text: // This file is part of Eigen, a lightweight C++ template library
  // for linear algebra.
  //
  // Copyright (C) 2014 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/.
  
  #include "main.h"
  
  #include <Eigen/CXX11/Tensor>
  
  using Eigen::Tensor;
  
  template <int DataLayout>
  static void test_simple_broadcasting()
  {
    Tensor<float, 4, DataLayout> tensor(2,3,5,7);
    tensor.setRandom();
    array<ptrdiff_t, 4> broadcasts;
    broadcasts[0] = 1;
    broadcasts[1] = 1;
    broadcasts[2] = 1;
    broadcasts[3] = 1;
  
    Tensor<float, 4, DataLayout> no_broadcast;
    no_broadcast = tensor.broadcast(broadcasts);
  
    VERIFY_IS_EQUAL(no_broadcast.dimension(0), 2);
    VERIFY_IS_EQUAL(no_broadcast.dimension(1), 3);
    VERIFY_IS_EQUAL(no_broadcast.dimension(2), 5);
    VERIFY_IS_EQUAL(no_broadcast.dimension(3), 7);
  
    for (int i = 0; i < 2; ++i) {
      for (int j = 0; j < 3; ++j) {
        for (int k = 0; k < 5; ++k) {
          for (int l = 0; l < 7; ++l) {
            VERIFY_IS_EQUAL(tensor(i,j,k,l), no_broadcast(i,j,k,l));
          }
        }
      }
    }
  
    broadcasts[0] = 2;
    broadcasts[1] = 3;
    broadcasts[2] = 1;
    broadcasts[3] = 4;
    Tensor<float, 4, DataLayout> broadcast;
    broadcast = tensor.broadcast(broadcasts);
  
    VERIFY_IS_EQUAL(broadcast.dimension(0), 4);
    VERIFY_IS_EQUAL(broadcast.dimension(1), 9);
    VERIFY_IS_EQUAL(broadcast.dimension(2), 5);
    VERIFY_IS_EQUAL(broadcast.dimension(3), 28);
  
    for (int i = 0; i < 4; ++i) {
      for (int j = 0; j < 9; ++j) {
        for (int k = 0; k < 5; ++k) {
          for (int l = 0; l < 28; ++l) {
            VERIFY_IS_EQUAL(tensor(i%2,j%3,k%5,l%7), broadcast(i,j,k,l));
          }
        }
      }
    }
  }
  
  
  template <int DataLayout>
  static void test_vectorized_broadcasting()
  {
    Tensor<float, 3, DataLayout> tensor(8,3,5);
    tensor.setRandom();
    array<ptrdiff_t, 3> broadcasts;
    broadcasts[0] = 2;
    broadcasts[1] = 3;
    broadcasts[2] = 4;
  
    Tensor<float, 3, DataLayout> broadcast;
    broadcast = tensor.broadcast(broadcasts);
  
    VERIFY_IS_EQUAL(broadcast.dimension(0), 16);
    VERIFY_IS_EQUAL(broadcast.dimension(1), 9);
    VERIFY_IS_EQUAL(broadcast.dimension(2), 20);
  
    for (int i = 0; i < 16; ++i) {
      for (int j = 0; j < 9; ++j) {
        for (int k = 0; k < 20; ++k) {
          VERIFY_IS_EQUAL(tensor(i%8,j%3,k%5), broadcast(i,j,k));
        }
      }
    }
  
    tensor.resize(11,3,5);
    tensor.setRandom();
    broadcast = tensor.broadcast(broadcasts);
  
    VERIFY_IS_EQUAL(broadcast.dimension(0), 22);
    VERIFY_IS_EQUAL(broadcast.dimension(1), 9);
    VERIFY_IS_EQUAL(broadcast.dimension(2), 20);
  
    for (int i = 0; i < 22; ++i) {
      for (int j = 0; j < 9; ++j) {
        for (int k = 0; k < 20; ++k) {
          VERIFY_IS_EQUAL(tensor(i%11,j%3,k%5), broadcast(i,j,k));
        }
      }
    }
  }
  
  
  template <int DataLayout>
  static void test_static_broadcasting()
  {
    Tensor<float, 3, DataLayout> tensor(8,3,5);
    tensor.setRandom();
  
  #ifdef EIGEN_HAS_CONSTEXPR
    Eigen::IndexList<Eigen::type2index<2>, Eigen::type2index<3>, Eigen::type2index<4>> broadcasts;
  #else
    Eigen::array<int, 3> broadcasts;
    broadcasts[0] = 2;
    broadcasts[1] = 3;
    broadcasts[2] = 4;
  #endif
  
    Tensor<float, 3, DataLayout> broadcast;
    broadcast = tensor.broadcast(broadcasts);
  
    VERIFY_IS_EQUAL(broadcast.dimension(0), 16);
    VERIFY_IS_EQUAL(broadcast.dimension(1), 9);
    VERIFY_IS_EQUAL(broadcast.dimension(2), 20);
  
    for (int i = 0; i < 16; ++i) {
      for (int j = 0; j < 9; ++j) {
        for (int k = 0; k < 20; ++k) {
          VERIFY_IS_EQUAL(tensor(i%8,j%3,k%5), broadcast(i,j,k));
        }
      }
    }
  
    tensor.resize(11,3,5);
    tensor.setRandom();
    broadcast = tensor.broadcast(broadcasts);
  
    VERIFY_IS_EQUAL(broadcast.dimension(0), 22);
    VERIFY_IS_EQUAL(broadcast.dimension(1), 9);
    VERIFY_IS_EQUAL(broadcast.dimension(2), 20);
  
    for (int i = 0; i < 22; ++i) {
      for (int j = 0; j < 9; ++j) {
        for (int k = 0; k < 20; ++k) {
          VERIFY_IS_EQUAL(tensor(i%11,j%3,k%5), broadcast(i,j,k));
        }
      }
    }
  }
  
  
  template <int DataLayout>
  static void test_fixed_size_broadcasting()
  {
    // Need to add a [] operator to the Size class for this to work
  #if 0
    Tensor<float, 1, DataLayout> t1(10);
    t1.setRandom();
    TensorFixedSize<float, Sizes<1>, DataLayout> t2;
    t2 = t2.constant(20.0f);
  
    Tensor<float, 1, DataLayout> t3 = t1 + t2.broadcast(Eigen::array<int, 1>{{10}});
    for (int i = 0; i < 10; ++i) {
      VERIFY_IS_APPROX(t3(i), t1(i) + t2(0));
    }
  
    TensorMap<TensorFixedSize<float, Sizes<1>, DataLayout> > t4(t2.data(), {{1}});
    Tensor<float, 1, DataLayout> t5 = t1 + t4.broadcast(Eigen::array<int, 1>{{10}});
    for (int i = 0; i < 10; ++i) {
      VERIFY_IS_APPROX(t5(i), t1(i) + t2(0));
    }
  #endif
  }
  
  
  void test_cxx11_tensor_broadcasting()
  {
    CALL_SUBTEST(test_simple_broadcasting<ColMajor>());
    CALL_SUBTEST(test_simple_broadcasting<RowMajor>());
    CALL_SUBTEST(test_vectorized_broadcasting<ColMajor>());
    CALL_SUBTEST(test_vectorized_broadcasting<RowMajor>());
    CALL_SUBTEST(test_static_broadcasting<ColMajor>());
    CALL_SUBTEST(test_static_broadcasting<RowMajor>());
    CALL_SUBTEST(test_fixed_size_broadcasting<ColMajor>());
    CALL_SUBTEST(test_fixed_size_broadcasting<RowMajor>());
  }
  