From 03f865d4b1c295b6b3a71a9ba703ef3349b5a670 Mon Sep 17 00:00:00 2001 From: Frank Dellaert Date: Thu, 12 Nov 2009 18:33:14 +0000 Subject: [PATCH] Used typedefs with similar naming convention. --- cpp/testBayesTree.cpp | 49 ++++++++++++++++++++++--------------------- 1 file changed, 25 insertions(+), 24 deletions(-) diff --git a/cpp/testBayesTree.cpp b/cpp/testBayesTree.cpp index cfa445672..bfc54e7b9 100644 --- a/cpp/testBayesTree.cpp +++ b/cpp/testBayesTree.cpp @@ -17,7 +17,8 @@ using namespace boost::assign; using namespace gtsam; -typedef BayesTree Gaussian; +typedef BayesTree SymbolicBayesTree; +typedef BayesTree GaussianBayesTree; // Conditionals for ASIA example from the tutorial with A and D evidence SymbolicConditional::shared_ptr B(new SymbolicConditional("B")), L( @@ -43,7 +44,7 @@ TEST( BayesTree, Front ) TEST( BayesTree, constructor ) { // Create using insert - BayesTree bayesTree; + SymbolicBayesTree bayesTree; bayesTree.insert(B); bayesTree.insert(L); bayesTree.insert(E); @@ -59,7 +60,7 @@ TEST( BayesTree, constructor ) expected_root.push_back(E); expected_root.push_back(L); expected_root.push_back(B); - boost::shared_ptr > actual_root = bayesTree.root(); + boost::shared_ptr actual_root = bayesTree.root(); CHECK(assert_equal(expected_root,*actual_root)); // Create from symbolic Bayes chain in which we want to discover cliques @@ -70,7 +71,7 @@ TEST( BayesTree, constructor ) ASIA.push_back(E); ASIA.push_back(L); ASIA.push_back(B); - BayesTree bayesTree2(ASIA); + SymbolicBayesTree bayesTree2(ASIA); //bayesTree2.print("bayesTree2"); // Check whether the same @@ -104,17 +105,17 @@ TEST( BayesTree, linear_smoother_shortcuts ) GaussianBayesNet chordalBayesNet = smoother.eliminate(ordering); // Create the Bayes tree - Gaussian bayesTree(chordalBayesNet); + GaussianBayesTree bayesTree(chordalBayesNet); LONGS_EQUAL(7,bayesTree.size()); // Check the conditional P(Root|Root) GaussianBayesNet empty; - Gaussian::sharedClique R = bayesTree.root(); + GaussianBayesTree::sharedClique R = bayesTree.root(); GaussianBayesNet actual1 = R->shortcut(R); CHECK(assert_equal(empty,actual1,1e-4)); // Check the conditional P(C2|Root) - Gaussian::sharedClique C2 = bayesTree["x5"]; + GaussianBayesTree::sharedClique C2 = bayesTree["x5"]; GaussianBayesNet actual2 = C2->shortcut(R); CHECK(assert_equal(empty,actual2,1e-4)); @@ -123,7 +124,7 @@ TEST( BayesTree, linear_smoother_shortcuts ) Matrix A56 = Matrix_(2,2,-0.382022,0.,0.,-0.382022); GaussianBayesNet expected3; push_front(expected3,"x5", zero(2), eye(2), "x6", A56, sigma3); - Gaussian::sharedClique C3 = bayesTree["x4"]; + GaussianBayesTree::sharedClique C3 = bayesTree["x4"]; GaussianBayesNet actual3 = C3->shortcut(R); CHECK(assert_equal(expected3,actual3,1e-4)); @@ -132,7 +133,7 @@ TEST( BayesTree, linear_smoother_shortcuts ) Matrix A46 = Matrix_(2,2,-0.146067,0.,0.,-0.146067); GaussianBayesNet expected4; push_front(expected4,"x4", zero(2), eye(2), "x6", A46, sigma4); - Gaussian::sharedClique C4 = bayesTree["x3"]; + GaussianBayesTree::sharedClique C4 = bayesTree["x3"]; GaussianBayesNet actual4 = C4->shortcut(R); CHECK(assert_equal(expected4,actual4,1e-4)); } @@ -173,7 +174,7 @@ TEST( BayesTree, balanced_smoother_marginals ) CHECK(assert_equal(expectedSolution,actualSolution,1e-4)); // Create the Bayes tree - Gaussian bayesTree(chordalBayesNet); + GaussianBayesTree bayesTree(chordalBayesNet); LONGS_EQUAL(7,bayesTree.size()); // Check marginal on x1 @@ -212,23 +213,23 @@ TEST( BayesTree, balanced_smoother_shortcuts ) // Create the Bayes tree GaussianBayesNet chordalBayesNet = smoother.eliminate(ordering); - Gaussian bayesTree(chordalBayesNet); + GaussianBayesTree bayesTree(chordalBayesNet); // Check the conditional P(Root|Root) GaussianBayesNet empty; - Gaussian::sharedClique R = bayesTree.root(); + GaussianBayesTree::sharedClique R = bayesTree.root(); GaussianBayesNet actual1 = R->shortcut(R); CHECK(assert_equal(empty,actual1,1e-4)); // Check the conditional P(C2|Root) - Gaussian::sharedClique C2 = bayesTree["x3"]; + GaussianBayesTree::sharedClique C2 = bayesTree["x3"]; GaussianBayesNet actual2 = C2->shortcut(R); CHECK(assert_equal(empty,actual2,1e-4)); // Check the conditional P(C3|Root), which should be equal to P(x2|x4) GaussianConditional::shared_ptr p_x2_x4 = chordalBayesNet["x2"]; GaussianBayesNet expected3; expected3.push_back(p_x2_x4); - Gaussian::sharedClique C3 = bayesTree["x1"]; + GaussianBayesTree::sharedClique C3 = bayesTree["x1"]; GaussianBayesNet actual3 = C3->shortcut(R); CHECK(assert_equal(expected3,actual3,1e-4)); } @@ -243,14 +244,14 @@ TEST( BayesTree, balanced_smoother_clique_marginals ) // Create the Bayes tree GaussianBayesNet chordalBayesNet = smoother.eliminate(ordering); - Gaussian bayesTree(chordalBayesNet); + GaussianBayesTree bayesTree(chordalBayesNet); // Check the clique marginal P(C3) GaussianBayesNet expected = simpleGaussian("x2",zero(2),sigmax2); Vector sigma = repeat(2, 0.707107); Matrix A12 = (-0.5)*eye(2); push_front(expected,"x1", zero(2), eye(2), "x2", A12, sigma); - Gaussian::sharedClique R = bayesTree.root(), C3 = bayesTree["x1"]; + GaussianBayesTree::sharedClique R = bayesTree.root(), C3 = bayesTree["x1"]; FactorGraph marginal = C3->marginal(R); GaussianBayesNet actual = eliminate(marginal,C3->keys()); CHECK(assert_equal(expected,actual,1e-4)); @@ -266,37 +267,37 @@ TEST( BayesTree, balanced_smoother_joint ) // Create the Bayes tree GaussianBayesNet chordalBayesNet = smoother.eliminate(ordering); - Gaussian bayesTree(chordalBayesNet); + GaussianBayesTree bayesTree(chordalBayesNet); // Conditional density elements reused by both tests Vector sigma = repeat(2, 0.786146); - Matrix A = (-0.00429185)*eye(2); + Matrix I = eye(2), A = -0.00429185*I; // Check the joint density P(x1,x7) factored as P(x1|x7)P(x7) GaussianBayesNet expected1 = simpleGaussian("x7", zero(2), sigmax7); - push_front(expected1,"x1", zero(2), eye(2), "x7", A, sigma); + push_front(expected1,"x1", zero(2), I, "x7", A, sigma); GaussianBayesNet actual1 = bayesTree.jointBayesNet("x1","x7"); CHECK(assert_equal(expected1,actual1,1e-4)); // Check the joint density P(x7,x1) factored as P(x7|x1)P(x1) GaussianBayesNet expected2 = simpleGaussian("x1", zero(2), sigmax1); - push_front(expected2,"x7", zero(2), eye(2), "x1", A, sigma); + push_front(expected2,"x7", zero(2), I, "x1", A, sigma); GaussianBayesNet actual2 = bayesTree.jointBayesNet("x7","x1"); CHECK(assert_equal(expected2,actual2,1e-4)); // Check the joint density P(x1,x4), i.e. with a root variable GaussianBayesNet expected3 = simpleGaussian("x4", zero(2), sigmax4); Vector sigma14 = repeat(2, 0.784465); - Matrix A14 = (-0.0769231)*eye(2); - push_front(expected3,"x1", zero(2), eye(2), "x4", A14, sigma14); + Matrix A14 = -0.0769231*I; + push_front(expected3,"x1", zero(2), I, "x4", A14, sigma14); GaussianBayesNet actual3 = bayesTree.jointBayesNet("x1","x4"); CHECK(assert_equal(expected3,actual3,1e-4)); // Check the joint density P(x4,x1), i.e. with a root variable, factored the other way GaussianBayesNet expected4 = simpleGaussian("x1", zero(2), sigmax1); Vector sigma41 = repeat(2, 0.668096); - Matrix A41 = (-0.055794)*eye(2); - push_front(expected4,"x4", zero(2), eye(2), "x1", A41, sigma41); + Matrix A41 = -0.055794*I; + push_front(expected4,"x4", zero(2), I, "x1", A41, sigma41); GaussianBayesNet actual4 = bayesTree.jointBayesNet("x4","x1"); CHECK(assert_equal(expected4,actual4,1e-4)); }