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Additional Nonlinear Hybrid #1277

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merged 11 commits into from
Aug 22, 2022
Merged

Additional Nonlinear Hybrid #1277

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aa48658
more tests running
varunagrawal Aug 12, 2022
77bea31
one more test passing
varunagrawal Aug 13, 2022
ac20cff
add incremental pruning to HybridGaussianISAM
varunagrawal Aug 16, 2022
83b8103
last test to get running
varunagrawal Aug 17, 2022
07f0101
check subset rather than equality for GaussianISAM pruning
varunagrawal Aug 21, 2022
29c19ee
handle HybridConditional and explicitly set Gaussian Factor Graphs to…
varunagrawal Aug 21, 2022
f6df641
remove custom orderings, let it happen automatically
varunagrawal Aug 21, 2022
6b792c0
add note about sumFrontals
varunagrawal Aug 21, 2022
893c5f7
cast to only HybridGaussianFactor
varunagrawal Aug 22, 2022
4c9c106
Merge pull request #1271 from borglab/feature/nonlinear-incremental
varunagrawal Aug 22, 2022
84456f4
Merge pull request #1273 from borglab/hybrid-incremental
varunagrawal Aug 22, 2022
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remove custom orderings, let it happen automatically
  • Loading branch information
@varunagrawal
varunagrawal committed Aug 21, 2022
commit f6df641b191ef7b1e4bd8b867c707abdc86a4549
111 changes: 23 additions & 88 deletions gtsam/hybrid/tests/testHybridIncremental.cpp
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Original file line number Diff line number Diff line change
Expand Up @@ -61,11 +61,6 @@ TEST(HybridGaussianElimination, IncrementalElimination) {
graph1.push_back(switching.linearizedFactorGraph.at(2)); // P(X2, X3 | M2)
graph1.push_back(switching.linearizedFactorGraph.at(5)); // P(M1)

// Create ordering.
Ordering ordering;
ordering += X(1);
ordering += X(2);

// Run update step
isam.update(graph1);

Expand All @@ -85,11 +80,6 @@ TEST(HybridGaussianElimination, IncrementalElimination) {
graph2.push_back(switching.linearizedFactorGraph.at(4)); // P(X3)
graph2.push_back(switching.linearizedFactorGraph.at(6)); // P(M1, M2)

// Create ordering.
Ordering ordering2;
ordering2 += X(2);
ordering2 += X(3);

isam.update(graph2);

// Check that after the second update we have
Expand Down Expand Up @@ -336,12 +326,6 @@ TEST(HybridGaussianElimination, Incremental_approximate) {
graph1.push_back(switching.linearizedFactorGraph.at(i));
}

// Create ordering.
Ordering ordering;
for (size_t j = 1; j <= 4; j++) {
ordering += X(j);
}

// Run update with pruning
size_t maxComponents = 5;
incrementalHybrid.update(graph1);
Expand All @@ -364,10 +348,6 @@ TEST(HybridGaussianElimination, Incremental_approximate) {
graph2.push_back(switching.linearizedFactorGraph.at(4));
graph2.push_back(switching.linearizedFactorGraph.at(8));

Ordering ordering2;
ordering2 += X(4);
ordering2 += X(5);

// Run update with pruning a second time.
incrementalHybrid.update(graph2);
incrementalHybrid.prune(M(4), maxComponents);
Expand All @@ -382,14 +362,11 @@ TEST(HybridGaussianElimination, Incremental_approximate) {
}

/* ************************************************************************/
// Test for figuring out the optimal ordering to ensure we get
// a discrete graph after elimination.
// A GTSAM-only test for running inference on a single-legged robot.
// The leg links are represented by the chain X-Y-Z-W, where X is the base and
// W is the foot.
// We use BetweenFactor<Pose2> as constraints between each of the poses.
TEST(HybridGaussianISAM, NonTrivial) {
// This is a GTSAM-only test for running inference on a single legged robot.
// The leg links are represented by the chain X-Y-Z-W, where X is the base and
// W is the foot.
// We use BetweenFactor<Pose2> as constraints between each of the poses.

/*************** Run Round 1 ***************/
HybridNonlinearFactorGraph fg;

Expand Down Expand Up @@ -427,19 +404,11 @@ TEST(HybridGaussianISAM, NonTrivial) {

HybridGaussianISAM inc;

// Regular ordering going up the chain.
Ordering ordering;
ordering += W(0);
ordering += Z(0);
ordering += Y(0);
ordering += X(0);

// Update without pruning
// The result is a HybridBayesNet with no discrete variables
// (equivalent to a GaussianBayesNet).
// Factorization is:
// `P(X | measurements) = P(W0|Z0) P(Z0|Y0) P(Y0|X0) P(X0)`
// TODO(Varun) ClusterTree-inst.h L202 segfaults with custom ordering.
inc.update(gfg);

/*************** Run Round 2 ***************/
Expand Down Expand Up @@ -478,26 +447,12 @@ TEST(HybridGaussianISAM, NonTrivial) {
gfg = fg.linearize(initial);
fg = HybridNonlinearFactorGraph();

// Ordering for k=1.
// This ordering follows the intuition that we eliminate the previous
// timestep, and then the current timestep.
ordering = Ordering();
ordering += W(0);
ordering += Z(0);
ordering += Y(0);
ordering += X(0);
ordering += W(1);
ordering += Z(1);
ordering += Y(1);
ordering += X(1);

// Update without pruning
// The result is a HybridBayesNet with 1 discrete variable M(1).
// P(X | measurements) = P(W0|Z0, W1, M1) P(Z0|Y0, W1, M1) P(Y0|X0, W1, M1)
// P(X0 | X1, W1, M1) P(W1|Z1, X1, M1) P(Z1|Y1, X1, M1)
// P(Y1 | X1, M1)P(X1 | M1)P(M1)
// The MHS tree is a 1 level tree for time indices (1,) with 2 leaves.
// TODO(Varun) ClusterTree-inst.h L202 segfaults with custom ordering.
inc.update(gfg);

/*************** Run Round 3 ***************/
Expand Down Expand Up @@ -528,17 +483,6 @@ TEST(HybridGaussianISAM, NonTrivial) {
initial.insert(Z(2), Pose2(2.0, 2.0, 0.0));
initial.insert(W(2), Pose2(0.0, 3.0, 0.0));

// Ordering at k=2. Same intuition as before.
ordering = Ordering();
ordering += W(1);
ordering += Z(1);
ordering += Y(1);
ordering += X(1);
ordering += W(2);
ordering += Z(2);
ordering += Y(2);
ordering += X(2);

gfg = fg.linearize(initial);
fg = HybridNonlinearFactorGraph();

Expand Down Expand Up @@ -585,40 +529,31 @@ TEST(HybridGaussianISAM, NonTrivial) {
gfg = fg.linearize(initial);
fg = HybridNonlinearFactorGraph();

// Ordering at k=3. Same intuition as before.
ordering = Ordering();
ordering += W(2);
ordering += Z(2);
ordering += Y(2);
ordering += X(2);
ordering += W(3);
ordering += Z(3);
ordering += Y(3);
ordering += X(3);

// Keep pruning!
inc.update(gfg);
inc.prune(M(3), 3);
inc.print();

// The final discrete graph should not be empty since we have eliminated
// all continuous variables.
// EXPECT(!inc.remainingDiscreteGraph().empty());

// // Test if the optimal discrete mode assignment is (1, 1, 1).
// DiscreteValues optimal_assignment =
// inc.remainingDiscreteGraph().optimize(); DiscreteValues
// expected_assignment; expected_assignment[M(1)] = 1;
// expected_assignment[M(2)] = 1;
// expected_assignment[M(3)] = 1;
// EXPECT(assert_equal(expected_assignment, optimal_assignment));

// // Test if pruning worked correctly by checking that we only have 3
// leaves in
// // the last node.
// auto lastConditional = boost::dynamic_pointer_cast<GaussianMixture>(
// inc.hybridBayesNet().at(inc.hybridBayesNet().size() - 1));
// EXPECT_LONGS_EQUAL(3, lastConditional->nrComponents());
auto discreteTree = inc[M(3)]->conditional()->asDiscreteConditional();
EXPECT_LONGS_EQUAL(3, discreteTree->size());

// Test if the optimal discrete mode assignment is (1, 1, 1).
DiscreteFactorGraph discreteGraph;
discreteGraph.push_back(discreteTree);
DiscreteValues optimal_assignment = discreteGraph.optimize();

DiscreteValues expected_assignment;
expected_assignment[M(1)] = 1;
expected_assignment[M(2)] = 1;
expected_assignment[M(3)] = 1;

EXPECT(assert_equal(expected_assignment, optimal_assignment));

// Test if pruning worked correctly by checking that we only have 3 leaves in
// the last node.
auto lastConditional = inc[X(3)]->conditional()->asMixture();
EXPECT_LONGS_EQUAL(3, lastConditional->nrComponents());
}

/* ************************************************************************* */
Expand Down