kohonen (version 2.0.5)

som: Kohonen's self-organising maps

Description

Self-organising maps for mapping high-dimensional spectra or patterns to 2D; Euclidean distance is used. Modelled after the SOM function in package class.

Usage

som(data, grid=somgrid(), rlen = 100, alpha = c(0.05, 0.01),
    radius = quantile(nhbrdist, 0.67) * c(1, -1), init,
    toroidal = FALSE, n.hood, keep.data = TRUE)

Arguments

data

a matrix, with each row representing an object.

grid

a grid for the representatives: see 'somgrid'.

rlen

the number of times the complete data set will be presented to the network.

alpha

learning rate, a vector of two numbers indicating the amount of change. Default is to decline linearly from 0.05 to 0.01 over rlen updates.

radius

the radius of the neighbourhood, either given as a single number or a vector (start, stop). If it is given as a single number the radius will run from the given number to the negative value of that number; as soon as the neighbourhood gets sma

init

the initial representatives, represented as a matrix. If missing, chosen (without replacement) randomly from 'data'.

toroidal

if TRUE, the edges of the map are joined. Note that in a hexagonal toroidal map, the number of rows must be even.

n.hood

the shape of the neighbourhood, either "circular" or "square". The latter is the default for rectangular maps, the former for hexagonal maps.

keep.data

save data in return object.

Value

• an object of class "kohonen" with components
• data
  data matrix, only returned if keep.data == TRUE.
• grid
  the grid, an object of class "somgrid".
• codes
  a matrix of code vectors.
• changes
  vector of mean average deviations from code vectors.
• unit.classif
  winning units for all data objects, only returned if keep.data == TRUE.
• distances
  distances of objects to their corresponding winning unit, only returned if keep.data == TRUE.
• toroidal
  whether a toroidal map is used.
• method
  the type of som, here "som".

See Also

xyf, bdk, plot.kohonen

Examples

data(wines)
set.seed(7)

training <- sample(nrow(wines), 120)
Xtraining <- scale(wines[training, ])
Xtest <- scale(wines[-training, ],
               center = attr(Xtraining, "scaled:center"),
               scale = attr(Xtraining, "scaled:scale"))

som.wines <- som(Xtraining, grid = somgrid(5, 5, "hexagonal"))

som.prediction <- predict(som.wines, newdata = Xtest,
          trainX = Xtraining,
          trainY = factor(wine.classes[training]))
table(wine.classes[-training], som.prediction$prediction)