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| date | Tue, 10 Feb 2015 15:05:51 +0000 |
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| 1 <html> | |
| 2 <head> | |
| 3 <title> | |
| 4 NETLAB Reference Documentation | |
| 5 </title> | |
| 6 </head> | |
| 7 <body> | |
| 8 <H1> NETLAB Online Reference Documentation </H1> | |
| 9 Welcome to the NETLAB online reference documentation. | |
| 10 The NETLAB simulation software is designed to provide all the tools necessary | |
| 11 for principled and theoretically well founded application development. The | |
| 12 NETLAB library is based on the approach and techniques described in <I>Neural | |
| 13 Networks for Pattern Recognition </I>(Bishop, 1995). The library includes software | |
| 14 implementations of a wide range of data analysis techniques, many of which are | |
| 15 not widely available, and are rarely, if ever, included in standard neural | |
| 16 network simulation packages. | |
| 17 <p>The online reference documentation provides direct hypertext links to specific Netlab function descriptions. | |
| 18 <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 | |
| 19 </H1> | |
| 20 An alphabetic list of functions in Netlab.<p> | |
| 21 <DL> | |
| 22 <DT> | |
| 23 <CODE><a href="conffig.htm">conffig</a></CODE><DD> | |
| 24 Display a confusion matrix. | |
| 25 <DT> | |
| 26 <CODE><a href="confmat.htm">confmat</a></CODE><DD> | |
| 27 Compute a confusion matrix. | |
| 28 <DT> | |
| 29 <CODE><a href="conjgrad.htm">conjgrad</a></CODE><DD> | |
| 30 Conjugate gradients optimization. | |
| 31 <DT> | |
| 32 <CODE><a href="consist.htm">consist</a></CODE><DD> | |
| 33 Check that arguments are consistent. | |
| 34 <DT> | |
| 35 <CODE><a href="convertoldnet.htm">convertoldnet</a></CODE><DD> | |
| 36 Convert pre-2.3 release MLP and MDN nets to new format | |
| 37 <DT> | |
| 38 <CODE><a href="datread.htm">datread</a></CODE><DD> | |
| 39 Read data from an ascii file. | |
| 40 <DT> | |
| 41 <CODE><a href="datwrite.htm">datwrite</a></CODE><DD> | |
| 42 Write data to ascii file. | |
| 43 <DT> | |
| 44 <CODE><a href="dem2ddat.htm">dem2ddat</a></CODE><DD> | |
| 45 Generates two dimensional data for demos. | |
| 46 <DT> | |
| 47 <CODE><a href="demard.htm">demard</a></CODE><DD> | |
| 48 Automatic relevance determination using the MLP. | |
| 49 <DT> | |
| 50 <CODE><a href="demev1.htm">demev1</a></CODE><DD> | |
| 51 Demonstrate Bayesian regression for the MLP. | |
| 52 <DT> | |
| 53 <CODE><a href="demev2.htm">demev2</a></CODE><DD> | |
| 54 Demonstrate Bayesian classification for the MLP. | |
| 55 <DT> | |
| 56 <CODE><a href="demev3.htm">demev3</a></CODE><DD> | |
| 57 Demonstrate Bayesian regression for the RBF. | |
| 58 <DT> | |
| 59 <CODE><a href="demgauss.htm">demgauss</a></CODE><DD> | |
| 60 Demonstrate sampling from Gaussian distributions. | |
| 61 <DT> | |
| 62 <CODE><a href="demglm1.htm">demglm1</a></CODE><DD> | |
| 63 Demonstrate simple classification using a generalized linear model. | |
| 64 <DT> | |
| 65 <CODE><a href="demglm2.htm">demglm2</a></CODE><DD> | |
| 66 Demonstrate simple classification using a generalized linear model. | |
| 67 <DT> | |
| 68 <CODE><a href="demgmm1.htm">demgmm1</a></CODE><DD> | |
| 69 Demonstrate density modelling with a Gaussian mixture model. | |
| 70 <DT> | |
| 71 <CODE><a href="demgmm3.htm">demgmm3</a></CODE><DD> | |
| 72 Demonstrate density modelling with a Gaussian mixture model. | |
| 73 <DT> | |
| 74 <CODE><a href="demgmm4.htm">demgmm4</a></CODE><DD> | |
| 75 Demonstrate density modelling with a Gaussian mixture model. | |
| 76 <DT> | |
| 77 <CODE><a href="demgmm5.htm">demgmm5</a></CODE><DD> | |
| 78 Demonstrate density modelling with a PPCA mixture model. | |
| 79 <DT> | |
| 80 <CODE><a href="demgp.htm">demgp</a></CODE><DD> | |
| 81 Demonstrate simple regression using a Gaussian Process. | |
| 82 <DT> | |
| 83 <CODE><a href="demgpard.htm">demgpard</a></CODE><DD> | |
| 84 Demonstrate ARD using a Gaussian Process. | |
| 85 <DT> | |
| 86 <CODE><a href="demgpot.htm">demgpot</a></CODE><DD> | |
| 87 Computes the gradient of the negative log likelihood for a mixture model. | |
| 88 <DT> | |
| 89 <CODE><a href="demgtm1.htm">demgtm1</a></CODE><DD> | |
| 90 Demonstrate EM for GTM. | |
| 91 <DT> | |
| 92 <CODE><a href="demgtm2.htm">demgtm2</a></CODE><DD> | |
| 93 Demonstrate GTM for visualisation. | |
| 94 <DT> | |
| 95 <CODE><a href="demhint.htm">demhint</a></CODE><DD> | |
| 96 Demonstration of Hinton diagram for 2-layer feed-forward network. | |
| 97 <DT> | |
| 98 <CODE><a href="demhmc1.htm">demhmc1</a></CODE><DD> | |
| 99 Demonstrate Hybrid Monte Carlo sampling on mixture of two Gaussians. | |
| 100 <DT> | |
| 101 <CODE><a href="demhmc2.htm">demhmc2</a></CODE><DD> | |
| 102 Demonstrate Bayesian regression with Hybrid Monte Carlo sampling. | |
| 103 <DT> | |
| 104 <CODE><a href="demhmc3.htm">demhmc3</a></CODE><DD> | |
| 105 Demonstrate Bayesian regression with Hybrid Monte Carlo sampling. | |
| 106 <DT> | |
| 107 <CODE><a href="demkmean.htm">demkmean</a></CODE><DD> | |
| 108 Demonstrate simple clustering model trained with K-means. | |
| 109 <DT> | |
| 110 <CODE><a href="demknn1.htm">demknn1</a></CODE><DD> | |
| 111 Demonstrate nearest neighbour classifier. | |
| 112 <DT> | |
| 113 <CODE><a href="demmdn1.htm">demmdn1</a></CODE><DD> | |
| 114 Demonstrate fitting a multi-valued function using a Mixture Density Network. | |
| 115 <DT> | |
| 116 <CODE><a href="demmet1.htm">demmet1</a></CODE><DD> | |
| 117 Demonstrate Markov Chain Monte Carlo sampling on a Gaussian. | |
| 118 <DT> | |
| 119 <CODE><a href="demmlp1.htm">demmlp1</a></CODE><DD> | |
| 120 Demonstrate simple regression using a multi-layer perceptron | |
| 121 <DT> | |
| 122 <CODE><a href="demmlp2.htm">demmlp2</a></CODE><DD> | |
| 123 Demonstrate simple classification using a multi-layer perceptron | |
| 124 <DT> | |
| 125 <CODE><a href="demnlab.htm">demnlab</a></CODE><DD> | |
| 126 A front-end Graphical User Interface to the demos | |
| 127 <DT> | |
| 128 <CODE><a href="demns1.htm">demns1</a></CODE><DD> | |
| 129 Demonstrate Neuroscale for visualisation. | |
| 130 <DT> | |
| 131 <CODE><a href="demolgd1.htm">demolgd1</a></CODE><DD> | |
| 132 Demonstrate simple MLP optimisation with on-line gradient descent | |
| 133 <DT> | |
| 134 <CODE><a href="demopt1.htm">demopt1</a></CODE><DD> | |
| 135 Demonstrate different optimisers on Rosenbrock's function. | |
| 136 <DT> | |
| 137 <CODE><a href="dempot.htm">dempot</a></CODE><DD> | |
| 138 Computes the negative log likelihood for a mixture model. | |
| 139 <DT> | |
| 140 <CODE><a href="demprgp.htm">demprgp</a></CODE><DD> | |
| 141 Demonstrate sampling from a Gaussian Process prior. | |
| 142 <DT> | |
| 143 <CODE><a href="demprior.htm">demprior</a></CODE><DD> | |
| 144 Demonstrate sampling from a multi-parameter Gaussian prior. | |
| 145 <DT> | |
| 146 <CODE><a href="demrbf1.htm">demrbf1</a></CODE><DD> | |
| 147 Demonstrate simple regression using a radial basis function network. | |
| 148 <DT> | |
| 149 <CODE><a href="demsom1.htm">demsom1</a></CODE><DD> | |
| 150 Demonstrate SOM for visualisation. | |
| 151 <DT> | |
| 152 <CODE><a href="demtrain.htm">demtrain</a></CODE><DD> | |
| 153 Demonstrate training of MLP network. | |
| 154 <DT> | |
| 155 <CODE><a href="dist2.htm">dist2</a></CODE><DD> | |
| 156 Calculates squared distance between two sets of points. | |
| 157 <DT> | |
| 158 <CODE><a href="eigdec.htm">eigdec</a></CODE><DD> | |
| 159 Sorted eigendecomposition | |
| 160 <DT> | |
| 161 <CODE><a href="errbayes.htm">errbayes</a></CODE><DD> | |
| 162 Evaluate Bayesian error function for network. | |
| 163 <DT> | |
| 164 <CODE><a href="evidence.htm">evidence</a></CODE><DD> | |
| 165 Re-estimate hyperparameters using evidence approximation. | |
| 166 <DT> | |
| 167 <CODE><a href="fevbayes.htm">fevbayes</a></CODE><DD> | |
| 168 Evaluate Bayesian regularisation for network forward propagation. | |
| 169 <DT> | |
| 170 <CODE><a href="gauss.htm">gauss</a></CODE><DD> | |
| 171 Evaluate a Gaussian distribution. | |
| 172 <DT> | |
| 173 <CODE><a href="gbayes.htm">gbayes</a></CODE><DD> | |
| 174 Evaluate gradient of Bayesian error function for network. | |
| 175 <DT> | |
| 176 <CODE><a href="glm.htm">glm</a></CODE><DD> | |
| 177 Create a generalized linear model. | |
| 178 <DT> | |
| 179 <CODE><a href="glmderiv.htm">glmderiv</a></CODE><DD> | |
| 180 Evaluate derivatives of GLM outputs with respect to weights. | |
| 181 <DT> | |
| 182 <CODE><a href="glmerr.htm">glmerr</a></CODE><DD> | |
| 183 Evaluate error function for generalized linear model. | |
| 184 <DT> | |
| 185 <CODE><a href="glmevfwd.htm">glmevfwd</a></CODE><DD> | |
| 186 Forward propagation with evidence for GLM | |
| 187 <DT> | |
| 188 <CODE><a href="glmfwd.htm">glmfwd</a></CODE><DD> | |
| 189 Forward propagation through generalized linear model. | |
| 190 <DT> | |
| 191 <CODE><a href="glmgrad.htm">glmgrad</a></CODE><DD> | |
| 192 Evaluate gradient of error function for generalized linear model. | |
| 193 <DT> | |
| 194 <CODE><a href="glmhess.htm">glmhess</a></CODE><DD> | |
| 195 Evaluate the Hessian matrix for a generalised linear model. | |
| 196 <DT> | |
| 197 <CODE><a href="glminit.htm">glminit</a></CODE><DD> | |
| 198 Initialise the weights in a generalized linear model. | |
| 199 <DT> | |
| 200 <CODE><a href="glmpak.htm">glmpak</a></CODE><DD> | |
| 201 Combines weights and biases into one weights vector. | |
| 202 <DT> | |
| 203 <CODE><a href="glmtrain.htm">glmtrain</a></CODE><DD> | |
| 204 Specialised training of generalized linear model | |
| 205 <DT> | |
| 206 <CODE><a href="glmunpak.htm">glmunpak</a></CODE><DD> | |
| 207 Separates weights vector into weight and bias matrices. | |
| 208 <DT> | |
| 209 <CODE><a href="gmm.htm">gmm</a></CODE><DD> | |
| 210 Creates a Gaussian mixture model with specified architecture. | |
| 211 <DT> | |
| 212 <CODE><a href="gmmactiv.htm">gmmactiv</a></CODE><DD> | |
| 213 Computes the activations of a Gaussian mixture model. | |
| 214 <DT> | |
| 215 <CODE><a href="gmmem.htm">gmmem</a></CODE><DD> | |
| 216 EM algorithm for Gaussian mixture model. | |
| 217 <DT> | |
| 218 <CODE><a href="gmminit.htm">gmminit</a></CODE><DD> | |
| 219 Initialises Gaussian mixture model from data | |
| 220 <DT> | |
| 221 <CODE><a href="gmmpak.htm">gmmpak</a></CODE><DD> | |
| 222 Combines all the parameters in a Gaussian mixture model into one vector. | |
| 223 <DT> | |
| 224 <CODE><a href="gmmpost.htm">gmmpost</a></CODE><DD> | |
| 225 Computes the class posterior probabilities of a Gaussian mixture model. | |
| 226 <DT> | |
| 227 <CODE><a href="gmmprob.htm">gmmprob</a></CODE><DD> | |
| 228 Computes the data probability for a Gaussian mixture model. | |
| 229 <DT> | |
| 230 <CODE><a href="gmmsamp.htm">gmmsamp</a></CODE><DD> | |
| 231 Sample from a Gaussian mixture distribution. | |
| 232 <DT> | |
| 233 <CODE><a href="gmmunpak.htm">gmmunpak</a></CODE><DD> | |
| 234 Separates a vector of Gaussian mixture model parameters into its components. | |
| 235 <DT> | |
| 236 <CODE><a href="gp.htm">gp</a></CODE><DD> | |
| 237 Create a Gaussian Process. | |
| 238 <DT> | |
| 239 <CODE><a href="gpcovar.htm">gpcovar</a></CODE><DD> | |
| 240 Calculate the covariance for a Gaussian Process. | |
| 241 <DT> | |
| 242 <CODE><a href="gpcovarf.htm">gpcovarf</a></CODE><DD> | |
| 243 Calculate the covariance function for a Gaussian Process. | |
| 244 <DT> | |
| 245 <CODE><a href="gpcovarp.htm">gpcovarp</a></CODE><DD> | |
| 246 Calculate the prior covariance for a Gaussian Process. | |
| 247 <DT> | |
| 248 <CODE><a href="gperr.htm">gperr</a></CODE><DD> | |
| 249 Evaluate error function for Gaussian Process. | |
| 250 <DT> | |
| 251 <CODE><a href="gpfwd.htm">gpfwd</a></CODE><DD> | |
| 252 Forward propagation through Gaussian Process. | |
| 253 <DT> | |
| 254 <CODE><a href="gpgrad.htm">gpgrad</a></CODE><DD> | |
| 255 Evaluate error gradient for Gaussian Process. | |
| 256 <DT> | |
| 257 <CODE><a href="gpinit.htm">gpinit</a></CODE><DD> | |
| 258 Initialise Gaussian Process model. | |
| 259 <DT> | |
| 260 <CODE><a href="gppak.htm">gppak</a></CODE><DD> | |
| 261 Combines GP hyperparameters into one vector. | |
| 262 <DT> | |
| 263 <CODE><a href="gpunpak.htm">gpunpak</a></CODE><DD> | |
| 264 Separates hyperparameter vector into components. | |
| 265 <DT> | |
| 266 <CODE><a href="gradchek.htm">gradchek</a></CODE><DD> | |
| 267 Checks a user-defined gradient function using finite differences. | |
| 268 <DT> | |
| 269 <CODE><a href="graddesc.htm">graddesc</a></CODE><DD> | |
| 270 Gradient descent optimization. | |
| 271 <DT> | |
| 272 <CODE><a href="gsamp.htm">gsamp</a></CODE><DD> | |
| 273 Sample from a Gaussian distribution. | |
| 274 <DT> | |
| 275 <CODE><a href="gtm.htm">gtm</a></CODE><DD> | |
| 276 Create a Generative Topographic Map. | |
| 277 <DT> | |
| 278 <CODE><a href="gtmem.htm">gtmem</a></CODE><DD> | |
| 279 EM algorithm for Generative Topographic Mapping. | |
| 280 <DT> | |
| 281 <CODE><a href="gtmfwd.htm">gtmfwd</a></CODE><DD> | |
| 282 Forward propagation through GTM. | |
| 283 <DT> | |
| 284 <CODE><a href="gtminit.htm">gtminit</a></CODE><DD> | |
| 285 Initialise the weights and latent sample in a GTM. | |
| 286 <DT> | |
| 287 <CODE><a href="gtmlmean.htm">gtmlmean</a></CODE><DD> | |
| 288 Mean responsibility for data in a GTM. | |
| 289 <DT> | |
| 290 <CODE><a href="gtmlmode.htm">gtmlmode</a></CODE><DD> | |
| 291 Mode responsibility for data in a GTM. | |
| 292 <DT> | |
| 293 <CODE><a href="gtmmag.htm">gtmmag</a></CODE><DD> | |
| 294 Magnification factors for a GTM | |
| 295 <DT> | |
| 296 <CODE><a href="gtmpost.htm">gtmpost</a></CODE><DD> | |
| 297 Latent space responsibility for data in a GTM. | |
| 298 <DT> | |
| 299 <CODE><a href="gtmprob.htm">gtmprob</a></CODE><DD> | |
| 300 Probability for data under a GTM. | |
| 301 <DT> | |
| 302 <CODE><a href="hbayes.htm">hbayes</a></CODE><DD> | |
| 303 Evaluate Hessian of Bayesian error function for network. | |
| 304 <DT> | |
| 305 <CODE><a href="hesschek.htm">hesschek</a></CODE><DD> | |
| 306 Use central differences to confirm correct evaluation of Hessian matrix. | |
| 307 <DT> | |
| 308 <CODE><a href="hintmat.htm">hintmat</a></CODE><DD> | |
| 309 Evaluates the coordinates of the patches for a Hinton diagram. | |
| 310 <DT> | |
| 311 <CODE><a href="hinton.htm">hinton</a></CODE><DD> | |
| 312 Plot Hinton diagram for a weight matrix. | |
| 313 <DT> | |
| 314 <CODE><a href="histp.htm">histp</a></CODE><DD> | |
| 315 Histogram estimate of 1-dimensional probability distribution. | |
| 316 <DT> | |
| 317 <CODE><a href="hmc.htm">hmc</a></CODE><DD> | |
| 318 Hybrid Monte Carlo sampling. | |
| 319 <DT> | |
| 320 <CODE><a href="kmeans.htm">kmeans</a></CODE><DD> | |
| 321 Trains a k means cluster model. | |
| 322 <DT> | |
| 323 <CODE><a href="knn.htm">knn</a></CODE><DD> | |
| 324 Creates a K-nearest-neighbour classifier. | |
| 325 <DT> | |
| 326 <CODE><a href="knnfwd.htm">knnfwd</a></CODE><DD> | |
| 327 Forward propagation through a K-nearest-neighbour classifier. | |
| 328 <DT> | |
| 329 <CODE><a href="linef.htm">linef</a></CODE><DD> | |
| 330 Calculate function value along a line. | |
| 331 <DT> | |
| 332 <CODE><a href="linemin.htm">linemin</a></CODE><DD> | |
| 333 One dimensional minimization. | |
| 334 <DT> | |
| 335 <CODE><a href="maxitmess.htm">maxitmess</a></CODE><DD> | |
| 336 Create a standard error message when training reaches max. iterations. | |
| 337 <DT> | |
| 338 <CODE><a href="mdn.htm">mdn</a></CODE><DD> | |
| 339 Creates a Mixture Density Network with specified architecture. | |
| 340 <DT> | |
| 341 <CODE><a href="mdn2gmm.htm">mdn2gmm</a></CODE><DD> | |
| 342 Converts an MDN mixture data structure to array of GMMs. | |
| 343 <DT> | |
| 344 <CODE><a href="mdndist2.htm">mdndist2</a></CODE><DD> | |
| 345 Calculates squared distance between centres of Gaussian kernels and data | |
| 346 <DT> | |
| 347 <CODE><a href="mdnerr.htm">mdnerr</a></CODE><DD> | |
| 348 Evaluate error function for Mixture Density Network. | |
| 349 <DT> | |
| 350 <CODE><a href="mdnfwd.htm">mdnfwd</a></CODE><DD> | |
| 351 Forward propagation through Mixture Density Network. | |
| 352 <DT> | |
| 353 <CODE><a href="mdngrad.htm">mdngrad</a></CODE><DD> | |
| 354 Evaluate gradient of error function for Mixture Density Network. | |
| 355 <DT> | |
| 356 <CODE><a href="mdninit.htm">mdninit</a></CODE><DD> | |
| 357 Initialise the weights in a Mixture Density Network. | |
| 358 <DT> | |
| 359 <CODE><a href="mdnpak.htm">mdnpak</a></CODE><DD> | |
| 360 Combines weights and biases into one weights vector. | |
| 361 <DT> | |
| 362 <CODE><a href="mdnpost.htm">mdnpost</a></CODE><DD> | |
| 363 Computes the posterior probability for each MDN mixture component. | |
| 364 <DT> | |
| 365 <CODE><a href="mdnprob.htm">mdnprob</a></CODE><DD> | |
| 366 Computes the data probability likelihood for an MDN mixture structure. | |
| 367 <DT> | |
| 368 <CODE><a href="mdnunpak.htm">mdnunpak</a></CODE><DD> | |
| 369 Separates weights vector into weight and bias matrices. | |
| 370 <DT> | |
| 371 <CODE><a href="metrop.htm">metrop</a></CODE><DD> | |
| 372 Markov Chain Monte Carlo sampling with Metropolis algorithm. | |
| 373 <DT> | |
| 374 <CODE><a href="minbrack.htm">minbrack</a></CODE><DD> | |
| 375 Bracket a minimum of a function of one variable. | |
| 376 <DT> | |
| 377 <CODE><a href="mlp.htm">mlp</a></CODE><DD> | |
| 378 Create a 2-layer feedforward network. | |
| 379 <DT> | |
| 380 <CODE><a href="mlpbkp.htm">mlpbkp</a></CODE><DD> | |
| 381 Backpropagate gradient of error function for 2-layer network. | |
| 382 <DT> | |
| 383 <CODE><a href="mlpderiv.htm">mlpderiv</a></CODE><DD> | |
| 384 Evaluate derivatives of network outputs with respect to weights. | |
| 385 <DT> | |
| 386 <CODE><a href="mlperr.htm">mlperr</a></CODE><DD> | |
| 387 Evaluate error function for 2-layer network. | |
| 388 <DT> | |
| 389 <CODE><a href="mlpevfwd.htm">mlpevfwd</a></CODE><DD> | |
| 390 Forward propagation with evidence for MLP | |
| 391 <DT> | |
| 392 <CODE><a href="mlpfwd.htm">mlpfwd</a></CODE><DD> | |
| 393 Forward propagation through 2-layer network. | |
| 394 <DT> | |
| 395 <CODE><a href="mlpgrad.htm">mlpgrad</a></CODE><DD> | |
| 396 Evaluate gradient of error function for 2-layer network. | |
| 397 <DT> | |
| 398 <CODE><a href="mlphdotv.htm">mlphdotv</a></CODE><DD> | |
| 399 Evaluate the product of the data Hessian with a vector. | |
| 400 <DT> | |
| 401 <CODE><a href="mlphess.htm">mlphess</a></CODE><DD> | |
| 402 Evaluate the Hessian matrix for a multi-layer perceptron network. | |
| 403 <DT> | |
| 404 <CODE><a href="mlphint.htm">mlphint</a></CODE><DD> | |
| 405 Plot Hinton diagram for 2-layer feed-forward network. | |
| 406 <DT> | |
| 407 <CODE><a href="mlpinit.htm">mlpinit</a></CODE><DD> | |
| 408 Initialise the weights in a 2-layer feedforward network. | |
| 409 <DT> | |
| 410 <CODE><a href="mlppak.htm">mlppak</a></CODE><DD> | |
| 411 Combines weights and biases into one weights vector. | |
| 412 <DT> | |
| 413 <CODE><a href="mlpprior.htm">mlpprior</a></CODE><DD> | |
| 414 Create Gaussian prior for mlp. | |
| 415 <DT> | |
| 416 <CODE><a href="mlptrain.htm">mlptrain</a></CODE><DD> | |
| 417 Utility to train an MLP network for demtrain | |
| 418 <DT> | |
| 419 <CODE><a href="mlpunpak.htm">mlpunpak</a></CODE><DD> | |
| 420 Separates weights vector into weight and bias matrices. | |
| 421 <DT> | |
| 422 <CODE><a href="netderiv.htm">netderiv</a></CODE><DD> | |
| 423 Evaluate derivatives of network outputs by weights generically. | |
| 424 <DT> | |
| 425 <CODE><a href="neterr.htm">neterr</a></CODE><DD> | |
| 426 Evaluate network error function for generic optimizers | |
| 427 <DT> | |
| 428 <CODE><a href="netevfwd.htm">netevfwd</a></CODE><DD> | |
| 429 Generic forward propagation with evidence for network | |
| 430 <DT> | |
| 431 <CODE><a href="netgrad.htm">netgrad</a></CODE><DD> | |
| 432 Evaluate network error gradient for generic optimizers | |
| 433 <DT> | |
| 434 <CODE><a href="nethess.htm">nethess</a></CODE><DD> | |
| 435 Evaluate network Hessian | |
| 436 <DT> | |
| 437 <CODE><a href="netinit.htm">netinit</a></CODE><DD> | |
| 438 Initialise the weights in a network. | |
| 439 <DT> | |
| 440 <CODE><a href="netopt.htm">netopt</a></CODE><DD> | |
| 441 Optimize the weights in a network model. | |
| 442 <DT> | |
| 443 <CODE><a href="netpak.htm">netpak</a></CODE><DD> | |
| 444 Combines weights and biases into one weights vector. | |
| 445 <DT> | |
| 446 <CODE><a href="netunpak.htm">netunpak</a></CODE><DD> | |
| 447 Separates weights vector into weight and bias matrices. | |
| 448 <DT> | |
| 449 <CODE><a href="olgd.htm">olgd</a></CODE><DD> | |
| 450 On-line gradient descent optimization. | |
| 451 <DT> | |
| 452 <CODE><a href="pca.htm">pca</a></CODE><DD> | |
| 453 Principal Components Analysis | |
| 454 <DT> | |
| 455 <CODE><a href="plotmat.htm">plotmat</a></CODE><DD> | |
| 456 Display a matrix. | |
| 457 <DT> | |
| 458 <CODE><a href="ppca.htm">ppca</a></CODE><DD> | |
| 459 Probabilistic Principal Components Analysis | |
| 460 <DT> | |
| 461 <CODE><a href="quasinew.htm">quasinew</a></CODE><DD> | |
| 462 Quasi-Newton optimization. | |
| 463 <DT> | |
| 464 <CODE><a href="rbf.htm">rbf</a></CODE><DD> | |
| 465 Creates an RBF network with specified architecture | |
| 466 <DT> | |
| 467 <CODE><a href="rbfbkp.htm">rbfbkp</a></CODE><DD> | |
| 468 Backpropagate gradient of error function for RBF network. | |
| 469 <DT> | |
| 470 <CODE><a href="rbfderiv.htm">rbfderiv</a></CODE><DD> | |
| 471 Evaluate derivatives of RBF network outputs with respect to weights. | |
| 472 <DT> | |
| 473 <CODE><a href="rbferr.htm">rbferr</a></CODE><DD> | |
| 474 Evaluate error function for RBF network. | |
| 475 <DT> | |
| 476 <CODE><a href="rbfevfwd.htm">rbfevfwd</a></CODE><DD> | |
| 477 Forward propagation with evidence for RBF | |
| 478 <DT> | |
| 479 <CODE><a href="rbffwd.htm">rbffwd</a></CODE><DD> | |
| 480 Forward propagation through RBF network with linear outputs. | |
| 481 <DT> | |
| 482 <CODE><a href="rbfgrad.htm">rbfgrad</a></CODE><DD> | |
| 483 Evaluate gradient of error function for RBF network. | |
| 484 <DT> | |
| 485 <CODE><a href="rbfhess.htm">rbfhess</a></CODE><DD> | |
| 486 Evaluate the Hessian matrix for RBF network. | |
| 487 <DT> | |
| 488 <CODE><a href="rbfjacob.htm">rbfjacob</a></CODE><DD> | |
| 489 Evaluate derivatives of RBF network outputs with respect to inputs. | |
| 490 <DT> | |
| 491 <CODE><a href="rbfpak.htm">rbfpak</a></CODE><DD> | |
| 492 Combines all the parameters in an RBF network into one weights vector. | |
| 493 <DT> | |
| 494 <CODE><a href="rbfprior.htm">rbfprior</a></CODE><DD> | |
| 495 Create Gaussian prior and output layer mask for RBF. | |
| 496 <DT> | |
| 497 <CODE><a href="rbfsetbf.htm">rbfsetbf</a></CODE><DD> | |
| 498 Set basis functions of RBF from data. | |
| 499 <DT> | |
| 500 <CODE><a href="rbfsetfw.htm">rbfsetfw</a></CODE><DD> | |
| 501 Set basis function widths of RBF. | |
| 502 <DT> | |
| 503 <CODE><a href="rbftrain.htm">rbftrain</a></CODE><DD> | |
| 504 Two stage training of RBF network. | |
| 505 <DT> | |
| 506 <CODE><a href="rbfunpak.htm">rbfunpak</a></CODE><DD> | |
| 507 Separates a vector of RBF weights into its components. | |
| 508 <DT> | |
| 509 <CODE><a href="rosegrad.htm">rosegrad</a></CODE><DD> | |
| 510 Calculate gradient of Rosenbrock's function. | |
| 511 <DT> | |
| 512 <CODE><a href="rosen.htm">rosen</a></CODE><DD> | |
| 513 Calculate Rosenbrock's function. | |
| 514 <DT> | |
| 515 <CODE><a href="scg.htm">scg</a></CODE><DD> | |
| 516 Scaled conjugate gradient optimization. | |
| 517 <DT> | |
| 518 <CODE><a href="som.htm">som</a></CODE><DD> | |
| 519 Creates a Self-Organising Map. | |
| 520 <DT> | |
| 521 <CODE><a href="somfwd.htm">somfwd</a></CODE><DD> | |
| 522 Forward propagation through a Self-Organising Map. | |
| 523 <DT> | |
| 524 <CODE><a href="sompak.htm">sompak</a></CODE><DD> | |
| 525 Combines node weights into one weights matrix. | |
| 526 <DT> | |
| 527 <CODE><a href="somtrain.htm">somtrain</a></CODE><DD> | |
| 528 Kohonen training algorithm for SOM. | |
| 529 <DT> | |
| 530 <CODE><a href="somunpak.htm">somunpak</a></CODE><DD> | |
| 531 Replaces node weights in SOM. | |
| 532 </DL> | |
| 533 | |
| 534 <hr> | |
| 535 <p>Copyright (c) Christopher M Bishop, Ian T Nabney (1996, 1997) | |
| 536 </body> | |
| 537 </html> |