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// 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/.
#ifndef EIGEN_CXX11_TENSOR_TENSOR_MAP_H
#define EIGEN_CXX11_TENSOR_TENSOR_MAP_H
namespace Eigen {
template<int InnerStrideAtCompileTime, int OuterStrideAtCompileTime> class Stride;
/** \class TensorMap
* \ingroup CXX11_Tensor_Module
*
* \brief A tensor expression mapping an existing array of data.
*
*/
template<typename PlainObjectType> class TensorMap : public TensorBase<TensorMap<PlainObjectType> >
{
public:
typedef TensorMap<PlainObjectType> Self;
typedef typename PlainObjectType::Base Base;
typedef typename Eigen::internal::nested<Self>::type Nested;
typedef typename internal::traits<PlainObjectType>::StorageKind StorageKind;
typedef typename internal::traits<PlainObjectType>::Index Index;
typedef typename internal::traits<PlainObjectType>::Scalar Scalar;
typedef typename internal::packet_traits<Scalar>::type PacketScalar;
typedef typename NumTraits<Scalar>::Real RealScalar;
typedef typename Base::CoeffReturnType CoeffReturnType;
/* typedef typename internal::conditional<
bool(internal::is_lvalue<PlainObjectType>::value),
Scalar *,
const Scalar *>::type
PointerType;*/
typedef Scalar* PointerType;
typedef PointerType PointerArgType;
EIGEN_DEVICE_FUNC
EIGEN_STRONG_INLINE TensorMap(PointerArgType dataPtr, Index firstDimension) : m_data(dataPtr), m_dimensions({{firstDimension}}) {
// The number of dimensions used to construct a tensor must be equal to the rank of the tensor.
EIGEN_STATIC_ASSERT(1 == PlainObjectType::NumIndices, YOU_MADE_A_PROGRAMMING_MISTAKE)
}
#ifdef EIGEN_HAS_VARIADIC_TEMPLATES
template<typename... IndexTypes> EIGEN_DEVICE_FUNC
EIGEN_STRONG_INLINE TensorMap(PointerArgType dataPtr, Index firstDimension, IndexTypes... otherDimensions) : m_data(dataPtr), m_dimensions({{firstDimension, otherDimensions...}}) {
// The number of dimensions used to construct a tensor must be equal to the rank of the tensor.
EIGEN_STATIC_ASSERT(sizeof...(otherDimensions) + 1 == PlainObjectType::NumIndices, YOU_MADE_A_PROGRAMMING_MISTAKE)
}
#endif
EIGEN_DEVICE_FUNC
EIGEN_STRONG_INLINE Index dimension(Index n) const { return m_dimensions[n]; }
EIGEN_DEVICE_FUNC
EIGEN_STRONG_INLINE Index size() const { return internal::array_prod(m_dimensions); }
EIGEN_DEVICE_FUNC
EIGEN_STRONG_INLINE Scalar* data() { return m_data; }
EIGEN_DEVICE_FUNC
EIGEN_STRONG_INLINE const Scalar* data() const { return m_data; }
EIGEN_DEVICE_FUNC
EIGEN_STRONG_INLINE const Scalar& operator()(Index index) const
{
eigen_internal_assert(index >= 0 && index < size());
return m_data[index];
}
#ifdef EIGEN_HAS_VARIADIC_TEMPLATES
template<typename... IndexTypes> EIGEN_DEVICE_FUNC
EIGEN_STRONG_INLINE Scalar& operator()(Index firstIndex, IndexTypes... otherIndices)
{
static_assert(sizeof...(otherIndices) + 1 == PlainObjectType::NumIndices, "Number of indices used to access a tensor coefficient must be equal to the rank of the tensor.");
const Index index = internal::tensor_index_linearization_helper<Index, PlainObjectType::NumIndices, PlainObjectType::NumIndices - 1, PlainObjectType::Options&RowMajor>::run(array<Index, PlainObjectType::NumIndices>{{firstIndex, otherIndices...}}, m_dimensions);
return m_data[index];
}
#endif
template<typename OtherDerived>
EIGEN_DEVICE_FUNC
Self& operator=(const OtherDerived& other)
{
internal::TensorAssign<Self, const OtherDerived>::run(*this, other);
return *this;
}
private:
typename PlainObjectType::Scalar* m_data;
array<DenseIndex, PlainObjectType::NumIndices> m_dimensions;
};
} // end namespace Eigen
#endif // EIGEN_CXX11_TENSOR_TENSOR_MAP_H
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