Removed print and cout statements.

gtsam/linear/GaussianConditional.cpp

@@ -129,10 +129,7 @@ namespace gtsam {
     Vector soln = get_R().triangularView<Eigen::Upper>().solve(xS);
 
     // Check for indeterminant solution
-    if(soln.hasNaN()) {
+    if(soln.hasNaN()) throw IndeterminantLinearSystemException(keys().front());
-      std::cout << "GaussianConditional::solve failed: solution has NaN!!" << std::endl;
-      throw IndeterminantLinearSystemException(keys().front());
-    }
 
     // TODO, do we not have to scale by sigmas here? Copy/paste bug
 
@@ -183,10 +180,7 @@ namespace gtsam {
     frontalVec = gtsam::backSubstituteUpper(frontalVec, Matrix(get_R()));
 
     // Check for indeterminant solution
-    if (frontalVec.hasNaN()) {
+    if (frontalVec.hasNaN()) throw IndeterminantLinearSystemException(this->keys().front());
-      std::cout << "GaussianConditional::solveTransposeInPlace failed: frontalVec has NaN!!" << std::endl;
-      throw IndeterminantLinearSystemException(this->keys().front());
-    }
 
     for (const_iterator it = beginParents(); it!= endParents(); it++)
       gtsam::transposeMultiplyAdd(-1.0, Matrix(getA(it)), frontalVec, gy[*it]);
@@ -214,4 +208,3 @@ namespace gtsam {
   }
 
 }
-

gtsam/linear/HessianFactor.cpp

@@ -641,7 +641,6 @@ EliminateCholesky(const GaussianFactorGraph& factors, const Ordering& keys)
     // Erase the eliminated keys in the remaining factor
     jointFactor->keys_.erase(jointFactor->begin(), jointFactor->begin() + numberOfKeysToEliminate);
   } catch(CholeskyFailed&) {
-    std::cout << "EliminateCholesky failed!"  << std::endl;
     throw IndeterminantLinearSystemException(keys.front());
   }
 

gtsam/linear/JacobianFactor.cpp

@@ -116,10 +116,8 @@ JacobianFactor::JacobianFactor(const HessianFactor& factor) :
   boost::tie(maxrank, success) = choleskyCareful(Ab_.matrix());
 
   // Check for indefinite system
-  if (!success) {
+  if (!success)
-    std::cout << "JacobianFactor constructor from HessianFactor failed!" << std::endl;
     throw IndeterminantLinearSystemException(factor.keys().front());
-  }
 
   // Zero out lower triangle
   Ab_.matrix().topRows(maxrank).triangularView<Eigen::StrictlyLower>() =
@@ -693,10 +691,8 @@ GaussianConditional::shared_ptr JacobianFactor::splitConditional(
   Ab_.rowEnd() = Ab_.rowStart() + frontalDim;
   SharedDiagonal conditionalNoiseModel;
   if (model_) {
-    if ((DenseIndex) model_->dim() < frontalDim) {
+    if ((DenseIndex) model_->dim() < frontalDim)
-      std::cout << "Jacobian::splitConditional failed" << std::endl;
       throw IndeterminantLinearSystemException(this->keys().front());
-    }
     conditionalNoiseModel = noiseModel::Diagonal::Sigmas(
         model_->sigmas().segment(Ab_.rowStart(), Ab_.rows()));
   }

gtsam/linear/linearAlgorithms-inst.h

@@ -86,10 +86,7 @@ namespace gtsam
             Vector soln = c.get_R().triangularView<Eigen::Upper>().solve(xS);
 
             // Check for indeterminant solution
-            if(soln.hasNaN()) {
+            if(soln.hasNaN()) throw IndeterminantLinearSystemException(c.keys().front());
-              std::cout << "linearAlgorithms::OptimizeClique failed: solution has NaN!" << std::endl;
-              throw IndeterminantLinearSystemException(c.keys().front());
-            }
 
             // Insert solution into a VectorValues
             DenseIndex vectorPosition = 0;

gtsam/nonlinear/ISAM2-inl.h

@@ -115,7 +115,6 @@ template<class CLIQUE>
 bool optimizeWildfireNode(const boost::shared_ptr<CLIQUE>& clique, double threshold,
     FastSet<Key>& changed, const FastSet<Key>& replaced, VectorValues& delta, size_t& count)
 {
-  //clique->print("Input clique: ");
   // if none of the variables in this clique (frontal and separator!) changed
   // significantly, then by the running intersection property, none of the
   // cliques in the children need to be processed
@@ -193,10 +192,7 @@ bool optimizeWildfireNode(const boost::shared_ptr<CLIQUE>& clique, double thresh
         Vector soln = c.get_R().triangularView<Eigen::Upper>().solve(xS);
 
         // Check for indeterminant solution
-        if(soln.hasNaN()) {
+        if(soln.hasNaN()) throw IndeterminantLinearSystemException(c.keys().front());
-          std::cout << "iSAM2 failed: solution has NaN!!" << std::endl;
-          throw IndeterminantLinearSystemException(c.keys().front());
-        }
 
         // Insert solution into a VectorValues
         DenseIndex vectorPosition = 0;
@@ -307,4 +303,3 @@ int calculate_nnz(const boost::shared_ptr<CLIQUE>& clique) {
 
 }
 
-

gtsam/nonlinear/Marginals.cpp

@@ -38,38 +38,10 @@ Marginals::Marginals(const NonlinearFactorGraph& graph, const Values& solution,
 
   // Compute BayesTree
   factorization_ = factorization;
-  if(factorization_ == CHOLESKY) {
+  if(factorization_ == CHOLESKY)
     bayesTree_ = *graph_.eliminateMultifrontal(boost::none, EliminatePreferCholesky);
-  std::cout<<"performing Cholesky"<<std::endl;
+  else if(factorization_ == QR)
-  }
-  else if(factorization_ == QR) {
     bayesTree_ = *graph_.eliminateMultifrontal(boost::none, EliminateQR);
-    std::cout<<"performing QR"<<std::endl;
-  }
-}
-
-Marginals::Marginals(const NonlinearFactorGraph& graph, const Values& solution, const std::string& str) {
-  Factorization factorization;
-  if (str == "QR") factorization = QR;
-  else if (str == "CHOLESKY") factorization = CHOLESKY;
-  gttic(MarginalsConstructor);
-
-  // Linearize graph
-  graph_ = *graph.linearize(solution);
-
-  // Store values
-  values_ = solution;
-
-  // Compute BayesTree
-  factorization_ = factorization;
-  if(factorization_ == CHOLESKY) {
-    bayesTree_ = *graph_.eliminateMultifrontal(boost::none, EliminatePreferCholesky);
-  std::cout<<"performing Cholesky"<<std::endl;
-  }
-  else if(factorization_ == QR) {
-    bayesTree_ = *graph_.eliminateMultifrontal(boost::none, EliminateQR);
-    std::cout<<"performing QR"<<std::endl;
-  }
 }
 
 /* ************************************************************************* */
@@ -171,24 +143,4 @@ void JointMarginal::print(const std::string& s, const KeyFormatter& formatter) c
   cout << ".  Use 'at' or 'operator()' to query matrix blocks." << endl;
 }
 
-Marginals::Factorization Marginals::factorizationTranslator(const std::string& str) const {
-//  std::string s = str;  boost::algorithm::to_upper(s);
-  if (str == "QR") return Marginals::QR;
-  if (str == "CHOLESKY") return Marginals::CHOLESKY;
-
-  /* default is CHOLESKY */
-  return Marginals::CHOLESKY;
-}
-
-/* ************************************************************************* */
-std::string Marginals::factorizationTranslator(const Marginals::Factorization& value) const {
-  std::string s;
-  switch (value) {
-  case Marginals::QR:         s = "QR"; break;
-  case Marginals::CHOLESKY:   s = "CHOLESKY"; break;
-  default:                      s = "UNDEFINED"; break;
-  }
-  return s;
-}
-
 } /* namespace gtsam */

gtsam/nonlinear/Marginals.h

@@ -54,7 +54,6 @@ public:
    * @param factorization The linear decomposition mode - either Marginals::CHOLESKY (faster and suitable for most problems) or Marginals::QR (slower but more numerically stable for poorly-conditioned problems).
    */
   Marginals(const NonlinearFactorGraph& graph, const Values& solution, Factorization factorization = CHOLESKY);
-  Marginals(const NonlinearFactorGraph& graph, const Values& solution, const std::string& str);
 
   /** print */
   void print(const std::string& str = "Marginals: ", const KeyFormatter& keyFormatter = DefaultKeyFormatter) const;
@@ -72,16 +71,6 @@ public:
 
   /** Compute the joint marginal information of several variables */
   JointMarginal jointMarginalInformation(const std::vector<Key>& variables) const;
-
-  Factorization factorizationTranslator(const std::string& str) const;
-
-  std::string factorizationTranslator(const Marginals::Factorization& value) const;
-
-  void setFactorization(const std::string& factorization) { this->factorization_ = factorizationTranslator(factorization); }
-
-
-
-
 };
 
 /**