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In OCaml, the library function Oo.copy performs shallow copying of an object. In Python, the library's copy module provides shallow copy and deep copy of objects through the copy() and deepcopy() functions, respectively. [13] Programmers may define special methods __copy__() and __deepcopy__() in an object to provide custom copying implementation.
This complexity measure is related to Kolmogorov complexity, but the only function it uses is the recursive copy (i.e., the shallow copy). The underlying mechanism in this complexity measure is the starting point for some algorithms for lossless data compression, like LZ77, LZ78 and LZW. Even though it is based on an elementary principle of ...
This page was last edited on 31 May 2015, at 18:45 (UTC).; Text is available under the Creative Commons Attribution-ShareAlike 4.0 License; additional terms may apply ...
Folds can be regarded as consistently replacing the structural components of a data structure with functions and values. Lists, for example, are built up in many functional languages from two primitives: any list is either an empty list, commonly called nil ([]), or is constructed by prefixing an element in front of another list, creating what is called a cons node ( Cons(X1,Cons(X2,Cons ...
A Java example, when "copying" an object using simple assignment: Object original = new Object (); Object copy = null ; copy = original ; // does not copy object but only its reference The object is not duplicated, the variables 'original' and 'copy' are actually referring to the same object.
The emgr framework is a compact open source toolbox for gramian-based model reduction and compatible with OCTAVE and MATLAB. KerMor: An object-oriented MATLAB© library providing routines for model order reduction of nonlinear dynamical systems. Reduction can be achieved via subspace projection and approximation of nonlinearities via kernels ...
The information bottleneck method is a technique in information theory introduced by Naftali Tishby, Fernando C. Pereira, and William Bialek. [1] It is designed for finding the best tradeoff between accuracy and complexity (compression) when summarizing (e.g. clustering) a random variable X, given a joint probability distribution p(X,Y) between X and an observed relevant variable Y - and self ...
An autoencoder is a type of artificial neural network used to learn efficient codings of unlabeled data (unsupervised learning).An autoencoder learns two functions: an encoding function that transforms the input data, and a decoding function that recreates the input data from the encoded representation.