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In computer science, a generator is a routine that can be used to control the iteration behaviour of a loop.All generators are also iterators. [1] A generator is very similar to a function that returns an array, in that a generator has parameters, can be called, and generates a sequence of values.
A generator expression may be used in Python versions >= 2.4 which gives lazy evaluation over its input, and can be used with generators to iterate over 'infinite' input such as the count generator function which returns successive integers:
However, a generator is an object with persistent state, which can repeatedly enter and leave the same scope. A generator call can then be used in place of a list, or other structure whose elements will be iterated over. Whenever the for loop in the example requires the next item, the generator is called, and yields the next item.
IronPython is written entirely in C#, although some of its code is automatically generated by a code generator written in Python. IronPython is implemented on top of the Dynamic Language Runtime (DLR), a library running on top of the Common Language Infrastructure that provides dynamic typing and dynamic method dispatch, among other things, for ...
In Python, functions are first-class objects, just like strings, numbers, lists etc. This feature eliminates the need to write a function object in many cases. Any object with a __call__() method can be called using function-call syntax. An example is this accumulator class (based on Paul Graham's study on programming language syntax and ...
The C language does not have collections or a foreach construct. However, it has several standard data structures that can be used as collections, and foreach can be made easily with a macro.
As a precursor to the lambda functions introduced in C# 3.0, C#2.0 added anonymous delegates. These provide closure-like functionality to C#. [3] Code inside the body of an anonymous delegate has full read/write access to local variables, method parameters, and class members in scope of the delegate, excepting out and ref parameters.
An example of a Python generator returning an iterator for the Fibonacci numbers using Python's yield statement follows: def fibonacci ( limit ): a , b = 0 , 1 for _ in range ( limit ): yield a a , b = b , a + b for number in fibonacci ( 100 ): # The generator constructs an iterator print ( number )