public GaussianProcessModel(IDataset ds, string targetVariable, IEnumerable <string> allowedInputVariables, IEnumerable <int> rows,
IEnumerable <double> hyp, IMeanFunction meanFunction, ICovarianceFunction covarianceFunction,
bool scaleInputs = true)
: base()
{
this.name = ItemName;
this.description = ItemDescription;
this.meanFunction = (IMeanFunction)meanFunction.Clone();
this.covarianceFunction = (ICovarianceFunction)covarianceFunction.Clone();
this.targetVariable = targetVariable;
this.allowedInputVariables = allowedInputVariables.ToArray();
int nVariables = this.allowedInputVariables.Length;
meanParameter = hyp
.Take(this.meanFunction.GetNumberOfParameters(nVariables))
.ToArray();
covarianceParameter = hyp.Skip(this.meanFunction.GetNumberOfParameters(nVariables))
.Take(this.covarianceFunction.GetNumberOfParameters(nVariables))
.ToArray();
sqrSigmaNoise = Math.Exp(2.0 * hyp.Last());
try {
CalculateModel(ds, rows, scaleInputs);
} catch (alglib.alglibexception ae) {
// wrap exception so that calling code doesn't have to know about alglib implementation
throw new ArgumentException("There was a problem in the calculation of the Gaussian process model", ae);
}
}
public GaussianProcessModel(IDataset ds, string targetVariable, IEnumerable<string> allowedInputVariables, IEnumerable<int> rows,
IEnumerable<double> hyp, IMeanFunction meanFunction, ICovarianceFunction covarianceFunction,
bool scaleInputs = true)
: base(targetVariable) {
this.name = ItemName;
this.description = ItemDescription;
this.meanFunction = (IMeanFunction)meanFunction.Clone();
this.covarianceFunction = (ICovarianceFunction)covarianceFunction.Clone();
this.allowedInputVariables = allowedInputVariables.ToArray();
int nVariables = this.allowedInputVariables.Length;
meanParameter = hyp
.Take(this.meanFunction.GetNumberOfParameters(nVariables))
.ToArray();
covarianceParameter = hyp.Skip(this.meanFunction.GetNumberOfParameters(nVariables))
.Take(this.covarianceFunction.GetNumberOfParameters(nVariables))
.ToArray();
sqrSigmaNoise = Math.Exp(2.0 * hyp.Last());
try {
CalculateModel(ds, rows, scaleInputs);
}
catch (alglib.alglibexception ae) {
// wrap exception so that calling code doesn't have to know about alglib implementation
throw new ArgumentException("There was a problem in the calculation of the Gaussian process model", ae);
}
}
private IItem CreateSolution()
{
var problemData = ProblemData;
var ds = problemData.Dataset;
var targetVariable = problemData.TargetVariable;
var allowedInputVariables = problemData.AllowedInputVariables.ToArray();
var trainingRows = problemData.TrainingIndices.ToArray();
lock (problemStateLocker) {
var model = new GaussianProcessModel(ds, targetVariable, allowedInputVariables, trainingRows, bestHyperParameters, (IMeanFunction)meanFunc.Clone(), (ICovarianceFunction)covFunc.Clone());
model.FixParameters();
return(model.CreateRegressionSolution((IRegressionProblemData)ProblemData.Clone()));
}
}