wrap GaussianDensity::FromMeanAndStddev

gtsam/linear/GaussianDensity.cpp

@@ -23,9 +23,11 @@ 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 / sigma,
+                           Matrix::Identity(mean.size(), mean.size()) / sigma);
   }
 
   /* ************************************************************************* */
@@ -35,8 +37,8 @@ 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: ");
   }

gtsam/linear/GaussianDensity.h

@@ -24,11 +24,10 @@
 namespace gtsam {
 
   /**
-  * A Gaussian density.
-  *
-  * It is implemented as a GaussianConditional without parents.
+  * A GaussianDensity is a GaussianConditional without parents.
   * The negative log-probability is given by \f$ |Rx - d|^2 \f$
   * with \f$ \Lambda = \Sigma^{-1} = R^T R \f$ and \f$ \mu = R^{-1} d \f$
+  * @addtogroup linear
   */
   class GTSAM_EXPORT GaussianDensity : public GaussianConditional {
 
@@ -52,8 +51,9 @@ namespace gtsam {
     GaussianDensity(Key key, const Vector& d, const Matrix& R, const SharedDiagonal& noiseModel = SharedDiagonal()) :
       GaussianConditional(key, d, R, noiseModel) {}
 
-    /// Construct using a mean and variance
-    static GaussianDensity FromMeanAndStddev(Key key, const Vector& mean, const double& sigma);
+    /// Construct using a mean and standard deviation
+    static GaussianDensity FromMeanAndStddev(Key key, const Vector& mean,
+                                             double sigma);
 
     /// print
     void print(const std::string& = "GaussianDensity",

gtsam/linear/linear.i

@@ -490,14 +490,19 @@ virtual class GaussianConditional : gtsam::JacobianFactor {
 
 #include <gtsam/linear/GaussianDensity.h>
 virtual class GaussianDensity : gtsam::GaussianConditional {
-    //Constructors
-  GaussianDensity(size_t key, Vector d, Matrix R, const gtsam::noiseModel::Diagonal* sigmas);
+  // Constructors
+  GaussianDensity(gtsam::Key key, Vector d, Matrix R,
+                  const gtsam::noiseModel::Diagonal* sigmas);
 
-  //Standard Interface
+  static gtsam::GaussianDensity FromMeanAndStddev(gtsam::Key key,
+                                                  const Vector& mean,
+                                                  double sigma);
+
+  // Standard Interface
   void print(string s = "GaussianDensity",
              const gtsam::KeyFormatter& keyFormatter =
                  gtsam::DefaultKeyFormatter) const;
-  bool equals(const gtsam::GaussianDensity &cg, double tol) const;
+  bool equals(const gtsam::GaussianDensity& cg, double tol) const;
   Vector mean() const;
   Matrix covariance() const;
 };