remove all adaptors

examples/RangeISAMExample_plaza2.cpp

@@ -178,10 +178,10 @@ int main(int argc, char** argv) {
     initial.insert(i, predictedPose);
 
     // Check if there are range factors to be added
-    while (k < K && t >= boost::get<0>(triples[k])) {
-      size_t j = boost::get<1>(triples[k]);
+    while (k < K && t >= std::get<0>(triples[k])) {
+      size_t j = std::get<1>(triples[k]);
       Symbol landmark_key('L', j);
-      double range = boost::get<2>(triples[k]);
+      double range = std::get<2>(triples[k]);
       newFactors.emplace_shared<gtsam::RangeFactor<Pose2, Point2>>(
           i, landmark_key, range, rangeNoise);
       if (initializedLandmarks.count(landmark_key) == 0) {

examples/SolverComparer.cpp

@@ -49,8 +49,6 @@
 #include <boost/archive/binary_oarchive.hpp>
 #include <boost/program_options.hpp>
 #include <boost/range/algorithm/set_algorithm.hpp>
-#include <boost/range/adaptor/reversed.hpp>
-#include <boost/serialization/export.hpp>
 
 #include <fstream>
 #include <iostream>

gtsam/discrete/DecisionTreeFactor.h

@@ -268,5 +268,4 @@ namespace gtsam {
 // traits
 template <>
 struct traits<DecisionTreeFactor> : public Testable<DecisionTreeFactor> {};
-
 }  // namespace gtsam

gtsam/discrete/DiscreteBayesNet.cpp

@@ -58,8 +58,9 @@ DiscreteValues DiscreteBayesNet::sample() const {
 
 DiscreteValues DiscreteBayesNet::sample(DiscreteValues result) const {
   // sample each node in turn in topological sort order (parents first)
-  for (auto conditional : boost::adaptors::reverse(*this))
-    conditional->sampleInPlace(&result);
+  for (auto it = std::make_reverse_iterator(end()); it != std::make_reverse_iterator(begin()); ++it) {
+    (*it)->sampleInPlace(&result);
+  }
   return result;
 }
 

gtsam/discrete/DiscreteLookupDAG.cpp

@@ -20,6 +20,7 @@
 #include <gtsam/discrete/DiscreteLookupDAG.h>
 #include <gtsam/discrete/DiscreteValues.h>
 
+#include <iterator>
 #include <string>
 #include <utility>
 
@@ -118,8 +119,10 @@ DiscreteLookupDAG DiscreteLookupDAG::FromBayesNet(
 
 DiscreteValues DiscreteLookupDAG::argmax(DiscreteValues result) const {
   // Argmax each node in turn in topological sort order (parents first).
-  for (auto lookupTable : boost::adaptors::reverse(*this))
-    lookupTable->argmaxInPlace(&result);
+  for (auto it = std::make_reverse_iterator(end()); it != std::make_reverse_iterator(begin()); ++it) {
+    // dereference to get the sharedFactor to the lookup table
+    (*it)->argmaxInPlace(&result);
+  }
   return result;
 }
 /* ************************************************************************** */

gtsam/inference/BayesNet-inst.h

@@ -56,7 +56,9 @@ void BayesNet<CONDITIONAL>::dot(std::ostream& os,
   os << "\n";
 
   // Reverse order as typically Bayes nets stored in reverse topological sort.
-  for (auto conditional : boost::adaptors::reverse(*this)) {
+  for (auto it = std::make_reverse_iterator(this->end()); 
+      it != std::make_reverse_iterator(this->begin()); ++it) {
+    const auto& conditional = *it;
     auto frontals = conditional->frontals();
     const Key me = frontals.front();
     auto parents = conditional->parents();

gtsam/linear/GaussianBayesNet.cpp

@@ -20,8 +20,8 @@
 #include <gtsam/linear/GaussianBayesNet.h>
 #include <gtsam/linear/GaussianFactorGraph.h>
 
-#include <boost/range/adaptor/reversed.hpp>
 #include <fstream>
+#include <iterator>
 
 using namespace std;
 using namespace gtsam;
@@ -50,11 +50,11 @@ namespace gtsam {
     VectorValues solution = given;
     // (R*x)./sigmas = y by solving x=inv(R)*(y.*sigmas)
     // solve each node in reverse topological sort order (parents first)
-    for (auto cg : boost::adaptors::reverse(*this)) {
+    for (auto it = std::make_reverse_iterator(end()); it != std::make_reverse_iterator(begin()); ++it) {
       // 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:))
-      solution.insert(cg->solve(solution));
+      solution.insert((*it)->solve(solution));
     }
     return solution;
   }
@@ -69,8 +69,8 @@ namespace gtsam {
                                         std::mt19937_64* rng) const {
     VectorValues result(given);
     // sample each node in reverse topological sort order (parents first)
-    for (auto cg : boost::adaptors::reverse(*this)) {
-      const VectorValues sampled = cg->sample(result, rng);
+    for (auto it = std::make_reverse_iterator(end()); it != std::make_reverse_iterator(begin()); ++it) {
+      const VectorValues sampled = (*it)->sample(result, rng);
       result.insert(sampled);
     }
     return result;
@@ -131,8 +131,8 @@ namespace gtsam {
     VectorValues result;
     // TODO this looks pretty sketchy. result is passed as the parents argument
     //  as it's filled up by solving the gaussian conditionals.
-    for (auto cg: boost::adaptors::reverse(*this)) {
-      result.insert(cg->solveOtherRHS(result, rhs));
+    for (auto it = std::make_reverse_iterator(end()); it != std::make_reverse_iterator(begin()); ++it) {
+      result.insert((*it)->solveOtherRHS(result, rhs));
     }
     return result;
   }

gtsam/linear/HessianFactor.cpp

@@ -31,18 +31,12 @@
 
 #include <boost/format.hpp>
 #include <boost/tuple/tuple.hpp>
-#include <boost/range/adaptor/transformed.hpp>
-#include <boost/range/adaptor/map.hpp>
-#include <boost/range/algorithm/copy.hpp>
 
 #include <sstream>
 #include <limits>
+#include "gtsam/base/Vector.h"
 
 using namespace std;
-namespace br {
-using namespace boost::range;
-using namespace boost::adaptors;
-}
 
 namespace gtsam {
 
@@ -144,12 +138,20 @@ namespace {
 DenseIndex _getSizeHF(const Vector& m) {
   return m.size();
 }
+
+std::vector<DenseIndex> _getSizeHFVec(const std::vector<Vector>& m) {
+  std::vector<DenseIndex> dims;
+  for (const Vector& v : m) {
+    dims.push_back(v.size());
+  }
+  return dims;
+}
 }
 
 /* ************************************************************************* */
 HessianFactor::HessianFactor(const KeyVector& js,
     const std::vector<Matrix>& Gs, const std::vector<Vector>& gs, double f) :
-    GaussianFactor(js), info_(gs | br::transformed(&_getSizeHF), true) {
+    GaussianFactor(js), info_(_getSizeHFVec(gs), true) {
   // Get the number of variables
   size_t variable_count = js.size();
 

gtsam/linear/JacobianFactor.cpp

@@ -32,10 +32,6 @@
 #include <gtsam/base/cholesky.h>
 
 #include <boost/format.hpp>
-#include <boost/array.hpp>
-#include <boost/range/algorithm/copy.hpp>
-#include <boost/range/adaptor/indirected.hpp>
-#include <boost/range/adaptor/map.hpp>
 
 #include <cmath>
 #include <sstream>
@@ -227,10 +223,10 @@ void JacobianFactor::JacobianFactorHelper(const GaussianFactorGraph& graph,
   gttic(allocate);
   Ab_ = VerticalBlockMatrix(varDims, m, true); // Allocate augmented matrix
   Base::keys_.resize(orderedSlots.size());
-  boost::range::copy(
-      // Get variable keys
-      orderedSlots | boost::adaptors::indirected | boost::adaptors::map_keys,
-      Base::keys_.begin());
+  // Copy keys in order
+  std::transform(orderedSlots.begin(), orderedSlots.end(),
+      Base::keys_.begin(),
+      [](const VariableSlots::const_iterator& it) {return it->first;});
   gttoc(allocate);
 
   // Loop over slots in combined factor and copy blocks from source factors

gtsam/linear/SubgraphPreconditioner.cpp

@@ -25,7 +25,6 @@
 #include <gtsam/base/Vector.h>
 
 #include <boost/algorithm/string.hpp>
-#include <boost/range/adaptor/reversed.hpp>
 
 #include <stdexcept>
 
@@ -205,7 +204,8 @@ void SubgraphPreconditioner::solve(const Vector &y, Vector &x) const {
   assert(x.size() == y.size());
 
   /* back substitute */
-  for (const auto &cg : boost::adaptors::reverse(Rc1_)) {
+  for (auto it = std::make_reverse_iterator(Rc1_.end()); it != std::make_reverse_iterator(Rc1_.begin()); ++it) {
+    auto& cg = *it;
     /* collect a subvector of x that consists of the parents of cg (S) */
     const KeyVector parentKeys(cg->beginParents(), cg->endParents());
     const KeyVector frontalKeys(cg->beginFrontals(), cg->endFrontals());

gtsam/linear/VectorValues.cpp

@@ -20,16 +20,11 @@
 
 #include <boost/bind/bind.hpp>
 #include <boost/range/numeric.hpp>
-#include <boost/range/adaptor/transformed.hpp>
-#include <boost/range/adaptor/map.hpp>
 
 using namespace std;
 
 namespace gtsam {
 
-  using boost::adaptors::transformed;
-  using boost::accumulate;
-
   /* ************************************************************************ */
   VectorValues::VectorValues(const VectorValues& first, const VectorValues& second)
   {

gtsam/linear/tests/testJacobianFactor.cpp

@@ -26,7 +26,6 @@
 #include <gtsam/linear/VectorValues.h>
 
 #include <boost/range/iterator_range.hpp>
-#include <boost/range/adaptor/map.hpp>
 
 using namespace std;
 using namespace gtsam;
@@ -131,7 +130,11 @@ TEST(JacobianFactor, constructors_and_accessors)
     blockMatrix(1) = terms[1].second;
     blockMatrix(2) = terms[2].second;
     blockMatrix(3) = b;
-    JacobianFactor actual(terms | boost::adaptors::map_keys, blockMatrix, noise);
+    // get a vector of keys from the terms
+    vector<Key> keys;
+    for (const auto& term : terms)
+      keys.push_back(term.first);
+    JacobianFactor actual(keys, blockMatrix, noise);
     EXPECT(assert_equal(expected, actual));
     LONGS_EQUAL((long)terms[2].first, (long)actual.keys().back());
     EXPECT(assert_equal(terms[2].second, actual.getA(actual.end() - 1)));

gtsam/linear/tests/testVectorValues.cpp

@@ -21,12 +21,9 @@
 
 #include <CppUnitLite/TestHarness.h>
 
-#include <boost/range/adaptor/map.hpp>
-
 #include <sstream>
 
 using namespace std;
-using boost::adaptors::map_keys;
 using namespace gtsam;
 
 /* ************************************************************************* */

gtsam/nonlinear/ISAM2-impl.h

@@ -28,13 +28,6 @@
 #include <gtsam/linear/GaussianBayesTree.h>
 #include <gtsam/linear/GaussianEliminationTree.h>
 
-#include <boost/range/adaptors.hpp>
-#include <boost/range/algorithm/copy.hpp>
-namespace br {
-using namespace boost::range;
-using namespace boost::adaptors;
-}  // namespace br
-
 #include <algorithm>
 #include <limits>
 #include <string>

gtsam/nonlinear/ISAM2.cpp

@@ -176,9 +176,11 @@ void ISAM2::recalculateBatch(const ISAM2UpdateParams& updateParams,
   gttic(recalculateBatch);
 
   gttic(add_keys);
-  br::copy(variableIndex_ | br::map_keys,
-           std::inserter(*affectedKeysSet, affectedKeysSet->end()));
 
+  // copy the keys from the variableIndex_ to the affectedKeysSet
+  for (const auto& [key, _] : variableIndex_) {
+    affectedKeysSet->insert(key);
+  }
   // Removed unused keys:
   VariableIndex affectedFactorsVarIndex = variableIndex_;
 

gtsam/symbolic/tests/testSymbolicBayesTree.cpp

@@ -22,11 +22,10 @@
 #include <gtsam/symbolic/SymbolicBayesTree.h>
 #include <gtsam/symbolic/tests/symbolicExampleGraphs.h>
 
-#include <boost/range/adaptor/indirected.hpp>
-using boost::adaptors::indirected;
-
 #include <CppUnitLite/TestHarness.h>
 #include <gtsam/base/TestableAssertions.h>
+#include <iterator>
+#include <type_traits>
 
 using namespace std;
 using namespace gtsam;
@@ -34,6 +33,24 @@ using namespace gtsam::symbol_shorthand;
 
 static bool debug = false;
 
+// Given a vector of shared pointers infer the type of the pointed-to objects
+template<typename T>
+using PointedToType = std::decay_t<decltype(**declval<T>().begin())>;
+
+// Given a vector of shared pointers infer the type of the pointed-to objects
+template<typename T>
+using ValuesVector = std::vector<PointedToType<T>>;
+
+// Return a vector of dereferenced values
+template<typename T>
+ValuesVector<T> deref(const T& v) {
+  ValuesVector<T> result;
+  for (auto& t : v)
+    result.push_back(*t);
+  return result;
+}
+
+
 /* ************************************************************************* */
 TEST(SymbolicBayesTree, clear) {
   SymbolicBayesTree bayesTree = asiaBayesTree;
@@ -111,8 +128,7 @@ TEST(BayesTree, removePath) {
   bayesTree.removePath(bayesTree[_C_], &bn, &orphans);
   SymbolicFactorGraph factors(bn);
   CHECK(assert_equal(expected, factors));
-  CHECK(assert_container_equal(expectedOrphans | indirected,
-                               orphans | indirected));
+  CHECK(assert_container_equal(deref(expectedOrphans), deref(orphans)));
 
   bayesTree = bayesTreeOrig;
 
@@ -127,8 +143,7 @@ TEST(BayesTree, removePath) {
   bayesTree.removePath(bayesTree[_E_], &bn2, &orphans2);
   SymbolicFactorGraph factors2(bn2);
   CHECK(assert_equal(expected2, factors2));
-  CHECK(assert_container_equal(expectedOrphans2 | indirected,
-                               orphans2 | indirected));
+  CHECK(assert_container_equal(deref(expectedOrphans2), deref(orphans2)));
 }
 
 /* ************************************************************************* */
@@ -147,8 +162,7 @@ TEST(BayesTree, removePath2) {
   CHECK(assert_equal(expected, factors));
   SymbolicBayesTree::Cliques expectedOrphans{bayesTree[_S_], bayesTree[_T_],
                                              bayesTree[_X_]};
-  CHECK(assert_container_equal(expectedOrphans | indirected,
-                               orphans | indirected));
+  CHECK(assert_container_equal(deref(expectedOrphans), deref(orphans)));
 }
 
 /* ************************************************************************* */
@@ -167,8 +181,7 @@ TEST(BayesTree, removePath3) {
   expected.emplace_shared<SymbolicFactor>(_T_, _E_, _L_);
   CHECK(assert_equal(expected, factors));
   SymbolicBayesTree::Cliques expectedOrphans{bayesTree[_S_], bayesTree[_X_]};
-  CHECK(assert_container_equal(expectedOrphans | indirected,
-                               orphans | indirected));
+  CHECK(assert_container_equal(deref(expectedOrphans), deref(orphans)));
 }
 
 void getAllCliques(const SymbolicBayesTree::sharedClique& subtree,
@@ -249,8 +262,7 @@ TEST(BayesTree, removeTop) {
   CHECK(assert_equal(expected, bn));
 
   SymbolicBayesTree::Cliques expectedOrphans{bayesTree[_T_], bayesTree[_X_]};
-  CHECK(assert_container_equal(expectedOrphans | indirected,
-                               orphans | indirected));
+  CHECK(assert_container_equal(deref(expectedOrphans), deref(orphans)));
 
   // Try removeTop again with a factor that should not change a thing
   // std::shared_ptr<IndexFactor> newFactor2(new IndexFactor(_B_));
@@ -261,8 +273,7 @@ TEST(BayesTree, removeTop) {
   SymbolicFactorGraph expected2;
   CHECK(assert_equal(expected2, factors2));
   SymbolicBayesTree::Cliques expectedOrphans2;
-  CHECK(assert_container_equal(expectedOrphans2 | indirected,
-                               orphans2 | indirected));
+  CHECK(assert_container_equal(deref(expectedOrphans2), deref(orphans2)));
 }
 
 /* ************************************************************************* */
@@ -286,8 +297,7 @@ TEST(BayesTree, removeTop2) {
   CHECK(assert_equal(expected, bn));
 
   SymbolicBayesTree::Cliques expectedOrphans{bayesTree[_S_], bayesTree[_X_]};
-  CHECK(assert_container_equal(expectedOrphans | indirected,
-                               orphans | indirected));
+  CHECK(assert_container_equal(deref(expectedOrphans), deref(orphans)));
 }
 
 /* ************************************************************************* */

gtsam_unstable/discrete/tests/testLoopyBelief.cpp

@@ -11,7 +11,6 @@
 #include <gtsam/discrete/DiscreteConditional.h>
 #include <gtsam/inference/VariableIndex.h>
 
-#include <boost/range/adaptor/map.hpp>
 #include <fstream>
 #include <iostream>
 
@@ -47,7 +46,7 @@ class LoopyBelief {
     void print(const std::string& s = "") const {
       cout << s << ":" << endl;
       star->print("Star graph: ");
-      for (Key key : correctedBeliefIndices | boost::adaptors::map_keys) {
+      for (const auto& [key, _] : correctedBeliefIndices) {
         cout << "Belief factor index for " << key << ": "
              << correctedBeliefIndices.at(key) << endl;
       }
@@ -71,7 +70,7 @@ class LoopyBelief {
   /// print
   void print(const std::string& s = "") const {
     cout << s << ":" << endl;
-    for (Key key : starGraphs_ | boost::adaptors::map_keys) {
+    for (const auto& [key, _] : starGraphs_) {
       starGraphs_.at(key).print((boost::format("Node %d:") % key).str());
     }
   }
@@ -85,7 +84,7 @@ class LoopyBelief {
     DiscreteFactorGraph::shared_ptr beliefs(new DiscreteFactorGraph());
     std::map<Key, std::map<Key, DiscreteFactor::shared_ptr> > allMessages;
     // Eliminate each star graph
-    for (Key key : starGraphs_ | boost::adaptors::map_keys) {
+    for (const auto& [key, _] : starGraphs_) {
       //      cout << "***** Node " << key << "*****" << endl;
       // initialize belief to the unary factor from the original graph
       DecisionTreeFactor::shared_ptr beliefAtKey;
@@ -94,8 +93,7 @@ class LoopyBelief {
       std::map<Key, DiscreteFactor::shared_ptr> messages;
 
       // eliminate each neighbor in this star graph one by one
-      for (Key neighbor : starGraphs_.at(key).correctedBeliefIndices |
-                              boost::adaptors::map_keys) {
+      for (const auto& [neighbor, _] : starGraphs_.at(key).correctedBeliefIndices) {
         DiscreteFactorGraph subGraph;
         for (size_t factor : starGraphs_.at(key).varIndex_[neighbor]) {
           subGraph.push_back(starGraphs_.at(key).star->at(factor));
@@ -143,11 +141,10 @@ class LoopyBelief {
 
     // Update corrected beliefs
     VariableIndex beliefFactors(*beliefs);
-    for (Key key : starGraphs_ | boost::adaptors::map_keys) {
+    for (const auto& [key, _] : starGraphs_) {
       std::map<Key, DiscreteFactor::shared_ptr> messages = allMessages[key];
-      for (Key neighbor : starGraphs_.at(key).correctedBeliefIndices |
-                              boost::adaptors::map_keys) {
-        DecisionTreeFactor correctedBelief =
+      for (const auto& [neighbor, _] : starGraphs_.at(key).correctedBeliefIndices) {
+              DecisionTreeFactor correctedBelief =
             (*std::dynamic_pointer_cast<DecisionTreeFactor>(
                 beliefs->at(beliefFactors[key].front()))) /
             (*std::dynamic_pointer_cast<DecisionTreeFactor>(
@@ -175,7 +172,7 @@ class LoopyBelief {
       const std::map<Key, DiscreteKey>& allDiscreteKeys) const {
     StarGraphs starGraphs;
     VariableIndex varIndex(graph);  ///< access to all factors of each node
-    for (Key key : varIndex | boost::adaptors::map_keys) {
+    for (const auto& [key, _] : varIndex) {
       // initialize to multiply with other unary factors later
       DecisionTreeFactor::shared_ptr prodOfUnaries;
 

gtsam_unstable/examples/SmartRangeExample_plaza1.cpp

@@ -179,9 +179,9 @@ int main(int argc, char** argv) {
     landmarkEstimates.insert(i, predictedPose);
 
     // Check if there are range factors to be added
-    while (k < K && t >= boost::get<0>(triples[k])) {
-      size_t j = boost::get<1>(triples[k]);
-      double range = boost::get<2>(triples[k]);
+    while (k < K && t >= std::get<0>(triples[k])) {
+      size_t j = std::get<1>(triples[k]);
+      double range = std::get<2>(triples[k]);
       if (i > start) {
         if (smart && totalCount < minK) {
           try {

gtsam_unstable/examples/SmartRangeExample_plaza2.cpp

@@ -160,9 +160,9 @@ int main(int argc, char** argv) {
     landmarkEstimates.insert(i, predictedPose);
 
     // Check if there are range factors to be added
-    while (k < K && t >= boost::get<0>(triples[k])) {
-      size_t j = boost::get<1>(triples[k]);
-      double range = boost::get<2>(triples[k]);
+    while (k < K && t >= std::get<0>(triples[k])) {
+      size_t j = std::get<1>(triples[k]);
+      double range = std::get<2>(triples[k]);
       RangeFactor<Pose2, Point2> factor(i, symbol('L', j), range, rangeNoise);
       // Throw out obvious outliers based on current landmark estimates
       Vector error = factor.unwhitenedError(landmarkEstimates);

gtsam_unstable/linear/LPInitSolver.cpp

@@ -66,7 +66,7 @@ GaussianFactorGraph::shared_ptr LPInitSolver::buildInitOfInitGraph() const {
 
   // create factor ||x||^2 and add to the graph
   const KeyDimMap& constrainedKeyDim = lp_.constrainedKeyDimMap();
-  for (Key key : constrainedKeyDim | boost::adaptors::map_keys) {
+  for (const auto& [key, _] : constrainedKeyDim) {
     size_t dim = constrainedKeyDim.at(key);
     initGraph->push_back(
         JacobianFactor(key, Matrix::Identity(dim, dim), Vector::Zero(dim)));
@@ -107,4 +107,4 @@ InequalityFactorGraph LPInitSolver::addSlackVariableToInequalities(
   return slackInequalities;
 }
 
-}
+}

timing/timeIncremental.cpp

@@ -27,7 +27,6 @@
 #include <boost/archive/binary_oarchive.hpp>
 #include <boost/archive/binary_iarchive.hpp>
 #include <boost/serialization/export.hpp>
-#include <boost/range/adaptor/reversed.hpp>
 
 using namespace std;
 using namespace gtsam;
@@ -225,9 +224,14 @@ int main(int argc, char *argv[]) {
   try {
     Marginals marginals(graph, values);
     int i=0;
-    for (Key key1: boost::adaptors::reverse(values.keys())) {
+    // Assign the keyvector to a named variable
+    auto keys = values.keys();
+    // Iterate over it in reverse
+    for (auto it1 = keys.rbegin(); it1 != keys.rend(); ++it1) {
+      Key key1 = *it1;
       int j=0;
-      for (Key key2: boost::adaptors::reverse(values.keys())) {
+      for (auto it2 = keys.rbegin(); it2 != keys.rend(); ++it2) {
+        Key key2 = *it2;
         if(i != j) {
           gttic_(jointMarginalInformation);
           KeyVector keys(2);