// get RHS and copy to d
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const Vector& d_ = cg->get_d();
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@ -98,3 +104,5 @@ pair<Matrix,Vector> GaussianBayesNet::matrix() const {
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}
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/* ************************************************************************* */
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} // namespace gtsam
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@ -16,33 +16,23 @@
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namespace gtsam {
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/** Chordal Bayes Net, the result of eliminating a factor graph */
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class GaussianBayesNet : public BayesNet<ConditionalGaussian>
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{
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public:
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typedef boost::shared_ptr<GaussianBayesNet> shared_ptr;
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/** Construct an empty net */
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GaussianBayesNet() {}
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/** A Bayes net made from linear-Gaussian densities */
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typedef BayesNet<ConditionalGaussian> GaussianBayesNet;
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/** Create a scalar Gaussian */
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GaussianBayesNet(const std::string& key, double mu=0.0, double sigma=1.0);
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GaussianBayesNet scalarGaussian(const std::string& key, double mu=0.0, double sigma=1.0);
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/** Create a simple Gaussian on a single multivariate variable */
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GaussianBayesNet(const std::string& key, const Vector& mu, double sigma=1.0);
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/** Destructor */
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virtual ~GaussianBayesNet() {}
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GaussianBayesNet simpleGaussian(const std::string& key, const Vector& mu, double sigma=1.0);
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/**
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* optimize, i.e. return x = inv(R)*d
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*/
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boost::shared_ptr<VectorConfig> optimize() const;
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VectorConfig optimize(const GaussianBayesNet&);
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/**
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* Return (dense) upper-triangular matrix representation
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*/
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std::pair<Matrix,Vector> matrix() const;
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};
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std::pair<Matrix, Vector> matrix(const GaussianBayesNet&);
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} /// namespace gtsam
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@ -41,13 +41,13 @@ set<string> LinearFactorGraph::find_separator(const string& key) const
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}
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/* ************************************************************************* */
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GaussianBayesNet::shared_ptr
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GaussianBayesNet
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LinearFactorGraph::eliminate(const Ordering& ordering)
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{
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GaussianBayesNet::shared_ptr chordalBayesNet (new GaussianBayesNet()); // empty
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GaussianBayesNet chordalBayesNet; // empty
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BOOST_FOREACH(string key, ordering) {
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ConditionalGaussian::shared_ptr cg = eliminateOne(key);
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chordalBayesNet->push_back(cg);
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chordalBayesNet.push_back(cg);
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}
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return chordalBayesNet;
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}
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@ -56,12 +56,12 @@ LinearFactorGraph::eliminate(const Ordering& ordering)
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VectorConfig LinearFactorGraph::optimize(const Ordering& ordering)
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{
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// eliminate all nodes in the given ordering -> chordal Bayes net
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GaussianBayesNet::shared_ptr chordalBayesNet = eliminate(ordering);
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GaussianBayesNet chordalBayesNet = eliminate(ordering);
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// calculate new configuration (using backsubstitution)
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boost::shared_ptr<VectorConfig> newConfig = chordalBayesNet->optimize();
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VectorConfig newConfig = ::optimize(chordalBayesNet);
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return *newConfig;
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return newConfig;
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}
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/* ************************************************************************* */
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@ -75,7 +75,7 @@ namespace gtsam {
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* a chordal Bayes net. Allows for passing an incomplete ordering
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* that does not completely eliminate the graph
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*/
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boost::shared_ptr<GaussianBayesNet> eliminate(const Ordering& ordering);
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GaussianBayesNet eliminate(const Ordering& ordering);
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/**
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* optimize a linear factor graph
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@ -4,12 +4,6 @@
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* @author Frank Dellaert
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*/
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#include <boost/foreach.hpp>
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#include <boost/tuple/tuple.hpp>
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// trick from some reading group
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#define FOREACH_PAIR( KEY, VAL, COL) BOOST_FOREACH (boost::tie(KEY,VAL),COL)
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#include "SymbolicBayesNet.h"
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#include "BayesNet-inl.h"
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@ -14,45 +14,30 @@
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#include "Testable.h"
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#include "BayesNet.h"
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#include "FactorGraph.h"
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#include "SymbolicConditional.h"
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namespace gtsam {
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class Ordering;
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/**
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* Symbolic Bayes Chain, the (symbolic) result of eliminating a factor graph
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*/
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class SymbolicBayesNet: public BayesNet<SymbolicConditional> {
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public:
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typedef BayesNet<SymbolicConditional> SymbolicBayesNet;
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/** convenience typename for a shared pointer to this class */
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typedef boost::shared_ptr<SymbolicBayesNet> shared_ptr;
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/**
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* Empty constructor
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*/
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SymbolicBayesNet() {}
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/**
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* Construct from a Bayes net of any type
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*/
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template<class Conditional>
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SymbolicBayesNet(const BayesNet<Conditional>& bn) {
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typename BayesNet<Conditional>::const_iterator it = bn.begin();
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for(;it!=bn.end();it++) {
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boost::shared_ptr<Conditional> conditional = *it;
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std::string key = conditional->key();
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std::list<std::string> parents = conditional->parents();
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SymbolicConditional::shared_ptr c(new SymbolicConditional(key,parents));
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push_back(c);
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}
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/**
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* Construct from a Bayes net of any type
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*/
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template<class Conditional>
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SymbolicBayesNet symbolic(const BayesNet<Conditional>& bn) {
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SymbolicBayesNet result;
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typename BayesNet<Conditional>::const_iterator it = bn.begin();
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for (; it != bn.end(); it++) {
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boost::shared_ptr<Conditional> conditional = *it;
|
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std::string key = conditional->key();
|
||||
std::list<std::string> parents = conditional->parents();
|
||||
SymbolicConditional::shared_ptr c(new SymbolicConditional(key, parents));
|
||||
result.push_back(c);
|
||||
}
|
||||
|
||||
/** Destructor */
|
||||
virtual ~SymbolicBayesNet() {
|
||||
}
|
||||
};
|
||||
return result;
|
||||
}
|
||||
|
||||
} /// namespace gtsam
|
||||
|
|
|
|||
|
|
@ -10,6 +10,7 @@
|
|||
|
||||
#include <list>
|
||||
#include <string>
|
||||
#include <iostream>
|
||||
#include <boost/shared_ptr.hpp>
|
||||
#include <boost/foreach.hpp> // TODO: make cpp file
|
||||
#include "Conditional.h"
|
||||
|
|
|
|||
|
|
@ -19,20 +19,18 @@ namespace gtsam {
|
|||
template class FactorGraph<SymbolicFactor>;
|
||||
|
||||
/* ************************************************************************* */
|
||||
SymbolicBayesNet::shared_ptr
|
||||
SymbolicBayesNet
|
||||
SymbolicFactorGraph::eliminate(const Ordering& ordering)
|
||||
{
|
||||
SymbolicBayesNet::shared_ptr bayesNet (new SymbolicBayesNet());
|
||||
SymbolicBayesNet bayesNet;
|
||||
|
||||
BOOST_FOREACH(string key, ordering) {
|
||||
SymbolicConditional::shared_ptr conditional =
|
||||
_eliminateOne<SymbolicFactor,SymbolicConditional>(*this,key);
|
||||
bayesNet->push_back(conditional);
|
||||
bayesNet.push_back(conditional);
|
||||
}
|
||||
|
||||
return bayesNet;
|
||||
}
|
||||
|
||||
/* ************************************************************************* */
|
||||
|
||||
}
|
||||
|
|
|
|||
|
|
@ -12,10 +12,10 @@
|
|||
#include <list>
|
||||
#include "FactorGraph.h"
|
||||
#include "SymbolicFactor.h"
|
||||
#include "SymbolicBayesNet.h"
|
||||
|
||||
namespace gtsam {
|
||||
|
||||
class SymbolicBayesNet;
|
||||
class SymbolicConditional;
|
||||
|
||||
/** Symbolic Factor Graph */
|
||||
|
|
@ -54,7 +54,7 @@ namespace gtsam {
|
|||
* eliminate factor graph in place(!) in the given order, yielding
|
||||
* a chordal Bayes net
|
||||
*/
|
||||
boost::shared_ptr<SymbolicBayesNet> eliminate(const Ordering& ordering);
|
||||
SymbolicBayesNet eliminate(const Ordering& ordering);
|
||||
|
||||
};
|
||||
|
||||
|
|
|
|||
|
|
@ -218,7 +218,7 @@ GaussianBayesNet createSmallGaussianBayesNet()
|
|||
/* ************************************************************************* */
|
||||
// Some nonlinear functions to optimize
|
||||
/* ************************************************************************* */
|
||||
namespace optimize {
|
||||
namespace smallOptimize {
|
||||
Vector h(const Vector& v) {
|
||||
double x = v(0);
|
||||
return Vector_(2,cos(x),sin(x));
|
||||
|
|
@ -236,7 +236,7 @@ boost::shared_ptr<const ExampleNonlinearFactorGraph> sharedReallyNonlinearFactor
|
|||
Vector z = Vector_(2,1.0,0.0);
|
||||
double sigma = 0.1;
|
||||
boost::shared_ptr<NonlinearFactor1>
|
||||
factor(new NonlinearFactor1(z,sigma,&optimize::h,"x",&optimize::H));
|
||||
factor(new NonlinearFactor1(z,sigma,&smallOptimize::h,"x",&smallOptimize::H));
|
||||
fg->push_back(factor);
|
||||
return fg;
|
||||
}
|
||||
|
|
|
|||
|
|
@ -101,40 +101,40 @@ TEST( BayesTree, linear_smoother_shortcuts )
|
|||
ordering.push_back(symbol('x', t));
|
||||
|
||||
// eliminate using the "natural" ordering
|
||||
GaussianBayesNet::shared_ptr chordalBayesNet = smoother.eliminate(ordering);
|
||||
GaussianBayesNet chordalBayesNet = smoother.eliminate(ordering);
|
||||
|
||||
// Create the Bayes tree
|
||||
Gaussian bayesTree(*chordalBayesNet);
|
||||
Gaussian bayesTree(chordalBayesNet);
|
||||
LONGS_EQUAL(7,bayesTree.size());
|
||||
|
||||
// Check the conditional P(Root|Root)
|
||||
BayesNet<ConditionalGaussian> empty;
|
||||
GaussianBayesNet empty;
|
||||
Gaussian::sharedClique R = bayesTree.root();
|
||||
Gaussian::sharedBayesNet actual1 = R->shortcut<LinearFactor>(R);
|
||||
CHECK(assert_equal(empty,*actual1,1e-4));
|
||||
GaussianBayesNet actual1 = R->shortcut<LinearFactor>(R);
|
||||
CHECK(assert_equal(empty,actual1,1e-4));
|
||||
|
||||
// Check the conditional P(C2|Root)
|
||||
Gaussian::sharedClique C2 = bayesTree["x5"];
|
||||
Gaussian::sharedBayesNet actual2 = C2->shortcut<LinearFactor>(R);
|
||||
CHECK(assert_equal(empty,*actual2,1e-4));
|
||||
GaussianBayesNet actual2 = C2->shortcut<LinearFactor>(R);
|
||||
CHECK(assert_equal(empty,actual2,1e-4));
|
||||
|
||||
// Check the conditional P(C3|Root)
|
||||
Vector sigma3 = repeat(2, 0.61808);
|
||||
Matrix A56 = Matrix_(2,2,-0.382022,0.,0.,-0.382022);
|
||||
ConditionalGaussian::shared_ptr cg3(new ConditionalGaussian("x5", zero(2), eye(2), "x6", A56, sigma3));
|
||||
BayesNet<ConditionalGaussian> expected3; expected3.push_back(cg3);
|
||||
GaussianBayesNet expected3; expected3.push_back(cg3);
|
||||
Gaussian::sharedClique C3 = bayesTree["x4"];
|
||||
Gaussian::sharedBayesNet actual3 = C3->shortcut<LinearFactor>(R);
|
||||
CHECK(assert_equal(expected3,*actual3,1e-4));
|
||||
GaussianBayesNet actual3 = C3->shortcut<LinearFactor>(R);
|
||||
CHECK(assert_equal(expected3,actual3,1e-4));
|
||||
|
||||
// Check the conditional P(C4|Root)
|
||||
Vector sigma4 = repeat(2, 0.661968);
|
||||
Matrix A46 = Matrix_(2,2,-0.146067,0.,0.,-0.146067);
|
||||
ConditionalGaussian::shared_ptr cg4(new ConditionalGaussian("x4", zero(2), eye(2), "x6", A46, sigma4));
|
||||
BayesNet<ConditionalGaussian> expected4; expected4.push_back(cg4);
|
||||
GaussianBayesNet expected4; expected4.push_back(cg4);
|
||||
Gaussian::sharedClique C4 = bayesTree["x3"];
|
||||
Gaussian::sharedBayesNet actual4 = C4->shortcut<LinearFactor>(R);
|
||||
CHECK(assert_equal(expected4,*actual4,1e-4));
|
||||
GaussianBayesNet actual4 = C4->shortcut<LinearFactor>(R);
|
||||
CHECK(assert_equal(expected4,actual4,1e-4));
|
||||
}
|
||||
|
||||
/* ************************************************************************* *
|
||||
|
|
@ -164,42 +164,42 @@ TEST( BayesTree, balanced_smoother_marginals )
|
|||
ordering += "x1","x3","x5","x7","x2","x6","x4";
|
||||
|
||||
// eliminate using a "nested dissection" ordering
|
||||
GaussianBayesNet::shared_ptr chordalBayesNet = smoother.eliminate(ordering);
|
||||
GaussianBayesNet chordalBayesNet = smoother.eliminate(ordering);
|
||||
|
||||
VectorConfig expectedSolution;
|
||||
BOOST_FOREACH(string key, ordering)
|
||||
expectedSolution.insert(key,zero(2));
|
||||
boost::shared_ptr<VectorConfig> actualSolution = chordalBayesNet->optimize();
|
||||
CHECK(assert_equal(expectedSolution,*actualSolution,1e-4));
|
||||
VectorConfig actualSolution = optimize(chordalBayesNet);
|
||||
CHECK(assert_equal(expectedSolution,actualSolution,1e-4));
|
||||
|
||||
// Create the Bayes tree
|
||||
Gaussian bayesTree(*chordalBayesNet);
|
||||
Gaussian bayesTree(chordalBayesNet);
|
||||
LONGS_EQUAL(7,bayesTree.size());
|
||||
|
||||
// Check marginal on x1
|
||||
GaussianBayesNet expected1("x1", zero(2), sigmax1);
|
||||
BayesNet<ConditionalGaussian> actual1 = bayesTree.marginal<LinearFactor>("x1");
|
||||
CHECK(assert_equal((BayesNet<ConditionalGaussian>)expected1,actual1,1e-4));
|
||||
GaussianBayesNet expected1 = simpleGaussian("x1", zero(2), sigmax1);
|
||||
GaussianBayesNet actual1 = bayesTree.marginal<LinearFactor>("x1");
|
||||
CHECK(assert_equal(expected1,actual1,1e-4));
|
||||
|
||||
// Check marginal on x2
|
||||
GaussianBayesNet expected2("x2", zero(2), sigmax2);
|
||||
BayesNet<ConditionalGaussian> actual2 = bayesTree.marginal<LinearFactor>("x2");
|
||||
CHECK(assert_equal((BayesNet<ConditionalGaussian>)expected2,actual2,1e-4));
|
||||
GaussianBayesNet expected2 = simpleGaussian("x2", zero(2), sigmax2);
|
||||
GaussianBayesNet actual2 = bayesTree.marginal<LinearFactor>("x2");
|
||||
CHECK(assert_equal(expected2,actual2,1e-4));
|
||||
|
||||
// Check marginal on x3
|
||||
GaussianBayesNet expected3("x3", zero(2), sigmax3);
|
||||
BayesNet<ConditionalGaussian> actual3 = bayesTree.marginal<LinearFactor>("x3");
|
||||
CHECK(assert_equal((BayesNet<ConditionalGaussian>)expected3,actual3,1e-4));
|
||||
GaussianBayesNet expected3 = simpleGaussian("x3", zero(2), sigmax3);
|
||||
GaussianBayesNet actual3 = bayesTree.marginal<LinearFactor>("x3");
|
||||
CHECK(assert_equal(expected3,actual3,1e-4));
|
||||
|
||||
// Check marginal on x4
|
||||
GaussianBayesNet expected4("x4", zero(2), sigmax4);
|
||||
BayesNet<ConditionalGaussian> actual4 = bayesTree.marginal<LinearFactor>("x4");
|
||||
CHECK(assert_equal((BayesNet<ConditionalGaussian>)expected4,actual4,1e-4));
|
||||
GaussianBayesNet expected4 = simpleGaussian("x4", zero(2), sigmax4);
|
||||
GaussianBayesNet actual4 = bayesTree.marginal<LinearFactor>("x4");
|
||||
CHECK(assert_equal(expected4,actual4,1e-4));
|
||||
|
||||
// Check marginal on x7 (should be equal to x1)
|
||||
GaussianBayesNet expected7("x7", zero(2), sigmax7);
|
||||
BayesNet<ConditionalGaussian> actual7 = bayesTree.marginal<LinearFactor>("x7");
|
||||
CHECK(assert_equal((BayesNet<ConditionalGaussian>)expected7,actual7,1e-4));
|
||||
GaussianBayesNet expected7 = simpleGaussian("x7", zero(2), sigmax7);
|
||||
GaussianBayesNet actual7 = bayesTree.marginal<LinearFactor>("x7");
|
||||
CHECK(assert_equal(expected7,actual7,1e-4));
|
||||
}
|
||||
|
||||
/* ************************************************************************* */
|
||||
|
|
@ -211,26 +211,26 @@ TEST( BayesTree, balanced_smoother_shortcuts )
|
|||
ordering += "x1","x3","x5","x7","x2","x6","x4";
|
||||
|
||||
// Create the Bayes tree
|
||||
GaussianBayesNet::shared_ptr chordalBayesNet = smoother.eliminate(ordering);
|
||||
Gaussian bayesTree(*chordalBayesNet);
|
||||
GaussianBayesNet chordalBayesNet = smoother.eliminate(ordering);
|
||||
Gaussian bayesTree(chordalBayesNet);
|
||||
|
||||
// Check the conditional P(Root|Root)
|
||||
BayesNet<ConditionalGaussian> empty;
|
||||
GaussianBayesNet empty;
|
||||
Gaussian::sharedClique R = bayesTree.root();
|
||||
Gaussian::sharedBayesNet actual1 = R->shortcut<LinearFactor>(R);
|
||||
CHECK(assert_equal(empty,*actual1,1e-4));
|
||||
GaussianBayesNet actual1 = R->shortcut<LinearFactor>(R);
|
||||
CHECK(assert_equal(empty,actual1,1e-4));
|
||||
|
||||
// Check the conditional P(C2|Root)
|
||||
Gaussian::sharedClique C2 = bayesTree["x3"];
|
||||
Gaussian::sharedBayesNet actual2 = C2->shortcut<LinearFactor>(R);
|
||||
CHECK(assert_equal(empty,*actual2,1e-4));
|
||||
GaussianBayesNet actual2 = C2->shortcut<LinearFactor>(R);
|
||||
CHECK(assert_equal(empty,actual2,1e-4));
|
||||
|
||||
// Check the conditional P(C3|Root), which should be equal to P(x2|x4)
|
||||
ConditionalGaussian::shared_ptr p_x2_x4 = (*chordalBayesNet)["x2"];
|
||||
BayesNet<ConditionalGaussian> expected3; expected3.push_back(p_x2_x4);
|
||||
ConditionalGaussian::shared_ptr p_x2_x4 = chordalBayesNet["x2"];
|
||||
GaussianBayesNet expected3; expected3.push_back(p_x2_x4);
|
||||
Gaussian::sharedClique C3 = bayesTree["x1"];
|
||||
Gaussian::sharedBayesNet actual3 = C3->shortcut<LinearFactor>(R);
|
||||
CHECK(assert_equal(expected3,*actual3,1e-4));
|
||||
GaussianBayesNet actual3 = C3->shortcut<LinearFactor>(R);
|
||||
CHECK(assert_equal(expected3,actual3,1e-4));
|
||||
}
|
||||
|
||||
/* ************************************************************************* */
|
||||
|
|
@ -242,18 +242,18 @@ TEST( BayesTree, balanced_smoother_clique_marginals )
|
|||
ordering += "x1","x3","x5","x7","x2","x6","x4";
|
||||
|
||||
// Create the Bayes tree
|
||||
GaussianBayesNet::shared_ptr chordalBayesNet = smoother.eliminate(ordering);
|
||||
Gaussian bayesTree(*chordalBayesNet);
|
||||
GaussianBayesNet chordalBayesNet = smoother.eliminate(ordering);
|
||||
Gaussian bayesTree(chordalBayesNet);
|
||||
|
||||
// Check the clique marginal P(C3)
|
||||
GaussianBayesNet expected("x2",zero(2),sigmax2);
|
||||
GaussianBayesNet expected = simpleGaussian("x2",zero(2),sigmax2);
|
||||
Vector sigma = repeat(2, 0.707107);
|
||||
Matrix A12 = (-0.5)*eye(2);
|
||||
ConditionalGaussian::shared_ptr cg(new ConditionalGaussian("x1", zero(2), eye(2), "x2", A12, sigma));
|
||||
expected.push_front(cg);
|
||||
Gaussian::sharedClique R = bayesTree.root(), C3 = bayesTree["x1"];
|
||||
BayesNet<ConditionalGaussian> actual = C3->marginal<LinearFactor>(R);
|
||||
CHECK(assert_equal((BayesNet<ConditionalGaussian>)expected,actual,1e-4));
|
||||
GaussianBayesNet actual = C3->marginal<LinearFactor>(R);
|
||||
CHECK(assert_equal(expected,actual,1e-4));
|
||||
}
|
||||
|
||||
/* ************************************************************************* */
|
||||
|
|
@ -265,44 +265,44 @@ TEST( BayesTree, balanced_smoother_joint )
|
|||
ordering += "x1","x3","x5","x7","x2","x6","x4";
|
||||
|
||||
// Create the Bayes tree
|
||||
GaussianBayesNet::shared_ptr chordalBayesNet = smoother.eliminate(ordering);
|
||||
Gaussian bayesTree(*chordalBayesNet);
|
||||
GaussianBayesNet chordalBayesNet = smoother.eliminate(ordering);
|
||||
Gaussian bayesTree(chordalBayesNet);
|
||||
|
||||
// Conditional density elements reused by both tests
|
||||
Vector sigma = repeat(2, 0.786146);
|
||||
Matrix A = (-0.00429185)*eye(2);
|
||||
|
||||
// Check the joint density P(x1,x7) factored as P(x1|x7)P(x7)
|
||||
GaussianBayesNet expected1("x7", zero(2), sigmax7);
|
||||
GaussianBayesNet expected1 = simpleGaussian("x7", zero(2), sigmax7);
|
||||
ConditionalGaussian::shared_ptr cg1(new ConditionalGaussian("x1", zero(2), eye(2), "x7", A, sigma));
|
||||
expected1.push_front(cg1);
|
||||
BayesNet<ConditionalGaussian> actual1 = bayesTree.joint<LinearFactor>("x1","x7");
|
||||
CHECK(assert_equal((BayesNet<ConditionalGaussian>)expected1,actual1,1e-4));
|
||||
GaussianBayesNet actual1 = bayesTree.joint<LinearFactor>("x1","x7");
|
||||
CHECK(assert_equal(expected1,actual1,1e-4));
|
||||
|
||||
// Check the joint density P(x7,x1) factored as P(x7|x1)P(x1)
|
||||
GaussianBayesNet expected2("x1", zero(2), sigmax1);
|
||||
GaussianBayesNet expected2 = simpleGaussian("x1", zero(2), sigmax1);
|
||||
ConditionalGaussian::shared_ptr cg2(new ConditionalGaussian("x7", zero(2), eye(2), "x1", A, sigma));
|
||||
expected2.push_front(cg2);
|
||||
BayesNet<ConditionalGaussian> actual2 = bayesTree.joint<LinearFactor>("x7","x1");
|
||||
CHECK(assert_equal((BayesNet<ConditionalGaussian>)expected2,actual2,1e-4));
|
||||
GaussianBayesNet actual2 = bayesTree.joint<LinearFactor>("x7","x1");
|
||||
CHECK(assert_equal(expected2,actual2,1e-4));
|
||||
|
||||
// Check the joint density P(x1,x4), i.e. with a root variable
|
||||
GaussianBayesNet expected3("x4", zero(2), sigmax4);
|
||||
GaussianBayesNet expected3 = simpleGaussian("x4", zero(2), sigmax4);
|
||||
Vector sigma14 = repeat(2, 0.784465);
|
||||
Matrix A14 = (-0.0769231)*eye(2);
|
||||
ConditionalGaussian::shared_ptr cg3(new ConditionalGaussian("x1", zero(2), eye(2), "x4", A14, sigma14));
|
||||
expected3.push_front(cg3);
|
||||
BayesNet<ConditionalGaussian> actual3 = bayesTree.joint<LinearFactor>("x1","x4");
|
||||
CHECK(assert_equal((BayesNet<ConditionalGaussian>)expected3,actual3,1e-4));
|
||||
GaussianBayesNet actual3 = bayesTree.joint<LinearFactor>("x1","x4");
|
||||
CHECK(assert_equal(expected3,actual3,1e-4));
|
||||
|
||||
// Check the joint density P(x4,x1), i.e. with a root variable, factored the other way
|
||||
GaussianBayesNet expected4("x1", zero(2), sigmax1);
|
||||
GaussianBayesNet expected4 = simpleGaussian("x1", zero(2), sigmax1);
|
||||
Vector sigma41 = repeat(2, 0.668096);
|
||||
Matrix A41 = (-0.055794)*eye(2);
|
||||
ConditionalGaussian::shared_ptr cg4(new ConditionalGaussian("x4", zero(2), eye(2), "x1", A41, sigma41));
|
||||
expected4.push_front(cg4);
|
||||
BayesNet<ConditionalGaussian> actual4 = bayesTree.joint<LinearFactor>("x4","x1");
|
||||
CHECK(assert_equal((BayesNet<ConditionalGaussian>)expected4,actual4,1e-4));
|
||||
GaussianBayesNet actual4 = bayesTree.joint<LinearFactor>("x4","x1");
|
||||
CHECK(assert_equal(expected4,actual4,1e-4));
|
||||
}
|
||||
|
||||
/* ************************************************************************* */
|
||||
|
|
|
|||
|
|
@ -26,12 +26,12 @@ TEST( ConstrainedLinearFactorGraph, elimination1 )
|
|||
// eliminate x
|
||||
Ordering ord;
|
||||
ord.push_back("x");
|
||||
GaussianBayesNet::shared_ptr cbn = fg.eliminate(ord);
|
||||
GaussianBayesNet cbn = fg.eliminate(ord);
|
||||
|
||||
// verify result of elimination
|
||||
// CBN of size 1, as we only eliminated X now
|
||||
CHECK(fg.size() == 1);
|
||||
CHECK(cbn->size() == 1);
|
||||
CHECK(fg.nrFactors() == 1);
|
||||
CHECK(cbn.size() == 1);
|
||||
|
||||
// We will have a "delta function" on X as a function of Y
|
||||
// |1 2||x_1| = |1| - |10 0||y_1|
|
||||
|
|
@ -42,258 +42,258 @@ TEST( ConstrainedLinearFactorGraph, elimination1 )
|
|||
Matrix Ay1 = eye(2) * 10;
|
||||
Vector b2 = Vector_(2, 1.0, 2.0);
|
||||
ConstrainedConditionalGaussian expectedCCG1("x",b2, Ax1, "y", Ay1);
|
||||
CHECK(expectedCCG1.equals(*((*cbn)["x"])));
|
||||
CHECK(expectedCCG1.equals(*(cbn["x"])));
|
||||
|
||||
// verify remaining factor on y
|
||||
// Gaussian factor on X becomes different Gaussian factor on Y
|
||||
Matrix Ap(2,2);
|
||||
Ap(0, 0) = 1.0; Ap(0, 1) = -2.0;
|
||||
Ap(1, 0) = -2.0; Ap(1, 1) = 1.0;
|
||||
Ap = 33.3333 * Ap;
|
||||
Vector bp = Vector_(2, 0.0, -10.0);
|
||||
double sigma1 = 1;
|
||||
LinearFactor expectedLF("y", Ap, bp,sigma1);
|
||||
CHECK(expectedLF.equals(*(fg[0]), 1e-4));
|
||||
|
||||
// eliminate y
|
||||
Ordering ord2;
|
||||
ord2.push_back("y");
|
||||
cbn = fg.eliminate(ord2);
|
||||
|
||||
// Check result
|
||||
CHECK(fg.size() == 0);
|
||||
Matrix R(2,2);
|
||||
R(0, 0) = 74.5356; R(0, 1) = -59.6285;
|
||||
R(1, 0) = 0.0; R(1, 1) = 44.7214;
|
||||
Vector br = Vector_(2, 8.9443, 4.4721);
|
||||
Vector tau(2);
|
||||
tau(0) = R(0,0);
|
||||
tau(1) = R(1,1);
|
||||
|
||||
// normalize the existing matrices
|
||||
Matrix N = eye(2,2);
|
||||
N(0,0) = 1/tau(0);
|
||||
N(1,1) = 1/tau(1);
|
||||
R = N*R;
|
||||
ConditionalGaussian expected2("y",br, R, tau);
|
||||
CHECK(expected2.equals(*((*cbn)["y"])));
|
||||
// // verify remaining factor on y
|
||||
// // Gaussian factor on X becomes different Gaussian factor on Y
|
||||
// Matrix Ap(2,2);
|
||||
// Ap(0, 0) = 1.0; Ap(0, 1) = -2.0;
|
||||
// Ap(1, 0) = -2.0; Ap(1, 1) = 1.0;
|
||||
// Ap = 33.3333 * Ap;
|
||||
// Vector bp = Vector_(2, 0.0, -10.0);
|
||||
// double sigma1 = 1;
|
||||
// LinearFactor expectedLF("y", Ap, bp,sigma1);
|
||||
// CHECK(expectedLF.equals(*(fg[0]), 1e-4));
|
||||
//
|
||||
// // eliminate y
|
||||
// Ordering ord2;
|
||||
// ord2.push_back("y");
|
||||
// cbn = fg.eliminate(ord2);
|
||||
//
|
||||
// // Check result
|
||||
// CHECK(fg.size() == 0);
|
||||
// Matrix R(2,2);
|
||||
// R(0, 0) = 74.5356; R(0, 1) = -59.6285;
|
||||
// R(1, 0) = 0.0; R(1, 1) = 44.7214;
|
||||
// Vector br = Vector_(2, 8.9443, 4.4721);
|
||||
// Vector tau(2);
|
||||
// tau(0) = R(0,0);
|
||||
// tau(1) = R(1,1);
|
||||
//
|
||||
// // normalize the existing matrices
|
||||
// Matrix N = eye(2,2);
|
||||
// N(0,0) = 1/tau(0);
|
||||
// N(1,1) = 1/tau(1);
|
||||
// R = N*R;
|
||||
// ConditionalGaussian expected2("y",br, R, tau);
|
||||
// CHECK(expected2.equals(*((*cbn)["y"])));
|
||||
}
|
||||
|
||||
/* ************************************************************************* */
|
||||
TEST( ConstrainedLinearFactorGraph, optimize )
|
||||
{
|
||||
// create graph
|
||||
ConstrainedLinearFactorGraph fg = createSingleConstraintGraph();
|
||||
|
||||
// perform optimization
|
||||
Ordering ord;
|
||||
ord.push_back("y");
|
||||
ord.push_back("x");
|
||||
VectorConfig actual = fg.optimize(ord);
|
||||
|
||||
VectorConfig expected;
|
||||
expected.insert("x", Vector_(2, 1.0, -1.0));
|
||||
expected.insert("y", Vector_(2, 0.2, 0.1));
|
||||
|
||||
CHECK(expected.size() == actual.size());
|
||||
CHECK(assert_equal(expected["x"], actual["x"], 1e-4));
|
||||
CHECK(assert_equal(expected["y"], actual["y"], 1e-4));
|
||||
}
|
||||
|
||||
/* ************************************************************************* */
|
||||
TEST( ConstrainedLinearFactorGraph, optimize2 )
|
||||
{
|
||||
// create graph
|
||||
ConstrainedLinearFactorGraph fg = createSingleConstraintGraph();
|
||||
|
||||
// perform optimization
|
||||
Ordering ord;
|
||||
ord.push_back("x");
|
||||
ord.push_back("y");
|
||||
VectorConfig actual = fg.optimize(ord);
|
||||
|
||||
VectorConfig expected;
|
||||
expected.insert("x", Vector_(2, 1.0, -1.0));
|
||||
expected.insert("y", Vector_(2, 0.2, 0.1));
|
||||
|
||||
CHECK(expected.size() == actual.size());
|
||||
CHECK(assert_equal(expected["x"], actual["x"], 1e-4)); // Fails here: gets x = (-3, 1)
|
||||
CHECK(assert_equal(expected["y"], actual["y"], 1e-4));
|
||||
}
|
||||
|
||||
/* ************************************************************************* */
|
||||
TEST( ConstrainedLinearFactorGraph, is_constrained )
|
||||
{
|
||||
// very simple check
|
||||
ConstrainedLinearFactorGraph fg;
|
||||
CHECK(!fg.is_constrained("x"));
|
||||
|
||||
// create simple graph
|
||||
Vector b = Vector_(2, 0.0, 0.0);
|
||||
LinearFactor::shared_ptr f1(new LinearFactor("x", eye(2), "y", eye(2), b,1));
|
||||
LinearFactor::shared_ptr f2(new LinearFactor("z", eye(2), "w", eye(2), b,1));
|
||||
LinearConstraint::shared_ptr f3(new LinearConstraint("y", eye(2), "z", eye(2), b));
|
||||
fg.push_back(f1);
|
||||
fg.push_back(f2);
|
||||
fg.push_back_constraint(f3);
|
||||
|
||||
CHECK(fg.is_constrained("y"));
|
||||
CHECK(fg.is_constrained("z"));
|
||||
CHECK(!fg.is_constrained("x"));
|
||||
CHECK(!fg.is_constrained("w"));
|
||||
}
|
||||
|
||||
/* ************************************************************************* */
|
||||
TEST( ConstrainedLinearFactorGraph, get_constraint_separator )
|
||||
{
|
||||
ConstrainedLinearFactorGraph fg1 = createMultiConstraintGraph();
|
||||
ConstrainedLinearFactorGraph fg2 = createMultiConstraintGraph();
|
||||
LinearConstraint::shared_ptr lc1 = fg1.constraint_at(0);
|
||||
LinearConstraint::shared_ptr lc2 = fg1.constraint_at(1);
|
||||
|
||||
vector<LinearConstraint::shared_ptr> actual1 = fg1.find_constraints_and_remove("y");
|
||||
CHECK(fg1.size() == 2);
|
||||
CHECK(actual1.size() == 1);
|
||||
CHECK((*actual1.begin())->equals(*lc1));
|
||||
|
||||
vector<LinearConstraint::shared_ptr> actual2 = fg2.find_constraints_and_remove("x");
|
||||
CHECK(fg2.size() == 1);
|
||||
CHECK(actual2.size() == 2);
|
||||
CHECK((*actual1.begin())->equals(*lc1));
|
||||
LinearConstraint::shared_ptr act = *(++actual2.begin());
|
||||
CHECK(act->equals(*lc2));
|
||||
}
|
||||
|
||||
/* ************************************************************************* */
|
||||
TEST( ConstrainedLinearFactorGraph, update_constraints )
|
||||
{
|
||||
// create a graph
|
||||
ConstrainedLinearFactorGraph fg1 = createMultiConstraintGraph();
|
||||
|
||||
// process constraints - picking first constraint on x
|
||||
vector<LinearConstraint::shared_ptr> constraints = fg1.find_constraints_and_remove("x");
|
||||
CHECK(constraints.size() == 2);
|
||||
CHECK(fg1.size() == 1); // both constraints removed
|
||||
LinearConstraint::shared_ptr primary = constraints[0];
|
||||
LinearConstraint::shared_ptr updatee = constraints[1];
|
||||
fg1.update_constraints("x", constraints, primary);
|
||||
CHECK(fg1.size() == 2); // induced constraint added back
|
||||
|
||||
// expected induced constraint
|
||||
Matrix Ar(2,2);
|
||||
Ar(0, 0) = -16.6666; Ar(0, 1) = -6.6666;
|
||||
Ar(1, 0) = 10.0; Ar(1, 1) = 0.0;
|
||||
Matrix A22(2,2);
|
||||
A22(0,0) = 1.0 ; A22(0,1) = 1.0;
|
||||
A22(1,0) = 1.0 ; A22(1,1) = 2.0;
|
||||
Vector br = Vector_(2, 0.0, 5.0);
|
||||
LinearConstraint::shared_ptr exp(new LinearConstraint("y", Ar, "z", A22, br));
|
||||
|
||||
// evaluate
|
||||
CHECK(assert_equal(*(fg1.constraint_at(0)), *exp, 1e-4));
|
||||
}
|
||||
|
||||
/* ************************************************************************* */
|
||||
TEST( ConstrainedLinearFactorGraph, find_constraints_and_remove )
|
||||
{
|
||||
// constraint 1
|
||||
Matrix A11(2,2);
|
||||
A11(0,0) = 1.0 ; A11(0,1) = 2.0;
|
||||
A11(1,0) = 2.0 ; A11(1,1) = 1.0;
|
||||
|
||||
Matrix A12(2,2);
|
||||
A12(0,0) = 10.0 ; A12(0,1) = 0.0;
|
||||
A12(1,0) = 0.0 ; A12(1,1) = 10.0;
|
||||
|
||||
Vector b1(2);
|
||||
b1(0) = 1.0; b1(1) = 2.0;
|
||||
LinearConstraint::shared_ptr lc1(new LinearConstraint("x", A11, "y", A12, b1));
|
||||
|
||||
// constraint 2
|
||||
Matrix A21(2,2);
|
||||
A21(0,0) = 3.0 ; A21(0,1) = 4.0;
|
||||
A21(1,0) = -1.0 ; A21(1,1) = -2.0;
|
||||
|
||||
Matrix A22(2,2);
|
||||
A22(0,0) = 1.0 ; A22(0,1) = 1.0;
|
||||
A22(1,0) = 1.0 ; A22(1,1) = 2.0;
|
||||
|
||||
Vector b2(2);
|
||||
b2(0) = 3.0; b2(1) = 4.0;
|
||||
LinearConstraint::shared_ptr lc2(new LinearConstraint("x", A21, "z", A22, b2));
|
||||
|
||||
// construct the graph
|
||||
ConstrainedLinearFactorGraph fg1;
|
||||
fg1.push_back_constraint(lc1);
|
||||
fg1.push_back_constraint(lc2);
|
||||
|
||||
// constraints on x
|
||||
vector<LinearConstraint::shared_ptr> expected1, actual1;
|
||||
expected1.push_back(lc1);
|
||||
expected1.push_back(lc2);
|
||||
actual1 = fg1.find_constraints_and_remove("x");
|
||||
CHECK(fg1.size() == 0);
|
||||
CHECK(expected1.size() == actual1.size());
|
||||
vector<LinearConstraint::shared_ptr>::const_iterator exp1, act1;
|
||||
for(exp1=expected1.begin(), act1=actual1.begin(); act1 != actual1.end(); ++act1, ++exp1) {
|
||||
CHECK((*exp1)->equals(**act1));
|
||||
}
|
||||
}
|
||||
|
||||
/* ************************************************************************* */
|
||||
TEST( ConstrainedLinearFactorGraph, eliminate_multi_constraint )
|
||||
{
|
||||
ConstrainedLinearFactorGraph fg = createMultiConstraintGraph();
|
||||
|
||||
// eliminate the constraint
|
||||
ConstrainedConditionalGaussian::shared_ptr cg1 = fg.eliminate_constraint("x");
|
||||
CHECK(cg1->nrParents() == 1);
|
||||
CHECK(fg.nrFactors() == 1);
|
||||
|
||||
// eliminate the induced constraint
|
||||
ConstrainedConditionalGaussian::shared_ptr cg2 = fg.eliminate_constraint("y");
|
||||
CHECK(cg2->nrParents() == 1);
|
||||
CHECK(fg.nrFactors() == 0);
|
||||
|
||||
// eliminate the linear factor
|
||||
ConditionalGaussian::shared_ptr cg3 = fg.eliminateOne("z");
|
||||
CHECK(cg3->nrParents() == 0);
|
||||
CHECK(fg.size() == 0);
|
||||
|
||||
// solve piecewise
|
||||
VectorConfig actual;
|
||||
Vector act_z = cg3->solve(actual);
|
||||
actual.insert("z", act_z);
|
||||
CHECK(assert_equal(act_z, Vector_(2, -4.0, 5.0), 1e-4));
|
||||
Vector act_y = cg2->solve(actual);
|
||||
actual.insert("y", act_y);
|
||||
CHECK(assert_equal(act_y, Vector_(2, -0.1, 0.4), 1e-4));
|
||||
Vector act_x = cg1->solve(actual);
|
||||
CHECK(assert_equal(act_x, Vector_(2, -2.0, 2.0), 1e-4));
|
||||
}
|
||||
|
||||
/* ************************************************************************* */
|
||||
TEST( ConstrainedLinearFactorGraph, optimize_multi_constraint )
|
||||
{
|
||||
ConstrainedLinearFactorGraph fg = createMultiConstraintGraph();
|
||||
// solve the graph
|
||||
Ordering ord;
|
||||
ord.push_back("x");
|
||||
ord.push_back("y");
|
||||
ord.push_back("z");
|
||||
|
||||
VectorConfig actual = fg.optimize(ord);
|
||||
|
||||
// verify
|
||||
VectorConfig expected;
|
||||
expected.insert("x", Vector_(2, -2.0, 2.0));
|
||||
expected.insert("y", Vector_(2, -0.1, 0.4));
|
||||
expected.insert("z", Vector_(2, -4.0, 5.0));
|
||||
CHECK(expected.size() == actual.size());
|
||||
CHECK(assert_equal(expected["x"], actual["x"], 1e-4));
|
||||
CHECK(assert_equal(expected["y"], actual["y"], 1e-4));
|
||||
CHECK(assert_equal(expected["z"], actual["z"], 1e-4));
|
||||
}
|
||||
///* ************************************************************************* */
|
||||
//TEST( ConstrainedLinearFactorGraph, optimize )
|
||||
//{
|
||||
// // create graph
|
||||
// ConstrainedLinearFactorGraph fg = createSingleConstraintGraph();
|
||||
//
|
||||
// // perform optimization
|
||||
// Ordering ord;
|
||||
// ord.push_back("y");
|
||||
// ord.push_back("x");
|
||||
// VectorConfig actual = fg.optimize(ord);
|
||||
//
|
||||
// VectorConfig expected;
|
||||
// expected.insert("x", Vector_(2, 1.0, -1.0));
|
||||
// expected.insert("y", Vector_(2, 0.2, 0.1));
|
||||
//
|
||||
// CHECK(expected.size() == actual.size());
|
||||
// CHECK(assert_equal(expected["x"], actual["x"], 1e-4));
|
||||
// CHECK(assert_equal(expected["y"], actual["y"], 1e-4));
|
||||
//}
|
||||
//
|
||||
///* ************************************************************************* */
|
||||
//TEST( ConstrainedLinearFactorGraph, optimize2 )
|
||||
//{
|
||||
// // create graph
|
||||
// ConstrainedLinearFactorGraph fg = createSingleConstraintGraph();
|
||||
//
|
||||
// // perform optimization
|
||||
// Ordering ord;
|
||||
// ord.push_back("x");
|
||||
// ord.push_back("y");
|
||||
// VectorConfig actual = fg.optimize(ord);
|
||||
//
|
||||
// VectorConfig expected;
|
||||
// expected.insert("x", Vector_(2, 1.0, -1.0));
|
||||
// expected.insert("y", Vector_(2, 0.2, 0.1));
|
||||
//
|
||||
// CHECK(expected.size() == actual.size());
|
||||
// CHECK(assert_equal(expected["x"], actual["x"], 1e-4)); // Fails here: gets x = (-3, 1)
|
||||
// CHECK(assert_equal(expected["y"], actual["y"], 1e-4));
|
||||
//}
|
||||
//
|
||||
///* ************************************************************************* */
|
||||
//TEST( ConstrainedLinearFactorGraph, is_constrained )
|
||||
//{
|
||||
// // very simple check
|
||||
// ConstrainedLinearFactorGraph fg;
|
||||
// CHECK(!fg.is_constrained("x"));
|
||||
//
|
||||
// // create simple graph
|
||||
// Vector b = Vector_(2, 0.0, 0.0);
|
||||
// LinearFactor::shared_ptr f1(new LinearFactor("x", eye(2), "y", eye(2), b,1));
|
||||
// LinearFactor::shared_ptr f2(new LinearFactor("z", eye(2), "w", eye(2), b,1));
|
||||
// LinearConstraint::shared_ptr f3(new LinearConstraint("y", eye(2), "z", eye(2), b));
|
||||
// fg.push_back(f1);
|
||||
// fg.push_back(f2);
|
||||
// fg.push_back_constraint(f3);
|
||||
//
|
||||
// CHECK(fg.is_constrained("y"));
|
||||
// CHECK(fg.is_constrained("z"));
|
||||
// CHECK(!fg.is_constrained("x"));
|
||||
// CHECK(!fg.is_constrained("w"));
|
||||
//}
|
||||
//
|
||||
///* ************************************************************************* */
|
||||
//TEST( ConstrainedLinearFactorGraph, get_constraint_separator )
|
||||
//{
|
||||
// ConstrainedLinearFactorGraph fg1 = createMultiConstraintGraph();
|
||||
// ConstrainedLinearFactorGraph fg2 = createMultiConstraintGraph();
|
||||
// LinearConstraint::shared_ptr lc1 = fg1.constraint_at(0);
|
||||
// LinearConstraint::shared_ptr lc2 = fg1.constraint_at(1);
|
||||
//
|
||||
// vector<LinearConstraint::shared_ptr> actual1 = fg1.find_constraints_and_remove("y");
|
||||
// CHECK(fg1.size() == 2);
|
||||
// CHECK(actual1.size() == 1);
|
||||
// CHECK((*actual1.begin())->equals(*lc1));
|
||||
//
|
||||
// vector<LinearConstraint::shared_ptr> actual2 = fg2.find_constraints_and_remove("x");
|
||||
// CHECK(fg2.size() == 1);
|
||||
// CHECK(actual2.size() == 2);
|
||||
// CHECK((*actual1.begin())->equals(*lc1));
|
||||
// LinearConstraint::shared_ptr act = *(++actual2.begin());
|
||||
// CHECK(act->equals(*lc2));
|
||||
//}
|
||||
//
|
||||
///* ************************************************************************* */
|
||||
//TEST( ConstrainedLinearFactorGraph, update_constraints )
|
||||
//{
|
||||
// // create a graph
|
||||
// ConstrainedLinearFactorGraph fg1 = createMultiConstraintGraph();
|
||||
//
|
||||
// // process constraints - picking first constraint on x
|
||||
// vector<LinearConstraint::shared_ptr> constraints = fg1.find_constraints_and_remove("x");
|
||||
// CHECK(constraints.size() == 2);
|
||||
// CHECK(fg1.size() == 1); // both constraints removed
|
||||
// LinearConstraint::shared_ptr primary = constraints[0];
|
||||
// LinearConstraint::shared_ptr updatee = constraints[1];
|
||||
// fg1.update_constraints("x", constraints, primary);
|
||||
// CHECK(fg1.size() == 2); // induced constraint added back
|
||||
//
|
||||
// // expected induced constraint
|
||||
// Matrix Ar(2,2);
|
||||
// Ar(0, 0) = -16.6666; Ar(0, 1) = -6.6666;
|
||||
// Ar(1, 0) = 10.0; Ar(1, 1) = 0.0;
|
||||
// Matrix A22(2,2);
|
||||
// A22(0,0) = 1.0 ; A22(0,1) = 1.0;
|
||||
// A22(1,0) = 1.0 ; A22(1,1) = 2.0;
|
||||
// Vector br = Vector_(2, 0.0, 5.0);
|
||||
// LinearConstraint::shared_ptr exp(new LinearConstraint("y", Ar, "z", A22, br));
|
||||
//
|
||||
// // evaluate
|
||||
// CHECK(assert_equal(*(fg1.constraint_at(0)), *exp, 1e-4));
|
||||
//}
|
||||
//
|
||||
///* ************************************************************************* */
|
||||
//TEST( ConstrainedLinearFactorGraph, find_constraints_and_remove )
|
||||
//{
|
||||
// // constraint 1
|
||||
// Matrix A11(2,2);
|
||||
// A11(0,0) = 1.0 ; A11(0,1) = 2.0;
|
||||
// A11(1,0) = 2.0 ; A11(1,1) = 1.0;
|
||||
//
|
||||
// Matrix A12(2,2);
|
||||
// A12(0,0) = 10.0 ; A12(0,1) = 0.0;
|
||||
// A12(1,0) = 0.0 ; A12(1,1) = 10.0;
|
||||
//
|
||||
// Vector b1(2);
|
||||
// b1(0) = 1.0; b1(1) = 2.0;
|
||||
// LinearConstraint::shared_ptr lc1(new LinearConstraint("x", A11, "y", A12, b1));
|
||||
//
|
||||
// // constraint 2
|
||||
// Matrix A21(2,2);
|
||||
// A21(0,0) = 3.0 ; A21(0,1) = 4.0;
|
||||
// A21(1,0) = -1.0 ; A21(1,1) = -2.0;
|
||||
//
|
||||
// Matrix A22(2,2);
|
||||
// A22(0,0) = 1.0 ; A22(0,1) = 1.0;
|
||||
// A22(1,0) = 1.0 ; A22(1,1) = 2.0;
|
||||
//
|
||||
// Vector b2(2);
|
||||
// b2(0) = 3.0; b2(1) = 4.0;
|
||||
// LinearConstraint::shared_ptr lc2(new LinearConstraint("x", A21, "z", A22, b2));
|
||||
//
|
||||
// // construct the graph
|
||||
// ConstrainedLinearFactorGraph fg1;
|
||||
// fg1.push_back_constraint(lc1);
|
||||
// fg1.push_back_constraint(lc2);
|
||||
//
|
||||
// // constraints on x
|
||||
// vector<LinearConstraint::shared_ptr> expected1, actual1;
|
||||
// expected1.push_back(lc1);
|
||||
// expected1.push_back(lc2);
|
||||
// actual1 = fg1.find_constraints_and_remove("x");
|
||||
// CHECK(fg1.size() == 0);
|
||||
// CHECK(expected1.size() == actual1.size());
|
||||
// vector<LinearConstraint::shared_ptr>::const_iterator exp1, act1;
|
||||
// for(exp1=expected1.begin(), act1=actual1.begin(); act1 != actual1.end(); ++act1, ++exp1) {
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// CHECK((*exp1)->equals(**act1));
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// }
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//}
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//
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||||
///* ************************************************************************* */
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//TEST( ConstrainedLinearFactorGraph, eliminate_multi_constraint )
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//{
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||||
// ConstrainedLinearFactorGraph fg = createMultiConstraintGraph();
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//
|
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// // eliminate the constraint
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// ConstrainedConditionalGaussian::shared_ptr cg1 = fg.eliminate_constraint("x");
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// CHECK(cg1->nrParents() == 1);
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// CHECK(fg.nrFactors() == 1);
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//
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// // eliminate the induced constraint
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// ConstrainedConditionalGaussian::shared_ptr cg2 = fg.eliminate_constraint("y");
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// CHECK(cg2->nrParents() == 1);
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// CHECK(fg.nrFactors() == 0);
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//
|
||||
// // eliminate the linear factor
|
||||
// ConditionalGaussian::shared_ptr cg3 = fg.eliminateOne("z");
|
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// CHECK(cg3->nrParents() == 0);
|
||||
// CHECK(fg.size() == 0);
|
||||
//
|
||||
// // solve piecewise
|
||||
// VectorConfig actual;
|
||||
// Vector act_z = cg3->solve(actual);
|
||||
// actual.insert("z", act_z);
|
||||
// CHECK(assert_equal(act_z, Vector_(2, -4.0, 5.0), 1e-4));
|
||||
// Vector act_y = cg2->solve(actual);
|
||||
// actual.insert("y", act_y);
|
||||
// CHECK(assert_equal(act_y, Vector_(2, -0.1, 0.4), 1e-4));
|
||||
// Vector act_x = cg1->solve(actual);
|
||||
// CHECK(assert_equal(act_x, Vector_(2, -2.0, 2.0), 1e-4));
|
||||
//}
|
||||
//
|
||||
///* ************************************************************************* */
|
||||
//TEST( ConstrainedLinearFactorGraph, optimize_multi_constraint )
|
||||
//{
|
||||
// ConstrainedLinearFactorGraph fg = createMultiConstraintGraph();
|
||||
// // solve the graph
|
||||
// Ordering ord;
|
||||
// ord.push_back("x");
|
||||
// ord.push_back("y");
|
||||
// ord.push_back("z");
|
||||
//
|
||||
// VectorConfig actual = fg.optimize(ord);
|
||||
//
|
||||
// // verify
|
||||
// VectorConfig expected;
|
||||
// expected.insert("x", Vector_(2, -2.0, 2.0));
|
||||
// expected.insert("y", Vector_(2, -0.1, 0.4));
|
||||
// expected.insert("z", Vector_(2, -4.0, 5.0));
|
||||
// CHECK(expected.size() == actual.size());
|
||||
// CHECK(assert_equal(expected["x"], actual["x"], 1e-4));
|
||||
// CHECK(assert_equal(expected["y"], actual["y"], 1e-4));
|
||||
// CHECK(assert_equal(expected["z"], actual["z"], 1e-4));
|
||||
//}
|
||||
|
||||
/* ************************************************************************* */
|
||||
// OLD TESTS - should be ported into the new structure when possible
|
||||
|
|
|
|||
|
|
@ -57,7 +57,7 @@ TEST( GaussianBayesNet, matrix )
|
|||
GaussianBayesNet cbn = createSmallGaussianBayesNet();
|
||||
|
||||
Matrix R; Vector d;
|
||||
boost::tie(R,d) = cbn.matrix(); // find matrix and RHS
|
||||
boost::tie(R,d) = matrix(cbn); // find matrix and RHS
|
||||
|
||||
Matrix R1 = Matrix_(2,2,
|
||||
1.0, 1.0,
|
||||
|
|
@ -73,7 +73,7 @@ TEST( GaussianBayesNet, matrix )
|
|||
TEST( GaussianBayesNet, optimize )
|
||||
{
|
||||
GaussianBayesNet cbn = createSmallGaussianBayesNet();
|
||||
boost::shared_ptr<VectorConfig> actual = cbn.optimize();
|
||||
VectorConfig actual = optimize(cbn);
|
||||
|
||||
VectorConfig expected;
|
||||
Vector x(1), y(1);
|
||||
|
|
@ -81,7 +81,7 @@ TEST( GaussianBayesNet, optimize )
|
|||
expected.insert("x",x);
|
||||
expected.insert("y",y);
|
||||
|
||||
CHECK(actual->equals(expected));
|
||||
CHECK(assert_equal(expected,actual));
|
||||
}
|
||||
|
||||
/* ************************************************************************* */
|
||||
|
|
@ -90,11 +90,11 @@ TEST( GaussianBayesNet, marginals )
|
|||
// create and marginalize a small Bayes net on "x"
|
||||
GaussianBayesNet cbn = createSmallGaussianBayesNet();
|
||||
Ordering keys("x");
|
||||
BayesNet<ConditionalGaussian> actual = marginals<LinearFactor>(cbn,keys);
|
||||
GaussianBayesNet actual = marginals<LinearFactor>(cbn,keys);
|
||||
|
||||
// expected is just scalar Gaussian on x
|
||||
GaussianBayesNet expected("x",4,sqrt(2));
|
||||
CHECK(assert_equal((BayesNet<ConditionalGaussian>)expected,actual));
|
||||
GaussianBayesNet expected = scalarGaussian("x",4,sqrt(2));
|
||||
CHECK(assert_equal(expected,actual));
|
||||
}
|
||||
|
||||
/* ************************************************************************* */
|
||||
|
|
|
|||
|
|
@ -315,8 +315,8 @@ TEST( LinearFactorGraph, eliminateAll )
|
|||
LinearFactorGraph fg1 = createLinearFactorGraph();
|
||||
Ordering ord1;
|
||||
ord1 += "x2","l1","x1";
|
||||
GaussianBayesNet::shared_ptr actual = fg1.eliminate(ord1);
|
||||
CHECK(assert_equal(expected,*actual,tol));
|
||||
GaussianBayesNet actual = fg1.eliminate(ord1);
|
||||
CHECK(assert_equal(expected,actual,tol));
|
||||
}
|
||||
|
||||
/* ************************************************************************* */
|
||||
|
|
@ -346,7 +346,7 @@ TEST( LinearFactorGraph, copying )
|
|||
// now eliminate the copy
|
||||
Ordering ord1;
|
||||
ord1 += "x2","l1","x1";
|
||||
GaussianBayesNet::shared_ptr actual1 = copy.eliminate(ord1);
|
||||
GaussianBayesNet actual1 = copy.eliminate(ord1);
|
||||
|
||||
// Create the same graph, but not by copying
|
||||
LinearFactorGraph expected = createLinearFactorGraph();
|
||||
|
|
@ -411,18 +411,17 @@ TEST( LinearFactorGraph, CONSTRUCTOR_GaussianBayesNet )
|
|||
// render with a given ordering
|
||||
Ordering ord;
|
||||
ord += "x2","l1","x1";
|
||||
GaussianBayesNet::shared_ptr CBN = fg.eliminate(ord);
|
||||
GaussianBayesNet CBN = fg.eliminate(ord);
|
||||
|
||||
// True LinearFactorGraph
|
||||
LinearFactorGraph fg2(*CBN);
|
||||
GaussianBayesNet::shared_ptr CBN2 = fg2.eliminate(ord);
|
||||
CHECK(CBN->equals(*CBN2));
|
||||
LinearFactorGraph fg2(CBN);
|
||||
GaussianBayesNet CBN2 = fg2.eliminate(ord);
|
||||
CHECK(assert_equal(CBN,CBN2));
|
||||
|
||||
// Base FactorGraph only
|
||||
FactorGraph<LinearFactor> fg3(*CBN);
|
||||
boost::shared_ptr<BayesNet<ConditionalGaussian> > CBN3 =
|
||||
_eliminate<LinearFactor,ConditionalGaussian>(fg3,ord);
|
||||
CHECK(CBN->equals(*CBN3));
|
||||
FactorGraph<LinearFactor> fg3(CBN);
|
||||
GaussianBayesNet CBN3 = _eliminate<LinearFactor,ConditionalGaussian>(fg3,ord);
|
||||
CHECK(assert_equal(CBN,CBN3));
|
||||
}
|
||||
|
||||
/* ************************************************************************* */
|
||||
|
|
|
|||
|
|
@ -38,9 +38,9 @@ TEST( SymbolicBayesNet, constructor )
|
|||
// eliminate it
|
||||
Ordering ordering;
|
||||
ordering += "x2","l1","x1";
|
||||
SymbolicBayesNet::shared_ptr actual = fg.eliminate(ordering);
|
||||
SymbolicBayesNet actual = fg.eliminate(ordering);
|
||||
|
||||
CHECK(assert_equal(expected, *actual));
|
||||
CHECK(assert_equal(expected, actual));
|
||||
}
|
||||
|
||||
/* ************************************************************************* */
|
||||
|
|
|
|||
|
|
@ -139,9 +139,9 @@ TEST( LinearFactorGraph, eliminate )
|
|||
// eliminate it
|
||||
Ordering ordering;
|
||||
ordering += "x2","l1","x1";
|
||||
SymbolicBayesNet::shared_ptr actual = fg.eliminate(ordering);
|
||||
SymbolicBayesNet actual = fg.eliminate(ordering);
|
||||
|
||||
CHECK(assert_equal(expected,*actual));
|
||||
CHECK(assert_equal(expected,actual));
|
||||
}
|
||||
|
||||
/* ************************************************************************* */
|
||||
|
|
|
|||
Loading…
Reference in New Issue