From 8070dc963753142bb86c4ed698d91fd623ed28e7 Mon Sep 17 00:00:00 2001 From: ziejd2 Date: Thu, 28 Sep 2017 15:04:40 -0500 Subject: BNW using Octave instead of Matlab. This version of BNW should perform the same as the original version. The only difference is that it uses Octave instead of Matlab when running BayesNet Toolbox during parameter learning. I am calling this BNW_1.02. It can be accessed at: compbio.uthsc.edu/BNW_1.02 --- sourcecodes/bnt-master/nethelp3.3/mdnfwd.htm | 71 ++++++++++++++++++++++++++++ 1 file changed, 71 insertions(+) create mode 100644 sourcecodes/bnt-master/nethelp3.3/mdnfwd.htm (limited to 'sourcecodes/bnt-master/nethelp3.3/mdnfwd.htm') diff --git a/sourcecodes/bnt-master/nethelp3.3/mdnfwd.htm b/sourcecodes/bnt-master/nethelp3.3/mdnfwd.htm new file mode 100644 index 00000000..67c5c5d3 --- /dev/null +++ b/sourcecodes/bnt-master/nethelp3.3/mdnfwd.htm @@ -0,0 +1,71 @@ + + + +Netlab Reference Manual mdnfwd + + + +

mdnfwd +

+

+Purpose +

+Forward propagation through Mixture Density Network. + +

+Synopsis +

+
+mixparams = mdnfwd(net, x)
+[mixparams, y, z] = mdnfwd(net, x)
+[mixparams, y, z, a] = mdnfwd(net, x)
+
+ + +

+Description +

+ +mixparams = mdnfwd(net, x) takes a mixture density network data +structure net and a matrix x of input vectors, and forward +propagates the inputs through the network to generate a structure +mixparams which contains the parameters of several mixture models. +Each row of x represents +one input vector and the corresponding row of the matrices in mixparams +represents the parameters of a mixture model for the conditional probability +of target vectors given the input vector. This is not represented as an array +of gmm structures to improve the efficiency of MDN training. + +

The fields in mixparams are +

+
+  type = 'mdnmixes'
+  ncentres = number of mixture components
+  dimtarget = dimension of target space
+  mixcoeffs = mixing coefficients
+  centres = means of Gaussians: stored as one row per pattern
+  covars = covariances of Gaussians
+  nparams = number of parameters
+
+ + +

[mixparams, y, z] = mdnfwd(net, x) also generates a matrix y of +the outputs of the MLP and a matrix z of the hidden +unit activations where each row corresponds to one pattern. + +

[mixparams, y, z, a] = mlpfwd(net, x) also returns a matrix a +giving the summed inputs to each output unit, where each row +corresponds to one pattern. + +

+See Also +

+mdn, mdn2gmm, mdnerr, mdngrad, mlpfwd
+Pages: +Index +
+

Copyright (c) Ian T Nabney (1996-9) +

David J Evans (1998) + + + \ No newline at end of file -- cgit 1.4.1