% Netlab Toolbox
% Version 3.3.1 18-Jun-2004
%
% conffig - Display a confusion matrix.
% confmat - Compute a confusion matrix.
% conjgrad - Conjugate gradients optimization.
% consist - Check that arguments are consistent.
% convertoldnet- Convert pre-2.3 release MLP and MDN nets to new format
% datread - Read data from an ascii file.
% datwrite - Write data to ascii file.
% dem2ddat - Generates two dimensional data for demos.
% demard - Automatic relevance determination using the MLP.
% demev1 - Demonstrate Bayesian regression for the MLP.
% demev2 - Demonstrate Bayesian classification for the MLP.
% demev3 - Demonstrate Bayesian regression for the RBF.
% demgauss - Demonstrate sampling from Gaussian distributions.
% demglm1 - Demonstrate simple classification using a generalized linear model.
% demglm2 - Demonstrate simple classification using a generalized linear model.
% demgmm1 - Demonstrate density modelling with a Gaussian mixture model.
% demgmm3 - Demonstrate density modelling with a Gaussian mixture model.
% demgmm4 - Demonstrate density modelling with a Gaussian mixture model.
% demgmm5 - Demonstrate density modelling with a PPCA mixture model.
% demgp - Demonstrate simple regression using a Gaussian Process.
% demgpard - Demonstrate ARD using a Gaussian Process.
% demgpot - Computes the gradient of the negative log likelihood for a mixture model.
% demgtm1 - Demonstrate EM for GTM.
% demgtm2 - Demonstrate GTM for visualisation.
% demhint - Demonstration of Hinton diagram for 2-layer feed-forward network.
% demhmc1 - Demonstrate Hybrid Monte Carlo sampling on mixture of two Gaussians.
% demhmc2 - Demonstrate Bayesian regression with Hybrid Monte Carlo sampling.
% demhmc3 - Demonstrate Bayesian regression with Hybrid Monte Carlo sampling.
% demkmean - Demonstrate simple clustering model trained with K-means.
% demknn1 - Demonstrate nearest neighbour classifier.
% demmdn1 - Demonstrate fitting a multi-valued function using a Mixture Density Network.
% demmet1 - Demonstrate Markov Chain Monte Carlo sampling on a Gaussian.
% demmlp1 - Demonstrate simple regression using a multi-layer perceptron
% demmlp2 - Demonstrate simple classification using a multi-layer perceptron
% demnlab - A front-end Graphical User Interface to the demos
% demns1 - Demonstrate Neuroscale for visualisation.
% demolgd1 - Demonstrate simple MLP optimisation with on-line gradient descent
% demopt1 - Demonstrate different optimisers on Rosenbrock's function.
% dempot - Computes the negative log likelihood for a mixture model.
% demprgp - Demonstrate sampling from a Gaussian Process prior.
% demprior - Demonstrate sampling from a multi-parameter Gaussian prior.
% demrbf1 - Demonstrate simple regression using a radial basis function network.
% demsom1 - Demonstrate SOM for visualisation.
% demtrain - Demonstrate training of MLP network.
% dist2 - Calculates squared distance between two sets of points.
% eigdec - Sorted eigendecomposition
% errbayes - Evaluate Bayesian error function for network.
% evidence - Re-estimate hyperparameters using evidence approximation.
% fevbayes - Evaluate Bayesian regularisation for network forward propagation.
% gauss - Evaluate a Gaussian distribution.
% gbayes - Evaluate gradient of Bayesian error function for network.
% glm - Create a generalized linear model.
% glmderiv - Evaluate derivatives of GLM outputs with respect to weights.
% glmerr - Evaluate error function for generalized linear model.
% glmevfwd - Forward propagation with evidence for GLM
% glmfwd - Forward propagation through generalized linear model.
% glmgrad - Evaluate gradient of error function for generalized linear model.
% glmhess - Evaluate the Hessian matrix for a generalised linear model.
% glminit - Initialise the weights in a generalized linear model.
% glmpak - Combines weights and biases into one weights vector.
% glmtrain - Specialised training of generalized linear model
% glmunpak - Separates weights vector into weight and bias matrices.
% gmm - Creates a Gaussian mixture model with specified architecture.
% gmmactiv - Computes the activations of a Gaussian mixture model.
% gmmem - EM algorithm for Gaussian mixture model.
% gmminit - Initialises Gaussian mixture model from data
% gmmpak - Combines all the parameters in a Gaussian mixture model into one vector.
% gmmpost - Computes the class posterior probabilities of a Gaussian mixture model.
% gmmprob - Computes the data probability for a Gaussian mixture model.
% gmmsamp - Sample from a Gaussian mixture distribution.
% gmmunpak - Separates a vector of Gaussian mixture model parameters into its components.
% gp - Create a Gaussian Process.
% gpcovar - Calculate the covariance for a Gaussian Process.
% gpcovarf - Calculate the covariance function for a Gaussian Process.
% gpcovarp - Calculate the prior covariance for a Gaussian Process.
% gperr - Evaluate error function for Gaussian Process.
% gpfwd - Forward propagation through Gaussian Process.
% gpgrad - Evaluate error gradient for Gaussian Process.
% gpinit - Initialise Gaussian Process model.
% gppak - Combines GP hyperparameters into one vector.
% gpunpak - Separates hyperparameter vector into components.
% gradchek - Checks a user-defined gradient function using finite differences.
% graddesc - Gradient descent optimization.
% gsamp - Sample from a Gaussian distribution.
% gtm - Create a Generative Topographic Map.
% gtmem - EM algorithm for Generative Topographic Mapping.
% gtmfwd - Forward propagation through GTM.
% gtminit - Initialise the weights and latent sample in a GTM.
% gtmlmean - Mean responsibility for data in a GTM.
% gtmlmode - Mode responsibility for data in a GTM.
% gtmmag - Magnification factors for a GTM
% gtmpost - Latent space responsibility for data in a GTM.
% gtmprob - Probability for data under a GTM.
% hbayes - Evaluate Hessian of Bayesian error function for network.
% hesschek - Use central differences to confirm correct evaluation of Hessian matrix.
% hintmat - Evaluates the coordinates of the patches for a Hinton diagram.
% hinton - Plot Hinton diagram for a weight matrix.
% histp - Histogram estimate of 1-dimensional probability distribution.
% hmc - Hybrid Monte Carlo sampling.
% kmeans - Trains a k means cluster model.
% knn - Creates a K-nearest-neighbour classifier.
% knnfwd - Forward propagation through a K-nearest-neighbour classifier.
% linef - Calculate function value along a line.
% linemin - One dimensional minimization.
% maxitmess- Create a standard error message when training reaches max. iterations.
% mdn - Creates a Mixture Density Network with specified architecture.
% mdn2gmm - Converts an MDN mixture data structure to array of GMMs.
% mdndist2 - Calculates squared distance between centres of Gaussian kernels and data
% mdnerr - Evaluate error function for Mixture Density Network.
% mdnfwd - Forward propagation through Mixture Density Network.
% mdngrad - Evaluate gradient of error function for Mixture Density Network.
% mdninit - Initialise the weights in a Mixture Density Network.
% mdnpak - Combines weights and biases into one weights vector.
% mdnpost - Computes the posterior probability for each MDN mixture component.
% mdnprob - Computes the data probability likelihood for an MDN mixture structure.
% mdnunpak - Separates weights vector into weight and bias matrices.
% metrop - Markov Chain Monte Carlo sampling with Metropolis algorithm.
% minbrack - Bracket a minimum of a function of one variable.
% mlp - Create a 2-layer feedforward network.
% mlpbkp - Backpropagate gradient of error function for
评论0