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Lazy evaluation Typing Abstract data types Algebraic data types Data is immutable Type classes Garbage collection First appeared Common Lisp: No [1] Simulated with thunks [2] Dynamic [3] Yes [4] Extension [5] No [6]? Yes: 1984 Scheme: No [7] Yes [8] Dynamic [7] Yes [9] Simulated with thunks [10] No [11] No: Yes: 1975 Racket: No: Default in Lazy ...
In Python 3.x the range() function [28] returns a generator which computes elements of the list on demand. Elements are only generated when they are needed (e.g., when print(r[3]) is evaluated in the following example), so this is an example of lazy or deferred evaluation: >>>
Introduced in Python 2.2 as an optional feature and finalized in version 2.3, generators are Python's mechanism for lazy evaluation of a function that would otherwise return a space-prohibitive or computationally intensive list. This is an example to lazily generate the prime numbers:
In software engineering, double-checked locking (also known as "double-checked locking optimization" [1]) is a software design pattern used to reduce the overhead of acquiring a lock by testing the locking criterion (the "lock hint") before acquiring the lock.
Python 3.0, released in 2008, was a major revision not completely backward-compatible with earlier versions. Python 2.7.18, released in 2020, was the last release of Python 2. [37] Python consistently ranks as one of the most popular programming languages, and has gained widespread use in the machine learning community. [38] [39] [40] [41]
In computer programming, lazy initialization is the tactic of delaying the creation of an object, the calculation of a value, or some other expensive process until the first time it is needed.
Bears receiver Keenan Allen said that issues ran deeper than that and went back to the offseason. “Too nice of a guy," Allen said, according to Kalyn Kahler of ESPN, via Dan Wiederer of the ...
The main advantage gained in employing a lazy learning method is that the target function will be approximated locally, such as in the k-nearest neighbor algorithm. Because the target function is approximated locally for each query to the system, lazy learning systems can simultaneously solve multiple problems and deal successfully with changes ...