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gptk (version 1.03)

Gaussian Processes Tool-Kit

Description

The gptk package implements a general-purpose toolkit for Gaussian process regression with an RBF covariance function. 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

1.03

License

AGPL-3

Maintainer

Alfredo A Kalaitzis

Last Published

July 26th, 2011

Functions in gptk (1.03)

kernDiagGradient

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

Return default options for GP model.

Display a model.

cmpndNoiseParamInit

CMPND noise parameter initialisation.

Gaussian Process Sampling Demo

Gradient of WHITE kernel with respect to input locations.

Extract a parameter vector from a GP model.

Initialise a kernel structure.

gpPosteriorSample

Plot Samples from a GP Posterior.

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

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

Evaluate the output of an Gaussian process model.

Run some tests on the specified kernel.

Compute the gradient wrt the kernel parameters.

Gaussian Process Optimisation Demo

gpPosteriorMeanVar

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

Gradient wrapper for a GP model.

cmpndKernParamInit

CMPND kernel parameter initialisation.

modelExtractParam

Extract the parameters of a model.

Gradient of RBF kernel with respect to input locations.

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

gpLogLikeGradients

Compute the gradients for the parameters and X.

modelGradientCheck

Check gradients of given model.

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

Compute the gradient of the kernel wrt X.

optimiDefaultConstraint

Returns function for parameter constraint.

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

demInterpolation

Gaussian Process Interpolation Demo

Gaussian Process 2D Covariance Demo

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

Optimise the inducing variable based kernel.

Sample from a Gaussian with a given covariance.

gpUpdateKernels

Update the kernels that are needed.

gaussianNoiseOut

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

gaussianNoiseParamInit

GAUSSIAN noise parameter initialisation.

gpLogLikelihood

Compute the log likelihood of a GP.

Return indices of present data.

Compute the matrix m given the model.

gpScaleBiasGradient

Compute the log likelihood gradient wrt the scales.

rbfKernDiagGradX

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

Wrapper function for GP objective.

Test the gradients of the likelihood wrt the covariance.

Noise model's parameter initialisation.

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

Constrains a parameter.

Gaussian Process Plotter

gpMeanFunctionGradient

Compute the log likelihood gradient wrt the scales.

modelExpandParam

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

Sparse objective function gradients wrt Covariance functions for inducing variables.

whiteKernParamInit

WHITE kernel parameter initialisation.

rbfKernParamInit

RBF kernel parameter initialisation.

multiKernParamInit

MULTI kernel parameter initialisation.

Optimise the given function using (scaled) conjugate gradients.

Plot Samples from a GP.

whiteKernDiagGradX

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

Initialise a noise structure.

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

Compute the kernel given the parameters and X.

modelOutputGrad

Compute derivatives with respect to params of model outputs.

Kernel parameter initialisation.

Give the output of a model for given X.

Return indices of given block.

Expand a parameter vector into a GP model.

Gaussian Process Regression Demo