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| author | ziejd2 | 2017-09-28 15:04:40 -0500 |
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| committer | ziejd2 | 2017-09-28 15:04:40 -0500 |
| commit | 8070dc963753142bb86c4ed698d91fd623ed28e7 (patch) | |
| tree | d0f6dd8fc46a49b819aa55c1a90faa14d8448883 /sourcecodes/bnt-master/nethelp3.3/mdn.htm | |
| parent | 7cc31810d53176e805532b2789955f4eedbce6bb (diff) | |
| download | BNW-8070dc963753142bb86c4ed698d91fd623ed28e7.tar.gz | |
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
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diff --git a/sourcecodes/bnt-master/nethelp3.3/mdn.htm b/sourcecodes/bnt-master/nethelp3.3/mdn.htm new file mode 100644 index 00000000..5e5b83a8 --- /dev/null +++ b/sourcecodes/bnt-master/nethelp3.3/mdn.htm @@ -0,0 +1,84 @@ +<html> +<head> +<title> +Netlab Reference Manual mdn +</title> +</head> +<body> +<H1> mdn +</H1> +<h2> +Purpose +</h2> +Creates a Mixture Density Network with specified architecture. + +<p><h2> +Synopsis +</h2> +<PRE> +net = mdn(nin, nhidden, ncentres, dimtarget) +net = mdn(nin, nhidden, ncentres, dimtarget, mixtype, ... + prior, beta) +</PRE> + + +<p><h2> +Description +</h2> +<CODE>net = mdn(nin, nhidden, ncentres, dimtarget)</CODE> takes the number of +inputs, +hidden units for a 2-layer feed-forward +network and the number of centres and target dimension for the +mixture model whose parameters are set from the outputs of the neural network. +The fifth argument <CODE>mixtype</CODE> is used to define the type of mixture +model. (Currently there is only one type supported: a mixture of Gaussians with +a single covariance parameter for each component.) For this model, +the mixture coefficients are computed from a group of softmax outputs, +the centres are equal to a group of linear outputs, and the variances are +obtained by applying the exponential function to a third group of outputs. + +<p>The network is initialised by a call to <CODE>mlp</CODE>, and the arguments +<CODE>prior</CODE>, and <CODE>beta</CODE> have the same role as for that function. +Weight initialisation uses the Matlab function <CODE>randn</CODE> + and so the seed for the random weight initialization can be +set using <CODE>randn('state', s)</CODE> where <CODE>s</CODE> is the seed value. +A specialised data structure (rather than <CODE>gmm</CODE>) +is used for the mixture model outputs to improve +the efficiency of error and gradient calculations in network training. +The fields are described in <CODE>mdnfwd</CODE> where they are set up. + +<p>The fields in <CODE>net</CODE> are +<PRE> + + type = 'mdn' + nin = number of input variables + nout = dimension of target space (not number of network outputs) + nwts = total number of weights and biases + mdnmixes = data structure for mixture model output + mlp = data structure for MLP network +</PRE> + + +<p><h2> +Example +</h2> +<PRE> + +net = mdn(2, 4, 3, 1, 'spherical'); +</PRE> + +This creates a Mixture Density Network with 2 inputs and 4 hidden units. +The mixture model has 3 components and the target space has dimension 1. + +<p><h2> +See Also +</h2> +<CODE><a href="mdnfwd.htm">mdnfwd</a></CODE>, <CODE><a href="mdnerr.htm">mdnerr</a></CODE>, <CODE><a href="mdn2gmm.htm">mdn2gmm</a></CODE>, <CODE><a href="mdngrad.htm">mdngrad</a></CODE>, <CODE><a href="mdnpak.htm">mdnpak</a></CODE>, <CODE><a href="mdnunpak.htm">mdnunpak</a></CODE>, <CODE><a href="mlp.htm">mlp</a></CODE><hr> +<b>Pages:</b> +<a href="index.htm">Index</a> +<hr> +<p>Copyright (c) Ian T Nabney (1996-9) +<p>David J Evans (1998) + +</body> +</html> \ No newline at end of file |