### DisTools introductory 2D example, non-Euclidean dissimilarities

Get rid of old figures, generate a trainset and a testset and give them proper names

`delfigs`
`AT = setname(gendatb,'TrainSet')`
` AS = setname(gendatb,'TestSet')`

Define a set of untrained classifiers all based on Fisher’s Linear Discriminant: in the original 2D feature space, in the dissimilarity space based on Euclidean (Minkowsky-2) distances, in a dissimilarity space based on squared Euclidean distances and in a dissimilarity space based on Minkowsky-1 (L1) distances. Give them proper names and train them by `AT`.

`U1 = fisherc;`
`U2 = proxm([],'m',2)*fisherc;`
`U2 = setname(U2,'DisSpaceL2-1');`
`U3 = proxm([],'m',2)*mapm('power',2)*fisherc;`
`U3 = setname(U3,'DisSpaceL2-2');`
`U4 = proxm([],'m',1)*fisherc;`
`U4 = setname(U4,'DisSpaceL1');`
`W = AT*{U1,U2,U3,U4};`

Show results in a scatter plot. By enlarging the gridsize (default setting is 30) to, e.g. 300, a more accurate plot is obtained. However, plotting may take considerably more time.

```gridsize 300 scatterd(AT)```
` plotc(W)`

Classify trainset and testset and show results.

`testc({AT,AS},W)`

Finally you may have a look at the long spiral experiment:

`exp_spiral`