static GaussianProcessCovarianceOptimizationProblem()
{
// cumbersome initialization because of ConstrainedValueParameters
maternIso1 = new CovarianceMaternIso(); SetConstrainedValueParameter(maternIso1.DParameter, 1);
maternIso3 = new CovarianceMaternIso(); SetConstrainedValueParameter(maternIso3.DParameter, 3);
maternIso5 = new CovarianceMaternIso(); SetConstrainedValueParameter(maternIso5.DParameter, 5);
piecewisePoly0 = new CovariancePiecewisePolynomial(); SetConstrainedValueParameter(piecewisePoly0.VParameter, 0);
piecewisePoly1 = new CovariancePiecewisePolynomial(); SetConstrainedValueParameter(piecewisePoly1.VParameter, 1);
piecewisePoly2 = new CovariancePiecewisePolynomial(); SetConstrainedValueParameter(piecewisePoly2.VParameter, 2);
piecewisePoly3 = new CovariancePiecewisePolynomial(); SetConstrainedValueParameter(piecewisePoly3.VParameter, 3);
poly2 = new CovariancePolynomial(); poly2.DegreeParameter.Value.Value = 2;
poly3 = new CovariancePolynomial(); poly3.DegreeParameter.Value.Value = 3;
spectralMixture1 = new CovarianceSpectralMixture(); spectralMixture1.QParameter.Value.Value = 1;
spectralMixture3 = new CovarianceSpectralMixture(); spectralMixture3.QParameter.Value.Value = 3;
spectralMixture5 = new CovarianceSpectralMixture(); spectralMixture5.QParameter.Value.Value = 5;
linear = new CovarianceLinear();
linearArd = new CovarianceLinearArd();
neuralNetwork = new CovarianceNeuralNetwork();
periodic = new CovariancePeriodic();
ratQuadraticArd = new CovarianceRationalQuadraticArd();
ratQuadraticIso = new CovarianceRationalQuadraticIso();
sqrExpArd = new CovarianceSquaredExponentialArd();
sqrExpIso = new CovarianceSquaredExponentialIso();
}
private CovariancePeriodic(CovariancePeriodic original, Cloner cloner)
: base(original, cloner) {
}
private CovariancePeriodic(CovariancePeriodic original, Cloner cloner)
: base(original, cloner)
{
}