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gptk (version 1.08)

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

1.08

License

BSD_2_clause + file LICENSE

Maintainer

Alfredo A Kalaitzis

Last Published

March 7th, 2014

Functions in gptk (1.08)

Constrains a parameter.

Gaussian Process 2D Covariance Demo

Initialise a noise structure.

cmpndNoiseParamInit

CMPND noise parameter initialisation.

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

demAutoOptimiseGp

Gaussian Process Optimisation Demo

Gaussian Process Sampling Demo

Evaluate the output of an Gaussian process model.

gpScaleBiasGradient

Compute the log likelihood gradient wrt the scales.

modelGradientCheck

Check gradients of given model.

rbfKernDiagGradX

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

Display a model.

cmpndKernParamInit

CMPND kernel parameter initialisation.

Gradient wrapper for a GP model.

gpMeanFunctionGradient

Compute the log likelihood gradient wrt the scales.

Sample from a Gaussian with a given covariance.

Sparse objective function gradients wrt Covariance functions for inducing variables.

gaussianNoiseOut

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

Gaussian Process Plotter

whiteKernDiagGradX

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

Gradient of RBF kernel with respect to input locations.

Compute the kernel given the parameters and X.

Return indices of given block.

Expand a parameter vector into a GP model.

Gaussian Process Optimisation Demo

gpLogLikeGradients

Compute the gradients for the parameters and X.

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

modelExpandParam

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

Give the output of a model for given X.

gpUpdateKernels

Update the kernels that are needed.

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

modelExtractParam

Extract the parameters of a model.

Return indices of present data.

kernDiagGradient

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

Compute the matrix m given the model.

gaussianNoiseParamInit

GAUSSIAN noise parameter initialisation.

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

Gaussian Process Regression Demo

Test the gradients of the likelihood wrt the covariance.

Initialise a kernel structure.

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

Compute the gradient wrt the kernel parameters.

Kernel parameter initialisation.

Run some tests on the specified kernel.

gpLogLikelihood

Compute the log likelihood of a GP.

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

gpPosteriorSample

Plot Samples from a GP Posterior.

optimiDefaultConstraint

Returns function for parameter constraint.

Optimise the inducing variable based kernel.

Extract a parameter vector from a GP model.

Noise model's parameter initialisation.

Optimise the given function using (scaled) conjugate gradients.

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

whiteKernParamInit

WHITE kernel parameter initialisation.

Return default options for GP model.

rbfKernParamInit

RBF kernel parameter initialisation.

Compute the gradient of the kernel wrt X.

gpPosteriorMeanVar

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

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

multiKernParamInit

MULTI kernel parameter initialisation.

Plot Samples from a GP.

Gradient of WHITE kernel with respect to input locations.

Wrapper function for GP objective.

modelOutputGrad

Compute derivatives with respect to params of model outputs.

demInterpolation

Gaussian Process Interpolation Demo