151 lines
5.0 KiB
C++
151 lines
5.0 KiB
C++
// Ceres Solver - A fast non-linear least squares minimizer
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// Copyright 2015 Google Inc. All rights reserved.
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// http://ceres-solver.org/
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//
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// Redistribution and use in source and binary forms, with or without
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// modification, are permitted provided that the following conditions are met:
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//
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// * Redistributions of source code must retain the above copyright notice,
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// this list of conditions and the following disclaimer.
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// * Redistributions in binary form must reproduce the above copyright notice,
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// this list of conditions and the following disclaimer in the documentation
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// and/or other materials provided with the distribution.
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// * Neither the name of Google Inc. nor the names of its contributors may be
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// used to endorse or promote products derived from this software without
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// specific prior written permission.
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//
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// THIS SOFTWARE IS PROVIDED BY THE COPYRIGHT HOLDERS AND CONTRIBUTORS "AS IS"
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// AND ANY EXPRESS OR IMPLIED WARRANTIES, INCLUDING, BUT NOT LIMITED TO, THE
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// IMPLIED WARRANTIES OF MERCHANTABILITY AND FITNESS FOR A PARTICULAR PURPOSE
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// ARE DISCLAIMED. IN NO EVENT SHALL THE COPYRIGHT OWNER OR CONTRIBUTORS BE
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// LIABLE FOR ANY DIRECT, INDIRECT, INCIDENTAL, SPECIAL, EXEMPLARY, OR
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// CONSEQUENTIAL DAMAGES (INCLUDING, BUT NOT LIMITED TO, PROCUREMENT OF
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// SUBSTITUTE GOODS OR SERVICES; LOSS OF USE, DATA, OR PROFITS; OR BUSINESS
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// INTERRUPTION) HOWEVER CAUSED AND ON ANY THEORY OF LIABILITY, WHETHER IN
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// CONTRACT, STRICT LIABILITY, OR TORT (INCLUDING NEGLIGENCE OR OTHERWISE)
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// ARISING IN ANY WAY OUT OF THE USE OF THIS SOFTWARE, EVEN IF ADVISED OF THE
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// POSSIBILITY OF SUCH DAMAGE.
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//
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// Author: Sameer Agarwal (sameeragarwal@google.com)
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// David Gallup (dgallup@google.com)
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// This include must come before any #ifndef check on Ceres compile options.
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#include "ceres/internal/port.h"
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#ifndef CERES_NO_SUITESPARSE
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#include "ceres/canonical_views_clustering.h"
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#include "ceres/collections_port.h"
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#include "ceres/graph.h"
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#include "gtest/gtest.h"
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namespace ceres {
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namespace internal {
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const int kVertexIds[] = {0, 1, 2, 3};
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class CanonicalViewsTest : public ::testing::Test {
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protected:
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virtual void SetUp() {
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// The graph structure is as follows.
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//
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// Vertex weights: 0 2 2 0
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// V0-----V1-----V2-----V3
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// Edge weights: 0.8 0.9 0.3
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const double kVertexWeights[] = {0.0, 2.0, 2.0, -1.0};
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for (int i = 0; i < 4; ++i) {
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graph_.AddVertex(i, kVertexWeights[i]);
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}
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// Create self edges.
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// CanonicalViews requires that every view "sees" itself.
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for (int i = 0; i < 4; ++i) {
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graph_.AddEdge(i, i, 1.0);
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}
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// Create three edges.
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const double kEdgeWeights[] = {0.8, 0.9, 0.3};
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for (int i = 0; i < 3; ++i) {
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// The graph interface is directed, so remember to create both
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// edges.
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graph_.AddEdge(kVertexIds[i], kVertexIds[i + 1], kEdgeWeights[i]);
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}
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}
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void ComputeClustering() {
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ComputeCanonicalViewsClustering(options_, graph_, ¢ers_, &membership_);
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}
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WeightedGraph<int> graph_;
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CanonicalViewsClusteringOptions options_;
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std::vector<int> centers_;
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HashMap<int, int> membership_;
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};
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TEST_F(CanonicalViewsTest, ComputeCanonicalViewsTest) {
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options_.min_views = 0;
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options_.size_penalty_weight = 0.5;
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options_.similarity_penalty_weight = 0.0;
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options_.view_score_weight = 0.0;
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ComputeClustering();
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// 2 canonical views.
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EXPECT_EQ(centers_.size(), 2);
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EXPECT_EQ(centers_[0], kVertexIds[1]);
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EXPECT_EQ(centers_[1], kVertexIds[3]);
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// Check cluster membership.
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EXPECT_EQ(FindOrDie(membership_, kVertexIds[0]), 0);
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EXPECT_EQ(FindOrDie(membership_, kVertexIds[1]), 0);
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EXPECT_EQ(FindOrDie(membership_, kVertexIds[2]), 0);
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EXPECT_EQ(FindOrDie(membership_, kVertexIds[3]), 1);
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}
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// Increases size penalty so the second canonical view won't be
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// chosen.
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TEST_F(CanonicalViewsTest, SizePenaltyTest) {
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options_.min_views = 0;
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options_.size_penalty_weight = 2.0;
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options_.similarity_penalty_weight = 0.0;
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options_.view_score_weight = 0.0;
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ComputeClustering();
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// 1 canonical view.
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EXPECT_EQ(centers_.size(), 1);
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EXPECT_EQ(centers_[0], kVertexIds[1]);
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}
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// Increases view score weight so vertex 2 will be chosen.
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TEST_F(CanonicalViewsTest, ViewScoreTest) {
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options_.min_views = 0;
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options_.size_penalty_weight = 0.5;
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options_.similarity_penalty_weight = 0.0;
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options_.view_score_weight = 1.0;
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ComputeClustering();
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// 2 canonical views.
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EXPECT_EQ(centers_.size(), 2);
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EXPECT_EQ(centers_[0], kVertexIds[1]);
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EXPECT_EQ(centers_[1], kVertexIds[2]);
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}
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// Increases similarity penalty so vertex 2 won't be chosen despite
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// it's view score.
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TEST_F(CanonicalViewsTest, SimilarityPenaltyTest) {
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options_.min_views = 0;
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options_.size_penalty_weight = 0.5;
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options_.similarity_penalty_weight = 3.0;
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options_.view_score_weight = 1.0;
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ComputeClustering();
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// 2 canonical views.
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EXPECT_EQ(centers_.size(), 1);
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EXPECT_EQ(centers_[0], kVertexIds[1]);
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}
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} // namespace internal
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} // namespace ceres
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#endif // CERES_NO_SUITESPARSE
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