package owl
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On This Page
- Create N-dimensional array
- 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
- Neural network related functions
- Tensor Calculus
- Experimental functions
- Fucntions of in-place modification
- Matrix functions
- Stats & distribution functions
Owl - An OCaml Numerical Library
Install
dune-project
Dependency
Authors
Maintainers
Sources
owl-base-0.3.7.tbz
sha256=28d6c909f8f91cd8fd61fd1079b2f0e4bf8917bf33e2da96607caf63c73d0a39
md5=16454681ed82d527edf25eaee668c88a
doc/owl/Owl_dense_ndarray_d/index.html
Module Owl_dense_ndarray_d
type arr = (float, Bigarray.float64_elt, Bigarray.c_layout) Bigarray.Genarray.tCreate N-dimensional array
val empty : int array -> arrval zeros : int array -> arrval ones : int array -> arrval bernoulli : ?p:float -> int array -> arrObtain basic properties
val shape : arr -> int arrayval num_dims : arr -> intval nth_dim : arr -> int -> intval numel : arr -> intval nnz : arr -> intval density : arr -> floatval size_in_bytes : arr -> intval strides : arr -> int arrayval slice_size : arr -> int arrayval ind : arr -> int -> int arrayval i1d : arr -> int array -> intManipulate a N-dimensional array
val get_fancy : Owl_types.index list -> arr -> arrval set_fancy : Owl_types.index list -> arr -> arr -> unitval reset : arr -> unitval top : arr -> int -> int array arrayval bottom : arr -> int -> int array arrayval mmap : Unix.file_descr -> ?pos:int64 -> bool -> int array -> arrIterate array elements
Examine array elements or compare two arrays
val is_zero : arr -> boolval is_positive : arr -> boolval is_negative : arr -> boolval is_nonpositive : arr -> boolval is_nonnegative : arr -> boolval is_normal : arr -> boolval not_nan : arr -> boolval not_inf : arr -> boolInput/Output functions
val save : arr -> string -> unitval load : string -> arrUnary mathematical operations
val log_sum_exp' : arr -> floatBinary mathematical operations
Neural network related functions
val conv1d : ?padding:Owl_types.padding -> arr -> arr -> int array -> arrval conv2d : ?padding:Owl_types.padding -> arr -> arr -> int array -> arrval conv3d : ?padding:Owl_types.padding -> arr -> arr -> int array -> arrval max_pool1d :
?padding:Owl_types.padding ->
arr ->
int array ->
int array ->
arrval max_pool2d :
?padding:Owl_types.padding ->
arr ->
int array ->
int array ->
arrval max_pool3d :
?padding:Owl_types.padding ->
arr ->
int array ->
int array ->
arrval avg_pool1d :
?padding:Owl_types.padding ->
arr ->
int array ->
int array ->
arrval avg_pool2d :
?padding:Owl_types.padding ->
arr ->
int array ->
int array ->
arrval avg_pool3d :
?padding:Owl_types.padding ->
arr ->
int array ->
int array ->
arrval max_pool2d_argmax :
?padding:Owl_types.padding ->
arr ->
int array ->
int array ->
arr * (int64, Bigarray.int64_elt, Bigarray.c_layout) Bigarray.Genarray.tval max_pool1d_backward :
Owl_types.padding ->
arr ->
int array ->
int array ->
arr ->
arrval max_pool2d_backward :
Owl_types.padding ->
arr ->
int array ->
int array ->
arr ->
arrval max_pool3d_backward :
Owl_types.padding ->
arr ->
int array ->
int array ->
arr ->
arrval avg_pool1d_backward :
Owl_types.padding ->
arr ->
int array ->
int array ->
arr ->
arrval avg_pool2d_backward :
Owl_types.padding ->
arr ->
int array ->
int array ->
arr ->
arrval avg_pool3d_backward :
Owl_types.padding ->
arr ->
int array ->
int array ->
arr ->
arrTensor Calculus
Experimental functions
Fucntions of in-place modification
val sort_ : arr -> unitval conj_ : arr -> unitval abs_ : arr -> unitval neg_ : arr -> unitval reci_ : arr -> unitval signum_ : arr -> unitval sqr_ : arr -> unitval sqrt_ : arr -> unitval cbrt_ : arr -> unitval exp_ : arr -> unitval exp2_ : arr -> unitval exp10_ : arr -> unitval expm1_ : arr -> unitval log_ : arr -> unitval log2_ : arr -> unitval log10_ : arr -> unitval log1p_ : arr -> unitval sin_ : arr -> unitval cos_ : arr -> unitval tan_ : arr -> unitval asin_ : arr -> unitval acos_ : arr -> unitval atan_ : arr -> unitval sinh_ : arr -> unitval cosh_ : arr -> unitval tanh_ : arr -> unitval asinh_ : arr -> unitval acosh_ : arr -> unitval atanh_ : arr -> unitval floor_ : arr -> unitval ceil_ : arr -> unitval round_ : arr -> unitval trunc_ : arr -> unitval fix_ : arr -> unitval erf_ : arr -> unitval erfc_ : arr -> unitval relu_ : arr -> unitval softplus_ : arr -> unitval softsign_ : arr -> unitval sigmoid_ : arr -> unitval softmax_ : arr -> unitval cumsum_ : ?axis:int -> arr -> unitval cumprod_ : ?axis:int -> arr -> unitval cummin_ : ?axis:int -> arr -> unitval cummax_ : ?axis:int -> arr -> unitval dropout_ : ?rate:float -> arr -> unitMatrix functions
val row_num : arr -> intval col_num : arr -> intStats & distribution functions
sectionYPositions = computeSectionYPositions($el), 10)"
x-init="setTimeout(() => sectionYPositions = computeSectionYPositions($el), 10)"
>
On This Page
- Create N-dimensional array
- 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
- Neural network related functions
- Tensor Calculus
- Experimental functions
- Fucntions of in-place modification
- Matrix functions
- Stats & distribution functions