public GaussianProcessLeaf()
{
var gp = new GaussianProcessRegression();
gp.CovarianceFunctionParameter.Value = new CovarianceRationalQuadraticIso();
gp.MeanFunctionParameter.Value = new MeanLinear();
Parameters.Add(new FixedValueParameter <IntValue>(TriesParameterName, "Number of restarts (default = 10)", new IntValue(10)));
Parameters.Add(new ValueParameter <GaussianProcessRegression>(RegressionParameterName, "The algorithm creating Gaussian process models", gp));
}
private GaussianProcessRegression CreateGaussianProcessRegressionSample() {
var gpr = new GaussianProcessRegression();
var provider = new VariousInstanceProvider();
var instance = provider.GetDataDescriptors().Where(x => x.Name.Contains("Spatial co-evolution")).Single();
var regProblem = new RegressionProblem();
regProblem.Load(provider.LoadData(instance));
#region Algorithm Configuration
gpr.Name = "Gaussian Process Regression";
gpr.Description = "A Gaussian process regression algorithm which solves the spatial co-evolution benchmark problem";
gpr.Problem = regProblem;
gpr.CovarianceFunction = new CovarianceSquaredExponentialIso();
gpr.MeanFunction = new MeanConst();
gpr.MinimizationIterations = 20;
gpr.Seed = 0;
gpr.SetSeedRandomly = true;
#endregion
gpr.Engine = new ParallelEngine.ParallelEngine();
return gpr;
}
private GaussianProcessRegression(GaussianProcessRegression original, Cloner cloner)
: base(original, cloner) {
RegisterEventHandlers();
}
private GaussianProcessRegression(GaussianProcessRegression original, Cloner cloner)
: base(original, cloner)
{
RegisterEventHandlers();
}
