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Matrix 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 ``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 definition
type('a, 'b) t = ('a, 'b, Stdlib.Bigarray.c_layout)Stdlib.Bigarray.Genarray.t
N-dimensional array type, i.e. Bigarray Genarray type.
``init m n f`` creates a matrix ``x`` of shape ``m x n``, then using ``f`` to initialise the elements in ``x``. The input of ``f`` is 1-dimensional index of the matrix. You need to explicitly convert it if you need 2D index. The function ``Owl_utils.ind`` can help you.
``init_2d m n f`` s almost the same as ``init`` but ``f`` receives 2D index as input. It is more convenient since you don't have to convert the index by yourself, but this also means ``init_2d`` is slower than ``init``.
``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``.
``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.
``sequential ~a ~step m n`` creates an ``m`` by ``n`` matrix. The elements in ``x`` are initialised sequentiallly from ``~a`` and is increased by ``~step``.
The default value of ``~a`` is zero whilst the default value of ``~step`` is one.
``uniform m n`` creates an ``m`` by ``n`` matrix where all the elements follow a uniform distribution in ``(0,1)`` interval. ``uniform ~scale:a m n`` adjusts the interval to ``(0,a)``.
``gaussian m n`` creates an ``m`` by ``n`` matrix where all the elements in ``x`` follow a Gaussian distribution with specified sigma. By default ``sigma = 1``.
``linspace a b n`` linearly divides the interval ``a,b`` into ``n`` pieces by creating an ``m`` by ``1`` row vector. E.g., ``linspace 0. 5. 5`` will create a row vector ``0;1;2;3;4;5``.
``meshgrid a1 b1 a2 b2 n1 n2`` is similar to the ``meshgrid`` function in Matlab. It returns two matrices ``x`` and ``y`` where the row vectors in ``x`` are linearly spaced between ``a1,b1`` by ``n1`` whilst the column vectors in ``y`` are linearly spaced between ``(a2,b2)`` by ``n2``.
val meshup : ('a, 'b)t->('a, 'b)t->('a, 'b)t * ('a, 'b)t
``meshup x y`` creates mesh grids by using two row vectors ``x`` and ``y``.
``diagm k v`` creates a diagonal matrix using the elements in ``v`` as diagonal values. ``k`` specifies the main diagonal index. If ``k > 0`` then it is above the main diagonal, if ``k < 0`` then it is below the main diagonal. This function is the same as the ``diag`` function in Matlab.
``triu k x`` returns the element on and above the ``k``th diagonal of ``x``. ``k = 0`` is the main diagonal, ``k > 0`` is above the main diagonal, and ``k < 0`` is below the main diagonal.
``tril k x`` returns the element on and below the ``k``th diagonal of ``x``. ``k = 0`` is the main diagonal, ``k > 0`` is above the main diagonal, and ``k < 0`` is below the main diagonal.
val symmetric : ?upper:bool ->('a, 'b)t->('a, 'b)t
``symmetric ~upper x`` creates a symmetric matrix using either upper or lower triangular part of ``x``. If ``upper`` is ``true`` then it uses the upper part, if ``upper`` is ``false``, then ``symmetric`` uses the lower part. By default ``upper`` is true.
val hermitian :
?upper:bool ->(Stdlib.Complex.t, 'a)t->(Stdlib.Complex.t, 'a)t
``hermitian ~upper x`` creates a hermitian matrix based on ``x``. By default, the upper triangular part is used for creating the hermitian matrix, but you use the lower part by setting ``upper=false``
val bidiagonal : ?upper:bool ->('a, 'b)t->('a, 'b)t->('a, 'b)t
``bidiagonal upper dv ev`` creates a bidiagonal matrix using ``dv`` and ``ev``. Both ``dv`` and ``ev`` are row vectors. ``dv`` is the main diagonal. If ``upper`` is ``true`` then ``ev`` is superdiagonal; if ``upper`` is ``false`` then ``ev`` is subdiagonal. By default, ``upper`` is ``true``.
NOTE: because the diagonal elements in a hermitian matrix must be real, the function set the imaginary part of the diagonal elements to zero by default. In other words, if the diagonal elements of ``x`` have non-zero imaginary parts, the imaginary parts will be dropped without a warning.
``toeplitz ~c r`` generates a toeplitz matrix using ``r`` and ``c``. Both ``r`` and ``c`` are row vectors of the same length. If the first elements of ``c`` is different from that of ``r``, ``r``'s first element will be used.
Note: 1) If ``c`` is not passed in, then ``c = r`` will be used. 2) If ``c`` is not passed in and ``r`` is complex, the ``c = conj r`` will be used. 3) If ``r`` and ``c`` have different length, then the result is a rectangular matrix.
``hankel ~r c`` generates a hankel matrix using ``r`` and ``c``. ``c`` will be the first column and ``r`` will be the last row of the returned matrix.
Note: 1) If only ``c`` is passed in, the elelments below the anti-diagnoal are zero. 2) If the last element of ``c`` is different from the first element of ``r`` then the first element of ``c`` prevails. 3) ``c`` and ``r`` can have different length, the return will be an rectangular matrix.
``hadamard k n`` constructs a hadamard matrix of order ``n``. For a hadamard ``H``, we have ``H'*H = n*I``. Currrently, this function handles only the cases where ``n``, ``n/12``, or ``n/20`` is a power of 2.
``magic k n`` constructs a ``n x n`` magic square matrix ``x``. The elements in ``x`` are consecutive numbers increasing from ``1`` to ``n^2``. ``n`` must ``n >= 3``.
There are three different algorithms to deal with ``n`` is odd, singly even, and doubly even respectively.
``set x i j a`` sets the element ``(i,j)`` of ``x`` to value ``a``. The shorthand for ``set x i j a`` is ``x.,j <- a``
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`` 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.
``get_fancy s x`` returns a copy of the slice in ``x``. The slice is defined by ``a`` which is an ``int array``. Please refer to the same function in the ``Owl_dense_ndarray_generic`` documentation for more details.
``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 ``slice`` 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 ``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 : int list list->('a, 'b)t->('a, 'b)t-> unit
``set_slice 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]``.
``row x i`` returns row ``i`` of ``x``. Note: Unlike ``col``, the return value is simply a view onto the original row in ``x``, so modifying ``row``'s value also alters ``x``.
``rows x a`` returns the rows (defined in an int array ``a``) of ``x``. The returned rows will be combined into a new dense matrix. The order of rows in the new matrix is the same as that in the array ``a``.
``reshape x s`` returns a new ``m`` by ``n`` matrix from the ``m'`` by ``n'`` matrix ``x``. Note that ``(m * n)`` must be equal to ``(m' * n')``, and the returned matrix shares the same memory with the original ``x``.
``flatten x`` reshape ``x`` into a ``1`` by ``n`` row vector 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.
``concat_vh`` is used to assemble small parts of matrices into a bigger one. E.g. ``| [|a; b; c|]; [|d; e; f|]; [|g; h; i|] |`` will be concatenated into a big matrix as follows.
Please refer to :doc:`owl_dense_ndarray_generic`. for details.
val concatenate : ?axis:int ->('a, 'b)t array->('a, 'b)t
``concatenate ~axis:1 x`` concatenates an array of matrices along the second dimension. For the matrices 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 on a marix, i.e., rows.
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.
``ctranspose x`` performs conjugate transpose of a complex matrix ``x``. If ``x`` is a real matrix, then ``ctranspose x`` is equivalent to ``transpose 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.
``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.
``iteri f x`` iterates all the elements in ``x`` and applies the user defined function ``f : int -> int -> float -> 'a``. ``f i j v`` takes three parameters, ``i`` and ``j`` are the coordinates of current element, and ``v`` is its value.
``mapi f x`` maps each element in ``x`` to a new value by applying ``f : int -> int -> float -> float``. The first two parameters are the coordinates of the element, and the third parameter is the value.
``map f x`` is similar to ``mapi f x`` except the coordinates of the current element is not passed to the function ``f : float -> float``
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 : int -> int -> float -> bool`` to filter out certain elements in ``x``. An element will be included if ``f`` returns ``true``. The returned result is a list of coordinates of the selected elements.
Similar to `filteri` but 2d indices ``(i,j)`` are returned.
val iter2i_2d :
(int ->int ->'a->'c-> unit)->('a, 'b)t->('c, 'd)t->
unit
Similar to `iter2i` but 2d indices ``(i,j)`` are passed to the user function.
val map2i_2d :
(int ->int ->'a->'a->'a)->('a, 'b)t->('a, 'b)t->('a, 'b)t
Similar to `map2i` but 2d indices ``(i,j)`` are passed to the user function.
val iter2i : (int ->'a->'b-> unit)->('a, 'c)t->('b, 'd)t-> unit
Similar to ``iteri`` but applies to two matrices ``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 is 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_rows : (int ->('a, 'b)t-> unit)->('a, 'b)t-> unit
``iteri_rows f x`` iterates every row in ``x`` and applies function ``f : int -> mat -> unit`` to each of them.
val iter_rows : (('a, 'b)t-> unit)->('a, 'b)t-> unit
Similar to ``iteri_rows`` except row number is not passed to ``f``.
val iter2i_rows :
(int ->('a, 'b)t->('a, 'b)t-> unit)->('a, 'b)t->('a, 'b)t->
unit
``iter2_rows f x y`` iterates rows of two matrices ``x`` and ```y``.
val iter2_rows :
(('a, 'b)t->('a, 'b)t-> unit)->('a, 'b)t->('a, 'b)t->
unit
Similar to ``iter2iter2i_rows`` but without passing in indices.
val iteri_cols : (int ->('a, 'b)t-> unit)->('a, 'b)t-> unit
``iteri_cols f x`` iterates every column in ``x`` and applies function ``f : int -> mat -> unit`` to each of them. Column number is passed to ``f`` as the first parameter.
val iter_cols : (('a, 'b)t-> unit)->('a, 'b)t-> unit
Similar to ``iteri_cols`` except col number is not passed to ``f``.
val filteri_rows : (int ->('a, 'b)t-> bool)->('a, 'b)t->int array
``filteri_rows f x`` uses function ``f : int -> mat -> bool`` to check each row in ``x``, then returns an int array containing the indices of those rows which satisfy the function ``f``.
val filter_rows : (('a, 'b)t-> bool)->('a, 'b)t->int array
Similar to ``filteri_rows`` except that the row indices are not passed to ``f``.
val filteri_cols : (int ->('a, 'b)t-> bool)->('a, 'b)t->int array
``filteri_cols f x`` uses function ``f : int -> mat -> bool`` to check each column in ``x``, then returns an int array containing the indices of those columns which satisfy the function ``f``.
val filter_cols : (('a, 'b)t-> bool)->('a, 'b)t->int array
Similar to ``filteri_cols`` except that the column indices are not passed to ``f``.
val fold_rows : ('c->('a, 'b)t->'c)->'c->('a, 'b)t->'c
``fold_rows f a x`` folds all the rows in ``x`` using function ``f``. The order of folding is from the first row to the last one.
val fold_cols : ('c->('a, 'b)t->'c)->'c->('a, 'b)t->'c
``fold_cols f a x`` folds all the columns in ``x`` using function ``f``. The order of folding is from the first column to the last one.
val mapi_rows : (int ->('a, 'b)t->'c)->('a, 'b)t->'c array
``mapi_rows f x`` maps every row in ``x`` to a type ``'a`` value by applying function ``f : int -> mat -> 'a`` to each of them. The results is an array of all the returned values.
val map_rows : (('a, 'b)t->'c)->('a, 'b)t->'c array
Similar to ``mapi_rows`` except row number is not passed to ``f``.
val mapi_cols : (int ->('a, 'b)t->'c)->('a, 'b)t->'c array
``mapi_cols f x`` maps every column in ``x`` to a type ``'a`` value by applying function ``f : int -> mat -> 'a``.
val map_cols : (('a, 'b)t->'c)->('a, 'b)t->'c array
Similar to ``mapi_cols`` except column number is not passed to ``f``.
val mapi_by_row :
int ->(int ->('a, 'b)t->('a, 'b)t)->('a, 'b)t->('a, 'b)t
``mapi_by_row d f x`` applies ``f`` to each row of a ``m`` by ``n`` matrix ``x``, then uses the returned ``d`` dimensional row vectors to assemble a new ``m`` by ``d`` matrix.
val map_by_row : int ->(('a, 'b)t->('a, 'b)t)->('a, 'b)t->('a, 'b)t
``map_by_row d f x`` is similar to ``mapi_by_row`` except that the row indices are not passed to ``f``.
val mapi_by_col :
int ->(int ->('a, 'b)t->('a, 'b)t)->('a, 'b)t->('a, 'b)t
``mapi_by_col d f x`` applies ``f`` to each column of a ``m`` by ``n`` matrix ``x``, then uses the returned ``d`` dimensional column vectors to assemble a new ``d`` by ``n`` matrix.
val map_by_col : int ->(('a, 'b)t->('a, 'b)t)->('a, 'b)t->('a, 'b)t
``map_by_col d f x`` is similar to ``mapi_by_col`` except that the column indices are not passed to ``f``.
val mapi_at_row : (int ->'a->'a)->('a, 'b)t->int ->('a, 'b)t
``mapi_at_row f x i`` creates a new matrix by applying function ``f`` only to the ``i``th row in matrix ``x``.
val map_at_row : ('a->'a)->('a, 'b)t->int ->('a, 'b)t
``map_at_row f x i`` is similar to ``mapi_at_row`` except that the coordinates of an element is not passed to ``f``.
val mapi_at_col : (int ->'a->'a)->('a, 'b)t->int ->('a, 'b)t
``mapi_at_col f x j`` creates a new matrix by applying function ``f`` only to the ``j``th column in matrix ``x``.
val map_at_col : ('a->'a)->('a, 'b)t->int ->('a, 'b)t
``map_at_col f x i`` is similar to ``mapi_at_col`` except that the coordinates of an element is not passed to ``f``.
``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``.
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``.
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``.
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``.
Note: the threshold check is exclusive for passed in ``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``.
Randomisation functions
val draw_rows :
?replacement:bool ->('a, 'b)t->int ->('a, 'b)t * int array
``draw_rows x m`` draws ``m`` rows randomly from ``x``. The row indices are also returned in an int array along with the selected rows. The parameter ``replacement`` indicates whether the drawing is by replacement or not.
val draw_cols :
?replacement:bool ->('a, 'b)t->int ->('a, 'b)t * int array
``draw_cols x m`` draws ``m`` cols randomly from ``x``. The column indices are also returned in an int array along with the selected columns. The parameter ``replacement`` indicates whether the drawing is by replacement or not.
val draw_rows2 :
?replacement:bool ->('a, 'b)t->('a, 'b)t->int ->('a, 'b)t * ('a, 'b)t * int array
``draw_rows2 x y c`` is similar to ``draw_rows`` but applies to two matrices.
val draw_cols2 :
?replacement:bool ->('a, 'b)t->('a, 'b)t->int ->('a, 'b)t * ('a, 'b)t * int array
``draw_col2 x y c`` is similar to ``draw_cols`` but applies to two matrices.
``shuffle x`` shuffles all the elements in ``x`` by first shuffling along the rows then shuffling along columns. It is equivalent to ``shuffle_cols (shuffle_rows x)``.
``load f`` loads a matrix from file ``f``. The file must be previously saved by using ``save`` function.
val save_txt : ?sep:string ->?append:bool ->('a, 'b)t->string -> unit
``save_txt ~sep ~append x f`` saves the matrix ``x`` into a text file ``f`` delimited by the specified string ``sep`` (default: tab). If ``append`` is ``false`` (it is by default), an existing file will be truncated and overwritten. If ``append`` is ``true`` and the file exists, new rows will be appended to it. Files are created, if necessary, with the AND of 0o644 and the user's umask value. Note that the operation can be very time consuming.
``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.
``conj x`` computes the conjugate of the elements in ``x`` and returns the result in a new matrix. If the passed in ``x`` is a real matrix, the function simply returns a copy of the original ``x``.
``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.
``softsign x`` computes the softsign function ``x / (1 + abs(x))`` of the elements in ``x`` and returns the result in a new matrix.
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.
``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
``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 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``.
val mat2gray : ?amin:'a->?amax:'a->('a, 'b)t->('a, 'b)t
``mat2gray ~amin ~amax x`` converts the matrix ``x`` to the intensity image. The elements in ``x`` are clipped by ``amin`` and ``amax``, and they will be between ``0.`` and ``1.`` after conversion to represents the intensity.
``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 sqr_nrm2 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``.
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``.
``cov ~a`` calculates the covariance matrix of ``a`` wherein each row represents one observation and each column represents one random variable. ``a`` is normalised by the number of observations-1. If there is only one observation, it is normalised by ``1``.
``cov ~a ~b`` takes two matrices as inputs. The functions flatten ``a`` and ``b`` first then returns a ``2 x 2`` matrix, so two must have the same number of elements.
``kron a b`` calculates the Kronecker product between the matrices ``a`` and ``b``. If ``a`` is an ``m x n`` matrix and ``b`` is a ``p x q`` matrix, then ``kron(a,b)`` is an ``m*p x n*q`` matrix formed by taking all possible products between the elements of ``a`` and the matrix ``b``.
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``.
``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, 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``.
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 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 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``.