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update testGaussianMixture

release/4.3a0
Varun Agrawal 2024-12-31 15:54:07 -05:00
parent 462a5b8b3a
commit 5e1931eb98
1 changed files with 17 additions and 10 deletions
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27
gtsam/hybrid/tests/testGaussianMixture.cpp
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@ -20,6 +20,7 @@
#include <gtsam/discrete/DecisionTreeFactor.h>
#include <gtsam/discrete/DiscreteConditional.h>
#include <gtsam/discrete/DiscreteKey.h>
#include <gtsam/discrete/DiscreteTableConditional.h>
#include <gtsam/hybrid/HybridBayesNet.h>
#include <gtsam/hybrid/HybridGaussianConditional.h>
#include <gtsam/hybrid/HybridGaussianFactorGraph.h>
@ -79,8 +80,9 @@ TEST(GaussianMixture, GaussianMixtureModel) {
double midway = mu1 - mu0;
auto eliminationResult =
gmm.toFactorGraph({{Z(0), Vector1(midway)}}).eliminateSequential();
auto pMid = *eliminationResult->at(0)->asDiscrete();
EXPECT(assert_equal(DiscreteConditional(m, "60/40"), pMid));
auto pMid = std::dynamic_pointer_cast<DiscreteTableConditional>(
eliminationResult->at(0)->asDiscrete());
EXPECT(assert_equal(DiscreteTableConditional(m, "60/40"), *pMid));
// Everywhere else, the result should be a sigmoid.
for (const double shift : {-4, -2, 0, 2, 4}) {
@ -90,7 +92,8 @@ TEST(GaussianMixture, GaussianMixtureModel) {
// Workflow 1: convert HBN to HFG and solve
auto eliminationResult1 =
gmm.toFactorGraph({{Z(0), Vector1(z)}}).eliminateSequential();
auto posterior1 = *eliminationResult1->at(0)->asDiscrete();
auto posterior1 = *std::dynamic_pointer_cast<DiscreteTableConditional>(
eliminationResult1->at(0)->asDiscrete());
EXPECT_DOUBLES_EQUAL(expected, posterior1(m1Assignment), 1e-8);
// Workflow 2: directly specify HFG and solve
@ -99,7 +102,8 @@ TEST(GaussianMixture, GaussianMixtureModel) {
m, std::vector{Gaussian(mu0, sigma, z), Gaussian(mu1, sigma, z)});
hfg1.push_back(mixing);
auto eliminationResult2 = hfg1.eliminateSequential();
auto posterior2 = *eliminationResult2->at(0)->asDiscrete();
auto posterior2 = *std::dynamic_pointer_cast<DiscreteTableConditional>(
eliminationResult2->at(0)->asDiscrete());
EXPECT_DOUBLES_EQUAL(expected, posterior2(m1Assignment), 1e-8);
}
}
@ -133,13 +137,14 @@ TEST(GaussianMixture, GaussianMixtureModel2) {
// Eliminate the graph!
auto eliminationResultMax = gfg.eliminateSequential();
// Equality of posteriors asserts that the elimination is correct (same ratios
// for all modes)
// Equality of posteriors asserts that the elimination is correct
// (same ratios for all modes)
EXPECT(assert_equal(expectedDiscretePosterior,
eliminationResultMax->discretePosterior(vv)));
auto pMax = *eliminationResultMax->at(0)->asDiscrete();
EXPECT(assert_equal(DiscreteConditional(m, "42/58"), pMax, 1e-4));
auto pMax = *std::dynamic_pointer_cast<DiscreteTableConditional>(
eliminationResultMax->at(0)->asDiscrete());
EXPECT(assert_equal(DiscreteTableConditional(m, "42/58"), pMax, 1e-4));
// Everywhere else, the result should be a bell curve like function.
for (const double shift : {-4, -2, 0, 2, 4}) {
@ -149,7 +154,8 @@ TEST(GaussianMixture, GaussianMixtureModel2) {
// Workflow 1: convert HBN to HFG and solve
auto eliminationResult1 =
gmm.toFactorGraph({{Z(0), Vector1(z)}}).eliminateSequential();
auto posterior1 = *eliminationResult1->at(0)->asDiscrete();
auto posterior1 = *std::dynamic_pointer_cast<DiscreteTableConditional>(
eliminationResult1->at(0)->asDiscrete());
EXPECT_DOUBLES_EQUAL(expected, posterior1(m1Assignment), 1e-8);
// Workflow 2: directly specify HFG and solve
@ -158,7 +164,8 @@ TEST(GaussianMixture, GaussianMixtureModel2) {
m, std::vector{Gaussian(mu0, sigma0, z), Gaussian(mu1, sigma1, z)});
hfg.push_back(mixing);
auto eliminationResult2 = hfg.eliminateSequential();
auto posterior2 = *eliminationResult2->at(0)->asDiscrete();
auto posterior2 = *std::dynamic_pointer_cast<DiscreteTableConditional>(
eliminationResult2->at(0)->asDiscrete());
EXPECT_DOUBLES_EQUAL(expected, posterior2(m1Assignment), 1e-8);
}
}