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kill DiscreteBayesNet::mode

release/4.3a0
Varun Agrawal 2024-07-15 12:26:49 -04:00
parent 4e66fff153
commit 4a04963197
4 changed files with 0 additions and 62 deletions
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4
gtsam/discrete/DiscreteBayesNet.cpp
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@ -65,10 +65,6 @@ DiscreteValues DiscreteBayesNet::sample(DiscreteValues result) const {
return result;
}
DiscreteValues DiscreteBayesNet::mode() const {
return DiscreteFactorGraph(*this).optimize();
}
/* *********************************************************************** */
std::string DiscreteBayesNet::markdown(
const KeyFormatter& keyFormatter,

8
gtsam/discrete/DiscreteBayesNet.h
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@ -124,14 +124,6 @@ class GTSAM_EXPORT DiscreteBayesNet: public BayesNet<DiscreteConditional> {
*/
DiscreteValues sample(DiscreteValues given) const;
/**
* @brief Compute the discrete assignment which gives the highest
* probability for the DiscreteBayesNet.
*
* @return DiscreteValues
*/
DiscreteValues mode() const;
///@}
/// @name Wrapper support
/// @{

1
gtsam/discrete/discrete.i
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@ -193,7 +193,6 @@ class DiscreteBayesNet {
gtsam::DiscreteValues sample() const;
gtsam::DiscreteValues sample(gtsam::DiscreteValues given) const;
gtsam::DiscreteValues mode() const;
string dot(
const gtsam::KeyFormatter& keyFormatter = gtsam::DefaultKeyFormatter,

49
gtsam/discrete/tests/testDiscreteBayesNet.cpp
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@ -120,55 +120,6 @@ TEST(DiscreteBayesNet, Asia) {
EXPECT(assert_equal(expectedSample, actualSample));
}
/* ************************************************************************* */
TEST(DiscreteBayesNet, Mode) {
DiscreteBayesNet asia;
// We need to order the Bayes net in bottom-up fashion
asia.add(XRay | Either = "95/5 2/98");
asia.add((Dyspnea | Either, Bronchitis) = "9/1 2/8 3/7 1/9");
asia.add((Either | Tuberculosis, LungCancer) = "F T T T");
asia.add(Tuberculosis | Asia = "99/1 95/5");
asia.add(LungCancer | Smoking = "99/1 90/10");
asia.add(Bronchitis | Smoking = "70/30 40/60");
asia.add(Asia, "99/1");
asia.add(Smoking % "50/50"); // Signature version
DiscreteValues actual = asia.mode();
// NOTE: Examined the DBN and found the optimal assignment.
DiscreteValues expected{
{Asia.first, 0}, {Smoking.first, 0}, {Tuberculosis.first, 0},
{LungCancer.first, 0}, {Bronchitis.first, 0}, {Either.first, 0},
{XRay.first, 0}, {Dyspnea.first, 0},
};
EXPECT(assert_equal(expected, actual));
}
/* ************************************************************************* */
TEST(DiscreteBayesNet, ModeEdgeCase) {
// Declare 2 keys
DiscreteKey A(0, 2), B(1, 2);
// Create Bayes net such that marginal on A is bigger for 0 than 1, but the
// MPE does not have A=0.
DiscreteBayesNet bayesNet;
bayesNet.add(B | A = "1/1 1/2");
bayesNet.add(A % "10/9");
// Which we verify using max-product:
DiscreteFactorGraph graph(bayesNet);
// The expected MPE is A=1, B=1
DiscreteValues expectedMPE = graph.optimize();
auto actualMPE = bayesNet.mode();
EXPECT(assert_equal(expectedMPE, actualMPE));
EXPECT_DOUBLES_EQUAL(0.315789, bayesNet(expectedMPE), 1e-5); // regression
}
/* ************************************************************************* */
TEST(DiscreteBayesNet, Sugar) {
DiscreteKey T(0, 2), L(1, 2), E(2, 2), C(8, 3), S(7, 2);