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In computer programming, redundant code is source code or compiled code in a computer program that is unnecessary, such as: recomputing a value that has previously been calculated [1] and is still available, code that is never executed (known as unreachable code),
Overall the algorithm is more efficient (in terms of the amount of data required) than the theoretically optimal max-dependency selection, yet produces a feature set with little pairwise redundancy. mRMR is an instance of a large class of filter methods which trade off between relevancy and redundancy in different ways. [34] [36]
Minimum redundancy feature selection is an algorithm frequently used in a method to accurately identify characteristics of genes and phenotypes and narrow down their relevance and is usually described in its pairing with relevant feature selection as Minimum Redundancy Maximum Relevance (mRMR).
Python is a multi-paradigm programming language. Object-oriented programming and structured programming are fully supported, and many of their features support functional programming and aspect-oriented programming (including metaprogramming [71] and metaobjects). [72]
Comparison of programming languages (algebraic data type) Comparison of programming languages (list comprehension) Comparison of programming languages (string functions) Conditional (computer programming) Control flow; Corecursion; Cubes (OLAP server) CuPy; Cycle detection; Cycle sort; Cyclic redundancy check; Cython
In computer science and information theory, a Huffman code is a particular type of optimal prefix code that is commonly used for lossless data compression.The process of finding or using such a code is Huffman coding, an algorithm developed by David A. Huffman while he was a Sc.D. student at MIT, and published in the 1952 paper "A Method for the Construction of Minimum-Redundancy Codes".
Don't repeat yourself" (DRY), also known as "duplication is evil", is a principle of software development aimed at reducing repetition of information which is likely to change, replacing it with abstractions that are less likely to change, or using data normalization which avoids redundancy in the first place.
A textbook formulation is: "People are part of the system. The design should match the user's experience, expectations, and mental models." [13]The principle aims to leverage the existing knowledge of users to minimize the learning curve, for instance by designing interfaces that borrow heavily from "functionally similar or analogous programs with which your users are likely to be familiar". [2]