Package jgpml.covariancefunctions
Interface CovarianceFunction
- All Known Implementing Classes:
CovLINard
,CovLINone
,CovNNone
,CovNNoneNoise
,CovNoise
,CovSEard
,CovSEiso
,CovSum
public interface CovarianceFunction
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Method Summary
Modifier and TypeMethodDescriptionJama.Matrix
compute
(Jama.Matrix loghyper, Jama.Matrix X) Compute covariance matrix of a dataset XJama.Matrix[]
compute
(Jama.Matrix loghyper, Jama.Matrix X, Jama.Matrix Xstar) Compute compute test set covariancesJama.Matrix
computeDerivatives
(Jama.Matrix loghyper, Jama.Matrix X, int index) Coompute the derivatives of thisCovarianceFunction
with respect to the hyperparameter with indexidx
int
Returns the number of hyperparameters of thisCovarianceFunction
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Method Details
-
numParameters
int numParameters()Returns the number of hyperparameters of thisCovarianceFunction
- Returns:
- number of hyperparameters
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compute
Jama.Matrix compute(Jama.Matrix loghyper, Jama.Matrix X) Compute covariance matrix of a dataset X- Parameters:
loghyper
- columnMatrix
of hyperparametersX
- input dataset- Returns:
- K covariance
Matrix
-
compute
Jama.Matrix[] compute(Jama.Matrix loghyper, Jama.Matrix X, Jama.Matrix Xstar) Compute compute test set covariances- Parameters:
loghyper
- columnMatrix
of hyperparametersX
- input datasetXstar
- test set- Returns:
- [K(Xstar, Xstar) K(X,Xstar)]
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computeDerivatives
Jama.Matrix computeDerivatives(Jama.Matrix loghyper, Jama.Matrix X, int index) Coompute the derivatives of thisCovarianceFunction
with respect to the hyperparameter with indexidx
- Parameters:
loghyper
- hyperparametersX
- input datasetindex
- hyperparameter index- Returns:
Matrix
of derivatives
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