Gaussian Process Library. This module contains assorted functions that support the computation of covariance, constructing covariance matrices etc.
Covariance functions store log parameters. Functions are needed to return the covariance and its derivative. Derivatives are with respect to the actual parameters, NOT their logs.
Copyright (C) 2011 Sean Holden. sbh11@cl.cam.ac.uk.
- class CovarianceFunction a where
- trueHyper :: a -> DVector
- covariance :: a -> DVector -> DVector -> Double
- dCovarianceDParameters :: a -> DVector -> DVector -> DVector
- makeCovarianceFromList :: a -> [Double] -> a
- makeListFromCovariance :: a -> [Double]
- covarianceMatrix :: CovarianceFunction c => c -> Inputs -> CovarianceMatrix
- covarianceWithPoint :: CovarianceFunction c => c -> Inputs -> Input -> DVector
- covarianceWithPoints :: CovarianceFunction c => c -> Inputs -> [Input] -> [DVector]
Documentation
class CovarianceFunction a whereSource
trueHyper :: a -> DVectorSource
The actual hyperparameter values.
The covariance
covariance :: a -> DVector -> DVector -> DoubleSource
Derivative of covariance with respect to parameters
dCovarianceDParameters :: a -> DVector -> DVector -> DVectorSource
Construct using log parameters.
makeCovarianceFromList :: a -> [Double] -> aSource
Get log parameters.
makeListFromCovariance :: a -> [Double]Source
covarianceMatrix :: CovarianceFunction c => c -> Inputs -> CovarianceMatrixSource
Construct a matrix of covariances from a covariance and a design matrix.
covarianceWithPoint :: CovarianceFunction c => c -> Inputs -> Input -> DVectorSource
Constructs the column vector required when a new input is included. Constructed as a matrix to avoid further work elsewhere.
covarianceWithPoints :: CovarianceFunction c => c -> Inputs -> [Input] -> [DVector]Source
covarianceWithPoint applied to a list of points to produce a list of vectors.