Extended support for Tensors:

  * Added ability to map a region of the memory to a tensor
  * Added basic support for unary and binary coefficient wise expressions, such as addition or square root
  * Provided an emulation layer to make it possible to compile the code with compilers (such as nvcc) that don't support cxx11.

1 files changed, 101 insertions, 0 deletions

diff --git a/unsupported/Eigen/CXX11/src/Tensor/TensorMap.h b/unsupported/Eigen/CXX11/src/Tensor/TensorMap.h
new file mode 100644
index 000000000..7dec1e08d
--- /dev/null
+++ b/unsupported/Eigen/CXX11/src/Tensor/TensorMap.h
@@ -0,0 +1,101 @@
+// 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