package owl

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package owl
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include module type of struct include Owl_dense_ndarray_generic end

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 x; in case both x and y have the same magnitudes, x is less than x 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 ('a, 'b) t = ('a, 'b, Bigarray.c_layout) Bigarray.Genarray.t

N-dimensional array abstract type

type ('a, 'b) kind = ('a, 'b) Bigarray.kind

Type of the ndarray, e.g., Bigarray.Float32, Bigarray.Complex64, and etc.

Create N-dimensional array
val empty : ('a, 'b) kind -> int array -> ('a, 'b) t

empty 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 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 type Bigarray.Float64. 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 index_1d_nd 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.

val zeros : ('a, 'b) kind -> int array -> ('a, 'b) t

zeros Bigarray.Complex32 [|3;4;5|] creates a three-diemensional array of type Bigarray.Complex32. 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.

val ones : ('a, 'b) kind -> int array -> ('a, 'b) t

ones Bigarray.Complex32 [|3;4;5|] creates a three-diemensional array of type Bigarray.Complex32. 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.

val uniform : ?scale:float -> ('a, 'b) kind -> 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 : ?sigma:float -> ('a, 'b) kind -> int array -> ('a, 'b) t

gaussian 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 -> ?seed:int -> 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.

Obtain basic properties
val shape : ('a, 'b) t -> int array

shape x returns the shape of ndarray x.

val num_dims : ('a, 'b) t -> int

num_dims x returns the number of dimensions of ndarray x.

val nth_dim : ('a, 'b) t -> int -> int

nth_dim x returns the size of the nth dimension of x.

val numel : ('a, 'b) t -> int

numel x returns the number of elements in x.

val nnz : ('a, 'b) t -> int

nnz x returns the number of non-zero elements in x.

val density : ('a, 'b) t -> float

density x returns the percentage of non-zero elements in x.

val size_in_bytes : ('a, 'b) t -> int

size_in_bytes x returns the size of x in bytes in memory.

val same_shape : ('a, 'b) t -> ('a, 'b) t -> bool

same_shape x y checks whether x and y has the same shape or not.

val kind : ('a, 'b) t -> ('a, 'b) kind

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.

val strides : ('a, 'b) t -> int array

strides x calcuates the strides of x. E.g., if x is of shape [|3;4;5|], the returned strides will be [|20;5;1|].

val slice_size : ('a, 'b) t -> int array

slice_size calculates the slice size in each dimension, E.g., if x is of shape [|3;4;5|], the returned slice size will be |60; 20; 5|.

val index_1d_nd : int -> int array -> int array

index_1d_nd i stride converts one-dimensional index i to n-dimensional index according to the passed in stride.

NOTE: you need to pass in stride, not the shape of x!

val index_nd_1d : int array -> int array -> int

index_nd_1d i shp converts n-dimensional index i to one-dimensional index according to the passed in stride.

NOTE: you need to pass in stride, not the shape of x!

Manipulate a N-dimensional array
val get : ('a, 'b) t -> int array -> 'a

get x i returns the value at i in x. E.g., get x [|0;2;1|] returns the value at [|0;2;1|] in x.

val set : ('a, 'b) t -> int array -> 'a -> unit

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_slice : Owl_types.index list -> ('a, 'b) t -> ('a, 'b) t

slice 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_slice : Owl_types.index list -> ('a, 'b) t -> ('a, 'b) t -> unit

set_slice 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 slice function.

val get_slice_simple : int list list -> ('a, 'b) t -> ('a, 'b) t

get_slice_simple axis x aims to provide a simpler version of get_slice. This function assumes that every list element in the passed in in 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_simple : int list list -> ('a, 'b) t -> ('a, 'b) t -> unit

set_slice_simple axis x y aims to provide a simpler version of set_slice. This function assumes that every list element in the passed in in list list represents a range, i.e., R constructor.

E.g., [[];[0;3];[0]] is equivalent to [R []; R [0;3]; R [0]] .

val sub_left : ('a, 'b) t -> int -> int -> ('a, 'b) t

Some as Bigarray.sub_left, please refer to Bigarray documentation.

val slice_left : ('a, 'b) t -> int array -> ('a, 'b) t

Same as Bigarray.slice_left, please refer to Bigarray documentation.

val copy_to : ('a, 'b) t -> ('a, 'b) t -> unit

copy_to src dst copies the data from ndarray src to dst.

val reset : ('a, 'b) t -> unit

reset x resets all the elements in x to zero.

val fill : ('a, 'b) t -> 'a -> unit

fill x a assigns the value a to the elements in x.

val copy : ('a, 'b) t -> ('a, 'b) t

copy x makes a copy 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.

val reshape : ('a, 'b) t -> int array -> ('a, 'b) t

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.

val flatten : ('a, 'b) t -> ('a, 'b) t

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.

val reverse : ('a, 'b) t -> ('a, 'b) t

reverse x reverse the order of all elements in the flattened x and returns the results in a new ndarray. The original x remains intact.

val flip : ?axis:int -> ('a, 'b) t -> ('a, 'b) t

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.

val rotate : ('a, 'b) t -> int -> ('a, 'b) t

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.

val swap : int -> int -> ('a, 'b) t -> ('a, 'b) t

swap i j x makes a copy of x, then swaps the data on axis i and j.

val tile : ('a, 'b) t -> int array -> ('a, 'b) t

tile x a tiles the data in x according the repitition specified by a. This function provides the exact behaviour as numpy.tile, please refer to the numpy's online documentation for details.

val repeat : ?axis:int -> ('a, 'b) t -> int -> ('a, 'b) t

repeat ~axis x a repeats the elements along axis for a times. The default value of ?axis is the highest dimension of x. This function is similar to numpy.repeat except that a is an integer instead of an array.

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

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 : ('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.

val pad : ?v:'a -> int list list -> ('a, 'b) t -> ('a, 'b) t

pad ~v:0. [[1;1]] x

val dropout : ?rate:float -> ?seed:int -> ('a, 'b) t -> ('a, 'b) t

dropout ~rate:0.3 x drops out 30% of the elements in x, in other words, by setting their values to zeros.

val top : ('a, 'b) t -> int -> int array array

top x n returns the indices of n greatest values of x. The indices are arranged according to the corresponding elelment values, from the greatest one to the smallest one.

val bottom : ('a, 'b) t -> int -> int array array

bottom x n returns the indices of n smallest values of x. The indices are arranged according to the corresponding elelment values, from the smallest one to the greatest one.

val sort : ('a, 'b) t -> unit

sort x performs in-place quicksort of the elelments in x.

val mmap : Unix.file_descr -> ?pos:int64 -> ('a, 'b) kind -> bool -> int array -> ('a, 'b) t

mmap fd kind layout shared dims ...

Iterate array elements
val iteri : ?axis:int option array -> (int array -> 'a -> unit) -> ('a, 'b) t -> unit

iteri ~axis f x applies function f to each element in a slice defined by ~axis. If ~axis is not passed in, then iteri simply iterates all the elements in x.

val iter : ?axis:int option array -> ('a -> unit) -> ('a, 'b) t -> unit

iter ~axis f x is similar to iteri ~axis f x, excpet the index i of an element is not passed in f. Note that iter is much faster than iteri.

val mapi : ?axis:int option array -> (int array -> 'a -> 'a) -> ('a, 'b) t -> ('a, 'b) t

mapi ~axis f x makes a copy of x, then applies f to each element in a slice defined by ~axis. If ~axis is not passed in, then mapi simply iterates all the elements in x.

val map : ?axis:int option array -> ('a -> 'a) -> ('a, 'b) t -> ('a, 'b) t

map ~axis f x is similar to mapi ~axis f x except the index of the current element is not passed to the function f. Note that map is much faster than mapi.

val map2i : ?axis:int option array -> (int array -> 'a -> 'a -> 'a) -> ('a, 'b) t -> ('a, 'b) t -> ('a, 'b) t

map2i ~axis f x y applies f to two elements of the same position in a slice defined by ~axis in both x and y. If ~axis is not passed in, then map2i simply iterates all the elements in x and y. The two matrices mush have the same shape.

val map2 : ?axis:int option array -> ('a -> 'a -> 'a) -> ('a, 'b) t -> ('a, 'b) t -> ('a, 'b) t

map2 ~axis f x y is similar to map2i ~axis f x y except the index of the index of the current element is not passed to the function f.

val filteri : ?axis:int option array -> (int array -> 'a -> bool) -> ('a, 'b) t -> int array array

filteri ~axis f x uses f to filter out certain elements in a slice defined by ~axis. An element will be included if f returns true. The returned result is a list of indices of the selected elements.

val filter : ?axis:int option array -> ('a -> bool) -> ('a, 'b) t -> int array array

Similar to filteri, but the indices of the elements are not passed to f.

val foldi : ?axis:int option array -> (int array -> 'c -> 'a -> 'c) -> 'c -> ('a, 'b) t -> 'c

foldi ~axis f a x folds all the elements in a slice defined by ~axis with the function f. If ~axis is not passed in, then foldi simply folds all the elements in x.

val fold : ?axis:int option array -> ('c -> 'a -> 'c) -> 'c -> ('a, 'b) t -> 'c

Similar to foldi, except that the index of an element is not passed to f.

val fold__ : ?axis:int -> ('a -> 'a -> 'a) -> 'a -> ('a, 'b) t -> ('a, 'b) t

TODO: rename and add doc fold ~axis f a x folds the elements in x from left along specified axis using passed in function f. a is the initial element and in f acc b is the accumulater and b is one of the elemets in x along the same axis.

val cumulate : ?axis:int -> ('a -> 'a -> 'a) -> ('a, 'b) t -> ('a, 'b) t

TODO: rename and add doc accumulate ~axis f x scans the x along 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 acc a b. This function can be used to implement accumulate sum and prod funcgions.

val iteri_slice : int array -> (int array array -> ('a, 'b) t -> unit) -> ('a, 'b) t -> unit

iteri_slice s f x iterates the slices along the passed in axis indices s, and applies the function f to each of them. The order of iterating slices is based on the order of axis in s.

E.g., for a three-dimensional ndarray of shape [|2;2;2|], iteri_slice [|1;0|] f x will access the slices in the following order: [ [0]; [0]; [] ], [ [1]; [0]; [] ], [ [1]; [1]; [] ]. Also note the slice passed in f is a copy of the original data.

val iter_slice : int array -> (('a, 'b) t -> unit) -> ('a, 'b) t -> unit

Similar to iteri_slice, except that the index of a slice is not passed to f.

val iter2i : (int array -> '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 of a slice is not passed to f.

Examine array elements or compare two arrays
val exists : ('a -> bool) -> ('a, 'b) t -> bool

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.

val not_exists : ('a -> bool) -> ('a, 'b) t -> bool

not_exists f x checks all the elements in x, the function returns true only if all the elements fail to satisfy f : float -> bool.

val for_all : ('a -> bool) -> ('a, 'b) t -> bool

for_all f x checks all the elements in x, the function returns true if and only if all the elements pass the check of function f.

val is_zero : ('a, 'b) t -> bool

is_zero x returns true if all the elements in x are zeros.

val is_positive : ('a, 'b) t -> bool

is_positive x returns true if all the elements in x are positive.

val is_negative : ('a, 'b) t -> bool

is_negative x returns true if all the elements in x are negative.

val is_nonpositive : ('a, 'b) t -> bool

is_nonpositive returns true if all the elements in x are non-positive.

val is_nonnegative : ('a, 'b) t -> bool

is_nonnegative returns true if all the elements in x are non-negative.

val is_normal : ('a, 'b) t -> bool

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

https://www.gnu.org/software/libc/manual/html_node/Floating-Point-Classes.html https://www.gnu.org/software/libc/manual/html_node/Infinity-and-NaN.html#Infinity-and-NaN

val not_nan : ('a, 'b) t -> bool

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.

val not_inf : ('a, 'b) t -> bool

not_inf x returns false if there is any positive or negative INF element in x. Otherwise, the function returns true.

val equal : ('a, 'b) t -> ('a, 'b) t -> bool

equal x y returns true if two ('a, 'b) trices x and y are equal.

val not_equal : ('a, 'b) t -> ('a, 'b) t -> bool

not_equal x y returns true if there is at least one element in x is not equal to that in y.

val greater : ('a, 'b) t -> ('a, 'b) t -> bool

greater x y returns true if all the elements in x are greater than the corresponding elements in y.

val less : ('a, 'b) t -> ('a, 'b) t -> bool

less x y returns true if all the elements in x are smaller than the corresponding elements in y.

val greater_equal : ('a, 'b) t -> ('a, 'b) t -> bool

greater_equal x y returns true if all the elements in x are not smaller than the corresponding elements in y.

val less_equal : ('a, 'b) t -> ('a, 'b) t -> bool

less_equal x y returns true if all the elements in x are not greater than the corresponding elements in y.

val elt_equal : ('a, 'b) t -> ('a, 'b) t -> ('a, 'b) t

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.

The function supports broadcast operation.

val elt_less : ('a, 'b) t -> ('a, 'b) t -> ('a, 'b) t

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.

The function supports broadcast operation.

val elt_greater : ('a, 'b) t -> ('a, 'b) t -> ('a, 'b) t

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.

The function supports broadcast operation.

val equal_scalar : ('a, 'b) t -> 'a -> bool

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.

val not_equal_scalar : ('a, 'b) t -> 'a -> bool

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.

val less_scalar : ('a, 'b) t -> 'a -> bool

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.

val greater_scalar : ('a, 'b) t -> 'a -> bool

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.

val less_equal_scalar : ('a, 'b) t -> 'a -> bool

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.

val greater_equal_scalar : ('a, 'b) t -> 'a -> bool

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.

val elt_equal_scalar : ('a, 'b) t -> 'a -> ('a, 'b) t

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.

val elt_less_scalar : ('a, 'b) t -> 'a -> ('a, 'b) t

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.

val elt_greater_scalar : ('a, 'b) t -> 'a -> ('a, 'b) t

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.

val to_array : ('a, 'b) t -> 'a array

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.

val save : ('a, 'b) t -> string -> unit

save x s serialises a ndarray x to a file of name s.

val load : ('a, 'b) kind -> string -> ('a, 'b) t

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.

Unary mathematical operations
val re_c2s : (Stdlib.Complex.t, Bigarray.complex32_elt) t -> (float, 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, Bigarray.complex64_elt) t -> (float, 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, Bigarray.complex32_elt) t -> (float, 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, Bigarray.complex64_elt) t -> (float, Bigarray.float64_elt) t

im_d2z x returns all the imaginary components of x in a new ndarray of same shape.

val sum : ?axis:int -> ('a, 'b) t -> ('a, 'b) t

sum ~axis x sums the elements in x along specified axis.

val sum' : ('a, 'b) t -> 'a

sum' x returns the sumtion of all elements in x.

val prod : ?axis:int -> ('a, 'b) t -> ('a, 'b) t

prod ~axis x multiples the elements in x along specified axis.

val prod' : ('a, 'b) t -> 'a

prod x returns the product of all elements in x along passed in axises.

val mean : ?axis:int -> ('a, 'b) t -> ('a, 'b) t

mean ~axis x calculates the mean along specified axis.

val mean' : ('a, 'b) t -> 'a

mean' x calculates the mean of all the elements in x.

val var : ?axis:int -> ('a, 'b) t -> ('a, 'b) t

var ~axis x calculates the variance along specified axis.

val var' : ('a, 'b) t -> 'a

var' x calculates the variance of all the elements in x.

val std : ?axis:int -> ('a, 'b) t -> ('a, 'b) t

std ~axis calculates the standard deviation along specified axis.

val std' : ('a, 'b) t -> 'a

std' x calculates the standard deviation of all the elements in x.

val min : ?axis:int -> ('a, 'b) t -> ('a, 'b) t

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.

val min' : ('a, 'b) t -> 'a

min' x is similar to min but returns the minimum of all elements in x in scalar value.

val max : ?axis:int -> ('a, 'b) t -> ('a, 'b) t

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.

val max' : ('a, 'b) t -> 'a

max' x is similar to max but returns the maximum of all elements in x in scalar value.

val minmax : ?axis:int -> ('a, 'b) t -> ('a, 'b) t * ('a, 'b) t

minmax' x returns (min_v, max_v), min_v is the minimum value in x while max_v is the maximum.

val minmax' : ('a, 'b) t -> 'a * 'a

minmax' x returns (min_v, max_v), min_v is the minimum value in x while max_v is the maximum.

val min_i : ('a, 'b) t -> 'a * int array

min_i x returns the minimum of all elements in x as well as its index.

val max_i : ('a, 'b) t -> 'a * int array

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.

val abs : ('a, 'b) t -> ('a, 'b) t

abs x returns the absolute value of all elements in x in a new ndarray.

val abs_c2s : (Stdlib.Complex.t, Bigarray.complex32_elt) t -> (float, Bigarray.float32_elt) t

abs_c2s x is similar to abs but takes complex32 as input.

val abs_z2d : (Stdlib.Complex.t, Bigarray.complex64_elt) t -> (float, Bigarray.float64_elt) t

abs_z2d x is similar to abs but takes complex64 as input.

val abs2 : ('a, 'b) t -> ('a, 'b) t

abs2 x returns the square of absolute value of all elements in x in a new ndarray.

val abs2_c2s : (Stdlib.Complex.t, Bigarray.complex32_elt) t -> (float, Bigarray.float32_elt) t

abs2_c2s x is similar to abs2 but takes complex32 as input.

val abs2_z2d : (Stdlib.Complex.t, Bigarray.complex64_elt) t -> (float, Bigarray.float64_elt) t

abs2_z2d x is similar to abs2 but takes complex64 as input.

val conj : ('a, 'b) t -> ('a, 'b) t

conj x returns the conjugate of the complex x.

val neg : ('a, 'b) t -> ('a, 'b) t

neg x negates the elements in x and returns the result in a new ndarray.

val reci : ('a, 'b) t -> ('a, 'b) t

reci x computes the reciprocal of every elements in x and returns the result in a new ndarray.

val reci_tol : ?tol:'a -> ('a, 'b) t -> ('a, 'b) t

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.

val signum : (float, 'a) t -> (float, 'a) t

signum computes the sign value (-1 for negative numbers, 0 (or -0) for zero, 1 for positive numbers, nan for nan).

val sqr : ('a, 'b) t -> ('a, 'b) t

sqr x computes the square of the elements in x and returns the result in a new ndarray.

val sqrt : ('a, 'b) t -> ('a, 'b) t

sqrt x computes the square root of the elements in x and returns the result in a new ndarray.

val cbrt : ('a, 'b) t -> ('a, 'b) t

cbrt x computes the cubic root of the elements in x and returns the result in a new ndarray.

val exp : ('a, 'b) t -> ('a, 'b) t

exp x computes the exponential of the elements in x and returns the result in a new ndarray.

val exp2 : ('a, 'b) t -> ('a, 'b) t

exp2 x computes the base-2 exponential of the elements in x and returns the result in a new ndarray.

val exp10 : ('a, 'b) t -> ('a, 'b) t

exp10 x computes the base-10 exponential of the elements in x and returns the result in a new ndarray.

val expm1 : ('a, 'b) t -> ('a, 'b) t

expm1 x computes exp x -. 1. of the elements in x and returns the result in a new ndarray.

val log : ('a, 'b) t -> ('a, 'b) t

log x computes the logarithm of the elements in x and returns the result in a new ndarray.

val log10 : ('a, 'b) t -> ('a, 'b) t

log10 x computes the base-10 logarithm of the elements in x and returns the result in a new ndarray.

val log2 : ('a, 'b) t -> ('a, 'b) t

log2 x computes the base-2 logarithm of the elements in x and returns the result in a new ndarray.

val log1p : ('a, 'b) t -> ('a, 'b) t

log1p x computes log (1 + x) of the elements in x and returns the result in a new ndarray.

val sin : ('a, 'b) t -> ('a, 'b) t

sin x computes the sine of the elements in x and returns the result in a new ndarray.

val cos : ('a, 'b) t -> ('a, 'b) t

cos x computes the cosine of the elements in x and returns the result in a new ndarray.

val tan : ('a, 'b) t -> ('a, 'b) t

tan x computes the tangent of the elements in x and returns the result in a new ndarray.

val asin : ('a, 'b) t -> ('a, 'b) t

asin x computes the arc sine of the elements in x and returns the result in a new ndarray.

val acos : ('a, 'b) t -> ('a, 'b) t

acos x computes the arc cosine of the elements in x and returns the result in a new ndarray.

val atan : ('a, 'b) t -> ('a, 'b) t

atan x computes the arc tangent of the elements in x and returns the result in a new ndarray.

val sinh : ('a, 'b) t -> ('a, 'b) t

sinh x computes the hyperbolic sine of the elements in x and returns the result in a new ndarray.

val cosh : ('a, 'b) t -> ('a, 'b) t

cosh x computes the hyperbolic cosine of the elements in x and returns the result in a new ndarray.

val tanh : ('a, 'b) t -> ('a, 'b) t

tanh x computes the hyperbolic tangent of the elements in x and returns the result in a new ndarray.

val asinh : ('a, 'b) t -> ('a, 'b) t

asinh x computes the hyperbolic arc sine of the elements in x and returns the result in a new ndarray.

val acosh : ('a, 'b) t -> ('a, 'b) t

acosh x computes the hyperbolic arc cosine of the elements in x and returns the result in a new ndarray.

val atanh : ('a, 'b) t -> ('a, 'b) t

atanh x computes the hyperbolic arc tangent of the elements in x and returns the result in a new ndarray.

val floor : ('a, 'b) t -> ('a, 'b) t

floor x computes the floor of the elements in x and returns the result in a new ndarray.

val ceil : ('a, 'b) t -> ('a, 'b) t

ceil x computes the ceiling of the elements in x and returns the result in a new ndarray.

val round : ('a, 'b) t -> ('a, 'b) t

round x rounds the elements in x and returns the result in a new ndarray.

val trunc : ('a, 'b) t -> ('a, 'b) t

trunc x computes the truncation of the elements in x and returns the result in a new ndarray.

val fix : ('a, 'b) t -> ('a, 'b) t

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.

val modf : ('a, 'b) t -> ('a, 'b) t * ('a, 'b) t

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.

val erf : (float, 'a) t -> (float, 'a) t

erf x computes the error function of the elements in x and returns the result in a new ndarray.

val erfc : (float, 'a) t -> (float, 'a) t

erfc x computes the complementary error function of the elements in x and returns the result in a new ndarray.

val logistic : (float, 'a) t -> (float, 'a) t

logistic x computes the logistic function 1/(1 + exp(-a) of the elements in x and returns the result in a new ndarray.

val relu : (float, 'a) t -> (float, 'a) t

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.

val softplus : (float, 'a) t -> (float, 'a) t

softplus x computes the softplus function log(1 + exp(x) of the elements in x and returns the result in a new ndarray.

val softsign : (float, 'a) t -> (float, 'a) t

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 : (float, 'a) t -> (float, 'a) t

softmax x computes the softmax functions (exp x) / (sum (exp x)) of all the elements in x and returns the result in a new array.

val sigmoid : (float, 'a) t -> (float, 'a) t

sigmoid x computes the sigmoid function 1 / (1 + exp (-x)) for each element in x.

val log_sum_exp' : (float, 'a) t -> float

log_sum_exp x computes the logarithm of the sum of exponentials of all the elements in x.

val l1norm : ?axis:int -> ('a, 'b) t -> ('a, 'b) t

l1norm x calculates the l1-norm of of x along specified axis.

val l1norm' : ('a, 'b) t -> 'a

l1norm x calculates the l1-norm of all the element in x.

val l2norm : ?axis:int -> ('a, 'b) t -> ('a, 'b) t

l2norm x calculates the l2-norm of of x along specified axis.

val l2norm' : ('a, 'b) t -> 'a

l2norm x calculates the l2-norm of all the element in x.

val l2norm_sqr : ?axis:int -> ('a, 'b) t -> ('a, 'b) t

l2norm x calculates the square l2-norm of of x along specified axis.

val l2norm_sqr' : ('a, 'b) t -> 'a

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 cumsum : ?axis:int -> ('a, 'b) t -> ('a, 'b) t

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.

val cumprod : ?axis:int -> ('a, 'b) t -> ('a, 'b) t

cumprod ~axis x : similar to cumsum but performs cumulative product of the elements along the given ~axis.

val cummin : ?axis:int -> ('a, 'b) t -> ('a, 'b) t

cummin ~axis x : performs cumulative min along axis dimension.

val cummax : ?axis:int -> ('a, 'b) t -> ('a, 'b) t

cummax ~axis x : performs cumulative max along axis dimension.

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.

Binary mathematical operations
val add : ('a, 'b) t -> ('a, 'b) t -> ('a, 'b) t

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.

val sub : ('a, 'b) t -> ('a, 'b) t -> ('a, 'b) t

sub x y subtracts all the elements in x and y elementwise, and returns the result in a new ndarray.

val mul : ('a, 'b) t -> ('a, 'b) t -> ('a, 'b) t

mul x y multiplies all the elements in x and y elementwise, and returns the result in a new ndarray.

val div : ('a, 'b) t -> ('a, 'b) t -> ('a, 'b) t

div x y divides all the elements in x and y elementwise, and returns the result in a new ndarray.

val add_scalar : ('a, 'b) t -> 'a -> ('a, 'b) t

add_scalar x a adds a scalar value a to each element in x, and returns the result in a new ndarray.

val sub_scalar : ('a, 'b) t -> 'a -> ('a, 'b) t

sub_scalar x a subtracts a scalar value a from each element in x, and returns the result in a new ndarray.

val mul_scalar : ('a, 'b) t -> 'a -> ('a, 'b) t

mul_scalar x a multiplies each element in x by a scalar value a, and returns the result in a new ndarray.

val div_scalar : ('a, 'b) t -> 'a -> ('a, 'b) t

div_scalar x a divides each element in x by a scalar value a, and returns the result in a new ndarray.

val scalar_add : 'a -> ('a, 'b) t -> ('a, 'b) t

scalar_add a x adds a scalar value a to each element in x, and returns the result in a new ndarray.

val scalar_sub : 'a -> ('a, 'b) t -> ('a, 'b) t

scalar_sub a x subtracts each element in x from a scalar value a, and returns the result in a new ndarray.

val scalar_mul : 'a -> ('a, 'b) t -> ('a, 'b) t

scalar_mul a x multiplies each element in x by a scalar value a, and returns the result in a new ndarray.

val scalar_div : 'a -> ('a, 'b) t -> ('a, 'b) t

scalar_div a x divides a scalar value a by each element in x, and returns the result in a new ndarray.

val pow : ('a, 'b) t -> ('a, 'b) t -> ('a, 'b) t

pow x y computes pow(a, b) of all the elements in x and y elementwise, and returns the result in a new ndarray.

val scalar_pow : 'a -> ('a, 'b) t -> ('a, 'b) t

scalar_pow a x computes the power value of a scalar value a using the elements in a ndarray x.

val pow_scalar : ('a, 'b) t -> 'a -> ('a, 'b) t

pow_scalar x a computes each element in x power to a.

val atan2 : (float, 'a) t -> (float, 'a) t -> (float, 'a) t

atan2 x y computes atan2(a, b) of all the elements in x and y elementwise, and returns the result in a new ndarray.

val scalar_atan2 : float -> (float, 'a) t -> (float, 'a) t

scalar_atan2 a x

val atan2_scalar : (float, 'a) t -> float -> (float, 'a) t

scalar_atan2 x a

val hypot : (float, 'a) t -> (float, 'a) t -> (float, 'a) t

hypot x y computes sqrt(x*x + y*y) of all the elements in x and y elementwise, and returns the result in a new ndarray.

val min2 : ('a, 'b) t -> ('a, 'b) t -> ('a, 'b) t

min2 x y computes the minimum of all the elements in x and y elementwise, and returns the result in a new ndarray.

val max2 : ('a, 'b) t -> ('a, 'b) t -> ('a, 'b) t

max2 x y computes the maximum of all the elements in x and y elementwise, and returns the result in a new ndarray.

val fmod : (float, 'a) t -> (float, 'a) t -> (float, 'a) t

fmod x y performs float mod division.

val fmod_scalar : (float, 'a) t -> float -> (float, 'a) t

fmod_scalar x a performs mod division between x and scalar a.

val scalar_fmod : float -> (float, 'a) t -> (float, 'a) t

scalar_fmod x a performs mod division between scalar a and x.

val ssqr' : ('a, 'b) t -> 'a -> 'a

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.

val ssqr_diff' : ('a, 'b) t -> ('a, 'b) t -> 'a

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.

val clip_by_l2norm : float -> (float, 'a) t -> (float, 'a) t

clip_by_l2norm t x clips the x according to the threshold set by t.

Cast functions
val cast : ('a, 'b) kind -> ('c, 'd) t -> ('a, 'b) t

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 type casting functions such as cast_s2d, cast_c2z, and etc.

val cast_s2d : (float, Bigarray.float32_elt) t -> (float, Bigarray.float64_elt) t

cast_s2d x casts x from float32 to float64.

val cast_d2s : (float, Bigarray.float64_elt) t -> (float, Bigarray.float32_elt) t

cast_d2s x casts x from float64 to float32.

val cast_c2z : (Stdlib.Complex.t, Bigarray.complex32_elt) t -> (Stdlib.Complex.t, Bigarray.complex64_elt) t

cast_c2z x casts x from complex32 to complex64.

val cast_z2c : (Stdlib.Complex.t, Bigarray.complex64_elt) t -> (Stdlib.Complex.t, Bigarray.complex32_elt) t

cast_z2c x casts x from complex64 to complex32.

val cast_s2c : (float, Bigarray.float32_elt) t -> (Stdlib.Complex.t, Bigarray.complex32_elt) t

cast_s2c x casts x from float32 to complex32.

val cast_d2z : (float, Bigarray.float64_elt) t -> (Stdlib.Complex.t, Bigarray.complex64_elt) t

cast_d2z x casts x from float64 to complex64.

val cast_s2z : (float, Bigarray.float32_elt) t -> (Stdlib.Complex.t, Bigarray.complex64_elt) t

cast_s2z x casts x from float32 to complex64.

val cast_d2c : (float, Bigarray.float64_elt) t -> (Stdlib.Complex.t, Bigarray.complex32_elt) t

cast_d2c x casts x from float64 to complex32.

val conv1d : ?padding:Owl_types.padding -> (float, 'a) t -> (float, 'a) t -> int array -> (float, 'a) t

val conv2d : ?padding:Owl_types.padding -> (float, 'a) t -> (float, 'a) t -> int array -> (float, 'a) t

val conv3d : ?padding:Owl_types.padding -> (float, 'a) t -> (float, 'a) t -> int array -> (float, 'a) t

val max_pool1d : ?padding:Owl_types.padding -> (float, 'a) t -> int array -> int array -> (float, 'a) t

val max_pool2d : ?padding:Owl_types.padding -> (float, 'a) t -> int array -> int array -> (float, 'a) t

val max_pool3d : ?padding:Owl_types.padding -> (float, 'a) t -> int array -> int array -> (float, 'a) t

val avg_pool1d : ?padding:Owl_types.padding -> (float, 'a) t -> int array -> int array -> (float, 'a) t

val avg_pool2d : ?padding:Owl_types.padding -> (float, 'a) t -> int array -> int array -> (float, 'a) t

val avg_pool3d : ?padding:Owl_types.padding -> (float, 'a) t -> int array -> int array -> (float, 'a) t

val max_pool2d_argmax : ?padding:Owl_types.padding -> (float, 'a) t -> int array -> int array -> (float, 'a) t * (int64, Bigarray.int64_elt) t

val conv1d_backward_input : (float, 'a) t -> (float, 'a) t -> int array -> (float, 'a) t -> (float, 'a) t

val conv1d_backward_kernel : (float, 'a) t -> (float, 'a) t -> int array -> (float, 'a) t -> (float, 'a) t

val conv2d_backward_input : (float, 'a) t -> (float, 'a) t -> int array -> (float, 'a) t -> (float, 'a) t

val conv2d_backward_kernel : (float, 'a) t -> (float, 'a) t -> int array -> (float, 'a) t -> (float, 'a) t

val conv3d_backward_input : (float, 'a) t -> (float, 'a) t -> int array -> (float, 'a) t -> (float, 'a) t

val conv3d_backward_kernel : (float, 'a) t -> (float, 'a) t -> int array -> (float, 'a) t -> (float, 'a) t

val max_pool1d_backward : Owl_types.padding -> (float, 'a) t -> int array -> int array -> (float, 'a) t -> (float, 'a) t

val max_pool2d_backward : Owl_types.padding -> (float, 'a) t -> int array -> int array -> (float, 'a) t -> (float, 'a) t

val avg_pool1d_backward : Owl_types.padding -> (float, 'a) t -> int array -> int array -> (float, 'a) t -> (float, 'a) t

val avg_pool2d_backward : Owl_types.padding -> (float, 'a) t -> int array -> int array -> (float, 'a) t -> (float, 'a) t

Some helper and experimental 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.

val print_element : ('a, 'b) kind -> 'a -> unit

print_element kind a prints the value of a single element.

val print_index : int array -> unit

print_index i prints out the index of an element.

val _check_transpose_axis : int array -> int -> unit

_check_transpose_axis a d checks whether a is a legiti('a, 'b) te transpose index.

val sum_slices : ?axis:int -> ('a, 'b) t -> ('a, 'b) t

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, fast but uses more memory.

val calc_conv1d_output_shape : Owl_types.padding -> int -> int -> int -> int

val calc_conv2d_output_shape : Owl_types.padding -> int -> int -> int -> int -> int -> int -> int * int

val calc_conv3d_output_shape : Owl_types.padding -> int -> int -> int -> int -> int -> int -> int -> int -> int -> int * int * int

val slice_along_dim0 : ('a, 'b) t -> int array -> ('a, 'b) t
val draw_along_dim0 : ('a, 'b) t -> int -> ('a, 'b) t * int array
Fucntions of in-place modification
val add_ : ('a, 'b) t -> ('a, 'b) t -> unit

add_ x y is simiar to add function but the output is written to x. The broadcast operation only allows broadcasting y over x, so you need to make sure x is big enough to hold the output result.

val sub_ : ('a, 'b) t -> ('a, 'b) t -> unit

sub_ x y is simiar to sub function but the output is written to x. The broadcast operation only allows broadcasting y over x, so you need to make sure x is big enough to hold the output result.

val mul_ : ('a, 'b) t -> ('a, 'b) t -> unit

mul_ x y is simiar to mul function but the output is written to x. The broadcast operation only allows broadcasting y over x, so you need to make sure x is big enough to hold the output result.

val div_ : ('a, 'b) t -> ('a, 'b) t -> unit

div_ x y is simiar to div function but the output is written to x. The broadcast operation only allows broadcasting y over x, so you need to make sure x is big enough to hold the output result.

val pow_ : ('a, 'b) t -> ('a, 'b) t -> unit

pow_ x y is simiar to pow function but the output is written to x. The broadcast operation only allows broadcasting y over x, so you need to make sure x is big enough to hold the output result.

val atan2_ : ('a, 'b) t -> ('a, 'b) t -> unit

atan2_ x y is simiar to atan2 function but the output is written to x. The broadcast operation only allows broadcasting y over x, so you need to make sure x is big enough to hold the output result.

val hypot_ : ('a, 'b) t -> ('a, 'b) t -> unit

hypot_ x y is simiar to hypot function but the output is written to x. The broadcast operation only allows broadcasting y over x, so you need to make sure x is big enough to hold the output result.

val fmod_ : ('a, 'b) t -> ('a, 'b) t -> unit

fmod_ x y is simiar to fmod function but the output is written to x. The broadcast operation only allows broadcasting y over x, so you need to make sure x is big enough to hold the output result.

val min2_ : ('a, 'b) t -> ('a, 'b) t -> unit

min2_ x y is simiar to min2 function but the output is written to x. The broadcast operation only allows broadcasting y over x, so you need to make sure x is big enough to hold the output result.

val max2_ : ('a, 'b) t -> ('a, 'b) t -> unit

max2_ x y is simiar to max2 function but the output is written to x. The broadcast operation only allows broadcasting y over x, so you need to make sure x is big enough to hold the output result.

val add_scalar_ : ('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_ : ('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_ : ('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_ : ('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_ : ('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_ : ('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_ : ('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_ : '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_ : '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_ : '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_ : '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_ : '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_ : '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_ : 'a -> ('a, 'b) t -> unit

scalar_fmod_ a x is simiar to scalar_fmod function but the output is written to x.

val conj_ : ('a, 'b) t -> unit

conj_ x is similar to conj but output is written to x

val abs_ : ('a, 'b) t -> unit

abs_ x is similar to abs but output is written to x

val neg_ : ('a, 'b) t -> unit

neg_ x is similar to neg but output is written to x

val reci_ : ('a, 'b) t -> unit

reci_ x is similar to reci but output is written to x

val signum_ : ('a, 'b) t -> unit

signum_ x is similar to signum but output is written to x

val sqr_ : ('a, 'b) t -> unit

sqr_ x is similar to sqr but output is written to x

val sqrt_ : ('a, 'b) t -> unit

sqrt_ x is similar to sqrt but output is written to x

val cbrt_ : ('a, 'b) t -> unit

cbrt_ x is similar to cbrt but output is written to x

val exp_ : ('a, 'b) t -> unit

exp_ x is similar to exp_ but output is written to x

val exp2_ : ('a, 'b) t -> unit

exp2_ x is similar to exp2 but output is written to x

val exp10_ : ('a, 'b) t -> unit

exp2_ x is similar to exp2 but output is written to x

val expm1_ : ('a, 'b) t -> unit

expm1_ x is similar to expm1 but output is written to x

val log_ : ('a, 'b) t -> unit

log_ x is similar to log but output is written to x

val log2_ : ('a, 'b) t -> unit

log2_ x is similar to log2 but output is written to x

val log10_ : ('a, 'b) t -> unit

log10_ x is similar to log10 but output is written to x

val log1p_ : ('a, 'b) t -> unit

log1p_ x is similar to log1p but output is written to x

val sin_ : ('a, 'b) t -> unit

sin_ x is similar to sin but output is written to x

val cos_ : ('a, 'b) t -> unit

cos_ x is similar to cos but output is written to x

val tan_ : ('a, 'b) t -> unit

tan_ x is similar to tan but output is written to x

val asin_ : ('a, 'b) t -> unit

asin_ x is similar to asin but output is written to x

val acos_ : ('a, 'b) t -> unit

acos_ x is similar to acos but output is written to x

val atan_ : ('a, 'b) t -> unit

atan_ x is similar to atan but output is written to x

val sinh_ : ('a, 'b) t -> unit

sinh_ x is similar to sinh but output is written to x

val cosh_ : ('a, 'b) t -> unit

cosh_ x is similar to cosh but output is written to x

val tanh_ : ('a, 'b) t -> unit

tanh_ x is similar to tanh but output is written to x

val asinh_ : ('a, 'b) t -> unit

asinh_ x is similar to asinh but output is written to x

val acosh_ : ('a, 'b) t -> unit

acosh_ x is similar to acosh but output is written to x

val atanh_ : ('a, 'b) t -> unit

atanh_ x is similar to atanh but output is written to x

val floor_ : ('a, 'b) t -> unit

floor_ x is similar to floor but output is written to x

val ceil_ : ('a, 'b) t -> unit

ceil_ x is similar to ceil but output is written to x

val round_ : ('a, 'b) t -> unit

round_ x is similar to round but output is written to x

val trunc_ : ('a, 'b) t -> unit

trunc_ x is similar to trunc but output is written to x

val fix_ : ('a, 'b) t -> unit

fix_ x is similar to fix but output is written to x

val erf_ : ('a, 'b) t -> unit

erf_ x is similar to erf but output is written to x

val erfc_ : ('a, 'b) t -> unit

erfc_ x is similar to erfc but output is written to x

val relu_ : ('a, 'b) t -> unit

relu_ x is similar to relu but output is written to x

val softplus_ : ('a, 'b) t -> unit

softplus_ x is similar to softplus but output is written to x

val softsign_ : ('a, 'b) t -> unit

softsign_ x is similar to softsign but output is written to x

val sigmoid_ : ('a, 'b) t -> unit

sigmoid_ x is similar to sigmoid but output is written to x

val softmax_ : ('a, 'b) t -> unit

softmax_ x is similar to softmax but output is written to x

val cumsum_ : ?axis:int -> ('a, 'b) t -> unit

cumsum_ x is similar to cumsum but output is written to x

val cumprod_ : ?axis:int -> ('a, 'b) t -> unit

cumprod_ x is similar to cumprod but output is written to x

val cummin_ : ?axis:int -> ('a, 'b) t -> unit

cummin_ x is similar to cummin but output is written to x

val cummax_ : ?axis:int -> ('a, 'b) t -> unit

cummax_ x is similar to cummax but output is written to x

val dropout_ : ?rate:float -> ?seed:int -> ('a, 'b) t -> unit

dropout_ x is similar to dropout but output is written to x

val elt_equal_ : ('a, 'b) t -> ('a, 'b) t -> unit

elt_equal_ x y is simiar to elt_equal function but the output is written to x. The broadcast operation only allows broadcasting y over x, so you need to make sure x is big enough to hold the output result.

val elt_not_equal_ : ('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 x. The broadcast operation only allows broadcasting y over x, so you need to make sure x is big enough to hold the output result.

val elt_less_ : ('a, 'b) t -> ('a, 'b) t -> unit

elt_less_ x y is simiar to elt_less function but the output is written to x. The broadcast operation only allows broadcasting y over x, so you need to make sure x is big enough to hold the output result.

val elt_greater_ : ('a, 'b) t -> ('a, 'b) t -> unit

elt_greater_ x y is simiar to elt_greater function but the output is written to x. The broadcast operation only allows broadcasting y over x, so you need to make sure x is big enough to hold the output result.

val elt_less_equal_ : ('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 x. The broadcast operation only allows broadcasting y over x, so you need to make sure x is big enough to hold the output result.

val elt_greater_equal_ : ('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 x. The broadcast operation only allows broadcasting y over x, so you need to make sure x is big enough to hold the output result.

val elt_equal_scalar_ : ('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_ : ('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_ : ('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_ : ('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_ : ('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_ : ('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.

Matrix functions
type area = Owl_dense_ndarray_generic.area = {
  1. a : int;
  2. b : int;
  3. c : int;
  4. d : int;
}
val area : int -> int -> int -> int -> area
val copy_area_to : ('a, 'b) t -> area -> ('a, 'b) t -> area -> unit
val row_num : ('a, 'b) t -> int
val col_num : ('a, 'b) t -> int
val row : ('a, 'b) t -> int -> ('a, 'b) t
val col : ('a, 'b) t -> int -> ('a, 'b) t
val rows : ('a, 'b) t -> int array -> ('a, 'b) t
val cols : ('a, 'b) t -> int array -> ('a, 'b) t
val copy_row_to : ('a, 'b) t -> ('a, 'b) t -> int -> unit
val copy_col_to : ('a, 'b) t -> ('a, 'b) t -> int -> unit
val dot : ('a, 'b) t -> ('a, 'b) t -> ('a, 'b) t
val inv : ('a, 'b) t -> ('a, 'b) t
val diag : ?k:int -> ('a, 'b) t -> ('a, 'b) t
val trace : ('a, 'b) t -> 'a
val to_rows : ('a, 'b) t -> ('a, 'b) t array
val of_rows : ('a, 'b) t array -> ('a, 'b) t
val to_cols : ('a, 'b) t -> ('a, 'b) t array
val of_cols : ('a, 'b) t array -> ('a, 'b) t
val to_arrays : ('a, 'b) t -> 'a array array
val of_arrays : ('a, 'b) kind -> 'a array array -> ('a, 'b) t
val draw_rows : ?replacement:bool -> ('a, 'b) t -> int -> ('a, 'b) t * int array
val draw_cols : ?replacement:bool -> ('a, 'b) t -> int -> ('a, 'b) t * int array
val draw_rows2 : ?replacement:bool -> ('a, 'b) t -> ('a, 'b) t -> int -> ('a, 'b) t * ('a, 'b) t * int array
val draw_cols2 : ?replacement:bool -> ('a, 'b) t -> ('a, 'b) t -> int -> ('a, 'b) t * ('a, 'b) t * int array
include module type of struct include Operator end
include sig ... end
type ('a, 'b) op_t0 = ('a, 'b) Owl_dense_ndarray_generic.t
val (+$) : ('a, 'b) Owl_dense_ndarray_generic.t -> 'a -> ('a, 'b) Owl_dense_ndarray_generic.t
val (-$) : ('a, 'b) Owl_dense_ndarray_generic.t -> 'a -> ('a, 'b) Owl_dense_ndarray_generic.t
val (*$) : ('a, 'b) Owl_dense_ndarray_generic.t -> 'a -> ('a, 'b) Owl_dense_ndarray_generic.t
val (/$) : ('a, 'b) Owl_dense_ndarray_generic.t -> 'a -> ('a, 'b) Owl_dense_ndarray_generic.t
val ($+) : 'a -> ('a, 'b) Owl_dense_ndarray_generic.t -> ('a, 'b) Owl_dense_ndarray_generic.t
val ($-) : 'a -> ('a, 'b) Owl_dense_ndarray_generic.t -> ('a, 'b) Owl_dense_ndarray_generic.t
val ($*) : 'a -> ('a, 'b) Owl_dense_ndarray_generic.t -> ('a, 'b) Owl_dense_ndarray_generic.t
val ($/) : 'a -> ('a, 'b) Owl_dense_ndarray_generic.t -> ('a, 'b) Owl_dense_ndarray_generic.t
val (=) : ('a, 'b) Owl_dense_ndarray_generic.t -> ('a, 'b) Owl_dense_ndarray_generic.t -> bool
val (!=) : ('a, 'b) Owl_dense_ndarray_generic.t -> ('a, 'b) Owl_dense_ndarray_generic.t -> bool
val (<>) : ('a, 'b) Owl_dense_ndarray_generic.t -> ('a, 'b) Owl_dense_ndarray_generic.t -> bool
val (>) : ('a, 'b) Owl_dense_ndarray_generic.t -> ('a, 'b) Owl_dense_ndarray_generic.t -> bool
val (<) : ('a, 'b) Owl_dense_ndarray_generic.t -> ('a, 'b) Owl_dense_ndarray_generic.t -> bool
val (>=) : ('a, 'b) Owl_dense_ndarray_generic.t -> ('a, 'b) Owl_dense_ndarray_generic.t -> bool
val (<=) : ('a, 'b) Owl_dense_ndarray_generic.t -> ('a, 'b) Owl_dense_ndarray_generic.t -> bool
include sig ... end
type ('a, 'b) op_t1 = ('a, 'b) Owl_dense_ndarray_generic.t
val (=$) : ('a, 'b) Owl_dense_ndarray_generic.t -> 'a -> bool
val (!=$) : ('a, 'b) Owl_dense_ndarray_generic.t -> 'a -> bool
val (<>$) : ('a, 'b) Owl_dense_ndarray_generic.t -> 'a -> bool
val (<$) : ('a, 'b) Owl_dense_ndarray_generic.t -> 'a -> bool
val (>$) : ('a, 'b) Owl_dense_ndarray_generic.t -> 'a -> bool
val (<=$) : ('a, 'b) Owl_dense_ndarray_generic.t -> 'a -> bool
val (>=$) : ('a, 'b) Owl_dense_ndarray_generic.t -> 'a -> bool
val (=.$) : ('a, 'b) Owl_dense_ndarray_generic.t -> 'a -> ('a, 'b) Owl_dense_ndarray_generic.t
val (!=.$) : ('a, 'b) Owl_dense_ndarray_generic.t -> 'a -> ('a, 'b) Owl_dense_ndarray_generic.t
val (<>.$) : ('a, 'b) Owl_dense_ndarray_generic.t -> 'a -> ('a, 'b) Owl_dense_ndarray_generic.t
val (<.$) : ('a, 'b) Owl_dense_ndarray_generic.t -> 'a -> ('a, 'b) Owl_dense_ndarray_generic.t
val (>.$) : ('a, 'b) Owl_dense_ndarray_generic.t -> 'a -> ('a, 'b) Owl_dense_ndarray_generic.t
val (<=.$) : ('a, 'b) Owl_dense_ndarray_generic.t -> 'a -> ('a, 'b) Owl_dense_ndarray_generic.t
val (>=.$) : ('a, 'b) Owl_dense_ndarray_generic.t -> 'a -> ('a, 'b) Owl_dense_ndarray_generic.t
val (=~) : ?eps:float -> ('a, 'b) Owl_dense_ndarray_generic.t -> ('a, 'b) Owl_dense_ndarray_generic.t -> bool
val (=~$) : ?eps:float -> ('a, 'b) Owl_dense_ndarray_generic.t -> 'a -> bool
val (=~.) : ?eps:float -> ('a, 'b) Owl_dense_ndarray_generic.t -> ('a, 'b) Owl_dense_ndarray_generic.t -> ('a, 'b) Owl_dense_ndarray_generic.t
val (=~.$) : ?eps:float -> ('a, 'b) Owl_dense_ndarray_generic.t -> 'a -> ('a, 'b) Owl_dense_ndarray_generic.t
val (%) : (float, 'a) Owl_dense_ndarray_generic.t -> (float, 'a) Owl_dense_ndarray_generic.t -> (float, 'a) Owl_dense_ndarray_generic.t
val (%$) : (float, 'a) Owl_dense_ndarray_generic.t -> float -> (float, 'a) Owl_dense_ndarray_generic.t
val (**) : (float, 'a) Owl_dense_ndarray_generic.t -> (float, 'a) Owl_dense_ndarray_generic.t -> (float, 'a) Owl_dense_ndarray_generic.t
val ($**) : float -> (float, 'a) Owl_dense_ndarray_generic.t -> (float, 'a) Owl_dense_ndarray_generic.t
val (**$) : (float, 'a) Owl_dense_ndarray_generic.t -> float -> (float, 'a) Owl_dense_ndarray_generic.t
val (+=) : ('a, 'b) Owl_dense_ndarray_generic.t -> ('a, 'b) Owl_dense_ndarray_generic.t -> unit
val (-=) : ('a, 'b) Owl_dense_ndarray_generic.t -> ('a, 'b) Owl_dense_ndarray_generic.t -> unit
val (*=) : ('a, 'b) Owl_dense_ndarray_generic.t -> ('a, 'b) Owl_dense_ndarray_generic.t -> unit
val (/=) : ('a, 'b) Owl_dense_ndarray_generic.t -> ('a, 'b) Owl_dense_ndarray_generic.t -> unit
val (+$=) : ('a, 'b) Owl_dense_ndarray_generic.t -> 'a -> unit
val (-$=) : ('a, 'b) Owl_dense_ndarray_generic.t -> 'a -> unit
val (*$=) : ('a, 'b) Owl_dense_ndarray_generic.t -> 'a -> unit
val (/$=) : ('a, 'b) Owl_dense_ndarray_generic.t -> 'a -> unit
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