12345qiupeng
/
gtsam
1
0
Fork 0
Code Issues Pull Requests Packages Projects Releases Wiki Activity

Used typedefs with similar naming convention.

release/4.3a0
Frank Dellaert 2009-11-12 18:33:14 +00:00
parent 5857635ca1
commit 03f865d4b1
1 changed files with 25 additions and 24 deletions
Show Stats Download Patch File Download Diff File Expand all files Collapse all files

49
cpp/testBayesTree.cpp
View File

@ -17,7 +17,8 @@ using namespace boost::assign;
using namespace gtsam;
typedef BayesTree<GaussianConditional> Gaussian;
typedef BayesTree<SymbolicConditional> SymbolicBayesTree;
typedef BayesTree<GaussianConditional> 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<SymbolicConditional> 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<BayesNet<SymbolicConditional> > actual_root = bayesTree.root();
boost::shared_ptr<SymbolicBayesNet> 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<SymbolicConditional> 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<GaussianFactor>(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<GaussianFactor>(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<GaussianFactor>(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<GaussianFactor>(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<GaussianFactor>(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<GaussianFactor>(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<GaussianFactor>(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<GaussianFactor> marginal = C3->marginal<GaussianFactor>(R);
GaussianBayesNet actual = eliminate<GaussianFactor,GaussianConditional>(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<GaussianFactor>("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<GaussianFactor>("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<GaussianFactor>("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<GaussianFactor>("x4","x1");
CHECK(assert_equal(expected4,actual4,1e-4));
}