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Promela: Process or Protocol Meta Language; it is a verification modeling language. The language allows for the dynamic creation of concurrent processes to model, for example, distributed systems. Starlark: Starlark is a dialect of Python created by Google for Bazel. Model checkers like FizzBee uses Starlark/Python as the modeling language.
Functional programming – uses evaluation of mathematical functions and avoids state and mutable data; Generic programming – uses algorithms written in terms of to-be-specified-later types that are then instantiated as needed for specific types provided as parameters; Imperative programming – explicit statements that change a program state
PyCharm – Cross-platform Python IDE with code inspections available for analyzing code on-the-fly in the editor and bulk analysis of the whole project. PyDev – Eclipse-based Python IDE with code analysis available on-the-fly in the editor or at save time. Pylint – Static code analyzer. Quite stringent; includes many stylistic warnings as ...
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]
The implementation or design is regarded as a model of the system, whereas the specifications are properties that the model must satisfy. [2] An important class of model-checking methods has been developed for checking models of hardware and software designs where the specification is given by a temporal logic formula.
Server-side embedded languages are much more flexible, since almost any language can be built into a server. The aim of having fragments of server-side code embedded in a web page is to generate additional markup dynamically; the code itself disappears when the page is served, to be replaced by its output.
Supported data models (conceptual, logical, physical) Supported notations Forward engineering Reverse engineering Model/database comparison and synchronization Teamwork/repository Database Workbench: Conceptual, logical, physical IE (Crow’s foot) Yes Yes Update database and/or update model No Enterprise Architect
Pandas is built around data structures called Series and DataFrames. Data for these collections can be imported from various file formats such as comma-separated values, JSON, Parquet, SQL database tables or queries, and Microsoft Excel. [8] A Series is a 1-dimensional data structure built on top of NumPy's array.