wolffd@0:
wolffd@0:
wolffd@0:
wolffd@0: NETLAB Reference Documentation
wolffd@0:
wolffd@0:
wolffd@0:
wolffd@0: NETLAB Online Reference Documentation
wolffd@0: Welcome to the NETLAB online reference documentation.
wolffd@0: The NETLAB simulation software is designed to provide all the tools necessary
wolffd@0: for principled and theoretically well founded application development. The
wolffd@0: NETLAB library is based on the approach and techniques described in Neural
wolffd@0: Networks for Pattern Recognition (Bishop, 1995). The library includes software
wolffd@0: implementations of a wide range of data analysis techniques, many of which are
wolffd@0: not widely available, and are rarely, if ever, included in standard neural
wolffd@0: network simulation packages.
wolffd@0: The online reference documentation provides direct hypertext links to specific Netlab function descriptions.
wolffd@0:
If you have any comments or problems to report, please contact Ian Nabney (i.t.nabney@aston.ac.uk) or Christopher Bishop (c.m.bishop@aston.ac.uk).
Index
wolffd@0:
wolffd@0: An alphabetic list of functions in Netlab.
wolffd@0:
wolffd@0: -
wolffd@0:
conffig
-
wolffd@0: Display a confusion matrix.
wolffd@0:
-
wolffd@0:
confmat
-
wolffd@0: Compute a confusion matrix.
wolffd@0:
-
wolffd@0:
conjgrad
-
wolffd@0: Conjugate gradients optimization.
wolffd@0:
-
wolffd@0:
consist
-
wolffd@0: Check that arguments are consistent.
wolffd@0:
-
wolffd@0:
convertoldnet
-
wolffd@0: Convert pre-2.3 release MLP and MDN nets to new format
wolffd@0:
-
wolffd@0:
datread
-
wolffd@0: Read data from an ascii file.
wolffd@0:
-
wolffd@0:
datwrite
-
wolffd@0: Write data to ascii file.
wolffd@0:
-
wolffd@0:
dem2ddat
-
wolffd@0: Generates two dimensional data for demos.
wolffd@0:
-
wolffd@0:
demard
-
wolffd@0: Automatic relevance determination using the MLP.
wolffd@0:
-
wolffd@0:
demev1
-
wolffd@0: Demonstrate Bayesian regression for the MLP.
wolffd@0:
-
wolffd@0:
demev2
-
wolffd@0: Demonstrate Bayesian classification for the MLP.
wolffd@0:
-
wolffd@0:
demev3
-
wolffd@0: Demonstrate Bayesian regression for the RBF.
wolffd@0:
-
wolffd@0:
demgauss
-
wolffd@0: Demonstrate sampling from Gaussian distributions.
wolffd@0:
-
wolffd@0:
demglm1
-
wolffd@0: Demonstrate simple classification using a generalized linear model.
wolffd@0:
-
wolffd@0:
demglm2
-
wolffd@0: Demonstrate simple classification using a generalized linear model.
wolffd@0:
-
wolffd@0:
demgmm1
-
wolffd@0: Demonstrate density modelling with a Gaussian mixture model.
wolffd@0:
-
wolffd@0:
demgmm3
-
wolffd@0: Demonstrate density modelling with a Gaussian mixture model.
wolffd@0:
-
wolffd@0:
demgmm4
-
wolffd@0: Demonstrate density modelling with a Gaussian mixture model.
wolffd@0:
-
wolffd@0:
demgmm5
-
wolffd@0: Demonstrate density modelling with a PPCA mixture model.
wolffd@0:
-
wolffd@0:
demgp
-
wolffd@0: Demonstrate simple regression using a Gaussian Process.
wolffd@0:
-
wolffd@0:
demgpard
-
wolffd@0: Demonstrate ARD using a Gaussian Process.
wolffd@0:
-
wolffd@0:
demgpot
-
wolffd@0: Computes the gradient of the negative log likelihood for a mixture model.
wolffd@0:
-
wolffd@0:
demgtm1
-
wolffd@0: Demonstrate EM for GTM.
wolffd@0:
-
wolffd@0:
demgtm2
-
wolffd@0: Demonstrate GTM for visualisation.
wolffd@0:
-
wolffd@0:
demhint
-
wolffd@0: Demonstration of Hinton diagram for 2-layer feed-forward network.
wolffd@0:
-
wolffd@0:
demhmc1
-
wolffd@0: Demonstrate Hybrid Monte Carlo sampling on mixture of two Gaussians.
wolffd@0:
-
wolffd@0:
demhmc2
-
wolffd@0: Demonstrate Bayesian regression with Hybrid Monte Carlo sampling.
wolffd@0:
-
wolffd@0:
demhmc3
-
wolffd@0: Demonstrate Bayesian regression with Hybrid Monte Carlo sampling.
wolffd@0:
-
wolffd@0:
demkmean
-
wolffd@0: Demonstrate simple clustering model trained with K-means.
wolffd@0:
-
wolffd@0:
demknn1
-
wolffd@0: Demonstrate nearest neighbour classifier.
wolffd@0:
-
wolffd@0:
demmdn1
-
wolffd@0: Demonstrate fitting a multi-valued function using a Mixture Density Network.
wolffd@0:
-
wolffd@0:
demmet1
-
wolffd@0: Demonstrate Markov Chain Monte Carlo sampling on a Gaussian.
wolffd@0:
-
wolffd@0:
demmlp1
-
wolffd@0: Demonstrate simple regression using a multi-layer perceptron
wolffd@0:
-
wolffd@0:
demmlp2
-
wolffd@0: Demonstrate simple classification using a multi-layer perceptron
wolffd@0:
-
wolffd@0:
demnlab
-
wolffd@0: A front-end Graphical User Interface to the demos
wolffd@0:
-
wolffd@0:
demns1
-
wolffd@0: Demonstrate Neuroscale for visualisation.
wolffd@0:
-
wolffd@0:
demolgd1
-
wolffd@0: Demonstrate simple MLP optimisation with on-line gradient descent
wolffd@0:
-
wolffd@0:
demopt1
-
wolffd@0: Demonstrate different optimisers on Rosenbrock's function.
wolffd@0:
-
wolffd@0:
dempot
-
wolffd@0: Computes the negative log likelihood for a mixture model.
wolffd@0:
-
wolffd@0:
demprgp
-
wolffd@0: Demonstrate sampling from a Gaussian Process prior.
wolffd@0:
-
wolffd@0:
demprior
-
wolffd@0: Demonstrate sampling from a multi-parameter Gaussian prior.
wolffd@0:
-
wolffd@0:
demrbf1
-
wolffd@0: Demonstrate simple regression using a radial basis function network.
wolffd@0:
-
wolffd@0:
demsom1
-
wolffd@0: Demonstrate SOM for visualisation.
wolffd@0:
-
wolffd@0:
demtrain
-
wolffd@0: Demonstrate training of MLP network.
wolffd@0:
-
wolffd@0:
dist2
-
wolffd@0: Calculates squared distance between two sets of points.
wolffd@0:
-
wolffd@0:
eigdec
-
wolffd@0: Sorted eigendecomposition
wolffd@0:
-
wolffd@0:
errbayes
-
wolffd@0: Evaluate Bayesian error function for network.
wolffd@0:
-
wolffd@0:
evidence
-
wolffd@0: Re-estimate hyperparameters using evidence approximation.
wolffd@0:
-
wolffd@0:
fevbayes
-
wolffd@0: Evaluate Bayesian regularisation for network forward propagation.
wolffd@0:
-
wolffd@0:
gauss
-
wolffd@0: Evaluate a Gaussian distribution.
wolffd@0:
-
wolffd@0:
gbayes
-
wolffd@0: Evaluate gradient of Bayesian error function for network.
wolffd@0:
-
wolffd@0:
glm
-
wolffd@0: Create a generalized linear model.
wolffd@0:
-
wolffd@0:
glmderiv
-
wolffd@0: Evaluate derivatives of GLM outputs with respect to weights.
wolffd@0:
-
wolffd@0:
glmerr
-
wolffd@0: Evaluate error function for generalized linear model.
wolffd@0:
-
wolffd@0:
glmevfwd
-
wolffd@0: Forward propagation with evidence for GLM
wolffd@0:
-
wolffd@0:
glmfwd
-
wolffd@0: Forward propagation through generalized linear model.
wolffd@0:
-
wolffd@0:
glmgrad
-
wolffd@0: Evaluate gradient of error function for generalized linear model.
wolffd@0:
-
wolffd@0:
glmhess
-
wolffd@0: Evaluate the Hessian matrix for a generalised linear model.
wolffd@0:
-
wolffd@0:
glminit
-
wolffd@0: Initialise the weights in a generalized linear model.
wolffd@0:
-
wolffd@0:
glmpak
-
wolffd@0: Combines weights and biases into one weights vector.
wolffd@0:
-
wolffd@0:
glmtrain
-
wolffd@0: Specialised training of generalized linear model
wolffd@0:
-
wolffd@0:
glmunpak
-
wolffd@0: Separates weights vector into weight and bias matrices.
wolffd@0:
-
wolffd@0:
gmm
-
wolffd@0: Creates a Gaussian mixture model with specified architecture.
wolffd@0:
-
wolffd@0:
gmmactiv
-
wolffd@0: Computes the activations of a Gaussian mixture model.
wolffd@0:
-
wolffd@0:
gmmem
-
wolffd@0: EM algorithm for Gaussian mixture model.
wolffd@0:
-
wolffd@0:
gmminit
-
wolffd@0: Initialises Gaussian mixture model from data
wolffd@0:
-
wolffd@0:
gmmpak
-
wolffd@0: Combines all the parameters in a Gaussian mixture model into one vector.
wolffd@0:
-
wolffd@0:
gmmpost
-
wolffd@0: Computes the class posterior probabilities of a Gaussian mixture model.
wolffd@0:
-
wolffd@0:
gmmprob
-
wolffd@0: Computes the data probability for a Gaussian mixture model.
wolffd@0:
-
wolffd@0:
gmmsamp
-
wolffd@0: Sample from a Gaussian mixture distribution.
wolffd@0:
-
wolffd@0:
gmmunpak
-
wolffd@0: Separates a vector of Gaussian mixture model parameters into its components.
wolffd@0:
-
wolffd@0:
gp
-
wolffd@0: Create a Gaussian Process.
wolffd@0:
-
wolffd@0:
gpcovar
-
wolffd@0: Calculate the covariance for a Gaussian Process.
wolffd@0:
-
wolffd@0:
gpcovarf
-
wolffd@0: Calculate the covariance function for a Gaussian Process.
wolffd@0:
-
wolffd@0:
gpcovarp
-
wolffd@0: Calculate the prior covariance for a Gaussian Process.
wolffd@0:
-
wolffd@0:
gperr
-
wolffd@0: Evaluate error function for Gaussian Process.
wolffd@0:
-
wolffd@0:
gpfwd
-
wolffd@0: Forward propagation through Gaussian Process.
wolffd@0:
-
wolffd@0:
gpgrad
-
wolffd@0: Evaluate error gradient for Gaussian Process.
wolffd@0:
-
wolffd@0:
gpinit
-
wolffd@0: Initialise Gaussian Process model.
wolffd@0:
-
wolffd@0:
gppak
-
wolffd@0: Combines GP hyperparameters into one vector.
wolffd@0:
-
wolffd@0:
gpunpak
-
wolffd@0: Separates hyperparameter vector into components.
wolffd@0:
-
wolffd@0:
gradchek
-
wolffd@0: Checks a user-defined gradient function using finite differences.
wolffd@0:
-
wolffd@0:
graddesc
-
wolffd@0: Gradient descent optimization.
wolffd@0:
-
wolffd@0:
gsamp
-
wolffd@0: Sample from a Gaussian distribution.
wolffd@0:
-
wolffd@0:
gtm
-
wolffd@0: Create a Generative Topographic Map.
wolffd@0:
-
wolffd@0:
gtmem
-
wolffd@0: EM algorithm for Generative Topographic Mapping.
wolffd@0:
-
wolffd@0:
gtmfwd
-
wolffd@0: Forward propagation through GTM.
wolffd@0:
-
wolffd@0:
gtminit
-
wolffd@0: Initialise the weights and latent sample in a GTM.
wolffd@0:
-
wolffd@0:
gtmlmean
-
wolffd@0: Mean responsibility for data in a GTM.
wolffd@0:
-
wolffd@0:
gtmlmode
-
wolffd@0: Mode responsibility for data in a GTM.
wolffd@0:
-
wolffd@0:
gtmmag
-
wolffd@0: Magnification factors for a GTM
wolffd@0:
-
wolffd@0:
gtmpost
-
wolffd@0: Latent space responsibility for data in a GTM.
wolffd@0:
-
wolffd@0:
gtmprob
-
wolffd@0: Probability for data under a GTM.
wolffd@0:
-
wolffd@0:
hbayes
-
wolffd@0: Evaluate Hessian of Bayesian error function for network.
wolffd@0:
-
wolffd@0:
hesschek
-
wolffd@0: Use central differences to confirm correct evaluation of Hessian matrix.
wolffd@0:
-
wolffd@0:
hintmat
-
wolffd@0: Evaluates the coordinates of the patches for a Hinton diagram.
wolffd@0:
-
wolffd@0:
hinton
-
wolffd@0: Plot Hinton diagram for a weight matrix.
wolffd@0:
-
wolffd@0:
histp
-
wolffd@0: Histogram estimate of 1-dimensional probability distribution.
wolffd@0:
-
wolffd@0:
hmc
-
wolffd@0: Hybrid Monte Carlo sampling.
wolffd@0:
-
wolffd@0:
kmeans
-
wolffd@0: Trains a k means cluster model.
wolffd@0:
-
wolffd@0:
knn
-
wolffd@0: Creates a K-nearest-neighbour classifier.
wolffd@0:
-
wolffd@0:
knnfwd
-
wolffd@0: Forward propagation through a K-nearest-neighbour classifier.
wolffd@0:
-
wolffd@0:
linef
-
wolffd@0: Calculate function value along a line.
wolffd@0:
-
wolffd@0:
linemin
-
wolffd@0: One dimensional minimization.
wolffd@0:
-
wolffd@0:
maxitmess
-
wolffd@0: Create a standard error message when training reaches max. iterations.
wolffd@0:
-
wolffd@0:
mdn
-
wolffd@0: Creates a Mixture Density Network with specified architecture.
wolffd@0:
-
wolffd@0:
mdn2gmm
-
wolffd@0: Converts an MDN mixture data structure to array of GMMs.
wolffd@0:
-
wolffd@0:
mdndist2
-
wolffd@0: Calculates squared distance between centres of Gaussian kernels and data
wolffd@0:
-
wolffd@0:
mdnerr
-
wolffd@0: Evaluate error function for Mixture Density Network.
wolffd@0:
-
wolffd@0:
mdnfwd
-
wolffd@0: Forward propagation through Mixture Density Network.
wolffd@0:
-
wolffd@0:
mdngrad
-
wolffd@0: Evaluate gradient of error function for Mixture Density Network.
wolffd@0:
-
wolffd@0:
mdninit
-
wolffd@0: Initialise the weights in a Mixture Density Network.
wolffd@0:
-
wolffd@0:
mdnpak
-
wolffd@0: Combines weights and biases into one weights vector.
wolffd@0:
-
wolffd@0:
mdnpost
-
wolffd@0: Computes the posterior probability for each MDN mixture component.
wolffd@0:
-
wolffd@0:
mdnprob
-
wolffd@0: Computes the data probability likelihood for an MDN mixture structure.
wolffd@0:
-
wolffd@0:
mdnunpak
-
wolffd@0: Separates weights vector into weight and bias matrices.
wolffd@0:
-
wolffd@0:
metrop
-
wolffd@0: Markov Chain Monte Carlo sampling with Metropolis algorithm.
wolffd@0:
-
wolffd@0:
minbrack
-
wolffd@0: Bracket a minimum of a function of one variable.
wolffd@0:
-
wolffd@0:
mlp
-
wolffd@0: Create a 2-layer feedforward network.
wolffd@0:
-
wolffd@0:
mlpbkp
-
wolffd@0: Backpropagate gradient of error function for 2-layer network.
wolffd@0:
-
wolffd@0:
mlpderiv
-
wolffd@0: Evaluate derivatives of network outputs with respect to weights.
wolffd@0:
-
wolffd@0:
mlperr
-
wolffd@0: Evaluate error function for 2-layer network.
wolffd@0:
-
wolffd@0:
mlpevfwd
-
wolffd@0: Forward propagation with evidence for MLP
wolffd@0:
-
wolffd@0:
mlpfwd
-
wolffd@0: Forward propagation through 2-layer network.
wolffd@0:
-
wolffd@0:
mlpgrad
-
wolffd@0: Evaluate gradient of error function for 2-layer network.
wolffd@0:
-
wolffd@0:
mlphdotv
-
wolffd@0: Evaluate the product of the data Hessian with a vector.
wolffd@0:
-
wolffd@0:
mlphess
-
wolffd@0: Evaluate the Hessian matrix for a multi-layer perceptron network.
wolffd@0:
-
wolffd@0:
mlphint
-
wolffd@0: Plot Hinton diagram for 2-layer feed-forward network.
wolffd@0:
-
wolffd@0:
mlpinit
-
wolffd@0: Initialise the weights in a 2-layer feedforward network.
wolffd@0:
-
wolffd@0:
mlppak
-
wolffd@0: Combines weights and biases into one weights vector.
wolffd@0:
-
wolffd@0:
mlpprior
-
wolffd@0: Create Gaussian prior for mlp.
wolffd@0:
-
wolffd@0:
mlptrain
-
wolffd@0: Utility to train an MLP network for demtrain
wolffd@0:
-
wolffd@0:
mlpunpak
-
wolffd@0: Separates weights vector into weight and bias matrices.
wolffd@0:
-
wolffd@0:
netderiv
-
wolffd@0: Evaluate derivatives of network outputs by weights generically.
wolffd@0:
-
wolffd@0:
neterr
-
wolffd@0: Evaluate network error function for generic optimizers
wolffd@0:
-
wolffd@0:
netevfwd
-
wolffd@0: Generic forward propagation with evidence for network
wolffd@0:
-
wolffd@0:
netgrad
-
wolffd@0: Evaluate network error gradient for generic optimizers
wolffd@0:
-
wolffd@0:
nethess
-
wolffd@0: Evaluate network Hessian
wolffd@0:
-
wolffd@0:
netinit
-
wolffd@0: Initialise the weights in a network.
wolffd@0:
-
wolffd@0:
netopt
-
wolffd@0: Optimize the weights in a network model.
wolffd@0:
-
wolffd@0:
netpak
-
wolffd@0: Combines weights and biases into one weights vector.
wolffd@0:
-
wolffd@0:
netunpak
-
wolffd@0: Separates weights vector into weight and bias matrices.
wolffd@0:
-
wolffd@0:
olgd
-
wolffd@0: On-line gradient descent optimization.
wolffd@0:
-
wolffd@0:
pca
-
wolffd@0: Principal Components Analysis
wolffd@0:
-
wolffd@0:
plotmat
-
wolffd@0: Display a matrix.
wolffd@0:
-
wolffd@0:
ppca
-
wolffd@0: Probabilistic Principal Components Analysis
wolffd@0:
-
wolffd@0:
quasinew
-
wolffd@0: Quasi-Newton optimization.
wolffd@0:
-
wolffd@0:
rbf
-
wolffd@0: Creates an RBF network with specified architecture
wolffd@0:
-
wolffd@0:
rbfbkp
-
wolffd@0: Backpropagate gradient of error function for RBF network.
wolffd@0:
-
wolffd@0:
rbfderiv
-
wolffd@0: Evaluate derivatives of RBF network outputs with respect to weights.
wolffd@0:
-
wolffd@0:
rbferr
-
wolffd@0: Evaluate error function for RBF network.
wolffd@0:
-
wolffd@0:
rbfevfwd
-
wolffd@0: Forward propagation with evidence for RBF
wolffd@0:
-
wolffd@0:
rbffwd
-
wolffd@0: Forward propagation through RBF network with linear outputs.
wolffd@0:
-
wolffd@0:
rbfgrad
-
wolffd@0: Evaluate gradient of error function for RBF network.
wolffd@0:
-
wolffd@0:
rbfhess
-
wolffd@0: Evaluate the Hessian matrix for RBF network.
wolffd@0:
-
wolffd@0:
rbfjacob
-
wolffd@0: Evaluate derivatives of RBF network outputs with respect to inputs.
wolffd@0:
-
wolffd@0:
rbfpak
-
wolffd@0: Combines all the parameters in an RBF network into one weights vector.
wolffd@0:
-
wolffd@0:
rbfprior
-
wolffd@0: Create Gaussian prior and output layer mask for RBF.
wolffd@0:
-
wolffd@0:
rbfsetbf
-
wolffd@0: Set basis functions of RBF from data.
wolffd@0:
-
wolffd@0:
rbfsetfw
-
wolffd@0: Set basis function widths of RBF.
wolffd@0:
-
wolffd@0:
rbftrain
-
wolffd@0: Two stage training of RBF network.
wolffd@0:
-
wolffd@0:
rbfunpak
-
wolffd@0: Separates a vector of RBF weights into its components.
wolffd@0:
-
wolffd@0:
rosegrad
-
wolffd@0: Calculate gradient of Rosenbrock's function.
wolffd@0:
-
wolffd@0:
rosen
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wolffd@0: Calculate Rosenbrock's function.
wolffd@0:
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wolffd@0:
scg
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wolffd@0: Scaled conjugate gradient optimization.
wolffd@0:
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wolffd@0:
som
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wolffd@0: Creates a Self-Organising Map.
wolffd@0:
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wolffd@0:
somfwd
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wolffd@0: Forward propagation through a Self-Organising Map.
wolffd@0:
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wolffd@0:
sompak
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wolffd@0: Combines node weights into one weights matrix.
wolffd@0:
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wolffd@0:
somtrain
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wolffd@0: Kohonen training algorithm for SOM.
wolffd@0:
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wolffd@0:
somunpak
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wolffd@0: Replaces node weights in SOM.
wolffd@0:
wolffd@0:
wolffd@0:
wolffd@0: Copyright (c) Christopher M Bishop, Ian T Nabney (1996, 1997)
wolffd@0:
wolffd@0: