som.nn.do.train: Work hourse for hexagonal som training
In som.nn: Topological k-NN Classifier Based on Self-Organising Maps

The function is called by som.nn.train and som.nn.continue to train self-organising map with hexagonal tolology.

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som.nn.do.train(x, class.idx, kernel = "internal", xdim, ydim, toroidal, len,
  alpha, radius = 0, norm, norm.center, norm.scale, dist.fun, max.dist, name,
  continue, len.total, codes)

`x`	data.fame with training data. Samples are requested as rows and taken randomly for the training steps. All columns except of the class lables are considered to be attributes and parts of the training vector. One column is needed as class labels. The column with class lables is selected by the argument `class.col`. If class is not given, the first column is used as class labels.
`class.idx`	index of the column with as class labels (after beeing coerced to character).
`kernel`	kernel to be used for training.
`xdim`	dimension in x-direction.
`ydim`	dimension in y-direction.
`toroidal`	`logical`; if TRUE an endless som is trained as on the surface of a torus.
`len`	number of steps to be trained (steps - not epochs!).
`alpha`	initial training rate.
`radius`	inital radius for SOM training. Gaussian distance function is used, radius corresponds to sigma.
`norm`	logical; if TRUE, input data is normalised with `scale(x, TRUE, TRUE)`.
`dist.fun`	parameter for k-NN prediction. Function is used to calculate distance-dependent weights. Any distance function must accept the two parameters `x` (distance) and `sigma` (maximum distance to give a weight > 0.0).
`max.dist`	parameter for k-NN prediction. Parameter `sigma` for dist.fun. In order to avoid rounding issues, it is recommended not to use exact integers as limit, but values like 1.1 to make sure, that all neurons with distance 1 are included.
`name`	name for the model. Name will be stored as slot `model@name` in the trained model.
`continue`	logical; if TRUE, the codebook vectors of the model, given in argument `model` will be used as initial codes.
`len.total`	number of previuos training steps.
`model`	model of type `SOMnn`.