// Ceres Solver - A fast non-linear least squares minimizer
// Copyright 2013 Google Inc. All rights reserved.
// http://code.google.com/p/ceres-solver/
//
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// Author: sameeragarwal@google.com (Sameer Agarwal)

#include <limits>
#include <vector>

#include "ceres/block_random_access_diagonal_matrix.h"
#include "ceres/internal/eigen.h"
#include "glog/logging.h"
#include "gtest/gtest.h"
#include "Eigen/Cholesky"

namespace ceres {
namespace internal {

class BlockRandomAccessDiagonalMatrixTest : public ::testing::Test {
 public:
  void SetUp() {
    vector<int> blocks;
    blocks.push_back(3);
    blocks.push_back(4);
    blocks.push_back(5);
    const int num_rows = 3 + 4 + 5;
    num_nonzeros_ =  3 * 3 + 4 * 4 + 5 * 5;

    m_.reset(new BlockRandomAccessDiagonalMatrix(blocks));

    EXPECT_EQ(m_->num_rows(), num_rows);
    EXPECT_EQ(m_->num_cols(), num_rows);

    for (int i = 0; i < blocks.size(); ++i) {
      const int row_block_id = i;
      int col_block_id;
      int row;
      int col;
      int row_stride;
      int col_stride;

      for (int j = 0; j < blocks.size(); ++j) {
        col_block_id = j;
        CellInfo* cell =  m_->GetCell(row_block_id, col_block_id,
                                    &row, &col,
                                    &row_stride, &col_stride);
        // Off diagonal entries are not present.
        if (i != j) {
          EXPECT_TRUE(cell == NULL);
          continue;
        }

        EXPECT_TRUE(cell != NULL);
        EXPECT_EQ(row, 0);
        EXPECT_EQ(col, 0);
        EXPECT_EQ(row_stride, blocks[row_block_id]);
        EXPECT_EQ(col_stride, blocks[col_block_id]);

        // Write into the block
        MatrixRef(cell->values, row_stride, col_stride).block(
            row, col, blocks[row_block_id], blocks[col_block_id]) =
            (row_block_id + 1) * (col_block_id +1) *
            Matrix::Ones(blocks[row_block_id], blocks[col_block_id])
            + Matrix::Identity(blocks[row_block_id], blocks[row_block_id]);
      }
    }
  }

 protected:
  int num_nonzeros_;
  scoped_ptr<BlockRandomAccessDiagonalMatrix> m_;
};

TEST_F(BlockRandomAccessDiagonalMatrixTest, MatrixContents) {
  const TripletSparseMatrix* tsm = m_->matrix();
  EXPECT_EQ(tsm->num_nonzeros(), num_nonzeros_);
  EXPECT_EQ(tsm->max_num_nonzeros(), num_nonzeros_);

  Matrix dense;
  tsm->ToDenseMatrix(&dense);

  double kTolerance = 1e-14;

  // (0,0)
  EXPECT_NEAR((dense.block(0, 0, 3, 3) -
               (Matrix::Ones(3, 3) + Matrix::Identity(3, 3))).norm(),
              0.0,
              kTolerance);

  // (1,1)
  EXPECT_NEAR((dense.block(3, 3, 4, 4) -
               (2 * 2 * Matrix::Ones(4, 4) + Matrix::Identity(4, 4))).norm(),
              0.0,
              kTolerance);

  // (1,1)
  EXPECT_NEAR((dense.block(7, 7, 5, 5) -
               (3 * 3 * Matrix::Ones(5, 5) + Matrix::Identity(5, 5))).norm(),
              0.0,
              kTolerance);

  // There is nothing else in the matrix besides these four blocks.
  EXPECT_NEAR(dense.norm(),
              sqrt(6 * 1.0 + 3 * 4.0 +
                   12 * 16.0 + 4 * 25.0 +
                   20 * 81.0 + 5 * 100.0), kTolerance);
}

TEST_F(BlockRandomAccessDiagonalMatrixTest, RightMultiply) {
  double kTolerance = 1e-14;
  const TripletSparseMatrix* tsm = m_->matrix();
  Matrix dense;
  tsm->ToDenseMatrix(&dense);
  Vector x = Vector::Random(dense.rows());
  Vector expected_y = dense * x;
  Vector actual_y = Vector::Zero(dense.rows());
  m_->RightMultiply(x.data(),  actual_y.data());
  EXPECT_NEAR((expected_y - actual_y).norm(), 0, kTolerance);
}

TEST_F(BlockRandomAccessDiagonalMatrixTest, Invert) {
  double kTolerance = 1e-14;
  const TripletSparseMatrix* tsm = m_->matrix();
  Matrix dense;
  tsm->ToDenseMatrix(&dense);
  Matrix expected_inverse =
      dense.llt().solve(Matrix::Identity(dense.rows(), dense.rows()));

  m_->Invert();
  tsm->ToDenseMatrix(&dense);

  EXPECT_NEAR((expected_inverse - dense).norm(), 0.0, kTolerance);
}

}  // namespace internal
}  // namespace ceres