perClass Documentation
version 5.1 (31-May-2017)
 SDGAUSS  Gaussian model

    P=SDGAUSS(DATA,options)     % train from DATA
    P=SDGAUSS(MEAN,COV,PRIORS)  % create from component parameters
    P=SDGAUSS(MEAN,[],PRIORS)  % create from component parameters

 INPUT
   DATA     Labeled dataset
   MEAN     SDDATA with component means
   COV      cell array with covariance matrices
   PRIORS   vector with prior per component

 OUTPUT
   P        Gaussian model per class

 OPTIONS
   'prior'    Class priors (default: use priors from the training set)
   'no display'  Do not show progress of regularization optimization
  Regularization:
   'reg'      Automatic regularization
   'reg',R    Regularization constant added to diagonal
   'test',TS  Use a test/validation set TS to evaluate regularization
              Do not split DATA internally.
   'tsfrac',F Fraction of data used to validating error (default: 0.2)

 DESCRIPTION
 SDGAUSS trains a Gaussian model with full covariance matrix per class.
 The model may be regularized using 'reg' option by adding a constant to
 covariances' diagonals.  SDGAUSS may be trained on one class data set and
 used for detection.
 Alternatively, SDGAUSS can create Gaussian model directly from
 parameters. If COV is empty, it is intialized to unit covariance
 matrices.

 EXAMPLES
 p=sdgauss(data)   % Train gaussian model, no regularization
 p=sdgauss(data,'reg')   % run automatic regularization
 p=sdgauss(data,'reg',0.01)   % regularize by adding 0.01 on cov.diagonal

 pinit=sdgauss(sddata([0 0; 1 1; 2 2]), [],[0.3 0.3 0.3])

 READ MORE
http://perclass.com/doc/guide/classifiers.html#sdgauss

 SEE ALSO
 SDQUADRATIC, SDLINEAR, SDMIXTURE, SDNMEAN

sdgauss is referenced in examples: