> The Spider Objects

>       KNN k-nearest neighbours object

>        a = knn(hyperParam)

>        Generates a knn object with given hyperparamters.

>        Hyperparameters (with defaults)
>         k=1                  -- number of neighbours
>         batch=1              -- true if to be computed in batch
>                                  (requires more memory)
>         child=kernel;        -- kernel function (distances computation/
>                                  default is linear kernel)
>         output_preimage=0    -- whether output index from training sample
>                                  of preimage
>                                 instead of actual label

>        Model
>         dat                  -- data used for neighbours computation

>        Methods:
>         train, test
>        c1=[2,0];
>        c2=[-2,0];
>        X1=randn(50,2)+repmat(c1,50,1);
>        X2=randn(50,2)+repmat(c2,50,1);
>        d=data([X1;X2],[ones(50,1);-ones(50,1)]);
>        [r,a]=train(cv(knn({kernel('poly',3),'k=3'})),d);   use knn with 3 number of neighbours and polynomial kernel order 3
>        loss(r)

> | Reference : chapter 4 (Richard O. Duda and Peter E. Hart) k-nearest neighbor

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

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