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Diffstat (limited to 'src/Collapse/include/gudhi/Flag_complex_edge_collapser.h')
| -rw-r--r-- | src/Collapse/include/gudhi/Flag_complex_edge_collapser.h | 579 |
1 files changed, 266 insertions, 313 deletions
diff --git a/src/Collapse/include/gudhi/Flag_complex_edge_collapser.h b/src/Collapse/include/gudhi/Flag_complex_edge_collapser.h index 713c6608..d0b3fe4a 100644 --- a/src/Collapse/include/gudhi/Flag_complex_edge_collapser.h +++ b/src/Collapse/include/gudhi/Flag_complex_edge_collapser.h @@ -1,11 +1,12 @@ /* This file is part of the Gudhi Library - https://gudhi.inria.fr/ - which is released under MIT. * See file LICENSE or go to https://gudhi.inria.fr/licensing/ for full license details. - * Author(s): Siddharth Pritam + * Author(s): Siddharth Pritam, Marc Glisse * * Copyright (C) 2020 Inria * * Modification(s): * - 2020/03 Vincent Rouvreau: integration to the gudhi library + * - 2021 Marc Glisse: complete rewrite * - YYYY/MM Author: Description of the modification */ @@ -14,367 +15,319 @@ #include <gudhi/Debug_utils.h> -#include <boost/functional/hash.hpp> -#include <boost/iterator/iterator_facade.hpp> - -#include <Eigen/Sparse> -#include <Eigen/src/Core/util/Macros.h> // for EIGEN_VERSION_AT_LEAST +#include <boost/container/flat_map.hpp> +#include <boost/container/flat_set.hpp> #ifdef GUDHI_USE_TBB #include <tbb/parallel_sort.h> #endif -#include <iostream> -#include <utility> // for std::pair +#include <utility> #include <vector> -#include <unordered_map> -#include <unordered_set> -#include <set> -#include <tuple> // for std::tie -#include <algorithm> // for std::includes -#include <iterator> // for std::inserter -#include <type_traits> // for std::decay - -// Make compilation fail - required for external projects - https://github.com/GUDHI/gudhi-devel/issues/10 -#if !EIGEN_VERSION_AT_LEAST(3,1,0) -# error Edge Collapse is only available for Eigen3 >= 3.1.0 -#endif +#include <tuple> +#include <algorithm> +#include <limits> namespace Gudhi { namespace collapse { /** \private - * - * \brief Flag complex sparse matrix data structure. * - * \details - * This class stores a <a target="_blank" href="https://en.wikipedia.org/wiki/Clique_complex">Flag complex</a> - * in an <a target="_blank" href="https://eigen.tuxfamily.org/dox/group__TutorialSparse.html">Eigen sparse matrix</a>. + * \brief Flag complex sparse matrix data structure. * - * \tparam Vertex type must be a signed integer type. It admits a total order <. - * \tparam Filtration type for the value of the filtration function. Must be comparable with <. + * \tparam Vertex type must be an integer type. + * \tparam Filtration type for the value of the filtration function. */ -template<typename Vertex, typename Filtration> -class Flag_complex_edge_collapser { - public: - /** \brief Re-define Vertex as Vertex_handle type to ease the interface with `Gudhi::Proximity_graph`. */ - using Vertex_handle = Vertex; - /** \brief Re-define Filtration as Filtration_value type to ease the interface with `Gudhi::Proximity_graph`. */ - using Filtration_value = Filtration; - - private: - // internal numbering of vertices and edges - using IVertex = std::size_t; - using Edge_index = std::size_t; - using IEdge = std::pair<IVertex, IVertex>; - - // The sparse matrix data type - // (Eigen::SparseMatrix<Edge_index, Eigen::RowMajor> has slow insertions) - using Sparse_vector = Eigen::SparseVector<Edge_index>; - using Sparse_row_matrix = std::vector<Sparse_vector>; - - // Range of neighbors of a vertex - template<bool closed> - struct Neighbours { - class iterator : public boost::iterator_facade<iterator, - IVertex, /* value_type */ - std::input_iterator_tag, // or boost::single_pass_traversal_tag - IVertex /* reference */ > - { - public: - iterator():ptr(nullptr){} - iterator(Neighbours const*p):ptr(p){find_valid();} - private: - friend class boost::iterator_core_access; - Neighbours const*ptr; - void increment(){ - ++ptr->it; - find_valid(); - } - void find_valid(){ - auto& it = ptr->it; - do { - if(!it) { ptr=nullptr; break; } - if(IVertex(it.index()) == ptr->u) { - if(closed) break; - else continue; - } - Edge_index e = it.value(); - if(e <= ptr->ec->current_backward || ptr->ec->critical_edge_indicator_[e]) break; - } while(++it, true); - } - bool equal(iterator const& other) const { return ptr == other.ptr; } - IVertex dereference() const { return ptr->it.index(); } - }; - typedef iterator const_iterator; - mutable typename Sparse_vector::InnerIterator it; - Flag_complex_edge_collapser const*ec; - IVertex u; - iterator begin() const { return this; } - iterator end() const { return {}; } - explicit Neighbours(Flag_complex_edge_collapser const*p,IVertex u):it(p->sparse_row_adjacency_matrix_[u]),ec(p),u(u){} - }; - - // A range of row indices - using IVertex_vector = std::vector<IVertex>; - - public: - /** \brief Filtered_edge is a type to store an edge with its filtration value. */ - using Filtered_edge = std::tuple<Vertex_handle, Vertex_handle, Filtration_value>; - - private: - // Map from row index to its vertex handle - std::vector<Vertex_handle> row_to_vertex_; - - // Index of the current edge in the backwards walk. Edges <= current_backward are part of the temporary graph, - // while edges > current_backward are removed unless critical_edge_indicator_. - Edge_index current_backward = -1; - - // Map from IEdge to its index - std::unordered_map<IEdge, Edge_index, boost::hash<IEdge>> iedge_to_index_map_; - - // Boolean vector to indicate if the edge is critical. - std::vector<bool> critical_edge_indicator_; - - // Map from vertex handle to its row index - std::unordered_map<Vertex_handle, IVertex> vertex_to_row_; - - // Stores the Sparse matrix of Filtration values representing the original graph. - // The matrix rows and columns are indexed by IVertex. - Sparse_row_matrix sparse_row_adjacency_matrix_; - - // The input, a vector of filtered edges. - std::vector<Filtered_edge> f_edge_vector_; - - // Edge is the actual edge (u,v), with Vertex_handle u and v, not IVertex. - bool edge_is_dominated(Vertex_handle u, Vertex_handle v) const - { - const IVertex rw_u = vertex_to_row_.at(u); - const IVertex rw_v = vertex_to_row_.at(v); -#ifdef DEBUG_TRACES - std::cout << "The edge {" << u << ", " << v << "} is going for domination check." << std::endl; -#endif // DEBUG_TRACES - auto common_neighbours = open_common_neighbours_row_index(rw_u, rw_v); -#ifdef DEBUG_TRACES - std::cout << "And its common neighbours are." << std::endl; - for (auto neighbour : common_neighbours) { - std::cout << row_to_vertex_[neighbour] << ", " ; - } - std::cout<< std::endl; -#endif // DEBUG_TRACES - if (common_neighbours.size() == 1) - return true; - else - for (auto rw_c : common_neighbours) { - auto neighbours_c = neighbours_row_index<true>(rw_c); - // If neighbours_c contains the common neighbours. - if (std::includes(neighbours_c.begin(), neighbours_c.end(), - common_neighbours.begin(), common_neighbours.end())) - return true; - } - return false; +template<typename Vertex, typename Filtration_value> +struct Flag_complex_edge_collapser { + using Filtered_edge = std::tuple<Vertex, Vertex, Filtration_value>; + typedef std::pair<Vertex,Vertex> Edge; + struct Cmpi { template<class T, class U> bool operator()(T const&a, U const&b)const{return b<a; } }; + typedef boost::container::flat_map<Vertex, Filtration_value> Ngb_list; + typedef std::vector<Ngb_list> Neighbors; + Neighbors neighbors; // closed neighborhood + std::size_t num_vertices; + std::vector<std::tuple<Vertex, Vertex, Filtration_value>> res; + +#ifdef GUDHI_COLLAPSE_USE_DENSE_ARRAY + // Minimal matrix interface + // Using this matrix generally helps performance, but the memory use may be excessive for a very sparse graph + // (and in extreme cases the constant initialization of the matrix may start to dominate the running time). + // Are there cases where the matrix is too big but a hash table would help? + std::vector<Filtration_value> neighbors_data; + void init_neighbors_dense(){ + neighbors_data.clear(); + neighbors_data.resize(num_vertices*num_vertices, std::numeric_limits<Filtration_value>::infinity()); } + Filtration_value& neighbors_dense(Vertex i, Vertex j){return neighbors_data[num_vertices*j+i];} +#endif - // Returns the edges connecting u and v (extremities of crit) to their common neighbors (not themselves) - std::set<Edge_index> three_clique_indices(Edge_index crit) { - std::set<Edge_index> edge_indices; - - Vertex_handle u = std::get<0>(f_edge_vector_[crit]); - Vertex_handle v = std::get<1>(f_edge_vector_[crit]); - -#ifdef DEBUG_TRACES - std::cout << "The current critical edge to re-check criticality with filt value is : f {" << u << "," << v - << "} = " << std::get<2>(f_edge_vector_[crit]) << std::endl; -#endif // DEBUG_TRACES - auto rw_u = vertex_to_row_[u]; - auto rw_v = vertex_to_row_[v]; - - IVertex_vector common_neighbours = open_common_neighbours_row_index(rw_u, rw_v); + // This does not touch the events list, only the adjacency matrix(es) + void delay_neighbor(Vertex u, Vertex v, Filtration_value f) { + neighbors[u][v]=f; + neighbors[v][u]=f; +#ifdef GUDHI_COLLAPSE_USE_DENSE_ARRAY + neighbors_dense(u,v)=f; + neighbors_dense(v,u)=f; +#endif + } + void remove_neighbor(Vertex u, Vertex v) { + neighbors[u].erase(v); + neighbors[v].erase(u); +#ifdef GUDHI_COLLAPSE_USE_DENSE_ARRAY + neighbors_dense(u,v)=std::numeric_limits<Filtration_value>::infinity(); + neighbors_dense(v,u)=std::numeric_limits<Filtration_value>::infinity(); +#endif + } - for (auto rw_c : common_neighbours) { - IEdge e_with_new_nbhr_v = std::minmax(rw_u, rw_c); - IEdge e_with_new_nbhr_u = std::minmax(rw_v, rw_c); - edge_indices.emplace(iedge_to_index_map_[e_with_new_nbhr_v]); - edge_indices.emplace(iedge_to_index_map_[e_with_new_nbhr_u]); + template<class FilteredEdgeRange> + void read_edges(FilteredEdgeRange const&r){ + neighbors.resize(num_vertices); +#ifdef GUDHI_COLLAPSE_USE_DENSE_ARRAY + init_neighbors_dense(); +#endif + // Use the raw sequence to avoid maintaining the order + std::vector<typename Ngb_list::sequence_type> neighbors_seq(num_vertices); + for(auto&&e : r){ + using std::get; + Vertex u = get<0>(e); + Vertex v = get<1>(e); + Filtration_value f = get<2>(e); + neighbors_seq[u].emplace_back(v, f); + neighbors_seq[v].emplace_back(u, f); +#ifdef GUDHI_COLLAPSE_USE_DENSE_ARRAY + neighbors_dense(u,v)=f; + neighbors_dense(v,u)=f; +#endif + } + for(std::size_t i=0;i<neighbors_seq.size();++i){ + neighbors_seq[i].emplace_back(i, -std::numeric_limits<Filtration_value>::infinity()); + neighbors[i].adopt_sequence(std::move(neighbors_seq[i])); // calls sort +#ifdef GUDHI_COLLAPSE_USE_DENSE_ARRAY + neighbors_dense(i,i)=-std::numeric_limits<Filtration_value>::infinity(); +#endif } - return edge_indices; } - // Detect and set all edges that are becoming critical - template<typename FilteredEdgeOutput> - void set_edge_critical(Edge_index indx, Filtration_value filt, FilteredEdgeOutput filtered_edge_output) { -#ifdef DEBUG_TRACES - std::cout << "The curent index with filtration value " << indx << ", " << filt << " is primary critical" << - std::endl; -#endif // DEBUG_TRACES - std::set<Edge_index> effected_indices = three_clique_indices(indx); - // Cannot use boost::adaptors::reverse in such dynamic cases apparently - for (auto it = effected_indices.rbegin(); it != effected_indices.rend(); ++it) { - current_backward = *it; - Vertex_handle u = std::get<0>(f_edge_vector_[current_backward]); - Vertex_handle v = std::get<1>(f_edge_vector_[current_backward]); - // If current_backward is not critical so it should be processed, otherwise it stays in the graph - if (!critical_edge_indicator_[current_backward]) { - if (!edge_is_dominated(u, v)) { -#ifdef DEBUG_TRACES - std::cout << "The curent index became critical " << current_backward << std::endl; -#endif // DEBUG_TRACES - critical_edge_indicator_[current_backward] = true; - filtered_edge_output(u, v, filt); - std::set<Edge_index> inner_effected_indcs = three_clique_indices(current_backward); - for (auto inr_idx : inner_effected_indcs) { - if(inr_idx < current_backward) // && !critical_edge_indicator_[inr_idx] - effected_indices.emplace(inr_idx); - } -#ifdef DEBUG_TRACES - std::cout << "The following edge is critical with filt value: {" << u << "," << v << "}; " - << filt << std::endl; -#endif // DEBUG_TRACES - } + // Open neighborhood + // At some point it helped gcc to add __attribute__((noinline)) here, otherwise we had +50% on the running time + // on one example. It looks ok now, or I forgot which example that was. + void common_neighbors(boost::container::flat_set<Vertex>& e_ngb, + std::vector<std::pair<Filtration_value, Vertex>>& e_ngb_later, + Vertex u, Vertex v, Filtration_value f_event){ + // Using neighbors_dense here seems to hurt, even if we loop on the smaller of nu and nv. + Ngb_list const&nu = neighbors[u]; + Ngb_list const&nv = neighbors[v]; + auto ui = nu.begin(); + auto ue = nu.end(); + auto vi = nv.begin(); + auto ve = nv.end(); + assert(ui != ue && vi != ve); + while(ui != ue && vi != ve){ + Vertex w = ui->first; + if(w < vi->first) { ++ui; continue; } + if(w > vi->first) { ++vi; continue; } + // nu and nv are closed, so we need to exclude e here. + if(w != u && w != v) { + Filtration_value f = std::max(ui->second, vi->second); + if(f > f_event) + e_ngb_later.emplace_back(f, w); + else + e_ngb.insert(e_ngb.end(), w); } + ++ui; ++vi; } - // Clear the implicit "removed from graph" data structure - current_backward = -1; } - // Returns list of neighbors of a particular vertex. - template<bool closed> - auto neighbours_row_index(IVertex rw_u) const - { - return Neighbours<closed>(this, rw_u); + // Test if the neighborhood of e is included in the closed neighborhood of c + template<class Ngb> + bool is_dominated_by(Ngb const& e_ngb, Vertex c, Filtration_value f){ + // The best strategy probably depends on the distribution, how sparse / dense the adjacency matrix is, + // how (un)balanced the sizes of e_ngb and nc are. + // Some efficient operations on sets work best with bitsets, although the need for a map complicates things. +#ifdef GUDHI_COLLAPSE_USE_DENSE_ARRAY + for(auto v : e_ngb) { + // if(v==c)continue; + if(neighbors_dense(v,c) > f) return false; + } + return true; +#else + auto&&nc = neighbors[c]; + // if few neighbors, use dichotomy? Seems slower. + // I tried storing a copy of neighbors as a vector<absl::flat_hash_map> and using it for nc, but it was + // a bit slower here. It did help with neighbors[dominator].find(w) in the main function though, + // sometimes enough, sometimes not. + auto ci = nc.begin(); + auto ce = nc.end(); + auto eni = e_ngb.begin(); + auto ene = e_ngb.end(); + assert(eni != ene); + assert(ci != ce); + // if(*eni == c && ++eni == ene) return true; + for(;;){ + Vertex ve = *eni; + Vertex vc = ci->first; + while(ve > vc) { + // try a gallop strategy (exponential search)? Seems slower + if(++ci == ce) return false; + vc = ci->first; + } + if(ve < vc) return false; + // ve == vc + if(ci->second > f) return false; + if(++eni == ene)return true; + // If we stored an open neighborhood of c (excluding c), we would need to test for c here and before the loop + // if(*eni == c && ++eni == ene)return true; + if(++ci == ce) return false; + } +#endif } - // Returns the list of open neighbours of the edge :{u,v}. - IVertex_vector open_common_neighbours_row_index(IVertex rw_u, IVertex rw_v) const - { - auto non_zero_indices_u = neighbours_row_index<false>(rw_u); - auto non_zero_indices_v = neighbours_row_index<false>(rw_v); - IVertex_vector common; - std::set_intersection(non_zero_indices_u.begin(), non_zero_indices_u.end(), non_zero_indices_v.begin(), - non_zero_indices_v.end(), std::back_inserter(common)); - - return common; - } + template<class FilteredEdgeRange, class Delay> + void process_edges(FilteredEdgeRange const& edges, Delay&& delay) { + { + Vertex maxi = 0, maxj = 0; + for(auto& fe : edges) { + Vertex i = std::get<0>(fe); + Vertex j = std::get<1>(fe); + if (i > maxi) maxi = i; + if (j > maxj) maxj = j; + } + num_vertices = std::max(maxi, maxj) + 1; + } - // Insert a vertex in the data structure - IVertex insert_vertex(Vertex_handle vertex) { - auto n = row_to_vertex_.size(); - auto result = vertex_to_row_.emplace(vertex, n); - // If it was not already inserted - Value won't be updated by emplace if it is already present - if (result.second) { - // Expand the matrix. The size of rows is irrelevant. - sparse_row_adjacency_matrix_.emplace_back((std::numeric_limits<Eigen::Index>::max)()); - // Initializing the diagonal element of the adjency matrix corresponding to rw_b. - sparse_row_adjacency_matrix_[n].insert(n) = -1; // not an edge - // Must be done after reading its size() - row_to_vertex_.push_back(vertex); + read_edges(edges); + + boost::container::flat_set<Vertex> e_ngb; + e_ngb.reserve(num_vertices); + std::vector<std::pair<Filtration_value, Vertex>> e_ngb_later; + for(auto&e:edges) { + { + Vertex u = std::get<0>(e); + Vertex v = std::get<1>(e); + Filtration_value input_time = std::get<2>(e); + auto time = delay(input_time); + auto start_time = time; + e_ngb.clear(); + e_ngb_later.clear(); + common_neighbors(e_ngb, e_ngb_later, u, v, time); + // If we identify a good candidate (the first common neighbor) for being a dominator of e until infinity, + // we could check that a bit more cheaply. It does not seem to help though. + auto cmp1=[](auto const&a, auto const&b){return a.first > b.first;}; + auto e_ngb_later_begin=e_ngb_later.begin(); + auto e_ngb_later_end=e_ngb_later.end(); + bool heapified = false; + + bool dead = false; + while(true) { + Vertex dominator = -1; + // special case for size 1 + // if(e_ngb.size()==1){dominator=*e_ngb.begin();}else + // It is tempting to test the dominators in increasing order of filtration value, which is likely to reduce + // the number of calls to is_dominated_by before finding a dominator, but sorting, even partially / lazily, + // is very expensive. + for(auto c : e_ngb){ + if(is_dominated_by(e_ngb, c, time)){ + dominator = c; + break; + } + } + if(dominator==-1) break; + // Push as long as dominator remains a dominator. + // Iterate on times where at least one neighbor appears. + for (bool still_dominated = true; still_dominated; ) { + if(e_ngb_later_begin == e_ngb_later_end) { + dead = true; goto end_move; + } + if(!heapified) { + // Eagerly sorting can be slow + std::make_heap(e_ngb_later_begin, e_ngb_later_end, cmp1); + heapified=true; + } + time = e_ngb_later_begin->first; // first place it may become critical + // Update the neighborhood for this new time, while checking if any of the new neighbors break domination. + while (e_ngb_later_begin != e_ngb_later_end && e_ngb_later_begin->first <= time) { + Vertex w = e_ngb_later_begin->second; +#ifdef GUDHI_COLLAPSE_USE_DENSE_ARRAY + if (neighbors_dense(dominator,w) > e_ngb_later_begin->first) + still_dominated = false; +#else + auto& ngb_dom = neighbors[dominator]; + auto wit = ngb_dom.find(w); // neighborhood may be open or closed, it does not matter + if (wit == ngb_dom.end() || wit->second > e_ngb_later_begin->first) + still_dominated = false; +#endif + e_ngb.insert(w); + std::pop_heap(e_ngb_later_begin, e_ngb_later_end--, cmp1); + } + } // this doesn't seem to help that much... + } +end_move: + if(dead) { + remove_neighbor(u, v); + } else if(start_time != time) { + delay_neighbor(u, v, time); + res.emplace_back(u, v, time); + } else { + res.emplace_back(u, v, input_time); + } + } } - return result.first->second; } - // Insert an edge in the data structure - // @exception std::invalid_argument In debug mode, if u == v - IEdge insert_new_edge(Vertex_handle u, Vertex_handle v, Edge_index idx) - { - GUDHI_CHECK((u != v), std::invalid_argument("Flag_complex_edge_collapser::insert_new_edge with u == v")); - // The edge must not be added before, it should be a new edge. - IVertex rw_u = insert_vertex(u); - IVertex rw_v = insert_vertex(v); -#ifdef DEBUG_TRACES - std::cout << "Inserting the edge " << u <<", " << v << std::endl; -#endif // DEBUG_TRACES - sparse_row_adjacency_matrix_[rw_u].insert(rw_v) = idx; - sparse_row_adjacency_matrix_[rw_v].insert(rw_u) = idx; - return std::minmax(rw_u, rw_v); + std::vector<Filtered_edge> output() { + return std::move(res); } - public: - /** \brief Flag_complex_edge_collapser constructor from a range of filtered edges. - * - * @param[in] edges Range of Filtered edges range.There is no need the range to be sorted, as it will be performed in - * `Flag_complex_edge_collapser::process_edges`. - * - * \tparam FilteredEdgeRange must be a range for which std::begin and std::end return iterators on a - * `Flag_complex_edge_collapser::Filtered_edge`. - */ - template<typename FilteredEdgeRange> - Flag_complex_edge_collapser(const FilteredEdgeRange& edges) - : f_edge_vector_(std::begin(edges), std::end(edges)) { } +}; - /** \brief Performs edge collapse in a increasing sequence of the filtration value. - * - * \tparam filtered_edge_output is a functor that is called on the output edges, in non-decreasing order of - * filtration, as filtered_edge_output(u, v, f) where u and v are Vertex_handle representing the extremities of the - * edge, and f is its new Filtration_value. - */ - template<typename FilteredEdgeOutput> - void process_edges(FilteredEdgeOutput filtered_edge_output) { - // Sort edges - auto sort_by_filtration = [](const Filtered_edge& edge_a, const Filtered_edge& edge_b) -> bool - { - return (std::get<2>(edge_a) < std::get<2>(edge_b)); - }; +template<class R> R to_range(R&& r) { return std::move(r); } +template<class R, class T> R to_range(T&& t) { R r; r.insert(r.end(), t.begin(), t.end()); return r; } +template<class FilteredEdgeRange, class Delay> +auto flag_complex_collapse_edges(FilteredEdgeRange&& edges, Delay&&delay) { + // Would it help to label the points according to some spatial sorting? + auto first_edge_itr = std::begin(edges); + using Vertex = std::decay_t<decltype(std::get<0>(*first_edge_itr))>; + using Filtration_value = std::decay_t<decltype(std::get<2>(*first_edge_itr))>; + using Edge_collapser = Flag_complex_edge_collapser<Vertex, Filtration_value>; + if (first_edge_itr != std::end(edges)) { + auto edges2 = to_range<std::vector<typename Edge_collapser::Filtered_edge>>(std::forward<FilteredEdgeRange>(edges)); #ifdef GUDHI_USE_TBB - tbb::parallel_sort(f_edge_vector_.begin(), f_edge_vector_.end(), sort_by_filtration); + // I think this sorting is always negligible compared to the collapse, but parallelizing it shouldn't hurt. + tbb::parallel_sort(edges2.begin(), edges2.end(), + [](auto const&a, auto const&b){return std::get<2>(a)>std::get<2>(b);}); #else - std::sort(f_edge_vector_.begin(), f_edge_vector_.end(), sort_by_filtration); + std::sort(edges2.begin(), edges2.end(), [](auto const&a, auto const&b){return std::get<2>(a)>std::get<2>(b);}); #endif - - for (Edge_index endIdx = 0; endIdx < f_edge_vector_.size(); endIdx++) { - Filtered_edge fec = f_edge_vector_[endIdx]; - Vertex_handle u = std::get<0>(fec); - Vertex_handle v = std::get<1>(fec); - Filtration_value filt = std::get<2>(fec); - - // Inserts the edge in the sparse matrix to update the graph (G_i) - IEdge ie = insert_new_edge(u, v, endIdx); - - iedge_to_index_map_.emplace(ie, endIdx); - critical_edge_indicator_.push_back(false); - - if (!edge_is_dominated(u, v)) { - critical_edge_indicator_[endIdx] = true; - filtered_edge_output(u, v, filt); - if (endIdx > 1) - set_edge_critical(endIdx, filt, filtered_edge_output); - } - } + Edge_collapser edge_collapser; + edge_collapser.process_edges(edges2, std::forward<Delay>(delay)); + return edge_collapser.output(); } - -}; + return std::vector<typename Edge_collapser::Filtered_edge>(); +} /** \brief Implicitly constructs a flag complex from edges as an input, collapses edges while preserving the persistent - * homology and returns the remaining edges as a range. + * homology and returns the remaining edges as a range. The filtration value of vertices is irrelevant to this function. * - * \param[in] edges Range of Filtered edges.There is no need the range to be sorted, as it will be performed. + * \param[in] edges Range of Filtered edges. There is no need for the range to be sorted, as it will be done internally. * - * \tparam FilteredEdgeRange furnishes `std::begin` and `std::end` methods and returns an iterator on a - * FilteredEdge of type `std::tuple<Vertex_handle, Vertex_handle, Filtration_value>` where `Vertex_handle` is the type - * of a vertex index and `Filtration_value` is the type of an edge filtration value. + * \tparam FilteredEdgeRange Range of `std::tuple<Vertex_handle, Vertex_handle, Filtration_value>` + * where `Vertex_handle` is the type of a vertex index. * * \return Remaining edges after collapse as a range of * `std::tuple<Vertex_handle, Vertex_handle, Filtration_value>`. - * + * * \ingroup edge_collapse - * + * + * \note + * Advanced: Defining the macro GUDHI_COLLAPSE_USE_DENSE_ARRAY tells gudhi to allocate a square table of size the + * maximum vertex index. This usually speeds up the computation for dense graphs. However, for sparse graphs, the memory + * use may be problematic and initializing this large table may be slow. */ template<class FilteredEdgeRange> auto flag_complex_collapse_edges(const FilteredEdgeRange& edges) { - auto first_edge_itr = std::begin(edges); - using Vertex_handle = std::decay_t<decltype(std::get<0>(*first_edge_itr))>; - using Filtration_value = std::decay_t<decltype(std::get<2>(*first_edge_itr))>; - using Edge_collapser = Flag_complex_edge_collapser<Vertex_handle, Filtration_value>; - std::vector<typename Edge_collapser::Filtered_edge> remaining_edges; - if (first_edge_itr != std::end(edges)) { - Edge_collapser edge_collapser(edges); - edge_collapser.process_edges( - [&remaining_edges](Vertex_handle u, Vertex_handle v, Filtration_value filtration) { - // insert the edge - remaining_edges.emplace_back(u, v, filtration); - }); - } - return remaining_edges; + return flag_complex_collapse_edges(edges, [](auto const&d){return d;}); } } // namespace collapse |