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The leap year problem (also known as the leap year bug or the leap day bug) is a problem for both digital (computer-related) and non-digital documentation and data storage situations which results from errors in the calculation of which years are leap years, or from manipulating dates without regard to the difference between leap years and common years.
In Python 2 (and most other programming languages), unless explicitly requested, x / y performed integer division, returning a float only if either input was a float. However, because Python is a dynamically-typed language, it was not always possible to tell which operation was being performed, which often led to subtle bugs, thus prompting the ...
Error-analysis tells us how to design floating-point arithmetic, like IEEE Standard 754, moderately tolerant of well-meaning ignorance among programmers". [ 41 ] The special values such as infinity and NaN ensure that the floating-point arithmetic is algebraically complete: every floating-point operation produces a well-defined result and will ...
The "decimal" data type of the C# and Python programming languages, and the decimal formats of the IEEE 754-2008 standard, are designed to avoid the problems of binary floating-point representations when applied to human-entered exact decimal values, and make the arithmetic always behave as expected when numbers are printed in decimal.
A query string is a part of a uniform resource locator that assigns values to specified parameters.A query string commonly includes fields added to a base URL by a Web browser or other client application, for example as part of an HTML document, choosing the appearance of a page, or jumping to positions in multimedia content.
ISO 8601 is an international standard covering the worldwide exchange and communication of date and time-related data.It is maintained by the International Organization for Standardization (ISO) and was first published in 1988, with updates in 1991, 2000, 2004, and 2019, and an amendment in 2022. [1]
Word2vec is a group of related models that are used to produce word embeddings.These models are shallow, two-layer neural networks that are trained to reconstruct linguistic contexts of words.
This template fetches data from the centralized knowledge base Wikidata. To edit the data, click on "Wikidata item" in the left sidebar. Template parameters [Edit template data] Parameter Description Type Status No parameters specified See also {{ Wikidata }}, a user-friendly wrapper template for this module. {{ WikidataOI }}, a wrapper template for this module that adds an opt-in toggle ...