Empirical analysis of algorithms. Now you may think about standard empirical analysis methods like calculating theoretical complexity function on average. However, this approach has some downsides. The latest stay-of-the-art solution provides more complex analysis method (see original article).
Read more about this approach here.
In short, method includes two stages — the stage of preliminary research, the purpose of which is to test the hypothesis about the law of distribution of the algorithm’s labor intensity values as a discrete limited random variable, and the stage of the main study, which determines the values of the confidence labor intensity 𝑓𝛾(𝑛) as a function of the input length of the algorithm.
So, this repository contains system that allow you to fully automate such empirical analysis. Provide analysis to system, have a rest, relax, make a cup of coffee and get analysis result report upon completion!
See releases page. You can download installer or portbale version.
Check examples here.
Target .NET Standard is 2.1 for libraries, .NET Core is 3.1 for desktop app. Version of C# is 8.0. You can install all .NET dependencies using NuGet package manager.
Target C++ part of project is used C++17 standard. No external C++ dependencies are used.
You can read full instruction in project Wiki.
This project is licensed under the terms of the Apache License 2.0.
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