HasGP.Regression.Regression
Description
Regression is a module in the HasGP Gaussian process library. It implements basic Gaussian process regression. For the technical details see www.gaussianprocesses.org.
Copyright (C) 2011 Sean Holden. [email protected].
- gpRMain :: CovarianceFunction cF => cF -> Double -> Inputs -> Targets -> (DMatrix, DVector)
- gpRPredict :: CovarianceFunction cF => cF -> DMatrix -> DVector -> Inputs -> Targets -> Inputs -> (DVector, DVector)
- gpRPredict' :: CovarianceFunction cF => cF -> Double -> Inputs -> Targets -> Inputs -> (DVector, DVector)
- gpRLogEvidence :: DMatrix -> DVector -> Targets -> Double
- gpRGradLogEvidence :: CovarianceFunction cF => cF -> Double -> DMatrix -> DVector -> Inputs -> DVector
- gpRLogHyperToEvidence :: CovarianceFunction cF => cF -> Inputs -> Targets -> DVector -> (Double, DVector)
Documentation
Arguments
| :: CovarianceFunction cF | |
| => cF | |
| -> Double | The log noise variance |
| -> Inputs | |
| -> Targets | |
| -> (DMatrix, DVector) | L and alpha. |
Compute the main quantities required to do regression, specifically: the Cholesky decomposition L of the covariance matrix, and the parameters alpha such that L L^t y = alpha.
Arguments
| :: CovarianceFunction cF | |
| => cF | |
| -> DMatrix | l |
| -> DVector | alpha |
| -> Inputs | |
| -> Targets | |
| -> Inputs | The new inputs |
| -> (DVector, DVector) | Mean, variance |
Compute the expected value and variance for a collection of new points supplied as the rows of a matrix. Differs from gpRPredict' as l and alpha need to be computed in advance.
Arguments
| :: CovarianceFunction cF | |
| => cF | |
| -> Double | The log noise variance |
| -> Inputs | |
| -> Targets | |
| -> Inputs | The new inputs |
| -> (DVector, DVector) | Mean, variance |
Compute the expected value and variance for a collection of new points supplied as the rows of a matrix.
Compute the log of the marginal likelihood.
Arguments
| :: CovarianceFunction cF | |
| => cF | |
| -> Double | the log noise variance |
| -> DMatrix | l |
| -> DVector | alpha |
| -> Inputs | |
| -> DVector | Derivatives |
Compute the gradient of the log marginal likelihood. Output contains derivative with respect to noise variance followed by the derivatives with respect to the hyperparameters in the covariance function.