> The Spider Objects

>            GAUSS object

>        A=GAUSS([M],[S],H) returns a gauss object initialized with mean M,
>        std S and hyperparameters H. Can also be called with GAUSS(H)

>        Training will try to fit a single Gaussian to the data.
>        Testing will return the probability estimate, or if passed
>        an empty dataset will generate new data according to the
>        density learnt.

>        Hyperparameters:
>         l=50       -- number of data points to generate if asked to generate
>         assume=[]  -- can make assumptions: {'diag_cov','equal_cov'}
>                       where the matrix is diagonal ('diag_cov'),
>                       or diagonal with all elements equal ('equal_cov')

>        Model:
>         mean=0  -- centre of gauss distbn
>         cov=1   -- cov matrix, if single value assumes diagonal
>                    of matrix all has same values, if vector assumes it
>                    is the diagonal of the matrix, with 0s everywhere else

>        Methods:
>         train, test, generate

>        Examples:
>         gen(gauss)                                     generate data
>         gen(gauss('l=5;mean=[1 1];cov=0.01;'))         generate data

>         d=test(gauss([1 5],[1 -0.4 ; -0.4 1],'l=300'));
>         [d2 a]=train(gauss('l=200'),d);
>         d2=test(a);    train a gauss on some (gauss) data and then
>                        try to generate similar data
>         hold off; plot(d.X(:,1),d.X(:,2),'o')
>         hold on;  plot(d2.X(:,1),d2.X(:,2),'rx')

> | Reference : chapter 2 (Richard O. Duda and Peter E. Hart) Bayesian Decision Theory

> | Author : Richard O. Duda , Peter E. Hart

> | Link : http://www.amazon.com/exec/obidos/tg/detail/-/0471056693/002-6279399-2828812?v=glance