contains all cuv functionality More...

Namespaces
namespace	alex_conv
	wrappers of convolution operations by Alex Kriszevsky
namespace	integral_img
	integral image computation
namespace	sep_conv
	separable convolution

Classes
struct	allocator
	Allocator allows allocation, deallocation and copying depending on memory_space_type. More...
struct	allocator< value_type, size_type, host_memory_space >
	specialization of template allocator for host_memory_space More...
class	cuda_array
	Wrapper for a 2D CUDAArray. More...
class	dia_matrix
	Class for diagonal matrices. More...
class	filter_factory
class	interleaved_image
	a wrapper around a tensor to provide an interleaved (e.g. More...
class	matrix
	Basic matrix class. More...
struct	reference
	This objects acts like a reference to the object stored at the wrapped pointer. More...
struct	column_major
	Tag for column major matrices. More...
struct	row_major
	Tag for row major matrices. More...
struct	linear_memory_tag
	tag for linear memory More...
struct	pitched_memory_tag
	tag for pitched memory More...
struct	other_memory_layout
	converts from column to row-major and vice versa More...
struct	other_memory_layout< column_major >
	specialisation: converts from column to row-major More...
struct	other_memory_layout< row_major >
	specialisation: converts from row to column-major More...
struct	other_memory_space
	converts from dev to host memory space and vice versa More...
struct	other_memory_space< dev_memory_space >
	specialisation: converts from dev_memory_space to host_memory_space More...
struct	other_memory_space< host_memory_space >
	specialisation: converts from host_memory_space to dev_memory_space More...
class	memory
	simply keeps a pointer and deallocates it when destroyed More...
class	linear_memory
	represents contiguous memory More...
class	pitched_memory
	represents 2D non-contiguous ("pitched") memory More...
struct	tensor_info
	contains infos about shape and stride on host and in the tensor data space. More...
class	tensor
	represents an n-dimensional array on GPU or CPU. More...
class	tensor_view
	primarily used as result of tensor::operator[] More...
struct	switch_value_type
	create a tensor type with the same template parameters, but with switched value type More...
struct	switch_memory_layout_type
	create a tensor type with the same template parameters, but with switched memory_layout_type More...
struct	switch_memory_space_type
	create a tensor type with the same template parameters, but with switched memory_space_type More...
class	image_pyramid
	image pyramid decreasing in size logarithmically. More...
class	host_block_descriptor
	Dummy Block descriptor on host. More...
class	dev_block_descriptor
	Block descriptors on device this class is needed for DIA_Mat = Dense_Mat * Dense_Mat it stores all blocks of size SPARSE_DIA_BLOCK_SIZE x SPARSE_DIA_BLOCK_SIZE of a regluar grid where at least one diagonal crosses the block. More...
struct	vector_type_traits
struct	vector_type_traits< float4 >
struct	vector_type_traits< float3 >
struct	vector_type_traits< float2 >
struct	vector_type_traits< float1 >
struct	vector_type_traits< float >
struct	vector< 1 >
struct	vector< 2 >
struct	vector< 3 >
struct	vector< 4 >
struct	host_memory_space
	Tag for host memory. More...
struct	dev_memory_space
	Tag for device memory. More...
struct	FalseType
	defines "False" More...
struct	TrueType
	defines "True" More...
struct	IsSame
	Checks whether two types are equal. More...
struct	IsSame< T, T >
struct	IsDifferent
	Checks whether two types are different. More...
struct	IsDifferent< T, T >
struct	unconst
	Remove "const" from a type. More...
struct	unconst< const T >
struct	If
	Switch result depending on Condition. More...
struct	If< false, Then, Else >
struct	EnableIfC
	enable-if controlled creation of SFINAE conditions More...
struct	EnableIfC< false, T >
struct	EnableIf
struct	DisableIf

Typedefs
typedef boost::detail::multi_array::index_range < boost::detail::multi_array::index, boost::detail::multi_array::size_type >	index_range
	defines an index range, stolen from boost::multi_array
typedef index_range::index	index
	the index type used in index_range, useful for comparator syntax in

Enumerations
enum	reduce_functor { RF_ADD, RF_MEAN, RF_ADD_SQUARED, RF_MAX, RF_ARGMAX, RF_ARGMIN, RF_MIN, RF_MULT, RF_LOGADDEXP, RF_ADDEXP }
	Reduce functor to reduce a matrix to a row or column. More...
enum	NullaryFunctor { NF_FILL, NF_SEQ }
	Nullary functors for vectors and matrices. More...
enum	ScalarFunctor { SF_EXP, SF_SIN, SF_COS, SF_LOG, SF_SIGN, SF_SIGM, SF_DSIGM, SF_SQUARE, SF_SUBLIN, SF_ENERG, SF_INV, SF_SQRT, SF_NEGATE, SF_ABS, SF_SMAX, SF_POSLIN, SF_RECT, SF_DRECT, SF_COPY, SF_LOG1P, SF_POW, SF_DPOW, SF_ADD, SF_SUBTRACT, SF_RSUB, SF_MULT, SF_DIV, SF_RDIV, SF_LOGADDEXP, SF_MIN, SF_MAX, SF_ROBUST_ABS, SF_DROBUST_ABS, SF_EQ, SF_LT, SF_GT, SF_LEQ, SF_GEQ, SF_BERNOULLI_KL, SF_DBERNOULLI_KL, SF_AXPB, SF_TANH, SF_DTANH }
	Scalar Functors for vectors and matrices Applied pointwise to a vector/matrix. More...
enum	BinaryFunctor { BF_1ST, BF_2ND, BF_EQ, BF_AND, BF_OR, BF_POW, BF_ADD, BF_SUBTRACT, BF_MULT, BF_DIV, BF_MIN, BF_MAX, BF_ATAN2, BF_NORM, BF_LOGADDEXP, BF_LOGCE_OF_LOGISTIC, BF_BERNOULLI_KL, BF_DBERNOULLI_KL, BF_AXPY, BF_XPBY, BF_AXPBY, BF_EPSILON_INSENSITIVE_LOSS, BF_DEPSILON_INSENSITIVE_LOSS, BF_HINGE_LOSS, BF_DHINGE_LOSS, BF_SQHINGE_LOSS, BF_DSQHINGE_LOSS }
	Binary functors for vectors and matrices. More...

Functions
template<class Archive , class value_type , class index_type >
void	serialize (Archive &ar, cuv::dia_matrix< value_type, host_memory_space, index_type > &m, const unsigned int version)
	Serialize/deserialize a host-dia-matrix to/from an archive.
template void	serialize (boost::archive::binary_oarchive &, dia_matrix< float, host_memory_space, unsigned int > &, unsigned int)
	explicit instantiation of serialization for dia-matrices in binary oarchives
template<class V , class M , class L , class S >
void	fill (tensor< V, M, L > &v, const V &p)
	used in implementation of tensor.operator= for value_type argument
template<class V , class M0 , class M1 , class L0 , class L1 >
bool	copy_memory (tensor< V, M0, L0 > &dst, const tensor< V, M1, L1 > &src, bool force_dst_contiguous)
	tries to copy memory, succeeds if shapes match AND both tensors are c_contiguous of 2dcopyable.
template<class V , class M0 , class M1 , class L0 , class L1 >
void	copy_memory (tensor< V, M0, L0 > &dst, const tensor< V, M1, L1 > &src, linear_memory_tag)
	copies between different memory spaces
template<class V , class M0 , class M1 , class L0 , class L1 >
void	copy_memory (tensor< V, M0, L0 > &dst, const tensor< V, M1, L1 > &src, pitched_memory_tag)
	copies between different memory spaces
template<class V , class V2 , class M , class M2 , class L >
bool	equal_shape (const tensor< V, M, L > &a, const tensor< V2, M2, L > &b)
	test whether two tensors have the same shape
template<class Dst , class Src >
void	convert (Dst &dst, const Src &src)
	Convert matrices and vectors between different formats.
template<class T , class S , class I >
void	gaussian_pyramid_downsample (tensor< T, S, row_major > &dst, const cuda_array< T, S, I > &src, const unsigned int interleaved_channels)
	sample down an image by a factor of 2
template<class T , class S , class I >
void	gaussian_pyramid_upsample (tensor< T, S, row_major > &dst, const cuda_array< T, S, I > &src)
template<class TDest , class T , class S , class I >
void	get_pixel_classes (tensor< TDest, S, row_major > &dst, const cuda_array< T, S, I > &src, float scale_fact)
template<class T , class S , class I >
void	gaussian (tensor< T, S, row_major > &dst, const cuda_array< T, S, I > &src)
template<class __value_typeA , class __value_typeB , class __memory_space_type , class __memory_layout_type >
void	image_move (tensor< __value_typeA, __memory_space_type, __memory_layout_type > &dst, const tensor< __value_typeB, __memory_space_type, __memory_layout_type > &src, const unsigned int &image_width, const unsigned int &image_height, const unsigned int &num_maps, const int &xshift, const int &yshift)
	Shift images by given amount.
template<class __value_type , class __memory_layout_type , class __index_type >
void	densedense_to_dia (dia_matrix< __value_type, dev_memory_space, __index_type > &C, const dev_block_descriptor< __value_type, __index_type > &Cbd, const tensor< __value_type, dev_memory_space, __memory_layout_type > &A, const tensor< __value_type, dev_memory_space, __memory_layout_type > &B, const __value_type &factAB=1.f, const __value_type &factC=0.f)
	DIA_Mat <- Dense_Mat * Dense_Mat_transposed.
template<class __value_type , class __memory_layout_type , class __index_type >
void	densedense_to_dia (dia_matrix< __value_type, host_memory_space, __index_type > &C, const host_block_descriptor< __value_type, __index_type > &Cbd, const tensor< __value_type, host_memory_space, __memory_layout_type > &A, const tensor< __value_type, host_memory_space, __memory_layout_type > &B, const __value_type &factAB=1.f, const __value_type &factC=0.f)
template<class T , class M >
void	avg_diagonals (cuv::tensor< T, M > &dst, const cuv::dia_matrix< T, M > &dia)
	get the average of all diagonals of a diagonal matrix
template<class V , class M , class __memory_layout , class __index_type >
tensor< V, M, __memory_layout > *	blockview (tensor< V, M, __memory_layout > &matrix, __index_type start_rows, __index_type num_rows, __index_type start_cols, __index_type num_cols)
	Generate a view to a block inside an existing matrix.
template<class V , class M , class L >
void	prod (tensor< V, M, L > &C, const tensor< V, M, L > &A, const tensor< V, M, L > &B, char transA='n', char transB='n', const float &factAB=1.f, const float &factC=0.f)
	Matrix multiplication and other BLAS3 functionality.
template<class V , class M , class L >
void	prod (tensor< V, M, L > &C, const tensor< V, M, L > &A, const tensor< V, M, typename other_memory_layout< L >::type > &B, const float &factAB=1.f, const float &factC=0.f)
template<class V , class M , class L >
void	prod (tensor< V, M, L > &C, const tensor< V, M, typename other_memory_layout< L >::type > &A, const tensor< V, M, L > &B, const float &factAB=1.f, const float &factC=0.f)
template<class V , class M , class L >
void	prod (tensor< V, M, L > &C, const dia_matrix< V, M > &A, const tensor< V, M, L > &B, char transA='n', char transB='n', const float &factAB=1.f, const float &factC=0.f)
template<class V , class M , class L >
void	transpose (tensor< V, M, L > &dst, const tensor< V, M, L > &src)
	Transpose a matrix.
template<class V , class T , class M >
cuv::tensor< V, T, typename other_memory_layout< M >::type > *	transposed_view_p (cuv::tensor< V, T, M > &src)
	Transpose a matrix by creating a view with different storage.
template<class V , class T , class M >
std::auto_ptr< cuv::tensor< V, T, typename other_memory_layout< M >::type > >	transposed_view (cuv::tensor< V, T, M > &src)
	As in.
template<class V , class T , class M >
const cuv::tensor< V, T, typename other_memory_layout < M >::type > *	transposed_view_p (const cuv::tensor< V, T, M > &src)
	Const variant of.
template<class V , class T , class M >
std::auto_ptr< const cuv::tensor< V, T, typename other_memory_layout< M >::type > >	transposed_view (const cuv::tensor< V, T, M > &src)
	As in.
template<class V , class __value_type2 , class M , class L >
void	reduce_to_col (tensor< V, M > &dst, const tensor< __value_type2, M, L > &src, reduce_functor rf=RF_ADD, const __value_type2 &factNew=1.f, const __value_type2 &factOld=0.f)
	Reduce a matrix to one column using specified reduce functor (or add them up by default)
template<class V , class __value_type2 , class M , class L >
void	reduce_to_row (tensor< V, M > &dst, const tensor< __value_type2, M, L > &src, reduce_functor rf=RF_ADD, const __value_type2 &factNew=1.f, const __value_type2 &factOld=0.f)
	Reduce a matrix to one row using specified reduce functor (or add them up by default)
template<class V , class M , class L >
tensor< V, M >	sum (const tensor< V, M, L > &src, const int &axis)
	Convenience function that creates a new vector and performs reduction by summing along given axis.
template<class V , class M >
void	spmv (tensor< V, M > &dst, const dia_matrix< V, M > &A, const tensor< V, M > &v, char transA='n', const float &factAv=1.f, const float &factC=0.f)
	Calculates product of a sparse matrix and a vector.
template<class V , class V2 , class M , class L >
void	matrix_op_vec (tensor< V, M, L > &Dst, const tensor< V, M, L > &Src, const tensor< V2, M > &v, int axis, BinaryFunctor bf, float factNew=1.f, float factOld=0.f, int n_params=0, float param0=0.f, float param1=0.f)
	Apply a binary functor on one axis of a tensor and a n-1-dimensional tensor.
template<class V , class M , class L >
void	matrix_plus_col (tensor< V, M, L > &A, const tensor< V, M > &v)
	Add a vector to each column of a matrix A.
template<class V , class M , class L >
void	matrix_times_col (tensor< V, M, L > &A, const tensor< V, M > &v)
	Multiply each column of a matrix A pointwise with a vector v.
template<class V , class M , class L >
void	matrix_divide_col (tensor< V, M, L > &A, const tensor< V, M > &v)
	Devide each column of a matrix A pointwise by a vector v.
template<class V , class M , class L >
void	matrix_plus_row (tensor< V, M, L > &A, const tensor< V, M > &v)
	Add a vector to each row of a matrix A.
template<class V , class M , class L >
void	matrix_times_row (tensor< V, M, L > &A, const tensor< V, M > &v)
	Multiply each row of a matrix A pointwise with a vector v.
template<class V , class M , class L >
void	matrix_divide_row (tensor< V, M, L > &A, const tensor< V, M > &v)
	Devide each row of a matrix A pointwise by a vector v.
template<class V , class M , class T >
void	fill_rnd_uniform (tensor< V, M, T > &dst)
	Fill a matrix/vector with random numbers uniformly distributed between zero and one.
template<class V , class M , class T >
void	rnd_binarize (tensor< V, M, T > &dst)
	Binarize a matrix/vector to 1 or 0 with probability given by current values.
template<class V , class M , class T >
void	add_rnd_normal (tensor< V, M, T > &dst, const float &std=1.0f)
	Add random numbers (normally distributed, mean 0) to a matrix/vector.
void	initialize_mersenne_twister_seeds (unsigned int seed=0)
	Initialize Mersenne twister to generate random numbers on GPU.
void	deinit_rng (unsigned int seed=0)
	destruction counterpart to
template<class __value_type , class __memory_space_type , class S >
void	rprop (tensor< __value_type, __memory_space_type > &W, tensor< __value_type, __memory_space_type > &dW, tensor< S, __memory_space_type > &dW_old, tensor< __value_type, __memory_space_type > &rate, const float &decay=0.0f, const float &sparsedecay=0.0f)
	Does a gradient descent step using the "RPROP" algorithm.
template<class __value_type , class __memory_space_type , class S >
void	rprop (tensor< __value_type, __memory_space_type, column_major > &W, tensor< __value_type, __memory_space_type, column_major > &dW, tensor< S, __memory_space_type, column_major > &dW_old, tensor< __value_type, __memory_space_type, column_major > &rate, const float &decay=0.0f, const float &sparsedecay=0.0f)
	This is an overloaded member function, provided for convenience. It differs from the above function only in what argument(s) it accepts. casting column major to row major since working on linear memory anyway.
template<class __value_type , class __memory_space_type >
void	learn_step_weight_decay (tensor< __value_type, __memory_space_type > &W, const tensor< __value_type, __memory_space_type > &dW, const float &learnrate, const float &decay=0.0f, const float &sparsedecay=0.0f)
	Do a step of gradient descent with optional weight decay.
template<class V , class M >
void	learn_step_weight_decay_momentum (tensor< V, M > &W, tensor< V, M > &momentum, const tensor< V, M > &dW, const float &learnrate, const float &momentum_weight=0.9, const float &decay=0.0f, const float &sparsedecay=0.0f)
	Same as learn_step_weight_decay, but with momentum.
template<class __value_type , class __memory_space_type >
void	learn_step_weight_decay (tensor< __value_type, __memory_space_type, column_major > &W, const tensor< __value_type, __memory_space_type, column_major > &dW, const float &learnrate, const float &decay=0.0f, const float &sparsedecay=0.0f)
	This is an overloaded member function, provided for convenience. It differs from the above function only in what argument(s) it accepts. casting column major to row major since working on linear memory anyway.
template<class __value_type , class __memory_space_type >
void	apply_0ary_functor (tensor< __value_type, __memory_space_type > &v, const NullaryFunctor &sf)
	Apply a pointwise nullary functor to a vector.
template<class __value_type , class __memory_space_type >
void	apply_0ary_functor (tensor< __value_type, __memory_space_type, column_major > &v, const NullaryFunctor &sf)
	This is an overloaded member function, provided for convenience. It differs from the above function only in what argument(s) it accepts.
template<class V1 , class M >
void	apply_0ary_functor (tensor< V1, M > &v, const NullaryFunctor &sf, const V1 &param)
	This is an overloaded member function, provided for convenience. It differs from the above function only in what argument(s) it accepts.
template<class V1 , class M >
void	apply_0ary_functor (tensor< V1, M, column_major > &v, const NullaryFunctor &sf, const V1 &param)
	This is an overloaded member function, provided for convenience. It differs from the above function only in what argument(s) it accepts.
template<class __value_type , class __memory_space_type , class __memory_layout_type >
void	sequence (tensor< __value_type, __memory_space_type, __memory_layout_type > &v)
	Fill a vector with a sequence of numbers.
template<class __value_type , class __memory_space_type , class __memory_layout_type , class S >
void	fill (tensor< __value_type, __memory_space_type, __memory_layout_type > &v, const S &p)
	Fill a vector with a value.
template<class D >
void	apply_scalar_functor (D &v, const ScalarFunctor &sf, const tensor< unsigned char, typename D::memory_space_type, typename D::memory_layout_type > *mask=NULL)
	apply a scalar functor to all elements of v.
template<class D , class S >
void	apply_scalar_functor (D &dst, const S &src, const ScalarFunctor &sf, const tensor< unsigned char, typename D::memory_space_type, typename D::memory_layout_type > *mask=NULL)
	This is an overloaded member function, provided for convenience. It differs from the above function only in what argument(s) it accepts. apply a scalar functor to all elements of src.
template<class D >
void	apply_scalar_functor (D &v, const ScalarFunctor &sf, const typename D::value_type &p, const tensor< unsigned char, typename D::memory_space_type, typename D::memory_layout_type > *mask=NULL)
	This is an overloaded member function, provided for convenience. It differs from the above function only in what argument(s) it accepts. in-place, one parameter.
template<class D , class S >
void	apply_scalar_functor (D &dst, const S &src, const ScalarFunctor &sf, const typename S::value_type &p, const tensor< unsigned char, typename D::memory_space_type, typename D::memory_layout_type > *mask=NULL)
	This is an overloaded member function, provided for convenience. It differs from the above function only in what argument(s) it accepts.one parameter.
template<class D >
void	apply_scalar_functor (D &v, const ScalarFunctor &sf, const typename D::value_type &p, const typename D::value_type &p2, const tensor< unsigned char, typename D::memory_space_type, typename D::memory_layout_type > *mask=NULL)
	This is an overloaded member function, provided for convenience. It differs from the above function only in what argument(s) it accepts.in-place, two parameters.
template<class D , class S >
void	apply_scalar_functor (D &dst, const S &src, const ScalarFunctor &sf, const typename S::value_type &p, const typename S::value_type &p2, const tensor< unsigned char, typename D::memory_space_type, typename D::memory_layout_type > *mask=NULL)
	This is an overloaded member function, provided for convenience. It differs from the above function only in what argument(s) it accepts.
template<class D , class S >
void	apply_binary_functor (D &v, const S &w, const BinaryFunctor &bf)
	in-place, no parameters.
template<class D , class S , class S2 >
void	apply_binary_functor (D &v, const S &w, const S2 &w2, const BinaryFunctor &bf)
	This is an overloaded member function, provided for convenience. It differs from the above function only in what argument(s) it accepts.
template<class D , class S >
void	apply_binary_functor (D &v, const S &w, const BinaryFunctor &bf, const typename S::value_type &param)
	This is an overloaded member function, provided for convenience. It differs from the above function only in what argument(s) it accepts.
template<class D , class S , class S2 >
void	apply_binary_functor (D &v, const S &w, const S2 &w2, const BinaryFunctor &bf, const typename S::value_type &param)
	This is an overloaded member function, provided for convenience. It differs from the above function only in what argument(s) it accepts.
template<class D , class S >
void	apply_binary_functor (D &v, const S &w, const BinaryFunctor &bf, const typename S::value_type &param, const typename S::value_type &param2)
	This is an overloaded member function, provided for convenience. It differs from the above function only in what argument(s) it accepts.
template<class D , class S , class S2 >
void	apply_binary_functor (D &v, const S &w, const S2 &w2, const BinaryFunctor &bf, const typename S::value_type &param, const typename S::value_type &param2)
	This is an overloaded member function, provided for convenience. It differs from the above function only in what argument(s) it accepts.
template<class __value_type , class __memory_space_type , class __memory_layout_type >
void	copy (tensor< __value_type, __memory_space_type, __memory_layout_type > &dst, const tensor< __value_type, __memory_space_type, __memory_layout_type > &src)
	Copy one vector into another.
template<class __value_type , class __memory_space_type >
bool	has_inf (const tensor< __value_type, __memory_space_type > &v)
	Check whether a float vector contains "Inf" or "-Inf".
template<class __value_type , class __memory_space_type >
bool	has_inf (const tensor< __value_type, __memory_space_type, column_major > &v)
template<class __value_type , class __memory_space_type >
bool	has_nan (const tensor< __value_type, __memory_space_type > &v)
	Check whether a float vector contains "NaN".
template<class __value_type , class __memory_space_type >
bool	has_nan (const tensor< __value_type, __memory_space_type, column_major > &v)
template<class __value_type , class __memory_space_type >
unsigned int	count (const tensor< __value_type, __memory_space_type > &v, const __value_type &s)
	Count the elements with a certain scalar value in a vector.
template<class __value_type , class __memory_space_type >
unsigned int	count (const tensor< __value_type, __memory_space_type, column_major > &v, const __value_type &s)
template<class __value_type , class __memory_space_type >
float	sum (const tensor< __value_type, __memory_space_type > &v)
	Return the sum of a vector.
template<class __value_type , class __memory_space_type >
float	sum (const tensor< __value_type, __memory_space_type, column_major > &v)
template<class __value_type , class __memory_space_type >
float	norm2 (const tensor< __value_type, __memory_space_type > &v)
	Return the two-norm or Euclidean norm of a vector.
template<class __value_type , class __memory_space_type >
float	norm2 (const tensor< __value_type, __memory_space_type, column_major > &v)
template<class __value_type , class __memory_space_type >
float	diff_norm2 (const tensor< __value_type, __memory_space_type > &v, const tensor< __value_type, __memory_space_type > &w)
	Return the two-norm or of the difference of two vectors.
template<class __value_type , class __memory_space_type >
float	diff_norm2 (const tensor< __value_type, __memory_space_type, column_major > &v, const tensor< __value_type, __memory_space_type, column_major > &w)
template<class __value_type , class __memory_space_type >
float	norm1 (const tensor< __value_type, __memory_space_type > &v)
	Return the one-norm or sum-norm of a vector.
template<class __value_type , class __memory_space_type >
float	norm1 (const tensor< __value_type, __memory_space_type, column_major > &v)
template<class __value_type , class __memory_space_type >
float	minimum (const tensor< __value_type, __memory_space_type > &v)
	Return the minimum entry of a vector.
template<class __value_type , class __memory_space_type >
float	minimum (const tensor< __value_type, __memory_space_type, column_major > &v)
template<class __value_type , class __memory_space_type >
float	maximum (const tensor< __value_type, __memory_space_type > &v)
	Return the maximum entry of a vector.
template<class __value_type , class __memory_space_type >
float	maximum (const tensor< __value_type, __memory_space_type, column_major > &v)
template<class __value_type , class __memory_space_type >
float	mean (const tensor< __value_type, __memory_space_type > &v)
	Return the mean of the entries of a vector.
template<class __value_type , class __memory_space_type >
float	mean (const tensor< __value_type, __memory_space_type, column_major > &v)
template<class __value_type , class __memory_space_type >
float	var (const tensor< __value_type, __memory_space_type > &v)
	Return the variation of the entries of a vector.
template<class __value_type , class __memory_space_type >
float	var (const tensor< __value_type, __memory_space_type, column_major > &v)
template<class __value_type , class __memory_space_type >
tensor< __value_type, __memory_space_type > ::index_type	arg_max (const tensor< __value_type, __memory_space_type > &v)
	Return the index of the maximum element.
template<class __value_type , class __memory_space_type >
tensor< __value_type, __memory_space_type, column_major >::index_type	arg_max (const tensor< __value_type, __memory_space_type, column_major > &v)
template<class __value_type , class __memory_space_type >
tensor< __value_type, __memory_space_type > ::index_type	arg_min (const tensor< __value_type, __memory_space_type > &v)
	Return the index of the minimum element.
template<class __value_type , class __memory_space_type >
tensor< __value_type, __memory_space_type, column_major >::index_type	arg_min (const tensor< __value_type, __memory_space_type, column_major > &v)
void	cuvAssertFailed (const char *msg)
	fail with an error message, a stack trace and a runtime_exception (the nicest failures you've seen ^^!)
void	checkCudaError (const char *msg)
	check whether cuda thinks there was an error and fail with msg, if this is the case
void	initCUDA (int dev=0)
	Initializes CUDA context.
void	exitCUDA ()
	quit cuda
void	safeThreadSync ()
	synch threads from plain (non-cuda) C++
int	getFreeDeviceMemory ()
	Get available memory on device.
int	getMaxDeviceMemory ()
	Get device memory.
int	countDevices ()
	Returns number of CUDA devices.
int	getCurrentDevice ()
	Get device id of current CUDA context.

Detailed Description

contains all cuv functionality

Typedef Documentation

typedef index_range::index cuv::index

the index type used in index_range, useful for comparator syntax in

See Also: index_range

Definition at line 129 of file tensor.hpp.

typedef boost::detail::multi_array::index_range<boost::detail::multi_array::index,boost::detail::multi_array::size_type> cuv::index_range

defines an index range, stolen from boost::multi_array

examples:

index_range(1,3)

index(1) <= index_range() < index(3)

Definition at line 125 of file tensor.hpp.

Function Documentation

template<class T , class M >

void cuv::avg_diagonals	(	cuv::tensor< T, M > &	dst,
		const cuv::dia_matrix< T, M > &	dia
	)

get the average of all diagonals of a diagonal matrix

Parameters

dst	the vector where the results are stored in
dia	the diagonal matrix where the diagonals are supposed to be summed

template<class V , class M , class __memory_layout , class __index_type >

tensor<V,M,__memory_layout>* cuv::blockview	(	tensor< V, M, __memory_layout > &	matrix,
		__index_type	start_rows,
		__index_type	num_rows,
		__index_type	start_cols,
		__index_type	num_cols
	)

Generate a view to a block inside an existing matrix.

Parameters

matrix	Matrix to generate a view from
start_rows	First row in block
num_rows	Number of rows in block
start_cols	First column in block
num_cols	Number of columns in block

Returns

View of specified block inside matrix

    Returns a matrix of size num_rows x num_cols that is a view to the entries of matrix,
    starting at entry start_rows,start_cols to entry start_rows+num_rows,start_cols+num_cols.
    For row major matrices, this only works with start_cols=0 and num_cols=matrix.w().
    For column major matrices, this only works with start_rows=0 and num_rows=matrix.h().

template<class __value_type , class __memory_layout_type , class __index_type >

void cuv::densedense_to_dia	(	dia_matrix< __value_type, dev_memory_space, __index_type > &	C,
		const dev_block_descriptor< __value_type, __index_type > &	Cbd,
		const tensor< __value_type, dev_memory_space, __memory_layout_type > &	A,
		const tensor< __value_type, dev_memory_space, __memory_layout_type > &	B,
		const __value_type &	factAB = `1.f`,
		const __value_type &	factC = `0.f`
	)

DIA_Mat <- Dense_Mat * Dense_Mat_transposed.

This is one special case for matrix multiplication, where the second matrix is transposed and only the elements on the diagonals of a DIA matrix must be computed. The function is needed for the backwards-pass of a neural network.

Parameters

C	target matrix
Cbd	block descriptor (is not changed, so you can re-use the bd for all matrices with same layout)
A	A as in C=A*B'
B	B as in C=A*B'
factAB	the result of AB is multiplied with this factAB and then added to factCC
factC	the result of AB is multiplied with this factAB and then added to factCC

template<class __value_typeA , class __value_typeB , class __memory_space_type , class __memory_layout_type >

void cuv::image_move	(	tensor< __value_typeA, __memory_space_type, __memory_layout_type > &	dst,
		const tensor< __value_typeB, __memory_space_type, __memory_layout_type > &	src,
		const unsigned int &	image_width,
		const unsigned int &	image_height,
		const unsigned int &	num_maps,
		const int &	xshift,
		const int &	yshift
	)

Shift images by given amount.

Parameters

dst	where the moved images are written
src	unsigned char where original images are taken from
src_image_size	width and height of image in source
dst_image_size	width and height of image in destination
src_num_maps	how many maps there are in src
xshift	how much to shift right
yshift	how much to shift down

template<class Archive , class value_type , class index_type >

void cuv::serialize	(	Archive &	ar,
		cuv::dia_matrix< value_type, host_memory_space, index_type > &	m,
		const unsigned int	version
	)

Serialize/deserialize a host-dia-matrix to/from an archive.

Parameters

ar	the archive
m	the dia-matrix to serialize
version	not used

Definition at line 60 of file sparse_matrix_io.hpp.

Namespaces

Classes

Typedefs

Enumerations

Functions

Detailed Description

Typedef Documentation

Function Documentation