blob: 816e24ebb165b4c0e0aa01ec64e3a03144d19818 (
plain)
1
2
3
4
5
6
7
8
9
10
11
12
13
14
15
16
17
18
19
20
21
22
23
24
25
26
27
28
29
30
31
32
33
34
35
36
37
38
39
40
41
42
43
44
45
46
47
48
49
50
51
52
53
54
55
56
57
58
59
60
61
62
63
64
65
66
67
68
69
70
71
72
73
74
75
76
77
78
79
80
81
82
83
84
85
86
87
88
89
90
91
92
93
94
95
96
97
98
99
100
101
102
103
104
105
106
107
108
109
110
111
112
113
114
115
116
117
118
119
120
121
122
123
124
125
126
127
128
129
130
131
132
133
134
135
136
137
138
139
140
141
142
143
144
145
146
147
148
149
150
151
152
153
154
155
156
157
158
159
160
161
162
163
164
165
166
167
168
169
170
171
172
173
174
175
176
177
178
179
180
181
182
183
184
185
186
187
188
189
190
191
192
193
194
195
196
197
198
199
200
201
202
203
204
205
206
207
208
209
210
211
212
213
214
215
216
217
218
219
220
221
222
223
224
225
226
227
228
229
230
231
232
233
234
235
236
237
238
239
240
241
242
243
244
245
246
247
248
249
250
251
252
253
254
255
256
257
258
259
260
261
262
263
264
265
266
267
268
269
270
271
272
273
274
275
276
277
278
279
280
281
282
283
284
285
286
287
288
289
290
291
292
293
294
295
296
297
298
299
300
301
302
303
304
305
306
307
308
309
310
311
312
313
314
315
316
317
318
319
320
321
322
323
324
325
326
327
328
329
330
331
332
333
334
335
336
337
338
339
340
341
342
343
344
345
346
347
348
349
350
351
352
353
354
355
356
357
358
359
360
361
362
363
364
365
366
367
368
369
370
371
372
373
374
375
376
377
378
379
380
381
382
383
384
385
386
387
388
389
390
391
392
393
394
395
396
397
398
399
400
401
402
403
404
405
406
407
408
409
410
411
412
413
414
415
416
417
418
419
420
421
422
423
424
425
426
427
428
429
430
431
432
433
434
435
436
437
438
439
440
441
442
443
444
445
446
447
448
449
450
451
452
453
454
455
456
457
458
459
460
461
462
463
464
465
466
467
468
469
470
471
472
473
474
475
476
477
478
479
480
481
482
483
484
485
486
487
488
489
490
491
492
493
494
495
496
497
498
499
500
501
502
503
504
505
506
507
508
509
510
511
512
513
514
515
516
517
518
519
520
521
522
523
524
525
526
527
528
529
530
531
532
533
534
535
536
537
|
<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 necessary
for principled and theoretically well founded application development. The
NETLAB library is based on the approach and techniques described in <I>Neural
Networks for Pattern Recognition </I>(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.
<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>
Gradient descent optimization.
<DT>
<CODE><a href="gsamp.htm">gsamp</a></CODE><DD>
Sample from a Gaussian distribution.
<DT>
<CODE><a href="gtm.htm">gtm</a></CODE><DD>
Create a Generative Topographic Map.
<DT>
<CODE><a href="gtmem.htm">gtmem</a></CODE><DD>
EM algorithm for Generative Topographic Mapping.
<DT>
<CODE><a href="gtmfwd.htm">gtmfwd</a></CODE><DD>
Forward propagation through GTM.
<DT>
<CODE><a href="gtminit.htm">gtminit</a></CODE><DD>
Initialise the weights and latent sample in a GTM.
<DT>
<CODE><a href="gtmlmean.htm">gtmlmean</a></CODE><DD>
Mean responsibility for data in a GTM.
<DT>
<CODE><a href="gtmlmode.htm">gtmlmode</a></CODE><DD>
Mode responsibility for data in a GTM.
<DT>
<CODE><a href="gtmmag.htm">gtmmag</a></CODE><DD>
Magnification factors for a GTM
<DT>
<CODE><a href="gtmpost.htm">gtmpost</a></CODE><DD>
Latent space responsibility for data in a GTM.
<DT>
<CODE><a href="gtmprob.htm">gtmprob</a></CODE><DD>
Probability for data under a GTM.
<DT>
<CODE><a href="hbayes.htm">hbayes</a></CODE><DD>
Evaluate Hessian of Bayesian error function for network.
<DT>
<CODE><a href="hesschek.htm">hesschek</a></CODE><DD>
Use central differences to confirm correct evaluation of Hessian matrix.
<DT>
<CODE><a href="hintmat.htm">hintmat</a></CODE><DD>
Evaluates the coordinates of the patches for a Hinton diagram.
<DT>
<CODE><a href="hinton.htm">hinton</a></CODE><DD>
Plot Hinton diagram for a weight matrix.
<DT>
<CODE><a href="histp.htm">histp</a></CODE><DD>
Histogram estimate of 1-dimensional probability distribution.
<DT>
<CODE><a href="hmc.htm">hmc</a></CODE><DD>
Hybrid Monte Carlo sampling.
<DT>
<CODE><a href="kmeans.htm">kmeans</a></CODE><DD>
Trains a k means cluster model.
<DT>
<CODE><a href="knn.htm">knn</a></CODE><DD>
Creates a K-nearest-neighbour classifier.
<DT>
<CODE><a href="knnfwd.htm">knnfwd</a></CODE><DD>
Forward propagation through a K-nearest-neighbour classifier.
<DT>
<CODE><a href="linef.htm">linef</a></CODE><DD>
Calculate function value along a line.
<DT>
<CODE><a href="linemin.htm">linemin</a></CODE><DD>
One dimensional minimization.
<DT>
<CODE><a href="maxitmess.htm">maxitmess</a></CODE><DD>
Create a standard error message when training reaches max. iterations.
<DT>
<CODE><a href="mdn.htm">mdn</a></CODE><DD>
Creates a Mixture Density Network with specified architecture.
<DT>
<CODE><a href="mdn2gmm.htm">mdn2gmm</a></CODE><DD>
Converts an MDN mixture data structure to array of GMMs.
<DT>
<CODE><a href="mdndist2.htm">mdndist2</a></CODE><DD>
Calculates squared distance between centres of Gaussian kernels and data
<DT>
<CODE><a href="mdnerr.htm">mdnerr</a></CODE><DD>
Evaluate error function for Mixture Density Network.
<DT>
<CODE><a href="mdnfwd.htm">mdnfwd</a></CODE><DD>
Forward propagation through Mixture Density Network.
<DT>
<CODE><a href="mdngrad.htm">mdngrad</a></CODE><DD>
Evaluate gradient of error function for Mixture Density Network.
<DT>
<CODE><a href="mdninit.htm">mdninit</a></CODE><DD>
Initialise the weights in a Mixture Density Network.
<DT>
<CODE><a href="mdnpak.htm">mdnpak</a></CODE><DD>
Combines weights and biases into one weights vector.
<DT>
<CODE><a href="mdnpost.htm">mdnpost</a></CODE><DD>
Computes the posterior probability for each MDN mixture component.
<DT>
<CODE><a href="mdnprob.htm">mdnprob</a></CODE><DD>
Computes the data probability likelihood for an MDN mixture structure.
<DT>
<CODE><a href="mdnunpak.htm">mdnunpak</a></CODE><DD>
Separates weights vector into weight and bias matrices.
<DT>
<CODE><a href="metrop.htm">metrop</a></CODE><DD>
Markov Chain Monte Carlo sampling with Metropolis algorithm.
<DT>
<CODE><a href="minbrack.htm">minbrack</a></CODE><DD>
Bracket a minimum of a function of one variable.
<DT>
<CODE><a href="mlp.htm">mlp</a></CODE><DD>
Create a 2-layer feedforward network.
<DT>
<CODE><a href="mlpbkp.htm">mlpbkp</a></CODE><DD>
Backpropagate gradient of error function for 2-layer network.
<DT>
<CODE><a href="mlpderiv.htm">mlpderiv</a></CODE><DD>
Evaluate derivatives of network outputs with respect to weights.
<DT>
<CODE><a href="mlperr.htm">mlperr</a></CODE><DD>
Evaluate error function for 2-layer network.
<DT>
<CODE><a href="mlpevfwd.htm">mlpevfwd</a></CODE><DD>
Forward propagation with evidence for MLP
<DT>
<CODE><a href="mlpfwd.htm">mlpfwd</a></CODE><DD>
Forward propagation through 2-layer network.
<DT>
<CODE><a href="mlpgrad.htm">mlpgrad</a></CODE><DD>
Evaluate gradient of error function for 2-layer network.
<DT>
<CODE><a href="mlphdotv.htm">mlphdotv</a></CODE><DD>
Evaluate the product of the data Hessian with a vector.
<DT>
<CODE><a href="mlphess.htm">mlphess</a></CODE><DD>
Evaluate the Hessian matrix for a multi-layer perceptron network.
<DT>
<CODE><a href="mlphint.htm">mlphint</a></CODE><DD>
Plot Hinton diagram for 2-layer feed-forward network.
<DT>
<CODE><a href="mlpinit.htm">mlpinit</a></CODE><DD>
Initialise the weights in a 2-layer feedforward network.
<DT>
<CODE><a href="mlppak.htm">mlppak</a></CODE><DD>
Combines weights and biases into one weights vector.
<DT>
<CODE><a href="mlpprior.htm">mlpprior</a></CODE><DD>
Create Gaussian prior for mlp.
<DT>
<CODE><a href="mlptrain.htm">mlptrain</a></CODE><DD>
Utility to train an MLP network for demtrain
<DT>
<CODE><a href="mlpunpak.htm">mlpunpak</a></CODE><DD>
Separates weights vector into weight and bias matrices.
<DT>
<CODE><a href="netderiv.htm">netderiv</a></CODE><DD>
Evaluate derivatives of network outputs by weights generically.
<DT>
<CODE><a href="neterr.htm">neterr</a></CODE><DD>
Evaluate network error function for generic optimizers
<DT>
<CODE><a href="netevfwd.htm">netevfwd</a></CODE><DD>
Generic forward propagation with evidence for network
<DT>
<CODE><a href="netgrad.htm">netgrad</a></CODE><DD>
Evaluate network error gradient for generic optimizers
<DT>
<CODE><a href="nethess.htm">nethess</a></CODE><DD>
Evaluate network Hessian
<DT>
<CODE><a href="netinit.htm">netinit</a></CODE><DD>
Initialise the weights in a network.
<DT>
<CODE><a href="netopt.htm">netopt</a></CODE><DD>
Optimize the weights in a network model.
<DT>
<CODE><a href="netpak.htm">netpak</a></CODE><DD>
Combines weights and biases into one weights vector.
<DT>
<CODE><a href="netunpak.htm">netunpak</a></CODE><DD>
Separates weights vector into weight and bias matrices.
<DT>
<CODE><a href="olgd.htm">olgd</a></CODE><DD>
On-line gradient descent optimization.
<DT>
<CODE><a href="pca.htm">pca</a></CODE><DD>
Principal Components Analysis
<DT>
<CODE><a href="plotmat.htm">plotmat</a></CODE><DD>
Display a matrix.
<DT>
<CODE><a href="ppca.htm">ppca</a></CODE><DD>
Probabilistic Principal Components Analysis
<DT>
<CODE><a href="quasinew.htm">quasinew</a></CODE><DD>
Quasi-Newton optimization.
<DT>
<CODE><a href="rbf.htm">rbf</a></CODE><DD>
Creates an RBF network with specified architecture
<DT>
<CODE><a href="rbfbkp.htm">rbfbkp</a></CODE><DD>
Backpropagate gradient of error function for RBF network.
<DT>
<CODE><a href="rbfderiv.htm">rbfderiv</a></CODE><DD>
Evaluate derivatives of RBF network outputs with respect to weights.
<DT>
<CODE><a href="rbferr.htm">rbferr</a></CODE><DD>
Evaluate error function for RBF network.
<DT>
<CODE><a href="rbfevfwd.htm">rbfevfwd</a></CODE><DD>
Forward propagation with evidence for RBF
<DT>
<CODE><a href="rbffwd.htm">rbffwd</a></CODE><DD>
Forward propagation through RBF network with linear outputs.
<DT>
<CODE><a href="rbfgrad.htm">rbfgrad</a></CODE><DD>
Evaluate gradient of error function for RBF network.
<DT>
<CODE><a href="rbfhess.htm">rbfhess</a></CODE><DD>
Evaluate the Hessian matrix for RBF network.
<DT>
<CODE><a href="rbfjacob.htm">rbfjacob</a></CODE><DD>
Evaluate derivatives of RBF network outputs with respect to inputs.
<DT>
<CODE><a href="rbfpak.htm">rbfpak</a></CODE><DD>
Combines all the parameters in an RBF network into one weights vector.
<DT>
<CODE><a href="rbfprior.htm">rbfprior</a></CODE><DD>
Create Gaussian prior and output layer mask for RBF.
<DT>
<CODE><a href="rbfsetbf.htm">rbfsetbf</a></CODE><DD>
Set basis functions of RBF from data.
<DT>
<CODE><a href="rbfsetfw.htm">rbfsetfw</a></CODE><DD>
Set basis function widths of RBF.
<DT>
<CODE><a href="rbftrain.htm">rbftrain</a></CODE><DD>
Two stage training of RBF network.
<DT>
<CODE><a href="rbfunpak.htm">rbfunpak</a></CODE><DD>
Separates a vector of RBF weights into its components.
<DT>
<CODE><a href="rosegrad.htm">rosegrad</a></CODE><DD>
Calculate gradient of Rosenbrock's function.
<DT>
<CODE><a href="rosen.htm">rosen</a></CODE><DD>
Calculate Rosenbrock's function.
<DT>
<CODE><a href="scg.htm">scg</a></CODE><DD>
Scaled conjugate gradient optimization.
<DT>
<CODE><a href="som.htm">som</a></CODE><DD>
Creates a Self-Organising Map.
<DT>
<CODE><a href="somfwd.htm">somfwd</a></CODE><DD>
Forward propagation through a Self-Organising Map.
<DT>
<CODE><a href="sompak.htm">sompak</a></CODE><DD>
Combines node weights into one weights matrix.
<DT>
<CODE><a href="somtrain.htm">somtrain</a></CODE><DD>
Kohonen training algorithm for SOM.
<DT>
<CODE><a href="somunpak.htm">somunpak</a></CODE><DD>
Replaces node weights in SOM.
</DL>
<hr>
<p>Copyright (c) Christopher M Bishop, Ian T Nabney (1996, 1997)
</body>
</html>
|
