som.nn.do.train: Work hourse for hexagonal som training

Description

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

Usage

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,
  strict,
  name,
  continue,
  len.total,
  codes = NULL
)

Value

    S4 object of type \code{\link{SOMnn}} with the trained model

Arguments

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.
strict: difference of maximum votes to assign class label (if the difference between the to two votes is smaller or equal to strict, unknown is predicted). default = 0.3.
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.
codes: codes of a model to be used for initialisation.