| // 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: Sameer Agarwal (sameeragarwal@google.com) |
| // David Gallup (dgallup@google.com) |
| |
| #include "ceres/canonical_views_clustering.h" |
| |
| #include <unordered_map> |
| |
| #include "ceres/graph.h" |
| #include "gtest/gtest.h" |
| |
| namespace ceres { |
| namespace internal { |
| |
| const int kVertexIds[] = {0, 1, 2, 3}; |
| class CanonicalViewsTest : public ::testing::Test { |
| protected: |
| void SetUp() final { |
| // The graph structure is as follows. |
| // |
| // Vertex weights: 0 2 2 0 |
| // V0-----V1-----V2-----V3 |
| // Edge weights: 0.8 0.9 0.3 |
| const double kVertexWeights[] = {0.0, 2.0, 2.0, -1.0}; |
| for (int i = 0; i < 4; ++i) { |
| graph_.AddVertex(i, kVertexWeights[i]); |
| } |
| // Create self edges. |
| // CanonicalViews requires that every view "sees" itself. |
| for (int i = 0; i < 4; ++i) { |
| graph_.AddEdge(i, i, 1.0); |
| } |
| |
| // Create three edges. |
| const double kEdgeWeights[] = {0.8, 0.9, 0.3}; |
| for (int i = 0; i < 3; ++i) { |
| // The graph interface is directed, so remember to create both |
| // edges. |
| graph_.AddEdge(kVertexIds[i], kVertexIds[i + 1], kEdgeWeights[i]); |
| } |
| } |
| |
| void ComputeClustering() { |
| ComputeCanonicalViewsClustering(options_, graph_, ¢ers_, &membership_); |
| } |
| |
| WeightedGraph<int> graph_; |
| |
| CanonicalViewsClusteringOptions options_; |
| std::vector<int> centers_; |
| std::unordered_map<int, int> membership_; |
| }; |
| |
| TEST_F(CanonicalViewsTest, ComputeCanonicalViewsTest) { |
| options_.min_views = 0; |
| options_.size_penalty_weight = 0.5; |
| options_.similarity_penalty_weight = 0.0; |
| options_.view_score_weight = 0.0; |
| ComputeClustering(); |
| |
| // 2 canonical views. |
| EXPECT_EQ(centers_.size(), 2); |
| EXPECT_EQ(centers_[0], kVertexIds[1]); |
| EXPECT_EQ(centers_[1], kVertexIds[3]); |
| |
| // Check cluster membership. |
| EXPECT_EQ(FindOrDie(membership_, kVertexIds[0]), 0); |
| EXPECT_EQ(FindOrDie(membership_, kVertexIds[1]), 0); |
| EXPECT_EQ(FindOrDie(membership_, kVertexIds[2]), 0); |
| EXPECT_EQ(FindOrDie(membership_, kVertexIds[3]), 1); |
| } |
| |
| // Increases size penalty so the second canonical view won't be |
| // chosen. |
| TEST_F(CanonicalViewsTest, SizePenaltyTest) { |
| options_.min_views = 0; |
| options_.size_penalty_weight = 2.0; |
| options_.similarity_penalty_weight = 0.0; |
| options_.view_score_weight = 0.0; |
| ComputeClustering(); |
| |
| // 1 canonical view. |
| EXPECT_EQ(centers_.size(), 1); |
| EXPECT_EQ(centers_[0], kVertexIds[1]); |
| } |
| |
| // Increases view score weight so vertex 2 will be chosen. |
| TEST_F(CanonicalViewsTest, ViewScoreTest) { |
| options_.min_views = 0; |
| options_.size_penalty_weight = 0.5; |
| options_.similarity_penalty_weight = 0.0; |
| options_.view_score_weight = 1.0; |
| ComputeClustering(); |
| |
| // 2 canonical views. |
| EXPECT_EQ(centers_.size(), 2); |
| EXPECT_EQ(centers_[0], kVertexIds[1]); |
| EXPECT_EQ(centers_[1], kVertexIds[2]); |
| } |
| |
| // Increases similarity penalty so vertex 2 won't be chosen despite |
| // it's view score. |
| TEST_F(CanonicalViewsTest, SimilarityPenaltyTest) { |
| options_.min_views = 0; |
| options_.size_penalty_weight = 0.5; |
| options_.similarity_penalty_weight = 3.0; |
| options_.view_score_weight = 1.0; |
| ComputeClustering(); |
| |
| // 2 canonical views. |
| EXPECT_EQ(centers_.size(), 1); |
| EXPECT_EQ(centers_[0], kVertexIds[1]); |
| } |
| |
| } // namespace internal |
| } // namespace ceres |