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In Python, non-innermost-local and not-declared-global accessible names are all aliases. Among dynamically-typed languages, Python is moderately type-checked. Implicit conversion is defined for numeric types (as well as booleans), so one may validly multiply a complex number by an integer (for instance) without explicit casting.
Since 7 October 2024, Python 3.13 is the latest stable release, and it and, for few more months, 3.12 are the only releases with active support including for bug fixes (as opposed to just for security) and Python 3.9, [55] is the oldest supported version of Python (albeit in the 'security support' phase), due to Python 3.8 reaching end-of-life.
The closely related code point U+2262 ≢ NOT IDENTICAL TO (≢, ≢) is the same symbol with a slash through it, indicating the negation of its mathematical meaning. [ 1 ] In LaTeX mathematical formulas, the code \equiv produces the triple bar symbol and \not\equiv produces the negated triple bar symbol ≢ {\displaystyle \not ...
Hyphen: Dash, Hyphen-minus-Hyphen-minus: Dash, Hyphen, Minus sign ☞ Index: Manicule, Obelus (medieval usage) · Interpunct: Full-stop, Period, Decimal separator, Dot operator ‽ Interrobang (combined 'Question mark' and 'Exclamation mark') Inverted question and exclamation marks ¡ Inverted exclamation mark: Exclamation mark, Interrobang ...
The numerical 3-d matching problem is problem [SP16] of Garey and Johnson. [1] They claim it is NP-complete, and refer to, [2] but the claim is not proved at that source. The NP-hardness of the related problem 3-partition is done in [1] by a reduction from 3-dimensional matching via 4-partition. To prove NP-completeness of the numerical 3 ...
A hyphenation algorithm is a set of rules, especially one codified for implementation in a computer program, that decides at which points a word can be broken over two lines with a hyphen. For example, a hyphenation algorithm might decide that impeachment can be broken as impeach-ment or im-peachment but not impe-achment.
According to the authors' note, [3] CBOW is faster while skip-gram does a better job for infrequent words. After the model is trained, the learned word embeddings are positioned in the vector space such that words that share common contexts in the corpus — that is, words that are semantically and syntactically similar — are located close to ...
A Unicode character is assigned a unique Name (na). [1] The name is composed of uppercase letters A–Z, digits 0–9, hyphen-minus and space.Some sequences are excluded: names beginning with a space or hyphen, names ending with a space or hyphen, repeated spaces or hyphens, and space after hyphen are not allowed.