package owl
OCaml Scientific and Engineering Computing
Install
Dune Dependency
Authors
Maintainers
Sources
owl-1.2.tbz
sha256=3817a2e4391922c8a2225b4e33ca95da6809246994e6bf291a300c82d8cac6c5
sha512=68a21f540cb4a289419f35cd152d132af36f1000fb41f98bab6e100698820379e36d650c5aa70a0126513451b354f86a28ea4ecf6f1d3b196b5b5e56f0fac9bd
doc/owl/Owl_dense_ndarray_c/index.html
Module Owl_dense_ndarray_c
Source
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 ->
arr
Source
val dilated_conv2d :
?padding:Owl_types_common.padding ->
arr ->
arr ->
int array ->
int array ->
arr
Source
val dilated_conv3d :
?padding:Owl_types_common.padding ->
arr ->
arr ->
int array ->
int array ->
arr
Source
val max_pool1d_backward :
Owl_types_common.padding ->
arr ->
int array ->
int array ->
arr ->
arr
Source
val max_pool2d_backward :
Owl_types_common.padding ->
arr ->
int array ->
int array ->
arr ->
arr
Source
val max_pool3d_backward :
Owl_types_common.padding ->
arr ->
int array ->
int array ->
arr ->
arr
Source
val avg_pool1d_backward :
Owl_types_common.padding ->
arr ->
int array ->
int array ->
arr ->
arr
Source
val avg_pool2d_backward :
Owl_types_common.padding ->
arr ->
int array ->
int array ->
arr ->
arr
Source
val avg_pool3d_backward :
Owl_types_common.padding ->
arr ->
int array ->
int array ->
arr ->
arr
Neural 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.t
Source
val dilated_conv1d_ :
out:arr ->
?padding:Owl_types.padding ->
arr ->
arr ->
int array ->
int array ->
unit
Source
val dilated_conv2d_ :
out:arr ->
?padding:Owl_types.padding ->
arr ->
arr ->
int array ->
int array ->
unit
Source
val dilated_conv3d_ :
out:arr ->
?padding:Owl_types.padding ->
arr ->
arr ->
int array ->
int array ->
unit
Source
val transpose_conv1d_ :
out:arr ->
?padding:Owl_types.padding ->
arr ->
arr ->
int array ->
unit
Source
val transpose_conv2d_ :
out:arr ->
?padding:Owl_types.padding ->
arr ->
arr ->
int array ->
unit
Source
val transpose_conv3d_ :
out:arr ->
?padding:Owl_types.padding ->
arr ->
arr ->
int array ->
unit
Source
val max_pool1d_ :
out:arr ->
?padding:Owl_types.padding ->
arr ->
int array ->
int array ->
unit
Source
val max_pool2d_ :
out:arr ->
?padding:Owl_types.padding ->
arr ->
int array ->
int array ->
unit
Source
val max_pool3d_ :
out:arr ->
?padding:Owl_types.padding ->
arr ->
int array ->
int array ->
unit
Source
val avg_pool1d_ :
out:arr ->
?padding:Owl_types.padding ->
arr ->
int array ->
int array ->
unit
Source
val avg_pool2d_ :
out:arr ->
?padding:Owl_types.padding ->
arr ->
int array ->
int array ->
unit
Source
val avg_pool3d_ :
out:arr ->
?padding:Owl_types.padding ->
arr ->
int array ->
int array ->
unit
Source
val max_pool1d_backward_ :
out:arr ->
Owl_types.padding ->
arr ->
int array ->
int array ->
arr ->
unit
Source
val max_pool2d_backward_ :
out:arr ->
Owl_types.padding ->
arr ->
int array ->
int array ->
arr ->
unit
Source
val max_pool3d_backward_ :
out:arr ->
Owl_types.padding ->
arr ->
int array ->
int array ->
arr ->
unit
Source
val avg_pool1d_backward_ :
out:arr ->
Owl_types.padding ->
arr ->
int array ->
int array ->
arr ->
unit
Source
val avg_pool2d_backward_ :
out:arr ->
Owl_types.padding ->
arr ->
int array ->
int array ->
arr ->
unit
Source
val avg_pool3d_backward_ :
out:arr ->
Owl_types.padding ->
arr ->
int array ->
int array ->
arr ->
unit
include 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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