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In object-oriented programming, the iterator pattern is a design pattern in which an iterator is used to traverse a container and access the container's elements. The iterator pattern decouples algorithms from containers; in some cases, algorithms are necessarily container-specific and thus cannot be decoupled.
The loop calls the Iterator::next method on the iterator before executing the loop body. If Iterator::next returns Some(_), the value inside is assigned to the pattern and the loop body is executed; if it returns None, the loop is terminated.
Iterators generalize pointers to elements of an array (which indeed can be used as iterators), and their syntax is designed to resemble that of C pointer arithmetic, where the * and -> operators are used to reference the element to which the iterator points and pointer arithmetic operators like ++ are used to modify iterators in the traversal ...
Python does not contain the classical for loop, rather a foreach loop is used to iterate over the output of the built-in range() function which returns an iterable sequence of integers. for i in range ( 1 , 6 ): # gives i values from 1 to 5 inclusive (but not 6) # statements print ( i ) # if we want 6 we must do the following for i in range ( 1 ...
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.
The Nial example of the inner product of two arrays can be implemented using the native matrix multiplication operator. If a is a row vector of size [1 n] and b is a corresponding column vector of size [n 1]. a * b; By contrast, the entrywise product is implemented as: a .* b;
In version 2.2 of Python, "new-style" classes were introduced. With new-style classes, objects and types were unified, allowing the subclassing of types. Even entirely new types can be defined, complete with custom behavior for infix operators. This allows for many radical things to be done syntactically within Python.
In other array types, a slice can be replaced by an array of different size, with subsequent elements being renumbered accordingly – as in Python's list assignment "A[5:5] = [10,20,30]", that inserts three new elements (10, 20, and 30) before element "A[5]".