NETLAB Online Reference Documentation

Welcome to the NETLAB online reference documentation. The NETLAB simulation software is designed to provide all the tools necessary for principled and theoretically well founded application development. The NETLAB library is based on the approach and techniques described in Neural Networks for Pattern Recognition (Bishop, 1995). The library includes software implementations of a wide range of data analysis techniques, many of which are not widely available, and are rarely, if ever, included in standard neural network simulation packages.

The online reference documentation provides direct hypertext links to specific Netlab function descriptions.

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

An alphabetic list of functions in Netlab.

conffig: Display a confusion matrix.
confmat: Compute a confusion matrix.
conjgrad: Conjugate gradients optimization.
consist: Check that arguments are consistent.
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.
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.
demgmm2: Demonstrate density modelling with a Gaussian mixture model.
demgmm3: Demonstrate density modelling with a Gaussian mixture model.
demgpot: Computes the gradient of the negative log likelihood for a mixture model.
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
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.
demprior: Demonstrate sampling from a multi-parameter Gaussian prior.
demrbf1: Demonstrate simple regression using a radial basis function network.
demtrain: Demonstrate training of MLP network.
dist2: Calculates squared distance between two sets of points.
evidence: Re-estimate hyperparameters using evidence approximation.
gauss: Evaluate a Gaussian distribution.
glm: Create a generalized linear model.
glmerr: Evaluate error function for generalized linear model.
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.
gradchek: Checks a user-defined gradient function using finite differences.
graddesc: Gradient descent optimization.
gsamp: Sample from a Gaussian distribution.
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.
linef: Calculate function value along a line.
linemin: One dimensional minimization.
mdn: Creates a Mixture Density Network with specified architecture.
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.
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 2-layer network.
mlpderiv: Evaluate derivatives of network outputs with respect to weights.
mlperr: Evaluate error function for 2-layer network.
mlpfwd: Forward propagation through 2-layer network.
mlpgrad: Evaluate gradient of error function for 2-layer network.
mlphdotv: Evaluate the product of the data Hessian with a vector.
mlphess: Evaluate the Hessian matrix for a multi-layer perceptron network.
mlphint: Plot Hinton diagram for 2-layer feed-forward network.
mlpinit: Initialise the weights in a 2-layer feedforward network.
mlppak: Combines weights and biases into one weights vector.
mlpprior: Create Gaussian prior for mlp.
mlptrain: Utility to train an MLP network for demtrain
mlpunpak: Separates weights vector into weight and bias matrices.
neterr: Evaluate network error function for generic optimizers
netgrad: Evaluate network error gradient for generic optimizers
nethess: Evaluate network Hessian
netopt: Optimize the weights in a network model.
olgd: On-line gradient descent optimization.
plotmat: Display a matrix.
quasinew: Quasi-Newton optimization.
rbf: Creates an RBF network with specified architecture
rbferr: Evaluate error function for RBF network.
rbffwd: Forward propagation through RBF network with linear outputs.
rbfgrad: Evaluate gradient of error function for RBF network.
rbfpak: Combines all the parameters in an RBF network into one weights vector.
rbftrain: Two stage training of RBF network.
rbfunpak: Separates a vector of RBF weights into its components.
rosegrad: Calculate gradient of Rosenbrock's function.
rosen: Calculate Rosenbrock's function.
scg: Scaled conjugate gradient optimization.