som.nn.run.kernel: calls the specified kernel for som training.

In som.nn: Topological k-NN Classifier Based on Self-Organising Maps

calls the specified kernel for som training.

Description

calls the specified kernel for som training.

Usage

som.nn.run.kernel(
  data,
  classes = "no classes",
  kernel = c("internal", "SOM"),
  xdim,
  ydim,
  len = 100,
  alpha = 0.05,
  radius = 1,
  init,
  toroidal = FALSE
)

Arguments

data	numeric matrix or data.frame with training data. Only numeric columns of data.frame are used for training.
classes	character vector with class labels (only necessary for supervised training kernels).
kernel	kernel to be used
xdim	number of neurons in x
ydim	number of neurons in y
len	number of steps to be trained (steps - not epochs!).
alpha	initial learning rate (decreased to 0).
radius	initial radius (decreased to 1).
init	numeric matrix or data.frame with codes for initialisation.
toroidal	true if doughnut-shaped som.

Value

    list with elements \code{codes} and \code{grid}.

som.nn documentation built on May 29, 2024, 6:50 a.m.