unsupported/Eigen/CXX11/src/Tensor/Tensor.h - mirror - Git at Google

 // This file is part of Eigen, a lightweight C++ template library
 // for linear algebra.
 //
 // Copyright (C) 2013 Christian Seiler <christian@iwakd.de>
 //
 // 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_H
 #define EIGEN_CXX11_TENSOR_TENSOR_H

 namespace Eigen {

 /** \class Tensor
   * \ingroup CXX11_Tensor_Module
   *
   * \brief The tensor class.
   *
   * The %Tensor class is the work-horse for all \em dense tensors within Eigen.
   *
   * The %Tensor class encompasses only dynamic-size objects so far.
   *
   * The first two template parameters are required:
   * \tparam Scalar_ \anchor tensor_tparam_scalar Numeric type, e.g. float, double, int or std::complex<float>.
   *                 User defined scalar types are supported as well (see \ref user_defined_scalars "here").
   * \tparam NumIndices_ Number of indices (i.e. rank of the tensor)
   *
   * The remaining template parameters are optional -- in most cases you don't have to worry about them.
   * \tparam Options_ \anchor tensor_tparam_options A combination of either \b #RowMajor or \b #ColMajor, and of either
   *                 \b #AutoAlign or \b #DontAlign.
   *                 The former controls \ref TopicStorageOrders "storage order", and defaults to column-major. The latter controls alignment, which is required
   *                 for vectorization. It defaults to aligning tensors. Note that tensors currently do not support any operations that profit from vectorization.
   *                 Support for such operations (i.e. adding two tensors etc.) is planned.
   *
   * You can access elements of tensors using normal subscripting:
   *
   * \code
   * Eigen::Tensor<double, 4> t(10, 10, 10, 10);
   * t(0, 1, 2, 3) = 42.0;
   * \endcode
   *
   * This class can be extended with the help of the plugin mechanism described on the page
   * \ref TopicCustomizingEigen by defining the preprocessor symbol \c EIGEN_TENSOR_PLUGIN.
   *
   * <i><b>Some notes:</b></i>
   *
   * <dl>
   * <dt><b>Relation to other parts of Eigen:</b></dt>
   * <dd>The midterm developement goal for this class is to have a similar hierarchy as Eigen uses for matrices, so that
   * taking blocks or using tensors in expressions is easily possible, including an interface with the vector/matrix code
   * by providing .asMatrix() and .asVector() (or similar) methods for rank 2 and 1 tensors. However, currently, the %Tensor
   * class does not provide any of these features and is only available as a stand-alone class that just allows for
   * coefficient access. Also, when fixed-size tensors are implemented, the number of template arguments is likely to
   * change dramatically.</dd>
   * </dl>
   *
   * \ref TopicStorageOrders
   */
 template<typename Scalar_, std::size_t NumIndices_, int Options_ = 0>
 class Tensor;

 namespace internal {
 template<typename Scalar_, std::size_t NumIndices_, int Options_>
 struct traits<Tensor<Scalar_, NumIndices_, Options_>>
 {
   typedef Scalar_ Scalar;
   typedef Dense StorageKind;
   typedef DenseIndex Index;
   enum {
     Options = Options_
   };
 };

 template<typename Index, std::size_t NumIndices, std::size_t n, bool RowMajor>
 struct tensor_index_linearization_helper
 {
   constexpr static inline Index run(std::array<Index, NumIndices> const& indices, std::array<Index, NumIndices> const& dimensions)
   {
     return std_array_get<RowMajor ? n : (NumIndices - n - 1)>(indices) +
       std_array_get<RowMajor ? n : (NumIndices - n - 1)>(dimensions) *
         tensor_index_linearization_helper<Index, NumIndices, n - 1, RowMajor>::run(indices, dimensions);
   }
 };

 template<typename Index, std::size_t NumIndices, bool RowMajor>
 struct tensor_index_linearization_helper<Index, NumIndices, 0, RowMajor>
 {
   constexpr static inline Index run(std::array<Index, NumIndices> const& indices, std::array<Index, NumIndices> const&)
   {
     return std_array_get<RowMajor ? 0 : NumIndices - 1>(indices);
   }
 };
 } // end namespace internal

 template<typename Scalar_, std::size_t NumIndices_, int Options_>
 class Tensor
 {
     static_assert(NumIndices_ >= 1, "A tensor must have at least one index.");

   public:
     typedef Tensor<Scalar_, NumIndices_, Options_> Self;
     typedef typename internal::traits<Self>::StorageKind StorageKind;
     typedef typename internal::traits<Self>::Index Index;
     typedef typename internal::traits<Self>::Scalar Scalar;
     typedef typename internal::packet_traits<Scalar>::type PacketScalar;
     typedef typename NumTraits<Scalar>::Real RealScalar;
     typedef Self DenseType;

     constexpr static int Options = Options_;
     constexpr static std::size_t NumIndices = NumIndices_;

   protected:
     TensorStorage<Scalar, NumIndices, Dynamic, Options> m_storage;

   public:
     EIGEN_STRONG_INLINE Index                         dimension(std::size_t n) const { return m_storage.dimensions()[n]; }
     EIGEN_STRONG_INLINE std::array<Index, NumIndices> dimensions()             const { return m_storage.dimensions(); }
     EIGEN_STRONG_INLINE Index                         size()                   const { return internal::array_prod(m_storage.dimensions()); }
     EIGEN_STRONG_INLINE Scalar                        *data()                        { return m_storage.data(); }
     EIGEN_STRONG_INLINE const Scalar                  *data()                  const { return m_storage.data(); }

     // This makes EIGEN_INITIALIZE_COEFFS_IF_THAT_OPTION_IS_ENABLED
     // work, because that uses base().coeffRef() - and we don't yet
     // implement a similar class hierarchy
     inline Self& base()             { return *this; }
     inline const Self& base() const { return *this; }

     void setZero()
     {
       // FIXME: until we have implemented packet access and the
       //        expression engine w.r.t. nullary ops, use this
       //        as a kludge. Only works with POD types, but for
       //        any standard usage, this shouldn't be a problem
       memset((void *)data(), 0, size() * sizeof(Scalar));
     }

     inline Self& operator=(Self const& other)
     {
       m_storage = other.m_storage;
       return *this;
     }

     template<typename... IndexTypes>
     inline const Scalar& coeff(Index firstIndex, Index secondIndex, IndexTypes... otherIndices) const
     {
       static_assert(sizeof...(otherIndices) + 2 == NumIndices, "Number of indices used to access a tensor coefficient must be equal to the rank of the tensor.");
       return coeff(std::array<Index, NumIndices>{{firstIndex, secondIndex, otherIndices...}});
     }

     inline const Scalar& coeff(const std::array<Index, NumIndices>& indices) const
     {
       eigen_internal_assert(checkIndexRange(indices));
       return m_storage.data()[linearizedIndex(indices)];
     }

     inline const Scalar& coeff(Index index) const
     {
       eigen_internal_assert(index >= 0 && index < size());
       return m_storage.data()[index];
     }

     template<typename... IndexTypes>
     inline Scalar& coeffRef(Index firstIndex, Index secondIndex, IndexTypes... otherIndices)
     {
       static_assert(sizeof...(otherIndices) + 2 == NumIndices, "Number of indices used to access a tensor coefficient must be equal to the rank of the tensor.");
       return coeffRef(std::array<Index, NumIndices>{{firstIndex, secondIndex, otherIndices...}});
     }

     inline Scalar& coeffRef(const std::array<Index, NumIndices>& indices)
     {
       eigen_internal_assert(checkIndexRange(indices));
       return m_storage.data()[linearizedIndex(indices)];
     }

     inline Scalar& coeffRef(Index index)
     {
       eigen_internal_assert(index >= 0 && index < size());
       return m_storage.data()[index];
     }

     template<typename... IndexTypes>
     inline const Scalar& operator()(Index firstIndex, Index secondIndex, IndexTypes... otherIndices) const
     {
       static_assert(sizeof...(otherIndices) + 2 == NumIndices, "Number of indices used to access a tensor coefficient must be equal to the rank of the tensor.");
       return this->operator()(std::array<Index, NumIndices>{{firstIndex, secondIndex, otherIndices...}});
     }

     inline const Scalar& operator()(const std::array<Index, NumIndices>& indices) const
     {
       eigen_assert(checkIndexRange(indices));
       return coeff(indices);
     }

     inline const Scalar& operator()(Index index) const
     {
       eigen_internal_assert(index >= 0 && index < size());
       return coeff(index);
     }

     inline const Scalar& operator[](Index index) const
     {
       static_assert(NumIndices == 1, "The bracket operator is only for vectors, use the parenthesis operator instead.");
       return coeff(index);
     }

     template<typename... IndexTypes>
     inline Scalar& operator()(Index firstIndex, Index secondIndex, IndexTypes... otherIndices)
     {
       static_assert(sizeof...(otherIndices) + 2 == NumIndices, "Number of indices used to access a tensor coefficient must be equal to the rank of the tensor.");
       return operator()(std::array<Index, NumIndices>{{firstIndex, secondIndex, otherIndices...}});
     }

     inline Scalar& operator()(const std::array<Index, NumIndices>& indices)
     {
       eigen_assert(checkIndexRange(indices));
       return coeffRef(indices);
     }

     inline Scalar& operator()(Index index)
     {
       eigen_assert(index >= 0 && index < size());
       return coeffRef(index);
     }

     inline Scalar& operator[](Index index)
     {
       static_assert(NumIndices == 1, "The bracket operator is only for vectors, use the parenthesis operator instead.");
       return coeffRef(index);
     }

     inline Tensor()
       : m_storage()
     {
     }

     inline Tensor(const Self& other)
       : m_storage(other.m_storage)
     {
     }

     inline Tensor(Self&& other)
       : m_storage(other.m_storage)
     {
     }

     template<typename... IndexTypes>
     inline Tensor(Index firstDimension, IndexTypes... otherDimensions)
       : m_storage()
     {
       static_assert(sizeof...(otherDimensions) + 1 == NumIndices, "Number of dimensions used to construct a tensor must be equal to the rank of the tensor.");
       resize(std::array<Index, NumIndices>{{firstDimension, otherDimensions...}});
     }

     inline Tensor(std::array<Index, NumIndices> dimensions)
       : m_storage(internal::array_prod(dimensions), dimensions)
     {
       EIGEN_INITIALIZE_COEFFS_IF_THAT_OPTION_IS_ENABLED
     }

     template<typename... IndexTypes>
     void resize(Index firstDimension, IndexTypes... otherDimensions)
     {
       static_assert(sizeof...(otherDimensions) + 1 == NumIndices, "Number of dimensions used to resize a tensor must be equal to the rank of the tensor.");
       resize(std::array<Index, NumIndices>{{firstDimension, otherDimensions...}});
     }

     void resize(const std::array<Index, NumIndices>& dimensions)
     {
       std::size_t i;
       Index size = Index(1);
       for (i = 0; i < NumIndices; i++) {
         internal::check_rows_cols_for_overflow<Dynamic>::run(size, dimensions[i]);
         size *= dimensions[i];
       }
       #ifdef EIGEN_INITIALIZE_COEFFS
         bool size_changed = size != this->size();
         m_storage.resize(size, dimensions);
         if(size_changed) EIGEN_INITIALIZE_COEFFS_IF_THAT_OPTION_IS_ENABLED
       #else
         m_storage.resize(size, dimensions);
       #endif
     }

   protected:
     bool checkIndexRange(const std::array<Index, NumIndices>& indices) const
     {
       using internal::array_apply_and_reduce;
       using internal::array_zip_and_reduce;
       using internal::greater_equal_zero_op;
       using internal::logical_and_op;
       using internal::lesser_op;

       return
         // check whether the indices are all >= 0
         array_apply_and_reduce<logical_and_op, greater_equal_zero_op>(indices) &&
         // check whether the indices fit in the dimensions
         array_zip_and_reduce<logical_and_op, lesser_op>(indices, m_storage.dimensions());
     }

     inline Index linearizedIndex(const std::array<Index, NumIndices>& indices) const
     {
       return internal::tensor_index_linearization_helper<Index, NumIndices, NumIndices - 1, Options&RowMajor>::run(indices, m_storage.dimensions());
     }
 };

 } // end namespace Eigen

 #endif // EIGEN_CXX11_TENSOR_TENSOR_H

 /*
  * kate: space-indent on; indent-width 2; mixedindent off; indent-mode cstyle;
  */
	// This file is part of Eigen, a lightweight C++ template library
	// for linear algebra.
	//
	// Copyright (C) 2013 Christian Seiler <christian@iwakd.de>
	//
	// 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_H
	#define EIGEN_CXX11_TENSOR_TENSOR_H

	namespace Eigen {

	/** \class Tensor
	* \ingroup CXX11_Tensor_Module
	*
	* \brief The tensor class.
	*
	* The %Tensor class is the work-horse for all \em dense tensors within Eigen.
	*
	* The %Tensor class encompasses only dynamic-size objects so far.
	*
	* The first two template parameters are required:
	* \tparam Scalar_ \anchor tensor_tparam_scalar Numeric type, e.g. float, double, int or std::complex<float>.
	* User defined scalar types are supported as well (see \ref user_defined_scalars "here").
	* \tparam NumIndices_ Number of indices (i.e. rank of the tensor)
	*
	* The remaining template parameters are optional -- in most cases you don't have to worry about them.
	* \tparam Options_ \anchor tensor_tparam_options A combination of either \b #RowMajor or \b #ColMajor, and of either
	* \b #AutoAlign or \b #DontAlign.
	* The former controls \ref TopicStorageOrders "storage order", and defaults to column-major. The latter controls alignment, which is required
	* for vectorization. It defaults to aligning tensors. Note that tensors currently do not support any operations that profit from vectorization.
	* Support for such operations (i.e. adding two tensors etc.) is planned.
	*
	* You can access elements of tensors using normal subscripting:
	*
	* \code
	* Eigen::Tensor<double, 4> t(10, 10, 10, 10);
	* t(0, 1, 2, 3) = 42.0;
	* \endcode
	*
	* This class can be extended with the help of the plugin mechanism described on the page
	* \ref TopicCustomizingEigen by defining the preprocessor symbol \c EIGEN_TENSOR_PLUGIN.
	*
	* <i><b>Some notes:</b></i>
	*
	* <dl>
	* <dt><b>Relation to other parts of Eigen:</b></dt>
	* <dd>The midterm developement goal for this class is to have a similar hierarchy as Eigen uses for matrices, so that
	* taking blocks or using tensors in expressions is easily possible, including an interface with the vector/matrix code
	* by providing .asMatrix() and .asVector() (or similar) methods for rank 2 and 1 tensors. However, currently, the %Tensor
	* class does not provide any of these features and is only available as a stand-alone class that just allows for
	* coefficient access. Also, when fixed-size tensors are implemented, the number of template arguments is likely to
	* change dramatically.</dd>
	* </dl>
	*
	* \ref TopicStorageOrders
	*/
	template<typename Scalar_, std::size_t NumIndices_, int Options_ = 0>
	class Tensor;

	namespace internal {
	template<typename Scalar_, std::size_t NumIndices_, int Options_>
	struct traits<Tensor<Scalar_, NumIndices_, Options_>>
	{
	typedef Scalar_ Scalar;
	typedef Dense StorageKind;
	typedef DenseIndex Index;
	enum {
	Options = Options_
	};
	};

	template<typename Index, std::size_t NumIndices, std::size_t n, bool RowMajor>
	struct tensor_index_linearization_helper
	{
	constexpr static inline Index run(std::array<Index, NumIndices> const& indices, std::array<Index, NumIndices> const& dimensions)
	{
	return std_array_get<RowMajor ? n : (NumIndices - n - 1)>(indices) +
	std_array_get<RowMajor ? n : (NumIndices - n - 1)>(dimensions) *
	tensor_index_linearization_helper<Index, NumIndices, n - 1, RowMajor>::run(indices, dimensions);
	}
	};

	template<typename Index, std::size_t NumIndices, bool RowMajor>
	struct tensor_index_linearization_helper<Index, NumIndices, 0, RowMajor>
	{
	constexpr static inline Index run(std::array<Index, NumIndices> const& indices, std::array<Index, NumIndices> const&)
	{
	return std_array_get<RowMajor ? 0 : NumIndices - 1>(indices);
	}
	};
	} // end namespace internal

	template<typename Scalar_, std::size_t NumIndices_, int Options_>
	class Tensor
	{
	static_assert(NumIndices_ >= 1, "A tensor must have at least one index.");

	public:
	typedef Tensor<Scalar_, NumIndices_, Options_> Self;
	typedef typename internal::traits<Self>::StorageKind StorageKind;
	typedef typename internal::traits<Self>::Index Index;
	typedef typename internal::traits<Self>::Scalar Scalar;
	typedef typename internal::packet_traits<Scalar>::type PacketScalar;
	typedef typename NumTraits<Scalar>::Real RealScalar;
	typedef Self DenseType;

	constexpr static int Options = Options_;
	constexpr static std::size_t NumIndices = NumIndices_;

	protected:
	TensorStorage<Scalar, NumIndices, Dynamic, Options> m_storage;

	public:
	EIGEN_STRONG_INLINE Index dimension(std::size_t n) const { return m_storage.dimensions()[n]; }
	EIGEN_STRONG_INLINE std::array<Index, NumIndices> dimensions() const { return m_storage.dimensions(); }
	EIGEN_STRONG_INLINE Index size() const { return internal::array_prod(m_storage.dimensions()); }
	EIGEN_STRONG_INLINE Scalar *data() { return m_storage.data(); }
	EIGEN_STRONG_INLINE const Scalar *data() const { return m_storage.data(); }

	// This makes EIGEN_INITIALIZE_COEFFS_IF_THAT_OPTION_IS_ENABLED
	// work, because that uses base().coeffRef() - and we don't yet
	// implement a similar class hierarchy
	inline Self& base() { return *this; }
	inline const Self& base() const { return *this; }

	void setZero()
	{
	// FIXME: until we have implemented packet access and the
	// expression engine w.r.t. nullary ops, use this
	// as a kludge. Only works with POD types, but for
	// any standard usage, this shouldn't be a problem
	memset((void )data(), 0, size() sizeof(Scalar));
	}

	inline Self& operator=(Self const& other)
	{
	m_storage = other.m_storage;
	return *this;
	}

	template<typename... IndexTypes>
	inline const Scalar& coeff(Index firstIndex, Index secondIndex, IndexTypes... otherIndices) const
	{
	static_assert(sizeof...(otherIndices) + 2 == NumIndices, "Number of indices used to access a tensor coefficient must be equal to the rank of the tensor.");
	return coeff(std::array<Index, NumIndices>{{firstIndex, secondIndex, otherIndices...}});
	}

	inline const Scalar& coeff(const std::array<Index, NumIndices>& indices) const
	{
	eigen_internal_assert(checkIndexRange(indices));
	return m_storage.data()[linearizedIndex(indices)];
	}

	inline const Scalar& coeff(Index index) const
	{
	eigen_internal_assert(index >= 0 && index < size());
	return m_storage.data()[index];
	}

	template<typename... IndexTypes>
	inline Scalar& coeffRef(Index firstIndex, Index secondIndex, IndexTypes... otherIndices)
	{
	static_assert(sizeof...(otherIndices) + 2 == NumIndices, "Number of indices used to access a tensor coefficient must be equal to the rank of the tensor.");
	return coeffRef(std::array<Index, NumIndices>{{firstIndex, secondIndex, otherIndices...}});
	}

	inline Scalar& coeffRef(const std::array<Index, NumIndices>& indices)
	{
	eigen_internal_assert(checkIndexRange(indices));
	return m_storage.data()[linearizedIndex(indices)];
	}

	inline Scalar& coeffRef(Index index)
	{
	eigen_internal_assert(index >= 0 && index < size());
	return m_storage.data()[index];
	}

	template<typename... IndexTypes>
	inline const Scalar& operator()(Index firstIndex, Index secondIndex, IndexTypes... otherIndices) const
	{
	static_assert(sizeof...(otherIndices) + 2 == NumIndices, "Number of indices used to access a tensor coefficient must be equal to the rank of the tensor.");
	return this->operator()(std::array<Index, NumIndices>{{firstIndex, secondIndex, otherIndices...}});
	}

	inline const Scalar& operator()(const std::array<Index, NumIndices>& indices) const
	{
	eigen_assert(checkIndexRange(indices));
	return coeff(indices);
	}

	inline const Scalar& operator()(Index index) const
	{
	eigen_internal_assert(index >= 0 && index < size());
	return coeff(index);
	}

	inline const Scalar& operator[](Index index) const
	{
	static_assert(NumIndices == 1, "The bracket operator is only for vectors, use the parenthesis operator instead.");
	return coeff(index);
	}

	template<typename... IndexTypes>
	inline Scalar& operator()(Index firstIndex, Index secondIndex, IndexTypes... otherIndices)
	{
	static_assert(sizeof...(otherIndices) + 2 == NumIndices, "Number of indices used to access a tensor coefficient must be equal to the rank of the tensor.");
	return operator()(std::array<Index, NumIndices>{{firstIndex, secondIndex, otherIndices...}});
	}

	inline Scalar& operator()(const std::array<Index, NumIndices>& indices)
	{
	eigen_assert(checkIndexRange(indices));
	return coeffRef(indices);
	}

	inline Scalar& operator()(Index index)
	{
	eigen_assert(index >= 0 && index < size());
	return coeffRef(index);
	}

	inline Scalar& operator[](Index index)
	{
	static_assert(NumIndices == 1, "The bracket operator is only for vectors, use the parenthesis operator instead.");
	return coeffRef(index);
	}

	inline Tensor()
	: m_storage()
	{
	}

	inline Tensor(const Self& other)
	: m_storage(other.m_storage)
	{
	}

	inline Tensor(Self&& other)
	: m_storage(other.m_storage)
	{
	}

	template<typename... IndexTypes>
	inline Tensor(Index firstDimension, IndexTypes... otherDimensions)
	: m_storage()
	{
	static_assert(sizeof...(otherDimensions) + 1 == NumIndices, "Number of dimensions used to construct a tensor must be equal to the rank of the tensor.");
	resize(std::array<Index, NumIndices>{{firstDimension, otherDimensions...}});
	}

	inline Tensor(std::array<Index, NumIndices> dimensions)
	: m_storage(internal::array_prod(dimensions), dimensions)
	{
	EIGEN_INITIALIZE_COEFFS_IF_THAT_OPTION_IS_ENABLED
	}

	template<typename... IndexTypes>
	void resize(Index firstDimension, IndexTypes... otherDimensions)
	{
	static_assert(sizeof...(otherDimensions) + 1 == NumIndices, "Number of dimensions used to resize a tensor must be equal to the rank of the tensor.");
	resize(std::array<Index, NumIndices>{{firstDimension, otherDimensions...}});
	}

	void resize(const std::array<Index, NumIndices>& dimensions)
	{
	std::size_t i;
	Index size = Index(1);
	for (i = 0; i < NumIndices; i++) {
	internal::check_rows_cols_for_overflow<Dynamic>::run(size, dimensions[i]);
	size *= dimensions[i];
	}
	#ifdef EIGEN_INITIALIZE_COEFFS
	bool size_changed = size != this->size();
	m_storage.resize(size, dimensions);
	if(size_changed) EIGEN_INITIALIZE_COEFFS_IF_THAT_OPTION_IS_ENABLED
	#else
	m_storage.resize(size, dimensions);
	#endif
	}

	protected:
	bool checkIndexRange(const std::array<Index, NumIndices>& indices) const
	{
	using internal::array_apply_and_reduce;
	using internal::array_zip_and_reduce;
	using internal::greater_equal_zero_op;
	using internal::logical_and_op;
	using internal::lesser_op;

	return
	// check whether the indices are all >= 0
	array_apply_and_reduce<logical_and_op, greater_equal_zero_op>(indices) &&
	// check whether the indices fit in the dimensions
	array_zip_and_reduce<logical_and_op, lesser_op>(indices, m_storage.dimensions());
	}

	inline Index linearizedIndex(const std::array<Index, NumIndices>& indices) const
	{
	return internal::tensor_index_linearization_helper<Index, NumIndices, NumIndices - 1, Options&RowMajor>::run(indices, m_storage.dimensions());
	}
	};

	} // end namespace Eigen

	#endif // EIGEN_CXX11_TENSOR_TENSOR_H

	/*
	* kate: space-indent on; indent-width 2; mixedindent off; indent-mode cstyle;
	*/