> The Spider Objects

>            FISHER object

>        A=FISHER(H) returns a fisher object initialized with hyperparameters H.

>        Calculates a Fisher/Correlation score for each feature to implement
>         feature selection.

>        Hyperparameters, and their defaults
>         feat=[]              -- number of features
>         output_rank=1        -- set to 1 if only the feature ranking matters
>                                 (does not perform any classification on the data)
>                                 otherwise performs classification using
>                                 weights given by individual correlation scores
>         method=2             -- useful only for multi-class. Set the how to combine
>                                 the score of different one-vs-rest fisher's score.
>                                 (2 = take the sum, 1 = take the max)
>        Model
>         w                    -- the weights
>         b0                   -- the threshold (when using all features)
>         rank                 -- the ranking of the features
>         d                    -- training set

>        Methods:
>         train, test, get_w

>        Example:
>        d=gen(toy); a=fisher; a.feat=10; a.output_rank=1;[r,a]=train(a,d);
>        a.rank    - lists the chosen features in  order of importance

>        Note:
>         To use for Furey et al. method (e.g correlation coefficients + svm)
>         use:  chain(fisher('output_rank=1'),svm)

> | Reference : Neural Networks for Pattern Recognition

> | Author : C. Bishop