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.
The generic module supports operations for the following Bigarry element types: Int8_signed, Int8_unsigned, Int16_signed, Int16_unsigned, Int32, Int64, Float32, Float64, Complex32, Complex64.
``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``.
``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.
``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.
``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``
``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``. Currently, 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.
``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.
Sourceval 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.
This function is used for extended indexing operator since ocaml 4.10.0. The indexing and slicing syntax become much ligher.
Sourceval 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]``.
Sourceval 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]``.
Sourceval get_slice_ext : int list array->('a, 'b)t->('a, 'b)t
Please refer to Ndarray document.
Sourceval set_slice_ext : int list array->('a, 'b)t->('a, 'b)t-> unit
``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.
``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.
``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.
``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.
Iteration functions
Sourceval iteri : (int ->'a-> unit)->('a, 'b)t-> unit
``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.
``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 elements in ``x`` along the same axis. Note that ``i`` is 1d index of ``b``.
``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``.
``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.
``iter2_rows f x y`` iterates rows of two matrices ``x`` and ```y``.
Sourceval 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.
Sourceval 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.
Sourceval iter_cols : (('a, 'b)t-> unit)->('a, 'b)t-> unit
Similar to ``iteri_cols`` except col number is not passed to ``f``.
``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``.
``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``.
``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.
Similar to ``mapi_cols`` except column number is not passed to ``f``.
Sourceval 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.
Sourceval 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``.
Sourceval 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.
Sourceval 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``.
``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``.
``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``.
``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``.
``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``.
``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``.
``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``.
``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``.
``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``.
``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``.
``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``.
``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``.
``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.
``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.
``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.
Sourceval save_txt : ?sep:string ->?append:bool ->out:string ->('a, 'b)t-> unit
``save_txt ~sep ~append ~out x`` saves the matrix ``x`` into a text file ``out`` 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.
``load_txt ~sep k f`` load a text file ``f`` into a matrix of type ``k``. The delimitor is specified by ``sep`` which can be a regular expression.
Sourceval save_npy : out:string ->('a, 'b)t-> unit
``save_npy ~out x`` saves the matrix ``x`` into a npy file ``out``. This function is implemented using npy-ocaml https://github.com/LaurentMazare/npy-ocaml.
``load_npy file`` load a npy ``file`` into a matrix of type ``k``. If the matrix is in the file is not of type ``k``, fails with ``file: incorrect format``. This function is implemented using npy-ocaml https://github.com/LaurentMazare/npy-ocaml.
``im_d2z x`` returns all the imaginary components of ``x`` in a new ndarray of same shape.
Sourceval min : ?axis:int ->?keep_dims:bool ->('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.
``min' x`` is similar to ``min`` but returns the minimum of all elements in ``x`` in scalar value.
Sourceval max : ?axis:int ->?keep_dims:bool ->('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.
``max_i x`` returns the maximum of all elements in ``x`` as well as its index.
Sourceval 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 default ``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.
``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.
``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.
``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.
``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``.
``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``.
``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.
``copy_ ~out src`` copies the data from ndarray ``src`` to destination ``out``.
Sourceval reshape_ : out:('a, 'b)t->('a, 'b)t-> unit
TODO
Sourceval 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``.
Sourceval sum_ : out:('a, 'b)t->axis:int ->('a, 'b)t-> unit
TODO
Sourceval min_ : out:('a, 'b)t->axis:int ->('a, 'b)t-> unit
TODO
Sourceval max_ : out:('a, 'b)t->axis:int ->('a, 'b)t-> unit
TODO
Sourceval add_ : ?out:('a, 'b)t->('a, 'b)t->('a, 'b)t-> unit
``add_ x y`` is similar to ``add`` function but the output is written to ``out``. You need to make sure ``out`` is big enough to hold the output result.
Sourceval sub_ : ?out:('a, 'b)t->('a, 'b)t->('a, 'b)t-> unit
``sub_ x y`` is similar to ``sub`` function but the output is written to ``out``. You need to make sure ``out`` is big enough to hold the output result.
Sourceval mul_ : ?out:('a, 'b)t->('a, 'b)t->('a, 'b)t-> unit
``mul_ x y`` is similar to ``mul`` function but the output is written to ``out``. You need to make sure ``out`` is big enough to hold the output result.
Sourceval div_ : ?out:('a, 'b)t->('a, 'b)t->('a, 'b)t-> unit
``div_ x y`` is similar to ``div`` function but the output is written to ``out``. You need to make sure ``out`` is big enough to hold the output result.
Sourceval pow_ : ?out:('a, 'b)t->('a, 'b)t->('a, 'b)t-> unit
``pow_ x y`` is similar to ``pow`` function but the output is written to ``out``. You need to make sure ``out`` is big enough to hold the output result.
Sourceval atan2_ : ?out:('a, 'b)t->('a, 'b)t->('a, 'b)t-> unit
``atan2_ x y`` is similar to ``atan2`` function but the output is written to ``out``. You need to make sure ``out`` is big enough to hold the output result.
Sourceval hypot_ : ?out:('a, 'b)t->('a, 'b)t->('a, 'b)t-> unit
``hypot_ x y`` is similar to ``hypot`` function but the output is written to ``out``. You need to make sure ``out`` is big enough to hold the output result.
Sourceval fmod_ : ?out:('a, 'b)t->('a, 'b)t->('a, 'b)t-> unit
``fmod_ x y`` is similar to ``fmod`` function but the output is written to ``out``. You need to make sure ``out`` is big enough to hold the output result.
Sourceval min2_ : ?out:('a, 'b)t->('a, 'b)t->('a, 'b)t-> unit
``min2_ x y`` is similar to ``min2`` function but the output is written to ``out``. You need to make sure ``out`` is big enough to hold the output result.
Sourceval max2_ : ?out:('a, 'b)t->('a, 'b)t->('a, 'b)t-> unit
``max2_ x y`` is similar to ``max2`` function but the output is written to ``out``. You need to make sure ``out`` is big enough to hold the output result.
Sourceval add_scalar_ : ?out:('a, 'b)t->('a, 'b)t->'a-> unit
``add_scalar_ x y`` is similar to ``add_scalar`` function but the output is written to ``x``.
Sourceval sub_scalar_ : ?out:('a, 'b)t->('a, 'b)t->'a-> unit
``sub_scalar_ x y`` is similar to ``sub_scalar`` function but the output is written to ``x``.
Sourceval mul_scalar_ : ?out:('a, 'b)t->('a, 'b)t->'a-> unit
``mul_scalar_ x y`` is similar to ``mul_scalar`` function but the output is written to ``x``.
Sourceval div_scalar_ : ?out:('a, 'b)t->('a, 'b)t->'a-> unit
``div_scalar_ x y`` is similar to ``div_scalar`` function but the output is written to ``x``.
Sourceval pow_scalar_ : ?out:('a, 'b)t->('a, 'b)t->'a-> unit
``pow_scalar_ x y`` is similar to ``pow_scalar`` function but the output is written to ``x``.
Sourceval atan2_scalar_ : ?out:('a, 'b)t->('a, 'b)t->'a-> unit
``atan2_scalar_ x y`` is similar to ``atan2_scalar`` function but the output is written to ``x``.
Sourceval fmod_scalar_ : ?out:('a, 'b)t->('a, 'b)t->'a-> unit
``fmod_scalar_ x y`` is similar to ``fmod_scalar`` function but the output is written to ``x``.
Sourceval scalar_add_ : ?out:('a, 'b)t->'a->('a, 'b)t-> unit
``scalar_add_ a x`` is similar to ``scalar_add`` function but the output is written to ``x``.
Sourceval scalar_sub_ : ?out:('a, 'b)t->'a->('a, 'b)t-> unit
``scalar_sub_ a x`` is similar to ``scalar_sub`` function but the output is written to ``x``.
Sourceval scalar_mul_ : ?out:('a, 'b)t->'a->('a, 'b)t-> unit
``scalar_mul_ a x`` is similar to ``scalar_mul`` function but the output is written to ``x``.
Sourceval scalar_div_ : ?out:('a, 'b)t->'a->('a, 'b)t-> unit
``scalar_div_ a x`` is similar to ``scalar_div`` function but the output is written to ``x``.
Sourceval scalar_pow_ : ?out:('a, 'b)t->'a->('a, 'b)t-> unit
``scalar_pow_ a x`` is similar to ``scalar_pow`` function but the output is written to ``x``.
Sourceval scalar_atan2_ : ?out:('a, 'b)t->'a->('a, 'b)t-> unit
``scalar_atan2_ a x`` is similar to ``scalar_atan2`` function but the output is written to ``x``.
Sourceval scalar_fmod_ : ?out:('a, 'b)t->'a->('a, 'b)t-> unit
``scalar_fmod_ a x`` is similar to ``scalar_fmod`` function but the output is written to ``x``.
Sourceval fma_ : ?out:('a, 'b)t->('a, 'b)t->('a, 'b)t->('a, 'b)t-> unit
``fma_ ~out x y z`` is similar to ``fma x y z`` function but the output is written to ``out``.
Sourceval dot_ :
?transa:bool ->?transb:bool ->?alpha:'a->?beta:'a->c:('a, 'b)t->('a, 'b)t->('a, 'b)t->
unit
Refer to :doc:`owl_dense_matrix_generic`
Sourceval conj_ : ?out:('a, 'b)t->('a, 'b)t-> unit
``conj_ x`` is similar to ``conj`` but output is written to ``x``
``erf_ x`` is similar to ``erf`` but output is written to ``x``
Sourceval erfc_ : ?out:('a, 'b)t->('a, 'b)t-> unit
``erfc_ x`` is similar to ``erfc`` but output is written to ``x``
Sourceval relu_ : ?out:('a, 'b)t->('a, 'b)t-> unit
``relu_ x`` is similar to ``relu`` but output is written to ``x``
Sourceval softplus_ : ?out:('a, 'b)t->('a, 'b)t-> unit
``softplus_ x`` is similar to ``softplus`` but output is written to ``x``
Sourceval softsign_ : ?out:('a, 'b)t->('a, 'b)t-> unit
``softsign_ x`` is similar to ``softsign`` but output is written to ``x``
Sourceval sigmoid_ : ?out:('a, 'b)t->('a, 'b)t-> unit
``sigmoid_ x`` is similar to ``sigmoid`` but output is written to ``x``
Sourceval softmax_ : ?out:('a, 'b)t->?axis:int ->('a, 'b)t-> unit
``softmax_ x`` is similar to ``softmax`` but output is written to ``x``
Sourceval cumsum_ : ?out:('a, 'b)t->?axis:int ->('a, 'b)t-> unit
``cumsum_ x`` is similar to ``cumsum`` but output is written to ``x``
Sourceval cumprod_ : ?out:('a, 'b)t->?axis:int ->('a, 'b)t-> unit
``cumprod_ x`` is similar to ``cumprod`` but output is written to ``x``
Sourceval cummin_ : ?out:('a, 'b)t->?axis:int ->('a, 'b)t-> unit
``cummin_ x`` is similar to ``cummin`` but output is written to ``x``
Sourceval cummax_ : ?out:('a, 'b)t->?axis:int ->('a, 'b)t-> unit
``cummax_ x`` is similar to ``cummax`` but output is written to ``x``
Sourceval dropout_ : ?out:('a, 'b)t->?rate:float ->('a, 'b)t-> unit
``dropout_ x`` is similar to ``dropout`` but output is written to ``x``
Sourceval elt_equal_ : ?out:('a, 'b)t->('a, 'b)t->('a, 'b)t-> unit
``elt_equal_ x y`` is similar 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.
Sourceval elt_not_equal_ : ?out:('a, 'b)t->('a, 'b)t->('a, 'b)t-> unit
``elt_not_equal_ x y`` is similar 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.
Sourceval elt_less_ : ?out:('a, 'b)t->('a, 'b)t->('a, 'b)t-> unit
``elt_less_ x y`` is similar 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.
Sourceval elt_greater_ : ?out:('a, 'b)t->('a, 'b)t->('a, 'b)t-> unit
``elt_greater_ x y`` is similar 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.
Sourceval elt_less_equal_ : ?out:('a, 'b)t->('a, 'b)t->('a, 'b)t-> unit
``elt_less_equal_ x y`` is similar 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.
Sourceval elt_greater_equal_ : ?out:('a, 'b)t->('a, 'b)t->('a, 'b)t-> unit
``elt_greater_equal_ x y`` is similar 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.
Sourceval elt_equal_scalar_ : ?out:('a, 'b)t->('a, 'b)t->'a-> unit
``elt_equal_scalar_ x a`` is similar to ``elt_equal_scalar`` function but the output is written to ``x``.
Sourceval elt_not_equal_scalar_ : ?out:('a, 'b)t->('a, 'b)t->'a-> unit
``elt_not_equal_scalar_ x a`` is similar to ``elt_not_equal_scalar`` function but the output is written to ``x``.
Sourceval elt_less_scalar_ : ?out:('a, 'b)t->('a, 'b)t->'a-> unit
``elt_less_scalar_ x a`` is similar to ``elt_less_scalar`` function but the output is written to ``x``.
Sourceval elt_greater_scalar_ : ?out:('a, 'b)t->('a, 'b)t->'a-> unit
``elt_greater_scalar_ x a`` is similar to ``elt_greater_scalar`` function but the output is written to ``x``.
Sourceval elt_less_equal_scalar_ : ?out:('a, 'b)t->('a, 'b)t->'a-> unit
``elt_less_equal_scalar_ x a`` is similar to ``elt_less_equal_scalar`` function but the output is written to ``x``.
Sourceval elt_greater_equal_scalar_ : ?out:('a, 'b)t->('a, 'b)t->'a-> unit
``elt_greater_equal_scalar_ x a`` is similar to ``elt_greater_equal_scalar`` function but the output is written to ``x``.