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author Daniel Wolff
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Daniel@0 1 <html>
Daniel@0 2 <head>
Daniel@0 3 <title>
Daniel@0 4 Netlab Reference Manual gmm
Daniel@0 5 </title>
Daniel@0 6 </head>
Daniel@0 7 <body>
Daniel@0 8 <H1> gmm
Daniel@0 9 </H1>
Daniel@0 10 <h2>
Daniel@0 11 Purpose
Daniel@0 12 </h2>
Daniel@0 13 Creates a Gaussian mixture model with specified architecture.
Daniel@0 14
Daniel@0 15 <p><h2>
Daniel@0 16 Synopsis
Daniel@0 17 </h2>
Daniel@0 18 <PRE>
Daniel@0 19 mix = gmm(dim, ncentres, covartype)
Daniel@0 20 mix = gmm(dim, ncentres, covartype, ppca_dim)
Daniel@0 21 </PRE>
Daniel@0 22
Daniel@0 23
Daniel@0 24 <p><h2>
Daniel@0 25 Description
Daniel@0 26 </h2>
Daniel@0 27
Daniel@0 28 <CODE>mix = gmm(dim, ncentres, covartype)</CODE> takes
Daniel@0 29 the dimension of the space <CODE>dim</CODE>, the number of centres in the
Daniel@0 30 mixture model and the type of the mixture model, and returns a data
Daniel@0 31 structure <CODE>mix</CODE>.
Daniel@0 32 The mixture model type defines the covariance structure of each component
Daniel@0 33 Gaussian:
Daniel@0 34 <PRE>
Daniel@0 35
Daniel@0 36 'spherical' = single variance parameter for each component: stored as a vector
Daniel@0 37 'diag' = diagonal matrix for each component: stored as rows of a matrix
Daniel@0 38 'full' = full matrix for each component: stored as 3d array
Daniel@0 39 'ppca' = probabilistic PCA: stored as principal components (in a 3d array
Daniel@0 40 and associated variances and off-subspace noise
Daniel@0 41 </PRE>
Daniel@0 42
Daniel@0 43 <CODE>mix = gmm(dim, ncentres, covartype, ppca_dim)</CODE> also sets the dimension of
Daniel@0 44 the PPCA sub-spaces: the default value is one.
Daniel@0 45
Daniel@0 46 <p>The priors are initialised to equal values summing to one, and the covariances
Daniel@0 47 are all the identity matrix (or equivalent). The centres are
Daniel@0 48 initialised randomly from a zero mean unit variance Gaussian. This makes use
Daniel@0 49 of the MATLAB function <CODE>randn</CODE> and so the seed for the random weight
Daniel@0 50 initialisation can be set using <CODE>randn('state', s)</CODE> where <CODE>s</CODE> is the
Daniel@0 51 state value.
Daniel@0 52
Daniel@0 53 <p>The fields in <CODE>mix</CODE> are
Daniel@0 54 <PRE>
Daniel@0 55
Daniel@0 56 type = 'gmm'
Daniel@0 57 nin = the dimension of the space
Daniel@0 58 ncentres = number of mixture components
Daniel@0 59 covartype = string for type of variance model
Daniel@0 60 priors = mixing coefficients
Daniel@0 61 centres = means of Gaussians: stored as rows of a matrix
Daniel@0 62 covars = covariances of Gaussians
Daniel@0 63 </PRE>
Daniel@0 64
Daniel@0 65 The additional fields for mixtures of PPCA are
Daniel@0 66 <PRE>
Daniel@0 67
Daniel@0 68 U = principal component subspaces
Daniel@0 69 lambda = in-space covariances: stored as rows of a matrix
Daniel@0 70 </PRE>
Daniel@0 71
Daniel@0 72 The off-subspace noise is stored in <CODE>covars</CODE>.
Daniel@0 73
Daniel@0 74 <p><h2>
Daniel@0 75 Example
Daniel@0 76 </h2>
Daniel@0 77 <PRE>
Daniel@0 78
Daniel@0 79 mix = gmm(2, 4, 'spherical');
Daniel@0 80 </PRE>
Daniel@0 81
Daniel@0 82 This creates a Gaussian mixture model with 4 components in 2 dimensions.
Daniel@0 83 The covariance structure is a spherical model.
Daniel@0 84
Daniel@0 85 <p><h2>
Daniel@0 86 See Also
Daniel@0 87 </h2>
Daniel@0 88 <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>
Daniel@0 89 <b>Pages:</b>
Daniel@0 90 <a href="index.htm">Index</a>
Daniel@0 91 <hr>
Daniel@0 92 <p>Copyright (c) Ian T Nabney (1996-9)
Daniel@0 93
Daniel@0 94
Daniel@0 95 </body>
Daniel@0 96 </html>