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YAML (/ ˈ j æ m əl /, rhymes with camel [4]) was first proposed by Clark Evans in 2001, [15] who designed it together with Ingy döt Net [16] and Oren Ben-Kiki. [16]Originally YAML was said to mean Yet Another Markup Language, [17] because it was released in an era that saw a proliferation of markup languages for presentation and connectivity (HTML, XML, SGML, etc.).
Monaco Editor (Visual Studio Code) Implementation nestable full parsers pattern-based parser pattern-based parser parsers Syntax highlight Over 110 languages 129 languages: Yes mixed mode: HTML + JavaScript and CSS, PHP, EJS; single mode: JavaScript, Java, JSON, CSS, Python, Ruby, XML, YAML (pluggable)
YAML version 1.2 is a superset of JSON; prior versions were not strictly compatible. For example, escaping a slash / with a backslash \ is valid in JSON, but was not valid in YAML. [46] YAML supports comments, while JSON does not. [46] [44] [21]
MessagePack is more compact than JSON, but imposes limitations on array and integer sizes.On the other hand, it allows binary data and non-UTF-8 encoded strings. In JSON, map keys have to be strings, but in MessagePack there is no such limitation and any type can be a map key, including types like maps and arrays, and, like YAML, numbers.
GitHub Copilot is a code completion and automatic programming tool developed by GitHub and OpenAI that assists users of Visual Studio Code, Visual Studio, Neovim, and JetBrains integrated development environments (IDEs) by autocompleting code. [1]
For example, 3.14 will be serialized to 3.140 000 000 000 000 124 344 978 758 017 532 527 446 746 826 171 875. ^ XML data bindings and SOAP serialization tools provide type-safe XML serialization of programming data structures into XML.
Here is an example of a cylinder as given in VPython's documentation (in older VPython implementations, the module to import is vpython, not visual): from visual import * # Import the visual module rod = cylinder ( pos = ( 0 , 2 , 1 ), axis = ( 5 , 0 , 0 ), radius = 1 )
A training data set is a data set of examples used during the learning process and is used to fit the parameters (e.g., weights) of, for example, a classifier. [9] [10]For classification tasks, a supervised learning algorithm looks at the training data set to determine, or learn, the optimal combinations of variables that will generate a good predictive model. [11]