Merge pull request #1501 from borglab/fix-1496
Varun Agrawal
GitHub
commit
1121ece0eb
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@ -31,6 +31,7 @@ option(GTSAM_FORCE_STATIC_LIB "Force gtsam to be a static library,
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option(GTSAM_USE_QUATERNIONS "Enable/Disable using an internal Quaternion representation for rotations instead of rotation matrices. If enable, Rot3::EXPMAP is enforced by default." OFF)
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option(GTSAM_POSE3_EXPMAP "Enable/Disable using Pose3::EXPMAP as the default mode. If disabled, Pose3::FIRST_ORDER will be used." ON)
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option(GTSAM_ROT3_EXPMAP "Ignore if GTSAM_USE_QUATERNIONS is OFF (Rot3::EXPMAP by default). Otherwise, enable Rot3::EXPMAP, or if disabled, use Rot3::CAYLEY." ON)
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option(GTSAM_DT_MERGING "Enable/Disable merging of equal leaf nodes in DecisionTrees. This leads to significant speed up and memory savings." ON)
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option(GTSAM_ENABLE_CONSISTENCY_CHECKS "Enable/Disable expensive consistency checks" OFF)
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option(GTSAM_ENABLE_MEMORY_SANITIZER "Enable/Disable memory sanitizer" OFF)
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option(GTSAM_WITH_TBB "Use Intel Threaded Building Blocks (TBB) if available" ON)
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@ -90,6 +90,7 @@ print_enabled_config(${GTSAM_ENABLE_CONSISTENCY_CHECKS} "Runtime consistency c
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print_enabled_config(${GTSAM_ENABLE_MEMORY_SANITIZER} "Build with Memory Sanitizer ")
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print_enabled_config(${GTSAM_ROT3_EXPMAP} "Rot3 retract is full ExpMap ")
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print_enabled_config(${GTSAM_POSE3_EXPMAP} "Pose3 retract is full ExpMap ")
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print_enabled_config(${GTSAM_DT_MERGING} "Enable branch merging in DecisionTree")
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print_enabled_config(${GTSAM_ALLOW_DEPRECATED_SINCE_V43} "Allow features deprecated in GTSAM 4.3")
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print_enabled_config(${GTSAM_SUPPORT_NESTED_DISSECTION} "Metis-based Nested Dissection ")
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print_enabled_config(${GTSAM_TANGENT_PREINTEGRATION} "Use tangent-space preintegration")
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@ -39,6 +39,9 @@
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#cmakedefine GTSAM_ROT3_EXPMAP
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#endif
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// Whether to enable merging of equal leaf nodes in the Discrete Decision Tree.
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#cmakedefine GTSAM_DT_MERGING
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// Whether we are using TBB (if TBB was found and GTSAM_WITH_TBB is enabled in CMake)
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#cmakedefine GTSAM_USE_TBB
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@ -53,26 +53,17 @@ namespace gtsam {
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/** constant stored in this leaf */
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Y constant_;
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/** The number of assignments contained within this leaf.
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* Particularly useful when leaves have been pruned.
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*/
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size_t nrAssignments_;
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/// Default constructor for serialization.
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Leaf() {}
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/// Constructor from constant
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Leaf(const Y& constant, size_t nrAssignments = 1)
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: constant_(constant), nrAssignments_(nrAssignments) {}
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Leaf(const Y& constant) : constant_(constant) {}
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/// Return the constant
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const Y& constant() const {
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return constant_;
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}
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/// Return the number of assignments contained within this leaf.
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size_t nrAssignments() const { return nrAssignments_; }
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/// Leaf-Leaf equality
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bool sameLeaf(const Leaf& q) const override {
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return constant_ == q.constant_;
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@ -93,8 +84,7 @@ namespace gtsam {
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/// print
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void print(const std::string& s, const LabelFormatter& labelFormatter,
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const ValueFormatter& valueFormatter) const override {
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std::cout << s << " Leaf [" << nrAssignments() << "] "
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<< valueFormatter(constant_) << std::endl;
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std::cout << s << " Leaf " << valueFormatter(constant_) << std::endl;
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}
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/** Write graphviz format to stream `os`. */
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@ -114,14 +104,14 @@ namespace gtsam {
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/** apply unary operator */
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NodePtr apply(const Unary& op) const override {
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NodePtr f(new Leaf(op(constant_), nrAssignments_));
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NodePtr f(new Leaf(op(constant_)));
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return f;
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}
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/// Apply unary operator with assignment
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NodePtr apply(const UnaryAssignment& op,
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const Assignment<L>& assignment) const override {
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NodePtr f(new Leaf(op(assignment, constant_), nrAssignments_));
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NodePtr f(new Leaf(op(assignment, constant_)));
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return f;
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}
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@ -137,7 +127,7 @@ namespace gtsam {
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// Applying binary operator to two leaves results in a leaf
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NodePtr apply_g_op_fL(const Leaf& fL, const Binary& op) const override {
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// fL op gL
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NodePtr h(new Leaf(op(fL.constant_, constant_), nrAssignments_));
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NodePtr h(new Leaf(op(fL.constant_, constant_)));
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return h;
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}
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@ -148,7 +138,7 @@ namespace gtsam {
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/** choose a branch, create new memory ! */
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NodePtr choose(const L& label, size_t index) const override {
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return NodePtr(new Leaf(constant(), nrAssignments()));
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return NodePtr(new Leaf(constant()));
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}
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bool isLeaf() const override { return true; }
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@ -163,7 +153,6 @@ namespace gtsam {
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void serialize(ARCHIVE& ar, const unsigned int /*version*/) {
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ar & BOOST_SERIALIZATION_BASE_OBJECT_NVP(Base);
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ar& BOOST_SERIALIZATION_NVP(constant_);
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ar& BOOST_SERIALIZATION_NVP(nrAssignments_);
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}
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#endif
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}; // Leaf
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@ -199,26 +188,50 @@ namespace gtsam {
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#endif
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}
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/// If all branches of a choice node f are the same, just return a branch.
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static NodePtr Unique(const ChoicePtr& f) {
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#ifndef GTSAM_DT_NO_PRUNING
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if (f->allSame_) {
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assert(f->branches().size() > 0);
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NodePtr f0 = f->branches_[0];
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/**
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* @brief Merge branches with equal leaf values for every choice node in a
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* decision tree. If all branches are the same (i.e. have the same leaf
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* value), replace the choice node with the equivalent leaf node.
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*
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* This function applies the branch merging (if enabled) recursively on the
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* decision tree represented by the root node passed in as the argument. It
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* recurses to the leaf nodes and merges branches with equal leaf values in
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* a bottom-up fashion.
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*
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* Thus, if all branches of a choice node `f` are the same,
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* just return a single branch at each recursion step.
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*
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* @param node The root node of the decision tree.
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* @return NodePtr
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*/
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static NodePtr Unique(const NodePtr& node) {
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if (auto choice = std::dynamic_pointer_cast<const Choice>(node)) {
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// Choice node, we recurse!
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// Make non-const copy so we can update
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auto f = std::make_shared<Choice>(choice->label(), choice->nrChoices());
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size_t nrAssignments = 0;
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for(auto branch: f->branches()) {
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assert(branch->isLeaf());
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nrAssignments +=
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std::dynamic_pointer_cast<const Leaf>(branch)->nrAssignments();
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// Iterate over all the branches
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for (size_t i = 0; i < choice->nrChoices(); i++) {
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auto branch = choice->branches_[i];
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f->push_back(Unique(branch));
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}
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#ifdef GTSAM_DT_MERGING
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// If all the branches are the same, we can merge them into one
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if (f->allSame_) {
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assert(f->branches().size() > 0);
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NodePtr f0 = f->branches_[0];
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NodePtr newLeaf(
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new Leaf(std::dynamic_pointer_cast<const Leaf>(f0)->constant()));
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return newLeaf;
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}
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NodePtr newLeaf(
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new Leaf(std::dynamic_pointer_cast<const Leaf>(f0)->constant(),
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nrAssignments));
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return newLeaf;
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} else
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#endif
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return f;
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} else {
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// Leaf node, return as is
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return node;
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}
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}
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bool isLeaf() const override { return false; }
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@ -439,8 +452,10 @@ namespace gtsam {
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// second case, not label of interest, just recurse
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auto r = std::make_shared<Choice>(label_, branches_.size());
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for (auto&& branch : branches_)
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for (auto&& branch : branches_) {
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r->push_back(branch->choose(label, index));
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}
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return Unique(r);
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}
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@ -464,13 +479,11 @@ namespace gtsam {
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// DecisionTree
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/****************************************************************************/
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template<typename L, typename Y>
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DecisionTree<L, Y>::DecisionTree() {
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}
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DecisionTree<L, Y>::DecisionTree() {}
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template<typename L, typename Y>
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DecisionTree<L, Y>::DecisionTree(const NodePtr& root) :
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root_(root) {
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}
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root_(root) {}
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/****************************************************************************/
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template<typename L, typename Y>
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@ -586,7 +599,8 @@ namespace gtsam {
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auto choiceOnLabel = std::make_shared<Choice>(label, end - begin);
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for (Iterator it = begin; it != end; it++)
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choiceOnLabel->push_back(it->root_);
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return Choice::Unique(choiceOnLabel);
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// If no reordering, no need to call Choice::Unique
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return choiceOnLabel;
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} else {
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// Set up a new choice on the highest label
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auto choiceOnHighestLabel =
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@ -605,21 +619,21 @@ namespace gtsam {
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NodePtr fi = compose(functions.begin(), functions.end(), label);
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choiceOnHighestLabel->push_back(fi);
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}
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return Choice::Unique(choiceOnHighestLabel);
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return choiceOnHighestLabel;
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}
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}
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/****************************************************************************/
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// "create" is a bit of a complicated thing, but very useful.
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// "build" is a bit of a complicated thing, but very useful.
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// It takes a range of labels and a corresponding range of values,
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// and creates a decision tree, as follows:
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// and builds a decision tree, as follows:
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// - if there is only one label, creates a choice node with values in leaves
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// - otherwise, it evenly splits up the range of values and creates a tree for
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// each sub-range, and assigns that tree to first label's choices
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// Example:
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// create([B A],[1 2 3 4]) would call
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// create([A],[1 2])
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// create([A],[3 4])
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// build([B A],[1 2 3 4]) would call
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// build([A],[1 2])
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// build([A],[3 4])
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// and produce
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// B=0
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// A=0: 1
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@ -632,7 +646,7 @@ namespace gtsam {
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// However, it will be *way* faster if labels are given highest to lowest.
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template<typename L, typename Y>
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template<typename It, typename ValueIt>
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typename DecisionTree<L, Y>::NodePtr DecisionTree<L, Y>::create(
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typename DecisionTree<L, Y>::NodePtr DecisionTree<L, Y>::build(
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It begin, It end, ValueIt beginY, ValueIt endY) const {
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// get crucial counts
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size_t nrChoices = begin->second;
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@ -650,9 +664,10 @@ namespace gtsam {
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throw std::invalid_argument("DecisionTree::create invalid argument");
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}
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auto choice = std::make_shared<Choice>(begin->first, endY - beginY);
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for (ValueIt y = beginY; y != endY; y++)
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for (ValueIt y = beginY; y != endY; y++) {
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choice->push_back(NodePtr(new Leaf(*y)));
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return Choice::Unique(choice);
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}
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return choice;
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}
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// Recursive case: perform "Shannon expansion"
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@ -661,12 +676,27 @@ namespace gtsam {
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std::vector<DecisionTree> functions;
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size_t split = size / nrChoices;
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for (size_t i = 0; i < nrChoices; i++, beginY += split) {
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NodePtr f = create<It, ValueIt>(labelC, end, beginY, beginY + split);
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NodePtr f = build<It, ValueIt>(labelC, end, beginY, beginY + split);
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functions.emplace_back(f);
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}
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return compose(functions.begin(), functions.end(), begin->first);
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}
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/****************************************************************************/
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// Top-level factory method, which takes a range of labels and a corresponding
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// range of values, and creates a decision tree.
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template<typename L, typename Y>
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template<typename It, typename ValueIt>
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typename DecisionTree<L, Y>::NodePtr DecisionTree<L, Y>::create(
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It begin, It end, ValueIt beginY, ValueIt endY) const {
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auto node = build(begin, end, beginY, endY);
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if (auto choice = std::dynamic_pointer_cast<const Choice>(node)) {
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return Choice::Unique(choice);
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} else {
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return node;
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}
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}
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/****************************************************************************/
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template <typename L, typename Y>
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template <typename M, typename X>
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@ -681,7 +711,7 @@ namespace gtsam {
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// If leaf, apply unary conversion "op" and create a unique leaf.
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using MXLeaf = typename DecisionTree<M, X>::Leaf;
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if (auto leaf = std::dynamic_pointer_cast<const MXLeaf>(f)) {
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return NodePtr(new Leaf(Y_of_X(leaf->constant()), leaf->nrAssignments()));
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return NodePtr(new Leaf(Y_of_X(leaf->constant())));
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}
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// Check if Choice
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@ -699,7 +729,7 @@ namespace gtsam {
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for (auto&& branch : choice->branches()) {
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functions.emplace_back(convertFrom<M, X>(branch, L_of_M, Y_of_X));
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}
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return LY::compose(functions.begin(), functions.end(), newLabel);
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return Choice::Unique(LY::compose(functions.begin(), functions.end(), newLabel));
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}
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/****************************************************************************/
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@ -828,16 +858,6 @@ namespace gtsam {
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return total;
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}
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/****************************************************************************/
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template <typename L, typename Y>
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size_t DecisionTree<L, Y>::nrAssignments() const {
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size_t n = 0;
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this->visitLeaf([&n](const DecisionTree<L, Y>::Leaf& leaf) {
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n += leaf.nrAssignments();
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});
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return n;
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}
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/****************************************************************************/
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// fold is just done with a visit
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template <typename L, typename Y>
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@ -150,11 +150,19 @@ namespace gtsam {
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NodePtr root_;
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protected:
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/**
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/**
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* Internal recursive function to create from keys, cardinalities,
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* and Y values
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*/
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template<typename It, typename ValueIt>
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template <typename It, typename ValueIt>
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NodePtr build(It begin, It end, ValueIt beginY, ValueIt endY) const;
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/** Internal helper function to create from
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* keys, cardinalities, and Y values.
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* Calls `build` which builds thetree bottom-up,
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* before we prune in a top-down fashion.
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*/
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template <typename It, typename ValueIt>
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NodePtr create(It begin, It end, ValueIt beginY, ValueIt endY) const;
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/**
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@ -320,42 +328,6 @@ namespace gtsam {
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/// Return the number of leaves in the tree.
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size_t nrLeaves() const;
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/**
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* @brief This is a convenience function which returns the total number of
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* leaf assignments in the decision tree.
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* This function is not used for anymajor operations within the discrete
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* factor graph framework.
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*
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* Leaf assignments represent the cardinality of each leaf node, e.g. in a
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* binary tree each leaf has 2 assignments. This includes counts removed
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* from implicit pruning hence, it will always be >= nrLeaves().
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*
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* E.g. we have a decision tree as below, where each node has 2 branches:
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*
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* Choice(m1)
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* 0 Choice(m0)
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* 0 0 Leaf 0.0
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* 0 1 Leaf 0.0
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* 1 Choice(m0)
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* 1 0 Leaf 1.0
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* 1 1 Leaf 2.0
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*
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* In the unpruned form, the tree will have 4 assignments, 2 for each key,
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* and 4 leaves.
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*
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* In the pruned form, the number of assignments is still 4 but the number
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* of leaves is now 3, as below:
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*
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* Choice(m1)
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* 0 Leaf 0.0
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* 1 Choice(m0)
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* 1 0 Leaf 1.0
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* 1 1 Leaf 2.0
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*
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* @return size_t
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*/
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size_t nrAssignments() const;
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/**
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* @brief Fold a binary function over the tree, returning accumulator.
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*
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@ -20,7 +20,6 @@
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#include <gtsam/discrete/DiscreteKey.h> // make sure we have traits
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#include <gtsam/discrete/DiscreteValues.h>
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// headers first to make sure no missing headers
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//#define GTSAM_DT_NO_PRUNING
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#include <gtsam/discrete/AlgebraicDecisionTree.h>
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#include <gtsam/discrete/DecisionTree-inl.h> // for convert only
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#define DISABLE_TIMING
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@ -179,7 +178,11 @@ TEST(ADT, joint) {
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dot(joint, "Asia-ASTLBEX");
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joint = apply(joint, pD, &mul);
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dot(joint, "Asia-ASTLBEXD");
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#ifdef GTSAM_DT_MERGING
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EXPECT_LONGS_EQUAL(346, muls);
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#else
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EXPECT_LONGS_EQUAL(508, muls);
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#endif
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gttoc_(asiaJoint);
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tictoc_getNode(asiaJointNode, asiaJoint);
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elapsed = asiaJointNode->secs() + asiaJointNode->wall();
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@ -240,7 +243,11 @@ TEST(ADT, inference) {
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dot(joint, "Joint-Product-ASTLBEX");
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joint = apply(joint, pD, &mul);
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dot(joint, "Joint-Product-ASTLBEXD");
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#ifdef GTSAM_DT_MERGING
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EXPECT_LONGS_EQUAL(370, (long)muls); // different ordering
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#else
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EXPECT_LONGS_EQUAL(508, (long)muls); // different ordering
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#endif
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gttoc_(asiaProd);
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tictoc_getNode(asiaProdNode, asiaProd);
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elapsed = asiaProdNode->secs() + asiaProdNode->wall();
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|
|
@ -258,7 +265,11 @@ TEST(ADT, inference) {
|
|||
dot(marginal, "Joint-Sum-ADBLE");
|
||||
marginal = marginal.combine(E, &add_);
|
||||
dot(marginal, "Joint-Sum-ADBL");
|
||||
#ifdef GTSAM_DT_MERGING
|
||||
EXPECT_LONGS_EQUAL(161, (long)adds);
|
||||
#else
|
||||
EXPECT_LONGS_EQUAL(240, (long)adds);
|
||||
#endif
|
||||
gttoc_(asiaSum);
|
||||
tictoc_getNode(asiaSumNode, asiaSum);
|
||||
elapsed = asiaSumNode->secs() + asiaSumNode->wall();
|
||||
|
|
@ -296,7 +307,11 @@ TEST(ADT, factor_graph) {
|
|||
fg = apply(fg, pX, &mul);
|
||||
fg = apply(fg, pD, &mul);
|
||||
dot(fg, "FactorGraph");
|
||||
#ifdef GTSAM_DT_MERGING
|
||||
EXPECT_LONGS_EQUAL(158, (long)muls);
|
||||
#else
|
||||
EXPECT_LONGS_EQUAL(188, (long)muls);
|
||||
#endif
|
||||
gttoc_(asiaFG);
|
||||
tictoc_getNode(asiaFGNode, asiaFG);
|
||||
elapsed = asiaFGNode->secs() + asiaFGNode->wall();
|
||||
|
|
@ -315,7 +330,11 @@ TEST(ADT, factor_graph) {
|
|||
dot(fg, "Marginalized-3E");
|
||||
fg = fg.combine(L, &add_);
|
||||
dot(fg, "Marginalized-2L");
|
||||
#ifdef GTSAM_DT_MERGING
|
||||
LONGS_EQUAL(49, adds);
|
||||
#else
|
||||
LONGS_EQUAL(62, adds);
|
||||
#endif
|
||||
gttoc_(marg);
|
||||
tictoc_getNode(margNode, marg);
|
||||
elapsed = margNode->secs() + margNode->wall();
|
||||
|
|
|
|||
|
|
@ -18,7 +18,6 @@
|
|||
*/
|
||||
|
||||
// #define DT_DEBUG_MEMORY
|
||||
// #define GTSAM_DT_NO_PRUNING
|
||||
#define DISABLE_DOT
|
||||
#include <CppUnitLite/TestHarness.h>
|
||||
#include <gtsam/base/Testable.h>
|
||||
|
|
@ -234,7 +233,11 @@ TEST(DecisionTree, Example) {
|
|||
|
||||
// Test choose 0
|
||||
DT actual0 = notba.choose(A, 0);
|
||||
#ifdef GTSAM_DT_MERGING
|
||||
EXPECT(assert_equal(DT(0.0), actual0));
|
||||
#else
|
||||
EXPECT(assert_equal(DT({0.0, 0.0}), actual0));
|
||||
#endif
|
||||
DOT(actual0);
|
||||
|
||||
// Test choose 1
|
||||
|
|
@ -367,49 +370,6 @@ TEST(DecisionTree, Containers) {
|
|||
StringContainerTree converted(stringIntTree, container_of_int);
|
||||
}
|
||||
|
||||
/* ************************************************************************** */
|
||||
// Test nrAssignments.
|
||||
TEST(DecisionTree, NrAssignments) {
|
||||
const std::pair<string, size_t> A("A", 2), B("B", 2), C("C", 2);
|
||||
DT tree({A, B, C}, "1 1 1 1 1 1 1 1");
|
||||
EXPECT(tree.root_->isLeaf());
|
||||
auto leaf = std::dynamic_pointer_cast<const DT::Leaf>(tree.root_);
|
||||
EXPECT_LONGS_EQUAL(8, leaf->nrAssignments());
|
||||
|
||||
DT tree2({C, B, A}, "1 1 1 2 3 4 5 5");
|
||||
/* The tree is
|
||||
Choice(C)
|
||||
0 Choice(B)
|
||||
0 0 Leaf 1
|
||||
0 1 Choice(A)
|
||||
0 1 0 Leaf 1
|
||||
0 1 1 Leaf 2
|
||||
1 Choice(B)
|
||||
1 0 Choice(A)
|
||||
1 0 0 Leaf 3
|
||||
1 0 1 Leaf 4
|
||||
1 1 Leaf 5
|
||||
*/
|
||||
|
||||
auto root = std::dynamic_pointer_cast<const DT::Choice>(tree2.root_);
|
||||
CHECK(root);
|
||||
auto choice0 = std::dynamic_pointer_cast<const DT::Choice>(root->branches()[0]);
|
||||
CHECK(choice0);
|
||||
EXPECT(choice0->branches()[0]->isLeaf());
|
||||
auto choice00 = std::dynamic_pointer_cast<const DT::Leaf>(choice0->branches()[0]);
|
||||
CHECK(choice00);
|
||||
EXPECT_LONGS_EQUAL(2, choice00->nrAssignments());
|
||||
|
||||
auto choice1 = std::dynamic_pointer_cast<const DT::Choice>(root->branches()[1]);
|
||||
CHECK(choice1);
|
||||
auto choice10 = std::dynamic_pointer_cast<const DT::Choice>(choice1->branches()[0]);
|
||||
CHECK(choice10);
|
||||
auto choice11 = std::dynamic_pointer_cast<const DT::Leaf>(choice1->branches()[1]);
|
||||
CHECK(choice11);
|
||||
EXPECT(choice11->isLeaf());
|
||||
EXPECT_LONGS_EQUAL(2, choice11->nrAssignments());
|
||||
}
|
||||
|
||||
/* ************************************************************************** */
|
||||
// Test visit.
|
||||
TEST(DecisionTree, visit) {
|
||||
|
|
@ -449,10 +409,15 @@ TEST(DecisionTree, VisitWithPruned) {
|
|||
};
|
||||
tree.visitWith(func);
|
||||
|
||||
#ifdef GTSAM_DT_MERGING
|
||||
EXPECT_LONGS_EQUAL(6, choices.size());
|
||||
#else
|
||||
EXPECT_LONGS_EQUAL(8, choices.size());
|
||||
#endif
|
||||
|
||||
Assignment<string> expectedAssignment;
|
||||
|
||||
#ifdef GTSAM_DT_MERGING
|
||||
expectedAssignment = {{"B", 0}, {"C", 0}};
|
||||
EXPECT(expectedAssignment == choices.at(0));
|
||||
|
||||
|
|
@ -470,6 +435,25 @@ TEST(DecisionTree, VisitWithPruned) {
|
|||
|
||||
expectedAssignment = {{"A", 1}, {"B", 1}, {"C", 1}};
|
||||
EXPECT(expectedAssignment == choices.at(5));
|
||||
#else
|
||||
expectedAssignment = {{"A", 0}, {"B", 0}, {"C", 0}};
|
||||
EXPECT(expectedAssignment == choices.at(0));
|
||||
|
||||
expectedAssignment = {{"A", 1}, {"B", 0}, {"C", 0}};
|
||||
EXPECT(expectedAssignment == choices.at(1));
|
||||
|
||||
expectedAssignment = {{"A", 0}, {"B", 1}, {"C", 0}};
|
||||
EXPECT(expectedAssignment == choices.at(2));
|
||||
|
||||
expectedAssignment = {{"A", 1}, {"B", 1}, {"C", 0}};
|
||||
EXPECT(expectedAssignment == choices.at(3));
|
||||
|
||||
expectedAssignment = {{"A", 0}, {"B", 0}, {"C", 1}};
|
||||
EXPECT(expectedAssignment == choices.at(4));
|
||||
|
||||
expectedAssignment = {{"A", 1}, {"B", 0}, {"C", 1}};
|
||||
EXPECT(expectedAssignment == choices.at(5));
|
||||
#endif
|
||||
}
|
||||
|
||||
/* ************************************************************************** */
|
||||
|
|
@ -480,7 +464,11 @@ TEST(DecisionTree, fold) {
|
|||
DT tree(B, DT(A, 1, 1), DT(A, 2, 3));
|
||||
auto add = [](const int& y, double x) { return y + x; };
|
||||
double sum = tree.fold(add, 0.0);
|
||||
EXPECT_DOUBLES_EQUAL(6.0, sum, 1e-9); // Note, not 7, due to pruning!
|
||||
#ifdef GTSAM_DT_MERGING
|
||||
EXPECT_DOUBLES_EQUAL(6.0, sum, 1e-9); // Note, not 7, due to merging!
|
||||
#else
|
||||
EXPECT_DOUBLES_EQUAL(7.0, sum, 1e-9);
|
||||
#endif
|
||||
}
|
||||
|
||||
/* ************************************************************************** */
|
||||
|
|
@ -532,9 +520,14 @@ TEST(DecisionTree, threshold) {
|
|||
auto threshold = [](int value) { return value < 5 ? 0 : value; };
|
||||
DT thresholded(tree, threshold);
|
||||
|
||||
#ifdef GTSAM_DT_MERGING
|
||||
// Check number of leaves equal to zero now = 2
|
||||
// Note: it is 2, because the pruned branches are counted as 1!
|
||||
EXPECT_LONGS_EQUAL(2, thresholded.fold(count, 0));
|
||||
#else
|
||||
// if GTSAM_DT_MERGING is disabled, the count will be larger
|
||||
EXPECT_LONGS_EQUAL(5, thresholded.fold(count, 0));
|
||||
#endif
|
||||
}
|
||||
|
||||
/* ************************************************************************** */
|
||||
|
|
@ -570,8 +563,13 @@ TEST(DecisionTree, ApplyWithAssignment) {
|
|||
};
|
||||
DT prunedTree2 = prunedTree.apply(counter);
|
||||
|
||||
#ifdef GTSAM_DT_MERGING
|
||||
// Check if apply doesn't enumerate all leaves.
|
||||
EXPECT_LONGS_EQUAL(5, count);
|
||||
#else
|
||||
// if GTSAM_DT_MERGING is disabled, the count will be full
|
||||
EXPECT_LONGS_EQUAL(8, count);
|
||||
#endif
|
||||
}
|
||||
|
||||
/* ************************************************************************* */
|
||||
|
|
|
|||
|
|
@ -22,6 +22,8 @@
|
|||
#include <gtsam/discrete/DecisionTreeFactor.h>
|
||||
#include <gtsam/discrete/DiscreteConditional.h>
|
||||
#include <gtsam/inference/Symbol.h>
|
||||
#include <gtsam/discrete/DiscreteFactorGraph.h>
|
||||
#include <gtsam/discrete/DiscreteBayesNet.h>
|
||||
|
||||
|
||||
using namespace std;
|
||||
|
|
|
|||
|
|
@ -15,17 +15,20 @@
|
|||
* @author Duy-Nguyen Ta
|
||||
*/
|
||||
|
||||
#include <CppUnitLite/TestHarness.h>
|
||||
#include <gtsam/base/TestableAssertions.h>
|
||||
#include <gtsam/discrete/DiscreteBayesTree.h>
|
||||
#include <gtsam/discrete/DiscreteEliminationTree.h>
|
||||
#include <gtsam/discrete/DiscreteFactor.h>
|
||||
#include <gtsam/discrete/DiscreteFactorGraph.h>
|
||||
#include <gtsam/discrete/DiscreteEliminationTree.h>
|
||||
#include <gtsam/discrete/DiscreteBayesTree.h>
|
||||
#include <gtsam/inference/BayesNet.h>
|
||||
|
||||
#include <CppUnitLite/TestHarness.h>
|
||||
#include <gtsam/inference/Symbol.h>
|
||||
|
||||
using namespace std;
|
||||
using namespace gtsam;
|
||||
|
||||
using symbol_shorthand::M;
|
||||
|
||||
/* ************************************************************************* */
|
||||
TEST_UNSAFE(DiscreteFactorGraph, debugScheduler) {
|
||||
DiscreteKey PC(0, 4), ME(1, 4), AI(2, 4), A(3, 3);
|
||||
|
|
@ -345,6 +348,7 @@ TEST(DiscreteFactorGraph, markdown) {
|
|||
values[1] = 0;
|
||||
EXPECT_DOUBLES_EQUAL(0.3, graph[0]->operator()(values), 1e-9);
|
||||
}
|
||||
|
||||
/* ************************************************************************* */
|
||||
int main() {
|
||||
TestResult tr;
|
||||
|
|
|
|||
|
|
@ -70,8 +70,7 @@ Ordering HybridGaussianISAM::GetOrdering(
|
|||
/* ************************************************************************* */
|
||||
void HybridGaussianISAM::updateInternal(
|
||||
const HybridGaussianFactorGraph& newFactors,
|
||||
HybridBayesTree::Cliques* orphans,
|
||||
const std::optional<size_t>& maxNrLeaves,
|
||||
HybridBayesTree::Cliques* orphans, const std::optional<size_t>& maxNrLeaves,
|
||||
const std::optional<Ordering>& ordering,
|
||||
const HybridBayesTree::Eliminate& function) {
|
||||
// Remove the contaminated part of the Bayes tree
|
||||
|
|
@ -101,8 +100,8 @@ void HybridGaussianISAM::updateInternal(
|
|||
}
|
||||
|
||||
// eliminate all factors (top, added, orphans) into a new Bayes tree
|
||||
HybridBayesTree::shared_ptr bayesTree =
|
||||
factors.eliminateMultifrontal(elimination_ordering, function, std::cref(index));
|
||||
HybridBayesTree::shared_ptr bayesTree = factors.eliminateMultifrontal(
|
||||
elimination_ordering, function, std::cref(index));
|
||||
|
||||
if (maxNrLeaves) {
|
||||
bayesTree->prune(*maxNrLeaves);
|
||||
|
|
|
|||
|
|
@ -108,7 +108,7 @@ TEST(GaussianMixtureFactor, Printing) {
|
|||
std::string expected =
|
||||
R"(Hybrid [x1 x2; 1]{
|
||||
Choice(1)
|
||||
0 Leaf [1] :
|
||||
0 Leaf :
|
||||
A[x1] = [
|
||||
0;
|
||||
0
|
||||
|
|
@ -120,7 +120,7 @@ TEST(GaussianMixtureFactor, Printing) {
|
|||
b = [ 0 0 ]
|
||||
No noise model
|
||||
|
||||
1 Leaf [1] :
|
||||
1 Leaf :
|
||||
A[x1] = [
|
||||
0;
|
||||
0
|
||||
|
|
|
|||
|
|
@ -296,8 +296,12 @@ TEST(HybridBayesNet, UpdateDiscreteConditionals) {
|
|||
std::make_shared<DecisionTreeFactor>(
|
||||
discreteConditionals.prune(maxNrLeaves));
|
||||
|
||||
#ifdef GTSAM_DT_MERGING
|
||||
EXPECT_LONGS_EQUAL(maxNrLeaves + 2 /*2 zero leaves*/,
|
||||
prunedDecisionTree->nrLeaves());
|
||||
#else
|
||||
EXPECT_LONGS_EQUAL(8 /*full tree*/, prunedDecisionTree->nrLeaves());
|
||||
#endif
|
||||
|
||||
// regression
|
||||
DiscreteKeys dkeys{{M(0), 2}, {M(1), 2}, {M(2), 2}};
|
||||
|
|
|
|||
|
|
@ -481,6 +481,7 @@ TEST(HybridFactorGraph, Printing) {
|
|||
const auto [hybridBayesNet, remainingFactorGraph] =
|
||||
linearizedFactorGraph.eliminatePartialSequential(ordering);
|
||||
|
||||
#ifdef GTSAM_DT_MERGING
|
||||
string expected_hybridFactorGraph = R"(
|
||||
size: 7
|
||||
factor 0:
|
||||
|
|
@ -492,7 +493,7 @@ factor 0:
|
|||
factor 1:
|
||||
Hybrid [x0 x1; m0]{
|
||||
Choice(m0)
|
||||
0 Leaf [1] :
|
||||
0 Leaf :
|
||||
A[x0] = [
|
||||
-1
|
||||
]
|
||||
|
|
@ -502,7 +503,7 @@ Hybrid [x0 x1; m0]{
|
|||
b = [ -1 ]
|
||||
No noise model
|
||||
|
||||
1 Leaf [1] :
|
||||
1 Leaf :
|
||||
A[x0] = [
|
||||
-1
|
||||
]
|
||||
|
|
@ -516,7 +517,7 @@ Hybrid [x0 x1; m0]{
|
|||
factor 2:
|
||||
Hybrid [x1 x2; m1]{
|
||||
Choice(m1)
|
||||
0 Leaf [1] :
|
||||
0 Leaf :
|
||||
A[x1] = [
|
||||
-1
|
||||
]
|
||||
|
|
@ -526,7 +527,7 @@ Hybrid [x1 x2; m1]{
|
|||
b = [ -1 ]
|
||||
No noise model
|
||||
|
||||
1 Leaf [1] :
|
||||
1 Leaf :
|
||||
A[x1] = [
|
||||
-1
|
||||
]
|
||||
|
|
@ -550,18 +551,104 @@ factor 4:
|
|||
b = [ -10 ]
|
||||
No noise model
|
||||
factor 5: P( m0 ):
|
||||
Leaf [2] 0.5
|
||||
Leaf 0.5
|
||||
|
||||
factor 6: P( m1 | m0 ):
|
||||
Choice(m1)
|
||||
0 Choice(m0)
|
||||
0 0 Leaf [1] 0.33333333
|
||||
0 1 Leaf [1] 0.6
|
||||
0 0 Leaf 0.33333333
|
||||
0 1 Leaf 0.6
|
||||
1 Choice(m0)
|
||||
1 0 Leaf [1] 0.66666667
|
||||
1 1 Leaf [1] 0.4
|
||||
1 0 Leaf 0.66666667
|
||||
1 1 Leaf 0.4
|
||||
|
||||
)";
|
||||
#else
|
||||
string expected_hybridFactorGraph = R"(
|
||||
size: 7
|
||||
factor 0:
|
||||
A[x0] = [
|
||||
10
|
||||
]
|
||||
b = [ -10 ]
|
||||
No noise model
|
||||
factor 1:
|
||||
Hybrid [x0 x1; m0]{
|
||||
Choice(m0)
|
||||
0 Leaf:
|
||||
A[x0] = [
|
||||
-1
|
||||
]
|
||||
A[x1] = [
|
||||
1
|
||||
]
|
||||
b = [ -1 ]
|
||||
No noise model
|
||||
|
||||
1 Leaf:
|
||||
A[x0] = [
|
||||
-1
|
||||
]
|
||||
A[x1] = [
|
||||
1
|
||||
]
|
||||
b = [ -0 ]
|
||||
No noise model
|
||||
|
||||
}
|
||||
factor 2:
|
||||
Hybrid [x1 x2; m1]{
|
||||
Choice(m1)
|
||||
0 Leaf:
|
||||
A[x1] = [
|
||||
-1
|
||||
]
|
||||
A[x2] = [
|
||||
1
|
||||
]
|
||||
b = [ -1 ]
|
||||
No noise model
|
||||
|
||||
1 Leaf:
|
||||
A[x1] = [
|
||||
-1
|
||||
]
|
||||
A[x2] = [
|
||||
1
|
||||
]
|
||||
b = [ -0 ]
|
||||
No noise model
|
||||
|
||||
}
|
||||
factor 3:
|
||||
A[x1] = [
|
||||
10
|
||||
]
|
||||
b = [ -10 ]
|
||||
No noise model
|
||||
factor 4:
|
||||
A[x2] = [
|
||||
10
|
||||
]
|
||||
b = [ -10 ]
|
||||
No noise model
|
||||
factor 5: P( m0 ):
|
||||
Choice(m0)
|
||||
0 Leaf 0.5
|
||||
1 Leaf 0.5
|
||||
|
||||
factor 6: P( m1 | m0 ):
|
||||
Choice(m1)
|
||||
0 Choice(m0)
|
||||
0 0 Leaf 0.33333333
|
||||
0 1 Leaf 0.6
|
||||
1 Choice(m0)
|
||||
1 0 Leaf 0.66666667
|
||||
1 1 Leaf 0.4
|
||||
|
||||
)";
|
||||
#endif
|
||||
|
||||
EXPECT(assert_print_equal(expected_hybridFactorGraph, linearizedFactorGraph));
|
||||
|
||||
// Expected output for hybridBayesNet.
|
||||
|
|
@ -570,13 +657,13 @@ size: 3
|
|||
conditional 0: Hybrid P( x0 | x1 m0)
|
||||
Discrete Keys = (m0, 2),
|
||||
Choice(m0)
|
||||
0 Leaf [1] p(x0 | x1)
|
||||
0 Leaf p(x0 | x1)
|
||||
R = [ 10.0499 ]
|
||||
S[x1] = [ -0.0995037 ]
|
||||
d = [ -9.85087 ]
|
||||
No noise model
|
||||
|
||||
1 Leaf [1] p(x0 | x1)
|
||||
1 Leaf p(x0 | x1)
|
||||
R = [ 10.0499 ]
|
||||
S[x1] = [ -0.0995037 ]
|
||||
d = [ -9.95037 ]
|
||||
|
|
@ -586,26 +673,26 @@ conditional 1: Hybrid P( x1 | x2 m0 m1)
|
|||
Discrete Keys = (m0, 2), (m1, 2),
|
||||
Choice(m1)
|
||||
0 Choice(m0)
|
||||
0 0 Leaf [1] p(x1 | x2)
|
||||
0 0 Leaf p(x1 | x2)
|
||||
R = [ 10.099 ]
|
||||
S[x2] = [ -0.0990196 ]
|
||||
d = [ -9.99901 ]
|
||||
No noise model
|
||||
|
||||
0 1 Leaf [1] p(x1 | x2)
|
||||
0 1 Leaf p(x1 | x2)
|
||||
R = [ 10.099 ]
|
||||
S[x2] = [ -0.0990196 ]
|
||||
d = [ -9.90098 ]
|
||||
No noise model
|
||||
|
||||
1 Choice(m0)
|
||||
1 0 Leaf [1] p(x1 | x2)
|
||||
1 0 Leaf p(x1 | x2)
|
||||
R = [ 10.099 ]
|
||||
S[x2] = [ -0.0990196 ]
|
||||
d = [ -10.098 ]
|
||||
No noise model
|
||||
|
||||
1 1 Leaf [1] p(x1 | x2)
|
||||
1 1 Leaf p(x1 | x2)
|
||||
R = [ 10.099 ]
|
||||
S[x2] = [ -0.0990196 ]
|
||||
d = [ -10 ]
|
||||
|
|
@ -615,14 +702,14 @@ conditional 2: Hybrid P( x2 | m0 m1)
|
|||
Discrete Keys = (m0, 2), (m1, 2),
|
||||
Choice(m1)
|
||||
0 Choice(m0)
|
||||
0 0 Leaf [1] p(x2)
|
||||
0 0 Leaf p(x2)
|
||||
R = [ 10.0494 ]
|
||||
d = [ -10.1489 ]
|
||||
mean: 1 elements
|
||||
x2: -1.0099
|
||||
No noise model
|
||||
|
||||
0 1 Leaf [1] p(x2)
|
||||
0 1 Leaf p(x2)
|
||||
R = [ 10.0494 ]
|
||||
d = [ -10.1479 ]
|
||||
mean: 1 elements
|
||||
|
|
@ -630,14 +717,14 @@ conditional 2: Hybrid P( x2 | m0 m1)
|
|||
No noise model
|
||||
|
||||
1 Choice(m0)
|
||||
1 0 Leaf [1] p(x2)
|
||||
1 0 Leaf p(x2)
|
||||
R = [ 10.0494 ]
|
||||
d = [ -10.0504 ]
|
||||
mean: 1 elements
|
||||
x2: -1.0001
|
||||
No noise model
|
||||
|
||||
1 1 Leaf [1] p(x2)
|
||||
1 1 Leaf p(x2)
|
||||
R = [ 10.0494 ]
|
||||
d = [ -10.0494 ]
|
||||
mean: 1 elements
|
||||
|
|
|
|||
|
|
@ -63,8 +63,8 @@ TEST(MixtureFactor, Printing) {
|
|||
R"(Hybrid [x1 x2; 1]
|
||||
MixtureFactor
|
||||
Choice(1)
|
||||
0 Leaf [1] Nonlinear factor on 2 keys
|
||||
1 Leaf [1] Nonlinear factor on 2 keys
|
||||
0 Leaf Nonlinear factor on 2 keys
|
||||
1 Leaf Nonlinear factor on 2 keys
|
||||
)";
|
||||
EXPECT(assert_print_equal(expected, mixtureFactor));
|
||||
}
|
||||
|
|
|
|||
|
|
@ -10,10 +10,11 @@ Author: Fan Jiang
|
|||
"""
|
||||
import unittest
|
||||
|
||||
import gtsam
|
||||
import numpy as np
|
||||
from gtsam.utils.test_case import GtsamTestCase
|
||||
|
||||
import gtsam
|
||||
|
||||
|
||||
class TestRobust(GtsamTestCase):
|
||||
|
||||
|
|
@ -37,7 +38,7 @@ class TestRobust(GtsamTestCase):
|
|||
v = gtsam.Values()
|
||||
v.insert(0, 0.0)
|
||||
|
||||
self.assertAlmostEquals(f.error(v), 0.125)
|
||||
self.assertAlmostEqual(f.error(v), 0.125)
|
||||
|
||||
if __name__ == "__main__":
|
||||
unittest.main()
|
||||
|
|
|
|||
Loading…
Reference in New Issue