map_values eliminated

gtsam/linear/Errors.cpp

@@ -17,7 +17,6 @@
  * @author  Christian Potthast
  */
 
-#include <boost/range/adaptor/map.hpp>
 #include <gtsam/linear/Errors.h>
 #include <gtsam/linear/VectorValues.h>
 
@@ -28,7 +27,7 @@ namespace gtsam {
 /* ************************************************************************* */
 Errors createErrors(const VectorValues& V) {
   Errors result;
-  for (const Vector& e : V | boost::adaptors::map_values) {
+  for (const auto& [key, e] : V) {
     result.push_back(e);
   }
   return result;

gtsam/linear/Preconditioner.cpp

@@ -14,7 +14,6 @@
 #include <gtsam/linear/NoiseModel.h>
 #include <memory>
 #include <boost/algorithm/string.hpp>
-#include <boost/range/adaptor/map.hpp>
 #include <iostream>
 #include <vector>
 
@@ -145,8 +144,9 @@ void BlockJacobiPreconditioner::build(
 
   /* getting the block diagonals over the factors */
   std::map<Key, Matrix> hessianMap =gfg.hessianBlockDiagonal();
-  for (const Matrix& hessian: hessianMap | boost::adaptors::map_values)
+  for (const auto& [key, hessian]: hessianMap) {
     blocks.push_back(hessian);
+  }
 
   /* if necessary, allocating the memory for cacheing the factorization results */
   if ( nnz > bufferSize_ ) {

gtsam/linear/VectorValues.cpp

@@ -28,7 +28,6 @@ using namespace std;
 namespace gtsam {
 
   using boost::adaptors::transformed;
-  using boost::adaptors::map_values;
   using boost::accumulate;
 
   /* ************************************************************************ */
@@ -129,8 +128,9 @@ namespace gtsam {
   /* ************************************************************************ */
   void VectorValues::setZero()
   {
-    for(Vector& v: values_ | map_values)
-      v.setZero();
+    for(auto& [key, value] : *this) {
+      value.setZero();
+    }
   }
 
   /* ************************************************************************ */
@@ -178,14 +178,15 @@ namespace gtsam {
   Vector VectorValues::vector() const {
     // Count dimensions
     DenseIndex totalDim = 0;
-    for (const Vector& v : *this | map_values) totalDim += v.size();
+    for (const auto& [key, value] : *this)
+      totalDim += value.size();
 
     // Copy vectors
     Vector result(totalDim);
     DenseIndex pos = 0;
-    for (const Vector& v : *this | map_values) {
-      result.segment(pos, v.size()) = v;
-      pos += v.size();
+    for (const auto& [key, value] : *this) {
+      result.segment(pos, value.size()) = value;
+      pos += value.size();
     }
 
     return result;
@@ -196,7 +197,7 @@ namespace gtsam {
   {
     // Count dimensions
     DenseIndex totalDim = 0;
-    for(size_t dim: keys | map_values)
+    for (const auto& [key, dim] : keys)
       totalDim += dim;
     Vector result(totalDim);
     size_t j = 0;
@@ -236,8 +237,6 @@ namespace gtsam {
     if(this->size() != v.size())
       throw invalid_argument("VectorValues::dot called with a VectorValues of different structure");
     double result = 0.0;
-    typedef std::tuple<value_type, value_type> ValuePair;
-    using boost::adaptors::map_values;
     auto this_it = this->begin();
     auto v_it = v.begin();
     for(; this_it != this->end(); ++this_it, ++v_it) {
@@ -258,9 +257,9 @@ namespace gtsam {
   /* ************************************************************************ */
   double VectorValues::squaredNorm() const {
     double sumSquares = 0.0;
-    using boost::adaptors::map_values;
-    for(const Vector& v: *this | map_values)
-      sumSquares += v.squaredNorm();
+    for(const auto& [key, value]: *this) {
+      sumSquares += value.squaredNorm();
+    }
     return sumSquares;
   }
 
@@ -374,8 +373,9 @@ namespace gtsam {
   /* ************************************************************************ */
   VectorValues& VectorValues::operator*=(double alpha)
   {
-    for(Vector& v: *this | map_values)
-      v *= alpha;
+    for (auto& [key, value]: *this) {
+      value *= alpha;
+    }
     return *this;
   }
 

gtsam/nonlinear/Expression-inl.h

@@ -229,8 +229,13 @@ typename Expression<T>::KeysAndDims Expression<T>::keysAndDims() const {
   dims(map);
   size_t n = map.size();
   KeysAndDims pair = std::make_pair(KeyVector(n), FastVector<int>(n));
-  boost::copy(map | boost::adaptors::map_keys, pair.first.begin());
-  boost::copy(map | boost::adaptors::map_values, pair.second.begin());
+  // Copy map into pair of vectors
+  auto key_it = pair.first.begin();
+  auto dim_it = pair.second.begin();
+  for (const auto& [key, value] : map) {
+    *key_it++ = key;
+    *dim_it++ = value;
+  }
   return pair;
 }
 

gtsam/nonlinear/LevenbergMarquardtOptimizer.cpp

@@ -29,7 +29,6 @@
 #include <gtsam/base/timing.h>
 
 #include <boost/format.hpp>
-#include <boost/range/adaptor/map.hpp>
 
 #include <cmath>
 #include <fstream>
@@ -41,7 +40,6 @@ using namespace std;
 
 namespace gtsam {
 
-using boost::adaptors::map_values;
 typedef internal::LevenbergMarquardtState State;
 
 /* ************************************************************************* */
@@ -281,8 +279,8 @@ GaussianFactorGraph::shared_ptr LevenbergMarquardtOptimizer::iterate() {
   VectorValues sqrtHessianDiagonal;
   if (params_.diagonalDamping) {
     sqrtHessianDiagonal = linear->hessianDiagonal();
-    for (Vector& v : sqrtHessianDiagonal | map_values) {
-      v = v.cwiseMax(params_.minDiagonal).cwiseMin(params_.maxDiagonal).cwiseSqrt();
+    for (auto& [key, value] : sqrtHessianDiagonal) {
+      value = value.cwiseMax(params_.minDiagonal).cwiseMin(params_.maxDiagonal).cwiseSqrt();
     }
   }
 

tests/testGaussianFactorGraphB.cpp

@@ -26,10 +26,6 @@
 
 #include <CppUnitLite/TestHarness.h>
 
-#include <boost/tuple/tuple.hpp>
-#include <boost/range/adaptor/map.hpp>
-namespace br { using namespace boost::range; using namespace boost::adaptors; }
-
 #include <string.h>
 #include <iostream>
 
@@ -450,7 +446,7 @@ TEST( GaussianFactorGraph, conditional_sigma_failure) {
   GaussianBayesTree actBT = *lfg.eliminateMultifrontal();
 
   // Check that all sigmas in an unconstrained bayes tree are set to one
-  for(const GaussianBayesTree::sharedClique& clique: actBT.nodes() | br::map_values) {
+  for (const auto& [key, clique]: actBT.nodes()) {
     GaussianConditional::shared_ptr conditional = clique->conditional();
     //size_t dim = conditional->rows();
     //EXPECT(assert_equal(gtsam::Vector::Ones(dim), conditional->get_model()->sigmas(), tol));

tests/testGaussianISAM.cpp

@@ -22,9 +22,6 @@
 #include <gtsam/linear/GaussianISAM.h>
 #include <gtsam/inference/Ordering.h>
 
-#include <boost/range/adaptor/map.hpp>
-namespace br { using namespace boost::adaptors; using namespace boost::range; }
-
 using namespace std;
 using namespace gtsam;
 using namespace example;
@@ -53,7 +50,7 @@ TEST( ISAM, iSAM_smoother )
   GaussianBayesTree expected = *smoother.eliminateMultifrontal(ordering);
 
   // Verify sigmas in the bayes tree
-  for(const GaussianBayesTree::sharedClique& clique: expected.nodes() | br::map_values) {
+  for (const auto& [key, clique] : expected.nodes()) {
     GaussianConditional::shared_ptr conditional = clique->conditional();
     EXPECT(!conditional->get_model());
   }

tests/testGaussianISAM2.cpp

@@ -24,8 +24,6 @@
 
 #include <CppUnitLite/TestHarness.h>
 
-#include <boost/range/adaptor/map.hpp>
-namespace br { using namespace boost::adaptors; using namespace boost::range; }
 
 using namespace std;
 using namespace gtsam;
@@ -249,7 +247,7 @@ bool isam_check(const NonlinearFactorGraph& fullgraph, const Values& fullinit, c
   // Check gradient at each node
   bool nodeGradientsOk = true;
   typedef ISAM2::sharedClique sharedClique;
-  for(const sharedClique& clique: isam.nodes() | br::map_values) {
+  for (const auto& [key, clique] : isam.nodes()) {
     // Compute expected gradient
     GaussianFactorGraph jfg;
     jfg += clique->conditional();
@@ -453,7 +451,7 @@ TEST(ISAM2, swapFactors)
 
   // Check gradient at each node
   typedef ISAM2::sharedClique sharedClique;
-  for(const sharedClique& clique: isam.nodes() | br::map_values) {
+  for (const auto& [key, clique]: isam.nodes()) {
     // Compute expected gradient
     GaussianFactorGraph jfg;
     jfg += clique->conditional();
@@ -578,7 +576,7 @@ TEST(ISAM2, constrained_ordering)
 
   // Check gradient at each node
   typedef ISAM2::sharedClique sharedClique;
-  for(const sharedClique& clique: isam.nodes() | br::map_values) {
+  for (const auto& [key, clique]: isam.nodes()) {
     // Compute expected gradient
     GaussianFactorGraph jfg;
     jfg += clique->conditional();
@@ -620,9 +618,11 @@ namespace {
   bool checkMarginalizeLeaves(ISAM2& isam, const FastList<Key>& leafKeys) {
     Matrix expectedAugmentedHessian, expected3AugmentedHessian;
     KeyVector toKeep;
-    for(Key j: isam.getDelta() | br::map_keys)
-      if(find(leafKeys.begin(), leafKeys.end(), j) == leafKeys.end())
-        toKeep.push_back(j);
+    for (const auto& [key, clique]: isam.getDelta()) {
+      if(find(leafKeys.begin(), leafKeys.end(), key) == leafKeys.end()) {
+        toKeep.push_back(key);
+      }
+    }
 
     // Calculate expected marginal from iSAM2 tree
     expectedAugmentedHessian = GaussianFactorGraph(isam).marginal(toKeep, EliminateQR)->augmentedHessian();

tests/testNonlinearOptimizer.cpp

@@ -31,8 +31,6 @@
 
 #include <CppUnitLite/TestHarness.h>
 
-#include <boost/range/adaptor/map.hpp>
-using boost::adaptors::map_values;
 
 #include <iostream>
 #include <fstream>
@@ -302,7 +300,9 @@ TEST_UNSAFE(NonlinearOptimizer, MoreOptimization) {
     GaussianFactorGraph::shared_ptr linear = optimizer.linearize();
     VectorValues d = linear->hessianDiagonal();
     VectorValues sqrtHessianDiagonal = d;
-    for (Vector& v : sqrtHessianDiagonal | map_values) v = v.cwiseSqrt();
+    for (auto& [key, value] : sqrtHessianDiagonal) {
+      value = value.cwiseSqrt();
+    }
     GaussianFactorGraph damped = optimizer.buildDampedSystem(*linear, sqrtHessianDiagonal);
     VectorValues  expectedDiagonal = d + params.lambdaInitial * d;
     EXPECT(assert_equal(expectedDiagonal, damped.hessianDiagonal()));