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clean up tests and TODOs

release/4.3a0
Varun Agrawal 2024-10-29 13:50:10 -04:00
parent 095a4cd187
commit ae95c6e84a
1 changed files with 5 additions and 20 deletions
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25
gtsam/hybrid/tests/testHybridBayesTree.cpp
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@ -74,9 +74,7 @@ TEST(HybridGaussianFactorGraph,
hfg.add(HybridGaussianFactor(m1, two::components(X(1)))); hfg.add(HybridGaussianFactor(m1, two::components(X(1))));
hfg.add(DecisionTreeFactor(m1, {2, 8})); hfg.add(DecisionTreeFactor(m1, {2, 8}));
// TODO(Varun) Adding extra discrete variable not connected to continuous hfg.add(DecisionTreeFactor({m1, m2}, "1 2 3 4"));
// variable throws segfault
// hfg.add(DecisionTreeFactor({m1, m2, "1 2 3 4"));
HybridBayesTree::shared_ptr result = hfg.eliminateMultifrontal(); HybridBayesTree::shared_ptr result = hfg.eliminateMultifrontal();
@ -176,7 +174,7 @@ TEST(HybridGaussianFactorGraph, Switching) {
// Ordering(KeyVector{X(1), X(4), X(2), X(6), X(9), X(8), X(3), X(7), // Ordering(KeyVector{X(1), X(4), X(2), X(6), X(9), X(8), X(3), X(7),
// X(5), // X(5),
// M(1), M(4), M(2), M(6), M(8), M(3), M(7), M(5)}); // M(1), M(4), M(2), M(6), M(8), M(3), M(7), M(5)});
KeyVector ordering; Ordering ordering;
{ {
std::vector<int> naturalX(N); std::vector<int> naturalX(N);
@ -187,10 +185,6 @@ TEST(HybridGaussianFactorGraph, Switching) {
auto [ndX, lvls] = makeBinaryOrdering(ordX); auto [ndX, lvls] = makeBinaryOrdering(ordX);
std::copy(ndX.begin(), ndX.end(), std::back_inserter(ordering)); std::copy(ndX.begin(), ndX.end(), std::back_inserter(ordering));
// TODO(dellaert): this has no effect!
for (auto& l : lvls) {
l = -l;
}
} }
{ {
std::vector<int> naturalC(N - 1); std::vector<int> naturalC(N - 1);
@ -199,14 +193,11 @@ TEST(HybridGaussianFactorGraph, Switching) {
std::transform(naturalC.begin(), naturalC.end(), std::back_inserter(ordC), std::transform(naturalC.begin(), naturalC.end(), std::back_inserter(ordC),
[](int x) { return M(x); }); [](int x) { return M(x); });
// std::copy(ordC.begin(), ordC.end(), std::back_inserter(ordering));
const auto [ndC, lvls] = makeBinaryOrdering(ordC); const auto [ndC, lvls] = makeBinaryOrdering(ordC);
std::copy(ndC.begin(), ndC.end(), std::back_inserter(ordering)); std::copy(ndC.begin(), ndC.end(), std::back_inserter(ordering));
} }
auto ordering_full = Ordering(ordering);
const auto [hbt, remaining] = const auto [hbt, remaining] = hfg->eliminatePartialMultifrontal(ordering);
hfg->eliminatePartialMultifrontal(ordering_full);
// 12 cliques in the bayes tree and 0 remaining variables to eliminate. // 12 cliques in the bayes tree and 0 remaining variables to eliminate.
EXPECT_LONGS_EQUAL(12, hbt->size()); EXPECT_LONGS_EQUAL(12, hbt->size());
@ -230,7 +221,7 @@ TEST(HybridGaussianFactorGraph, SwitchingISAM) {
// Ordering(KeyVector{X(1), X(4), X(2), X(6), X(9), X(8), X(3), X(7), // Ordering(KeyVector{X(1), X(4), X(2), X(6), X(9), X(8), X(3), X(7),
// X(5), // X(5),
// M(1), M(4), M(2), M(6), M(8), M(3), M(7), M(5)}); // M(1), M(4), M(2), M(6), M(8), M(3), M(7), M(5)});
KeyVector ordering; Ordering ordering;
{ {
std::vector<int> naturalX(N); std::vector<int> naturalX(N);
@ -241,10 +232,6 @@ TEST(HybridGaussianFactorGraph, SwitchingISAM) {
auto [ndX, lvls] = makeBinaryOrdering(ordX); auto [ndX, lvls] = makeBinaryOrdering(ordX);
std::copy(ndX.begin(), ndX.end(), std::back_inserter(ordering)); std::copy(ndX.begin(), ndX.end(), std::back_inserter(ordering));
// TODO(dellaert): this has no effect!
for (auto& l : lvls) {
l = -l;
}
} }
{ {
std::vector<int> naturalC(N - 1); std::vector<int> naturalC(N - 1);
@ -257,10 +244,8 @@ TEST(HybridGaussianFactorGraph, SwitchingISAM) {
const auto [ndC, lvls] = makeBinaryOrdering(ordC); const auto [ndC, lvls] = makeBinaryOrdering(ordC);
std::copy(ndC.begin(), ndC.end(), std::back_inserter(ordering)); std::copy(ndC.begin(), ndC.end(), std::back_inserter(ordering));
} }
auto ordering_full = Ordering(ordering);
const auto [hbt, remaining] = const auto [hbt, remaining] = hfg->eliminatePartialMultifrontal(ordering);
hfg->eliminatePartialMultifrontal(ordering_full);
auto new_fg = makeSwitchingChain(12); auto new_fg = makeSwitchingChain(12);
auto isam = HybridGaussianISAM(*hbt); auto isam = HybridGaussianISAM(*hbt);