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<html><head><title>NETLAB Reference Documentation </title></head><body><H1> NETLAB Online Reference Documentation </H1>Welcome to the NETLAB online reference documentation.The NETLAB simulation software is designed to provide all the tools necessaryfor principled and theoretically well founded application development. TheNETLAB library is based on the approach and techniques described in <I>NeuralNetworks for Pattern Recognition </I>(Bishop, 1995). The library includes softwareimplementations of a wide range of data analysis techniques, many of which arenot widely available, and are rarely, if ever, included in standard neuralnetwork simulation packages.<p>The online reference documentation provides direct hypertext links to specific Netlab function descriptions.<p>If you have any comments or problems to report, please contact Ian Nabney (<a href="mailto:i.t.nabney@aston.ac.uk"><tt>i.t.nabney@aston.ac.uk</tt></a>) or Christopher Bishop (<a href="mailto:c.m.bishop@aston.ac.uk"><tt>c.m.bishop@aston.ac.uk</tt></a>).<H1> Index</H1>An alphabetic list of functions in Netlab.<p><DL><DT><CODE><a href="conffig.htm">conffig</a></CODE><DD> Display a confusion matrix. <DT><CODE><a href="confmat.htm">confmat</a></CODE><DD> Compute a confusion matrix. <DT><CODE><a href="conjgrad.htm">conjgrad</a></CODE><DD> Conjugate gradients optimization. <DT><CODE><a href="consist.htm">consist</a></CODE><DD> Check that arguments are consistent. <DT><CODE><a href="convertoldnet.htm">convertoldnet</a></CODE><DD> Convert pre-2.3 release MLP and MDN nets to new format <DT><CODE><a href="datread.htm">datread</a></CODE><DD> Read data from an ascii file. <DT><CODE><a href="datwrite.htm">datwrite</a></CODE><DD> Write data to ascii file. <DT><CODE><a href="dem2ddat.htm">dem2ddat</a></CODE><DD> Generates two dimensional data for demos. <DT><CODE><a href="demard.htm">demard</a></CODE><DD> Automatic relevance determination using the MLP. <DT><CODE><a href="demev1.htm">demev1</a></CODE><DD> Demonstrate Bayesian regression for the MLP. <DT><CODE><a href="demev2.htm">demev2</a></CODE><DD> Demonstrate Bayesian classification for the MLP. <DT><CODE><a href="demev3.htm">demev3</a></CODE><DD> Demonstrate Bayesian regression for the RBF. <DT><CODE><a href="demgauss.htm">demgauss</a></CODE><DD> Demonstrate sampling from Gaussian distributions. <DT><CODE><a href="demglm1.htm">demglm1</a></CODE><DD> Demonstrate simple classification using a generalized linear model. <DT><CODE><a href="demglm2.htm">demglm2</a></CODE><DD> Demonstrate simple classification using a generalized linear model. <DT><CODE><a href="demgmm1.htm">demgmm1</a></CODE><DD> Demonstrate density modelling with a Gaussian mixture model. <DT><CODE><a href="demgmm3.htm">demgmm3</a></CODE><DD> Demonstrate density modelling with a Gaussian mixture model. <DT><CODE><a href="demgmm4.htm">demgmm4</a></CODE><DD> Demonstrate density modelling with a Gaussian mixture model. <DT><CODE><a href="demgmm5.htm">demgmm5</a></CODE><DD> Demonstrate density modelling with a PPCA mixture model. <DT><CODE><a href="demgp.htm">demgp</a></CODE><DD> Demonstrate simple regression using a Gaussian Process. <DT><CODE><a href="demgpard.htm">demgpard</a></CODE><DD> Demonstrate ARD using a Gaussian Process. <DT><CODE><a href="demgpot.htm">demgpot</a></CODE><DD> Computes the gradient of the negative log likelihood for a mixture model. <DT><CODE><a href="demgtm1.htm">demgtm1</a></CODE><DD> Demonstrate EM for GTM. <DT><CODE><a href="demgtm2.htm">demgtm2</a></CODE><DD> Demonstrate GTM for visualisation. <DT><CODE><a href="demhint.htm">demhint</a></CODE><DD> Demonstration of Hinton diagram for 2-layer feed-forward network. <DT><CODE><a href="demhmc1.htm">demhmc1</a></CODE><DD> Demonstrate Hybrid Monte Carlo sampling on mixture of two Gaussians. <DT><CODE><a href="demhmc2.htm">demhmc2</a></CODE><DD> Demonstrate Bayesian regression with Hybrid Monte Carlo sampling. <DT><CODE><a href="demhmc3.htm">demhmc3</a></CODE><DD> Demonstrate Bayesian regression with Hybrid Monte Carlo sampling. <DT><CODE><a href="demkmean.htm">demkmean</a></CODE><DD> Demonstrate simple clustering model trained with K-means. <DT><CODE><a href="demknn1.htm">demknn1</a></CODE><DD> Demonstrate nearest neighbour classifier. <DT><CODE><a href="demmdn1.htm">demmdn1</a></CODE><DD> Demonstrate fitting a multi-valued function using a Mixture Density Network. <DT><CODE><a href="demmet1.htm">demmet1</a></CODE><DD> Demonstrate Markov Chain Monte Carlo sampling on a Gaussian. <DT><CODE><a href="demmlp1.htm">demmlp1</a></CODE><DD> Demonstrate simple regression using a multi-layer perceptron <DT><CODE><a href="demmlp2.htm">demmlp2</a></CODE><DD> Demonstrate simple classification using a multi-layer perceptron <DT><CODE><a href="demnlab.htm">demnlab</a></CODE><DD> A front-end Graphical User Interface to the demos <DT><CODE><a href="demns1.htm">demns1</a></CODE><DD> Demonstrate Neuroscale for visualisation. <DT><CODE><a href="demolgd1.htm">demolgd1</a></CODE><DD> Demonstrate simple MLP optimisation with on-line gradient descent <DT><CODE><a href="demopt1.htm">demopt1</a></CODE><DD> Demonstrate different optimisers on Rosenbrock's function. <DT><CODE><a href="dempot.htm">dempot</a></CODE><DD> Computes the negative log likelihood for a mixture model. <DT><CODE><a href="demprgp.htm">demprgp</a></CODE><DD> Demonstrate sampling from a Gaussian Process prior. <DT><CODE><a href="demprior.htm">demprior</a></CODE><DD> Demonstrate sampling from a multi-parameter Gaussian prior. <DT><CODE><a href="demrbf1.htm">demrbf1</a></CODE><DD> Demonstrate simple regression using a radial basis function network. <DT><CODE><a href="demsom1.htm">demsom1</a></CODE><DD> Demonstrate SOM for visualisation. <DT><CODE><a href="demtrain.htm">demtrain</a></CODE><DD> Demonstrate training of MLP network. <DT><CODE><a href="dist2.htm">dist2</a></CODE><DD> Calculates squared distance between two sets of points. <DT><CODE><a href="eigdec.htm">eigdec</a></CODE><DD> Sorted eigendecomposition <DT><CODE><a href="errbayes.htm">errbayes</a></CODE><DD> Evaluate Bayesian error function for network. <DT><CODE><a href="evidence.htm">evidence</a></CODE><DD> Re-estimate hyperparameters using evidence approximation. <DT><CODE><a href="fevbayes.htm">fevbayes</a></CODE><DD> Evaluate Bayesian regularisation for network forward propagation. <DT><CODE><a href="gauss.htm">gauss</a></CODE><DD> Evaluate a Gaussian distribution. <DT><CODE><a href="gbayes.htm">gbayes</a></CODE><DD> Evaluate gradient of Bayesian error function for network. <DT><CODE><a href="glm.htm">glm</a></CODE><DD> Create a generalized linear model. <DT><CODE><a href="glmderiv.htm">glmderiv</a></CODE><DD> Evaluate derivatives of GLM outputs with respect to weights. <DT><CODE><a href="glmerr.htm">glmerr</a></CODE><DD> Evaluate error function for generalized linear model. <DT><CODE><a href="glmevfwd.htm">glmevfwd</a></CODE><DD> Forward propagation with evidence for GLM <DT><CODE><a href="glmfwd.htm">glmfwd</a></CODE><DD> Forward propagation through generalized linear model. <DT><CODE><a href="glmgrad.htm">glmgrad</a></CODE><DD> Evaluate gradient of error function for generalized linear model. <DT><CODE><a href="glmhess.htm">glmhess</a></CODE><DD> Evaluate the Hessian matrix for a generalised linear model. <DT><CODE><a href="glminit.htm">glminit</a></CODE><DD> Initialise the weights in a generalized linear model. <DT><CODE><a href="glmpak.htm">glmpak</a></CODE><DD> Combines weights and biases into one weights vector. <DT><CODE><a href="glmtrain.htm">glmtrain</a></CODE><DD> Specialised training of generalized linear model <DT><CODE><a href="glmunpak.htm">glmunpak</a></CODE><DD> Separates weights vector into weight and bias matrices. <DT><CODE><a href="gmm.htm">gmm</a></CODE><DD> Creates a Gaussian mixture model with specified architecture. <DT><CODE><a href="gmmactiv.htm">gmmactiv</a></CODE><DD> Computes the activations of a Gaussian mixture model. <DT><CODE><a href="gmmem.htm">gmmem</a></CODE><DD> EM algorithm for Gaussian mixture model. <DT><CODE><a href="gmminit.htm">gmminit</a></CODE><DD> Initialises Gaussian mixture model from data <DT><CODE><a href="gmmpak.htm">gmmpak</a></CODE><DD> Combines all the parameters in a Gaussian mixture model into one vector. <DT><CODE><a href="gmmpost.htm">gmmpost</a></CODE><DD> Computes the class posterior probabilities of a Gaussian mixture model. <DT><CODE><a href="gmmprob.htm">gmmprob</a></CODE><DD> Computes the data probability for a Gaussian mixture model. <DT><CODE><a href="gmmsamp.htm">gmmsamp</a></CODE><DD> Sample from a Gaussian mixture distribution. <DT><CODE><a href="gmmunpak.htm">gmmunpak</a></CODE><DD> Separates a vector of Gaussian mixture model parameters into its components. <DT><CODE><a href="gp.htm">gp</a></CODE><DD> Create a Gaussian Process. <DT><CODE><a href="gpcovar.htm">gpcovar</a></CODE><DD> Calculate the covariance for a Gaussian Process. <DT><CODE><a href="gpcovarf.htm">gpcovarf</a></CODE><DD> Calculate the covariance function for a Gaussian Process. <DT><CODE><a href="gpcovarp.htm">gpcovarp</a></CODE><DD> Calculate the prior covariance for a Gaussian Process. <DT><CODE><a href="gperr.htm">gperr</a></CODE><DD> Evaluate error function for Gaussian Process. <DT><CODE><a href="gpfwd.htm">gpfwd</a></CODE><DD> Forward propagation through Gaussian Process. <DT><CODE><a href="gpgrad.htm">gpgrad</a></CODE><DD> Evaluate error gradient for Gaussian Process. <DT><CODE><a href="gpinit.htm">gpinit</a></CODE><DD> Initialise Gaussian Process model. <DT><CODE><a href="gppak.htm">gppak</a></CODE><DD> Combines GP hyperparameters into one vector. <DT><CODE><a href="gpunpak.htm">gpunpak</a></CODE><DD> Separates hyperparameter vector into components. <DT><CODE><a href="gradchek.htm">gradchek</a></CODE><DD> Checks a user-defined gradient function using finite differences. <DT><CODE><a href="graddesc.htm">graddesc</a></CODE><DD>⌨️ 快捷键说明
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