> The Spider Objects

>       Pre-Image by ridge regression given KPCA coordinates

>        a = pmg_rr(alg,hyper)
>        generates a pre-image object; uses ridge regression learning
>        -------------------------------------------------------
>        hyperparameters:
>        child=kernel     kernel to work with.
>        kp=kpca		   kpca object
>        dn=sample data   data pool, if empty uses kp.dat

>        model:
>        rmap             learnt ridge regression object
>        mean_x           mean of data
>        rn=sample data coordinates from data pool
>        stats:
>        methods:
>        train         calls test
>        test			find the preimage

>        example:
>        k=kernel('rbf',0.75);
>        d=gen(toy2d);
>        d2=gen(toy2d('l=100'));

>        [r,kp]=train(kpca({k,'feat=50'}),d2);
>        p0=pmg_rr;p0.kp=kp; p0.child=k;
>        p0.dn=d2;
>        p0.rmap_model_selection=1;
>        [r,p1]=train(p0,d2);
>        r2=test(kp,d);
>        reconstruct=test(p1,r2)
>        reconstruct.X=reconstruct.X;

>        clf;
>        hold on;
>        colormap white;

>        plot3(d2.X(:,1),d2.X(:,2),d2.X(:,2)*0+10,'k.');
>        plot3(d.X(:,1),d.X(:,2),d.X(:,2)*0+10,'rx'); hold on;
>        plot3(reconstruct.X(:,1),reconstruct.X(:,2),reconstruct.X(:,2)*0+10,'o');

>        t=text(d.X(1,1),d.X(1,2)+0.005,d.X(1,2)*0+10,sprintf('Ideal Pre-Image'));
>        set(t,'Fontsize',10);
>        set(t,'Color',[1,0,0])

> | Reference : Learning To Find Pre-Images

> | Author : gökhan