// This file is part of Eigen, a lightweight C++ template library
// for linear algebra.
//
// Copyright (C) 2015 Benoit Steiner <benoit.steiner.goog@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/.
// SPDX-License-Identifier: MPL-2.0

#include "main.h"

#include <Eigen/Tensor>

struct Generator1D {
  Generator1D() {}

  float operator()(const array<Eigen::DenseIndex, 1>& coordinates) const { return coordinates[0]; }
};

template <int DataLayout>
static void test_1D() {
  Tensor<float, 1, DataLayout> vec(6);
  Tensor<float, 1, DataLayout> result = vec.generate(Generator1D());

  for (int i = 0; i < 6; ++i) {
    VERIFY_IS_EQUAL(result(i), i);
  }
}

struct Generator0D {
  float operator()(const array<Eigen::DenseIndex, 0>&) const { return 42.0f; }
};

template <int DataLayout>
static void test_0D() {
  Tensor<float, 0, DataLayout> scalar;
  Tensor<float, 0, DataLayout> result = scalar.generate(Generator0D());
  VERIFY_IS_EQUAL(result(), 42.0f);
}

struct Generator2D {
  Generator2D() {}

  float operator()(const array<Eigen::DenseIndex, 2>& coordinates) const {
    return 3 * coordinates[0] + 11 * coordinates[1];
  }
};

template <int DataLayout>
static void test_2D() {
  Tensor<float, 2, DataLayout> matrix(512, 512);
  Tensor<float, 2, DataLayout> result = matrix.generate(Generator2D());

  for (int i = 0; i < 512; ++i) {
    for (int j = 0; j < 512; ++j) {
      VERIFY_IS_EQUAL(result(i, j), 3 * i + 11 * j);
    }
  }
}

template <int DataLayout>
static void test_gaussian() {
  int rows = 32;
  int cols = 48;
  array<float, 2> means;
  means[0] = rows / 2.0f;
  means[1] = cols / 2.0f;
  array<float, 2> std_devs;
  std_devs[0] = 3.14f;
  std_devs[1] = 2.7f;
  internal::GaussianGenerator<float, Eigen::DenseIndex, 2> gaussian_gen(means, std_devs);

  Tensor<float, 2, DataLayout> matrix(rows, cols);
  Tensor<float, 2, DataLayout> result = matrix.generate(gaussian_gen);

  for (int i = 0; i < rows; ++i) {
    for (int j = 0; j < cols; ++j) {
      float g_rows = powf(rows / 2.0f - i, 2) / (3.14f * 3.14f) * 0.5f;
      float g_cols = powf(cols / 2.0f - j, 2) / (2.7f * 2.7f) * 0.5f;
      float gaussian = expf(-g_rows - g_cols);
      VERIFY_IS_EQUAL(result(i, j), gaussian);
    }
  }
}

EIGEN_DECLARE_TEST(tensor_generator) {
  CALL_SUBTEST(test_0D<ColMajor>());
  CALL_SUBTEST(test_0D<RowMajor>());
  CALL_SUBTEST(test_1D<ColMajor>());
  CALL_SUBTEST(test_1D<RowMajor>());
  CALL_SUBTEST(test_2D<ColMajor>());
  CALL_SUBTEST(test_2D<RowMajor>());
  CALL_SUBTEST(test_gaussian<ColMajor>());
  CALL_SUBTEST(test_gaussian<RowMajor>());
}