| // Ceres Solver - A fast non-linear least squares minimizer |
| // Copyright 2015 Google Inc. All rights reserved. |
| // http://ceres-solver.org/ |
| // |
| // Redistribution and use in source and binary forms, with or without |
| // modification, are permitted provided that the following conditions are met: |
| // |
| // * Redistributions of source code must retain the above copyright notice, |
| // this list of conditions and the following disclaimer. |
| // * Redistributions in binary form must reproduce the above copyright notice, |
| // this list of conditions and the following disclaimer in the documentation |
| // and/or other materials provided with the distribution. |
| // * Neither the name of Google Inc. nor the names of its contributors may be |
| // used to endorse or promote products derived from this software without |
| // specific prior written permission. |
| // |
| // THIS SOFTWARE IS PROVIDED BY THE COPYRIGHT HOLDERS AND CONTRIBUTORS "AS IS" |
| // AND ANY EXPRESS OR IMPLIED WARRANTIES, INCLUDING, BUT NOT LIMITED TO, THE |
| // IMPLIED WARRANTIES OF MERCHANTABILITY AND FITNESS FOR A PARTICULAR PURPOSE |
| // ARE DISCLAIMED. IN NO EVENT SHALL THE COPYRIGHT OWNER OR CONTRIBUTORS BE |
| // LIABLE FOR ANY DIRECT, INDIRECT, INCIDENTAL, SPECIAL, EXEMPLARY, OR |
| // CONSEQUENTIAL DAMAGES (INCLUDING, BUT NOT LIMITED TO, PROCUREMENT OF |
| // SUBSTITUTE GOODS OR SERVICES; LOSS OF USE, DATA, OR PROFITS; OR BUSINESS |
| // INTERRUPTION) HOWEVER CAUSED AND ON ANY THEORY OF LIABILITY, WHETHER IN |
| // CONTRACT, STRICT LIABILITY, OR TORT (INCLUDING NEGLIGENCE OR OTHERWISE) |
| // ARISING IN ANY WAY OUT OF THE USE OF THIS SOFTWARE, EVEN IF ADVISED OF THE |
| // POSSIBILITY OF SUCH DAMAGE. |
| // |
| // Author: David Gallup (dgallup@google.com) |
| // Sameer Agarwal (sameeragarwal@google.com) |
| |
| // This include must come before any #ifndef check on Ceres compile options. |
| #include "ceres/internal/port.h" |
| |
| #ifndef CERES_NO_SUITESPARSE |
| |
| #include "ceres/canonical_views_clustering.h" |
| |
| #include "ceres/collections_port.h" |
| #include "ceres/graph.h" |
| #include "ceres/internal/macros.h" |
| #include "ceres/map_util.h" |
| #include "glog/logging.h" |
| |
| namespace ceres { |
| namespace internal { |
| |
| using std::vector; |
| |
| typedef HashMap<int, int> IntMap; |
| typedef HashSet<int> IntSet; |
| |
| class CanonicalViewsClustering { |
| public: |
| CanonicalViewsClustering() {} |
| |
| // Compute the canonical views clustering of the vertices of the |
| // graph. centers will contain the vertices that are the identified |
| // as the canonical views/cluster centers, and membership is a map |
| // from vertices to cluster_ids. The i^th cluster center corresponds |
| // to the i^th cluster. It is possible depending on the |
| // configuration of the clustering algorithm that some of the |
| // vertices may not be assigned to any cluster. In this case they |
| // are assigned to a cluster with id = kInvalidClusterId. |
| void ComputeClustering(const CanonicalViewsClusteringOptions& options, |
| const WeightedGraph<int>& graph, |
| vector<int>* centers, |
| IntMap* membership); |
| |
| private: |
| void FindValidViews(IntSet* valid_views) const; |
| double ComputeClusteringQualityDifference(const int candidate, |
| const vector<int>& centers) const; |
| void UpdateCanonicalViewAssignments(const int canonical_view); |
| void ComputeClusterMembership(const vector<int>& centers, |
| IntMap* membership) const; |
| |
| CanonicalViewsClusteringOptions options_; |
| const WeightedGraph<int>* graph_; |
| // Maps a view to its representative canonical view (its cluster |
| // center). |
| IntMap view_to_canonical_view_; |
| // Maps a view to its similarity to its current cluster center. |
| HashMap<int, double> view_to_canonical_view_similarity_; |
| CERES_DISALLOW_COPY_AND_ASSIGN(CanonicalViewsClustering); |
| }; |
| |
| void ComputeCanonicalViewsClustering( |
| const CanonicalViewsClusteringOptions& options, |
| const WeightedGraph<int>& graph, |
| vector<int>* centers, |
| IntMap* membership) { |
| time_t start_time = time(NULL); |
| CanonicalViewsClustering cv; |
| cv.ComputeClustering(options, graph, centers, membership); |
| VLOG(2) << "Canonical views clustering time (secs): " |
| << time(NULL) - start_time; |
| } |
| |
| // Implementation of CanonicalViewsClustering |
| void CanonicalViewsClustering::ComputeClustering( |
| const CanonicalViewsClusteringOptions& options, |
| const WeightedGraph<int>& graph, |
| vector<int>* centers, |
| IntMap* membership) { |
| options_ = options; |
| CHECK_NOTNULL(centers)->clear(); |
| CHECK_NOTNULL(membership)->clear(); |
| graph_ = &graph; |
| |
| IntSet valid_views; |
| FindValidViews(&valid_views); |
| while (valid_views.size() > 0) { |
| // Find the next best canonical view. |
| double best_difference = -std::numeric_limits<double>::max(); |
| int best_view = 0; |
| |
| // TODO(sameeragarwal): Make this loop multi-threaded. |
| for (IntSet::const_iterator view = valid_views.begin(); |
| view != valid_views.end(); |
| ++view) { |
| const double difference = |
| ComputeClusteringQualityDifference(*view, *centers); |
| if (difference > best_difference) { |
| best_difference = difference; |
| best_view = *view; |
| } |
| } |
| |
| CHECK_GT(best_difference, -std::numeric_limits<double>::max()); |
| |
| // Add canonical view if quality improves, or if minimum is not |
| // yet met, otherwise break. |
| if ((best_difference <= 0) && |
| (centers->size() >= options_.min_views)) { |
| break; |
| } |
| |
| centers->push_back(best_view); |
| valid_views.erase(best_view); |
| UpdateCanonicalViewAssignments(best_view); |
| } |
| |
| ComputeClusterMembership(*centers, membership); |
| } |
| |
| // Return the set of vertices of the graph which have valid vertex |
| // weights. |
| void CanonicalViewsClustering::FindValidViews( |
| IntSet* valid_views) const { |
| const IntSet& views = graph_->vertices(); |
| for (IntSet::const_iterator view = views.begin(); |
| view != views.end(); |
| ++view) { |
| if (graph_->VertexWeight(*view) != WeightedGraph<int>::InvalidWeight()) { |
| valid_views->insert(*view); |
| } |
| } |
| } |
| |
| // Computes the difference in the quality score if 'candidate' were |
| // added to the set of canonical views. |
| double CanonicalViewsClustering::ComputeClusteringQualityDifference( |
| const int candidate, |
| const vector<int>& centers) const { |
| // View score. |
| double difference = |
| options_.view_score_weight * graph_->VertexWeight(candidate); |
| |
| // Compute how much the quality score changes if the candidate view |
| // was added to the list of canonical views and its nearest |
| // neighbors became members of its cluster. |
| const IntSet& neighbors = graph_->Neighbors(candidate); |
| for (IntSet::const_iterator neighbor = neighbors.begin(); |
| neighbor != neighbors.end(); |
| ++neighbor) { |
| const double old_similarity = |
| FindWithDefault(view_to_canonical_view_similarity_, *neighbor, 0.0); |
| const double new_similarity = graph_->EdgeWeight(*neighbor, candidate); |
| if (new_similarity > old_similarity) { |
| difference += new_similarity - old_similarity; |
| } |
| } |
| |
| // Number of views penalty. |
| difference -= options_.size_penalty_weight; |
| |
| // Orthogonality. |
| for (int i = 0; i < centers.size(); ++i) { |
| difference -= options_.similarity_penalty_weight * |
| graph_->EdgeWeight(centers[i], candidate); |
| } |
| |
| return difference; |
| } |
| |
| // Reassign views if they're more similar to the new canonical view. |
| void CanonicalViewsClustering::UpdateCanonicalViewAssignments( |
| const int canonical_view) { |
| const IntSet& neighbors = graph_->Neighbors(canonical_view); |
| for (IntSet::const_iterator neighbor = neighbors.begin(); |
| neighbor != neighbors.end(); |
| ++neighbor) { |
| const double old_similarity = |
| FindWithDefault(view_to_canonical_view_similarity_, *neighbor, 0.0); |
| const double new_similarity = |
| graph_->EdgeWeight(*neighbor, canonical_view); |
| if (new_similarity > old_similarity) { |
| view_to_canonical_view_[*neighbor] = canonical_view; |
| view_to_canonical_view_similarity_[*neighbor] = new_similarity; |
| } |
| } |
| } |
| |
| // Assign a cluster id to each view. |
| void CanonicalViewsClustering::ComputeClusterMembership( |
| const vector<int>& centers, |
| IntMap* membership) const { |
| CHECK_NOTNULL(membership)->clear(); |
| |
| // The i^th cluster has cluster id i. |
| IntMap center_to_cluster_id; |
| for (int i = 0; i < centers.size(); ++i) { |
| center_to_cluster_id[centers[i]] = i; |
| } |
| |
| static const int kInvalidClusterId = -1; |
| |
| const IntSet& views = graph_->vertices(); |
| for (IntSet::const_iterator view = views.begin(); |
| view != views.end(); |
| ++view) { |
| IntMap::const_iterator it = |
| view_to_canonical_view_.find(*view); |
| int cluster_id = kInvalidClusterId; |
| if (it != view_to_canonical_view_.end()) { |
| cluster_id = FindOrDie(center_to_cluster_id, it->second); |
| } |
| |
| InsertOrDie(membership, *view, cluster_id); |
| } |
| } |
| |
| } // namespace internal |
| } // namespace ceres |
| |
| #endif // CERES_NO_SUITESPARSE |