Added support for additional tensor operations:

* comparison (<, <=, ==, !=, ...)
  * selection
  * nullary ops such as random or constant generation
  * misc unary ops such as log(), exp(), or a user defined unaryExpr()
Cleaned up the code a little.

unsupported/Eigen/CXX11/src/Tensor/TensorBase.h

@@ -33,21 +33,25 @@ class TensorBase
     Derived& setZero() {
       return setConstant(Scalar(0));
     }
-
     Derived& setConstant(const Scalar& val) {
-      Scalar* data = derived().data();
+      return derived() = constant(val);
-      for (int i = 0; i < derived().size(); ++i) {
-        data[i] = val;
     }
-      return derived();
+    Derived& setRandom() {
+      return derived() = random();
     }
 
-    Derived& setRandom() {
+    // Nullary operators
-      Scalar* data = derived().data();
+    EIGEN_DEVICE_FUNC
-      for (int i = 0; i < derived().size(); ++i) {
+    EIGEN_STRONG_INLINE const TensorCwiseNullaryOp<internal::scalar_constant_op<Scalar>, const Derived>
-        data[i] = internal::random_default_impl<Scalar, false, false>::run();
+    constant(const Scalar& value) const {
+      return TensorCwiseNullaryOp<internal::scalar_constant_op<Scalar>, const Derived>
+          (internal::scalar_constant_op<Scalar>(value));
     }
-      return derived();
+
+    EIGEN_DEVICE_FUNC
+    EIGEN_STRONG_INLINE const TensorCwiseNullaryOp<internal::scalar_random_op<Scalar>, const Derived>
+    random() const {
+      return TensorCwiseNullaryOp<internal::scalar_random_op<Scalar>, const Derived>();
     }
 
     // Coefficient-wise unary operators
@@ -57,15 +61,31 @@ class TensorBase
 
     EIGEN_DEVICE_FUNC
     EIGEN_STRONG_INLINE const TensorCwiseUnaryOp<internal::scalar_sqrt_op<Scalar>, const Derived>
-    cwiseSqrt() const { return derived(); }
+    sqrt() const { return derived(); }
+
+    EIGEN_DEVICE_FUNC
+    EIGEN_STRONG_INLINE const TensorCwiseUnaryOp<internal::scalar_square_op<Scalar>, const Derived>
+    square() const { return derived(); }
+
+    EIGEN_DEVICE_FUNC
+    EIGEN_STRONG_INLINE const TensorCwiseUnaryOp<internal::scalar_inverse_op<Scalar>, const Derived>
+    inverse() const { return derived(); }
+
+    EIGEN_DEVICE_FUNC
+    EIGEN_STRONG_INLINE const TensorCwiseUnaryOp<internal::scalar_exp_op<Scalar>, const Derived>
+    exp() const { return derived(); }
+
+    EIGEN_DEVICE_FUNC
+    EIGEN_STRONG_INLINE const TensorCwiseUnaryOp<internal::scalar_log_op<Scalar>, const Derived>
+    log() const { return derived(); }
 
     EIGEN_DEVICE_FUNC
     EIGEN_STRONG_INLINE const TensorCwiseUnaryOp<internal::scalar_abs_op<Scalar>, const Derived>
-    cwiseAbs() const { return derived(); }
+    abs() const { return derived(); }
 
     EIGEN_DEVICE_FUNC
     EIGEN_STRONG_INLINE const TensorCwiseUnaryOp<internal::scalar_pow_op<Scalar>, const Derived>
-    cwisePow(Scalar exponent) const {
+    pow(Scalar exponent) const {
       return TensorCwiseUnaryOp<internal::scalar_pow_op<Scalar>, const Derived>
           (derived(), internal::scalar_pow_op<Scalar>(exponent));
     }
@@ -77,6 +97,30 @@ class TensorBase
           (derived(), internal::scalar_multiple_op<Scalar>(scale));
     }
 
+    EIGEN_DEVICE_FUNC
+    EIGEN_STRONG_INLINE const TensorCwiseBinaryOp<internal::scalar_max_op<Scalar>, const Derived, const TensorCwiseNullaryOp<internal::scalar_constant_op<Scalar>, const Derived> >
+    cwiseMax(Scalar threshold) const {
+      return cwiseMax(constant(threshold));
+    }
+
+    EIGEN_DEVICE_FUNC
+    EIGEN_STRONG_INLINE const TensorCwiseBinaryOp<internal::scalar_min_op<Scalar>, const Derived, const TensorCwiseNullaryOp<internal::scalar_constant_op<Scalar>, const Derived> >
+    cwiseMin(Scalar threshold) const {
+      return cwiseMin(constant(threshold));
+    }
+
+    template <typename CustomUnaryOp> EIGEN_DEVICE_FUNC
+    EIGEN_STRONG_INLINE const TensorCwiseUnaryOp<CustomUnaryOp, const Derived>
+    unaryExpr(const CustomUnaryOp& func) const {
+      return TensorCwiseUnaryOp<CustomUnaryOp, const Derived>(derived(), func);
+    }
+
+    template <typename NewType> EIGEN_DEVICE_FUNC
+    EIGEN_STRONG_INLINE const TensorCwiseUnaryOp<internal::scalar_cast_op<Scalar, NewType>, const Derived>
+    cast() const {
+      return derived();
+    }
+
     // Coefficient-wise binary operators.
     template<typename OtherDerived> EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE
     const TensorCwiseBinaryOp<internal::scalar_sum_op<Scalar>, const Derived, const OtherDerived>
@@ -90,6 +134,71 @@ class TensorBase
       return TensorCwiseBinaryOp<internal::scalar_difference_op<Scalar>, const Derived, const OtherDerived>(derived(), other.derived());
     }
 
+    template<typename OtherDerived> EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE
+    const TensorCwiseBinaryOp<internal::scalar_product_op<Scalar>, const Derived, const OtherDerived>
+    operator*(const OtherDerived& other) const  {
+      return TensorCwiseBinaryOp<internal::scalar_product_op<Scalar>, const Derived, const OtherDerived>(derived(), other.derived());
+    }
+
+    template<typename OtherDerived> EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE
+    const TensorCwiseBinaryOp<internal::scalar_quotient_op<Scalar>, const Derived, const OtherDerived>
+    operator/(const OtherDerived& other) const  {
+      return TensorCwiseBinaryOp<internal::scalar_quotient_op<Scalar>, const Derived, const OtherDerived>(derived(), other.derived());
+    }
+
+    template<typename OtherDerived> EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE
+    const TensorCwiseBinaryOp<internal::scalar_max_op<Scalar>, const Derived, const OtherDerived>
+    cwiseMax(const OtherDerived& other) const  {
+      return TensorCwiseBinaryOp<internal::scalar_max_op<Scalar>, const Derived, const OtherDerived>(derived(), other.derived());
+    }
+
+    template<typename OtherDerived> EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE
+    const TensorCwiseBinaryOp<internal::scalar_min_op<Scalar>, const Derived, const OtherDerived>
+    cwiseMin(const OtherDerived& other) const  {
+      return TensorCwiseBinaryOp<internal::scalar_min_op<Scalar>, const Derived, const OtherDerived>(derived(), other.derived());
+    }
+
+    // Comparisons and tests.
+    template<typename OtherDerived> EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE
+    const TensorCwiseBinaryOp<std::less<Scalar>, const Derived, const OtherDerived>
+    operator<(const OtherDerived& other) const  {
+      return TensorCwiseBinaryOp<std::less<Scalar>, const Derived, const OtherDerived>(derived(), other.derived());
+    }
+    template<typename OtherDerived> EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE
+    const TensorCwiseBinaryOp<std::less_equal<Scalar>, const Derived, const OtherDerived>
+    operator<=(const OtherDerived& other) const  {
+      return TensorCwiseBinaryOp<std::less_equal<Scalar>, const Derived, const OtherDerived>(derived(), other.derived());
+    }
+    template<typename OtherDerived> EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE
+    const TensorCwiseBinaryOp<std::greater<Scalar>, const Derived, const OtherDerived>
+    operator>(const OtherDerived& other) const  {
+      return TensorCwiseBinaryOp<std::greater<Scalar>, const Derived, const OtherDerived>(derived(), other.derived());
+    }
+    template<typename OtherDerived> EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE
+    const TensorCwiseBinaryOp<std::greater_equal<Scalar>, const Derived, const OtherDerived>
+    operator>=(const OtherDerived& other) const  {
+      return TensorCwiseBinaryOp<std::greater_equal<Scalar>, const Derived, const OtherDerived>(derived(), other.derived());
+    }
+
+    template<typename OtherDerived> EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE
+    const TensorCwiseBinaryOp<std::equal_to<Scalar>, const Derived, const OtherDerived>
+    operator==(const OtherDerived& other) const  {
+      return TensorCwiseBinaryOp<std::equal_to<Scalar>, const Derived, const OtherDerived>(derived(), other.derived());
+    }
+    template<typename OtherDerived> EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE
+    const TensorCwiseBinaryOp<std::not_equal_to<Scalar>, const Derived, const OtherDerived>
+    operator!=(const OtherDerived& other) const  {
+      return TensorCwiseBinaryOp<std::not_equal_to<Scalar>, const Derived, const OtherDerived>(derived(), other.derived());
+    }
+
+    // Coefficient-wise ternary operators.
+    template<typename ThenDerived,typename ElseDerived>
+    inline const TensorSelectOp<const Derived, const ThenDerived, const ElseDerived>
+    select(const ThenDerived& thenTensor, const ElseDerived& elseTensor) const{
+      return TensorSelectOp<const Derived, const ThenDerived, const ElseDerived>(derived(), thenTensor.derived(), elseTensor.derived());
+    }
+
+    // Select the device on which to evaluate the expression.
     template <typename DeviceType>
     TensorDevice<Derived, DeviceType> device(const DeviceType& device) {
       return TensorDevice<Derived, DeviceType>(device, derived());

unsupported/Eigen/CXX11/src/Tensor/TensorEvaluator.h

@@ -68,6 +68,42 @@ struct TensorEvaluator
 
 
 
+// -------------------- CwiseNullaryOp --------------------
+
+template<typename NullaryOp, typename PlainObjectType>
+struct TensorEvaluator<const TensorCwiseNullaryOp<NullaryOp, PlainObjectType> >
+{
+  typedef TensorCwiseNullaryOp<NullaryOp, PlainObjectType> XprType;
+
+  enum {
+    IsAligned = true,
+    PacketAccess = internal::functor_traits<NullaryOp>::PacketAccess,
+  };
+
+  TensorEvaluator(const XprType& op)
+    : m_functor(op.functor())
+  { }
+
+  typedef typename XprType::Index Index;
+  typedef typename XprType::CoeffReturnType CoeffReturnType;
+  typedef typename XprType::PacketReturnType PacketReturnType;
+
+  EIGEN_DEVICE_FUNC CoeffReturnType coeff(Index index) const
+  {
+    return m_functor(index);
+  }
+
+  template<int LoadMode>
+  EIGEN_DEVICE_FUNC PacketReturnType packet(Index index) const
+  {
+    return m_functor.packetOp(index);
+  }
+
+ private:
+  const NullaryOp m_functor;
+};
+
+
 
 // -------------------- CwiseUnaryOp --------------------
 
@@ -146,6 +182,54 @@ struct TensorEvaluator<const TensorCwiseBinaryOp<BinaryOp, LeftArgType, RightArg
   TensorEvaluator<RightArgType> m_rightImpl;
 };
 
+
+// -------------------- SelectOp --------------------
+
+template<typename IfArgType, typename ThenArgType, typename ElseArgType>
+struct TensorEvaluator<const TensorSelectOp<IfArgType, ThenArgType, ElseArgType> >
+{
+  typedef TensorSelectOp<IfArgType, ThenArgType, ElseArgType> XprType;
+
+  enum {
+    IsAligned = TensorEvaluator<ThenArgType>::IsAligned & TensorEvaluator<ElseArgType>::IsAligned,
+    PacketAccess = TensorEvaluator<ThenArgType>::PacketAccess & TensorEvaluator<ElseArgType>::PacketAccess/* &
+                                                                                                             TensorEvaluator<IfArgType>::PacketAccess*/,
+  };
+
+  TensorEvaluator(const XprType& op)
+    : m_condImpl(op.ifExpression()),
+      m_thenImpl(op.thenExpression()),
+      m_elseImpl(op.elseExpression())
+  { }
+
+  typedef typename XprType::Index Index;
+  typedef typename XprType::CoeffReturnType CoeffReturnType;
+  typedef typename XprType::PacketReturnType PacketReturnType;
+
+  EIGEN_DEVICE_FUNC CoeffReturnType coeff(Index index) const
+  {
+    return m_condImpl.coeff(index) ? m_thenImpl.coeff(index) : m_elseImpl.coeff(index);
+  }
+  template<int LoadMode>
+  EIGEN_DEVICE_FUNC PacketReturnType packet(Index index) const
+  {
+    static const int PacketSize = internal::unpacket_traits<PacketReturnType>::size;
+    internal::Selector<PacketSize> select;
+    for (Index i = 0; i < PacketSize; ++i) {
+      select.select[i] = m_condImpl.coeff(index+i);
+    }
+    return internal::pblend(select,
+                            m_thenImpl.template packet<LoadMode>(index),
+                            m_elseImpl.template packet<LoadMode>(index));
+  }
+
+ private:
+  TensorEvaluator<IfArgType> m_condImpl;
+  TensorEvaluator<ThenArgType> m_thenImpl;
+  TensorEvaluator<ElseArgType> m_elseImpl;
+};
+
+
 } // end namespace Eigen
 
 #endif // EIGEN_CXX11_TENSOR_TENSOR_EVALUATOR_H

unsupported/Eigen/CXX11/src/Tensor/TensorExpr.h

@@ -17,6 +17,9 @@ namespace Eigen {
   *
   * \brief Tensor expression classes.
   *
+  * The TensorCwiseNullaryOp class applies a nullary operators to an expression. This
+  * is typically used to generate constants.
+  *
   * The TensorCwiseUnaryOp class represents an expression where a unary operator
   * (e.g. cwiseSqrt) is applied to an expression.
   *
@@ -24,6 +27,46 @@ namespace Eigen {
   * (e.g. addition) is applied to a lhs and a rhs expression.
   *
   */
+namespace internal {
+template<typename NullaryOp, typename PlainObjectType>
+struct traits<TensorCwiseNullaryOp<NullaryOp, PlainObjectType> >
+    : traits<PlainObjectType>
+{
+  typedef typename PlainObjectType::Packet Packet;
+  typedef typename PlainObjectType::Scalar Scalar;
+  typedef typename PlainObjectType::Nested XprTypeNested;
+  typedef typename remove_reference<XprTypeNested>::type _XprTypeNested;
+};
+
+}  // end namespace internal
+
+
+
+template<typename NullaryOp, typename PlainObjectType>
+class TensorCwiseNullaryOp : public TensorBase<TensorCwiseNullaryOp<NullaryOp, PlainObjectType> >
+{
+  public:
+    typedef typename Eigen::internal::traits<TensorCwiseNullaryOp>::Scalar Scalar;
+    typedef typename Eigen::internal::traits<TensorCwiseNullaryOp>::Packet Packet;
+    typedef typename Eigen::NumTraits<Scalar>::Real RealScalar;
+    typedef typename PlainObjectType::CoeffReturnType CoeffReturnType;
+    typedef typename PlainObjectType::PacketReturnType PacketReturnType;
+    typedef TensorCwiseNullaryOp<NullaryOp, PlainObjectType> Nested;
+    typedef typename Eigen::internal::traits<TensorCwiseNullaryOp>::StorageKind StorageKind;
+    typedef typename Eigen::internal::traits<TensorCwiseNullaryOp>::Index Index;
+
+    EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE TensorCwiseNullaryOp(const NullaryOp& func = NullaryOp())
+      : m_functor(func) {}
+
+    EIGEN_DEVICE_FUNC
+    const NullaryOp& functor() const { return m_functor; }
+
+  protected:
+    // todo: add tensor dimension to be able to do some sanity checks
+    const NullaryOp m_functor;
+};
+
+
 
 namespace internal {
 template<typename UnaryOp, typename XprType>
@@ -160,6 +203,72 @@ class TensorCwiseBinaryOp : public TensorBase<TensorCwiseBinaryOp<BinaryOp, LhsX
     const BinaryOp m_functor;
 };
 
+
+namespace internal {
+template<typename IfXprType, typename ThenXprType, typename ElseXprType>
+struct traits<TensorSelectOp<IfXprType, ThenXprType, ElseXprType> >
+ : traits<ThenXprType>
+{
+  typedef typename traits<ThenXprType>::Scalar Scalar;
+  typedef typename internal::packet_traits<Scalar>::type Packet;
+  typedef typename promote_storage_type<typename traits<ThenXprType>::StorageKind,
+                                        typename traits<ElseXprType>::StorageKind>::ret StorageKind;
+  typedef typename promote_index_type<typename traits<ElseXprType>::Index,
+                                      typename traits<ThenXprType>::Index>::type Index;
+  typedef typename IfXprType::Nested IfNested;
+  typedef typename ThenXprType::Nested ThenNested;
+  typedef typename ElseXprType::Nested ElseNested;
+};
+
+template<typename IfXprType, typename ThenXprType, typename ElseXprType>
+struct eval<TensorSelectOp<IfXprType, ThenXprType, ElseXprType>, Eigen::Dense>
+{
+  typedef const TensorSelectOp<IfXprType, ThenXprType, ElseXprType>& type;
+};
+
+template<typename IfXprType, typename ThenXprType, typename ElseXprType>
+struct nested<TensorSelectOp<IfXprType, ThenXprType, ElseXprType>, 1, typename eval<TensorSelectOp<IfXprType, ThenXprType, ElseXprType> >::type>
+{
+  typedef TensorSelectOp<IfXprType, ThenXprType, ElseXprType> type;
+};
+
+}  // end namespace internal
+
+
+template<typename IfXprType, typename ThenXprType, typename ElseXprType>
+class TensorSelectOp : public TensorBase<TensorSelectOp<IfXprType, ThenXprType, ElseXprType> >
+{
+  public:
+    typedef typename Eigen::internal::traits<TensorSelectOp>::Scalar Scalar;
+    typedef typename Eigen::internal::traits<TensorSelectOp>::Packet Packet;
+    typedef typename Eigen::NumTraits<Scalar>::Real RealScalar;
+    typedef typename internal::promote_storage_type<typename ThenXprType::CoeffReturnType,
+                                                    typename ElseXprType::CoeffReturnType>::ret CoeffReturnType;
+    typedef typename internal::promote_storage_type<typename ThenXprType::PacketReturnType,
+                                                    typename ElseXprType::PacketReturnType>::ret PacketReturnType;
+    typedef typename Eigen::internal::nested<TensorSelectOp>::type Nested;
+    typedef typename Eigen::internal::traits<TensorSelectOp>::StorageKind StorageKind;
+    typedef typename Eigen::internal::traits<TensorSelectOp>::Index Index;
+
+    TensorSelectOp(const IfXprType& a_condition,
+                   const ThenXprType& a_then,
+                   const ElseXprType& a_else)
+      : m_condition(a_condition), m_then(a_then), m_else(a_else)
+    { }
+
+    const IfXprType& ifExpression() const { return m_condition; }
+
+    const ThenXprType& thenExpression() const { return m_then; }
+
+    const ElseXprType& elseExpression() const { return m_else; }
+
+  protected:
+    typename IfXprType::Nested m_condition;
+    typename ThenXprType::Nested m_then;
+    typename ElseXprType::Nested m_else;
+};
+
+
 } // end namespace Eigen
 
 #endif // EIGEN_CXX11_TENSOR_TENSOR_EXPR_H

unsupported/Eigen/CXX11/src/Tensor/TensorForwardDeclarations.h

@@ -17,8 +17,10 @@ template<typename Scalar_, typename Dimensions, int Options_ = 0> class TensorFi
 template<typename PlainObjectType, int Options_ = Unaligned> class TensorMap;
 template<typename Derived> class TensorBase;
 
+template<typename NullaryOp, typename PlainObjectType> class TensorCwiseNullaryOp;
 template<typename UnaryOp, typename XprType> class TensorCwiseUnaryOp;
 template<typename BinaryOp, typename LeftXprType, typename RightXprType> class TensorCwiseBinaryOp;
+template<typename IfXprType, typename ThenXprType, typename ElseXprType> class TensorSelectOp;
 
 template<typename ExpressionType, typename DeviceType> class TensorDevice;
 

unsupported/Eigen/CXX11/src/Tensor/TensorMap.h

@@ -45,33 +45,37 @@ template<typename PlainObjectType, int Options_> class TensorMap : public Tensor
 
     static const int Options = Options_;
 
+    static const std::size_t NumIndices = PlainObjectType::NumIndices;
+    typedef typename PlainObjectType::Dimensions Dimensions;
+
+
     enum {
       IsAligned = bool(EIGEN_ALIGN) && ((int(Options_)&Aligned)==Aligned),
       PacketAccess = true,
     };
 
     EIGEN_DEVICE_FUNC
-    EIGEN_STRONG_INLINE TensorMap(PointerArgType dataPtr, Index firstDimension) : m_data(dataPtr), m_dimensions(array<DenseIndex, PlainObjectType::NumIndices>({{firstDimension}})) {
+    EIGEN_STRONG_INLINE TensorMap(PointerArgType dataPtr, Index firstDimension) : m_data(dataPtr), m_dimensions(array<DenseIndex, NumIndices>(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)
+      EIGEN_STATIC_ASSERT(1 == 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(array<DenseIndex, PlainObjectType::NumIndices>({{firstDimension, otherDimensions...}})) {
+    EIGEN_STRONG_INLINE TensorMap(PointerArgType dataPtr, Index firstDimension, IndexTypes... otherDimensions) : m_data(dataPtr), m_dimensions(array<DenseIndex, NumIndices>({{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)
+      EIGEN_STATIC_ASSERT(sizeof...(otherDimensions) + 1 == NumIndices, YOU_MADE_A_PROGRAMMING_MISTAKE)
     }
 #endif
 
-    inline TensorMap(PointerArgType dataPtr, const array<Index, PlainObjectType::NumIndices>& dimensions)
+    inline TensorMap(PointerArgType dataPtr, const array<Index, NumIndices>& dimensions)
       : m_data(dataPtr), m_dimensions(dimensions)
     { }
 
     EIGEN_DEVICE_FUNC
     EIGEN_STRONG_INLINE Index dimension(Index n) const { return m_dimensions[n]; }
     EIGEN_DEVICE_FUNC
-    EIGEN_STRONG_INLINE const typename PlainObjectType::Dimensions& dimensions() const { return m_dimensions; }
+    EIGEN_STRONG_INLINE const Dimensions& dimensions() const { return m_dimensions; }
     EIGEN_DEVICE_FUNC
     EIGEN_STRONG_INLINE Index size() const { return m_dimensions.TotalSize(); }
     EIGEN_DEVICE_FUNC
@@ -80,7 +84,7 @@ template<typename PlainObjectType, int Options_> class TensorMap : public Tensor
     EIGEN_STRONG_INLINE const Scalar* data() const { return m_data; }
 
     EIGEN_DEVICE_FUNC
-    EIGEN_STRONG_INLINE const Scalar& operator()(const array<Index, PlainObjectType::NumIndices>& indices) const
+    EIGEN_STRONG_INLINE const Scalar& operator()(const array<Index, NumIndices>& indices) const
     {
       //      eigen_assert(checkIndexRange(indices));
       if (PlainObjectType::Options&RowMajor) {
@@ -96,12 +100,12 @@ template<typename PlainObjectType, int Options_> class TensorMap : public Tensor
     template<typename... IndexTypes> EIGEN_DEVICE_FUNC
     EIGEN_STRONG_INLINE const Scalar& operator()(Index firstIndex, IndexTypes... otherIndices) const
     {
-      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.");
+      static_assert(sizeof...(otherIndices) + 1 == NumIndices, "Number of indices used to access a tensor coefficient must be equal to the rank of the tensor.");
       if (PlainObjectType::Options&RowMajor) {
-        const Index index = m_dimensions.IndexOfRowMajor(array<Index, PlainObjectType::NumIndices>{{firstIndex, otherIndices...}});
+        const Index index = m_dimensions.IndexOfRowMajor(array<Index, NumIndices>{{firstIndex, otherIndices...}});
         return m_data[index];
       } else {
-        const Index index = m_dimensions.IndexOfColMajor(array<Index, PlainObjectType::NumIndices>{{firstIndex, otherIndices...}});
+        const Index index = m_dimensions.IndexOfColMajor(array<Index, NumIndices>{{firstIndex, otherIndices...}});
         return m_data[index];
       }
     }
@@ -159,7 +163,7 @@ template<typename PlainObjectType, int Options_> class TensorMap : public Tensor
 #endif
 
     EIGEN_DEVICE_FUNC
-    EIGEN_STRONG_INLINE Scalar& operator()(const array<Index, PlainObjectType::NumIndices>& indices)
+    EIGEN_STRONG_INLINE Scalar& operator()(const array<Index, NumIndices>& indices)
     {
       //      eigen_assert(checkIndexRange(indices));
       if (PlainObjectType::Options&RowMajor) {
@@ -175,12 +179,12 @@ template<typename PlainObjectType, int Options_> class TensorMap : public Tensor
     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.");
+      static_assert(sizeof...(otherIndices) + 1 == NumIndices, "Number of indices used to access a tensor coefficient must be equal to the rank of the tensor.");
       if (PlainObjectType::Options&RowMajor) {
-        const Index index = m_dimensions.IndexOfRowMajor(array<Index, PlainObjectType::NumIndices>{{firstIndex, otherIndices...}});
+        const Index index = m_dimensions.IndexOfRowMajor(array<Index, NumIndices>{{firstIndex, otherIndices...}});
         return m_data[index];
       } else {
-        const Index index = m_dimensions.IndexOfColMajor(array<Index, PlainObjectType::NumIndices>{{firstIndex, otherIndices...}});
+        const Index index = m_dimensions.IndexOfColMajor(array<Index, NumIndices>{{firstIndex, otherIndices...}});
         return m_data[index];
       }
     }
@@ -247,8 +251,8 @@ template<typename PlainObjectType, int Options_> class TensorMap : public Tensor
     }
 
   private:
-    typename PlainObjectType::Scalar* m_data;
+    Scalar* m_data;
-    typename PlainObjectType::Dimensions m_dimensions;
+    Dimensions m_dimensions;
 };
 
 } // end namespace Eigen