> The Spider Objects

>            GET_MEAN object

>         R=get_mean(A,[L])

>         An object which calculates the mean(s) R of results with algorithms A,
>         together with the standard error (std. dev / sqrt(number of trials))
>         optionally calculating the loss with loss type L (see help loss)
>         e.g 'class_loss',.. (NOTE: input is a string, not a loss object).
>         when trained, e.g:  train(get_mean(cv(svm)),gen(toy))

>         Hyperparameters:
>          take_average=0       -- method of taking average over groups
>                                  1: average over inside leaf of tree of groups
>                                  2: average over outside leaf of tree of groups
>                                  0: try to guess which is most appropriate

>               'take_average' is necessary because if you have a group
>                of groups of algorithms you could wish to take the mean in two
>                different ways: e.g with group({group({a b} group({c d}))})
>                you may either wish to average a&b and c&d or a&c and b&d.

>                NOTE: The same effect can usually be achieved by doing
>                      get_mean(r) and get_mean(r') with a data object r

>         Alternatively, can be used in a feed
>         forward network, e.g: train(chain({ group(cv(svm)) get_mean }),gen(toy))

>         Can also be called like a function with
>         A=get_mean(D,[L]) calculates the mean for groups of data objects D.