Improvements in linear

gtsam/linear/GaussianBayesNet.cpp

@@ -26,6 +26,9 @@
 using namespace std;
 using namespace gtsam;
 
+// In Wrappers we have no access to this so have a default ready
+static std::mt19937_64 kRandomNumberGenerator(42);
+
 namespace gtsam {
 
   // Instantiate base class
@@ -37,28 +40,50 @@ namespace gtsam {
     return Base::equals(bn, tol);
   }
 
-  /* ************************************************************************* */
-  VectorValues GaussianBayesNet::optimize() const
-  {
-    VectorValues soln; // no missing variables -> just create an empty vector
-    return optimize(soln);
+  /* ************************************************************************ */
+  VectorValues GaussianBayesNet::optimize() const {
+    VectorValues solution;  // no missing variables -> create an empty vector
+    return optimize(solution);
   }
 
-  /* ************************************************************************* */
-  VectorValues GaussianBayesNet::optimize(
-      const VectorValues& solutionForMissing) const {
-    VectorValues soln(solutionForMissing); // possibly empty
+  VectorValues GaussianBayesNet::optimize(VectorValues solution) const {
     // (R*x)./sigmas = y by solving x=inv(R)*(y.*sigmas)
-    /** solve each node in turn in topological sort order (parents first)*/
-    for (auto cg: boost::adaptors::reverse(*this)) {
+    // solve each node in reverse topological sort order (parents first)
+    for (auto cg : boost::adaptors::reverse(*this)) {
       // i^th part of R*x=y, x=inv(R)*y
-      // (Rii*xi + R_i*x(i+1:))./si = yi <-> xi = inv(Rii)*(yi.*si - R_i*x(i+1:))
-      soln.insert(cg->solve(soln));
+      // (Rii*xi + R_i*x(i+1:))./si = yi =>
+      // xi = inv(Rii)*(yi.*si - R_i*x(i+1:))
+      solution.insert(cg->solve(solution));
     }
-    return soln;
+    return solution;
   }
 
-  /* ************************************************************************* */
+  /* ************************************************************************ */
+  VectorValues GaussianBayesNet::sample(std::mt19937_64* rng) const {
+    VectorValues result;  // no missing variables -> create an empty vector
+    return sample(result, rng);
+  }
+
+  VectorValues GaussianBayesNet::sample(VectorValues result,
+                                        std::mt19937_64* rng) const {
+    // sample each node in reverse topological sort order (parents first)
+    for (auto cg : boost::adaptors::reverse(*this)) {
+      const VectorValues sampled = cg->sample(result, rng);
+      result.insert(sampled);
+    }
+    return result;
+  }
+
+  /* ************************************************************************ */
+  VectorValues GaussianBayesNet::sample() const {
+    return sample(&kRandomNumberGenerator);
+  }
+
+  VectorValues GaussianBayesNet::sample(VectorValues given) const {
+    return sample(given, &kRandomNumberGenerator);
+  }
+
+  /* ************************************************************************ */
   VectorValues GaussianBayesNet::optimizeGradientSearch() const
   {
     gttic(GaussianBayesTree_optimizeGradientSearch);

gtsam/linear/GaussianBayesNet.h

@@ -88,11 +88,35 @@ namespace gtsam {
     /// @name Standard Interface
     /// @{
 
-    /// Solve the GaussianBayesNet, i.e. return \f$ x = R^{-1}*d \f$, by back-substitution
+    /// Solve the GaussianBayesNet, i.e. return \f$ x = R^{-1}*d \f$, by
+    /// back-substitution
     VectorValues optimize() const;
 
-    /// Version of optimize for incomplete BayesNet, needs solution for missing variables
-    VectorValues optimize(const VectorValues& solutionForMissing) const;
+    /// Version of optimize for incomplete BayesNet, given missing variables
+    VectorValues optimize(const VectorValues given) const;
+
+    /**
+     * Sample using ancestral sampling
+     * Example:
+     *   std::mt19937_64 rng(42);
+     *   auto sample = gbn.sample(&rng);
+     */
+    VectorValues sample(std::mt19937_64* rng) const;
+
+    /**
+     * Sample from an incomplete BayesNet, given missing variables
+     * Example:
+     *   std::mt19937_64 rng(42);
+     *   VectorValues given = ...;
+     *   auto sample = gbn.sample(given, &rng);
+     */
+    VectorValues sample(VectorValues given, std::mt19937_64* rng) const;
+
+    /// Sample using ancestral sampling, use default rng
+    VectorValues sample() const;
+
+    /// Sample from an incomplete BayesNet, use default rng
+    VectorValues sample(VectorValues given) const;
 
     /**
      * Return ordering corresponding to a topological sort.

gtsam/linear/GaussianConditional.cpp

@@ -18,6 +18,7 @@
 #include <gtsam/linear/linearExceptions.h>
 #include <gtsam/linear/GaussianConditional.h>
 #include <gtsam/linear/VectorValues.h>
+#include <gtsam/linear/Sampler.h>
 
 #include <boost/format.hpp>
 #ifdef __GNUC__
@@ -34,6 +35,9 @@
 #include <list>
 #include <string>
 
+// In Wrappers we have no access to this so have a default ready
+static std::mt19937_64 kRandomNumberGenerator(42);
+
 using namespace std;
 
 namespace gtsam {
@@ -43,19 +47,47 @@ namespace gtsam {
     Key key, const Vector& d, const Matrix& R, const SharedDiagonal& sigmas) :
   BaseFactor(key, R, d, sigmas), BaseConditional(1) {}
 
-  /* ************************************************************************* */
-  GaussianConditional::GaussianConditional(
-    Key key, const Vector& d, const Matrix& R,
-    Key name1, const Matrix& S, const SharedDiagonal& sigmas) :
-  BaseFactor(key, R, name1, S, d, sigmas), BaseConditional(1) {}
+  /* ************************************************************************ */
+  GaussianConditional::GaussianConditional(Key key, const Vector& d,
+                                           const Matrix& R, Key parent1,
+                                           const Matrix& S,
+                                           const SharedDiagonal& sigmas)
+      : BaseFactor(key, R, parent1, S, d, sigmas), BaseConditional(1) {}
+
+  /* ************************************************************************ */
+  GaussianConditional::GaussianConditional(Key key, const Vector& d,
+                                           const Matrix& R, Key parent1,
+                                           const Matrix& S, Key parent2,
+                                           const Matrix& T,
+                                           const SharedDiagonal& sigmas)
+      : BaseFactor(key, R, parent1, S, parent2, T, d, sigmas),
+        BaseConditional(1) {}
+
+  /* ************************************************************************ */
+  GaussianConditional GaussianConditional::FromMeanAndStddev(
+      Key key, const Matrix& A, Key parent, const Vector& b, double sigma) {
+    // |Rx + Sy - d| = |x-(Ay + b)|/sigma
+    const Matrix R = Matrix::Identity(b.size(), b.size());
+    const Matrix S = -A;
+    const Vector d = b;
+    return GaussianConditional(key, d, R, parent, S,
+                               noiseModel::Isotropic::Sigma(b.size(), sigma));
+  }
 
-  /* ************************************************************************* */
-  GaussianConditional::GaussianConditional(
-    Key key, const Vector& d, const Matrix& R,
-    Key name1, const Matrix& S, Key name2, const Matrix& T, const SharedDiagonal& sigmas) :
-  BaseFactor(key, R, name1, S, name2, T, d, sigmas), BaseConditional(1) {}
+  /* ************************************************************************ */
+  GaussianConditional GaussianConditional::FromMeanAndStddev(
+      Key key, const Matrix& A1, Key parent1, const Matrix& A2, Key parent2,
+      const Vector& b, double sigma) {
+    // |Rx + Sy + Tz - d| = |x-(A1 y + A2 z + b)|/sigma
+    const Matrix R = Matrix::Identity(b.size(), b.size());
+    const Matrix S = -A1;
+    const Matrix T = -A2;
+    const Vector d = b;
+    return GaussianConditional(key, d, R, parent1, S, parent2, T,
+                               noiseModel::Isotropic::Sigma(b.size(), sigma));
+  }
 
-  /* ************************************************************************* */
+  /* ************************************************************************ */
   void GaussianConditional::print(const string &s, const KeyFormatter& formatter) const {
     cout << s << "  Conditional density ";
     for (const_iterator it = beginFrontals(); it != endFrontals(); ++it) {
@@ -192,7 +224,44 @@ namespace gtsam {
     }
   }
 
-  /* ************************************************************************* */
+  /* ************************************************************************ */
+  VectorValues GaussianConditional::sample(const VectorValues& parentsValues,
+                                           std::mt19937_64* rng) const {
+    if (nrFrontals() != 1) {
+      throw std::invalid_argument(
+          "GaussianConditional::sample can only be called on single variable "
+          "conditionals");
+    }
+    if (!model_) {
+      throw std::invalid_argument(
+          "GaussianConditional::sample can only be called if a diagonal noise "
+          "model was specified at construction.");
+    }
+    VectorValues solution = solve(parentsValues);
+    Key key = firstFrontalKey();
+    const Vector& sigmas = model_->sigmas();
+    solution[key] += Sampler::sampleDiagonal(sigmas, rng);
+    return solution;
+  }
+
+  VectorValues GaussianConditional::sample(std::mt19937_64* rng) const {
+    if (nrParents() != 0)
+      throw std::invalid_argument(
+          "sample() can only be invoked on no-parent prior");
+    VectorValues values;
+    return sample(values);
+  }
+
+  /* ************************************************************************ */
+  VectorValues GaussianConditional::sample() const {
+    return sample(&kRandomNumberGenerator);
+  }
+
+  VectorValues GaussianConditional::sample(const VectorValues& given) const {
+    return sample(given, &kRandomNumberGenerator);
+  }
+
+  /* ************************************************************************ */
 #ifdef GTSAM_ALLOW_DEPRECATED_SINCE_V42
   void GTSAM_DEPRECATED
   GaussianConditional::scaleFrontalsBySigma(VectorValues& gy) const {

gtsam/linear/GaussianConditional.h

@@ -26,12 +26,15 @@
 #include <gtsam/inference/Conditional.h>
 #include <gtsam/linear/VectorValues.h>
 
+#include <random> // for std::mt19937_64 
+
 namespace gtsam {
 
   /**
-  * A conditional Gaussian functions as the node in a Bayes network
+  * A GaussianConditional functions as the node in a Bayes network.
   * It has a set of parents y,z, etc. and implements a probability density on x.
   * The negative log-probability is given by \f$ \frac{1}{2} |Rx - (d - Sy - Tz - ...)|^2 \f$
+  * @addtogroup linear
   */
   class GTSAM_EXPORT GaussianConditional :
     public JacobianFactor,
@@ -43,6 +46,9 @@ namespace gtsam {
     typedef JacobianFactor BaseFactor; ///< Typedef to our factor base class
     typedef Conditional<BaseFactor, This> BaseConditional; ///< Typedef to our conditional base class
 
+    /// @name Constructors
+    /// @{
+
     /** default constructor needed for serialization */
     GaussianConditional() {}
 
@@ -51,13 +57,14 @@ namespace gtsam {
       const SharedDiagonal& sigmas = SharedDiagonal());
 
     /** constructor with only one parent |Rx+Sy-d| */
-    GaussianConditional(Key key, const Vector& d, const Matrix& R,
-      Key name1, const Matrix& S, const SharedDiagonal& sigmas = SharedDiagonal());
+    GaussianConditional(Key key, const Vector& d, const Matrix& R, Key parent1,
+                        const Matrix& S,
+                        const SharedDiagonal& sigmas = SharedDiagonal());
 
     /** constructor with two parents |Rx+Sy+Tz-d| */
-    GaussianConditional(Key key, const Vector& d, const Matrix& R,
-      Key name1, const Matrix& S, Key name2, const Matrix& T,
-      const SharedDiagonal& sigmas = SharedDiagonal());
+    GaussianConditional(Key key, const Vector& d, const Matrix& R, Key parent1,
+                        const Matrix& S, Key parent2, const Matrix& T,
+                        const SharedDiagonal& sigmas = SharedDiagonal());
 
     /** Constructor with arbitrary number of frontals and parents.
     *   @tparam TERMS A container whose value type is std::pair<Key, Matrix>, specifying the
@@ -76,6 +83,17 @@ namespace gtsam {
       const KEYS& keys, size_t nrFrontals, const VerticalBlockMatrix& augmentedMatrix,
       const SharedDiagonal& sigmas = SharedDiagonal());
 
+    /// Construct from mean A1 p1 + b and standard deviation.
+    static GaussianConditional FromMeanAndStddev(Key key, const Matrix& A,
+                                                 Key parent, const Vector& b,
+                                                 double sigma);
+
+    /// Construct from mean A1 p1 + A2 p2 + b and standard deviation.
+    static GaussianConditional FromMeanAndStddev(Key key,  //
+                                                 const Matrix& A1, Key parent1,
+                                                 const Matrix& A2, Key parent2,
+                                                 const Vector& b, double sigma);
+
     /** Combine several GaussianConditional into a single dense GC.  The conditionals enumerated by
     *   \c first and \c last must be in increasing order, meaning that the parents of any
     *   conditional may not include a conditional coming before it.
@@ -86,13 +104,21 @@ namespace gtsam {
     template<typename ITERATOR>
     static shared_ptr Combine(ITERATOR firstConditional, ITERATOR lastConditional);
 
+    /// @}
+    /// @name Testable
+    /// @{
+
     /** print */
     void print(const std::string& = "GaussianConditional",
       const KeyFormatter& formatter = DefaultKeyFormatter) const override;
 
     /** equals function */
     bool equals(const GaussianFactor&cg, double tol = 1e-9) const override;
 
+    /// @}
+    /// @name Standard Interface
+    /// @{
+
     /** Return a view of the upper-triangular R block of the conditional */
     constABlock R() const { return Ab_.range(0, nrFrontals()); }
 
@@ -125,10 +151,39 @@ namespace gtsam {
     /** Performs transpose backsubstition in place on values */
     void solveTransposeInPlace(VectorValues& gy) const;
 
+    /**
+     * Sample from conditional, zero parent version
+     * Example:
+     *   std::mt19937_64 rng(42);
+     *   auto sample = gbn.sample(&rng);
+     */
+    VectorValues sample(std::mt19937_64* rng) const;
+
+    /**
+     * Sample from conditional, given missing variables
+     * Example:
+     *   std::mt19937_64 rng(42);
+     *   VectorValues given = ...;
+     *   auto sample = gbn.sample(given, &rng);
+     */
+    VectorValues sample(const VectorValues& parentsValues,
+                        std::mt19937_64* rng) const;
+
+    /// Sample, use default rng
+    VectorValues sample() const;
+
+    /// Sample with given values, use default rng
+    VectorValues sample(const VectorValues& parentsValues) const;
+
+    /// @}
+
 #ifdef GTSAM_ALLOW_DEPRECATED_SINCE_V42
+    /// @name Deprecated
+    /// @{
     /** Scale the values in \c gy according to the sigmas for the frontal variables in this
      *  conditional. */
     void GTSAM_DEPRECATED scaleFrontalsBySigma(VectorValues& gy) const;
+    /// @}
 #endif
 
    private:

gtsam/linear/GaussianDensity.cpp

@@ -23,9 +23,12 @@ using namespace std;
 namespace gtsam {
 
   /* ************************************************************************* */
-  GaussianDensity GaussianDensity::FromMeanAndStddev(Key key, const Vector& mean, const double& sigma)
-  {
-    return GaussianDensity(key, mean / sigma, Matrix::Identity(mean.size(), mean.size()) / sigma);
+  GaussianDensity GaussianDensity::FromMeanAndStddev(Key key,
+                                                     const Vector& mean,
+                                                     double sigma) {
+    return GaussianDensity(key, mean,
+                           Matrix::Identity(mean.size(), mean.size()),
+                           noiseModel::Isotropic::Sigma(mean.size(), sigma));
   }
 
   /* ************************************************************************* */
@@ -35,8 +38,8 @@ namespace gtsam {
     for(const_iterator it = beginFrontals(); it != endFrontals(); ++it)
       cout << (boost::format("[%1%]")%(formatter(*it))).str() << " ";
     cout << endl;
-    gtsam::print(Matrix(R()), "R: ");
-    gtsam::print(Vector(d()), "d: ");
+    gtsam::print(mean(), "mean: ");
+    gtsam::print(covariance(), "covariance: ");
     if(model_)
       model_->print("Noise model: ");
   }