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Last data update: 2014.03.03

R: Kohonen's self-organising maps

som	R Documentation

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 smaller than one only the winning unit will be updated. The default is to start with a value that covers 2/3 of all unit-to-unit distances.
`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".

Author(s)

Ron Wehrens

References

"Self-organizing maps", 3rd Ed., T. Kohonen, New York: Springer (2001)

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)