344 lines
13 KiB
C++
344 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 testGaussianISAM.cpp
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* @brief Unit tests for GaussianISAM
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* @author Michael Kaess
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*/
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#include <boost/foreach.hpp>
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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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#define GTSAM_MAGIC_KEY
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#include <gtsam/nonlinear/Ordering.h>
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#include <gtsam/linear/GaussianBayesNet.h>
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#include <gtsam/inference/ISAM-inl.h>
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#include <gtsam/linear/GaussianISAM.h>
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#include <gtsam/slam/smallExample.h>
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using namespace std;
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using namespace gtsam;
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using namespace example;
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/* ************************************************************************* */
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// Some numbers that should be consistent among all smoother tests
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double sigmax1 = 0.786153, sigmax2 = 1.0/1.47292, sigmax3 = 0.671512, sigmax4 =
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0.669534, sigmax5 = sigmax3, sigmax6 = sigmax2, sigmax7 = sigmax1;
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const double tol = 1e-4;
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/* ************************************************************************* */
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TEST( ISAM, iSAM_smoother )
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{
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Ordering ordering;
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for (int t = 1; t <= 7; t++) ordering += Symbol('x', t);
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// Create smoother with 7 nodes
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GaussianFactorGraph smoother = createSmoother(7, ordering).first;
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// run iSAM for every factor
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GaussianISAM actual;
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BOOST_FOREACH(boost::shared_ptr<GaussianFactor> factor, smoother) {
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GaussianFactorGraph factorGraph;
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factorGraph.push_back(factor);
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actual.update(factorGraph);
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}
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// Create expected Bayes Tree by solving smoother with "natural" ordering
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GaussianISAM expected(*Inference::Eliminate(smoother));
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// Check whether BayesTree is correct
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CHECK(assert_equal(expected, actual));
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// obtain solution
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VectorValues e(vector<size_t>(7,2)); // expected solution
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e.makeZero();
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VectorValues optimized = optimize(actual); // actual solution
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CHECK(assert_equal(e, optimized));
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}
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/* ************************************************************************* */
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// SL-FIX TEST( ISAM, iSAM_smoother2 )
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//{
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// // Create smoother with 7 nodes
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// GaussianFactorGraph smoother = createSmoother(7);
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//
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// // Create initial tree from first 4 timestamps in reverse order !
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// Ordering ord; ord += "x4","x3","x2","x1";
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// GaussianFactorGraph factors1;
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// for (int i=0;i<7;i++) factors1.push_back(smoother[i]);
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// GaussianISAM actual(*Inference::Eliminate(factors1));
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//
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// // run iSAM with remaining factors
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// GaussianFactorGraph factors2;
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// for (int i=7;i<13;i++) factors2.push_back(smoother[i]);
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// actual.update(factors2);
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//
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// // Create expected Bayes Tree by solving smoother with "natural" ordering
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// Ordering ordering;
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// for (int t = 1; t <= 7; t++) ordering += symbol('x', t);
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// GaussianISAM expected(smoother.eliminate(ordering));
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//
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// CHECK(assert_equal(expected, actual));
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//}
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/* ************************************************************************* *
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Bayes tree for smoother with "natural" ordering:
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C1 x6 x7
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C2 x5 : x6
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C3 x4 : x5
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C4 x3 : x4
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C5 x2 : x3
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C6 x1 : x2
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**************************************************************************** */
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TEST( BayesTree, linear_smoother_shortcuts )
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{
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// Create smoother with 7 nodes
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Ordering ordering;
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GaussianFactorGraph smoother;
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boost::tie(smoother, ordering) = createSmoother(7);
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// eliminate using the "natural" ordering
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GaussianBayesNet chordalBayesNet = *Inference::Eliminate(smoother);
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// Create the Bayes tree
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GaussianISAM bayesTree(chordalBayesNet);
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LONGS_EQUAL(6,bayesTree.size());
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// Check the conditional P(Root|Root)
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GaussianBayesNet empty;
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GaussianISAM::sharedClique R = bayesTree.root();
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GaussianBayesNet actual1 = R->shortcut<GaussianFactorGraph>(R);
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CHECK(assert_equal(empty,actual1,tol));
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// Check the conditional P(C2|Root)
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GaussianISAM::sharedClique C2 = bayesTree[ordering["x5"]];
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GaussianBayesNet actual2 = C2->shortcut<GaussianFactorGraph>(R);
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CHECK(assert_equal(empty,actual2,tol));
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// Check the conditional P(C3|Root)
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double sigma3 = 0.61808;
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Matrix A56 = Matrix_(2,2,-0.382022,0.,0.,-0.382022);
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GaussianBayesNet expected3;
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push_front(expected3,ordering["x5"], zero(2), eye(2)/sigma3, ordering["x6"], A56/sigma3, ones(2));
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GaussianISAM::sharedClique C3 = bayesTree[ordering["x4"]];
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GaussianBayesNet actual3 = C3->shortcut<GaussianFactorGraph>(R);
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CHECK(assert_equal(expected3,actual3,tol));
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// Check the conditional P(C4|Root)
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double sigma4 = 0.661968;
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Matrix A46 = Matrix_(2,2,-0.146067,0.,0.,-0.146067);
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GaussianBayesNet expected4;
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push_front(expected4, ordering["x4"], zero(2), eye(2)/sigma4, ordering["x6"], A46/sigma4, ones(2));
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GaussianISAM::sharedClique C4 = bayesTree[ordering["x3"]];
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GaussianBayesNet actual4 = C4->shortcut<GaussianFactorGraph>(R);
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CHECK(assert_equal(expected4,actual4,tol));
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}
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/* ************************************************************************* *
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Bayes tree for smoother with "nested dissection" ordering:
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Node[x1] P(x1 | x2)
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Node[x3] P(x3 | x2 x4)
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Node[x5] P(x5 | x4 x6)
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Node[x7] P(x7 | x6)
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Node[x2] P(x2 | x4)
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Node[x6] P(x6 | x4)
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Node[x4] P(x4)
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becomes
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C1 x5 x6 x4
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C2 x3 x2 : x4
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C3 x1 : x2
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C4 x7 : x6
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************************************************************************* */
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TEST( BayesTree, balanced_smoother_marginals )
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{
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// Create smoother with 7 nodes
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Ordering ordering;
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ordering += "x1","x3","x5","x7","x2","x6","x4";
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GaussianFactorGraph smoother = createSmoother(7, ordering).first;
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// Create the Bayes tree
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GaussianBayesNet chordalBayesNet = *Inference::Eliminate(smoother);
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VectorValues expectedSolution(7, 2);
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expectedSolution.makeZero();
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VectorValues actualSolution = optimize(chordalBayesNet);
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CHECK(assert_equal(expectedSolution,actualSolution,tol));
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// Create the Bayes tree
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GaussianISAM bayesTree(chordalBayesNet);
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LONGS_EQUAL(4,bayesTree.size());
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double tol=1e-5;
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// Check marginal on x1
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GaussianBayesNet expected1 = simpleGaussian(ordering["x1"], zero(2), sigmax1);
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GaussianBayesNet actual1 = bayesTree.marginalBayesNet<GaussianFactorGraph>(ordering["x1"]);
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CHECK(assert_equal(expected1,actual1,tol));
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// Check marginal on x2
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double sigx2 = 0.68712938; // FIXME: this should be corrected analytically
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GaussianBayesNet expected2 = simpleGaussian(ordering["x2"], zero(2), sigx2);
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GaussianBayesNet actual2 = bayesTree.marginalBayesNet<GaussianFactorGraph>(ordering["x2"]);
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CHECK(assert_equal(expected2,actual2,tol)); // FAILS
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// Check marginal on x3
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GaussianBayesNet expected3 = simpleGaussian(ordering["x3"], zero(2), sigmax3);
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GaussianBayesNet actual3 = bayesTree.marginalBayesNet<GaussianFactorGraph>(ordering["x3"]);
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CHECK(assert_equal(expected3,actual3,tol));
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// Check marginal on x4
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GaussianBayesNet expected4 = simpleGaussian(ordering["x4"], zero(2), sigmax4);
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GaussianBayesNet actual4 = bayesTree.marginalBayesNet<GaussianFactorGraph>(ordering["x4"]);
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CHECK(assert_equal(expected4,actual4,tol));
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// Check marginal on x7 (should be equal to x1)
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GaussianBayesNet expected7 = simpleGaussian(ordering["x7"], zero(2), sigmax7);
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GaussianBayesNet actual7 = bayesTree.marginalBayesNet<GaussianFactorGraph>(ordering["x7"]);
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CHECK(assert_equal(expected7,actual7,tol));
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}
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/* ************************************************************************* */
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TEST( BayesTree, balanced_smoother_shortcuts )
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{
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// Create smoother with 7 nodes
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Ordering ordering;
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ordering += "x1","x3","x5","x7","x2","x6","x4";
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GaussianFactorGraph smoother = createSmoother(7, ordering).first;
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// Create the Bayes tree
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GaussianBayesNet chordalBayesNet = *Inference::Eliminate(smoother);
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GaussianISAM bayesTree(chordalBayesNet);
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// Check the conditional P(Root|Root)
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GaussianBayesNet empty;
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GaussianISAM::sharedClique R = bayesTree.root();
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GaussianBayesNet actual1 = R->shortcut<GaussianFactorGraph>(R);
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CHECK(assert_equal(empty,actual1,tol));
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// Check the conditional P(C2|Root)
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GaussianISAM::sharedClique C2 = bayesTree[ordering["x3"]];
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GaussianBayesNet actual2 = C2->shortcut<GaussianFactorGraph>(R);
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CHECK(assert_equal(empty,actual2,tol));
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// Check the conditional P(C3|Root), which should be equal to P(x2|x4)
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GaussianConditional::shared_ptr p_x2_x4 = chordalBayesNet[ordering["x2"]];
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GaussianBayesNet expected3; expected3.push_back(p_x2_x4);
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GaussianISAM::sharedClique C3 = bayesTree[ordering["x1"]];
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GaussianBayesNet actual3 = C3->shortcut<GaussianFactorGraph>(R);
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CHECK(assert_equal(expected3,actual3,tol));
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}
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/* ************************************************************************* */
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TEST( BayesTree, balanced_smoother_clique_marginals )
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{
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// Create smoother with 7 nodes
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Ordering ordering;
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ordering += "x1","x3","x5","x7","x2","x6","x4";
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GaussianFactorGraph smoother = createSmoother(7, ordering).first;
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// Create the Bayes tree
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GaussianBayesNet chordalBayesNet = *Inference::Eliminate(smoother);
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GaussianISAM bayesTree(chordalBayesNet);
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// Check the clique marginal P(C3)
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double sigmax2_alt = 1/1.45533; // THIS NEEDS TO BE CHECKED!
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GaussianBayesNet expected = simpleGaussian(ordering["x2"],zero(2),sigmax2_alt);
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push_front(expected,ordering["x1"], zero(2), eye(2)*sqrt(2), ordering["x2"], -eye(2)*sqrt(2)/2, ones(2));
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GaussianISAM::sharedClique R = bayesTree.root(), C3 = bayesTree[ordering["x1"]];
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GaussianFactorGraph marginal = C3->marginal<GaussianFactorGraph>(R);
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GaussianVariableIndex<> varIndex(marginal);
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Permutation toFront(Permutation::PullToFront(C3->keys(), varIndex.size()));
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Permutation toFrontInverse(*toFront.inverse());
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varIndex.permute(toFront);
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BOOST_FOREACH(const GaussianFactor::shared_ptr& factor, marginal) {
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factor->permuteWithInverse(toFrontInverse); }
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GaussianBayesNet actual = *Inference::EliminateUntil(marginal, C3->keys().size(), varIndex);
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actual.permuteWithInverse(toFront);
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CHECK(assert_equal(expected,actual,tol));
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}
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/* ************************************************************************* */
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// SL-FIX TEST( BayesTree, balanced_smoother_joint )
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//{
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// // Create smoother with 7 nodes
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// GaussianFactorGraph smoother = createSmoother(7);
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// Ordering ordering;
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// ordering += "x1","x3","x5","x7","x2","x6","x4";
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//
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// // Create the Bayes tree, expected to look like:
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// // x5 x6 x4
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// // x3 x2 : x4
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// // x1 : x2
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// // x7 : x6
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// GaussianBayesNet chordalBayesNet = smoother.eliminate(ordering);
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// GaussianISAM bayesTree(chordalBayesNet);
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//
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// // Conditional density elements reused by both tests
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// const Vector sigma = ones(2);
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// const Matrix I = eye(2), A = -0.00429185*I;
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//
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// // Check the joint density P(x1,x7) factored as P(x1|x7)P(x7)
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// GaussianBayesNet expected1;
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// // Why does the sign get flipped on the prior?
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// GaussianConditional::shared_ptr
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// parent1(new GaussianConditional("x7", zero(2), -1*I/sigmax7, ones(2)));
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// expected1.push_front(parent1);
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// push_front(expected1,"x1", zero(2), I/sigmax7, "x7", A/sigmax7, sigma);
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// GaussianBayesNet actual1 = bayesTree.jointBayesNet<GaussianFactor>("x1","x7");
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// CHECK(assert_equal(expected1,actual1,tol));
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//
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// // Check the joint density P(x7,x1) factored as P(x7|x1)P(x1)
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// GaussianBayesNet expected2;
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// GaussianConditional::shared_ptr
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// parent2(new GaussianConditional("x1", zero(2), -1*I/sigmax1, ones(2)));
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// expected2.push_front(parent2);
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// push_front(expected2,"x7", zero(2), I/sigmax1, "x1", A/sigmax1, sigma);
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// GaussianBayesNet actual2 = bayesTree.jointBayesNet<GaussianFactor>("x7","x1");
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// CHECK(assert_equal(expected2,actual2,tol));
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//
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// // Check the joint density P(x1,x4), i.e. with a root variable
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// GaussianBayesNet expected3;
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// GaussianConditional::shared_ptr
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// parent3(new GaussianConditional("x4", zero(2), I/sigmax4, ones(2)));
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// expected3.push_front(parent3);
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// double sig14 = 0.784465;
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// Matrix A14 = -0.0769231*I;
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// push_front(expected3,"x1", zero(2), I/sig14, "x4", A14/sig14, sigma);
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// GaussianBayesNet actual3 = bayesTree.jointBayesNet<GaussianFactor>("x1","x4");
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// CHECK(assert_equal(expected3,actual3,tol));
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//
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// // Check the joint density P(x4,x1), i.e. with a root variable, factored the other way
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// GaussianBayesNet expected4;
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// GaussianConditional::shared_ptr
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// parent4(new GaussianConditional("x1", zero(2), -1.0*I/sigmax1, ones(2)));
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// expected4.push_front(parent4);
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// double sig41 = 0.668096;
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// Matrix A41 = -0.055794*I;
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// push_front(expected4,"x4", zero(2), I/sig41, "x1", A41/sig41, sigma);
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// GaussianBayesNet actual4 = bayesTree.jointBayesNet<GaussianFactor>("x4","x1");
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// CHECK(assert_equal(expected4,actual4,tol));
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//}
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/* ************************************************************************* */
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int main() { TestResult tr; return TestRegistry::runAllTests(tr);}
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/* ************************************************************************* */
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