Minor change to shortcut
Abhijit Kundu
parent
2d0ce1c3ca
commit
bfc033d3f7
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@ -102,114 +102,112 @@ namespace gtsam {
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return changed;
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return changed;
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}
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}
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/* ************************************************************************* */
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/* ************************************************************************* */
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// The shortcut density is a conditional P(S|R) of the separator of this
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// The shortcut density is a conditional P(S|R) of the separator of this
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// clique on the root. We can compute it recursively from the parent shortcut
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// clique on the root. We can compute it recursively from the parent shortcut
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// P(Sp|R) as \int P(Fp|Sp) P(Sp|R), where Fp are the frontal nodes in p
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// P(Sp|R) as \int P(Fp|Sp) P(Sp|R), where Fp are the frontal nodes in p
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/* ************************************************************************* */
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/* ************************************************************************* */
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template<class DERIVED, class CONDITIONAL>
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template<class DERIVED, class CONDITIONAL>
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BayesNet<CONDITIONAL> BayesTreeCliqueBase<DERIVED, CONDITIONAL>::shortcut(
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BayesNet<CONDITIONAL> BayesTreeCliqueBase<DERIVED, CONDITIONAL>::shortcut(
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derived_ptr R, Eliminate function) const{
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derived_ptr R, Eliminate function) const{
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static const bool debug = false;
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static const bool debug = false;
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BayesNet<ConditionalType> p_S_R; //shortcut P(S|R)
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BayesNet<ConditionalType> p_S_R; //shortcut P(S|R) This is empty now
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//Check if the ShortCut already exists
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//Check if the ShortCut already exists
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if(!cachedShortcut_){
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if(!cachedShortcut_){
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// A first base case is when this clique or its parent is the root,
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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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// in which case we return an empty Bayes net.
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derived_ptr parent(parent_.lock());
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derived_ptr parent(parent_.lock());
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if (R.get() != this && parent != R) {
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if (R.get() == this || parent == R) {
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// The root conditional
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BayesNet<ConditionalType> empty;
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FactorGraph<FactorType> p_R(BayesNet<ConditionalType>(R->conditional()));
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return empty;
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}
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// The root conditional
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// The parent clique has a ConditionalType for each frontal node in Fp
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FactorGraph<FactorType> p_R(BayesNet<ConditionalType>(R->conditional()));
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// so we can obtain P(Fp|Sp) in factor graph form
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FactorGraph<FactorType> p_Fp_Sp(BayesNet<ConditionalType>(parent->conditional()));
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// The parent clique has a ConditionalType for each frontal node in Fp
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// If not the base case, obtain the parent shortcut P(Sp|R) as factors
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// so we can obtain P(Fp|Sp) in factor graph form
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FactorGraph<FactorType> p_Sp_R(parent->shortcut(R, function));
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FactorGraph<FactorType> p_Fp_Sp(BayesNet<ConditionalType>(parent->conditional()));
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// If not the base case, obtain the parent shortcut P(Sp|R) as factors
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// now combine P(Cp|R) = P(Fp|Sp) * P(Sp|R)
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FactorGraph<FactorType> p_Sp_R(parent->shortcut(R, function));
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FactorGraph<FactorType> p_Cp_R;
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p_Cp_R.push_back(p_R);
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p_Cp_R.push_back(p_Fp_Sp);
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p_Cp_R.push_back(p_Sp_R);
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// now combine P(Cp|R) = P(Fp|Sp) * P(Sp|R)
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// Eliminate into a Bayes net with ordering designed to integrate out
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FactorGraph<FactorType> p_Cp_R;
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// any variables not in *our* separator. Variables to integrate out must be
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p_Cp_R.push_back(p_R);
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// eliminated first hence the desired ordering is [Cp\S S].
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p_Cp_R.push_back(p_Fp_Sp);
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// However, an added wrinkle is that Cp might overlap with the root.
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p_Cp_R.push_back(p_Sp_R);
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// Keys corresponding to the root should not be added to the ordering at all.
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// Eliminate into a Bayes net with ordering designed to integrate out
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if(debug) {
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// any variables not in *our* separator. Variables to integrate out must be
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p_R.print("p_R: ");
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// eliminated first hence the desired ordering is [Cp\S S].
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p_Fp_Sp.print("p_Fp_Sp: ");
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// However, an added wrinkle is that Cp might overlap with the root.
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p_Sp_R.print("p_Sp_R: ");
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// Keys corresponding to the root should not be added to the ordering at all.
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}
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if(debug) {
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// We want to factor into a conditional of the clique variables given the
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p_R.print("p_R: ");
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// root and the marginal on the root, integrating out all other variables.
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p_Fp_Sp.print("p_Fp_Sp: ");
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// The integrands include any parents of this clique and the variables of
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p_Sp_R.print("p_Sp_R: ");
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// the parent clique.
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}
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FastSet<Index> variablesAtBack;
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FastSet<Index> separator;
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size_t uniqueRootVariables = 0;
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BOOST_FOREACH(const Index separatorIndex, this->conditional()->parents()) {
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variablesAtBack.insert(separatorIndex);
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separator.insert(separatorIndex);
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if(debug) std::cout << "At back (this): " << separatorIndex << std::endl;
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}
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BOOST_FOREACH(const Index key, R->conditional()->keys()) {
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if(variablesAtBack.insert(key).second)
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++ uniqueRootVariables;
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if(debug) std::cout << "At back (root): " << key << std::endl;
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}
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// We want to factor into a conditional of the clique variables given the
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Permutation toBack = Permutation::PushToBack(
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// root and the marginal on the root, integrating out all other variables.
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std::vector<Index>(variablesAtBack.begin(), variablesAtBack.end()),
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// The integrands include any parents of this clique and the variables of
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R->conditional()->lastFrontalKey() + 1);
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// the parent clique.
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Permutation::shared_ptr toBackInverse(toBack.inverse());
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FastSet<Index> variablesAtBack;
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BOOST_FOREACH(const typename FactorType::shared_ptr& factor, p_Cp_R) {
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FastSet<Index> separator;
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factor->permuteWithInverse(*toBackInverse); }
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size_t uniqueRootVariables = 0;
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typename BayesNet<ConditionalType>::shared_ptr eliminated(EliminationTree<
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BOOST_FOREACH(const Index separatorIndex, this->conditional()->parents()) {
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FactorType>::Create(p_Cp_R)->eliminate(function));
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variablesAtBack.insert(separatorIndex);
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separator.insert(separatorIndex);
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if(debug) std::cout << "At back (this): " << separatorIndex << std::endl;
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}
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BOOST_FOREACH(const Index key, R->conditional()->keys()) {
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if(variablesAtBack.insert(key).second)
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++ uniqueRootVariables;
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if(debug) std::cout << "At back (root): " << key << std::endl;
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}
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Permutation toBack = Permutation::PushToBack(
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// Take only the conditionals for p(S|R). We check for each variable being
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std::vector<Index>(variablesAtBack.begin(), variablesAtBack.end()),
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// in the separator set because if some separator variables overlap with
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R->conditional()->lastFrontalKey() + 1);
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// root variables, we cannot rely on the number of root variables, and also
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Permutation::shared_ptr toBackInverse(toBack.inverse());
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// want to include those variables in the conditional.
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BOOST_FOREACH(const typename FactorType::shared_ptr& factor, p_Cp_R) {
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BOOST_REVERSE_FOREACH(typename ConditionalType::shared_ptr conditional, *eliminated) {
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factor->permuteWithInverse(*toBackInverse); }
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assert(conditional->nrFrontals() == 1);
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typename BayesNet<ConditionalType>::shared_ptr eliminated(EliminationTree<
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if(separator.find(toBack[conditional->firstFrontalKey()]) != separator.end()) {
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FactorType>::Create(p_Cp_R)->eliminate(function));
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if(debug)
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conditional->print("Taking C|R conditional: ");
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p_S_R.push_front(conditional);
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}
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if(p_S_R.size() == separator.size())
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break;
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}
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// Take only the conditionals for p(S|R). We check for each variable being
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// Undo the permutation
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// in the separator set because if some separator variables overlap with
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if(debug) toBack.print("toBack: ");
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// root variables, we cannot rely on the number of root variables, and also
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p_S_R.permuteWithInverse(toBack);
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// want to include those variables in the conditional.
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}
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BOOST_REVERSE_FOREACH(typename ConditionalType::shared_ptr conditional, *eliminated) {
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assert(conditional->nrFrontals() == 1);
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if(separator.find(toBack[conditional->firstFrontalKey()]) != separator.end()) {
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if(debug)
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conditional->print("Taking C|R conditional: ");
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p_S_R.push_front(conditional);
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}
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if(p_S_R.size() == separator.size())
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break;
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}
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// Undo the permutation
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cachedShortcut_ = p_S_R;
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if(debug) toBack.print("toBack: ");
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}
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p_S_R.permuteWithInverse(toBack);
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else
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p_S_R = *cachedShortcut_; // return the cached version
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assertInvariants();
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assertInvariants();
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cachedShortcut_ = p_S_R;
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}
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else
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p_S_R = *cachedShortcut_;
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// return the shortcut P(S|R)
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// return the shortcut P(S|R)
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return p_S_R;
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return p_S_R;
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}
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}
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/* ************************************************************************* */
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/* ************************************************************************* */
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