GiveAGap 1.0.2

C# Library to perfrom Variable Selection according to the Give-A-Gap algortithm.

There is a newer version of this package available.
See the version list below for details.
Install-Package GiveAGap -Version 1.0.2
dotnet add package GiveAGap --version 1.0.2
<PackageReference Include="GiveAGap" Version="1.0.2" />
For projects that support PackageReference, copy this XML node into the project file to reference the package.
paket add GiveAGap --version 1.0.2
The NuGet Team does not provide support for this client. Please contact its maintainers for support.

Headings

Install the package and then add at least this line of code below to run once the algorithm

Code Block

var mutationProb = 0.1f;
var crossOverProb = 0.8f;
var dimVincoli = 1000;
var dimVars = 15;
var trPerc = 0.5;
var vdPerc = 0.25;
var myTest = new SynthTest(dimVincoli, dimVars, trPerc, vdPerc, 1, 1); // or Create another test expanding SynthTest.cs
GeneticAlgorithmSETUP setup = new GeneticAlgorithmSETUP(myTest, dimVincoli, dimVars, trPerc, vdPerc, 1, crossOverProb, mutationProb);
setup.minFit = 0.45;
setup.forcedINOUT = Vector<double>.Build.Dense(setup.dimVars); // modify your ForcedInOutClass
var forcedINOUT = setup.forcedINOUT;
var result = setup.Start(0.8,30,true); // Termination Conditions (minFit,minGen,and)
Console.WriteLine("SELECTED: " + result.Item1);
Console.WriteLine("In " + result.Item2 + " generations");
Console.WriteLine("In " + result.Item3 + " seconds");
Console.WriteLine("Fitness on TS " + result.Item4);
Console.WriteLine("Fitness on TR VD " + result.Item5);
Console.WriteLine("*****************");

Explaination

Basically you need to choose mutation and crossover probability, then setup the algorithm giving him the size of the input Matrix, and then calling setup.Start() the algorithm starts and when execution finishes you get a tuple with all element you need for the analysis

Headings

Install the package and then add at least this line of code below to run once the algorithm

Code Block

var mutationProb = 0.1f;
var crossOverProb = 0.8f;
var dimVincoli = 1000;
var dimVars = 15;
var trPerc = 0.5;
var vdPerc = 0.25;
var myTest = new SynthTest(dimVincoli, dimVars, trPerc, vdPerc, 1, 1); // or Create another test expanding SynthTest.cs
GeneticAlgorithmSETUP setup = new GeneticAlgorithmSETUP(myTest, dimVincoli, dimVars, trPerc, vdPerc, 1, crossOverProb, mutationProb);
setup.minFit = 0.45;
setup.forcedINOUT = Vector<double>.Build.Dense(setup.dimVars); // modify your ForcedInOutClass
var forcedINOUT = setup.forcedINOUT;
var result = setup.Start(0.8,30,true); // Termination Conditions (minFit,minGen,and)
Console.WriteLine("SELECTED: " + result.Item1);
Console.WriteLine("In " + result.Item2 + " generations");
Console.WriteLine("In " + result.Item3 + " seconds");
Console.WriteLine("Fitness on TS " + result.Item4);
Console.WriteLine("Fitness on TR VD " + result.Item5);
Console.WriteLine("*****************");

Explaination

Basically you need to choose mutation and crossover probability, then setup the algorithm giving him the size of the input Matrix, and then calling setup.Start() the algorithm starts and when execution finishes you get a tuple with all element you need for the analysis

Dependencies

This package has no dependencies.

This package is not used by any popular GitHub repositories.

Version History

Version Downloads Last updated
1.0.3 274 11/11/2017
1.0.2 272 11/5/2017
1.0.1 237 11/5/2017
1.0.0 283 11/5/2017