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make header functions as inline

release/4.3a0
Varun Agrawal 2023-01-03 05:18:13 -05:00
parent e01f7e7456
commit 9e7fcc81cd
1 changed files with 4 additions and 4 deletions
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8
gtsam/hybrid/tests/TinyHybridExample.h
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@ -34,7 +34,7 @@ const DiscreteKey mode{M(0), 2};
* Create a tiny two variable hybrid model which represents * Create a tiny two variable hybrid model which represents
* the generative probability P(z,x,mode) = P(z|x,mode)P(x)P(mode). * the generative probability P(z,x,mode) = P(z|x,mode)P(x)P(mode).
*/ */
HybridBayesNet createHybridBayesNet(int num_measurements = 1) { inline HybridBayesNet createHybridBayesNet(int num_measurements = 1) {
HybridBayesNet bayesNet; HybridBayesNet bayesNet;
// Create Gaussian mixture z_i = x0 + noise for each measurement. // Create Gaussian mixture z_i = x0 + noise for each measurement.
@ -61,8 +61,8 @@ HybridBayesNet createHybridBayesNet(int num_measurements = 1) {
/** /**
* Convert a hybrid Bayes net to a hybrid Gaussian factor graph. * Convert a hybrid Bayes net to a hybrid Gaussian factor graph.
*/ */
HybridGaussianFactorGraph convertBayesNet(const HybridBayesNet& bayesNet, inline HybridGaussianFactorGraph convertBayesNet(
const VectorValues& measurements) { const HybridBayesNet& bayesNet, 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++) {
@ -81,7 +81,7 @@ HybridGaussianFactorGraph convertBayesNet(const HybridBayesNet& bayesNet,
* continuous variable x0. If no measurements are given, they are sampled from * continuous variable x0. If no measurements are given, they are sampled from
* the generative Bayes net model HybridBayesNet::Example(num_measurements) * the generative Bayes net model HybridBayesNet::Example(num_measurements)
*/ */
HybridGaussianFactorGraph createHybridGaussianFactorGraph( inline HybridGaussianFactorGraph createHybridGaussianFactorGraph(
int num_measurements = 1, int num_measurements = 1,
boost::optional<VectorValues> measurements = boost::none) { boost::optional<VectorValues> measurements = boost::none) {
auto bayesNet = createHybridBayesNet(num_measurements); auto bayesNet = createHybridBayesNet(num_measurements);