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// Ceres Solver - A fast non-linear least squares minimizer
// Copyright 2010, 2011, 2012 Google Inc. All rights reserved.
// http://code.google.com/p/ceres-solver/
//
// 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)
// 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 "gtest/gtest.h"
namespace ceres {
namespace internal {
const int kVertexIds[] = {0, 1, 2, 3};
class CanonicalViewsTest : public ::testing::Test {
protected:
virtual void SetUp() {
// 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_);
}
Graph<int> graph_;
CanonicalViewsClusteringOptions options_;
vector<int> centers_;
HashMap<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
#endif // CERES_NO_SUITESPARSE
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