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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.
The default implementation of Object.clone() performs a shallow copy. When a class desires a deep copy or some other custom behavior, they must implement that in their own clone() method after they obtain the copy from the superclass. The syntax for calling clone in Java is (assuming obj is a variable of a class type that has a public clone ...
The functions were mapped to key combinations using the ⌘ Command key as a special modifier, which is held down while also pressing X for cut, C for copy, or V for paste. These few keyboard shortcuts allow the user to perform all the basic editing operations, and the keys are clustered at the left end of the bottom row of the standard QWERTY ...
A delta can be defined in 2 ways, symmetric delta and directed delta.A symmetric delta can be expressed as (,) = (),where and represent two versions.. A directed delta, also called a change, is a sequence of (elementary) change operations which, when applied to one version , yields another version (note the correspondence to transaction logs in databases).
The softmax function, also known as softargmax [1]: 184 or normalized exponential function, [2]: 198 converts a vector of K real numbers into a probability distribution of K possible outcomes. It is a generalization of the logistic function to multiple dimensions, and is used in multinomial logistic regression .
Data structure alignment is the way data is arranged and accessed in computer memory.It consists of three separate but related issues: data alignment, data structure padding, and packing.
The Pandas and Polars Python libraries implement the Pearson correlation coefficient calculation as the default option for the methods pandas.DataFrame.corr and polars.corr, respectively. Wolfram Mathematica via the Correlation function, or (with the P value) with CorrelationTest. The Boost C++ library via the correlation_coefficient function.
Google JAX is a machine learning framework for transforming numerical functions. [ 71 ] [ 72 ] [ 73 ] It is described as bringing together a modified version of autograd (automatic obtaining of the gradient function through differentiation of a function) and TensorFlow's XLA (Accelerated Linear Algebra).