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Bootstrap (formerly Twitter Bootstrap) is a free and open-source CSS framework directed at responsive, mobile-first front-end web development. It contains HTML , CSS and (optionally) JavaScript -based design templates for typography , forms , buttons , navigation , and other interface components.
Form validation framework(s) AngularJS: XHR, JSONP Yes i18n and l10n Karma (unit testing), Protractor (end-to-end testing) Content Security Policy (CSP), XSRF Templates Caching Form validation (client-side) EmberJS: Yes Yes Yes Ember Data QUnit Handlebars qooxdoo: Yes Data binding i18n Testrunner Form Validation SproutCore: Yes Yes
If cross-validation is used to decide which features to use, an inner cross-validation to carry out the feature selection on every training set must be performed. [30] Performing mean-centering, rescaling, dimensionality reduction, outlier removal or any other data-dependent preprocessing using the entire data set.
The studentized bootstrap, also called bootstrap-t, is computed analogously to the standard confidence interval, but replaces the quantiles from the normal or student approximation by the quantiles from the bootstrap distribution of the Student's t-test (see Davison and Hinkley 1997, equ. 5.7 p. 194 and Efron and Tibshirani 1993 equ 12.22, p. 160):
To demonstrate specificity Inheritance Inheritance is a key feature in CSS; it relies on the ancestor-descendant relationship to operate. Inheritance is the mechanism by which properties are applied not only to a specified element but also to its descendants. Inheritance relies on the document tree, which is the hierarchy of XHTML elements in a page based on nesting. Descendant elements may ...
PHP has a direct module interface called SAPI for different web servers; [272] in case of PHP 5 and Apache 2.0 on Windows, it is provided in form of a DLL file called php5apache2.dll, [273] which is a module that, among other functions, provides an interface between PHP and the web server, implemented in a form that the server understands. This ...
Data reconciliation is a technique that targets at correcting measurement errors that are due to measurement noise, i.e. random errors.From a statistical point of view the main assumption is that no systematic errors exist in the set of measurements, since they may bias the reconciliation results and reduce the robustness of the reconciliation.
It verifies that the software functions properly even when it receives invalid or unexpected inputs, thereby establishing the robustness of input validation and error-management routines. [citation needed] Software fault injection, in the form of fuzzing, is an example of failure testing.