package owl
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OCaml Scientific and Engineering Computing
Install
dune-project
Dependency
Authors
Maintainers
Sources
owl-1.0.0.tbz
sha256=d91ba09488edd602dad845f68db1f980a601bdbb55d9516e3b59681eca20debe
sha512=9b31c3474a94c3b11d1dedba00639e770737e61f2e724a1288066ed976e4d0f8afe891a430e17ecf525fbca92e433d71d1b66d3ba17d4e299a4f8fdc3b902461
doc/owl/Owl_dense_ndarray_z/index.html
Module Owl_dense_ndarray_zSource
include Owl_dense_ndarray_intf.Common with type elt := elt and type arr := arr
include Owl_base_dense_ndarray_intf.Common
with type elt := elt
with type arr := arr
Iterate array elements
Examination & Comparison
Create N-dimensional array
``unit_basis k n i`` returns a unit basis vector with ``i``th element set to 1.
Obtain basic properties
Manipulate a N-dimensional array
Iterate array elements
Examine array elements or compare two arrays
Input/Output functions
Unary mathematical operations
Binary mathematical operations
Tensor Calculus
Experimental functions
Functions of in-place modification
include Owl_dense_ndarray_intf.NN with type arr := arr
include Owl_base_dense_ndarray_intf.NN with type arr := arr
Source
val dilated_conv1d :
?padding:Owl_types_common.padding ->
arr ->
arr ->
int array ->
int array ->
arrSource
val dilated_conv2d :
?padding:Owl_types_common.padding ->
arr ->
arr ->
int array ->
int array ->
arrSource
val dilated_conv3d :
?padding:Owl_types_common.padding ->
arr ->
arr ->
int array ->
int array ->
arrSource
val max_pool1d_backward :
Owl_types_common.padding ->
arr ->
int array ->
int array ->
arr ->
arrSource
val max_pool2d_backward :
Owl_types_common.padding ->
arr ->
int array ->
int array ->
arr ->
arrSource
val max_pool3d_backward :
Owl_types_common.padding ->
arr ->
int array ->
int array ->
arr ->
arrSource
val avg_pool1d_backward :
Owl_types_common.padding ->
arr ->
int array ->
int array ->
arr ->
arrSource
val avg_pool2d_backward :
Owl_types_common.padding ->
arr ->
int array ->
int array ->
arr ->
arrSource
val avg_pool3d_backward :
Owl_types_common.padding ->
arr ->
int array ->
int array ->
arr ->
arrNeural network related functions
Source
val max_pool2d_argmax :
?padding:Owl_types.padding ->
arr ->
int array ->
int array ->
arr * (int64, Bigarray.int64_elt, Bigarray.c_layout) Bigarray.Genarray.tSource
val dilated_conv1d_ :
out:arr ->
?padding:Owl_types.padding ->
arr ->
arr ->
int array ->
int array ->
unitSource
val dilated_conv2d_ :
out:arr ->
?padding:Owl_types.padding ->
arr ->
arr ->
int array ->
int array ->
unitSource
val dilated_conv3d_ :
out:arr ->
?padding:Owl_types.padding ->
arr ->
arr ->
int array ->
int array ->
unitSource
val transpose_conv1d_ :
out:arr ->
?padding:Owl_types.padding ->
arr ->
arr ->
int array ->
unitSource
val transpose_conv2d_ :
out:arr ->
?padding:Owl_types.padding ->
arr ->
arr ->
int array ->
unitSource
val transpose_conv3d_ :
out:arr ->
?padding:Owl_types.padding ->
arr ->
arr ->
int array ->
unitSource
val max_pool1d_ :
out:arr ->
?padding:Owl_types.padding ->
arr ->
int array ->
int array ->
unitSource
val max_pool2d_ :
out:arr ->
?padding:Owl_types.padding ->
arr ->
int array ->
int array ->
unitSource
val max_pool3d_ :
out:arr ->
?padding:Owl_types.padding ->
arr ->
int array ->
int array ->
unitSource
val avg_pool1d_ :
out:arr ->
?padding:Owl_types.padding ->
arr ->
int array ->
int array ->
unitSource
val avg_pool2d_ :
out:arr ->
?padding:Owl_types.padding ->
arr ->
int array ->
int array ->
unitSource
val avg_pool3d_ :
out:arr ->
?padding:Owl_types.padding ->
arr ->
int array ->
int array ->
unitSource
val max_pool1d_backward_ :
out:arr ->
Owl_types.padding ->
arr ->
int array ->
int array ->
arr ->
unitSource
val max_pool2d_backward_ :
out:arr ->
Owl_types.padding ->
arr ->
int array ->
int array ->
arr ->
unitSource
val max_pool3d_backward_ :
out:arr ->
Owl_types.padding ->
arr ->
int array ->
int array ->
arr ->
unitSource
val avg_pool1d_backward_ :
out:arr ->
Owl_types.padding ->
arr ->
int array ->
int array ->
arr ->
unitSource
val avg_pool2d_backward_ :
out:arr ->
Owl_types.padding ->
arr ->
int array ->
int array ->
arr ->
unitSource
val avg_pool3d_backward_ :
out:arr ->
Owl_types.padding ->
arr ->
int array ->
int array ->
arr ->
unitinclude Owl_dense_ndarray_intf.Complex
with type elt := elt
and type arr := arr
and type cast_arr := cast_arr
Complex operations
``complex re im`` constructs a complex ndarray/matrix from ``re`` and ``im``. ``re`` and ``im`` contain the real and imaginary part of ``x`` respectively.
Note that both ``re`` and ``im`` can be complex but must have same type. The real part of ``re`` will be the real part of ``x`` and the imaginary part of ``im`` will be the imaginary part of ``x``.
``polar rho theta`` constructs a complex ndarray/matrix from polar coordinates ``rho`` and ``theta``. ``rho`` contains the magnitudes and ``theta`` contains phase angles. Note that the behaviour is undefined if ``rho`` has negative elelments or ``theta`` has infinity elelments.
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