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In Python, if a name is intended to be "private", it is prefixed by one or two underscores. Private variables are enforced in Python only by convention. Names can also be suffixed with an underscore to prevent conflict with Python keywords. Prefixing with double underscores changes behaviour in classes with regard to name mangling.
Camel case is named after the "hump" of its protruding capital letter, similar to the hump of common camels.. Camel case (sometimes stylized autologically as camelCase or CamelCase, also known as camel caps or more formally as medial capitals) is the practice of writing phrases without spaces or punctuation and with capitalized words.
It is a commonly used naming convention in computing, for example for variable and subroutine names, and for filenames. One study has found that readers can recognize snake case values more quickly than camel case. However, "subjects were trained mainly in the underscore style", so the possibility of bias cannot be eliminated. [1]
Common elements of coding style include: Indentation and whitespace character use – Ensures consistent block structures and improves readability.; Naming conventions – Standardizes how variables, functions, and classes are named, typically adhering to camelCase, snake case, or PascalCase, depending on the language.
When Wikipedia was founded on January 15, 2001, it used the wiki engine UseModWiki, which only supported CamelCase links at that time. These links took the form of plaintext camelcase words, such as "WikiCase", and the displayed title of the page this linked to would split this text at each capital letter, producing "Wiki Case". [1]
WASHINGTON (Reuters) -China-based DJI and Autel Robotics could be banned from selling new drones in the United States market under an annual military bill set to be voted on later this week by the ...
Lexical tokenization is related to the type of tokenization used in large language models (LLMs) but with two differences. First, lexical tokenization is usually based on a lexical grammar, whereas LLM tokenizers are usually probability-based. Second, LLM tokenizers perform a second step that converts the tokens into numerical values.
Image credits: moxie_walter Humanity domesticated cats much later than dogs - in fact, about two and a half times later. So it's not surprising that cats continue to demonstrate specific features ...