Fixed indentation

cpp/NoiseModel.h

@@ -15,23 +15,23 @@
 
 namespace gtsam {
 
-class SharedDiagonal; // forward declare, defined at end
+	class SharedDiagonal; // forward declare, defined at end
 
-namespace noiseModel {
+	namespace noiseModel {
 
-/**
+		/**
 		 * noiseModel::Base is the abstract base class for all noise models.
 		 *
 		 * Noise models must implement a 'whiten' function to normalize an error vector,
 		 * and an 'unwhiten' function to unnormalize an error vector.
 		 */
-class Base : public Testable<Base> {
+		class Base : public Testable<Base> {
 
-protected:
+		protected:
 
 			size_t dim_;
 
-public:
+		public:
 
 			Base(size_t dim):dim_(dim) {}
 			virtual ~Base() {}
@@ -60,9 +60,9 @@ public:
 			virtual void unwhitenInPlace(Vector& v) const {
 				v = unwhiten(v);
 			}
-};
+		};
 
-/**
+		/**
 		 * Gaussian implements the mathematical model
 		 *  |R*x|^2 = |y|^2 with R'*R=inv(Sigma)
 		 * where
@@ -72,9 +72,9 @@ public:
 		 *   |y|^2 = y'*y = x'*R'*R*x
 		 * Various derived classes are available that are more efficient.
 		 */
-struct Gaussian: public Base {
+		struct Gaussian: public Base {
 
-private:
+		private:
 
 			// TODO: store as boost upper-triangular or whatever is passed from above
 			/* Matrix square root of information matrix (R) */
@@ -83,50 +83,50 @@ private:
 			/**
 			 * Return R itself, but note that Whiten(H) is cheaper than R*H
 			 */
-const Matrix& thisR() const {
+			const Matrix& thisR() const {
 				// should never happen
 				if (!sqrt_information_) throw std::runtime_error("Gaussian: has no R matrix");
 				return *sqrt_information_;
 			}
 
-protected:
+		protected:
 
 			/** protected constructor takes square root information matrix */
 			Gaussian(size_t dim, const boost::optional<Matrix>& sqrt_information = boost::none) :
 				Base(dim), sqrt_information_(sqrt_information) {
 			}
 
-public:
+		public:
 
 			typedef boost::shared_ptr<Gaussian> shared_ptr;
 
-/**
+			/**
 			 * A Gaussian noise model created by specifying a square root information matrix.
 			 * @param smart, check if can be simplified to derived class
 			 */
-static shared_ptr SqrtInformation(const Matrix& R) {
+			static shared_ptr SqrtInformation(const Matrix& R) {
 				return shared_ptr(new Gaussian(R.size1(),R));
-}
+			}
 
-/**
+			/**
 			 * A Gaussian noise model created by specifying a covariance matrix.
 			 * @param smart, check if can be simplified to derived class
 			 */
-static shared_ptr Covariance(const Matrix& covariance, bool smart=false);
+			static shared_ptr Covariance(const Matrix& covariance, bool smart=false);
 
-/**
+			/**
 			 * A Gaussian noise model created by specifying an information matrix.
 			 */
-static shared_ptr Information(const Matrix& Q)  {
+			static shared_ptr Information(const Matrix& Q)  {
 				return shared_ptr(new Gaussian(Q.size1(),square_root_positive(Q)));
-}
+			}
 
 			virtual void print(const std::string& name) const;
 			virtual bool equals(const Base& expected, double tol) const;
-virtual Vector whiten(const Vector& v) const;
+			virtual Vector whiten(const Vector& v) const;
 			virtual Vector unwhiten(const Vector& v) const;
 
-/**
+			/**
 			 * Mahalanobis distance v'*R'*R*v = <R*v,R*v>
 			 */
 			virtual double Mahalanobis(const Vector& v) const;
@@ -142,46 +142,46 @@ virtual Vector whiten(const Vector& v) const;
 			 */
 			virtual void WhitenInPlace(Matrix& H) const;
 
-/**
+			/**
 			 * Whiten a system, in place as well
 			 */
-inline void WhitenSystem(Matrix& A, Vector& b) const {
+			inline void WhitenSystem(Matrix& A, Vector& b) const {
 				WhitenInPlace(A);
 				whitenInPlace(b);
-}
+			}
 
-/**
+			/**
 			 * Apply appropriately weighted QR factorization to the system [A b]
 			 *               Q'  *   [A b]  =  [R d]
 			 * Dimensions: (r*m) * m*(n+1) = r*(n+1)
 			 * @param Ab is the m*(n+1) augmented system matrix [A b]
 			 * @return in-place QR factorization [R d]. Below-diagonal is undefined !!!!!
 			 */
-virtual SharedDiagonal QR(Matrix& Ab) const;
+			virtual SharedDiagonal QR(Matrix& Ab) const;
 
 			/**
 			 * Return R itself, but note that Whiten(H) is cheaper than R*H
 			 */
-virtual Matrix R() const { return thisR();}
+			virtual Matrix R() const { return thisR();}
 
-/**
+			/**
 			 * Simple check for constrained-ness
 			 */
-virtual bool isConstrained() const {return false;}
+			virtual bool isConstrained() const {return false;}
 
-}; // Gaussian
+		}; // Gaussian
 
 
-// FD: does not work, ambiguous overload :-(
-// inline Vector operator*(const Gaussian& R, const Vector& v) {return R.whiten(v);}
+		// FD: does not work, ambiguous overload :-(
+		// inline Vector operator*(const Gaussian& R, const Vector& v) {return R.whiten(v);}
 
-/**
+		/**
 		 * A diagonal noise model implements a diagonal covariance matrix, with the
 		 * elements of the diagonal specified in a Vector.  This class has no public
 		 * constructors, instead, use the static constructor functions Sigmas etc...
 		 */
-class Diagonal : public Gaussian {
-protected:
+		class Diagonal : public Gaussian {
+		protected:
 
 			/** sigmas and reciprocal */
 			Vector sigmas_, invsigmas_;
@@ -189,7 +189,7 @@ protected:
 			/** protected constructor takes sigmas */
 			Diagonal(const Vector& sigmas);
 
-public:
+		public:
 
 			typedef boost::shared_ptr<Diagonal> shared_ptr;
 
@@ -237,9 +237,9 @@ public:
 			virtual Matrix R() const {
 				return diag(invsigmas_);
 			}
-}; // Diagonal
+		}; // Diagonal
 
-/**
+		/**
 		 * A Constrained constrained model is a specialization of Diagonal which allows
 		 * some or all of the sigmas to be zero, forcing the error to be zero there.
 		 * All other Gaussian models are guaranteed to have a non-singular square-root
@@ -247,8 +247,8 @@ public:
 		 * singular noise models, specifically: whiten will return zero on those
 		 * components that have zero sigma *and* zero error, infinity otherwise.
 		 */
-class Constrained : public Diagonal {
-protected:
+		class Constrained : public Diagonal {
+		protected:
 
 			// Constrained does not have member variables
 			// Instead (possibly zero) sigmas are stored in Diagonal Base class
@@ -256,7 +256,7 @@ protected:
 			/** protected constructor takes sigmas */
 			Constrained(const Vector& sigmas) :Diagonal(sigmas) {}
 
-public:
+		public:
 
 			typedef boost::shared_ptr<Constrained> shared_ptr;
 
@@ -311,21 +311,21 @@ public:
 			 */
 			virtual bool isConstrained() const {return true;}
 
-}; // Constrained
+		}; // Constrained
 
-/**
+		/**
 		 * An isotropic noise model corresponds to a scaled diagonal covariance
 		 * To construct, use one of the static methods
 		 */
-class Isotropic : public Diagonal {
-protected:
+		class Isotropic : public Diagonal {
+		protected:
 			double sigma_, invsigma_;
 
 			/** protected constructor takes sigma */
 			Isotropic(size_t dim, double sigma) :
 				Diagonal(repeat(dim, sigma)),sigma_(sigma),invsigma_(1.0/sigma) {}
 
-public:
+		public:
 
 			typedef boost::shared_ptr<Isotropic> shared_ptr;
 
@@ -366,17 +366,17 @@ public:
 			 */
 			virtual Vector sample() const;
 
-};
+		};
 
-/**
+		/**
 		 * Unit: i.i.d. unit-variance noise on all m dimensions.
 		 */
-class Unit : public Isotropic {
-protected:
+		class Unit : public Isotropic {
+		protected:
 
 			Unit(size_t dim): Isotropic(dim,1.0) {}
 
-public:
+		public:
 
 			typedef boost::shared_ptr<Unit> shared_ptr;
 
@@ -393,9 +393,9 @@ public:
 			virtual Vector unwhiten(const Vector& v) const { return v; }
 			virtual Matrix Whiten(const Matrix& H) const { return H; }
 			virtual void WhitenInPlace(Matrix& H) const {}
-};
+		};
 
-} // namespace noiseModel
+	} // namespace noiseModel
 
-using namespace noiseModel;
+	using namespace noiseModel;
 } // namespace gtsam