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gaussm

GAUSSM

### Trainable mapping, mixture of Gaussians (MoG) density estimate

W = GAUSSM(A,K,R,S,M)
W = A*GAUSSM([],K,R,S,M)
W = A*GAUSSM(K,R,S,M)

 Input A Dataset K Number of Gaussians per class R,S,M Regularisation parameters, 0 <= R,S <= 1, see QDC

 Output W Mixture of Gaussians density estimate

### Description

Estimation of a PDF by the dataset A by a mixture of Gaussians procedure.  Use is made of EMCLUST(A,QDC,K). Unlabeled objects are neglected, unless  A is entirely unlabeled or double. Then all objects are used. If A is a  multi-class crisp labeled dataset the densities are estimated class by  class and then weighted and combined according their prior probabilities.  Use +A instead of A to obtain a single set of Gaussians. In all cases,  just single density estimator W is returned.

Note that it is necessary to set the label type of A to soft labels  (A = LABTYPE(A,'soft') in order to use the traditional EM algorithm  based on posterior probabilities instead of using crisp labels.

The mapping W may be applied to a new dataset B using DENSITY = B*W.

W = A*GAUSSM

uses a single Gaussian per class (K=1) and no regularisation. If  regulariisation is desired, also K should be supplied.

### Example(s)

a = gendatb;
w = a*gaussm(2);
scatterd(a)
plotm(w)

### See also

datasets, mappings, qdc, mogc, emclust, plotm, testc,

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