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1 <html>
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2 <head>
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3 <title>
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4 Netlab Reference Manual gtm
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5 </title>
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6 </head>
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7 <body>
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8 <H1> gtm
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9 </H1>
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10 <h2>
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11 Purpose
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12 </h2>
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13 Create a Generative Topographic Map.
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14
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15 <p><h2>
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16 Synopsis
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17 </h2>
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18 <PRE>
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19 net = gtm(dimlatent, nlatent, dimdata, ncentres, rbfunc)
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20 net = gtm(dimlatent, nlatent, dimdata, ncentres, rbfunc, prior)
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21 </PRE>
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22
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23
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24 <p><h2>
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25 Description
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26 </h2>
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27
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28 <p><CODE>net = gtm(dimlatent, nlatent, dimdata, ncentres, rbfunc)</CODE>,
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29 takes the dimension of the latent space <CODE>dimlatent</CODE>, the
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30 number of data points sampled in the latent space <CODE>nlatent</CODE>, the
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31 dimension of the data space <CODE>dimdata</CODE>, the number of centres in the
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32 RBF model <CODE>ncentres</CODE>, the activation function for the RBF
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33 <CODE>rbfunc</CODE>
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34 and returns a data structure <CODE>net</CODE>. The parameters in the
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35 RBF and GMM sub-models are set by calls to the corresponding creation routines
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36 <CODE>rbf</CODE> and <CODE>gmm</CODE>.
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37
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38 <p>The fields in <CODE>net</CODE> are
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39 <PRE>
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40 type = 'gtm'
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41 nin = dimension of data space
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42 dimlatent = dimension of latent space
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43 rbfnet = RBF network data structure
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44 gmmnet = GMM data structure
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45 X = sample of latent points
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46 </PRE>
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47
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48
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49 <p><CODE>net = gtm(dimlatent, nlatent, dimdata, ncentres, rbfunc, prior)</CODE>,
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50 sets a Gaussian zero mean prior on the
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51 parameters of the RBF model. <CODE>prior</CODE> must be a scalar and represents
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52 the inverse variance of the prior distribution. This gives rise to
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53 a weight decay term in the error function.
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54
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55 <p><h2>
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56 See Also
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57 </h2>
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58 <CODE><a href="gtmfwd.htm">gtmfwd</a></CODE>, <CODE><a href="gtmpost.htm">gtmpost</a></CODE>, <CODE><a href="rbf.htm">rbf</a></CODE>, <CODE><a href="gmm.htm">gmm</a></CODE><hr>
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59 <b>Pages:</b>
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60 <a href="index.htm">Index</a>
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61 <hr>
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62 <p>Copyright (c) Ian T Nabney (1996-9)
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63
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64
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65 </body>
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66 </html> |
