C++ VertexSet::end方法代码示例

  void OptimizableGraph::setFixed(HyperGraph::VertexSet& vset, bool fixed)
{
  for (HyperGraph::VertexSet::iterator it=vset.begin(); it!=vset.end(); ++it) {
    OptimizableGraph::Vertex* v = static_cast<OptimizableGraph::Vertex*>(*it);
    v->setFixed(fixed);
  }
}

void HyperDijkstra::connectedSubset(HyperGraph::VertexSet& connected, HyperGraph::VertexSet& visited,
                                    HyperGraph::VertexSet& startingSet,
                                    HyperGraph* g, HyperGraph::Vertex* v,
                                    HyperDijkstra::CostFunction* cost, double distance,
                                    double comparisonConditioner, double maxEdgeCost)
{
    typedef std::queue<HyperGraph::Vertex*> VertexDeque;
    visited.clear();
    connected.clear();
    VertexDeque frontier;
    HyperDijkstra dv(g);
    connected.insert(v);
    frontier.push(v);
    while (! frontier.empty()) {
        HyperGraph::Vertex* v0=frontier.front();
        frontier.pop();
        dv.shortestPaths(v0, cost, distance, comparisonConditioner, false, maxEdgeCost);
        for (HyperGraph::VertexSet::iterator it=dv.visited().begin(); it!=dv.visited().end(); ++it) {
            visited.insert(*it);
            if (startingSet.find(*it)==startingSet.end())
                continue;
            std::pair<HyperGraph::VertexSet::iterator, bool> insertOutcome=connected.insert(*it);
            if (insertOutcome.second) { // the node was not in the connectedSet;
                frontier.push(dynamic_cast<HyperGraph::Vertex*>(*it));
            }
        }
    }
}

void OptimizableGraph::discardTop(HyperGraph::VertexSet& vset)
{
  for (HyperGraph::VertexSet::iterator it=vset.begin(); it!=vset.end(); ++it) {
    OptimizableGraph::Vertex* v = static_cast<OptimizableGraph::Vertex*>(*it);
    v->discardTop();
  }
}

  bool SparseOptimizer::updateInitialization(HyperGraph::VertexSet& vset, HyperGraph::EdgeSet& eset)
  {
    std::vector<HyperGraph::Vertex*> newVertices;
    newVertices.reserve(vset.size());
    _activeVertices.reserve(_activeVertices.size() + vset.size());
    //for (HyperGraph::VertexSet::iterator it = vset.begin(); it != vset.end(); ++it)
      //_activeVertices.push_back(static_cast<OptimizableGraph::Vertex*>(*it));
    _activeEdges.reserve(_activeEdges.size() + eset.size());
    for (HyperGraph::EdgeSet::iterator it = eset.begin(); it != eset.end(); ++it)
      _activeEdges.push_back(static_cast<OptimizableGraph::Edge*>(*it));

    // update the index mapping
    size_t next = _ivMap.size();
    for (HyperGraph::VertexSet::iterator it = vset.begin(); it != vset.end(); ++it) {
      OptimizableGraph::Vertex* v=static_cast<OptimizableGraph::Vertex*>(*it);
      if (! v->fixed()){
        if (! v->marginalized()){
          v->setTempIndex(next);
          _ivMap.push_back(v);
          newVertices.push_back(v);
          _activeVertices.push_back(v);
          next++;
        }
        else // not supported right now
          abort();
      }
      else {
        v->setTempIndex(-1);
      }
    }

    //if (newVertices.size() != vset.size())
    //cerr << __PRETTY_FUNCTION__ << ": something went wrong " << PVAR(vset.size()) << " " << PVAR(newVertices.size()) << endl;
    return _solver->updateStructure(newVertices, eset);
  }

void OptimizableGraph::push(HyperGraph::VertexSet& vset) {
  for (HyperGraph::VertexSet::iterator it = vset.begin(); it != vset.end();
      it++) {
    OptimizableGraph::Vertex* v = static_cast<OptimizableGraph::Vertex*>(*it);
    v->push();
  }
}

void starsInEdge(StarSet& stars, HyperGraph::Edge* e, EdgeStarMap& esmap, HyperGraph::VertexSet& gauge){
  for (size_t i=0; i<e->vertices().size(); i++){
    OptimizableGraph::Vertex* v=(OptimizableGraph::Vertex*)e->vertices()[i];
    if (gauge.find(v)==gauge.end())
      starsInVertex(stars, v, esmap);
  }
}

  void SparseOptimizer::pop(HyperGraph::VertexSet& vlist)
  {
    for (HyperGraph::VertexSet::iterator it = vlist.begin(); it != vlist.end(); ++it){
      OptimizableGraph::Vertex* v = dynamic_cast<OptimizableGraph::Vertex*> (*it);
      if (v)
	v->pop();
      else
	cerr << "FATAL POP SET" << endl;
    }
  }

  void SparseOptimizer::push(HyperGraph::VertexSet& vlist)
  {
    for (HyperGraph::VertexSet::iterator it = vlist.begin(); it != vlist.end(); ++it) {
      OptimizableGraph::Vertex* v = dynamic_cast<OptimizableGraph::Vertex*>(*it);
      if (v)
	v->push();
      else 
	cerr << __FUNCTION__ << ": FATAL PUSH SET" << endl;
    }
  }

  void HyperDijkstra::shortestPaths(HyperGraph::VertexSet& vset, HyperDijkstra::CostFunction* cost, 
      double maxDistance, double comparisonConditioner, bool directed, double maxEdgeCost)
  {
    reset();
    std::priority_queue< AdjacencyMapEntry > frontier;
    for (HyperGraph::VertexSet::iterator vit=vset.begin(); vit!=vset.end(); ++vit){
      HyperGraph::Vertex* v=*vit;
      AdjacencyMap::iterator it=_adjacencyMap.find(v);
      assert(it!=_adjacencyMap.end());
      it->second._distance=0.;
      it->second._parent=0;
      frontier.push(it->second);
    }

    while(! frontier.empty()){
      AdjacencyMapEntry entry=frontier.top();
      frontier.pop();
      HyperGraph::Vertex* u=entry.child();
      AdjacencyMap::iterator ut=_adjacencyMap.find(u);
      assert(ut!=_adjacencyMap.end());
      double uDistance=ut->second.distance();

      std::pair< HyperGraph::VertexSet::iterator, bool> insertResult=_visited.insert(u); (void) insertResult;
      HyperGraph::EdgeSet::iterator et=u->edges().begin();
      while (et != u->edges().end()){
        HyperGraph::Edge* edge=*et;
        ++et;

        if (directed && edge->vertex(0) != u)
          continue;

        for (size_t i = 0; i < edge->vertices().size(); ++i) {
          HyperGraph::Vertex* z = edge->vertex(i);
          if (z == u)
            continue;

          double edgeDistance=(*cost)(edge, u, z);
          if (edgeDistance==std::numeric_limits< double >::max() || edgeDistance > maxEdgeCost)
            continue;
          double zDistance=uDistance+edgeDistance;
          //cerr << z->id() << " " << zDistance << endl;

          AdjacencyMap::iterator ot=_adjacencyMap.find(z);
          assert(ot!=_adjacencyMap.end());

          if (zDistance+comparisonConditioner<ot->second.distance() && zDistance<maxDistance){
            ot->second._distance=zDistance;
            ot->second._parent=u;
            ot->second._edge=edge;
            frontier.push(ot->second);
          }
        }
      }
    }
  }

int main(int argc, char** argv) {
  CommandArgs arg;


  std::string outputFilename;
  std::string inputFilename;

  arg.param("o", outputFilename, "", "output file name"); 
  arg.paramLeftOver("input-filename ", inputFilename, "", "graph file to read", true);
  arg.parseArgs(argc, argv);
  OptimizableGraph graph;
  if (!graph.load(inputFilename.c_str())){
    cerr << "Error: cannot load a file from \"" << inputFilename << "\", aborting." << endl;
    return 0;
  }
  HyperGraph::EdgeSet removedEdges;
  HyperGraph::VertexSet removedVertices;
  for (HyperGraph::EdgeSet::iterator it = graph.edges().begin(); it!=graph.edges().end(); it++) {
    HyperGraph::Edge* e = *it;
    EdgeSE2PointXY* edgePointXY = dynamic_cast<EdgeSE2PointXY*>(e);
    if (edgePointXY) {
      VertexSE2* pose    = dynamic_cast<VertexSE2*>(edgePointXY->vertex(0));
      VertexPointXY* landmark = dynamic_cast<VertexPointXY*>(edgePointXY->vertex(1));
      FeaturePointXYData * feature = new FeaturePointXYData();
      feature->setPositionMeasurement(edgePointXY->measurement());
      feature->setPositionInformation(edgePointXY->information());
      pose->addUserData(feature); 
      removedEdges.insert(edgePointXY);
      removedVertices.insert(landmark);
    }
  }
  
  for (HyperGraph::EdgeSet::iterator it = removedEdges.begin(); it!=removedEdges.end(); it++){
    OptimizableGraph::Edge* e = dynamic_cast<OptimizableGraph::Edge*>(*it);
    graph.removeEdge(e);
  }

  for (HyperGraph::VertexSet::iterator it = removedVertices.begin(); it!=removedVertices.end(); it++){
    OptimizableGraph::Vertex* v = dynamic_cast<OptimizableGraph::Vertex*>(*it);
    graph.removeVertex(v);
  }

  
  if (outputFilename.length()){
    graph.save(outputFilename.c_str());
  }
 
}

  bool SparseOptimizerIncremental::updateInitialization(HyperGraph::VertexSet& vset, HyperGraph::EdgeSet& eset)
  {
    if (batchStep) {
      return SparseOptimizerOnline::updateInitialization(vset, eset);
    }

    for (HyperGraph::VertexSet::iterator it = vset.begin(); it != vset.end(); ++it) {
      OptimizableGraph::Vertex* v = static_cast<OptimizableGraph::Vertex*>(*it);
      v->clearQuadraticForm(); // be sure that b is zero for this vertex
    }

    // get the touched vertices
    _touchedVertices.clear();
    for (HyperGraph::EdgeSet::iterator it = eset.begin(); it != eset.end(); ++it) {
      OptimizableGraph::Edge* e = static_cast<OptimizableGraph::Edge*>(*it);
      OptimizableGraph::Vertex* v1 = static_cast<OptimizableGraph::Vertex*>(e->vertices()[0]);
      OptimizableGraph::Vertex* v2 = static_cast<OptimizableGraph::Vertex*>(e->vertices()[1]);
      if (! v1->fixed())
        _touchedVertices.insert(v1);
      if (! v2->fixed())
        _touchedVertices.insert(v2);
    }
    //cerr << PVAR(_touchedVertices.size()) << endl;

    // updating the internal structures
    std::vector<HyperGraph::Vertex*> newVertices;
    newVertices.reserve(vset.size());
    _activeVertices.reserve(_activeVertices.size() + vset.size());
    _activeEdges.reserve(_activeEdges.size() + eset.size());
    for (HyperGraph::EdgeSet::iterator it = eset.begin(); it != eset.end(); ++it)
      _activeEdges.push_back(static_cast<OptimizableGraph::Edge*>(*it));
    //cerr << "updating internal done." << endl;

    // update the index mapping
    size_t next = _ivMap.size();
    for (HyperGraph::VertexSet::iterator it = vset.begin(); it != vset.end(); ++it) {
      OptimizableGraph::Vertex* v=static_cast<OptimizableGraph::Vertex*>(*it);
      if (! v->fixed()){
        if (! v->marginalized()){
          v->setHessianIndex(next);
          _ivMap.push_back(v);
          newVertices.push_back(v);
          _activeVertices.push_back(v);
          next++;
        } 
        else // not supported right now
          abort();
      }
      else {
        v->setHessianIndex(-1);
      }
    }
    //cerr << "updating index mapping done." << endl;

    // backup the tempindex and prepare sorting structure
    VertexBackup backupIdx[_touchedVertices.size()];
    memset(backupIdx, 0, sizeof(VertexBackup) * _touchedVertices.size());
    int idx = 0;
    for (HyperGraph::VertexSet::iterator it = _touchedVertices.begin(); it != _touchedVertices.end(); ++it) {
      OptimizableGraph::Vertex* v = static_cast<OptimizableGraph::Vertex*>(*it);
      backupIdx[idx].hessianIndex = v->hessianIndex();
      backupIdx[idx].vertex = v;
      backupIdx[idx].hessianData = v->hessianData();
      ++idx;
    }
    sort(backupIdx, backupIdx + _touchedVertices.size()); // sort according to the hessianIndex which is the same order as used later by the optimizer
    for (int i = 0; i < idx; ++i) {
      backupIdx[i].vertex->setHessianIndex(i);
    }
    //cerr << "backup tempindex done." << endl;

    // building the structure of the update
    _updateMat.clear(true); // get rid of the old matrix structure
    _updateMat.rowBlockIndices().clear();
    _updateMat.colBlockIndices().clear();
    _updateMat.blockCols().clear();

    // placing the current stuff in _updateMat
    MatrixXd* lastBlock = 0;
    int sizePoses = 0;
    for (int i = 0; i < idx; ++i) {
      OptimizableGraph::Vertex* v = backupIdx[i].vertex;
      int dim = v->dimension();
      sizePoses+=dim;
      _updateMat.rowBlockIndices().push_back(sizePoses);
      _updateMat.colBlockIndices().push_back(sizePoses);
      _updateMat.blockCols().push_back(SparseBlockMatrix<MatrixXd>::IntBlockMap());
      int ind = v->hessianIndex();
      //cerr << PVAR(ind) << endl;
      if (ind >= 0) {
        MatrixXd* m = _updateMat.block(ind, ind, true);
        v->mapHessianMemory(m->data());
        lastBlock = m;
      }
    }
    lastBlock->diagonal().array() += 1e-6; // HACK to get Eigen value > 0


    for (HyperGraph::EdgeSet::const_iterator it = eset.begin(); it != eset.end(); ++it) {
      OptimizableGraph::Edge* e = static_cast<OptimizableGraph::Edge*>(*it);
//.........这里部分代码省略.........

  void computeSimpleStars(StarSet& stars,
			  SparseOptimizer* optimizer,
			  EdgeLabeler* labeler,
			  EdgeCreator* creator,
			  OptimizableGraph::Vertex* gauge_,
			  std::string edgeTag,
			  std::string vertexTag,
			  int level,
			  int step,
			  int backboneIterations,
			  int starIterations,
			  double rejectionThreshold,
			  bool debug){

    cerr << "preforming the tree actions" << endl;
    HyperDijkstra d(optimizer);
    // compute a spanning tree based on the types of edges and vertices in the pool
    EdgeTypesCostFunction f(edgeTag, vertexTag, level);
    d.shortestPaths(gauge_,
        &f,
        std::numeric_limits< double >::max(),
        1e-6,
        false,
        std::numeric_limits< double >::max()/2);

    HyperDijkstra::computeTree(d.adjacencyMap());
    // constructs the stars on the backbone

    BackBoneTreeAction bact(optimizer, vertexTag, level, step);
    bact.init();

    cerr << "free edges size " << bact.freeEdges().size() << endl;

    // perform breadth-first visit of the visit tree and create the stars on the backbone
    d.visitAdjacencyMap(d.adjacencyMap(),&bact,true);
    stars.clear();

    for (VertexStarMultimap::iterator it=bact.vertexStarMultiMap().begin();
        it!=bact.vertexStarMultiMap().end(); it++){
      stars.insert(it->second);
    }
    cerr << "stars.size: " << stars.size() << endl;
    cerr << "size: " << bact.vertexStarMultiMap().size() << endl;


    //  for each star

    //    for all vertices in the backbone, select all edges leading/leaving from that vertex
    //    that are contained in freeEdges.

    //      mark the corresponding "open" vertices and add them to a multimap (vertex->star)

    //    select a gauge in the backbone

    //    push all vertices on the backbone

    //    compute an initial guess on the backbone

    //    one round of optimization backbone

    //    lock all vertices in the backbone

    //    push all "open" vertices

    //    for each open vertex,
    //      compute an initial guess given the backbone
    //      do some rounds of solveDirect
    //      if (fail)
    //        - remove the vertex and the edges in that vertex from the star
    //   - make the structures consistent

    //    pop all "open" vertices
    //    pop all "vertices" in the backbone
    //    unfix the vertices in the backbone

    int starNum=0;
    for (StarSet::iterator it=stars.begin(); it!=stars.end(); it++){
      Star* s =*it;
      HyperGraph::VertexSet backboneVertices = s->_lowLevelVertices;
      HyperGraph::EdgeSet backboneEdges = s->_lowLevelEdges;
      if (backboneEdges.empty())
	continue;


      // cerr << "optimizing backbone" << endl;
      // one of these  should be the gauge, to be simple we select the fisrt one in the backbone
      OptimizableGraph::VertexSet gauge;
      gauge.insert(*backboneVertices.begin());
      s->gauge()=gauge;
      s->optimizer()->push(backboneVertices);
      s->optimizer()->setFixed(gauge,true);
      s->optimizer()->initializeOptimization(backboneEdges);
      s->optimizer()->computeInitialGuess();
      s->optimizer()->optimize(backboneIterations);
      s->optimizer()->setFixed(backboneVertices, true);

      // cerr << "assignind edges.vertices not in bbone" << endl;
      HyperGraph::EdgeSet otherEdges;
      HyperGraph::VertexSet otherVertices;
      std::multimap<HyperGraph::Vertex*, HyperGraph::Edge*> vemap;
//.........这里部分代码省略.........

  bool SparseOptimizer::initializeOptimization
     (HyperGraph::VertexSet& vset, int level)
  {

    // Recorre todos los vertices introducidos en el optimizador.
    // Para cada vertice 'V' obtiene los edges de los que forma parte.
    // Para cada uno de esos edges, se mira si todos sus vertices estan en el
    // optimizador. Si lo estan, el edge se aniade a _activeEdges.
    // Si el vertice 'V' tiene algun edge con todos los demas vertices en el
    // optimizador, se aniade 'V' a _activeVertices

    // Al final se asignan unos indices internos para los vertices:
    // -1: vertices fijos
    // 0..n: vertices no fijos y NO marginalizables
    // n+1..m: vertices no fijos y marginalizables
    clearIndexMapping();
    _activeVertices.clear();
    _activeVertices.reserve(vset.size());
    _activeEdges.clear();

    set<Edge*> auxEdgeSet; // temporary structure to avoid duplicates

    for (HyperGraph::VertexSet::iterator
         it  = vset.begin();
         it != vset.end();
         it++)
    {
      OptimizableGraph::Vertex* v= (OptimizableGraph::Vertex*) *it;
      const OptimizableGraph::EdgeSet& vEdges=v->edges();
      // count if there are edges in that level. If not remove from the pool
      int levelEdges=0;
      for (OptimizableGraph::EdgeSet::const_iterator
           it  = vEdges.begin();
           it != vEdges.end();
           it++)
      {
        OptimizableGraph::Edge* e =
           reinterpret_cast<OptimizableGraph::Edge*>(*it);
        if (level < 0 || e->level() == level)
        {
          bool allVerticesOK = true;
          for (vector<HyperGraph::Vertex*>::const_iterator
               vit  = e->vertices().begin();
               vit != e->vertices().end();
               ++vit)
          {
            if (vset.find(*vit) == vset.end())
            {
              allVerticesOK = false;
              break;
            }
          }

          if (allVerticesOK)
          {
            auxEdgeSet.insert(reinterpret_cast<OptimizableGraph::Edge*>(*it));
            levelEdges++;
          }
        }
      }
      if (levelEdges)   _activeVertices.push_back(v);
    }

    _activeEdges.reserve(auxEdgeSet.size());
    for (set<Edge*>::iterator
         it = auxEdgeSet.begin();
         it != auxEdgeSet.end();
         ++it)
       _activeEdges.push_back(*it);

    sortVectorContainers();
    return buildIndexMapping(_activeVertices);
  }

  bool SparseOptimizer::initializeOptimization(HyperGraph::VertexSet& vset, int level){
    if (edges().size() == 0) {
      cerr << __PRETTY_FUNCTION__ << ": Attempt to initialize an empty graph" << endl;
      return false;
    }
    bool workspaceAllocated = _jacobianWorkspace.allocate(); (void) workspaceAllocated;
    assert(workspaceAllocated && "Error while allocating memory for the Jacobians");
    clearIndexMapping();
    _activeVertices.clear();
    _activeVertices.reserve(vset.size());
    _activeEdges.clear();
    set<Edge*> auxEdgeSet; // temporary structure to avoid duplicates
    for (HyperGraph::VertexSet::iterator it=vset.begin(); it!=vset.end(); ++it){
      OptimizableGraph::Vertex* v= (OptimizableGraph::Vertex*) *it;
      const OptimizableGraph::EdgeSet& vEdges=v->edges();
      // count if there are edges in that level. If not remove from the pool
      int levelEdges=0;
      for (OptimizableGraph::EdgeSet::const_iterator it=vEdges.begin(); it!=vEdges.end(); ++it){
        OptimizableGraph::Edge* e=reinterpret_cast<OptimizableGraph::Edge*>(*it);
        if (level < 0 || e->level() == level) {

          bool allVerticesOK = true;
          for (vector<HyperGraph::Vertex*>::const_iterator vit = e->vertices().begin(); vit != e->vertices().end(); ++vit) {
            if (vset.find(*vit) == vset.end()) {
              allVerticesOK = false;
              break;
            }
          }
          if (allVerticesOK && !e->allVerticesFixed()) {
            auxEdgeSet.insert(e);
            levelEdges++;
          }

        }
      }
      if (levelEdges){
        _activeVertices.push_back(v);

        // test for NANs in the current estimate if we are debugging
#      ifndef NDEBUG
        int estimateDim = v->estimateDimension();
        if (estimateDim > 0) {
          Eigen::VectorXd estimateData(estimateDim);
          if (v->getEstimateData(estimateData.data()) == true) {
            int k;
            bool hasNan = arrayHasNaN(estimateData.data(), estimateDim, &k);
            if (hasNan)
              cerr << __PRETTY_FUNCTION__ << ": Vertex " << v->id() << " contains a nan entry at index " << k << endl;
          }
        }
#      endif

      }
    }

    _activeEdges.reserve(auxEdgeSet.size());
    for (set<Edge*>::iterator it = auxEdgeSet.begin(); it != auxEdgeSet.end(); ++it)
      _activeEdges.push_back(*it);

    sortVectorContainers();
    return buildIndexMapping(_activeVertices);
  }

bool saveGnuplot(const std::string& gnudump, const HyperGraph::VertexSet& vertices, const HyperGraph::EdgeSet& edges)
{
  // seek for an action whose name is writeGnuplot in the library
  HyperGraphElementAction* saveGnuplot = HyperGraphActionLibrary::instance()->actionByName("writeGnuplot");
  if (! saveGnuplot ){
    cerr << __PRETTY_FUNCTION__ << ": no action \"writeGnuplot\" registered" << endl;
    return false;
  }
  WriteGnuplotAction::Parameters params;

  int maxDim = -1;
  int minDim = numeric_limits<int>::max();
  for (HyperGraph::VertexSet::const_iterator it = vertices.begin(); it != vertices.end(); ++it){
    OptimizableGraph::Vertex* v = static_cast<OptimizableGraph::Vertex*>(*it);
    int vdim = v->dimension();
    maxDim = (std::max)(vdim, maxDim);
    minDim = (std::min)(vdim, minDim);
  }

  string extension = getFileExtension(gnudump);
  if (extension.size() == 0)
    extension = "dat";
  string baseFilename = getPureFilename(gnudump);

  // check for odometry edges
  bool hasOdomEdge = false;
  bool hasLandmarkEdge = false;
  for (HyperGraph::EdgeSet::const_iterator it = edges.begin(); it != edges.end(); ++it) {
    OptimizableGraph::Edge* e = static_cast<OptimizableGraph::Edge*>(*it);
    if (e->vertices().size() == 2) {
      if (edgeAllVertsSameDim(e, maxDim))
        hasOdomEdge = true;
      else
        hasLandmarkEdge = true;
    }
    if (hasOdomEdge && hasLandmarkEdge)
      break;
  }

  bool fileStatus = true;
  if (hasOdomEdge) {
    string odomFilename = baseFilename + "_odom_edges." + extension;
    cerr << "# saving " << odomFilename << " ... ";
    ofstream fout(odomFilename.c_str());
    if (! fout) {
      cerr << "Unable to open file" << endl;
      return false;
    }
    params.os = &fout;

    // writing odometry edges
    for (HyperGraph::EdgeSet::const_iterator it = edges.begin(); it != edges.end(); ++it) {
      OptimizableGraph::Edge* e = static_cast<OptimizableGraph::Edge*>(*it);
      if (e->vertices().size() != 2 || ! edgeAllVertsSameDim(e, maxDim))
        continue;
      (*saveGnuplot)(e, &params);
    }
    cerr << "done." << endl;
  }

  if (hasLandmarkEdge) {
    string filename = baseFilename + "_landmarks_edges." + extension;
    cerr << "# saving " << filename << " ... ";
    ofstream fout(filename.c_str());
    if (! fout) {
      cerr << "Unable to open file" << endl;
      return false;
    }
    params.os = &fout;

    // writing landmark edges
    for (HyperGraph::EdgeSet::const_iterator it = edges.begin(); it != edges.end(); ++it) {
      OptimizableGraph::Edge* e = static_cast<OptimizableGraph::Edge*>(*it);
      if (e->vertices().size() != 2 || edgeAllVertsSameDim(e, maxDim))
        continue;
      (*saveGnuplot)(e, &params);
    }
    cerr << "done." << endl;
  }

  if (1) {
    string filename = baseFilename + "_edges." + extension;
    cerr << "# saving " << filename << " ... ";
    ofstream fout(filename.c_str());
    if (! fout) {
      cerr << "Unable to open file" << endl;
      return false;
    }
    params.os = &fout;

    // writing all edges
    for (HyperGraph::EdgeSet::const_iterator it = edges.begin(); it != edges.end(); ++it) {
      OptimizableGraph::Edge* e = static_cast<OptimizableGraph::Edge*>(*it);
      (*saveGnuplot)(e, &params);
    }
    cerr << "done." << endl;
  }

  if (1) {
    string filename = baseFilename + "_vertices." + extension;
//.........这里部分代码省略.........