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Fixed conversion arguments

release/4.3a0
Frank Dellaert 2023-01-02 12:47:30 -05:00
parent 021ee1a5d9
commit fbfc20b88d
1 changed files with 6 additions and 6 deletions
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12
gtsam/hybrid/tests/TinyHybridExample.h
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@ -61,12 +61,12 @@ HybridBayesNet createHybridBayesNet(int num_measurements = 1) {
} }
HybridGaussianFactorGraph convertBayesNet(const HybridBayesNet& bayesNet, HybridGaussianFactorGraph convertBayesNet(const HybridBayesNet& bayesNet,
const HybridValues& values) { const VectorValues& measurements) {
HybridGaussianFactorGraph fg; HybridGaussianFactorGraph fg;
int num_measurements = bayesNet.size() - 2; int num_measurements = bayesNet.size() - 2;
for (int i = 0; i < num_measurements; i++) { for (int i = 0; i < num_measurements; i++) {
auto conditional = bayesNet.atMixture(i); auto conditional = bayesNet.atMixture(i);
auto factor = conditional->likelihood(values.continuousSubset({Z(i)})); auto factor = conditional->likelihood({{Z(i), measurements.at(Z(i))}});
fg.push_back(factor); fg.push_back(factor);
} }
fg.push_back(bayesNet.atGaussian(num_measurements)); fg.push_back(bayesNet.atGaussian(num_measurements));
@ -79,14 +79,14 @@ HybridGaussianFactorGraph createHybridGaussianFactorGraph(
auto bayesNet = createHybridBayesNet(num_measurements); auto bayesNet = createHybridBayesNet(num_measurements);
if (deterministic) { if (deterministic) {
// Create a deterministic set of measurements: // Create a deterministic set of measurements:
HybridValues values{{}, {{M(0), 0}}}; VectorValues measurements;
for (int i = 0; i < num_measurements; i++) { for (int i = 0; i < num_measurements; i++) {
values.insert(Z(i), Vector1(5.0 + 0.1 * i)); measurements.insert(Z(i), Vector1(5.0 + 0.1 * i));
} }
return convertBayesNet(bayesNet, values); return convertBayesNet(bayesNet, measurements);
} else { } else {
// Create a random set of measurements: // Create a random set of measurements:
return convertBayesNet(bayesNet, bayesNet.sample()); return convertBayesNet(bayesNet, bayesNet.sample().continuous());
} }
} }