// This file is part of Eigen, a lightweight C++ template library
// for linear algebra.
//
// Copyright (C) 2008 Benoit Jacob <jacob.benoit.1@gmail.com>
//
// This Source Code Form is subject to the terms of the Mozilla
// Public License v. 2.0. If a copy of the MPL was not distributed
// with this file, You can obtain one at http://mozilla.org/MPL/2.0/.

#include "main.h"

template <typename MatrixType>
void matrixVisitor(const MatrixType& p) {
  typedef typename MatrixType::Scalar Scalar;

  Index rows = p.rows();
  Index cols = p.cols();

  // construct a random matrix where all coefficients are different
  MatrixType m;
  m = MatrixType::Random(rows, cols);
  for (Index i = 0; i < m.size(); i++)
    for (Index i2 = 0; i2 < i; i2++)
      while (m(i) == m(i2))  // yes, ==
        m(i) = internal::random<Scalar>();

  Scalar minc = Scalar(1000), maxc = Scalar(-1000);
  Index minrow = 0, mincol = 0, maxrow = 0, maxcol = 0;
  for (Index j = 0; j < cols; j++)
    for (Index i = 0; i < rows; i++) {
      if (m(i, j) < minc) {
        minc = m(i, j);
        minrow = i;
        mincol = j;
      }
      if (m(i, j) > maxc) {
        maxc = m(i, j);
        maxrow = i;
        maxcol = j;
      }
    }
  Index eigen_minrow, eigen_mincol, eigen_maxrow, eigen_maxcol;
  Scalar eigen_minc, eigen_maxc;
  eigen_minc = m.minCoeff(&eigen_minrow, &eigen_mincol);
  eigen_maxc = m.maxCoeff(&eigen_maxrow, &eigen_maxcol);
  VERIFY(minrow == eigen_minrow);
  VERIFY(maxrow == eigen_maxrow);
  VERIFY(mincol == eigen_mincol);
  VERIFY(maxcol == eigen_maxcol);
  VERIFY_IS_APPROX(minc, eigen_minc);
  VERIFY_IS_APPROX(maxc, eigen_maxc);
  VERIFY_IS_APPROX(minc, m.minCoeff());
  VERIFY_IS_APPROX(maxc, m.maxCoeff());

  eigen_maxc = (m.adjoint() * m).maxCoeff(&eigen_maxrow, &eigen_maxcol);
  eigen_maxc = (m.adjoint() * m).eval().maxCoeff(&maxrow, &maxcol);
  VERIFY(maxrow == eigen_maxrow);
  VERIFY(maxcol == eigen_maxcol);
}

template <typename VectorType>
void vectorVisitor(const VectorType& w) {
  typedef typename VectorType::Scalar Scalar;

  Index size = w.size();

  // construct a random vector where all coefficients are different
  VectorType v;
  v = VectorType::Random(size);
  for (Index i = 0; i < size; i++)
    for (Index i2 = 0; i2 < i; i2++)
      while (v(i) == v(i2))  // yes, ==
        v(i) = internal::random<Scalar>();

  Scalar minc = v(0), maxc = v(0);
  Index minidx = 0, maxidx = 0;
  for (Index i = 0; i < size; i++) {
    if (v(i) < minc) {
      minc = v(i);
      minidx = i;
    }
    if (v(i) > maxc) {
      maxc = v(i);
      maxidx = i;
    }
  }
  Index eigen_minidx, eigen_maxidx;
  Scalar eigen_minc, eigen_maxc;
  eigen_minc = v.minCoeff(&eigen_minidx);
  eigen_maxc = v.maxCoeff(&eigen_maxidx);
  VERIFY(minidx == eigen_minidx);
  VERIFY(maxidx == eigen_maxidx);
  VERIFY_IS_APPROX(minc, eigen_minc);
  VERIFY_IS_APPROX(maxc, eigen_maxc);
  VERIFY_IS_APPROX(minc, v.minCoeff());
  VERIFY_IS_APPROX(maxc, v.maxCoeff());

  Index idx0 = internal::random<Index>(0, size - 1);
  Index idx1 = eigen_minidx;
  Index idx2 = eigen_maxidx;
  VectorType v1(v), v2(v);
  v1(idx0) = v1(idx1);
  v2(idx0) = v2(idx2);
  v1.minCoeff(&eigen_minidx);
  v2.maxCoeff(&eigen_maxidx);
  VERIFY(eigen_minidx == (std::min)(idx0, idx1));
  VERIFY(eigen_maxidx == (std::min)(idx0, idx2));
}

EIGEN_DECLARE_TEST(visitor) {
  for (int i = 0; i < g_repeat; i++) {
    CALL_SUBTEST_1(matrixVisitor(Matrix<float, 1, 1>()));
    CALL_SUBTEST_2(matrixVisitor(Matrix2f()));
    CALL_SUBTEST_3(matrixVisitor(Matrix4d()));
    CALL_SUBTEST_4(matrixVisitor(MatrixXd(8, 12)));
    CALL_SUBTEST_5(
        matrixVisitor(Matrix<double, Dynamic, Dynamic, RowMajor>(20, 20)));
    CALL_SUBTEST_6(matrixVisitor(MatrixXi(8, 12)));
  }
  for (int i = 0; i < g_repeat; i++) {
    CALL_SUBTEST_7(vectorVisitor(Vector4f()));
    CALL_SUBTEST_7(vectorVisitor(Matrix<int, 12, 1>()));
    CALL_SUBTEST_8(vectorVisitor(VectorXd(10)));
    CALL_SUBTEST_9(vectorVisitor(RowVectorXd(10)));
    CALL_SUBTEST_10(vectorVisitor(VectorXf(33)));
  }
}