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gptk (version 1.0)

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 written by Neil D. Lawrence. See inst/doc/index.html for more details.

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Install

install.packages('gptk')

Monthly Downloads

38

Version

1.0

License

AGPL-3

Maintainer

Alfredo A Kalaitzis

Last Published

December 12th, 2010

Functions in gptk (1.0)

Extract a parameter vector from a GP model.

cmpndKernParamInit

CMPND kernel parameter initialisation.

Return default options for GP model.

Compute the matrix m given the model.

Gradient of WHITE kernel with respect to input locations.

gpPosteriorSample

Plot Samples from a GP Posterior.

Constrains a parameter.

gpLogLikelihood

Compute the log likelihood of a GP.

Test the gradients of the likelihood wrt the covariance.

rbfKernParamInit

RBF kernel parameter initialisation.

whiteKernDiagGradX

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

Gaussian Process Optimisation Demo

gaussianNoiseParamInit

GAUSSIAN noise parameter initialisation.

whiteKernParamInit

WHITE kernel parameter initialisation.

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

Give the output of a model for given X.

Gaussian Process Regression Demo

Wrapper function for GP objective.

Return indices of given block.

gpScaleBiasGradient

Compute the log likelihood gradient wrt the scales.

Gradient of RBF kernel with respect to input locations.

gpUpdateKernels

Update the kernels that are needed.

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

cmpndNoiseParamInit

CMPND noise parameter initialisation.

Optimise the inducing variable based kernel.

Gaussian Process Sampling Demo

Optimise the given function using (scaled) conjugate gradients.

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

optimiDefaultConstraint

Returns function for parameter constraint.

Compute the gradient wrt the kernel parameters.

Evaluate the output of an Gaussian process model.

Sample from a Gaussian with a given covariance.

gpLogLikeGradients

Compute the gradients for the parameters and X.

demInterpolation

Gaussian Process Interpolation Demo

rbfKernDiagGradX

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

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

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

modelExtractParam

Extract the parameters of a model.

Gaussian Process Plotter

Compute the gradient of the kernel wrt X.

gaussianNoiseOut

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

kernDiagGradient

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

Plot Samples from a GP.

Expand a parameter vector into a GP model.

gpPosteriorMeanVar

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

Return indices of present data.

gpMeanFunctionGradient

Compute the log likelihood gradient wrt the scales.

Kernel parameter initialisation.

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

Gaussian Process 2D Covariance Demo

modelOutputGrad

Compute derivatives with respect to params of model outputs.

Compute the kernel given the parameters and X.

multiKernParamInit

MULTI kernel parameter initialisation.

Initialise a noise structure.

Noise model's parameter initialisation.

Gradient wrapper for a GP model.

modelGradientCheck

Check gradients of given model.

Run some tests on the specified kernel.

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

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

Sparse objective function gradients wrt Covariance functions for inducing variables.

modelExpandParam

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

Display a model.

Initialise a kernel structure.

demAutoOptimiseGp

Gaussian Process Optimisation Demo