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Use new constructors

release/4.3a0
Frank Dellaert 2024-09-28 22:05:07 -07:00
parent e690ff817a
commit 85e57f1c20
2 changed files with 45 additions and 67 deletions
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7
gtsam/hybrid/HybridBayesNet.h
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@ -44,9 +44,14 @@ class GTSAM_EXPORT HybridBayesNet : public BayesNet<HybridConditional> {
/// @name Standard Constructors /// @name Standard Constructors
/// @{ /// @{
/** Construct empty Bayes net */ /// Construct empty Bayes net.
HybridBayesNet() = default; HybridBayesNet() = default;
/// Constructor that takes an initializer list of shared pointers.
HybridBayesNet(
std::initializer_list<HybridConditional::shared_ptr> conditionals)
: Base(conditionals) {}
/// @} /// @}
/// @name Testable /// @name Testable
/// @{ /// @{

101
gtsam/hybrid/tests/testHybridBayesNet.cpp
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@ -20,6 +20,7 @@
#include <gtsam/hybrid/HybridBayesNet.h> #include <gtsam/hybrid/HybridBayesNet.h>
#include <gtsam/hybrid/HybridBayesTree.h> #include <gtsam/hybrid/HybridBayesTree.h>
#include <gtsam/hybrid/HybridConditional.h>
#include <gtsam/nonlinear/NonlinearFactor.h> #include <gtsam/nonlinear/NonlinearFactor.h>
#include <gtsam/nonlinear/NonlinearFactorGraph.h> #include <gtsam/nonlinear/NonlinearFactorGraph.h>
@ -32,7 +33,6 @@
using namespace std; using namespace std;
using namespace gtsam; using namespace gtsam;
using noiseModel::Isotropic;
using symbol_shorthand::M; using symbol_shorthand::M;
using symbol_shorthand::X; using symbol_shorthand::X;
using symbol_shorthand::Z; using symbol_shorthand::Z;
@ -92,39 +92,51 @@ TEST(HybridBayesNet, Tiny) {
} }
/* ****************************************************************************/ /* ****************************************************************************/
// Test evaluate for a hybrid Bayes net P(X0|X1) P(X1|Asia) P(Asia). // Hybrid Bayes net P(X0|X1) P(X1|Asia) P(Asia).
TEST(HybridBayesNet, evaluateHybrid) { namespace different_sigmas {
const auto continuousConditional = GaussianConditional::sharedMeanAndStddev( const auto gc = GaussianConditional::sharedMeanAndStddev(X(0), 2 * I_1x1, X(1),
X(0), 2 * I_1x1, X(1), Vector1(-4.0), 5.0); Vector1(-4.0), 5.0);
const SharedDiagonal model0 = noiseModel::Diagonal::Sigmas(Vector1(2.0)), const std::vector<std::pair<Vector, double>> parms{{Vector1(5), 2.0},
model1 = noiseModel::Diagonal::Sigmas(Vector1(3.0)); {Vector1(2), 3.0}};
const auto hgc = std::make_shared<HybridGaussianConditional>(X(1), Asia, parms);
const auto conditional0 = std::make_shared<GaussianConditional>( const auto prior = std::make_shared<DiscreteConditional>(Asia, "99/1");
X(1), Vector1::Constant(5), I_1x1, model0), auto wrap = [](const auto& c) {
conditional1 = std::make_shared<GaussianConditional>( return std::make_shared<HybridConditional>(c);
X(1), Vector1::Constant(2), I_1x1, model1); };
const HybridBayesNet bayesNet{wrap(gc), wrap(hgc), wrap(prior)};
// Create hybrid Bayes net.
HybridBayesNet bayesNet;
bayesNet.push_back(continuousConditional);
bayesNet.emplace_shared<HybridGaussianConditional>(
Asia, std::vector{conditional0, conditional1});
bayesNet.emplace_shared<DiscreteConditional>(Asia, "99/1");
// Create values at which to evaluate. // Create values at which to evaluate.
HybridValues values; HybridValues values{{{X(0), Vector1(-6)}, {X(1), Vector1(1)}}, {{asiaKey, 0}}};
values.insert(asiaKey, 0); } // namespace different_sigmas
values.insert(X(0), Vector1(-6));
values.insert(X(1), Vector1(1));
const double conditionalProbability = /* ****************************************************************************/
continuousConditional->evaluate(values.continuous()); // Test evaluate for a hybrid Bayes net P(X0|X1) P(X1|Asia) P(Asia).
const double mixtureProbability = conditional0->evaluate(values.continuous()); TEST(HybridBayesNet, evaluateHybrid) {
using namespace different_sigmas;
const double conditionalProbability = gc->evaluate(values.continuous());
const double mixtureProbability = hgc->evaluate(values);
EXPECT_DOUBLES_EQUAL(conditionalProbability * mixtureProbability * 0.99, EXPECT_DOUBLES_EQUAL(conditionalProbability * mixtureProbability * 0.99,
bayesNet.evaluate(values), 1e-9); bayesNet.evaluate(values), 1e-9);
} }
/* ****************************************************************************/
// Test error for a hybrid Bayes net P(X0|X1) P(X1|Asia) P(Asia).
TEST(HybridBayesNet, Error) {
using namespace different_sigmas;
AlgebraicDecisionTree<Key> actual = bayesNet.errorTree(values.continuous());
// Regression.
// Manually added all the error values from the 3 conditional types.
AlgebraicDecisionTree<Key> expected(
{Asia}, std::vector<double>{2.33005033585, 5.38619084965});
EXPECT(assert_equal(expected, actual));
}
/* ****************************************************************************/ /* ****************************************************************************/
// Test choosing an assignment of conditionals // Test choosing an assignment of conditionals
TEST(HybridBayesNet, Choose) { TEST(HybridBayesNet, Choose) {
@ -154,45 +166,6 @@ TEST(HybridBayesNet, Choose) {
*gbn.at(3))); *gbn.at(3)));
} }
/* ****************************************************************************/
// Test error for a hybrid Bayes net P(X0|X1) P(X1|Asia) P(Asia).
TEST(HybridBayesNet, Error) {
const auto continuousConditional = GaussianConditional::sharedMeanAndStddev(
X(0), 2 * I_1x1, X(1), Vector1(-4.0), 5.0);
const SharedDiagonal model0 = noiseModel::Diagonal::Sigmas(Vector1(2.0)),
model1 = noiseModel::Diagonal::Sigmas(Vector1(3.0));
const auto conditional0 = std::make_shared<GaussianConditional>(
X(1), Vector1::Constant(5), I_1x1, model0),
conditional1 = std::make_shared<GaussianConditional>(
X(1), Vector1::Constant(2), I_1x1, model1);
auto gm = std::make_shared<HybridGaussianConditional>(
Asia, std::vector{conditional0, conditional1});
// Create hybrid Bayes net.
HybridBayesNet bayesNet;
bayesNet.push_back(continuousConditional);
bayesNet.push_back(gm);
bayesNet.emplace_shared<DiscreteConditional>(Asia, "99/1");
// Create values at which to evaluate.
HybridValues values;
values.insert(asiaKey, 0);
values.insert(X(0), Vector1(-6));
values.insert(X(1), Vector1(1));
AlgebraicDecisionTree<Key> actual_errors =
bayesNet.errorTree(values.continuous());
// Regression.
// Manually added all the error values from the 3 conditional types.
AlgebraicDecisionTree<Key> expected_errors(
{Asia}, std::vector<double>{2.33005033585, 5.38619084965});
EXPECT(assert_equal(expected_errors, actual_errors));
}
/* ****************************************************************************/ /* ****************************************************************************/
// Test Bayes net optimize // Test Bayes net optimize
TEST(HybridBayesNet, OptimizeAssignment) { TEST(HybridBayesNet, OptimizeAssignment) {