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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/gmm.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/gmm.htm b/sourcecodes/bnt-master/nethelp3.3/gmm.htm new file mode 100644 index 00000000..c815b3cb --- /dev/null +++ b/sourcecodes/bnt-master/nethelp3.3/gmm.htm @@ -0,0 +1,96 @@ +<html> +<head> +<title> +Netlab Reference Manual gmm +</title> +</head> +<body> +<H1> gmm +</H1> +<h2> +Purpose +</h2> +Creates a Gaussian mixture model with specified architecture. + +<p><h2> +Synopsis +</h2> +<PRE> +mix = gmm(dim, ncentres, covartype) +mix = gmm(dim, ncentres, covartype, ppca_dim) +</PRE> + + +<p><h2> +Description +</h2> + +<CODE>mix = gmm(dim, ncentres, covartype)</CODE> takes +the dimension of the space <CODE>dim</CODE>, the number of centres in the +mixture model and the type of the mixture model, and returns a data +structure <CODE>mix</CODE>. +The mixture model type defines the covariance structure of each component +Gaussian: +<PRE> + + 'spherical' = single variance parameter for each component: stored as a vector + 'diag' = diagonal matrix for each component: stored as rows of a matrix + 'full' = full matrix for each component: stored as 3d array + 'ppca' = probabilistic PCA: stored as principal components (in a 3d array + and associated variances and off-subspace noise +</PRE> + +<CODE>mix = gmm(dim, ncentres, covartype, ppca_dim)</CODE> also sets the dimension of +the PPCA sub-spaces: the default value is one. + +<p>The priors are initialised to equal values summing to one, and the covariances +are all the identity matrix (or equivalent). The centres are +initialised randomly from a zero mean unit variance Gaussian. This makes use +of the MATLAB function <CODE>randn</CODE> and so the seed for the random weight +initialisation can be set using <CODE>randn('state', s)</CODE> where <CODE>s</CODE> is the +state value. + +<p>The fields in <CODE>mix</CODE> are +<PRE> + + type = 'gmm' + nin = the dimension of the space + ncentres = number of mixture components + covartype = string for type of variance model + priors = mixing coefficients + centres = means of Gaussians: stored as rows of a matrix + covars = covariances of Gaussians +</PRE> + +The additional fields for mixtures of PPCA are +<PRE> + + U = principal component subspaces + lambda = in-space covariances: stored as rows of a matrix +</PRE> + +The off-subspace noise is stored in <CODE>covars</CODE>. + +<p><h2> +Example +</h2> +<PRE> + +mix = gmm(2, 4, 'spherical'); +</PRE> + +This creates a Gaussian mixture model with 4 components in 2 dimensions. +The covariance structure is a spherical model. + +<p><h2> +See Also +</h2> +<CODE><a href="gmmpak.htm">gmmpak</a></CODE>, <CODE><a href="gmmunpak.htm">gmmunpak</a></CODE>, <CODE><a href="gmmsamp.htm">gmmsamp</a></CODE>, <CODE><a href="gmminit.htm">gmminit</a></CODE>, <CODE><a href="gmmem.htm">gmmem</a></CODE>, <CODE><a href="gmmactiv.htm">gmmactiv</a></CODE>, <CODE><a href="gmmpost.htm">gmmpost</a></CODE>, <CODE><a href="gmmprob.htm">gmmprob</a></CODE><hr> +<b>Pages:</b> +<a href="index.htm">Index</a> +<hr> +<p>Copyright (c) Ian T Nabney (1996-9) + + +</body> +</html> \ No newline at end of file |