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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> takesthe dimension of the space <CODE>dim</CODE>, the number of centres in themixture model and the type of the mixture model, and returns a datastructure <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 ofthe PPCA sub-spaces: the default value is one.<p>The priors are initialised to equal values summing to one, and the covariancesare all the identity matrix (or equivalent). The centres areinitialised randomly from a zero mean unit variance Gaussian. This makes useof the MATLAB function <CODE>randn</CODE> and so the seed for the random weightinitialisation can be set using <CODE>randn('state', s)</CODE> where <CODE>s</CODE> is thestate 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>⌨️ 快捷键说明
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