MLTOOLS software Version 0.121 Saturday 14 Oct 2006 at 22:42 Copyright (c) 2006 Neil D. Lawrence This toolbox provides various machine learning tools, either through wrapping other toolboxes (such as NETLAB) or providing the tool directly. It was designed originally as part of splitting the GPLVM and FGPLVM toolboxes. Version 0.121 ------------- Various minor bug fixes and changes which seem to have gone undocumented. Version 0.12 ------------ Extended model type to be a generic container module for optimising any model. Added model test for testing a created model. The code is still in a bit of flux though with some design decisions not made and some code untested. Version 0.111 ------------- Fixed bug in kbr where bias parameter fields where still being referred to as b.Also acknowledged the fact that the isomap graph may not be fully connected in isomapEmbed, but don't yet deal with it properly. Finally added lleEmbed.m for wrapping the lle code. Version 0.11 ------------ Updated release for operation with FGPLVM toolbox 0.13. Structure of model creation changed and functions of the form modelOptions.m included for setting default options of the various machine learning models. Version 0.1 ----------- The first release of the toolbox with various wrappers for NETLAB functions. Also latent variable model visualisation code was moved into this toolbox. MATLAB Files ------------ Matlab files associated with the toolbox are: linearCreate.m: Create a linear model. isomapEmbed.m: Embed data set with Isomap. kpcaEmbed.m: Embed data set with kernel PCA. lvmClassVisualise.m: Callback function for visualising data in 2-D. imageVisualise.m: Helper code for showing an image during 2-D visualisation. kbrExpandParam.m: Update kernel based regression model with vector of parameters. kbrExtractParam.m: Extract weights from a kernel based regression model. kbrOptimise.m: Optimise a kernel based regression. kbrOut.m: Obtain the output of the kernel based regression model. kbrOutputGrad.m: Evaluate derivatives of kernel based regression model outputs with respect to parameters. linearExpandParam.m: Update linear model with vector of parameters. linearExtractParam.m: Extract weights from a linear model. linearOptimise.m: Optimise a linear model. linearOut.m: Obtain the output of the linear model. linearOutputGrad.m: Evaluate derivatives of linear model outputs with respect to parameters. mappingOptimise.m: Optimise the given model. mlpCreate.m: Wrapper for NETLAB's mlp `net'. mlpExpandParam.m: Update mlp model with new vector of parameters. mlpOut.m: Output of an MLP model (wrapper for the NETLAB function mlpfwd). mlpExtractParam.m: Wrapper for NETLAB's mlppak. mlpOptimise.m: Optimise MLP for given inputs and outputs. mlpOutputGrad.m: Evaluate derivatives of mlp model outputs with respect to parameters. modelExpandParam.m: Update a model structure with parameters. modelExtractParam.m: Extract the parameters of a model. modelOptimise.m: Optimise the given model. modelOut.m: Give the output of a model for given X. modelOutputGrad.m: Compute derivatives with respect to params of model outputs. rbfCreate.m: Wrapper for NETLAB's rbf `net'. rbfExpandParam.m: Update rbf model with new vector of parameters. rbfExtractParam.m: Wrapper for NETLAB's rbfpak. rbfOptimise.m: Optimise RBF for given inputs and outputs. rbfOut.m: Output of an RBF model (wrapper for the NETLAB function rbffwd). rbfOutputGrad.m: Evaluate derivatives of rbf model outputs with respect to parameters. lvmScatterPlot.m: 2-D scatter plot of the latent points. ppcaEmbed.m: Embed data set with probabilistic PCA. spectrumModify.m: Helper code for visualisation of spectrum data. spectrumVisualise.m: Helper code for showing an spectrum during 2-D visualisation. kbrCreate.m: Create a kernel based regression model. vectorModify.m: Helper code for visualisation of vectorial data. vectorVisualise.m: Helper code for plotting a vector during 2-D visualisation. lvmTwoDPlot.m: Helper function for plotting the labels in 2-D. modelLogLikeGradients.m: Compute a model's gradients wrt log likelihood. modelLogLikelihood.m: Compute a model log likelihood. lvmScatterPlotColor.m: 2-D scatter plot of the latent points with color - for Swiss Roll data. modelSamp.m: Give a sample from a model for given X. modelDisplay.m: Display a text output of a model. mlpDisplay.m: Display the multi-layer perceptron model. mlpOptions.m: Options for the multi-layered perceptron. kbrOptions.m: Kernel based regression options. rbfDisplay.m: Display an RBF network. linearOptions.m: Options for learning a linear model. modelCreate.m: Create a model of the specified type. mlpLogLikeGradients.m: Multi-layer perceptron gradients. mlpLogLikelihood.m: Multi-layer perceptron log likelihood. mlpParamInit.m: Initialise the parameters of an MLP model. modelTest.m: Run some tests on the specified model. rbfOptions.m: Default options for RBF network. lleEmbed.m: Embed data set with LLE. modelGradient.m: Gradient of error function to minimise for given model. modelObjective.m: Objective function to minimise for given model. modelOptions.m: Returns a default options structure for the given model. modelParamInit.m: Initialise the parameters of the model. modelTieParam.m: Tie parameters of a model together. linearDisplay.m: Display a linear model. linearLogLikeGradients.m: Linear model gradients. linearLogLikelihood.m: Linear model log likelihood. linearOutputGradX.m: Evaluate derivatives of linear model outputs with respect to inputs. linearParamInit.m: Initialise the parameters of an LINEAR model. mlpOutputGradX.m: Evaluate derivatives of mlp model outputs with respect to inputs. modelOutputGradX.m: Compute derivatives with respect to model inputs of model outputs. demMppca1.m: Demonstrate MPPCA on a artificial dataset. mogCreate.m: Create a mixtures of Gaussians model. imageModify.m: Helper code for visualisation of image data. mogUpdateMean.m: Update the means of an MOG model. mogUpdatePrior.m: Update the priors of an MOG model. smallrandEmbed.m: Embed data set with small random values. mogEstep.m: Do an E-step on an MOG model. mogOptimise.m: Optimise an MOG model. mogOptions.m: Sets the default options structure for MOG models. mogUpdateCovariance.m: Update the covariances of an MOG model.