### DisTools introductory 2D example

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')`

The following statements just show how in PRTools a 2D space can be mapped onto a 6D polynomial space in which every direction corresponds to an original feature raised to some power, here for degrees 1, 2 and 3.

`XP = AT*cmapm(2,[1 0; 0 1; 2 0; 0 2; 3 0; 0 3;]);`
``` +XP(1:5,:) % show result for first 5 objects ```

The next statements show how a a mapping `VM` to a Minkowsky-2 (i.e. Euclidean) set dissimilarities is defined using all training objects `AT` for representation. After that the training set is mapped into that space, constructing thereby square dissimilarity matrix `XM`.

`VM = AT*proxm([],'m',2); % the dissimilarity mapping`
` XM = AT*VM               % map the training set`
``` +XM(1:5,1:5)             % show the first 5 dissimilarities of 5 objects ```

Define a set of untrained classifiers all based on Fisher’s Linear Discriminant: in the original 2D feature space, in the 6D polynomial space, in the 100D dissimilarity space and in a 10D PCA projection of the last one. Give them proper names and train them by `AT`.

`U1 = fisherc;                         % Fisher in 2D feature space`
` U2 = cmapm(2,[1 0; 0 1; 2 0; 0 2; 3 0; 0 3;])*fisherc;`
` U2 = setname(U2,'PolyFisher');        % Fisher in polynomial space`
` U3 = proxm([],'m',2)*fisherc;`
` U3 = setname(U3,'DisSpaceFisher');    % Fisher in disspace`
` U4 = proxm([],'m',2)*pca([],10)*fisherc;`
` U4 = setname(U4,'DisSpacePCAFisher'); % Fisher in PCA disspace`
``` W = AT*{U1,U2,U3,U4};                 % Train them all ```

Show results in a scatter plot

`scatterd(AT)`
` plotc(W)`

Classify trainset and testset and show results.

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