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Original (Bloodshed) Dev-C++ is a free full-featured integrated development environment (IDE) distributed under the GNU General Public License for programming in C and C++. It was originally developed by Colin Laplace and was first released in 1998. It is written in Delphi . It is bundled with, and uses, the MinGW or TDM-GCC 64bit port of the ...
IDE License Windows Linux macOS Other platforms Debugger Assemblers Auto-complete Macros/templates Latest stable release; Fresh: EUPL and 2-clause BSD : Yes Yes No Unknown No FASM: Unknown
bloodshed .net /devpascal .html. Dev-Pascal is a free integrated development environment (IDE) distributed under the GNU General Public License for programming in Pascal and Object Pascal. It supports an ancient version of the Free Pascal compiler and GNU Pascal as backends. The IDE is written in Delphi. It can also handle the Insight Debugger.
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In 1989, C++ 2.0 was released, followed by the updated second edition of The C++ Programming Language in 1991. [25] New features in 2.0 included multiple inheritance, abstract classes, static member functions, const member functions, and protected members. In 1990, The Annotated C++ Reference Manual was published. This work became the basis for ...
C++20 replaced the prior version of the C++ standard, called C++17, and was later replaced by C++23.[1] The standard was technically finalized[2]by WG21at the meeting in Praguein February 2020,[3]had its final draft version announced in March 2020,[4]was approved on 4 September 2020,[5][6]and published in December 2020. [7] Features.
Bloodshed Dev-C++ is a full-featured Integrated Development Environment (IDE) for the C and C++ programming languages. It uses the MinGW port of the GCC (GNU Compiler Collection) as its compiler. Dev-C++ can also be used in combination with Cygwin or any other GCC-based compiler.
In statistics, naive Bayes classifiers are a family of linear "probabilistic classifiers" which assumes that the features are conditionally independent, given the target class. The strength (naivety) of this assumption is what gives the classifier its name. These classifiers are among the simplest Bayesian network models.