> The Spider Objects

>            INDEP object

>        A=INDEP(C,H) returns a indep object initialized with density
>        estimator(s) C and hyperparameters H.

>        Training will try to fit the estimator C to each feature independently,
>        C can be an array of algorithms, one for each feature.
>        Testing will return the density estimate for each data point tested,
>        or if passed the empty dataset will generate new
>        class data according to the densities in the model.

>        Hyperparameters:
>         l=50            -- number of data points to generate if asked to generate

>        Model:
>         child={gauss}   -- array of underlying density estimators

>        Methods:
>         train, test, generate

>        Examples:
>          gen(indep({gauss([-1]) gauss([1])}))
>          get_mean(train(cv(bayes(indep)),toy))      run naive bayes