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For function that manipulate strings, modern object-oriented languages, like C# and Java have immutable strings and return a copy (in newly allocated dynamic memory), while others, like C manipulate the original string unless the programmer copies data to a new string.
An array data structure can be mathematically modeled as an abstract data structure (an abstract array) with two operations get(A, I): the data stored in the element of the array A whose indices are the integer tuple I. set(A, I, V): the array that results by setting the value of that element to V. These operations are required to satisfy the ...
C (along with Python) allows juxtaposition for string literals, however, for strings stored as character arrays, the strcat function must be used. COBOL uses the STRING statement to concatenate string variables. MATLAB and Octave use the syntax "[x y]" to concatenate x and y.
The actual sizes of short int, int, and long int are available as the constants short max int, max int, and long max int etc. ^b Commonly used for characters. ^c The ALGOL 68, C and C++ languages do not specify the exact width of the integer types short , int , long , and ( C99 , C++11 ) long long , so they are implementation-dependent.
So, PHP can have non-consecutively numerically indexed arrays. The keys have to be of integer (floating point numbers are truncated to integer) or string type, while values can be of arbitrary types, including other arrays and objects. The arrays are heterogeneous: a single array can have keys of different types.
Following Lisp, other high-level programming languages which feature linked lists as primitive data structures have adopted an append. To append lists, as an operator, Haskell uses ++, OCaml uses @. Other languages use the + or ++ symbols to nondestructively concatenate a string, list, or array.
In addition to support for vectorized arithmetic and relational operations, these languages also vectorize common mathematical functions such as sine. For example, if x is an array, then y = sin (x) will result in an array y whose elements are sine of the corresponding elements of the array x. Vectorized index operations are also supported.
CuPy is an open source library for GPU-accelerated computing with Python programming language, providing support for multi-dimensional arrays, sparse matrices, and a variety of numerical algorithms implemented on top of them. [3] CuPy shares the same API set as NumPy and SciPy, allowing it to be a drop-in replacement to run NumPy/SciPy code on GPU.