Legend:
Library
Module
Module type
Parameter
Class
Class type
N-dimensional array module: including creation, manipulation, and various vectorised mathematical operations.
About the comparison of two complex numbers ``x`` and ``y``, Owl uses the following conventions: 1) ``x`` and ``y`` are equal iff both real and imaginary parts are equal; 2) ``x`` is less than ``y`` if the magnitude of ``x`` is less than the magnitude of ``y``; in case both ``x`` and ``y`` have the same magnitudes, ``x`` is less than ``y`` if the phase of ``x`` is less than the phase of ``y``; 3) less or equal, greater, greater or equal relation can be further defined atop of the aforementioned conventions.
Type definition
type('a, 'b) t = ('a, 'b, Stdlib.Bigarray.c_layout)Stdlib.Bigarray.Genarray.t
N-dimensional array type, i.e. Bigarray Genarray type.
type('a, 'b) kind = ('a, 'b)Stdlib.Bigarray.kind
Type of the ndarray, e.g., Bigarray.Float32, Bigarray.Complex64, and etc.
``empty Bigarray.Float64 |3;4;5|`` creates a three diemensional array of ``Bigarray.Float64`` type. Each dimension has the following size: 3, 4, and 5. The elements in the array are not initialised, they can be any value. ``empty`` is faster than ``zeros`` to create a ndarray.
The module only supports the following four types of ndarray: ``Bigarray.Float32``, ``Bigarray.Float64``, ``Bigarray.Complex32``, and ``Bigarray.Complex64``.
val create : ('a, 'b)kind->int array->'a->('a, 'b)t
``create Bigarray.Float64 |3;4;5| 2.`` creates a three-diemensional array of ``Bigarray.Float64`` type. Each dimension has the following size: 3, 4, and 5. The elements in the array are initialised to ``2.``
val init : ('a, 'b)kind->int array->(int ->'a)->('a, 'b)t
``init Bigarray.Float64 d f`` creates a ndarray ``x`` of shape ``d``, then using ``f`` to initialise the elements in ``x``. The input of ``f`` is 1-dimensional index of the ndarray. You need to explicitly convert it if you need N-dimensional index. The function ``ind`` can help you.
val init_nd : ('a, 'b)kind->int array->(int array->'a)->('a, 'b)t
``init_nd`` is almost the same as ``init`` but ``f`` receives n-dimensional index as input. It is more convenient since you don't have to convert the index by yourself, but this also means ``init_nd`` is slower than ``init``.
``zeros Bigarray.Complex32 |3;4;5|`` creates a three-diemensional array of ``Bigarray.Complex32`` type. Each dimension has the following size: 3, 4, and 5. The elements in the array are initialised to "zero". Depending on the ``kind``, zero can be ``0.`` or ``Complex.zero``.
``ones Bigarray.Complex32 |3;4;5|`` creates a three-diemensional array of ``Bigarray.Complex32`` type. Each dimension has the following size: 3, 4, and 5. The elements in the array are initialised to "one". Depending on the ``kind``, one can be ``1.`` or ``Complex.one``.
``eye m`` creates an ``m`` by ``m`` identity matrix.
val uniform : ('a, 'b)kind->?a:'a->?b:'a->int array->('a, 'b)t
``uniform Bigarray.Float64 |3;4;5|`` creates a three-diemensional array of type ``Bigarray.Float64``. Each dimension has the following size: 3, 4, and 5. The elements in the array follow a uniform distribution ``0,1``.
val gaussian : ('a, 'b)kind->?mu:'a->?sigma:'a->int array->('a, 'b)t
``gaussian Float64 |3;4;5|`` ...
val poisson : ('a, 'b)kind->mu:float ->int array->('a, 'b)t
``poisson Float64 |3;4;5|`` ...
val sequential : ('a, 'b)kind->?a:'a->?step:'a->int array->('a, 'b)t
``sequential Bigarray.Float64 |3;4;5| 2.`` creates a three-diemensional array of type ``Bigarray.Float64``. Each dimension has the following size: 3, 4, and 5. The elements in the array are assigned sequential values.
``?a`` specifies the starting value and the default value is zero; whilst ``?step`` specifies the step size with default value one.
val linspace : ('a, 'b)kind->'a->'a->int ->('a, 'b)t
``linspace k 0. 9. 10`` ...
val logspace : ('a, 'b)kind->?base:float ->'a->'a->int ->('a, 'b)t
``logspace k 0. 9. 10`` ...
val bernoulli : ('a, 'b)kind->?p:float ->int array->('a, 'b)t
``bernoulli k ~p:0.3 |2;3;4|``
val complex :
('a, 'b)kind->('c, 'd)kind->('a, 'b)t->('a, 'b)t->('c, 'd)t
``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``.
val polar :
('a, 'b)kind->('c, 'd)kind->('a, 'b)t->('a, 'b)t->('c, 'd)t
``complex 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.
val unit_basis : ('a, 'b)kind->int ->int ->('a, 'b)t
``unit_basis k n i`` returns a unit basis vector with ``i``th element set to 1.
``same_data x y`` checks whether ``x`` and ``y`` share the same underlying data in the memory. Namely, both variables point to the same memory address. This is done by checking the ``Data`` pointer in the Bigarray structure.
This function is very useful for avoiding unnecessary copying between two ndarrays especially if one has been reshaped or sliced.
``kind x`` returns the type of ndarray ``x``. It is one of the four possible values: ``Bigarray.Float32``, ``Bigarray.Float64``, ``Bigarray.Complex32``, and ``Bigarray.Complex64``.
``set x i a`` sets the value at ``i`` to ``a`` in ``x``.
val get_index : ('a, 'b)t->int array array->'a array
``get_index i x`` returns an array of element values specified by the indices ``i``. The length of array ``i`` equals the number of dimensions of ``x``. The arrays in ``i`` must have the same length, and each represents the indices in that dimension.
E.g., ``| [|1;2|]; [|3;4|] |`` returns the value of elements at position ``(1,3)`` and ``(2,4)`` respectively.
val set_index : ('a, 'b)t->int array array->'a array-> unit
``set_index i x a`` sets the value of elements in ``x`` according to the indices specified by ``i``. The length of array ``i`` equals the number of dimensions of ``x``. The arrays in ``i`` must have the same length, and each represents the indices in that dimension.
If the length of ``a`` equals to the length of ``i``, then each element will be assigned by the value in the corresponding position in ``x``. If the length of ``a`` equals to one, then all the elements will be assigned the same value.
val get_fancy : Owl_types.index list->('a, 'b)t->('a, 'b)t
``get_fancy s x`` returns a copy of the slice in ``x``. The slice is defined by ``a`` which is an ``int option array``. E.g., for a ndarray ``x`` of dimension ``|2; 2; 3|``, ``slice 0 x`` takes the following slices of index ``\(0,*,*\)``, i.e., ``|0;0;0|``, ``|0;0;1|``, ``|0;0;2|`` ... Also note that if the length of ``s`` is less than the number of dimensions of ``x``, ``slice`` function will append slice definition to higher diemensions by assuming all the elements in missing dimensions will be taken.
Basically, ``slice`` function offers very much the same semantic as that in numpy, i.e., start:stop:step grammar, so if you how to index and slice ndarray in numpy, you should not find it difficult to use this function. Please just refer to numpy documentation or my tutorial.
There are two differences between ``slice_left`` and ``slice``: ``slice_left`` does not make a copy but simply moving the pointer; ``slice_left`` can only make a slice from left-most axis whereas ``slice`` is much more flexible and can work on arbitrary axis which need not start from left-most side.
val set_fancy : Owl_types.index list->('a, 'b)t->('a, 'b)t-> unit
``set_fancy axis x y`` set the slice defined by ``axis`` in ``x`` according to the values in ``y``. ``y`` must have the same shape as the one defined by ``axis``.
About the slice definition of ``axis``, please refer to ``get_fancy`` function.
val get_slice : int list list->('a, 'b)t->('a, 'b)t
``get_slice axis x`` aims to provide a simpler version of ``get_fancy``. This function assumes that every list element in the passed in ``int list list`` represents a range, i.e., ``R`` constructor.
E.g., ``[];[0;3];[0]`` is equivalent to ``R []; R [0;3]; R [0]``.
val set_slice : int list list->('a, 'b)t->('a, 'b)t-> unit
``set_slice axis x y`` aims to provide a simpler version of ``set_fancy``. This function assumes that every list element in the passed in ``int list list`` represents a range, i.e., ``R`` constructor.
E.g., ``[];[0;3];[0]`` is equivalent to ``R []; R [0;3]; R [0]``.
Some as ``Bigarray.sub_left``, please refer to Bigarray documentation.
val sub_ndarray : int array->('a, 'b)t->('a, 'b)t array
``sub_ndarray parts x`` is similar to ``Bigarray.sub_left``. It splits the passed in ndarray ``x`` along the ``axis 0`` according to ``parts``. The elelments in ``parts`` do not need to be equal but they must sum up to the dimension along axis zero.
The returned sub-ndarrays share the same memory as ``x``. Because there is no copies made, this function is much faster than using `split` function to divide the lowest dimensionality of ``x``.
val resize : ?head:bool ->('a, 'b)t->int array->('a, 'b)t
``resize ~head x d`` resizes the ndarray ``x``. If there are less number of elelments in the new shape than the old one, the new ndarray shares part of the memeory with the old ``x``. ``head`` indicates the alignment between the new and old data, either from head or from tail. Note the data is flattened before the operation.
If there are more elements in the new shape ``d``. Then new memeory space will be allocated and the content of ``x`` will be copied to the new memory. The rest of the allocated space will be filled with zeros. The default value of ``head`` is ``true``.
``reshape x d`` transforms ``x`` into a new shape definted by ``d``. Note the ``reshape`` function will not make a copy of ``x``, the returned ndarray shares the same memory with the original ``x``.
One shape dimension (only one) can be set to ``-1``. In this case, the value is inferred from the length of the array and remaining dimensions.
``flatten x`` transforms ``x`` into a one-dimsonal array without making a copy. Therefore the returned value shares the same memory space with original ``x``.
``flip ~axis x`` flips a matrix/ndarray along ``axis``. By default ``axis = 0``. The result is returned in a new matrix/ndarray, so the original ``x`` remains intact.
``rotate x d`` rotates ``x`` clockwise ``d`` degrees. ``d`` must be multiple times of ``90``, otherwise the function will fail. If ``x`` is an n-dimensional array, then the function rotates the plane formed by the first and second dimensions.
val transpose : ?axis:int array->('a, 'b)t->('a, 'b)t
``transpose ~axis x`` makes a copy of ``x``, then transpose it according to ``~axis``. ``~axis`` must be a valid permutation of ``x`` dimension indices. E.g., for a three-dimensional ndarray, it can be ``2;1;0``, ``0;2;1``, ``1;2;0``, and etc.
``tile x a`` tiles the data in ``x`` according the repetition specified by ``a``. This function provides the exact behaviour as ``numpy.tile``, please refer to the numpy's online documentation for details.
``repeat x a`` repeats the elements of ``x`` according the repetition specified by ``a``. The i-th element of ``a`` specifies the number of times that the individual entries of the i-th dimension of ``x`` should be repeated.
val concat_vertical : ('a, 'b)t->('a, 'b)t->('a, 'b)t
``concat_vertical x y`` concatenates two ndarray ``x`` and ``y`` vertically. This is just a convenient function for concatenating two ndarrays along their lowest dimension, i.e. 0.
The associated operator is ``@||``, please refer to :doc:`owl_operator`.
val concat_horizontal : ('a, 'b)t->('a, 'b)t->('a, 'b)t
``concat_horizontal x y`` concatenates two ndarrays ``x`` and ``y`` horizontally. This is just a convenient function for concatenating two ndarrays along their highest dimension.
The associated operator is ``@=``, please refer to :doc:`owl_operator`.
``concat_vh`` is used to assemble small parts of matrices into a bigger one. E.g. In ``| [|a; b; c|]; [|d; e; f|]; [|g; h; i|] |``, wherein `a, b, c ... i` are matrices of different shapes. They will be concatenated into a big matrix as follows.
.. math:: \beginmatrix a & b & c \\ d & e & f \\ g & h & i \endmatrix
This is achieved by first concatenating along ``axis:1`` for each element in the array, then concatenating along ``axis:0``. The number of elements in each array needs not to be equal as long as the aggregated dimensions match. E.g., please check the following example.
.. code-block:: ocaml
let a00 = Mat.sequential 2 3 in let a01 = Mat.sequential 2 2 in let a02 = Mat.sequential 2 1 in let a10 = Mat.sequential 3 3 in let a11 = Mat.sequential 3 3 in Mat.concat_vh | [|a00; a01; a02|]; [|a10; a11|] |;;
val concatenate : ?axis:int ->('a, 'b)t array->('a, 'b)t
``concatenate ~axis:2 x`` concatenates an array of ndarrays along the third dimension. For the ndarrays in ``x``, they must have the same shape except the dimension specified by ``axis``. The default value of ``axis`` is 0, i.e., the lowest dimension of a matrix/ndarray.
val split : ?axis:int ->int array->('a, 'b)t->('a, 'b)t array
``split ~axis parts x`` splits an ndarray ``x`` into parts along the specified ``axis``. This function is the inverse operation of ``concatenate``. The elements in ``x`` must sum up to the dimension in the specified axis.
``split_vh parts x`` splits a passed in ndarray ``x`` along the first two dimensions, i.e. ``axis 0`` and ``axis 1``. This is the inverse operation of ``concat_vh`` function, and the function is very useful in dividing a big matrix into smaller (especially heterogeneous) parts.
For example, given a matrix ``x`` of shape ``|8;10|``, it is possible to split in the following ways.
val squeeze : ?axis:int array->('a, 'b)t->('a, 'b)t
``squeeze ~axis x`` removes single-dimensional entries from the shape of ``x``.
val expand : ?hi:bool ->('a, 'b)t->int ->('a, 'b)t
``expand x d`` reshapes ``x`` by increasing its rank from ``num_dims x`` to ``d``. The opposite operation is ``squeeze x``. The ``hi`` parameter is used to specify whether the expandsion is along high dimension (by setting ``true``), or along the low dimension (by setting ``false``). The default value is ``false``.
val pad : ?v:'a->int list list->('a, 'b)t->('a, 'b)t
``pad ~v p x`` pads a ndarray ``x`` with a constant value ``v``. The padding index ``p`` is a list of lists of 2 integers. These two integers denote padding width at both edges of one dimension of ``x``.
``top x n`` returns the indices of ``n`` greatest values of ``x``. The indices are arranged according to the corresponding element values, from the greatest one to the smallest one.
``bottom x n`` returns the indices of ``n`` smallest values of ``x``. The indices are arranged according to the corresponding element values, from the smallest one to the greatest one.
``sort1 ~axis x`` performs quicksort of the elements along specific ``axis`` in ``x``. A new copy is returned as result, the original ``x`` remains intact.
``sort x`` performs quicksort of the elelments in ``x``. A new copy is returned as result, the original ``x`` remains intact. If you want to perform in-place sorting, please use `sort_` instead.
val argsort : ('a, 'b)t->(int64, Stdlib.Bigarray.int64_elt)t
``argsort x`` returns the indices with which the elements in ``x`` are sorted in increasing order. Note that the returned index ndarray has the same shape as that of ``x``, and the indices are 1D indices.
val draw : ?axis:int ->('a, 'b)t->int ->('a, 'b)t * int array
``draw ~axis x n`` draws ``n`` samples from ``x`` along the specified ``axis``, with replacement. ``axis`` is set to zero by default. The return is a tuple of both samples and the indices of the selected samples.
val mmap :
Unix.file_descr ->?pos:int64 ->('a, 'b)kind->bool ->int array->('a, 'b)t
``iteri f x`` applies function ``f`` to each element in ``x``. Note that 1d index is passed to function ``f``, you need to convert it to nd-index by yourself.
``map f x`` is similar to ``mapi f x`` except the index is not passed.
val foldi :
?axis:int ->(int ->'a->'a->'a)->'a->('a, 'b)t->('a, 'b)t
``foldi ~axis f a x`` folds (or reduces) the elements in ``x`` from left along the specified ``axis`` using passed in function ``f``. ``a`` is the initial element and in ``f i acc b`` ``acc`` is the accumulater and ``b`` is one of the elemets in ``x`` along the same axis. Note that ``i`` is 1d index of ``b``.
val fold : ?axis:int ->('a->'a->'a)->'a->('a, 'b)t->('a, 'b)t
Similar to ``foldi``, except that the index of an element is not passed to ``f``.
val scani : ?axis:int ->(int ->'a->'a->'a)->('a, 'b)t->('a, 'b)t
``scan ~axis f x`` scans the ``x`` along the specified ``axis`` using passed in function ``f``. ``f acc a b`` returns an updated ``acc`` which will be passed in the next call to ``f i acc a``. This function can be used to implement accumulative operations such as ``sum`` and ``prod`` functions. Note that the ``i`` is 1d index of ``a`` in ``x``.
val scan : ?axis:int ->('a->'a->'a)->('a, 'b)t->('a, 'b)t
Similar to ``scani``, except that the index of an element is not passed to ``f``.
val filteri : (int ->'a-> bool)->('a, 'b)t->int array
``filteri f x`` uses ``f`` to filter out certain elements in ``x``. An element will be included if ``f`` returns ``true``. The returned result is an array of 1-dimensional indices of the selected elements. To obtain the n-dimensional indices, you need to convert it manulally with Owl's helper function.
Similar to ``filteri``, but the indices are not passed to ``f``.
val iter2i : (int ->'a->'b-> unit)->('a, 'c)t->('b, 'd)t-> unit
Similar to ``iteri`` but applies to two N-dimensional arrays ``x`` and ``y``. Both ``x`` and ``y`` must have the same shape.
val iter2 : ('a->'b-> unit)->('a, 'c)t->('b, 'd)t-> unit
Similar to ``iter2i``, except that the index not passed to ``f``.
val map2i : (int ->'a->'a->'a)->('a, 'b)t->('a, 'b)t->('a, 'b)t
``map2i f x y`` applies ``f`` to two elements of the same position in both ``x`` and ``y``. Note that 1d index is passed to funciton ``f``.
val map2 : ('a->'a->'a)->('a, 'b)t->('a, 'b)t->('a, 'b)t
``map2 f x y`` is similar to ``map2i f x y`` except the index is not passed.
val iteri_nd : (int array->'a-> unit)->('a, 'b)t-> unit
Similar to ``iteri`` but n-d indices are passed to the user function.
val mapi_nd : (int array->'a->'a)->('a, 'b)t->('a, 'b)t
Similar to ``mapi`` but n-d indices are passed to the user function.
val foldi_nd :
?axis:int ->(int array->'a->'a->'a)->'a->('a, 'b)t->('a, 'b)t
Similar to ``foldi`` but n-d indices are passed to the user function.
val scani_nd :
?axis:int ->(int array->'a->'a->'a)->('a, 'b)t->('a, 'b)t
Similar to ``scani`` but n-d indices are passed to the user function.
val filteri_nd : (int array->'a-> bool)->('a, 'b)t->int array array
Similar to ``filteri`` but n-d indices are returned.
val iter2i_nd :
(int array->'a->'c-> unit)->('a, 'b)t->('c, 'd)t->
unit
Similar to ``iter2i`` but n-d indices are passed to the user function.
val map2i_nd :
(int array->'a->'a->'a)->('a, 'b)t->('a, 'b)t->('a, 'b)t
Similar to ``map2i`` but n-d indices are passed to the user function.
val iteri_slice :
?axis:int ->(int ->('a, 'b)t-> unit)->('a, 'b)t->
unit
``iteri_slice ~axis f x`` iterates the slices along the specified ``axis`` in ``x`` and applies the function ``f``. The 1-d index of of the slice is passed in. By default, the ``axis`` is 0. Setting ``axis`` to the highest dimension is not allowed because in that case you can just use `iteri` to iterate all the elements in ``x`` which is more efficient.
Note that the slice is obtained by slicing left (due to Owl's C-layout ndarray) a sub-array out of ``x``. E.g., if ``x`` has shape ``|3;4;5|``, setting ``axis=0`` will iterate three ``4 x 5`` matrices. The slice shares the same memory with ``x`` so no copy is made.
val iter_slice : ?axis:int ->(('a, 'b)t-> unit)->('a, 'b)t-> unit
Similar to ``iteri_slice`` but slice index is not passed in.
val mapi_slice :
?axis:int ->(int ->('a, 'b)t->'c)->('a, 'b)t->'c array
``mapi_slice ~axis f x`` maps the slices along the specified ``axis`` in ``x`` and applies the function ``f``. By default, ``axis`` is 0. The index of of the slice is passed in.
Please refer to ``iteri_slice`` for more details.
val map_slice : ?axis:int ->(('a, 'b)t->'c)->('a, 'b)t->'c array
Similar to ``mapi_slice`` but slice index is not passed in.
``filteri_slice ~axis f x`` filters the slices along the specified ``axis`` in ``x``. The slices which satisfy the predicate ``f`` are returned in an array.
Please refer to ``iteri_slice`` for more details.
val filter_slice :
?axis:int ->(('a, 'b)t-> bool)->('a, 'b)t->('a, 'b)t array
Similar to ``filteri_slice`` but slice index is not passed in.
val foldi_slice :
?axis:int ->(int ->'c->('a, 'b)t->'c)->'c->('a, 'b)t->'c
``foldi_slice ~axis f a x`` fold (left) the slices along the specified ``axis`` in ``x``. The slices which satisfy the predicate ``f`` are returned in an array.
Please refer to ``iteri_slice`` for more details.
val fold_slice :
?axis:int ->('c->('a, 'b)t->'c)->'c->('a, 'b)t->'c
Similar to ``foldi_slice`` but slice index is not passed in.
``exists f x`` checks all the elements in ``x`` using ``f``. If at least one element satisfies ``f`` then the function returns ``true`` otherwise ``false``.
``is_normal x`` returns ``true`` if all the elelments in ``x`` are normal float numbers, i.e., not ``NaN``, not ``INF``, not ``SUBNORMAL``. Please refer to
``not_nan x`` returns ``false`` if there is any ``NaN`` element in ``x``. Otherwise, the function returns ``true`` indicating all the numbers in ``x`` are not ``NaN``.
``elt_equal x y`` performs element-wise ``=`` comparison of ``x`` and ``y``. Assume that ``a`` is from ``x`` and ``b`` is the corresponding element of ``a`` from ``y`` of the same position. The function returns another binary (``0`` and ``1``) ndarray/matrix wherein ``1`` indicates ``a = b``.
The function supports broadcast operation.
val elt_not_equal : ('a, 'b)t->('a, 'b)t->('a, 'b)t
``elt_not_equal x y`` performs element-wise ``!=`` comparison of ``x`` and ``y``. Assume that ``a`` is from ``x`` and ``b`` is the corresponding element of ``a`` from ``y`` of the same position. The function returns another binary (``0`` and ``1``) ndarray/matrix wherein ``1`` indicates ``a <> b``.
``elt_less x y`` performs element-wise ``<`` comparison of ``x`` and ``y``. Assume that ``a`` is from ``x`` and ``b`` is the corresponding element of ``a`` from ``y`` of the same position. The function returns another binary (``0`` and ``1``) ndarray/matrix wherein ``1`` indicates ``a < b``.
``elt_greater x y`` performs element-wise ``>`` comparison of ``x`` and ``y``. Assume that ``a`` is from ``x`` and ``b`` is the corresponding element of ``a`` from ``y`` of the same position. The function returns another binary (``0`` and ``1``) ndarray/matrix wherein ``1`` indicates ``a > b``.
The function supports broadcast operation.
val elt_less_equal : ('a, 'b)t->('a, 'b)t->('a, 'b)t
``elt_less_equal x y`` performs element-wise ``<=`` comparison of ``x`` and ``y``. Assume that ``a`` is from ``x`` and ``b`` is the corresponding element of ``a`` from ``y`` of the same position. The function returns another binary (``0`` and ``1``) ndarray/matrix wherein ``1`` indicates ``a <= b``.
The function supports broadcast operation.
val elt_greater_equal : ('a, 'b)t->('a, 'b)t->('a, 'b)t
``elt_greater_equal x y`` performs element-wise ``>=`` comparison of ``x`` and ``y``. Assume that ``a`` is from ``x`` and ``b`` is the corresponding element of ``a`` from ``y`` of the same position. The function returns another binary (``0`` and ``1``) ndarray/matrix wherein ``1`` indicates ``a >= b``.
``equal_scalar x a`` checks if all the elements in ``x`` are equal to ``a``. The function returns ``true`` iff for every element ``b`` in ``x``, ``b = a``.
``not_equal_scalar x a`` checks if all the elements in ``x`` are not equal to ``a``. The function returns ``true`` iff for every element ``b`` in ``x``, ``b <> a``.
``less_scalar x a`` checks if all the elements in ``x`` are less than ``a``. The function returns ``true`` iff for every element ``b`` in ``x``, ``b < a``.
``greater_scalar x a`` checks if all the elements in ``x`` are greater than ``a``. The function returns ``true`` iff for every element ``b`` in ``x``, ``b > a``.
``less_equal_scalar x a`` checks if all the elements in ``x`` are less or equal to ``a``. The function returns ``true`` iff for every element ``b`` in ``x``, ``b <= a``.
``greater_equal_scalar x a`` checks if all the elements in ``x`` are greater or equal to ``a``. The function returns ``true`` iff for every element ``b`` in ``x``, ``b >= a``.
``elt_equal_scalar x a`` performs element-wise ``=`` comparison of ``x`` and ``a``. Assume that ``b`` is one element from ``x`` The function returns another binary (``0`` and ``1``) ndarray/matrix wherein ``1`` of the corresponding position indicates ``a = b``, otherwise ``0``.
val elt_not_equal_scalar : ('a, 'b)t->'a->('a, 'b)t
``elt_not_equal_scalar x a`` performs element-wise ``!=`` comparison of ``x`` and ``a``. Assume that ``b`` is one element from ``x`` The function returns another binary (``0`` and ``1``) ndarray/matrix wherein ``1`` of the corresponding position indicates ``a <> b``, otherwise ``0``.
``elt_less_scalar x a`` performs element-wise ``<`` comparison of ``x`` and ``a``. Assume that ``b`` is one element from ``x`` The function returns another binary (``0`` and ``1``) ndarray/matrix wherein ``1`` of the corresponding position indicates ``a < b``, otherwise ``0``.
``elt_greater_scalar x a`` performs element-wise ``>`` comparison of ``x`` and ``a``. Assume that ``b`` is one element from ``x`` The function returns another binary (``0`` and ``1``) ndarray/matrix wherein ``1`` of the corresponding position indicates ``a > b``, otherwise ``0``.
val elt_less_equal_scalar : ('a, 'b)t->'a->('a, 'b)t
``elt_less_equal_scalar x a`` performs element-wise ``<=`` comparison of ``x`` and ``a``. Assume that ``b`` is one element from ``x`` The function returns another binary (``0`` and ``1``) ndarray/matrix wherein ``1`` of the corresponding position indicates ``a <= b``, otherwise ``0``.
val elt_greater_equal_scalar : ('a, 'b)t->'a->('a, 'b)t
``elt_greater_equal_scalar x a`` performs element-wise ``>=`` comparison of ``x`` and ``a``. Assume that ``b`` is one element from ``x`` The function returns another binary (``0`` and ``1``) ndarray/matrix wherein ``1`` of the corresponding position indicates ``a >= b``, otherwise ``0``.
val approx_equal : ?eps:float ->('a, 'b)t->('a, 'b)t-> bool
``approx_equal ~eps x y`` returns ``true`` if ``x`` and ``y`` are approximately equal, i.e., for any two elements ``a`` from ``x`` and ``b`` from ``y``, we have ``abs (a - b) < eps``. For complex numbers, the ``eps`` applies to both real and imaginary part.
Note: the threshold check is exclusive for passed in ``eps``, i.e., the threshold interval is ``(a-eps, a+eps)``.
val approx_equal_scalar : ?eps:float ->('a, 'b)t->'a-> bool
``approx_equal_scalar ~eps x a`` returns ``true`` all the elements in ``x`` are approximately equal to ``a``, i.e., ``abs (x - a) < eps``. For complex numbers, the ``eps`` applies to both real and imaginary part.
Note: the threshold check is exclusive for the passed in ``eps``.
val approx_elt_equal : ?eps:float ->('a, 'b)t->('a, 'b)t->('a, 'b)t
``approx_elt_equal ~eps x y`` compares the element-wise equality of ``x`` and ``y``, then returns another binary (i.e., ``0`` and ``1``) ndarray/matrix wherein ``1`` indicates that two corresponding elements ``a`` from ``x`` and ``b`` from ``y`` are considered as approximately equal, namely ``abs (a - b) < eps``.
val approx_elt_equal_scalar : ?eps:float ->('a, 'b)t->'a->('a, 'b)t
``approx_elt_equal_scalar ~eps x a`` compares all the elements of ``x`` to a scalar value ``a``, then returns another binary (i.e., ``0`` and ``1``) ndarray/matrix wherein ``1`` indicates that the element ``b`` from ``x`` is considered as approximately equal to ``a``, namely ``abs (a - b) < eps``.
Input/Output functions
val of_array : ('a, 'b)kind->'a array->int array->('a, 'b)t
``of_array k x d`` takes an array ``x`` and converts it into an ndarray of type ``k`` and shape ``d``.
``to_array x`` converts an ndarray ``x`` to OCaml's array type. Note that the ndarray ``x`` is flattened before convertion.
val print :
?max_row:int ->?max_col:int ->?header:bool ->?fmt:('a-> string)->('a, 'b)t->
unit
``print x`` prints all the elements in ``x`` as well as their indices. ``max_row`` and ``max_col`` specify the maximum number of rows and columns to display. ``header`` specifies whether or not to print out the headers. ``fmt`` is the function to format every element into string.
val pp_dsnda : Stdlib.Format.formatter ->('a, 'b)t-> unit
``pp_dsnda x`` prints ``x`` in OCaml toplevel. If the ndarray is too long, ``pp_dsnda`` only prints out parts of the ndarray.
``load k s`` loads previously serialised ndarray from file ``s`` into memory. It is necesssary to specify the type of the ndarray with paramater ``k``.
``save_npy ~out x`` saves the matrix ``x`` into a npy file ``out``. This function is implemented using npy-ocaml https://github.com/LaurentMazare/npy-ocaml.
``load_npy file`` load a npy ``file`` into a matrix of type ``k``. If the matrix is in the file is not of type ``k``, fails with ``file: incorrect format``. This function is implemented using npy-ocaml https://github.com/LaurentMazare/npy-ocaml.
Unary math operators
val re_c2s :
(Stdlib.Complex.t, Stdlib.Bigarray.complex32_elt)t->(float, Stdlib.Bigarray.float32_elt)t
``re_c2s x`` returns all the real components of ``x`` in a new ndarray of same shape.
val re_z2d :
(Stdlib.Complex.t, Stdlib.Bigarray.complex64_elt)t->(float, Stdlib.Bigarray.float64_elt)t
``re_d2z x`` returns all the real components of ``x`` in a new ndarray of same shape.
val im_c2s :
(Stdlib.Complex.t, Stdlib.Bigarray.complex32_elt)t->(float, Stdlib.Bigarray.float32_elt)t
``im_c2s x`` returns all the imaginary components of ``x`` in a new ndarray of same shape.
val im_z2d :
(Stdlib.Complex.t, Stdlib.Bigarray.complex64_elt)t->(float, Stdlib.Bigarray.float64_elt)t
``im_d2z x`` returns all the imaginary components of ``x`` in a new ndarray of same shape.
``min x`` returns the minimum of all elements in ``x`` along specified ``axis``. If no axis is specified, ``x`` will be flattened and the minimum of all the elements will be returned. For two complex numbers, the one with the smaller magnitude will be selected. If two magnitudes are the same, the one with the smaller phase will be selected.
``max x`` returns the maximum of all elements in ``x`` along specified ``axis``. If no axis is specified, ``x`` will be flattened and the maximum of all the elements will be returned. For two complex numbers, the one with the greater magnitude will be selected. If two magnitudes are the same, the one with the greater phase will be selected.
``max_i x`` returns the maximum of all elements in ``x`` as well as its index.
val minmax_i : ('a, 'b)t->('a * int array) * ('a * int array)
``minmax_i x`` returns ``((min_v,min_i), (max_v,max_i))`` where ``(min_v,min_i)`` is the minimum value in ``x`` along with its index while ``(max_v,max_i)`` is the maximum value along its index.
``reci_tol ~tol x`` computes the reciprocal of every element in ``x``. Different from ``reci``, ``reci_tol`` sets the elements whose ``abs`` value smaller than ``tol`` to zeros. If ``tol`` is not specified, the defautl ``Owl_utils.eps Float32`` will be used. For complex numbers, refer to Owl's doc to see how to compare.
``fix x`` rounds each element of ``x`` to the nearest integer toward zero. For positive elements, the behavior is the same as ``floor``. For negative ones, the behavior is the same as ``ceil``.
``modf x`` performs ``modf`` over all the elements in ``x``, the fractal part is saved in the first element of the returned tuple whereas the integer part is saved in the second element.
``relu x`` computes the rectified linear unit function ``max(x, 0)`` of the elements in ``x`` and returns the result in a new ndarray.
val elu : ?alpha:float ->(float, 'a)t->(float, 'a)t
``elu alpha x`` computes the exponential linear unit function ``x >= 0. ? x : (alpha * (exp(x) - 1))`` of the elements in ``x`` and returns the result in a new ndarray.
val leaky_relu : ?alpha:float ->(float, 'a)t->(float, 'a)t
``leaky_relu alpha x`` computes the leaky rectified linear unit function ``x >= 0. ? x : (alpha * x)`` of the elements in ``x`` and returns the result in a new ndarray.
``softsign x`` computes the softsign function ``x / (1 + abs(x))`` of the elements in ``x`` and returns the result in a new ndarray.
val softmax : ?axis:int ->(float, 'a)t->(float, 'a)t
``softmax x`` computes the softmax functions ``(exp x) / (sum (exp x))`` of all the elements along the specified ``axis`` in ``x`` and returns the result in a new ndarray.
By default, ``axis = -1``, i.e. along the highest dimension.
``l2norm_sqr x`` calculates the square of l2-norm (or l2norm, Euclidean norm) of all elements in ``x``. The function uses conjugate transpose in the product, hence it always returns a float number.
val vecnorm : ?axis:int ->?p:float ->('a, 'b)t->('a, 'b)t
``vecnorm ~axis ~p x`` calculates the generalised vector p-norm along the specified ``axis``. The generalised p-norm is defined as below.
``cumsum ~axis x`` : performs cumulative sum of the elements along the given axis ``~axis``. If ``~axis`` is ``None``, then the ``cumsum`` is performed along the lowest dimension. The returned result however always remains the same shape.
``cummax ~axis x`` : performs cumulative ``max`` along ``axis`` dimension.
val diff : ?axis:int ->?n:int ->('a, 'b)t->('a, 'b)t
``diff ~axis ~n x`` calculates the ``n``-th difference of ``x`` along the specified ``axis``.
Parameters: * ``axis``: axis to calculate the difference. The default value is the highest dimension. * ``n``: how many times to calculate the difference. The default value is 1.
Return: * The difference ndarray y. Note that the shape of ``y`` 1 less than that of ``x`` along specified axis.
val angle : (Stdlib.Complex.t, 'a)t->(Stdlib.Complex.t, 'a)t
``angle x`` calculates the phase angle of all complex numbers in ``x``.
val proj : (Stdlib.Complex.t, 'a)t->(Stdlib.Complex.t, 'a)t
``proj x`` computes the projection on Riemann sphere of all elelments in ``x``.
``add x y`` adds all the elements in ``x`` and ``y`` elementwise, and returns the result in a new ndarray.
General broadcast operation is automatically applied to add/sub/mul/div, etc. The function compares the dimension element-wise from the highest to the lowest with the following broadcast rules (same as numpy): 1. equal; 2. either is 1.
``ssqr x a`` computes the sum of squared differences of all the elements in ``x`` from constant ``a``. This function only computes the square of each element rather than the conjugate transpose as l2norm_sqr does.
``ssqr_diff x y`` computes the sum of squared differences of every elements in ``x`` and its corresponding element in ``y``.
val cross_entropy' : (float, 'a)t->(float, 'a)t-> float
``cross_entropy x y`` calculates the cross entropy between ``x`` and ``y`` using base ``e``.
val clip_by_value : ?amin:'a->?amax:'a->('a, 'b)t->('a, 'b)t
``clip_by_value ~amin ~amax x`` clips the elements in ``x`` based on ``amin`` and ``amax``. The elements smaller than ``amin`` will be set to ``amin``, and the elements greater than ``amax`` will be set to ``amax``.
``clip_by_l2norm t x`` clips the ``x`` according to the threshold set by ``t``.
val fma : ('a, 'b)t->('a, 'b)t->('a, 'b)t->('a, 'b)t
``fma x y z`` calculates the `fused multiply add`, i.e. ``(x * y) + z``.
Tensor Calculus
val contract1 : (int * int) array->('a, 'b)t->('a, 'b)t
``contract1 index_pairs x`` performs indices contraction (a.k.a tensor contraction) on ``x``. ``index_pairs`` is an array of contracted indices.
Caveat: Not well tested yet, use with care! Also, consider to use TTGT in future for better perfomance.
val contract2 : (int * int) array->('a, 'b)t->('a, 'b)t->('a, 'b)t
``contract2 index_pairs x y`` performs indices contraction (a.k.a tensor contraction) on two ndarrays ``x`` and ``y``. ``index_pairs`` is an array of contracted indices, the first element is the index of ``x``, the second is that of ``y``.
Caveat: Not well tested yet, use with care! Also, consider to use TTGT in future for better perfomance.
``cast kind x`` casts ``x`` of type ``('c, 'd) t`` to type ``('a, 'b) t`` specify by the passed in ``kind`` parameter. This function is a generalisation of the other casting functions such as ``cast_s2d``, ``cast_c2z``, and etc.
val cast_s2d :
(float, Stdlib.Bigarray.float32_elt)t->(float, Stdlib.Bigarray.float64_elt)t
``cast_s2d x`` casts ``x`` from ``float32`` to ``float64``.
val cast_d2s :
(float, Stdlib.Bigarray.float64_elt)t->(float, Stdlib.Bigarray.float32_elt)t
``cast_d2s x`` casts ``x`` from ``float64`` to ``float32``.
val cast_c2z :
(Stdlib.Complex.t, Stdlib.Bigarray.complex32_elt)t->(Stdlib.Complex.t, Stdlib.Bigarray.complex64_elt)t
``cast_c2z x`` casts ``x`` from ``complex32`` to ``complex64``.
val cast_z2c :
(Stdlib.Complex.t, Stdlib.Bigarray.complex64_elt)t->(Stdlib.Complex.t, Stdlib.Bigarray.complex32_elt)t
``cast_z2c x`` casts ``x`` from ``complex64`` to ``complex32``.
val cast_s2c :
(float, Stdlib.Bigarray.float32_elt)t->(Stdlib.Complex.t, Stdlib.Bigarray.complex32_elt)t
``cast_s2c x`` casts ``x`` from ``float32`` to ``complex32``.
val cast_d2z :
(float, Stdlib.Bigarray.float64_elt)t->(Stdlib.Complex.t, Stdlib.Bigarray.complex64_elt)t
``cast_d2z x`` casts ``x`` from ``float64`` to ``complex64``.
val cast_s2z :
(float, Stdlib.Bigarray.float32_elt)t->(Stdlib.Complex.t, Stdlib.Bigarray.complex64_elt)t
``cast_s2z x`` casts ``x`` from ``float32`` to ``complex64``.
val cast_d2c :
(float, Stdlib.Bigarray.float64_elt)t->(Stdlib.Complex.t, Stdlib.Bigarray.complex32_elt)t
``cast_d2c x`` casts ``x`` from ``float64`` to ``complex32``.
Neural network related
val conv1d :
?padding:Owl_types.padding ->('a, 'b)t->('a, 'b)t->int array->('a, 'b)t
TODO
val conv2d :
?padding:Owl_types.padding ->('a, 'b)t->('a, 'b)t->int array->('a, 'b)t
TODO
val conv3d :
?padding:Owl_types.padding ->('a, 'b)t->('a, 'b)t->int array->('a, 'b)t
TODO
val dilated_conv1d :
?padding:Owl_types.padding ->('a, 'b)t->('a, 'b)t->int array->int array->('a, 'b)t
TODO
val dilated_conv2d :
?padding:Owl_types.padding ->('a, 'b)t->('a, 'b)t->int array->int array->('a, 'b)t
TODO
val dilated_conv3d :
?padding:Owl_types.padding ->('a, 'b)t->('a, 'b)t->int array->int array->('a, 'b)t
TODO
val transpose_conv1d :
?padding:Owl_types.padding ->('a, 'b)t->('a, 'b)t->int array->('a, 'b)t
TODO
val transpose_conv2d :
?padding:Owl_types.padding ->('a, 'b)t->('a, 'b)t->int array->('a, 'b)t
TODO
val transpose_conv3d :
?padding:Owl_types.padding ->('a, 'b)t->('a, 'b)t->int array->('a, 'b)t
TODO
val max_pool1d :
?padding:Owl_types.padding ->('a, 'b)t->int array->int array->('a, 'b)t
TODO
val max_pool2d :
?padding:Owl_types.padding ->('a, 'b)t->int array->int array->('a, 'b)t
TODO
val max_pool3d :
?padding:Owl_types.padding ->('a, 'b)t->int array->int array->('a, 'b)t
TODO
val avg_pool1d :
?padding:Owl_types.padding ->('a, 'b)t->int array->int array->('a, 'b)t
TODO
val avg_pool2d :
?padding:Owl_types.padding ->('a, 'b)t->int array->int array->('a, 'b)t
TODO
val avg_pool3d :
?padding:Owl_types.padding ->('a, 'b)t->int array->int array->('a, 'b)t
val upsampling2d : ('a, 'b)t->int array->('a, 'b)t
TODO
val conv1d_backward_input :
('a, 'b)t->('a, 'b)t->int array->('a, 'b)t->('a, 'b)t
TODO
val conv1d_backward_kernel :
('a, 'b)t->('a, 'b)t->int array->('a, 'b)t->('a, 'b)t
TODO
val conv2d_backward_input :
('a, 'b)t->('a, 'b)t->int array->('a, 'b)t->('a, 'b)t
TODO
val conv2d_backward_kernel :
('a, 'b)t->('a, 'b)t->int array->('a, 'b)t->('a, 'b)t
TODO
val conv3d_backward_input :
('a, 'b)t->('a, 'b)t->int array->('a, 'b)t->('a, 'b)t
TODO
val conv3d_backward_kernel :
('a, 'b)t->('a, 'b)t->int array->('a, 'b)t->('a, 'b)t
TODO
val dilated_conv1d_backward_input :
('a, 'b)t->('a, 'b)t->int array->int array->('a, 'b)t->('a, 'b)t
TODO
val dilated_conv1d_backward_kernel :
('a, 'b)t->('a, 'b)t->int array->int array->('a, 'b)t->('a, 'b)t
TODO
val dilated_conv2d_backward_input :
('a, 'b)t->('a, 'b)t->int array->int array->('a, 'b)t->('a, 'b)t
TODO
val dilated_conv2d_backward_kernel :
('a, 'b)t->('a, 'b)t->int array->int array->('a, 'b)t->('a, 'b)t
TODO
val dilated_conv3d_backward_input :
('a, 'b)t->('a, 'b)t->int array->int array->('a, 'b)t->('a, 'b)t
TODO
val dilated_conv3d_backward_kernel :
('a, 'b)t->('a, 'b)t->int array->int array->('a, 'b)t->('a, 'b)t
TODO
val transpose_conv1d_backward_input :
('a, 'b)t->('a, 'b)t->int array->('a, 'b)t->('a, 'b)t
TODO
val transpose_conv1d_backward_kernel :
('a, 'b)t->('a, 'b)t->int array->('a, 'b)t->('a, 'b)t
TODO
val transpose_conv2d_backward_input :
('a, 'b)t->('a, 'b)t->int array->('a, 'b)t->('a, 'b)t
TODO
val transpose_conv2d_backward_kernel :
('a, 'b)t->('a, 'b)t->int array->('a, 'b)t->('a, 'b)t
TODO
val transpose_conv3d_backward_input :
('a, 'b)t->('a, 'b)t->int array->('a, 'b)t->('a, 'b)t
TODO
val transpose_conv3d_backward_kernel :
('a, 'b)t->('a, 'b)t->int array->('a, 'b)t->('a, 'b)t
TODO
val max_pool1d_backward :
Owl_types.padding ->('a, 'b)t->int array->int array->('a, 'b)t->('a, 'b)t
TODO
val max_pool2d_backward :
Owl_types.padding ->('a, 'b)t->int array->int array->('a, 'b)t->('a, 'b)t
TODO
val max_pool3d_backward :
Owl_types.padding ->('a, 'b)t->int array->int array->('a, 'b)t->('a, 'b)t
TODO
val avg_pool1d_backward :
Owl_types.padding ->('a, 'b)t->int array->int array->('a, 'b)t->('a, 'b)t
TODO
val avg_pool2d_backward :
Owl_types.padding ->('a, 'b)t->int array->int array->('a, 'b)t->('a, 'b)t
TODO
val avg_pool3d_backward :
Owl_types.padding ->('a, 'b)t->int array->int array->('a, 'b)t->('a, 'b)t
TODO
val upsampling2d_backward : ('a, 'b)t->int array->('a, 'b)t->('a, 'b)t
TODO
Helper functions
The following functions are helper functions for some other functions in both Ndarray and Ndview modules. In general, you are not supposed to use these functions directly.
``one_hot idx depth`` creates one-hot vectors according to the indices ndarray and the specified depth. If ``idx`` is rank N, then the return is rank N+1. More specifically, if ``idx`` is of shape ``|a;b;c|``, the return is of shape ``|a;b;c;depth|``.
``sum_slices ~axis:2 x`` for ``x`` of ``|2;3;4;5|``, it returns an ndarray of shape ``|4;5|``. Currently, the operation is done using ``gemm``, it is fast but consumes more memory.
val slide :
?axis:int ->?ofs:int ->?step:int ->window:int ->('a, 'b)t->('a, 'b)t
``slide ~axis ~window x`` generates a new ndarray by sliding a window along specified ``axis`` in ``x``. E.g., if ``x`` has shape ``|a;b;c|`` and ``axis = 1``, then ``|a; number of windows; window; c|`` is the shape of the returned ndarray.
Parameters: * ``axis`` is the axis for sliding, the default is -1, i.e. highest dimension. * ``ofs`` is the starting position of the sliding window. The default is 0. * ``step`` is the step size, the default is 1. * ``window`` is the size of the sliding window.
val transpose_ : out:('a, 'b)t->?axis:int array->('a, 'b)t-> unit
``transpose_ ~out x`` is similar to ``transpose x`` but the output is written to ``out``.
val repeat_ : out:('a, 'b)t->('a, 'b)t->int array-> unit
``repeat_ ~out x reps`` is similar to ``repeat x reps`` but the output is written to ``out``.
val tile_ : out:('a, 'b)t->('a, 'b)t->int array-> unit
``tile_ ~out x reps`` is similar to ``tile x reps`` but the output is written to ``out``.
val pad_ : out:('a, 'b)t->?v:'a->int list list->('a, 'b)t-> unit
``pad_ ~out ?v p x`` is similar to ``pad ?v p x`` but the output is written to ``out``.
val sum_ : out:('a, 'b)t->axis:int ->('a, 'b)t-> unit
TODO
val min_ : out:('a, 'b)t->axis:int ->('a, 'b)t-> unit
TODO
val max_ : out:('a, 'b)t->axis:int ->('a, 'b)t-> unit
TODO
val add_ : ?out:('a, 'b)t->('a, 'b)t->('a, 'b)t-> unit
``add_ x y`` is simiar to ``add`` function but the output is written to ``out``. You need to make sure ``out`` is big enough to hold the output result.
val sub_ : ?out:('a, 'b)t->('a, 'b)t->('a, 'b)t-> unit
``sub_ x y`` is simiar to ``sub`` function but the output is written to ``out``. You need to make sure ``out`` is big enough to hold the output result.
val mul_ : ?out:('a, 'b)t->('a, 'b)t->('a, 'b)t-> unit
``mul_ x y`` is simiar to ``mul`` function but the output is written to ``out``. You need to make sure ``out`` is big enough to hold the output result.
val div_ : ?out:('a, 'b)t->('a, 'b)t->('a, 'b)t-> unit
``div_ x y`` is simiar to ``div`` function but the output is written to ``out``. You need to make sure ``out`` is big enough to hold the output result.
val pow_ : ?out:('a, 'b)t->('a, 'b)t->('a, 'b)t-> unit
``pow_ x y`` is simiar to ``pow`` function but the output is written to ``out``. You need to make sure ``out`` is big enough to hold the output result.
val atan2_ : ?out:('a, 'b)t->('a, 'b)t->('a, 'b)t-> unit
``atan2_ x y`` is simiar to ``atan2`` function but the output is written to ``out``. You need to make sure ``out`` is big enough to hold the output result.
val hypot_ : ?out:('a, 'b)t->('a, 'b)t->('a, 'b)t-> unit
``hypot_ x y`` is simiar to ``hypot`` function but the output is written to ``out``. You need to make sure ``out`` is big enough to hold the output result.
val fmod_ : ?out:('a, 'b)t->('a, 'b)t->('a, 'b)t-> unit
``fmod_ x y`` is simiar to ``fmod`` function but the output is written to ``out``. You need to make sure ``out`` is big enough to hold the output result.
val min2_ : ?out:('a, 'b)t->('a, 'b)t->('a, 'b)t-> unit
``min2_ x y`` is simiar to ``min2`` function but the output is written to ``out``. You need to make sure ``out`` is big enough to hold the output result.
val max2_ : ?out:('a, 'b)t->('a, 'b)t->('a, 'b)t-> unit
``max2_ x y`` is simiar to ``max2`` function but the output is written to ``out``. You need to make sure ``out`` is big enough to hold the output result.
val add_scalar_ : ?out:('a, 'b)t->('a, 'b)t->'a-> unit
``add_scalar_ x y`` is simiar to ``add_scalar`` function but the output is written to ``x``.
val sub_scalar_ : ?out:('a, 'b)t->('a, 'b)t->'a-> unit
``sub_scalar_ x y`` is simiar to ``sub_scalar`` function but the output is written to ``x``.
val mul_scalar_ : ?out:('a, 'b)t->('a, 'b)t->'a-> unit
``mul_scalar_ x y`` is simiar to ``mul_scalar`` function but the output is written to ``x``.
val div_scalar_ : ?out:('a, 'b)t->('a, 'b)t->'a-> unit
``div_scalar_ x y`` is simiar to ``div_scalar`` function but the output is written to ``x``.
val pow_scalar_ : ?out:('a, 'b)t->('a, 'b)t->'a-> unit
``pow_scalar_ x y`` is simiar to ``pow_scalar`` function but the output is written to ``x``.
val atan2_scalar_ : ?out:('a, 'b)t->('a, 'b)t->'a-> unit
``atan2_scalar_ x y`` is simiar to ``atan2_scalar`` function but the output is written to ``x``.
val fmod_scalar_ : ?out:('a, 'b)t->('a, 'b)t->'a-> unit
``fmod_scalar_ x y`` is simiar to ``fmod_scalar`` function but the output is written to ``x``.
val scalar_add_ : ?out:('a, 'b)t->'a->('a, 'b)t-> unit
``scalar_add_ a x`` is simiar to ``scalar_add`` function but the output is written to ``x``.
val scalar_sub_ : ?out:('a, 'b)t->'a->('a, 'b)t-> unit
``scalar_sub_ a x`` is simiar to ``scalar_sub`` function but the output is written to ``x``.
val scalar_mul_ : ?out:('a, 'b)t->'a->('a, 'b)t-> unit
``scalar_mul_ a x`` is simiar to ``scalar_mul`` function but the output is written to ``x``.
val scalar_div_ : ?out:('a, 'b)t->'a->('a, 'b)t-> unit
``scalar_div_ a x`` is simiar to ``scalar_div`` function but the output is written to ``x``.
val scalar_pow_ : ?out:('a, 'b)t->'a->('a, 'b)t-> unit
``scalar_pow_ a x`` is simiar to ``scalar_pow`` function but the output is written to ``x``.
val scalar_atan2_ : ?out:('a, 'b)t->'a->('a, 'b)t-> unit
``scalar_atan2_ a x`` is simiar to ``scalar_atan2`` function but the output is written to ``x``.
val scalar_fmod_ : ?out:('a, 'b)t->'a->('a, 'b)t-> unit
``scalar_fmod_ a x`` is simiar to ``scalar_fmod`` function but the output is written to ``x``.
val clip_by_value_ :
?out:('a, 'b)t->?amin:'a->?amax:'a->('a, 'b)t->
unit
TODO
val clip_by_l2norm_ : ?out:('a, 'b)t->'a->('a, 'b)t-> unit
TODO
val fma_ : ?out:('a, 'b)t->('a, 'b)t->('a, 'b)t->('a, 'b)t-> unit
``fma_ ~out x y z`` is simiar to ``fma x y z`` function but the output is written to ``out``.
val dot_ :
?transa:bool ->?transb:bool ->?alpha:'a->?beta:'a->c:('a, 'b)t->('a, 'b)t->('a, 'b)t->
unit
``sigmoid_ x`` is similar to ``sigmoid`` but output is written to ``x``
val softmax_ : ?out:('a, 'b)t->?axis:int ->('a, 'b)t-> unit
``softmax_ x`` is similar to ``softmax`` but output is written to ``x``
val cumsum_ : ?out:('a, 'b)t->?axis:int ->('a, 'b)t-> unit
``cumsum_ x`` is similar to ``cumsum`` but output is written to ``x``
val cumprod_ : ?out:('a, 'b)t->?axis:int ->('a, 'b)t-> unit
``cumprod_ x`` is similar to ``cumprod`` but output is written to ``x``
val cummin_ : ?out:('a, 'b)t->?axis:int ->('a, 'b)t-> unit
``cummin_ x`` is similar to ``cummin`` but output is written to ``x``
val cummax_ : ?out:('a, 'b)t->?axis:int ->('a, 'b)t-> unit
``cummax_ x`` is similar to ``cummax`` but output is written to ``x``
val dropout_ : ?out:('a, 'b)t->?rate:float ->('a, 'b)t-> unit
``dropout_ x`` is similar to ``dropout`` but output is written to ``x``
val elt_equal_ : ?out:('a, 'b)t->('a, 'b)t->('a, 'b)t-> unit
``elt_equal_ x y`` is simiar to ``elt_equal`` function but the output is written to ``out``. You need to make sure ``out`` is big enough to hold the output result.
val elt_not_equal_ : ?out:('a, 'b)t->('a, 'b)t->('a, 'b)t-> unit
``elt_not_equal_ x y`` is simiar to ``elt_not_equal`` function but the output is written to ``out``. You need to make sure ``out`` is big enough to hold the output result.
val elt_less_ : ?out:('a, 'b)t->('a, 'b)t->('a, 'b)t-> unit
``elt_less_ x y`` is simiar to ``elt_less`` function but the output is written to ``out``. You need to make sure ``out`` is big enough to hold the output result.
val elt_greater_ : ?out:('a, 'b)t->('a, 'b)t->('a, 'b)t-> unit
``elt_greater_ x y`` is simiar to ``elt_greater`` function but the output is written to ``out``. You need to make sure ``out`` is big enough to hold the output result.
val elt_less_equal_ : ?out:('a, 'b)t->('a, 'b)t->('a, 'b)t-> unit
``elt_less_equal_ x y`` is simiar to ``elt_less_equal`` function but the output is written to ``out``. You need to make sure ``out`` is big enough to hold the output result.
val elt_greater_equal_ : ?out:('a, 'b)t->('a, 'b)t->('a, 'b)t-> unit
``elt_greater_equal_ x y`` is simiar to ``elt_greater_equal`` function but the output is written to ``out``. You need to make sure ``out`` is big enough to hold the output result.
val elt_equal_scalar_ : ?out:('a, 'b)t->('a, 'b)t->'a-> unit
``elt_equal_scalar_ x a`` is simiar to ``elt_equal_scalar`` function but the output is written to ``x``.
val elt_not_equal_scalar_ : ?out:('a, 'b)t->('a, 'b)t->'a-> unit
``elt_not_equal_scalar_ x a`` is simiar to ``elt_not_equal_scalar`` function but the output is written to ``x``.
val elt_less_scalar_ : ?out:('a, 'b)t->('a, 'b)t->'a-> unit
``elt_less_scalar_ x a`` is simiar to ``elt_less_scalar`` function but the output is written to ``x``.
val elt_greater_scalar_ : ?out:('a, 'b)t->('a, 'b)t->'a-> unit
``elt_greater_scalar_ x a`` is simiar to ``elt_greater_scalar`` function but the output is written to ``x``.
val elt_less_equal_scalar_ : ?out:('a, 'b)t->('a, 'b)t->'a-> unit
``elt_less_equal_scalar_ x a`` is simiar to ``elt_less_equal_scalar`` function but the output is written to ``x``.
val elt_greater_equal_scalar_ : ?out:('a, 'b)t->('a, 'b)t->'a-> unit
``elt_greater_equal_scalar_ x a`` is simiar to ``elt_greater_equal_scalar`` function but the output is written to ``x``.
val conv1d_ :
out:('a, 'b)t->?padding:Owl_types.padding ->('a, 'b)t->('a, 'b)t->int array->
unit
TODO
val conv2d_ :
out:('a, 'b)t->?padding:Owl_types.padding ->('a, 'b)t->('a, 'b)t->int array->
unit
TODO
val conv3d_ :
out:('a, 'b)t->?padding:Owl_types.padding ->('a, 'b)t->('a, 'b)t->int array->
unit
TODO
val dilated_conv1d_ :
out:('a, 'b)t->?padding:Owl_types.padding ->('a, 'b)t->('a, 'b)t->int array->int array->
unit
TODO
val dilated_conv2d_ :
out:('a, 'b)t->?padding:Owl_types.padding ->('a, 'b)t->('a, 'b)t->int array->int array->
unit
TODO
val dilated_conv3d_ :
out:('a, 'b)t->?padding:Owl_types.padding ->('a, 'b)t->('a, 'b)t->int array->int array->
unit
TODO
val transpose_conv1d_ :
out:('a, 'b)t->?padding:Owl_types.padding ->('a, 'b)t->('a, 'b)t->int array->
unit
TODO
val transpose_conv2d_ :
out:('a, 'b)t->?padding:Owl_types.padding ->('a, 'b)t->('a, 'b)t->int array->
unit
TODO
val transpose_conv3d_ :
out:('a, 'b)t->?padding:Owl_types.padding ->('a, 'b)t->('a, 'b)t->int array->
unit
TODO
val max_pool1d_ :
out:('a, 'b)t->?padding:Owl_types.padding ->('a, 'b)t->int array->int array->
unit
TODO
val max_pool2d_ :
out:('a, 'b)t->?padding:Owl_types.padding ->('a, 'b)t->int array->int array->
unit
TODO
val max_pool3d_ :
out:('a, 'b)t->?padding:Owl_types.padding ->('a, 'b)t->int array->int array->
unit
TODO
val avg_pool1d_ :
out:('a, 'b)t->?padding:Owl_types.padding ->('a, 'b)t->int array->int array->
unit
TODO
val avg_pool2d_ :
out:('a, 'b)t->?padding:Owl_types.padding ->('a, 'b)t->int array->int array->
unit
TODO
val avg_pool3d_ :
out:('a, 'b)t->?padding:Owl_types.padding ->('a, 'b)t->int array->int array->
unit
TODO
val upsampling2d_ : out:('a, 'b)t->('a, 'b)t->int array-> unit
TODO
val conv1d_backward_input_ :
out:('a, 'b)t->('a, 'b)t->('a, 'b)t->int array->('a, 'b)t->
unit
TODO
val conv1d_backward_kernel_ :
out:('a, 'b)t->('a, 'b)t->('a, 'b)t->int array->('a, 'b)t->
unit
TODO
val conv2d_backward_input_ :
out:('a, 'b)t->('a, 'b)t->('a, 'b)t->int array->('a, 'b)t->
unit
TODO
val conv2d_backward_kernel_ :
out:('a, 'b)t->('a, 'b)t->('a, 'b)t->int array->('a, 'b)t->
unit
TODO
val conv3d_backward_input_ :
out:('a, 'b)t->('a, 'b)t->('a, 'b)t->int array->('a, 'b)t->
unit
TODO
val conv3d_backward_kernel_ :
out:('a, 'b)t->('a, 'b)t->('a, 'b)t->int array->('a, 'b)t->
unit
TODO
val dilated_conv1d_backward_input_ :
out:('a, 'b)t->('a, 'b)t->('a, 'b)t->int array->int array->('a, 'b)t->
unit
TODO
val dilated_conv1d_backward_kernel_ :
out:('a, 'b)t->('a, 'b)t->('a, 'b)t->int array->int array->('a, 'b)t->
unit
TODO
val dilated_conv2d_backward_input_ :
out:('a, 'b)t->('a, 'b)t->('a, 'b)t->int array->int array->('a, 'b)t->
unit
TODO
val dilated_conv2d_backward_kernel_ :
out:('a, 'b)t->('a, 'b)t->('a, 'b)t->int array->int array->('a, 'b)t->
unit
TODO
val dilated_conv3d_backward_input_ :
out:('a, 'b)t->('a, 'b)t->('a, 'b)t->int array->int array->('a, 'b)t->
unit
TODO
val dilated_conv3d_backward_kernel_ :
out:('a, 'b)t->('a, 'b)t->('a, 'b)t->int array->int array->('a, 'b)t->
unit
TODO
val transpose_conv1d_backward_input_ :
out:('a, 'b)t->('a, 'b)t->('a, 'b)t->int array->('a, 'b)t->
unit
TODO
val transpose_conv1d_backward_kernel_ :
out:('a, 'b)t->('a, 'b)t->('a, 'b)t->int array->('a, 'b)t->
unit
TODO
val transpose_conv2d_backward_input_ :
out:('a, 'b)t->('a, 'b)t->('a, 'b)t->int array->('a, 'b)t->
unit
TODO
val transpose_conv2d_backward_kernel_ :
out:('a, 'b)t->('a, 'b)t->('a, 'b)t->int array->('a, 'b)t->
unit
TODO
val transpose_conv3d_backward_input_ :
out:('a, 'b)t->('a, 'b)t->('a, 'b)t->int array->('a, 'b)t->
unit
TODO
val transpose_conv3d_backward_kernel_ :
out:('a, 'b)t->('a, 'b)t->('a, 'b)t->int array->('a, 'b)t->
unit
TODO
val max_pool1d_backward_ :
out:('a, 'b)t->Owl_types.padding ->('a, 'b)t->int array->int array->('a, 'b)t->
unit
TODO
val max_pool2d_backward_ :
out:('a, 'b)t->Owl_types.padding ->('a, 'b)t->int array->int array->('a, 'b)t->
unit
TODO
val max_pool3d_backward_ :
out:('a, 'b)t->Owl_types.padding ->('a, 'b)t->int array->int array->('a, 'b)t->
unit
TODO
val avg_pool1d_backward_ :
out:('a, 'b)t->Owl_types.padding ->('a, 'b)t->int array->int array->('a, 'b)t->
unit
TODO
val avg_pool2d_backward_ :
out:('a, 'b)t->Owl_types.padding ->('a, 'b)t->int array->int array->('a, 'b)t->
unit
TODO
val avg_pool3d_backward_ :
out:('a, 'b)t->Owl_types.padding ->('a, 'b)t->int array->int array->('a, 'b)t->
unit
TODO
val upsampling2d_backward_ :
out:('a, 'b)t->('a, 'b)t->int array->('a, 'b)t->
unit
TODO
val fused_adagrad_ : ?out:('a, 'b)t->rate:'a->eps:'a->('a, 'b)t-> unit