// 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/compressed_row_sparse_matrix.h"

#include <numeric>

#include "ceres/casts.h"

#include "ceres/crs_matrix.h"

#include "ceres/internal/eigen.h"

#include "ceres/internal/scoped_ptr.h"

#include "ceres/linear_least_squares_problems.h"

#include "ceres/random.h"

#include "ceres/triplet_sparse_matrix.h"

#include "glog/logging.h"

#include "gtest/gtest.h"

#include "Eigen/SparseCore"

namespace ceres {

namespace internal {

using std::vector;

void CompareMatrices(const SparseMatrix* a, const SparseMatrix* b) {

  EXPECT_EQ(a->num_rows(), b->num_rows());

  EXPECT_EQ(a->num_cols(), b->num_cols());

  int num_rows = a->num_rows();

  int num_cols = a->num_cols();

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

    Vector x = Vector::Zero(num_cols);

    x(i) = 1.0;

    Vector y_a = Vector::Zero(num_rows);

    Vector y_b = Vector::Zero(num_rows);

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

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

    EXPECT_EQ((y_a - y_b).norm(), 0);

  }

}

class CompressedRowSparseMatrixTest : public ::testing::Test {

 protected:

  virtual void SetUp() {

    scoped_ptr<LinearLeastSquaresProblem> problem(

        CreateLinearLeastSquaresProblemFromId(1));

    CHECK_NOTNULL(problem.get());

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

    crsm.reset(CompressedRowSparseMatrix::FromTripletSparseMatrix(*tsm));

    num_rows = tsm->num_rows();

    num_cols = tsm->num_cols();

    vector<int>* row_blocks = crsm->mutable_row_blocks();

    row_blocks->resize(num_rows);

    std::fill(row_blocks->begin(), row_blocks->end(), 1);

    vector<int>* col_blocks = crsm->mutable_col_blocks();

    col_blocks->resize(num_cols);

    std::fill(col_blocks->begin(), col_blocks->end(), 1);

  }

  int num_rows;

  int num_cols;

  scoped_ptr<TripletSparseMatrix> tsm;

  scoped_ptr<CompressedRowSparseMatrix> crsm;

};

TEST_F(CompressedRowSparseMatrixTest, RightMultiply) {

  CompareMatrices(tsm.get(), crsm.get());

}

TEST_F(CompressedRowSparseMatrixTest, LeftMultiply) {

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

    Vector a = Vector::Zero(num_rows);

    a(i) = 1.0;

    Vector b1 = Vector::Zero(num_cols);

    Vector b2 = Vector::Zero(num_cols);

    tsm->LeftMultiply(a.data(), b1.data());

    crsm->LeftMultiply(a.data(), b2.data());

    EXPECT_EQ((b1 - b2).norm(), 0);

  }

}

TEST_F(CompressedRowSparseMatrixTest, ColumnNorm) {

  Vector b1 = Vector::Zero(num_cols);

  Vector b2 = Vector::Zero(num_cols);

  tsm->SquaredColumnNorm(b1.data());

  crsm->SquaredColumnNorm(b2.data());

  EXPECT_EQ((b1 - b2).norm(), 0);

}

TEST_F(CompressedRowSparseMatrixTest, Scale) {

  Vector scale(num_cols);

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

    scale(i) = i + 1;

  }

  tsm->ScaleColumns(scale.data());

  crsm->ScaleColumns(scale.data());

  CompareMatrices(tsm.get(), crsm.get());

}

TEST_F(CompressedRowSparseMatrixTest, DeleteRows) {

  // Clear the row and column blocks as these are purely scalar tests.

  crsm->mutable_row_blocks()->clear();

  crsm->mutable_col_blocks()->clear();

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

    tsm->Resize(num_rows - i, num_cols);

    crsm->DeleteRows(crsm->num_rows() - tsm->num_rows());

    CompareMatrices(tsm.get(), crsm.get());

  }

}

TEST_F(CompressedRowSparseMatrixTest, AppendRows) {

  // Clear the row and column blocks as these are purely scalar tests.

  crsm->mutable_row_blocks()->clear();

  crsm->mutable_col_blocks()->clear();

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

    TripletSparseMatrix tsm_appendage(*tsm);

    tsm_appendage.Resize(i, num_cols);

    tsm->AppendRows(tsm_appendage);

    scoped_ptr<CompressedRowSparseMatrix> crsm_appendage(

        CompressedRowSparseMatrix::FromTripletSparseMatrix(tsm_appendage));

    crsm->AppendRows(*crsm_appendage);

    CompareMatrices(tsm.get(), crsm.get());

  }

}

TEST_F(CompressedRowSparseMatrixTest, AppendAndDeleteBlockDiagonalMatrix) {

  int num_diagonal_rows = crsm->num_cols();

  scoped_array<double> diagonal(new double[num_diagonal_rows]);

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

    diagonal[i] = i;

  }

  vector<int> row_and_column_blocks;

  row_and_column_blocks.push_back(1);

  row_and_column_blocks.push_back(2);

  row_and_column_blocks.push_back(2);

  const vector<int> pre_row_blocks = crsm->row_blocks();

  const vector<int> pre_col_blocks = crsm->col_blocks();

  scoped_ptr<CompressedRowSparseMatrix> appendage(

      CompressedRowSparseMatrix::CreateBlockDiagonalMatrix(

          diagonal.get(), row_and_column_blocks));

  LOG(INFO) << appendage->row_blocks().size();

  crsm->AppendRows(*appendage);

  const vector<int> post_row_blocks = crsm->row_blocks();

  const vector<int> post_col_blocks = crsm->col_blocks();

  vector<int> expected_row_blocks = pre_row_blocks;

  expected_row_blocks.insert(expected_row_blocks.end(),

                             row_and_column_blocks.begin(),

                             row_and_column_blocks.end());

  vector<int> expected_col_blocks = pre_col_blocks;

  EXPECT_EQ(expected_row_blocks, crsm->row_blocks());

  EXPECT_EQ(expected_col_blocks, crsm->col_blocks());

  crsm->DeleteRows(num_diagonal_rows);

  EXPECT_EQ(crsm->row_blocks(), pre_row_blocks);

  EXPECT_EQ(crsm->col_blocks(), pre_col_blocks);

}

TEST_F(CompressedRowSparseMatrixTest, ToDenseMatrix) {

  Matrix tsm_dense;

  Matrix crsm_dense;

  tsm->ToDenseMatrix(&tsm_dense);

  crsm->ToDenseMatrix(&crsm_dense);

  EXPECT_EQ((tsm_dense - crsm_dense).norm(), 0.0);

}

TEST_F(CompressedRowSparseMatrixTest, ToCRSMatrix) {

  CRSMatrix crs_matrix;

  crsm->ToCRSMatrix(&crs_matrix);

  EXPECT_EQ(crsm->num_rows(), crs_matrix.num_rows);

  EXPECT_EQ(crsm->num_cols(), crs_matrix.num_cols);

  EXPECT_EQ(crsm->num_rows() + 1, crs_matrix.rows.size());

  EXPECT_EQ(crsm->num_nonzeros(), crs_matrix.cols.size());

  EXPECT_EQ(crsm->num_nonzeros(), crs_matrix.values.size());

  for (int i = 0; i < crsm->num_rows() + 1; ++i) {

    EXPECT_EQ(crsm->rows()[i], crs_matrix.rows[i]);

  }

  for (int i = 0; i < crsm->num_nonzeros(); ++i) {

    EXPECT_EQ(crsm->cols()[i], crs_matrix.cols[i]);

    EXPECT_EQ(crsm->values()[i], crs_matrix.values[i]);

  }

}

TEST(CompressedRowSparseMatrix, CreateBlockDiagonalMatrix) {

  vector<int> blocks;

  blocks.push_back(1);

  blocks.push_back(2);

  blocks.push_back(2);

  Vector diagonal(5);

  for (int i = 0; i < 5; ++i) {

    diagonal(i) = i + 1;

  }

  scoped_ptr<CompressedRowSparseMatrix> matrix(

      CompressedRowSparseMatrix::CreateBlockDiagonalMatrix(diagonal.data(),

                                                           blocks));

  EXPECT_EQ(matrix->num_rows(), 5);

  EXPECT_EQ(matrix->num_cols(), 5);

  EXPECT_EQ(matrix->num_nonzeros(), 9);

  EXPECT_EQ(blocks, matrix->row_blocks());

  EXPECT_EQ(blocks, matrix->col_blocks());

  Vector x(5);

  Vector y(5);

  x.setOnes();

  y.setZero();

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

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

    EXPECT_EQ(y[i], diagonal[i]);

  }

  y.setZero();

  matrix->LeftMultiply(x.data(), y.data());

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

    EXPECT_EQ(y[i], diagonal[i]);

  }

  Matrix dense;

  matrix->ToDenseMatrix(&dense);

  EXPECT_EQ((dense.diagonal() - diagonal).norm(), 0.0);

}

TEST(CompressedRowSparseMatrix, Transpose) {

  //  0  1  0  2  3  0

  //  4  6  7  0  0  8

  //  9 10  0 11 12  0

  // 13  0 14 15  9  0

  //  0 16 17  0  0  0

  // Block structure:

  //  A  A  A  A  B  B

  //  A  A  A  A  B  B

  //  A  A  A  A  B  B

  //  C  C  C  C  D  D

  //  C  C  C  C  D  D

  //  C  C  C  C  D  D

  CompressedRowSparseMatrix matrix(5, 6, 30);

  int* rows = matrix.mutable_rows();

  int* cols = matrix.mutable_cols();

  double* values = matrix.mutable_values();

  matrix.mutable_row_blocks()->push_back(3);

  matrix.mutable_row_blocks()->push_back(3);

  matrix.mutable_col_blocks()->push_back(4);

  matrix.mutable_col_blocks()->push_back(2);

  rows[0] = 0;

  cols[0] = 1;

  cols[1] = 3;

  cols[2] = 4;

  rows[1] = 3;

  cols[3] = 0;

  cols[4] = 1;

  cols[5] = 2;

  cols[6] = 5;

  rows[2] = 7;

  cols[7] = 0;

  cols[8] = 1;

  cols[9] = 3;

  cols[10] = 4;

  rows[3] = 11;

  cols[11] = 0;

  cols[12] = 2;

  cols[13] = 3;

  cols[14] = 4;

  rows[4] = 15;

  cols[15] = 1;

  cols[16] = 2;

  rows[5] = 17;

  std::copy(values, values + 17, cols);

  scoped_ptr<CompressedRowSparseMatrix> transpose(matrix.Transpose());

  ASSERT_EQ(transpose->row_blocks().size(), matrix.col_blocks().size());

  for (int i = 0; i < transpose->row_blocks().size(); ++i) {

    EXPECT_EQ(transpose->row_blocks()[i], matrix.col_blocks()[i]);

  }

  ASSERT_EQ(transpose->col_blocks().size(), matrix.row_blocks().size());

  for (int i = 0; i < transpose->col_blocks().size(); ++i) {

    EXPECT_EQ(transpose->col_blocks()[i], matrix.row_blocks()[i]);

  }

  Matrix dense_matrix;

  matrix.ToDenseMatrix(&dense_matrix);

  Matrix dense_transpose;

  transpose->ToDenseMatrix(&dense_transpose);

  EXPECT_NEAR((dense_matrix - dense_transpose.transpose()).norm(), 0.0, 1e-14);

}

TEST(CompressedRowSparseMatrix, FromTripletSparseMatrix) {

  TripletSparseMatrix::RandomMatrixOptions options;

  options.num_rows = 5;

  options.num_cols = 7;

  options.density = 0.5;

  const int kNumTrials = 10;

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

    scoped_ptr<TripletSparseMatrix> tsm(

        TripletSparseMatrix::CreateRandomMatrix(options));

    scoped_ptr<CompressedRowSparseMatrix> crsm(

        CompressedRowSparseMatrix::FromTripletSparseMatrix(*tsm));

    Matrix expected;

    tsm->ToDenseMatrix(&expected);

    Matrix actual;

    crsm->ToDenseMatrix(&actual);

    EXPECT_NEAR((expected - actual).norm() / actual.norm(),

                0.0,

                std::numeric_limits<double>::epsilon())

        << "\nexpected: \n"

        << expected << "\nactual: \n"

        << actual;

  }

}

TEST(CompressedRowSparseMatrix, FromTripletSparseMatrixTransposed) {

  TripletSparseMatrix::RandomMatrixOptions options;

  options.num_rows = 5;

  options.num_cols = 7;

  options.density = 0.5;

  const int kNumTrials = 10;

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

    scoped_ptr<TripletSparseMatrix> tsm(

        TripletSparseMatrix::CreateRandomMatrix(options));

    scoped_ptr<CompressedRowSparseMatrix> crsm(

        CompressedRowSparseMatrix::FromTripletSparseMatrixTransposed(*tsm));

    Matrix tmp;

    tsm->ToDenseMatrix(&tmp);

    Matrix expected = tmp.transpose();

    Matrix actual;

    crsm->ToDenseMatrix(&actual);

    EXPECT_NEAR((expected - actual).norm() / actual.norm(),

                0.0,

                std::numeric_limits<double>::epsilon())

        << "\nexpected: \n"

        << expected << "\nactual: \n"

        << actual;

  }

}

// TODO(sameeragarwal) Add tests for the random matrix creation methods.

}  // namespace internal

}  // namespace ceres