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  2. Universally unique identifier - Wikipedia

    en.wikipedia.org/wiki/Universally_unique_identifier

    As in other UUIDs, 4 bits are used to indicate version 4, and 2 or 3 bits to indicate the variant (10 2 or 110 2 for variants 1 and 2 respectively). Thus, for variant 1 (that is, most UUIDs) a random version 4 UUID will have 6 predetermined variant and version bits, leaving 122 bits for the randomly generated part, for a total of 2 122 , or 5.3 ...

  3. Unique identifier - Wikipedia

    en.wikipedia.org/wiki/Unique_identifier

    The above methods can be combined, hierarchically or singly, to create other generation schemes which guarantee uniqueness. [2] In many cases, a single object may have more than one unique identifier, each of which identifies it for a different purpose.

  4. tox (Python testing wrapper) - Wikipedia

    en.wikipedia.org/wiki/Tox_(Python_testing_wrapper)

    [1] [2] Its use began to become popular in the Python community from around 2015. [3] tox acts a wrapper for both virtual environments and test automation tools, to simplify the consistent testing of Python code across a range of environments. [4] It integrates the use of a virtualisation tool, such as virtualenv, with a test script such as ...

  5. Talk:Universally unique identifier - Wikipedia

    en.wikipedia.org/wiki/Talk:Universally_unique...

    A properly written Version 4 UUID generator --such as, for example, the randomUUID() function supported by most web browsers --*should* be creating UUIDs of all 4 kinds (8, 9, a, or b in the high hex digit of octet 8) in approximately equal numbers. Version 4 UUIDs all have a few bits hard-wired in order to indicate that they *are* Version 4 UUIDs.

  6. Training, validation, and test data sets - Wikipedia

    en.wikipedia.org/wiki/Training,_validation,_and...

    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]

  7. Randomness test - Wikipedia

    en.wikipedia.org/wiki/Randomness_test

    In some cases, data reveals an obvious non-random pattern, as with so-called "runs in the data" (such as expecting random 0–9 but finding "4 3 2 1 0 4 3 2 1..." and rarely going above 4). If a selected set of data fails the tests, then parameters can be changed or other randomized data can be used which does pass the tests for randomness.

  8. Python (programming language) - Wikipedia

    en.wikipedia.org/wiki/Python_(programming_language)

    IronPython allows running Python 2.7 programs (and an alpha, released in 2021, is also available for "Python 3.4, although features and behaviors from later versions may be included" [170]) on the .NET Common Language Runtime. [171] Jython compiles Python 2.7 to Java bytecode, allowing the use of the Java libraries from a Python program. [172]

  9. All-pairs testing - Wikipedia

    en.wikipedia.org/wiki/All-pairs_testing

    In most cases, a single input parameter or an interaction between two parameters is what causes a program's bugs. [2] Bugs involving interactions between three or more parameters are both progressively less common [3] and also progressively more expensive to find, such testing has as its limit the testing of all possible inputs. [4]