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date Tue, 10 Feb 2015 15:05:51 +0000
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+<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>
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