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author wolffd
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wolffd@0 1 <html>
wolffd@0 2 <head>
wolffd@0 3 <title>
wolffd@0 4 Netlab Reference Manual mdnfwd
wolffd@0 5 </title>
wolffd@0 6 </head>
wolffd@0 7 <body>
wolffd@0 8 <H1> mdnfwd
wolffd@0 9 </H1>
wolffd@0 10 <h2>
wolffd@0 11 Purpose
wolffd@0 12 </h2>
wolffd@0 13 Forward propagation through Mixture Density Network.
wolffd@0 14
wolffd@0 15 <p><h2>
wolffd@0 16 Synopsis
wolffd@0 17 </h2>
wolffd@0 18 <PRE>
wolffd@0 19 mixparams = mdnfwd(net, x)
wolffd@0 20 [mixparams, y, z] = mdnfwd(net, x)
wolffd@0 21 [mixparams, y, z, a] = mdnfwd(net, x)
wolffd@0 22 </PRE>
wolffd@0 23
wolffd@0 24
wolffd@0 25 <p><h2>
wolffd@0 26 Description
wolffd@0 27 </h2>
wolffd@0 28
wolffd@0 29 <CODE>mixparams = mdnfwd(net, x)</CODE> takes a mixture density network data
wolffd@0 30 structure <CODE>net</CODE> and a matrix <CODE>x</CODE> of input vectors, and forward
wolffd@0 31 propagates the inputs through the network to generate a structure
wolffd@0 32 <CODE>mixparams</CODE> which contains the parameters of several mixture models.
wolffd@0 33 Each row of <CODE>x</CODE> represents
wolffd@0 34 one input vector and the corresponding row of the matrices in <CODE>mixparams</CODE>
wolffd@0 35 represents the parameters of a mixture model for the conditional probability
wolffd@0 36 of target vectors given the input vector. This is not represented as an array
wolffd@0 37 of <CODE>gmm</CODE> structures to improve the efficiency of MDN training.
wolffd@0 38
wolffd@0 39 <p>The fields in <CODE>mixparams</CODE> are
wolffd@0 40 <PRE>
wolffd@0 41
wolffd@0 42 type = 'mdnmixes'
wolffd@0 43 ncentres = number of mixture components
wolffd@0 44 dimtarget = dimension of target space
wolffd@0 45 mixcoeffs = mixing coefficients
wolffd@0 46 centres = means of Gaussians: stored as one row per pattern
wolffd@0 47 covars = covariances of Gaussians
wolffd@0 48 nparams = number of parameters
wolffd@0 49 </PRE>
wolffd@0 50
wolffd@0 51
wolffd@0 52 <p><CODE>[mixparams, y, z] = mdnfwd(net, x)</CODE> also generates a matrix <CODE>y</CODE> of
wolffd@0 53 the outputs of the MLP and a matrix <CODE>z</CODE> of the hidden
wolffd@0 54 unit activations where each row corresponds to one pattern.
wolffd@0 55
wolffd@0 56 <p><CODE>[mixparams, y, z, a] = mlpfwd(net, x)</CODE> also returns a matrix <CODE>a</CODE>
wolffd@0 57 giving the summed inputs to each output unit, where each row
wolffd@0 58 corresponds to one pattern.
wolffd@0 59
wolffd@0 60 <p><h2>
wolffd@0 61 See Also
wolffd@0 62 </h2>
wolffd@0 63 <CODE><a href="mdn.htm">mdn</a></CODE>, <CODE><a href="mdn2gmm.htm">mdn2gmm</a></CODE>, <CODE><a href="mdnerr.htm">mdnerr</a></CODE>, <CODE><a href="mdngrad.htm">mdngrad</a></CODE>, <CODE><a href="mlpfwd.htm">mlpfwd</a></CODE><hr>
wolffd@0 64 <b>Pages:</b>
wolffd@0 65 <a href="index.htm">Index</a>
wolffd@0 66 <hr>
wolffd@0 67 <p>Copyright (c) Ian T Nabney (1996-9)
wolffd@0 68 <p>David J Evans (1998)
wolffd@0 69
wolffd@0 70 </body>
wolffd@0 71 </html>