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gptk (version 1.06)

Gaussian Processes Tool-Kit

Description

The gptk package implements a general-purpose toolkit for Gaussian process regression with a variety of covariance functions (e.g. RBF, Mattern, polynomial, etc). Based on a MATLAB implementation by Neil D. Lawrence. See inst/doc/index.html for more details.

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Version

Install

install.packages('gptk')

Monthly Downloads

38

Version

1.06

License

AGPL-3

Maintainer

Alfredo A Kalaitzis

Last Published

March 12th, 2013

Functions in gptk (1.06)

Expand a parameter vector into a GP model.

Gaussian Process Sampling Demo

gpLogLikelihood

Compute the log likelihood of a GP.

Optimise the given function using (scaled) conjugate gradients.

Constrains a parameter.

Plot a contour of the 2D Gaussian distribution with covariance matrix K.

cmpndNoiseParamInit

CMPND noise parameter initialisation.

Gradient wrapper for a GP model.

Gaussian Process 2D Covariance Demo

Sparse objective function gradients wrt Covariance functions for inducing variables.

cmpndKernParamInit

CMPND kernel parameter initialisation.

gpLogLikeGradients

Compute the gradients for the parameters and X.

Sample from a Gaussian with a given covariance.

gpMeanFunctionGradient

Compute the log likelihood gradient wrt the scales.

gpPosteriorMeanVar

Mean and variances of the posterior at points given by X.

Update the representations of A and D associated with the model.

Plot Samples from a GP.

Compute the gradient wrt the kernel parameters.

Return indices of present data.

Kernel parameter initialisation.

modelGradientCheck

Check gradients of given model.

rbfKernParamInit

RBF kernel parameter initialisation.

Optimise the inducing variable based kernel.

Create a GP model with inducing variables/pseudo-inputs.

Return indices of given block.

Give the output of a model for given X.

Gaussian Process Optimisation Demo

gaussianNoiseParamInit

GAUSSIAN noise parameter initialisation.

Compute the kernel given the parameters and X.

A function to move the axes crossing point to the origin.

multiKernParamInit

MULTI kernel parameter initialisation.

modelExpandParam

Update a model structure with new parameters or update the posterior processes.

demAutoOptimiseGp

Gaussian Process Optimisation Demo

gpScaleBiasGradient

Compute the log likelihood gradient wrt the scales.

Test the gradients of the gpCovGrads function and the gp models.

Return default options for GP model.

Gaussian Process Plotter

modelExtractParam

Extract the parameters of a model.

Gradient of RBF kernel with respect to input locations.

Initialise a kernel structure.

Give the output of the noise model given the mean and variance.

gaussianNoiseOut

Compute the output of the GAUSSIAN noise given the input mean and variance.

Model log-likelihood/objective error function and its gradient.

Update the vector `alpha' for computing posterior mean quickly.

Test the gradients of the likelihood wrt the covariance.

Gaussian Process Regression Demo

Wrapper function for GP objective.

Run some tests on the specified kernel.

whiteKernDiagGradX

Gradient of WHITE kernel's diagonal with respect to X.

demInterpolation

Gaussian Process Interpolation Demo

Gradient of WHITE kernel with respect to input locations.

Compute the gradient of the kernel wrt X.

Display a model.

whiteKernParamInit

WHITE kernel parameter initialisation.

Initialise a noise structure.

kernDiagGradient

Compute the gradient of the kernel's parameters for the diagonal.

optimiDefaultConstraint

Returns function for parameter constraint.

rbfKernDiagGradX

Gradient of RBF kernel's diagonal with respect to X.

modelOutputGrad

Compute derivatives with respect to params of model outputs.

Compute the matrix m given the model.

Extract a parameter vector from a GP model.

gpPosteriorSample

Plot Samples from a GP Posterior.

Evaluate the output of an Gaussian process model.

gpUpdateKernels

Update the kernels that are needed.

Noise model's parameter initialisation.