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

#include "ceres/block_sparse_matrix.h"

#include <string>

#include "ceres/casts.h"

#include "ceres/internal/eigen.h"

#include "ceres/internal/scoped_ptr.h"

#include "ceres/linear_least_squares_problems.h"

#include "ceres/triplet_sparse_matrix.h"

#include "glog/logging.h"

#include "gtest/gtest.h"

namespace ceres {

namespace internal {

class BlockSparseMatrixTest : public ::testing::Test {

 protected :

  virtual void SetUp() {

    scoped_ptr<LinearLeastSquaresProblem> problem(

        CreateLinearLeastSquaresProblemFromId(2));

    CHECK_NOTNULL(problem.get());

    A_.reset(down_cast<BlockSparseMatrix*>(problem->A.release()));

    problem.reset(CreateLinearLeastSquaresProblemFromId(1));

    CHECK_NOTNULL(problem.get());

    B_.reset(down_cast<TripletSparseMatrix*>(problem->A.release()));

    CHECK_EQ(A_->num_rows(), B_->num_rows());

    CHECK_EQ(A_->num_cols(), B_->num_cols());

    CHECK_EQ(A_->num_nonzeros(), B_->num_nonzeros());

  }

  scoped_ptr<BlockSparseMatrix> A_;

  scoped_ptr<TripletSparseMatrix> B_;

};

TEST_F(BlockSparseMatrixTest, SetZeroTest) {

  A_->SetZero();

  EXPECT_EQ(13, A_->num_nonzeros());

}

TEST_F(BlockSparseMatrixTest, RightMultiplyTest) {

  Vector y_a = Vector::Zero(A_->num_rows());

  Vector y_b = Vector::Zero(A_->num_rows());

  for (int i = 0; i < A_->num_cols(); ++i) {

    Vector x = Vector::Zero(A_->num_cols());

    x[i] = 1.0;

    A_->RightMultiply(x.data(), y_a.data());

    B_->RightMultiply(x.data(), y_b.data());

    EXPECT_LT((y_a - y_b).norm(), 1e-12);

  }

}

TEST_F(BlockSparseMatrixTest, LeftMultiplyTest) {

  Vector y_a = Vector::Zero(A_->num_cols());

  Vector y_b = Vector::Zero(A_->num_cols());

  for (int i = 0; i < A_->num_rows(); ++i) {

    Vector x = Vector::Zero(A_->num_rows());

    x[i] = 1.0;

    A_->LeftMultiply(x.data(), y_a.data());

    B_->LeftMultiply(x.data(), y_b.data());

    EXPECT_LT((y_a - y_b).norm(), 1e-12);

  }

}

TEST_F(BlockSparseMatrixTest, SquaredColumnNormTest) {

  Vector y_a = Vector::Zero(A_->num_cols());

  Vector y_b = Vector::Zero(A_->num_cols());

  A_->SquaredColumnNorm(y_a.data());

  B_->SquaredColumnNorm(y_b.data());

  EXPECT_LT((y_a - y_b).norm(), 1e-12);

}

TEST_F(BlockSparseMatrixTest, ToDenseMatrixTest) {

  Matrix m_a;

  Matrix m_b;

  A_->ToDenseMatrix(&m_a);

  B_->ToDenseMatrix(&m_b);

  EXPECT_LT((m_a - m_b).norm(), 1e-12);

}

TEST_F(BlockSparseMatrixTest, AppendRows) {

  scoped_ptr<LinearLeastSquaresProblem> problem(

      CreateLinearLeastSquaresProblemFromId(2));

  scoped_ptr<BlockSparseMatrix> m(

      down_cast<BlockSparseMatrix*>(problem->A.release()));

  A_->AppendRows(*m);

  EXPECT_EQ(A_->num_rows(), 2 * m->num_rows());

  EXPECT_EQ(A_->num_cols(), m->num_cols());

  problem.reset(CreateLinearLeastSquaresProblemFromId(1));

  scoped_ptr<TripletSparseMatrix> m2(

      down_cast<TripletSparseMatrix*>(problem->A.release()));

  B_->AppendRows(*m2);

  Vector y_a = Vector::Zero(A_->num_rows());

  Vector y_b = Vector::Zero(A_->num_rows());

  for (int i = 0; i < A_->num_cols(); ++i) {

    Vector x = Vector::Zero(A_->num_cols());

    x[i] = 1.0;

    y_a.setZero();

    y_b.setZero();

    A_->RightMultiply(x.data(), y_a.data());

    B_->RightMultiply(x.data(), y_b.data());

    EXPECT_LT((y_a - y_b).norm(), 1e-12);

  }

}

TEST_F(BlockSparseMatrixTest, AppendAndDeleteBlockDiagonalMatrix) {

  const std::vector<Block>& column_blocks = A_->block_structure()->cols;

  const int num_cols =

      column_blocks.back().size + column_blocks.back().position;

  Vector diagonal(num_cols);

  for (int i = 0; i < num_cols; ++i) {

    diagonal(i) = 2 * i * i + 1;

  }

  scoped_ptr<BlockSparseMatrix> appendage(

      BlockSparseMatrix::CreateDiagonalMatrix(diagonal.data(), column_blocks));

  A_->AppendRows(*appendage);

  Vector y_a, y_b;

  y_a.resize(A_->num_rows());

  y_b.resize(A_->num_rows());

  for (int i = 0; i < A_->num_cols(); ++i) {

    Vector x = Vector::Zero(A_->num_cols());

    x[i] = 1.0;

    y_a.setZero();

    y_b.setZero();

    A_->RightMultiply(x.data(), y_a.data());

    B_->RightMultiply(x.data(), y_b.data());

    EXPECT_LT((y_a.head(B_->num_rows()) - y_b.head(B_->num_rows())).norm(), 1e-12);

    Vector expected_tail = Vector::Zero(A_->num_cols());

    expected_tail(i) = diagonal(i);

    EXPECT_LT((y_a.tail(A_->num_cols()) - expected_tail).norm(), 1e-12);

  }

  A_->DeleteRowBlocks(column_blocks.size());

  EXPECT_EQ(A_->num_rows(), B_->num_rows());

  EXPECT_EQ(A_->num_cols(), B_->num_cols());

  y_a.resize(A_->num_rows());

  y_b.resize(A_->num_rows());

  for (int i = 0; i < A_->num_cols(); ++i) {

    Vector x = Vector::Zero(A_->num_cols());

    x[i] = 1.0;

    y_a.setZero();

    y_b.setZero();

    A_->RightMultiply(x.data(), y_a.data());

    B_->RightMultiply(x.data(), y_b.data());

    EXPECT_LT((y_a - y_b).norm(), 1e-12);

  }

}

TEST(BlockSparseMatrix, CreateDiagonalMatrix) {

  std::vector<Block> column_blocks;

  column_blocks.push_back(Block(2, 0));

  column_blocks.push_back(Block(1, 2));

  column_blocks.push_back(Block(3, 3));

  const int num_cols =

      column_blocks.back().size + column_blocks.back().position;

  Vector diagonal(num_cols);

  for (int i = 0; i < num_cols; ++i) {

    diagonal(i) = 2 * i * i + 1;

  }

  scoped_ptr<BlockSparseMatrix> m(

      BlockSparseMatrix::CreateDiagonalMatrix(diagonal.data(), column_blocks));

  const CompressedRowBlockStructure* bs = m->block_structure();

  EXPECT_EQ(bs->cols.size(), column_blocks.size());

  for (int i = 0; i < column_blocks.size(); ++i) {

    EXPECT_EQ(bs->cols[i].size, column_blocks[i].size);

    EXPECT_EQ(bs->cols[i].position, column_blocks[i].position);

  }

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

  Vector x = Vector::Ones(num_cols);

  Vector y = Vector::Zero(num_cols);

  m->RightMultiply(x.data(), y.data());

  for (int i = 0; i < num_cols; ++i) {

    EXPECT_NEAR(y[i], diagonal[i], std::numeric_limits<double>::epsilon());

  }

}

}  // namespace internal

}  // namespace ceres