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Reverted variable name change to ease reviewing

release/4.3a0
Frank Dellaert 2023-01-04 21:16:28 -08:00
parent 381087f48d
commit 26575921b0
2 changed files with 15 additions and 15 deletions
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14
gtsam/hybrid/tests/TinyHybridExample.h
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@ -33,15 +33,15 @@ const DiscreteKey mode{M(0), 2};
/**
* Create a tiny two variable hybrid model which represents
* the generative probability P(z,x,mode) = P(z|x,mode)P(x)P(mode).
* numMeasurements is the number of measurements of the continuous variable x0.
* num_measurements is the number of measurements of the continuous variable x0.
* If manyModes is true, then we introduce one mode per measurement.
*/
inline HybridBayesNet createHybridBayesNet(int numMeasurements = 1,
inline HybridBayesNet createHybridBayesNet(int num_measurements = 1,
bool manyModes = false) {
HybridBayesNet bayesNet;
// Create Gaussian mixture z_i = x0 + noise for each measurement.
for (int i = 0; i < numMeasurements; i++) {
for (int i = 0; i < num_measurements; i++) {
const auto mode_i = manyModes ? DiscreteKey{M(i), 2} : mode;
GaussianMixture gm({Z(i)}, {X(0)}, {mode_i},
{GaussianConditional::sharedMeanAndStddev(
@ -56,7 +56,7 @@ inline HybridBayesNet createHybridBayesNet(int numMeasurements = 1,
GaussianConditional::sharedMeanAndStddev(X(0), Vector1(5.0), 0.5));
// Add prior on mode.
const size_t nrModes = manyModes ? numMeasurements : 1;
const size_t nrModes = manyModes ? num_measurements : 1;
for (int i = 0; i < nrModes; i++) {
bayesNet.emplaceDiscrete(DiscreteKey{M(i), 2}, "4/6");
}
@ -67,13 +67,13 @@ inline HybridBayesNet createHybridBayesNet(int numMeasurements = 1,
* Create a tiny two variable hybrid factor graph which represents a discrete
* mode and a continuous variable x0, given a number of measurements of the
* continuous variable x0. If no measurements are given, they are sampled from
* the generative Bayes net model HybridBayesNet::Example(numMeasurements)
* the generative Bayes net model HybridBayesNet::Example(num_measurements)
*/
inline HybridGaussianFactorGraph createHybridGaussianFactorGraph(
int numMeasurements = 1,
int num_measurements = 1,
boost::optional<VectorValues> measurements = boost::none,
bool manyModes = false) {
auto bayesNet = createHybridBayesNet(numMeasurements, manyModes);
auto bayesNet = createHybridBayesNet(num_measurements, manyModes);
if (measurements) {
// Use the measurements to create a hybrid factor graph.
return bayesNet.toFactorGraph(*measurements);

16
gtsam/hybrid/tests/testHybridGaussianFactorGraph.cpp
View File

@ -621,9 +621,9 @@ TEST(HybridGaussianFactorGraph, ErrorAndProbPrimeTree) {
// assignment.
TEST(HybridGaussianFactorGraph, assembleGraphTree) {
using symbol_shorthand::Z;
const int numMeasurements = 1;
const int num_measurements = 1;
auto fg = tiny::createHybridGaussianFactorGraph(
numMeasurements, VectorValues{{Z(0), Vector1(5.0)}});
num_measurements, VectorValues{{Z(0), Vector1(5.0)}});
EXPECT_LONGS_EQUAL(3, fg.size());
// Assemble graph tree:
@ -662,9 +662,9 @@ TEST(HybridGaussianFactorGraph, assembleGraphTree) {
// Check that eliminating tiny net with 1 measurement yields correct result.
TEST(HybridGaussianFactorGraph, EliminateTiny1) {
using symbol_shorthand::Z;
const int numMeasurements = 1;
const int num_measurements = 1;
auto fg = tiny::createHybridGaussianFactorGraph(
numMeasurements, VectorValues{{Z(0), Vector1(5.0)}});
num_measurements, VectorValues{{Z(0), Vector1(5.0)}});
EXPECT_LONGS_EQUAL(3, fg.size());
// Create expected Bayes Net:
@ -696,9 +696,9 @@ TEST(HybridGaussianFactorGraph, EliminateTiny1) {
TEST(HybridGaussianFactorGraph, EliminateTiny2) {
// Create factor graph with 2 measurements such that posterior mean = 5.0.
using symbol_shorthand::Z;
const int numMeasurements = 2;
const int num_measurements = 2;
auto fg = tiny::createHybridGaussianFactorGraph(
numMeasurements,
num_measurements,
VectorValues{{Z(0), Vector1(4.0)}, {Z(1), Vector1(6.0)}});
EXPECT_LONGS_EQUAL(4, fg.size());
@ -731,11 +731,11 @@ TEST(HybridGaussianFactorGraph, EliminateTiny2) {
TEST(HybridGaussianFactorGraph, EliminateTiny22) {
// Create factor graph with 2 measurements such that posterior mean = 5.0.
using symbol_shorthand::Z;
const int numMeasurements = 2;
const int num_measurements = 2;
const bool manyModes = true;
// Create Bayes net and convert to factor graph.
auto bn = tiny::createHybridBayesNet(numMeasurements, manyModes);
auto bn = tiny::createHybridBayesNet(num_measurements, manyModes);
const VectorValues measurements{{Z(0), Vector1(4.0)}, {Z(1), Vector1(6.0)}};
auto fg = bn.toFactorGraph(measurements);
EXPECT_LONGS_EQUAL(5, fg.size());