N-dimensional array module: including creation, manipulation, and various vectorised mathematical operations.
About the comparison of two complex numbers ``x`` and ``y``, Owl uses the following conventions: 1) ``x`` and ``y`` are equal iff both real and imaginary parts are equal; 2) ``x`` is less than ``y`` if the magnitude of ``x`` is less than the magnitude of ``y``; in case both ``x`` and ``y`` have the same magnitudes, ``x`` is less than ``y`` if the phase of ``x`` is less than the phase of ``y``; 3) less or equal, greater, greater or equal relation can be further defined atop of the aforementioned conventions.
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.
``empty Bigarray.Float64 |3;4;5|`` creates a three diemensional array of ``Bigarray.Float64`` type. Each dimension has the following size: 3, 4, and 5. The elements in the array are not initialised, they can be any value. ``empty`` is faster than ``zeros`` to create a ndarray.
The module only supports the following four types of ndarray: ``Bigarray.Float32``, ``Bigarray.Float64``, ``Bigarray.Complex32``, and ``Bigarray.Complex64``.
``create Bigarray.Float64 |3;4;5| 2.`` creates a three-diemensional array of ``Bigarray.Float64`` type. Each dimension has the following size: 3, 4, and 5. The elements in the array are initialised to ``2.``
``init Bigarray.Float64 d f`` creates a ndarray ``x`` of shape ``d``, then using ``f`` to initialise the elements in ``x``. The input of ``f`` is 1-dimensional index of the ndarray. You need to explicitly convert it if you need N-dimensional index. The function ``ind`` can help you.
``init_nd`` is almost the same as ``init`` but ``f`` receives n-dimensional index as input. It is more convenient since you don't have to convert the index by yourself, but this also means ``init_nd`` is slower than ``init``.
``zeros Bigarray.Complex32 |3;4;5|`` creates a three-diemensional array of ``Bigarray.Complex32`` type. Each dimension has the following size: 3, 4, and 5. The elements in the array are initialised to "zero". Depending on the ``kind``, zero can be ``0.`` or ``Complex.zero``.
``ones Bigarray.Complex32 |3;4;5|`` creates a three-diemensional array of ``Bigarray.Complex32`` type. Each dimension has the following size: 3, 4, and 5. The elements in the array are initialised to "one". Depending on the ``kind``, one can be ``1.`` or ``Complex.one``.
``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``.
``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.
``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.
``same_data x y`` checks whether ``x`` and ``y`` share the same underlying data in the memory. Namely, both variables point to the same memory address. This is done by checking the ``Data`` pointer in the Bigarray structure.
This function is very useful for avoiding unnecessary copying between two ndarrays especially if one has been reshaped or sliced.
``kind x`` returns the type of ndarray ``x``. It is one of the four possible values: ``Bigarray.Float32``, ``Bigarray.Float64``, ``Bigarray.Complex32``, and ``Bigarray.Complex64``.
``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 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.
``get_fancy s x`` returns a copy of the slice in ``x``. The slice is defined by ``a`` which is an ``int option array``. E.g., for a ndarray ``x`` of dimension ``|2; 2; 3|``, ``slice 0 x`` takes the following slices of index ``\(0,*,*\)``, i.e., ``|0;0;0|``, ``|0;0;1|``, ``|0;0;2|`` ... Also note that if the length of ``s`` is less than the number of dimensions of ``x``, ``slice`` function will append slice definition to higher diemensions by assuming all the elements in missing dimensions will be taken.
Basically, ``slice`` function offers very much the same semantic as that in numpy, i.e., start:stop:step grammar, so if you how to index and slice ndarray in numpy, you should not find it difficult to use this function. Please just refer to numpy documentation or my tutorial.
There are two differences between ``slice_left`` and ``slice``: ``slice_left`` does not make a copy but simply moving the pointer; ``slice_left`` can only make a slice from left-most axis whereas ``slice`` is much more flexible and can work on arbitrary axis which need not start from left-most side.
``set_fancy axis x y`` set the slice defined by ``axis`` in ``x`` according to the values in ``y``. ``y`` must have the same shape as the one defined by ``axis``.
About the slice definition of ``axis``, please refer to ``get_fancy`` function.
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 ``int 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_fancy``. This function assumes that every list element in the passed in ``int list list`` represents a range, i.e., ``R`` constructor.
E.g., ``[];[0;3];[0]`` is equivalent to ``R []; R [0;3]; R [0]``.
Sourceval get_slice_ext : int list array->('a, 'b)t->('a, 'b)t
``get_slice_ext axis x`` is used for extended indexing operator since ocaml 4.10.0. The indexing and slicing syntax become much ligher.
E.g., ``x.%
;1;2
``.
Sourceval set_slice_ext : int list array->('a, 'b)t->('a, 'b)t-> unit
Similar to ``get_slice_ext axis x``, this function is used for extended indexing operator since ocaml 4.10.0. The indexing and slicing syntax become much ligher.
Some as ``Bigarray.sub_left``, please refer to Bigarray documentation.
Sourceval sub_ndarray : int array->('a, 'b)t->('a, 'b)t array
``sub_ndarray parts x`` is similar to ``Bigarray.sub_left``. It splits the passed in ndarray ``x`` along the ``axis 0`` according to ``parts``. The elelments in ``parts`` do not need to be equal but they must sum up to the dimension along axis zero.
The returned sub-ndarrays share the same memory as ``x``. Because there is no copies made, this function is much faster than using `split` function to divide the lowest dimensionality of ``x``.
``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 memory 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 memory space will be allocated and the content of ``x`` will be copied to the new memory. The rest of the allocated space will be filled with zeros. The default value of ``head`` is ``true``.
``reshape x d`` transforms ``x`` into a new shape definted by ``d``. Note the ``reshape`` function will not make a copy of ``x``, the returned ndarray shares the same memory with the original ``x``.
One shape dimension (only one) can be set to ``-1``. In this case, the value is inferred from the length of the array and remaining dimensions.
``flatten x`` transforms ``x`` into a one-dimsonal array without making a copy. Therefore the returned value shares the same memory space with original ``x``.
``flip ~axis x`` flips a matrix/ndarray along ``axis``. By default ``axis = 0``. The result is returned in a new matrix/ndarray, so the original ``x`` remains intact.
``rotate x d`` rotates ``x`` clockwise ``d`` degrees. ``d`` must be multiple times of ``90``, otherwise the function will fail. If ``x`` is an n-dimensional array, then the function rotates the plane formed by the first and second dimensions.
``transpose ~axis x`` makes a copy of ``x``, then transpose it according to ``~axis``. ``~axis`` must be a valid permutation of ``x`` dimension indices. E.g., for a three-dimensional ndarray, it can be ``2;1;0``, ``0;2;1``, ``1;2;0``, and etc.
``tile x a`` tiles the data in ``x`` according the repetition specified by ``a``. This function provides the exact behaviour as ``numpy.tile``, please refer to the numpy's online documentation for details.
``repeat x a`` repeats the elements of ``x`` according the repetition specified by ``a``. The i-th element of ``a`` specifies the number of times that the individual entries of the i-th dimension of ``x`` should be repeated.
``concat_vertical x y`` concatenates two ndarray ``x`` and ``y`` vertically. This is just a convenient function for concatenating two ndarrays along their lowest dimension, i.e. 0.
The associated operator is ``@||``, please refer to :doc:`owl_operator`.
``concat_horizontal x y`` concatenates two ndarrays ``x`` and ``y`` horizontally. This is just a convenient function for concatenating two ndarrays along their highest dimension.
The associated operator is ``@=``, please refer to :doc:`owl_operator`.
``concat_vh`` is used to assemble small parts of matrices into a bigger one. E.g. In ``| [|a; b; c|]; [|d; e; f|]; [|g; h; i|] |``, wherein `a, b, c ... i` are matrices of different shapes. They will be concatenated into a big matrix as follows.
.. math:: \beginmatrix a & b & c \\ d & e & f \\ g & h & i \endmatrix
This is achieved by first concatenating along ``axis:1`` for each element in the array, then concatenating along ``axis:0``. The number of elements in each array needs not to be equal as long as the aggregated dimensions match. E.g., please check the following example.
.. code-block:: ocaml
let a00 = Mat.sequential 2 3 in let a01 = Mat.sequential 2 2 in let a02 = Mat.sequential 2 1 in let a10 = Mat.sequential 3 3 in let a11 = Mat.sequential 3 3 in Mat.concat_vh | [|a00; a01; a02|]; [|a10; a11|] |;;
``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.
``stack ~axis x`` stacks an array of ndarrays along the ``axis`` dimension. For example, if ``x`` contains K ndarrays of shape |2;3|, then ``stack ~axis:1 x`` will return an ndarray of dimensions |2;K;3|. The ndarrays in ``x``, they must all have the same shape. The default value of ``axis`` is 0.
``split ~axis parts x`` splits an ndarray ``x`` into parts along the specified ``axis``. This function is the inverse operation of ``concatenate``. The elements in ``x`` must sum up to the dimension in the specified axis.
``split_vh parts x`` splits a passed in ndarray ``x`` along the first two dimensions, i.e. ``axis 0`` and ``axis 1``. This is the inverse operation of ``concat_vh`` function, and the function is very useful in dividing a big matrix into smaller (especially heterogeneous) parts.
For example, given a matrix ``x`` of shape ``|8;10|``, it is possible to split in the following ways.
``expand x d`` reshapes ``x`` by increasing its rank from ``num_dims x`` to ``d``. The opposite operation is ``squeeze x``. The ``hi`` parameter is used to specify whether the expandsion is along high dimension (by setting ``true``), or along the low dimension (by setting ``false``). The default value is ``false``.
Sourceval pad : ?v:'a->int list list->('a, 'b)t->('a, 'b)t
``pad ~v p x`` pads a ndarray ``x`` with a constant value ``v``. The padding index ``p`` is a list of lists of 2 integers. These two integers denote padding width at both edges of one dimension of ``x``.
``top x n`` returns the indices of ``n`` greatest values of ``x``. The indices are arranged according to the corresponding element values, from the greatest one to the smallest one.
``bottom x n`` returns the indices of ``n`` smallest values of ``x``. The indices are arranged according to the corresponding element values, from the smallest one to the greatest one.
``sort1 ~axis x`` performs quicksort of the elements along specific ``axis`` in ``x``. A new copy is returned as result, the original ``x`` remains intact.
``sort x`` performs quicksort of the elelments in ``x``. A new copy is returned as result, the original ``x`` remains intact. If you want to perform in-place sorting, please use `sort_` instead.
``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.
``draw ~axis x n`` draws ``n`` samples from ``x`` along the specified ``axis``, with replacement. ``axis`` is set to zero by default. The return is a tuple of both samples and the indices of the selected samples.
Sourceval iteri : (int ->'a-> unit)->('a, 'b)t-> unit
``iteri f x`` applies function ``f`` to each element in ``x``. Note that 1d index is passed to function ``f``, you need to convert it to nd-index by yourself.
``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`` to filter out certain elements in ``x``. An element will be included if ``f`` returns ``true``. The returned result is an array of 1-dimensional indices of the selected elements. To obtain the n-dimensional indices, you need to convert it manually with Owl's helper function.
Similar to ``map2i`` but n-d indices are passed to the user function.
Sourceval iteri_slice :
?axis:int ->(int ->('a, 'b)t-> unit)->('a, 'b)t->
unit
``iteri_slice ~axis f x`` iterates the slices along the specified ``axis`` in ``x`` and applies the function ``f``. The 1-d index of of the slice is passed in. By default, the ``axis`` is 0. Setting ``axis`` to the highest dimension is not allowed because in that case you can just use `iteri` to iterate all the elements in ``x`` which is more efficient.
Note that the slice is obtained by slicing left (due to Owl's C-layout ndarray) a sub-array out of ``x``. E.g., if ``x`` has shape ``|3;4;5|``, setting ``axis=0`` will iterate three ``4 x 5`` matrices. The slice shares the same memory with ``x`` so no copy is made.
Sourceval iter_slice : ?axis:int ->(('a, 'b)t-> unit)->('a, 'b)t-> unit
Similar to ``iteri_slice`` but slice index is not passed in.
``mapi_slice ~axis f x`` maps the slices along the specified ``axis`` in ``x`` and applies the function ``f``. By default, ``axis`` is 0. The index of of the slice is passed in.
``filteri_slice ~axis f x`` filters the slices along the specified ``axis`` in ``x``. The slices which satisfy the predicate ``f`` are returned in an array.
``foldi_slice ~axis f a x`` fold (left) the slices along the specified ``axis`` in ``x``. The slices which satisfy the predicate ``f`` are returned in an array.
``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``. 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)``.
``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``.
``to_array x`` converts an ndarray ``x`` to OCaml's array type. Note that the ndarray ``x`` is flattened before conversion.
Sourceval 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.
``load k s`` loads previously serialised ndarray from file ``s`` into memory. It is necessary to specify the type of the ndarray with parameter ``k``.
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.
``sem' x`` calculates the standard error of mean of all the elements in ``x``.
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.
``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.
``relu x`` computes the rectified linear unit function ``max(x, 0)`` of the elements in ``x`` and returns the result in a new ndarray.
Sourceval 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.
``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.
``softmax x`` computes the softmax functions ``(exp x) / (sum (exp x))`` of all the elements along the specified ``axis`` in ``x`` and returns the result in a new ndarray.
By default, ``axis = -1``, i.e. along the highest dimension.
``l2norm_sqr x`` calculates the square of l2-norm (or l2norm, Euclidean norm) of all elements in ``x``. The function uses conjugate transpose in the product, hence it always returns a float number.
``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.
``diff ~axis ~n x`` calculates the ``n``-th difference of ``x`` along the specified ``axis``.
Parameters: * ``axis``: axis to calculate the difference. The default value is the highest dimension. * ``n``: how many times to calculate the difference. The default value is 1.
Return: * The difference ndarray y. Note that the shape of ``y`` 1 less than that of ``x`` along specified axis.
``add x y`` adds all the elements in ``x`` and ``y`` elementwise, and returns the result in a new ndarray.
General broadcast operation is automatically applied to add/sub/mul/div, etc. The function compares the dimension element-wise from the highest to the lowest with the following broadcast rules (same as numpy): 1. equal; 2. either is 1.
``ssqr x a`` computes the sum of squared differences of all the elements in ``x`` from constant ``a``. This function only computes the square of each element rather than the conjugate transpose as l2norm_sqr does.
``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``.
``contract2 index_pairs x y`` performs indices contraction (a.k.a tensor contraction) on two ndarrays ``x`` and ``y``. ``index_pairs`` is an array of contracted indices, the first element is the index of ``x``, the second is that of ``y``.
Caveat: Not well tested yet, use with care! Also, consider to use TTGT in future for better performance.
``cast kind x`` casts ``x`` of type ``('c, 'd) t`` to type ``('a, 'b) t`` specify by the passed in ``kind`` parameter. This function is a generalisation of the other casting functions such as ``cast_s2d``, ``cast_c2z``, and etc.
The following functions are helper functions for some other functions in both Ndarray and Ndview modules. In general, you are not supposed to use these functions directly.
``one_hot idx depth`` creates one-hot vectors according to the indices ndarray and the specified depth. If ``idx`` is rank N, then the return is rank N+1. More specifically, if ``idx`` is of shape ``|a;b;c|``, the return is of shape ``|a;b;c;depth|``.
``sum_slices ~axis:2 x`` for ``x`` of ``|2;3;4;5|``, it returns an ndarray of shape ``|4;5|``. Currently, the operation is done using ``gemm``, it is fast but consumes more memory.
``slide ~axis ~window x`` generates a new ndarray by sliding a window along specified ``axis`` in ``x``. E.g., if ``x`` has shape ``|a;b;c|`` and ``axis = 1``, then ``|a; number of windows; window; c|`` is the shape of the returned ndarray.
Parameters: * ``axis`` is the axis for sliding, the default is -1, i.e. highest dimension. * ``ofs`` is the starting position of the sliding window. The default is 0. * ``step`` is the step size, the default is 1. * ``window`` is the size of the sliding window.
``copy_ ~out src`` copies the data from ndarray ``src`` to destination ``out``.
Sourceval reshape_ : out:('a, 'b)t->('a, 'b)t-> unit
TODO
Sourceval reverse_ : 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 repeat_ : out:('a, 'b)t->('a, 'b)t->int array-> unit
``repeat_ ~out x reps`` is similar to ``repeat x reps`` but the output is written to ``out``.
Sourceval tile_ : out:('a, 'b)t->('a, 'b)t->int array-> unit
``tile_ ~out x reps`` is similar to ``tile x reps`` but the output is written to ``out``.
Sourceval pad_ : out:('a, 'b)t->?v:'a->int list list->('a, 'b)t-> unit
``pad_ ~out ?v p x`` is similar to ``pad ?v p x`` but the output is written to ``out``.
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 clip_by_value_ :
?out:('a, 'b)t->?amin:'a->?amax:'a->('a, 'b)t->
unit
TODO
Sourceval clip_by_l2norm_ : ?out:('a, 'b)t->'a->('a, 'b)t-> unit
TODO
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``.