Simplify elimination

borglab · dellaert · Jan 17, 2023 · Jan 12, 2023 · Jan 12, 2023 · Jan 13, 2023
commit dfef2c202ff2dd86d8a36086ef114f680aa3438f
diff --git a/gtsam/hybrid/HybridGaussianFactorGraph.cpp b/gtsam/hybrid/HybridGaussianFactorGraph.cpp
@@ -271,19 +271,15 @@ hybridElimination(const HybridGaussianFactorGraph &factors,
   // If there are no more continuous parents, then we should create a
   // DiscreteFactor here, with the error for each discrete choice.
   if (continuousSeparator.empty()) {
-    auto factorProb = [&](const EliminationPair &conditionalAndFactor) {
-      // This is the probability q(μ) at the MLE point.
-      // conditionalAndFactor.second is a factor without keys, just containing the residual.
+    auto probPrime = [&](const GaussianMixtureFactor::sharedFactor &factor) {
+      // This is the unnormalized probability q(μ) at the mean.
+      // The factor has no keys, just contains the residual.
       static const VectorValues kEmpty;
-      // return exp(-conditionalAndFactor.first->logNormalizationConstant());
-      // return exp(-conditionalAndFactor.first->logNormalizationConstant() - conditionalAndFactor.second->error(kEmpty));
-      return exp( - conditionalAndFactor.second->error(kEmpty));
-      // return 1.0;
+      return factor? exp(-factor->error(kEmpty)) : 1.0;
     };
 
-    const DecisionTree<Key, double> fdt(eliminationResults, factorProb);
-    const auto discreteFactor =
-        boost::make_shared<DecisionTreeFactor>(discreteSeparator, fdt);
+    const auto discreteFactor = boost::make_shared<DecisionTreeFactor>(
+        discreteSeparator, DecisionTree<Key, double>(newFactors, probPrime));
 
     return {boost::make_shared<HybridConditional>(gaussianMixture),
             discreteFactor};

diff --git a/gtsam/hybrid/tests/testHybridGaussianFactorGraph.cpp b/gtsam/hybrid/tests/testHybridGaussianFactorGraph.cpp
@@ -652,7 +652,7 @@ TEST(HybridGaussianFactorGraph, assembleGraphTree) {
 // Check that the factor graph unnormalized probability is proportional to the
 // Bayes net probability for the given measurements.
 bool ratioTest(const HybridBayesNet &bn, const VectorValues &measurements,
-               const HybridGaussianFactorGraph &fg, size_t num_samples = 10) {
+               const HybridGaussianFactorGraph &fg, size_t num_samples = 100) {
   auto compute_ratio = [&](HybridValues *sample) -> double {
     sample->update(measurements);  // update sample with given measurements:
     return bn.evaluate(*sample) / fg.probPrime(*sample);
@@ -670,6 +670,28 @@ bool ratioTest(const HybridBayesNet &bn, const VectorValues &measurements,
   return true;
 }
 
+/* ****************************************************************************/
+// Check that the factor graph unnormalized probability is proportional to the
+// Bayes net probability for the given measurements.
+bool ratioTest(const HybridBayesNet &bn, const VectorValues &measurements,
+               const HybridBayesNet &posterior, size_t num_samples = 100) {
+  auto compute_ratio = [&](HybridValues *sample) -> double {
+    sample->update(measurements);  // update sample with given measurements:
+    // return bn.evaluate(*sample) / fg.probPrime(*sample);
+    return bn.evaluate(*sample) / posterior.evaluate(*sample);
+  };
+
+  HybridValues sample = bn.sample(&kRng);
+  double expected_ratio = compute_ratio(&sample);
+
+  // Test ratios for a number of independent samples:
+  for (size_t i = 0; i < num_samples; i++) {
+    HybridValues sample = bn.sample(&kRng);
+    if (std::abs(expected_ratio - compute_ratio(&sample)) > 1e-6) return false;
+  }
+  return true;
+}
+
 /* ****************************************************************************/
 // Check that eliminating tiny net with 1 measurement yields correct result.
 TEST(HybridGaussianFactorGraph, EliminateTiny1) {
@@ -678,6 +700,7 @@ TEST(HybridGaussianFactorGraph, EliminateTiny1) {
   const VectorValues measurements{{Z(0), Vector1(5.0)}};
   auto bn = tiny::createHybridBayesNet(num_measurements);
   auto fg = bn.toFactorGraph(measurements);
+  GTSAM_PRINT(bn);
   EXPECT_LONGS_EQUAL(4, fg.size());
 
   EXPECT(ratioTest(bn, measurements, fg));
@@ -701,6 +724,7 @@ TEST(HybridGaussianFactorGraph, EliminateTiny1) {
   // Test elimination
   const auto posterior = fg.eliminateSequential();
   EXPECT(assert_equal(expectedBayesNet, *posterior, 0.01));
+  GTSAM_PRINT(*posterior);
 
   EXPECT(ratioTest(bn, measurements, *posterior));
 }