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wolffd@0 1 <html>
wolffd@0 2 <head>
wolffd@0 3 <title>
wolffd@0 4 Netlab Reference Manual mdn2gmm
wolffd@0 5 </title>
wolffd@0 6 </head>
wolffd@0 7 <body>
wolffd@0 8 <H1> mdn2gmm
wolffd@0 9 </H1>
wolffd@0 10 <h2>
wolffd@0 11 Purpose
wolffd@0 12 </h2>
wolffd@0 13 Converts an MDN mixture data structure to array of GMMs.
wolffd@0 14
wolffd@0 15 <p><h2>
wolffd@0 16 Synopsis
wolffd@0 17 </h2>
wolffd@0 18 <PRE>
wolffd@0 19 gmmmixes = mdn2gmm(mdnmixes)
wolffd@0 20 </PRE>
wolffd@0 21
wolffd@0 22
wolffd@0 23 <p><h2>
wolffd@0 24 Description
wolffd@0 25 </h2>
wolffd@0 26 <CODE>gmmmixes = mdn2gmm(mdnmixes)</CODE> takes an MDN mixture data structure
wolffd@0 27 <CODE>mdnmixes</CODE>
wolffd@0 28 containing three matrices (for priors, centres and variances) where each
wolffd@0 29 row represents the corresponding parameter values for a different mixture model
wolffd@0 30 and creates an array of GMMs. These can then be used with the standard
wolffd@0 31 Netlab Gaussian mixture model functions.
wolffd@0 32
wolffd@0 33 <p><h2>
wolffd@0 34 Example
wolffd@0 35 </h2>
wolffd@0 36 <PRE>
wolffd@0 37
wolffd@0 38 mdnmixes = mdnfwd(net, x);
wolffd@0 39 mixes = mdn2gmm(mdnmixes);
wolffd@0 40 p = gmmprob(mixes(1), y);
wolffd@0 41 </PRE>
wolffd@0 42
wolffd@0 43 This creates an array GMM mixture models (one for each data point in
wolffd@0 44 <CODE>x</CODE>). The vector <CODE>p</CODE> is then filled with the conditional
wolffd@0 45 probabilities of the values <CODE>y</CODE> given <CODE>x(1,:)</CODE>.
wolffd@0 46
wolffd@0 47 <p><h2>
wolffd@0 48 See Also
wolffd@0 49 </h2>
wolffd@0 50 <CODE><a href="gmm.htm">gmm</a></CODE>, <CODE><a href="mdn.htm">mdn</a></CODE>, <CODE><a href="mdnfwd.htm">mdnfwd</a></CODE><hr>
wolffd@0 51 <b>Pages:</b>
wolffd@0 52 <a href="index.htm">Index</a>
wolffd@0 53 <hr>
wolffd@0 54 <p>Copyright (c) Ian T Nabney (1996-9)
wolffd@0 55 <p>David J Evans (1998)
wolffd@0 56
wolffd@0 57 </body>
wolffd@0 58 </html>