Direct Hybrid Factor Specification

gtsam/discrete/DiscreteBayesNet.cpp

@@ -18,8 +18,6 @@
 
 #include <gtsam/discrete/DiscreteBayesNet.h>
 #include <gtsam/discrete/DiscreteConditional.h>
-#include <gtsam/discrete/DiscreteFactorGraph.h>
-#include <gtsam/discrete/DiscreteLookupDAG.h>
 #include <gtsam/inference/FactorGraph-inst.h>
 
 namespace gtsam {

gtsam/hybrid/HybridGaussianConditional.cpp

@@ -232,8 +232,7 @@ std::shared_ptr<HybridGaussianFactor> HybridGaussianConditional::likelihood(
 
 /* ************************************************************************* */
 std::set<DiscreteKey> DiscreteKeysAsSet(const DiscreteKeys &discreteKeys) {
-  std::set<DiscreteKey> s;
-  s.insert(discreteKeys.begin(), discreteKeys.end());
+  std::set<DiscreteKey> s(discreteKeys.begin(), discreteKeys.end());
   return s;
 }
 

gtsam/hybrid/HybridGaussianFactor.cpp

@@ -30,8 +30,8 @@ namespace gtsam {
 
 /**
  * @brief Helper function to augment the [A|b] matrices in the factor components
- * with the normalizer values.
- * This is done by storing the normalizer value in
+ * with the additional scalar values.
+ * This is done by storing the value in
  * the `b` vector as an additional row.
  *
  * @param factors DecisionTree of GaussianFactors and arbitrary scalars.
@@ -56,7 +56,7 @@ HybridGaussianFactor::Factors augment(
                           const HybridGaussianFactor::sharedFactor &gf) {
     auto jf = std::dynamic_pointer_cast<JacobianFactor>(gf);
     if (!jf) return gf;
-    // If the log_normalizer is 0, do nothing
+    // If the value is 0, do nothing
     if (values(assignment) == 0.0) return gf;
 
     GaussianFactorGraph gfg;

gtsam/hybrid/HybridGaussianFactor.h

@@ -45,6 +45,15 @@ using GaussianFactorValuePair = std::pair<GaussianFactor::shared_ptr, double>;
  * where the set of discrete variables indexes to
  * the continuous gaussian distribution.
  *
+ * In factor graphs the error function typically returns 0.5*|A*x - b|^2, i.e.,
+ * the negative log-likelihood for a Gaussian noise model.
+ * In hybrid factor graphs we allow *adding* an arbitrary scalar dependent on
+ * the discrete assignment.
+ * For example, adding a 70/30 mode probability is supported by providing the
+ * scalars $-log(.7)$ and $-log(.3)$.
+ * Note that adding a common constant will not make any difference in the
+ * optimization, so $-log(70)$ and $-log(30)$ work just as well.
+ *
  * @ingroup hybrid
  */
 class GTSAM_EXPORT HybridGaussianFactor : public HybridFactor {

gtsam/hybrid/HybridNonlinearFactor.h

@@ -45,6 +45,17 @@ using NonlinearFactorValuePair = std::pair<NonlinearFactor::shared_ptr, double>;
  * This class stores all factors as HybridFactors which can then be typecast to
  * one of (NonlinearFactor, GaussianFactor) which can then be checked to perform
  * the correct operation.
+ *
+ * In factor graphs the error function typically returns 0.5*|h(x)-z|^2, i.e.,
+ * the negative log-likelihood for a Gaussian noise model.
+ * In hybrid factor graphs we allow *adding* an arbitrary scalar dependent on
+ * the discrete assignment.
+ * For example, adding a 70/30 mode probability is supported by providing the
+ * scalars $-log(.7)$ and $-log(.3)$.
+ * Note that adding a common constant will not make any difference in the
+ * optimization, so $-log(70)$ and $-log(30)$ work just as well.
+ *
+ * @ingroup hybrid
  */
 class HybridNonlinearFactor : public HybridFactor {
  public:
@@ -134,7 +145,7 @@ class HybridNonlinearFactor : public HybridFactor {
     auto errorFunc =
         [continuousValues](const std::pair<sharedFactor, double>& f) {
           auto [factor, val] = f;
-          return factor->error(continuousValues) + (0.5 * val);
+          return factor->error(continuousValues) + val;
         };
     DecisionTree<Key, double> result(factors_, errorFunc);
     return result;
@@ -153,7 +164,7 @@ class HybridNonlinearFactor : public HybridFactor {
     auto [factor, val] = factors_(discreteValues);
     // Compute the error for the selected factor
     const double factorError = factor->error(continuousValues);
-    return factorError + (0.5 * val);
+    return factorError + val;
   }
 
   /**

gtsam/hybrid/HybridValues.cpp

@@ -36,9 +36,12 @@ HybridValues::HybridValues(const VectorValues& cv, const DiscreteValues& dv,
 void HybridValues::print(const std::string& s,
                          const KeyFormatter& keyFormatter) const {
   std::cout << s << ": \n";
-  continuous_.print("  Continuous",
-                    keyFormatter);              // print continuous components
-  discrete_.print("  Discrete", keyFormatter);  // print discrete components
+  // print continuous components
+  continuous_.print("  Continuous", keyFormatter);
+  // print discrete components
+  discrete_.print("  Discrete", keyFormatter);
+  // print nonlinear components
+  nonlinear_.print("  Nonlinear", keyFormatter);
 }
 
 /* ************************************************************************* */

gtsam/hybrid/tests/testHybridGaussianFactor.cpp

@@ -396,7 +396,7 @@ namespace test_two_state_estimation {
 
 DiscreteKey m1(M(1), 2);
 
-void addMeasurement(HybridBayesNet& hbn, Key z_key, Key x_key, double sigma) {
+void addMeasurement(HybridBayesNet &hbn, Key z_key, Key x_key, double sigma) {
   auto measurement_model = noiseModel::Isotropic::Sigma(1, sigma);
   hbn.emplace_shared<GaussianConditional>(z_key, Vector1(0.0), I_1x1, x_key,
                                           -I_1x1, measurement_model);
@@ -420,7 +420,7 @@ static HybridGaussianConditional::shared_ptr CreateHybridMotionModel(
 
 /// Create two state Bayes network with 1 or two measurement models
 HybridBayesNet CreateBayesNet(
-    const HybridGaussianConditional::shared_ptr& hybridMotionModel,
+    const HybridGaussianConditional::shared_ptr &hybridMotionModel,
     bool add_second_measurement = false) {
   HybridBayesNet hbn;
 
@@ -443,9 +443,9 @@ HybridBayesNet CreateBayesNet(
 
 /// Approximate the discrete marginal P(m1) using importance sampling
 std::pair<double, double> approximateDiscreteMarginal(
-    const HybridBayesNet& hbn,
-    const HybridGaussianConditional::shared_ptr& hybridMotionModel,
-    const VectorValues& given, size_t N = 100000) {
+    const HybridBayesNet &hbn,
+    const HybridGaussianConditional::shared_ptr &hybridMotionModel,
+    const VectorValues &given, size_t N = 100000) {
   /// Create importance sampling network q(x0,x1,m) = p(x1|x0,m1) q(x0) P(m1),
   /// using q(x0) = N(z0, sigmaQ) to sample x0.
   HybridBayesNet q;
@@ -741,6 +741,170 @@ TEST(HybridGaussianFactor, TwoStateModel4) {
   EXPECT(assert_equal(expected, *(bn->at(2)->asDiscrete()), 0.002));
 }
 
+namespace test_direct_factor_graph {
+/**
+ * @brief Create a Factor Graph by directly specifying all
+ * the factors instead of creating conditionals first.
+ * This way we can directly provide the likelihoods and
+ * then perform linearization.
+ *
+ * @param values Initial values to linearize around.
+ * @param means The means of the HybridGaussianFactor components.
+ * @param sigmas The covariances of the HybridGaussianFactor components.
+ * @param m1 The discrete key.
+ * @return HybridGaussianFactorGraph
+ */
+static HybridGaussianFactorGraph CreateFactorGraph(
+    const gtsam::Values &values, const std::vector<double> &means,
+    const std::vector<double> &sigmas, DiscreteKey &m1,
+    double measurement_noise = 1e-3) {
+  auto model0 = noiseModel::Isotropic::Sigma(1, sigmas[0]);
+  auto model1 = noiseModel::Isotropic::Sigma(1, sigmas[1]);
+  auto prior_noise = noiseModel::Isotropic::Sigma(1, measurement_noise);
+
+  auto f0 =
+      std::make_shared<BetweenFactor<double>>(X(0), X(1), means[0], model0)
+          ->linearize(values);
+  auto f1 =
+      std::make_shared<BetweenFactor<double>>(X(0), X(1), means[1], model1)
+          ->linearize(values);
+
+  // Create HybridGaussianFactor
+  // We multiply by -2 since the we want the underlying scalar to be log(|2πΣ|)
+  std::vector<GaussianFactorValuePair> factors{
+      {f0, -2 * model0->logNormalizationConstant()},
+      {f1, -2 * model1->logNormalizationConstant()}};
+  HybridGaussianFactor motionFactor({X(0), X(1)}, m1, factors);
+
+  HybridGaussianFactorGraph hfg;
+  hfg.push_back(motionFactor);
+
+  hfg.push_back(PriorFactor<double>(X(0), values.at<double>(X(0)), prior_noise)
+                    .linearize(values));
+
+  return hfg;
+}
+}  // namespace test_direct_factor_graph
+
+/* ************************************************************************* */
+/**
+ * @brief Test components with differing means but the same covariances.
+ * The factor graph is
+ *     *-X1-*-X2
+ *          |
+ *          M1
+ */
+TEST(HybridGaussianFactor, DifferentMeansFG) {
+  using namespace test_direct_factor_graph;
+
+  DiscreteKey m1(M(1), 2);
+
+  Values values;
+  double x1 = 0.0, x2 = 1.75;
+  values.insert(X(0), x1);
+  values.insert(X(1), x2);
+
+  std::vector<double> means = {0.0, 2.0}, sigmas = {1e-0, 1e-0};
+
+  HybridGaussianFactorGraph hfg = CreateFactorGraph(values, means, sigmas, m1);
+
+  {
+    auto bn = hfg.eliminateSequential();
+    HybridValues actual = bn->optimize();
+
+    HybridValues expected(
+        VectorValues{{X(0), Vector1(0.0)}, {X(1), Vector1(-1.75)}},
+        DiscreteValues{{M(1), 0}});
+
+    EXPECT(assert_equal(expected, actual));
+
+    DiscreteValues dv0{{M(1), 0}};
+    VectorValues cont0 = bn->optimize(dv0);
+    double error0 = bn->error(HybridValues(cont0, dv0));
+    // regression
+    EXPECT_DOUBLES_EQUAL(0.69314718056, error0, 1e-9);
+
+    DiscreteValues dv1{{M(1), 1}};
+    VectorValues cont1 = bn->optimize(dv1);
+    double error1 = bn->error(HybridValues(cont1, dv1));
+    EXPECT_DOUBLES_EQUAL(error0, error1, 1e-9);
+  }
+
+  {
+    auto prior_noise = noiseModel::Isotropic::Sigma(1, 1e-3);
+    hfg.push_back(
+        PriorFactor<double>(X(1), means[1], prior_noise).linearize(values));
+
+    auto bn = hfg.eliminateSequential();
+    HybridValues actual = bn->optimize();
+
+    HybridValues expected(
+        VectorValues{{X(0), Vector1(0.0)}, {X(1), Vector1(0.25)}},
+        DiscreteValues{{M(1), 1}});
+
+    EXPECT(assert_equal(expected, actual));
+
+    {
+      DiscreteValues dv{{M(1), 0}};
+      VectorValues cont = bn->optimize(dv);
+      double error = bn->error(HybridValues(cont, dv));
+      // regression
+      EXPECT_DOUBLES_EQUAL(2.12692448787, error, 1e-9);
+    }
+    {
+      DiscreteValues dv{{M(1), 1}};
+      VectorValues cont = bn->optimize(dv);
+      double error = bn->error(HybridValues(cont, dv));
+      // regression
+      EXPECT_DOUBLES_EQUAL(0.126928487854, error, 1e-9);
+    }
+  }
+}
+
+/* ************************************************************************* */
+/**
+ * @brief Test components with differing covariances but the same means.
+ * The factor graph is
+ *     *-X1-*-X2
+ *          |
+ *          M1
+ */
+TEST(HybridGaussianFactor, DifferentCovariancesFG) {
+  using namespace test_direct_factor_graph;
+
+  DiscreteKey m1(M(1), 2);
+
+  Values values;
+  double x1 = 1.0, x2 = 1.0;
+  values.insert(X(0), x1);
+  values.insert(X(1), x2);
+
+  std::vector<double> means = {0.0, 0.0}, sigmas = {1e2, 1e-2};
+
+  // Create FG with HybridGaussianFactor and prior on X1
+  HybridGaussianFactorGraph fg = CreateFactorGraph(values, means, sigmas, m1);
+  auto hbn = fg.eliminateSequential();
+
+  VectorValues cv;
+  cv.insert(X(0), Vector1(0.0));
+  cv.insert(X(1), Vector1(0.0));
+
+  // Check that the error values at the MLE point μ.
+  AlgebraicDecisionTree<Key> errorTree = hbn->errorTree(cv);
+
+  DiscreteValues dv0{{M(1), 0}};
+  DiscreteValues dv1{{M(1), 1}};
+
+  // regression
+  EXPECT_DOUBLES_EQUAL(9.90348755254, errorTree(dv0), 1e-9);
+  EXPECT_DOUBLES_EQUAL(0.69314718056, errorTree(dv1), 1e-9);
+
+  DiscreteConditional expected_m1(m1, "0.5/0.5");
+  DiscreteConditional actual_m1 = *(hbn->at(2)->asDiscrete());
+
+  EXPECT(assert_equal(expected_m1, actual_m1));
+}
+
 /* ************************************************************************* */
 int main() {
   TestResult tr;