ApplicationMetrics provides interfaces and classes to allow simple logging of metric and instrumentation events from a client application.
ApplicationMetrics includes several implementations of metric logger classes which write metrics and instrumentation information to files, the console, and Windows Performance Monitor. It also contains base classes which allow users to easily create metric loggers for logging to relational databases, and big data platforms.
Install-Package ApplicationMetrics -Version 1.5.0
dotnet add package ApplicationMetrics --version 1.5.0
<PackageReference Include="ApplicationMetrics" Version="1.5.0" />
paket add ApplicationMetrics --version 1.5.0
#r "nuget: ApplicationMetrics, 1.5.0"
// Install ApplicationMetrics as a Cake Addin #addin nuget:?package=ApplicationMetrics&version=1.5.0 // Install ApplicationMetrics as a Cake Tool #tool nuget:?package=ApplicationMetrics&version=1.5.0
- FrameworkAbstraction (>= 1.6.0)
NuGet packages (2)
Showing the top 2 NuGet packages that depend on ApplicationMetrics:
Mathematics Modular Framework (MMF) is a framework which allows simple construction and rearrangement of workflows for finance, mathematics, and machine learning applications. It allows the packaging of a program unit that performs a mathematical function into a 'module', which has strongly defined and typed inputs and outputs. The outputs of modules can then be linked to the inputs of other modules to form a directed graph (known as a 'module graph') which defines the workflow. The hierarchy implicit in the module graph allows the workflow to be executed automatically in order of dependency when processed. Workflows can also be serialized to and from XML documents, and hence written to and read from persistent storage (file, database, network, etc...). MMF was created primarily for financial maths and machine learning, but works with any data types in the .NET framework (including custom classes defined in an application or framework), and hence is suitable for any application where workflows must be flexibly defined at runtime.
SimpleML provides implementations of basic machine learning algorithms.
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