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

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

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// Author: keir@google.com (Keir Mierle)

#include "ceres/small_blas.h"

#include <limits>

#include "gtest/gtest.h"

#include "ceres/internal/eigen.h"

namespace ceres {

namespace internal {

const double kTolerance = 3.0 * std::numeric_limits<double>::epsilon();

TEST(BLAS, MatrixMatrixMultiply) {

  const int kRowA = 3;

  const int kColA = 5;

  Matrix A(kRowA, kColA);

  A.setOnes();

  const int kRowB = 5;

  const int kColB = 7;

  Matrix B(kRowB, kColB);

  B.setOnes();

  for (int row_stride_c = kRowA; row_stride_c < 3 * kRowA; ++row_stride_c) {

    for (int col_stride_c = kColB; col_stride_c < 3 * kColB; ++col_stride_c) {

      Matrix C(row_stride_c, col_stride_c);

      C.setOnes();

      Matrix C_plus = C;

      Matrix C_minus = C;

      Matrix C_assign = C;

      Matrix C_plus_ref = C;

      Matrix C_minus_ref = C;

      Matrix C_assign_ref = C;

      for (int start_row_c = 0; start_row_c + kRowA < row_stride_c; ++start_row_c) {

        for (int start_col_c = 0; start_col_c + kColB < col_stride_c; ++start_col_c) {

          C_plus_ref.block(start_row_c, start_col_c, kRowA, kColB) +=

              A * B;

          MatrixMatrixMultiply<kRowA, kColA, kRowB, kColB, 1>(

              A.data(), kRowA, kColA,

              B.data(), kRowB, kColB,

              C_plus.data(), start_row_c, start_col_c, row_stride_c, col_stride_c);

          EXPECT_NEAR((C_plus_ref - C_plus).norm(), 0.0, kTolerance)

              << "C += A * B \n"

              << "row_stride_c : " << row_stride_c << "\n"

              << "col_stride_c : " << col_stride_c << "\n"

              << "start_row_c  : " << start_row_c << "\n"

              << "start_col_c  : " << start_col_c << "\n"

              << "Cref : \n" << C_plus_ref << "\n"

              << "C: \n" << C_plus;

          C_minus_ref.block(start_row_c, start_col_c, kRowA, kColB) -=

              A * B;

          MatrixMatrixMultiply<kRowA, kColA, kRowB, kColB, -1>(

              A.data(), kRowA, kColA,

              B.data(), kRowB, kColB,

              C_minus.data(), start_row_c, start_col_c, row_stride_c, col_stride_c);

           EXPECT_NEAR((C_minus_ref - C_minus).norm(), 0.0, kTolerance)

              << "C -= A * B \n"

              << "row_stride_c : " << row_stride_c << "\n"

              << "col_stride_c : " << col_stride_c << "\n"

              << "start_row_c  : " << start_row_c << "\n"

              << "start_col_c  : " << start_col_c << "\n"

              << "Cref : \n" << C_minus_ref << "\n"

              << "C: \n" << C_minus;

          C_assign_ref.block(start_row_c, start_col_c, kRowA, kColB) =

              A * B;

          MatrixMatrixMultiply<kRowA, kColA, kRowB, kColB, 0>(

              A.data(), kRowA, kColA,

              B.data(), kRowB, kColB,

              C_assign.data(), start_row_c, start_col_c, row_stride_c, col_stride_c);

          EXPECT_NEAR((C_assign_ref - C_assign).norm(), 0.0, kTolerance)

              << "C = A * B \n"

              << "row_stride_c : " << row_stride_c << "\n"

              << "col_stride_c : " << col_stride_c << "\n"

              << "start_row_c  : " << start_row_c << "\n"

              << "start_col_c  : " << start_col_c << "\n"

              << "Cref : \n" << C_assign_ref << "\n"

              << "C: \n" << C_assign;

        }

      }

    }

  }

}

TEST(BLAS, MatrixTransposeMatrixMultiply) {

  const int kRowA = 5;

  const int kColA = 3;

  Matrix A(kRowA, kColA);

  A.setOnes();

  const int kRowB = 5;

  const int kColB = 7;

  Matrix B(kRowB, kColB);

  B.setOnes();

  for (int row_stride_c = kColA; row_stride_c < 3 * kColA; ++row_stride_c) {

    for (int col_stride_c = kColB; col_stride_c <  3 * kColB; ++col_stride_c) {

      Matrix C(row_stride_c, col_stride_c);

      C.setOnes();

      Matrix C_plus = C;

      Matrix C_minus = C;

      Matrix C_assign = C;

      Matrix C_plus_ref = C;

      Matrix C_minus_ref = C;

      Matrix C_assign_ref = C;

      for (int start_row_c = 0; start_row_c + kColA < row_stride_c; ++start_row_c) {

        for (int start_col_c = 0; start_col_c + kColB < col_stride_c; ++start_col_c) {

          C_plus_ref.block(start_row_c, start_col_c, kColA, kColB) +=

              A.transpose() * B;

          MatrixTransposeMatrixMultiply<kRowA, kColA, kRowB, kColB, 1>(

              A.data(), kRowA, kColA,

              B.data(), kRowB, kColB,

              C_plus.data(), start_row_c, start_col_c, row_stride_c, col_stride_c);

          EXPECT_NEAR((C_plus_ref - C_plus).norm(), 0.0, kTolerance)

              << "C += A' * B \n"

              << "row_stride_c : " << row_stride_c << "\n"

              << "col_stride_c : " << col_stride_c << "\n"

              << "start_row_c  : " << start_row_c << "\n"

              << "start_col_c  : " << start_col_c << "\n"

              << "Cref : \n" << C_plus_ref << "\n"

              << "C: \n" << C_plus;

          C_minus_ref.block(start_row_c, start_col_c, kColA, kColB) -=

              A.transpose() * B;

          MatrixTransposeMatrixMultiply<kRowA, kColA, kRowB, kColB, -1>(

              A.data(), kRowA, kColA,

              B.data(), kRowB, kColB,

              C_minus.data(), start_row_c, start_col_c, row_stride_c, col_stride_c);

          EXPECT_NEAR((C_minus_ref - C_minus).norm(), 0.0, kTolerance)

              << "C -= A' * B \n"

              << "row_stride_c : " << row_stride_c << "\n"

              << "col_stride_c : " << col_stride_c << "\n"

              << "start_row_c  : " << start_row_c << "\n"

              << "start_col_c  : " << start_col_c << "\n"

              << "Cref : \n" << C_minus_ref << "\n"

              << "C: \n" << C_minus;

          C_assign_ref.block(start_row_c, start_col_c, kColA, kColB) =

              A.transpose() * B;

          MatrixTransposeMatrixMultiply<kRowA, kColA, kRowB, kColB, 0>(

              A.data(), kRowA, kColA,

              B.data(), kRowB, kColB,

              C_assign.data(), start_row_c, start_col_c, row_stride_c, col_stride_c);

          EXPECT_NEAR((C_assign_ref - C_assign).norm(), 0.0, kTolerance)

              << "C = A' * B \n"

              << "row_stride_c : " << row_stride_c << "\n"

              << "col_stride_c : " << col_stride_c << "\n"

              << "start_row_c  : " << start_row_c << "\n"

              << "start_col_c  : " << start_col_c << "\n"

              << "Cref : \n" << C_assign_ref << "\n"

              << "C: \n" << C_assign;

        }

      }

    }

  }

}

TEST(BLAS, MatrixVectorMultiply) {

  const int kRowA = 5;

  const int kColA = 3;

  Matrix A(kRowA, kColA);

  A.setOnes();

  Vector b(kColA);

  b.setOnes();

  Vector c(kRowA);

  c.setOnes();

  Vector c_plus = c;

  Vector c_minus = c;

  Vector c_assign = c;

  Vector c_plus_ref = c;

  Vector c_minus_ref = c;

  Vector c_assign_ref = c;

  c_plus_ref += A * b;

  MatrixVectorMultiply<kRowA, kColA, 1>(A.data(), kRowA, kColA,

                                        b.data(),

                                        c_plus.data());

  EXPECT_NEAR((c_plus_ref - c_plus).norm(), 0.0, kTolerance)

      << "c += A * b \n"

      << "c_ref : \n" << c_plus_ref << "\n"

      << "c: \n" << c_plus;

  c_minus_ref -= A * b;

  MatrixVectorMultiply<kRowA, kColA, -1>(A.data(), kRowA, kColA,

                                                 b.data(),

                                                 c_minus.data());

  EXPECT_NEAR((c_minus_ref - c_minus).norm(), 0.0, kTolerance)

      << "c += A * b \n"

      << "c_ref : \n" << c_minus_ref << "\n"

      << "c: \n" << c_minus;

  c_assign_ref = A * b;

  MatrixVectorMultiply<kRowA, kColA, 0>(A.data(), kRowA, kColA,

                                                  b.data(),

                                                  c_assign.data());

  EXPECT_NEAR((c_assign_ref - c_assign).norm(), 0.0, kTolerance)

      << "c += A * b \n"

      << "c_ref : \n" << c_assign_ref << "\n"

      << "c: \n" << c_assign;

}

TEST(BLAS, MatrixTransposeVectorMultiply) {

  const int kRowA = 5;

  const int kColA = 3;

  Matrix A(kRowA, kColA);

  A.setRandom();

  Vector b(kRowA);

  b.setRandom();

  Vector c(kColA);

  c.setOnes();

  Vector c_plus = c;

  Vector c_minus = c;

  Vector c_assign = c;

  Vector c_plus_ref = c;

  Vector c_minus_ref = c;

  Vector c_assign_ref = c;

  c_plus_ref += A.transpose() * b;

  MatrixTransposeVectorMultiply<kRowA, kColA, 1>(A.data(), kRowA, kColA,

                                                 b.data(),

                                                 c_plus.data());

  EXPECT_NEAR((c_plus_ref - c_plus).norm(), 0.0, kTolerance)

      << "c += A' * b \n"

      << "c_ref : \n" << c_plus_ref << "\n"

      << "c: \n" << c_plus;

  c_minus_ref -= A.transpose() * b;

  MatrixTransposeVectorMultiply<kRowA, kColA, -1>(A.data(), kRowA, kColA,

                                                  b.data(),

                                                  c_minus.data());

  EXPECT_NEAR((c_minus_ref - c_minus).norm(), 0.0, kTolerance)

      << "c += A' * b \n"

      << "c_ref : \n" << c_minus_ref << "\n"

      << "c: \n" << c_minus;

  c_assign_ref = A.transpose() * b;

  MatrixTransposeVectorMultiply<kRowA, kColA, 0>(A.data(), kRowA, kColA,

                                                  b.data(),

                                                  c_assign.data());

  EXPECT_NEAR((c_assign_ref - c_assign).norm(), 0.0, kTolerance)

      << "c += A' * b \n"

      << "c_ref : \n" << c_assign_ref << "\n"

      << "c: \n" << c_assign;

}

}  // namespace internal

}  // namespace ceres