Create mean/stddev constructors

gtsam/hybrid/HybridGaussianConditional.cpp

@@ -29,16 +29,20 @@
 
 #include <cstddef>
 
+#include "gtsam/linear/JacobianFactor.h"
+
 namespace gtsam {
 /* *******************************************************************************/
 struct HybridGaussianConditional::ConstructorHelper {
   std::optional<size_t> nrFrontals;
-  HybridGaussianFactor::FactorValuePairs pairs;
+  FactorValuePairs pairs;
+  Conditionals conditionals;
   double minNegLogConstant;
 
-  /// Compute all variables needed for the private constructor below.
+  /// Construct from tree of GaussianConditionals.
   ConstructorHelper(const Conditionals &conditionals)
-      : minNegLogConstant(std::numeric_limits<double>::infinity()) {
+      : conditionals(conditionals),
+        minNegLogConstant(std::numeric_limits<double>::infinity()) {
     auto func = [this](const GaussianConditional::shared_ptr &c)
         -> GaussianFactorValuePair {
       double value = 0.0;
@@ -51,38 +55,79 @@ struct HybridGaussianConditional::ConstructorHelper {
       }
       return {std::dynamic_pointer_cast<GaussianFactor>(c), value};
     };
-    pairs = HybridGaussianFactor::FactorValuePairs(conditionals, func);
+    pairs = FactorValuePairs(conditionals, func);
     if (!nrFrontals.has_value()) {
       throw std::runtime_error(
           "HybridGaussianConditional: need at least one frontal variable.");
     }
   }
+
+  /// Construct from means and a single sigma.
+  ConstructorHelper(Key x, const DiscreteKey mode,
+                    const std::vector<Vector> &means, double sigma)
+      : nrFrontals(1), minNegLogConstant(0) {
+    std::vector<GaussianConditional::shared_ptr> gcs;
+    for (const auto &mean : means) {
+      auto c = GaussianConditional::sharedMeanAndStddev(x, mean, sigma);
+      gcs.push_back(c);
+    }
+    conditionals = Conditionals({mode}, gcs);
+    pairs = FactorValuePairs(conditionals, [](const auto &c) {
+      return GaussianFactorValuePair{c, 0.0};
+    });
+  }
+
+  /// Construct from means and a sigmas.
+  ConstructorHelper(Key x, const DiscreteKey mode,
+                    const std::vector<std::pair<Vector, double>> &parameters)
+      : nrFrontals(1),
+        minNegLogConstant(std::numeric_limits<double>::infinity()) {
+    std::vector<GaussianConditional::shared_ptr> gcs;
+    std::vector<GaussianFactorValuePair> fvs;
+    for (const auto &[mean, sigma] : parameters) {
+      auto c = GaussianConditional::sharedMeanAndStddev(x, mean, sigma);
+      double value = c->negLogConstant();
+      minNegLogConstant = std::min(minNegLogConstant, value);
+      gcs.push_back(c);
+      fvs.push_back({c, value});
+    }
+    conditionals = Conditionals({mode}, gcs);
+    pairs = FactorValuePairs({mode}, fvs);
+  }
 };
 
 /* *******************************************************************************/
 HybridGaussianConditional::HybridGaussianConditional(
-    const DiscreteKeys &discreteParents,
+    const DiscreteKeys &discreteParents, const ConstructorHelper &helper)
-    const HybridGaussianConditional::Conditionals &conditionals,
-    const ConstructorHelper &helper)
     : BaseFactor(discreteParents, helper.pairs),
       BaseConditional(*helper.nrFrontals),
-      conditionals_(conditionals),
+      conditionals_(helper.conditionals),
       negLogConstant_(helper.minNegLogConstant) {}
 
-/* *******************************************************************************/
+HybridGaussianConditional::HybridGaussianConditional(
+    const DiscreteKey &mode,
+    const std::vector<GaussianConditional::shared_ptr> &conditionals)
+    : HybridGaussianConditional(DiscreteKeys{mode},
+                                Conditionals({mode}, conditionals)) {}
+
+HybridGaussianConditional::HybridGaussianConditional(
+    Key x, const DiscreteKey mode, const std::vector<Vector> &means,
+    double sigma)
+    : HybridGaussianConditional(DiscreteKeys{mode},
+                                ConstructorHelper(x, mode, means, sigma)) {}
+
+HybridGaussianConditional::HybridGaussianConditional(
+    Key x, const DiscreteKey mode,
+    const std::vector<std::pair<Vector, double>> &parameters)
+    : HybridGaussianConditional(DiscreteKeys{mode},
+                                ConstructorHelper(x, mode, parameters)) {}
+
 HybridGaussianConditional::HybridGaussianConditional(
     const DiscreteKeys &discreteParents,
     const HybridGaussianConditional::Conditionals &conditionals)
-    : HybridGaussianConditional(discreteParents, conditionals,
+    : HybridGaussianConditional(discreteParents,
                                 ConstructorHelper(conditionals)) {}
 
-/* *******************************************************************************/
-HybridGaussianConditional::HybridGaussianConditional(
-    const DiscreteKey &discreteParent,
-    const std::vector<GaussianConditional::shared_ptr> &conditionals)
-    : HybridGaussianConditional(DiscreteKeys{discreteParent},
-                                Conditionals({discreteParent}, conditionals)) {}
-
 /* *******************************************************************************/
 const HybridGaussianConditional::Conditionals &
 HybridGaussianConditional::conditionals() const {

gtsam/hybrid/HybridGaussianConditional.h

@@ -79,14 +79,36 @@ class GTSAM_EXPORT HybridGaussianConditional
   /**
    * @brief Construct from one discrete key and vector of conditionals.
    *
-   * @param discreteParent Single discrete parent variable
+   * @param mode Single discrete parent variable
    * @param conditionals Vector of conditionals with the same size as the
    * cardinality of the discrete parent.
    */
   HybridGaussianConditional(
-      const DiscreteKey &discreteParent,
+      const DiscreteKey &mode,
       const std::vector<GaussianConditional::shared_ptr> &conditionals);
 
+  /**
+   * @brief Construct from vector of means and a single sigma.
+   *
+   * @param x The continuous key.
+   * @param mode The discrete key.
+   * @param means The means for the Gaussian conditionals.
+   * @param sigma The standard deviation for the Gaussian conditionals.
+   */
+  HybridGaussianConditional(Key x, const DiscreteKey mode,
+                            const std::vector<Vector> &means, double sigma);
+
+  /**
+   * @brief Construct from vector of means and sigmas.
+   *
+   * @param x The continuous key.
+   * @param mode The discrete key.
+   * @param parameters The means and sigmas for the Gaussian conditionals.
+   */
+  HybridGaussianConditional(
+      Key x, const DiscreteKey mode,
+      const std::vector<std::pair<Vector, double>> &parameters);
+
   /**
    * @brief Construct from multiple discrete keys and conditional tree.
    *
@@ -186,10 +208,8 @@ class GTSAM_EXPORT HybridGaussianConditional
   struct ConstructorHelper;
 
   /// Private constructor that uses helper struct above.
-  HybridGaussianConditional(
+  HybridGaussianConditional(const DiscreteKeys &discreteParents,
-      const DiscreteKeys &discreteParents,
+                            const ConstructorHelper &helper);
-      const HybridGaussianConditional::Conditionals &conditionals,
-      const ConstructorHelper &helper);
 
   /// Convert to a DecisionTree of Gaussian factor graphs.
   GaussianFactorGraphTree asGaussianFactorGraphTree() const;

gtsam/hybrid/tests/testGaussianMixture.cpp

@@ -43,26 +43,6 @@ const DiscreteValues m1Assignment{{M(0), 1}};
 DiscreteConditional::shared_ptr mixing =
     std::make_shared<DiscreteConditional>(m, "60/40");
 
-/**
- * Create a simple Gaussian Mixture Model represented as p(z|m)P(m)
- * where m is a discrete variable and z is a continuous variable.
- * The "mode" m is binary and depending on m, we have 2 different means
- * μ1 and μ2 for the Gaussian density p(z|m).
- */
-HybridBayesNet GaussianMixtureModel(double mu0, double mu1, double sigma0,
-                                    double sigma1) {
-  HybridBayesNet hbn;
-  auto model0 = noiseModel::Isotropic::Sigma(1, sigma0);
-  auto model1 = noiseModel::Isotropic::Sigma(1, sigma1);
-  auto c0 = std::make_shared<GaussianConditional>(Z(0), Vector1(mu0), I_1x1,
-                                                  model0),
-       c1 = std::make_shared<GaussianConditional>(Z(0), Vector1(mu1), I_1x1,
-                                                  model1);
-  hbn.emplace_shared<HybridGaussianConditional>(m, std::vector{c0, c1});
-  hbn.push_back(mixing);
-  return hbn;
-}
-
 /// Gaussian density function
 double Gaussian(double mu, double sigma, double z) {
   return exp(-0.5 * pow((z - mu) / sigma, 2)) / sqrt(2 * M_PI * sigma * sigma);
@@ -99,7 +79,10 @@ TEST(GaussianMixture, GaussianMixtureModel) {
   double mu0 = 1.0, mu1 = 3.0;
   double sigma = 2.0;
 
-  auto hbn = GaussianMixtureModel(mu0, mu1, sigma, sigma);
+  HybridBayesNet hbn;
+  std::vector<Vector> means{Vector1(mu0), Vector1(mu1)};
+  hbn.emplace_shared<HybridGaussianConditional>(Z(0), m, means, sigma);
+  hbn.push_back(mixing);
 
   // At the halfway point between the means, we should get P(m|z)=0.5
   double midway = mu1 - mu0;
@@ -133,7 +116,11 @@ TEST(GaussianMixture, GaussianMixtureModel2) {
   double mu0 = 1.0, mu1 = 3.0;
   double sigma0 = 8.0, sigma1 = 4.0;
 
-  auto hbn = GaussianMixtureModel(mu0, mu1, sigma0, sigma1);
+  HybridBayesNet hbn;
+  std::vector<std::pair<Vector, double>> parameters{{Vector1(mu0), sigma0},
+                                                    {Vector1(mu1), sigma1}};
+  hbn.emplace_shared<HybridGaussianConditional>(Z(0), m, parameters);
+  hbn.push_back(mixing);
 
   // We get zMax=3.1333 by finding the maximum value of the function, at which
   // point the mode m==1 is about twice as probable as m==0.