// Ceres Solver - A fast non-linear least squares minimizer

// Copyright 2015 Google Inc. All rights reserved.

// http://ceres-solver.org/

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// Author: Sameer Agarwal (sameeragarwal@google.com)

//         David Gallup (dgallup@google.com)

#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_, &centers_, &membership_);

  }

  WeightedGraph<int> graph_;

  CanonicalViewsClusteringOptions options_;

  std::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