package owl
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OCaml Scientific and Engineering Computing
Install
dune-project
Dependency
Authors
Maintainers
Sources
owl-1.2.tbz
sha256=3817a2e4391922c8a2225b4e33ca95da6809246994e6bf291a300c82d8cac6c5
sha512=68a21f540cb4a289419f35cd152d132af36f1000fb41f98bab6e100698820379e36d650c5aa70a0126513451b354f86a28ea4ecf6f1d3b196b5b5e56f0fac9bd
doc/owl/Owl_dense_ndarray_c/index.html
Module Owl_dense_ndarray_cSource
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 ith 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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