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If ′ =, then for large the set is expected to have the fraction (1 - 1/e) (~63.2%) of the unique samples of , the rest being duplicates. [1] This kind of sample is known as a bootstrap sample. Sampling with replacement ensures each bootstrap is independent from its peers, as it does not depend on previous chosen samples when sampling.
There are numerous community efforts for the Web Components ecosystem. WebComponents.org [10] provides an interface to search for any existing Web Components, Custom Elements Everywhere [11] validates whether popular front-end frameworks are compatible and ready to use Web Components standard, with a set of pending bugs and available workarounds.
Otto announced Bootstrap 4 on October 29, 2014. [15] The first alpha version of Bootstrap 4 was released on August 19, 2015. [16] The first beta version was released on August 10, 2017. [17] Otto suspended work on Bootstrap 3 on September 6, 2016, to free up time to work on Bootstrap 4. Bootstrap 4 was finalized on January 18, 2018. [18]
PyCharm is an integrated development environment (IDE) used for programming in Python. It provides code analysis, a graphical debugger, an integrated unit tester, integration with version control systems, and supports web development with Django. PyCharm is developed by the Czech company JetBrains and built on their IntelliJ platform. [4]
[citation needed] It takes its name from the poem Beautiful Soup from Alice's Adventures in Wonderland [5] and is a reference to the term "tag soup" meaning poorly-structured HTML code. [6] Richardson continues to contribute to the project, [ 7 ] which is additionally supported by paid open-source maintainers from the company Tidelift.
Django (/ ˈ dʒ æ ŋ ɡ oʊ / JANG-goh; sometimes stylized as django) [6] is a free and open-source, Python-based web framework that runs on a web server. It follows the model–template–views (MTV) architectural pattern.
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The bootstrap sample is taken from the original by using sampling with replacement (e.g. we might 'resample' 5 times from [1,2,3,4,5] and get [2,5,4,4,1]), so, assuming N is sufficiently large, for all practical purposes there is virtually zero probability that it will be identical to the original "real" sample. This process is repeated a large ...