public override IRegressionModel Build(IRegressionProblemData pd, IRandom random, CancellationToken cancellationToken, out int numberOfParameters)
{
if (pd.Dataset.Rows < MinLeafSize(pd))
{
throw new ArgumentException("The number of training instances is too small to create a Gaussian process model");
}
Regression.Problem = new RegressionProblem {
ProblemData = pd
};
var cvscore = double.MaxValue;
GaussianProcessRegressionSolution sol = null;
for (var i = 0; i < Tries; i++)
{
var res = RegressionTreeUtilities.RunSubAlgorithm(Regression, random.Next(), cancellationToken);
var t = res.Select(x => x.Value).OfType <GaussianProcessRegressionSolution>().FirstOrDefault();
var score = ((DoubleValue)res["Negative log pseudo-likelihood (LOO-CV)"].Value).Value;
if (score >= cvscore || t == null || double.IsNaN(t.TrainingRSquared))
{
continue;
}
cvscore = score;
sol = t;
}
Regression.Runs.Clear();
if (sol == null)
{
throw new ArgumentException("Could not create Gaussian process model");
}
numberOfParameters = pd.Dataset.Rows + 1
+ Regression.CovarianceFunction.GetNumberOfParameters(pd.AllowedInputVariables.Count())
+ Regression.MeanFunction.GetNumberOfParameters(pd.AllowedInputVariables.Count());
return(sol.Model);
}
public override IRegressionModel Build(IRegressionProblemData pd, IRandom random, CancellationToken cancellationToken, out int noParameters)
{
if (pd.Dataset.Rows < MinLeafSize(pd))
{
throw new ArgumentException("The number of training instances is too small to create a linear model");
}
noParameters = pd.Dataset.Rows + 1;
Regression.Problem = new RegressionProblem {
ProblemData = pd
};
var res = RegressionTreeUtilities.RunSubAlgorithm(Regression, random.Next(), cancellationToken);
var t = res.Select(x => x.Value).OfType <IRegressionSolution>().FirstOrDefault();
if (t == null)
{
throw new ArgumentException("No RegressionSolution was provided by the algorithm");
}
return(t.Model);
}
