398 lines
13 KiB
C++
398 lines
13 KiB
C++
/* ----------------------------------------------------------------------------
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* GTSAM Copyright 2010, Georgia Tech Research Corporation,
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* Atlanta, Georgia 30332-0415
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* All Rights Reserved
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* Authors: Frank Dellaert, et al. (see THANKS for the full author list)
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* See LICENSE for the license information
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* -------------------------------------------------------------------------- */
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/**
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* @file testBayesTree.cpp
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* @brief Unit tests for Bayes Tree
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* @author Frank Dellaert
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* @author Michael Kaess
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* @author Viorela Ila
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*/
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#include <boost/assign/std/list.hpp> // for operator +=
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using namespace boost::assign;
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#include <gtsam/CppUnitLite/TestHarness.h>
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#include <gtsam/base/TestableAssertions.h>
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#include <gtsam/inference/SymbolicFactorGraph.h>
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#include <gtsam/inference/BayesTree-inl.h>
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#include <gtsam/inference/IndexFactor.h>
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#include <gtsam/inference/SymbolicSequentialSolver.h>
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using namespace gtsam;
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typedef BayesTree<IndexConditional> SymbolicBayesTree;
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///* ************************************************************************* */
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//// SLAM example from RSS sqrtSAM paper
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static const Index _x3_=0, _x2_=1, _x1_=2, _l2_=3, _l1_=4;
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//IndexConditional::shared_ptr
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// x3(new IndexConditional(_x3_)),
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// x2(new IndexConditional(_x2_,_x3_)),
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// x1(new IndexConditional(_x1_,_x2_,_x3_)),
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// l1(new IndexConditional(_l1_,_x1_,_x2_)),
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// l2(new IndexConditional(_l2_,_x1_,_x3_));
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//
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//// Bayes Tree for sqrtSAM example
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//SymbolicBayesTree createSlamSymbolicBayesTree(){
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// // Create using insert
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//// Ordering slamOrdering; slamOrdering += _x3_, _x2_, _x1_, _l2_, _l1_;
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// SymbolicBayesTree bayesTree_slam;
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// bayesTree_slam.insert(x3);
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// bayesTree_slam.insert(x2);
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// bayesTree_slam.insert(x1);
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// bayesTree_slam.insert(l2);
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// bayesTree_slam.insert(l1);
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// return bayesTree_slam;
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//}
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/* ************************************************************************* */
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// Conditionals for ASIA example from the tutorial with A and D evidence
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static const Index _X_=0, _T_=1, _S_=2, _E_=3, _L_=4, _B_=5;
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IndexConditional::shared_ptr
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B(new IndexConditional(_B_)),
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L(new IndexConditional(_L_, _B_)),
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E(new IndexConditional(_E_, _L_, _B_)),
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S(new IndexConditional(_S_, _L_, _B_)),
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T(new IndexConditional(_T_, _E_, _L_)),
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X(new IndexConditional(_X_, _E_));
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// Bayes Tree for Asia example
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SymbolicBayesTree createAsiaSymbolicBayesTree() {
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SymbolicBayesTree bayesTree;
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// Ordering asiaOrdering; asiaOrdering += _X_, _T_, _S_, _E_, _L_, _B_;
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bayesTree.insert(B);
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bayesTree.insert(L);
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bayesTree.insert(E);
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bayesTree.insert(S);
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bayesTree.insert(T);
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bayesTree.insert(X);
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return bayesTree;
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}
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/* ************************************************************************* */
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TEST( BayesTree, constructor )
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{
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// Create using insert
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SymbolicBayesTree bayesTree = createAsiaSymbolicBayesTree();
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// Check Size
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LONGS_EQUAL(4,bayesTree.size());
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// Check root
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BayesNet<IndexConditional> expected_root;
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expected_root.push_back(E);
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expected_root.push_back(L);
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expected_root.push_back(B);
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boost::shared_ptr<BayesNet<IndexConditional> > actual_root = bayesTree.root();
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CHECK(assert_equal(expected_root,*actual_root));
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// Create from symbolic Bayes chain in which we want to discover cliques
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BayesNet<IndexConditional> ASIA;
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ASIA.push_back(X);
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ASIA.push_back(T);
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ASIA.push_back(S);
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ASIA.push_back(E);
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ASIA.push_back(L);
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ASIA.push_back(B);
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SymbolicBayesTree bayesTree2(ASIA);
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// Check whether the same
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CHECK(assert_equal(bayesTree,bayesTree2));
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// CHECK findParentClique, should *not depend on order of parents*
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// Ordering ordering; ordering += _X_, _T_, _S_, _E_, _L_, _B_;
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// IndexTable<Symbol> index(ordering);
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list<Index> parents1; parents1 += _E_, _L_;
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CHECK(assert_equal(_E_, bayesTree.findParentClique(parents1)));
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list<Index> parents2; parents2 += _L_, _E_;
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CHECK(assert_equal(_E_, bayesTree.findParentClique(parents2)));
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list<Index> parents3; parents3 += _L_, _B_;
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CHECK(assert_equal(_L_, bayesTree.findParentClique(parents3)));
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}
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/* ************************************************************************* */
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TEST(BayesTree, clear)
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{
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// SymbolicBayesTree bayesTree = createAsiaSymbolicBayesTree();
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// bayesTree.clear();
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//
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// SymbolicBayesTree expected;
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//
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// // Check whether cleared BayesTree is equal to a new BayesTree
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// CHECK(assert_equal(expected, bayesTree));
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}
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/* ************************************************************************* *
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Bayes Tree for testing conversion to a forest of orphans needed for incremental.
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A,B
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C|A E|B
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D|C F|E
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*/
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/* ************************************************************************* */
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TEST( BayesTree, removePath )
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{
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const Index _A_=5, _B_=4, _C_=3, _D_=2, _E_=1, _F_=0;
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IndexConditional::shared_ptr
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A(new IndexConditional(_A_)),
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B(new IndexConditional(_B_, _A_)),
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C(new IndexConditional(_C_, _A_)),
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D(new IndexConditional(_D_, _C_)),
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E(new IndexConditional(_E_, _B_)),
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F(new IndexConditional(_F_, _E_));
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SymbolicBayesTree bayesTree;
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// Ordering ord; ord += _A_,_B_,_C_,_D_,_E_,_F_;
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bayesTree.insert(A);
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bayesTree.insert(B);
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bayesTree.insert(C);
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bayesTree.insert(D);
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bayesTree.insert(E);
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bayesTree.insert(F);
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// remove C, expected outcome: factor graph with ABC,
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// Bayes Tree now contains two orphan trees: D|C and E|B,F|E
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SymbolicFactorGraph expected;
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expected.push_factor(_B_,_A_);
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expected.push_factor(_A_);
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expected.push_factor(_C_,_A_);
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SymbolicBayesTree::Cliques expectedOrphans;
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expectedOrphans += bayesTree[_D_], bayesTree[_E_];
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BayesNet<IndexConditional> bn;
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SymbolicBayesTree::Cliques orphans;
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bayesTree.removePath(bayesTree[_C_], bn, orphans);
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SymbolicFactorGraph factors(bn);
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CHECK(assert_equal((SymbolicFactorGraph)expected, factors));
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CHECK(assert_equal(expectedOrphans, orphans));
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// remove E: factor graph with EB; E|B removed from second orphan tree
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SymbolicFactorGraph expected2;
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expected2.push_factor(_E_,_B_);
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SymbolicBayesTree::Cliques expectedOrphans2;
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expectedOrphans2 += bayesTree[_F_];
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BayesNet<IndexConditional> bn2;
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SymbolicBayesTree::Cliques orphans2;
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bayesTree.removePath(bayesTree[_E_], bn2, orphans2);
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SymbolicFactorGraph factors2(bn2);
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CHECK(assert_equal((SymbolicFactorGraph)expected2, factors2));
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CHECK(assert_equal(expectedOrphans2, orphans2));
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}
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/* ************************************************************************* */
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TEST( BayesTree, removePath2 )
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{
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SymbolicBayesTree bayesTree = createAsiaSymbolicBayesTree();
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// Call remove-path with clique B
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BayesNet<IndexConditional> bn;
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SymbolicBayesTree::Cliques orphans;
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bayesTree.removePath(bayesTree[_B_], bn, orphans);
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SymbolicFactorGraph factors(bn);
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// Check expected outcome
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SymbolicFactorGraph expected;
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expected.push_factor(_E_,_L_,_B_);
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expected.push_factor(_L_,_B_);
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expected.push_factor(_B_);
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CHECK(assert_equal(expected, factors));
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SymbolicBayesTree::Cliques expectedOrphans;
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expectedOrphans += bayesTree[_S_], bayesTree[_T_], bayesTree[_X_];
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CHECK(assert_equal(expectedOrphans, orphans));
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}
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/* ************************************************************************* */
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TEST( BayesTree, removePath3 )
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{
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SymbolicBayesTree bayesTree = createAsiaSymbolicBayesTree();
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// Call remove-path with clique S
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BayesNet<IndexConditional> bn;
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SymbolicBayesTree::Cliques orphans;
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bayesTree.removePath(bayesTree[_S_], bn, orphans);
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SymbolicFactorGraph factors(bn);
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// Check expected outcome
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SymbolicFactorGraph expected;
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expected.push_factor(_E_,_L_,_B_);
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expected.push_factor(_L_,_B_);
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expected.push_factor(_B_);
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expected.push_factor(_S_,_L_,_B_);
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CHECK(assert_equal(expected, factors));
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SymbolicBayesTree::Cliques expectedOrphans;
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expectedOrphans += bayesTree[_T_], bayesTree[_X_];
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CHECK(assert_equal(expectedOrphans, orphans));
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}
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/* ************************************************************************* */
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TEST( BayesTree, removeTop )
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{
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SymbolicBayesTree bayesTree = createAsiaSymbolicBayesTree();
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// create a new factor to be inserted
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boost::shared_ptr<IndexFactor> newFactor(new IndexFactor(_S_,_B_));
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// Remove the contaminated part of the Bayes tree
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BayesNet<IndexConditional> bn;
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SymbolicBayesTree::Cliques orphans;
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list<Index> keys; keys += _B_,_S_;
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bayesTree.removeTop(keys, bn, orphans);
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SymbolicFactorGraph factors(bn);
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// Check expected outcome
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SymbolicFactorGraph expected;
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expected.push_factor(_E_,_L_,_B_);
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expected.push_factor(_L_,_B_);
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expected.push_factor(_B_);
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expected.push_factor(_S_,_L_,_B_);
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CHECK(assert_equal(expected, factors));
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SymbolicBayesTree::Cliques expectedOrphans;
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expectedOrphans += bayesTree[_T_], bayesTree[_X_];
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CHECK(assert_equal(expectedOrphans, orphans));
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// Try removeTop again with a factor that should not change a thing
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boost::shared_ptr<IndexFactor> newFactor2(new IndexFactor(_B_));
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BayesNet<IndexConditional> bn2;
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SymbolicBayesTree::Cliques orphans2;
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keys.clear(); keys += _B_;
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bayesTree.removeTop(keys, bn2, orphans2);
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SymbolicFactorGraph factors2(bn2);
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SymbolicFactorGraph expected2;
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CHECK(assert_equal(expected2, factors2));
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SymbolicBayesTree::Cliques expectedOrphans2;
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CHECK(assert_equal(expectedOrphans2, orphans2));
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}
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/* ************************************************************************* */
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TEST( BayesTree, removeTop2 )
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{
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SymbolicBayesTree bayesTree = createAsiaSymbolicBayesTree();
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// create two factors to be inserted
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SymbolicFactorGraph newFactors;
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newFactors.push_factor(_B_);
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newFactors.push_factor(_S_);
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// Remove the contaminated part of the Bayes tree
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BayesNet<IndexConditional> bn;
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SymbolicBayesTree::Cliques orphans;
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list<Index> keys; keys += _B_,_S_;
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bayesTree.removeTop(keys, bn, orphans);
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SymbolicFactorGraph factors(bn);
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// Check expected outcome
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SymbolicFactorGraph expected;
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expected.push_factor(_E_,_L_,_B_);
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expected.push_factor(_L_,_B_);
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expected.push_factor(_B_);
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expected.push_factor(_S_,_L_,_B_);
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CHECK(assert_equal(expected, factors));
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SymbolicBayesTree::Cliques expectedOrphans;
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expectedOrphans += bayesTree[_T_], bayesTree[_X_];
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CHECK(assert_equal(expectedOrphans, orphans));
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}
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/* ************************************************************************* */
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TEST( BayesTree, removeTop3 )
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{
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const Index _x4_=5, _l5_=6;
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// simple test case that failed after COLAMD was fixed/activated
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IndexConditional::shared_ptr
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X(new IndexConditional(_l5_)),
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A(new IndexConditional(_x4_, _l5_)),
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B(new IndexConditional(_x2_, _x4_)),
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C(new IndexConditional(_x3_, _x2_));
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// Ordering newOrdering;
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// newOrdering += _x3_, _x2_, _x1_, _l2_, _l1_, _x4_, _l5_;
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SymbolicBayesTree bayesTree;
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bayesTree.insert(X);
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bayesTree.insert(A);
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bayesTree.insert(B);
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bayesTree.insert(C);
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// remove all
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list<Index> keys;
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keys += _l5_, _x2_, _x3_, _x4_;
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BayesNet<IndexConditional> bn;
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SymbolicBayesTree::Cliques orphans;
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bayesTree.removeTop(keys, bn, orphans);
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SymbolicFactorGraph factors(bn);
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CHECK(orphans.size() == 0);
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}
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///* ************************************************************************* */
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///**
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// * x2 - x3 - x4 - x5
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// * | / \ |
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// * x1 / \ x6
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// */
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//TEST( BayesTree, insert )
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//{
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// // construct bayes tree by split the graph along the separator x3 - x4
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// const Index _x1_=0, _x2_=1, _x6_=2, _x5_=3, _x3_=4, _x4_=5;
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// SymbolicFactorGraph fg1, fg2, fg3;
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// fg1.push_factor(_x3_, _x4_);
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// fg2.push_factor(_x1_, _x2_);
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// fg2.push_factor(_x2_, _x3_);
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// fg2.push_factor(_x1_, _x3_);
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// fg3.push_factor(_x5_, _x4_);
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// fg3.push_factor(_x6_, _x5_);
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// fg3.push_factor(_x6_, _x4_);
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//
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//// Ordering ordering1; ordering1 += _x3_, _x4_;
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//// Ordering ordering2; ordering2 += _x1_, _x2_;
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//// Ordering ordering3; ordering3 += _x6_, _x5_;
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//
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// BayesNet<IndexConditional> bn1, bn2, bn3;
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// bn1 = *SymbolicSequentialSolver::EliminateUntil(fg1, _x4_+1);
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// bn2 = *SymbolicSequentialSolver::EliminateUntil(fg2, _x2_+1);
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// bn3 = *SymbolicSequentialSolver::EliminateUntil(fg3, _x5_+1);
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//
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// // insert child cliques
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// SymbolicBayesTree actual;
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// list<SymbolicBayesTree::sharedClique> children;
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// SymbolicBayesTree::sharedClique r1 = actual.insert(bn2, children);
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// SymbolicBayesTree::sharedClique r2 = actual.insert(bn3, children);
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//
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// // insert root clique
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// children.push_back(r1);
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// children.push_back(r2);
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// actual.insert(bn1, children, true);
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//
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// // traditional way
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// SymbolicFactorGraph fg;
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// fg.push_factor(_x3_, _x4_);
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// fg.push_factor(_x1_, _x2_);
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// fg.push_factor(_x2_, _x3_);
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// fg.push_factor(_x1_, _x3_);
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// fg.push_factor(_x5_, _x4_);
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// fg.push_factor(_x6_, _x5_);
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// fg.push_factor(_x6_, _x4_);
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//
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//// Ordering ordering; ordering += _x1_, _x2_, _x6_, _x5_, _x3_, _x4_;
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// BayesNet<IndexConditional> bn(*SymbolicSequentialSolver(fg).eliminate());
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// SymbolicBayesTree expected(bn);
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// CHECK(assert_equal(expected, actual));
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//
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//}
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/* ************************************************************************* */
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int main() {
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TestResult tr;
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return TestRegistry::runAllTests(tr);
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}
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/* ************************************************************************* */
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