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Last data update: 2014.03.03

R: Supervised version of Kohonen's self-organising maps

bdk	R Documentation

Supervised version of Kohonen's self-organising maps

Description

Supervised version of self-organising maps for mapping high-dimensional spectra or patterns to 2D: the Bi-Directional Kohonen map. This is an alternating training of the X-space and the Y-space of the map, where for updating the X-space more weight is given to the features in Y, and vice versa. Weights start by default with values of (0.75, 0.25) and during training go to (0.5, 0.5). Prediction is done only using the X-space. For continuous Y, the Euclidean distance is used; for categorical Y the Tanimoto distance.

Usage

bdk(data, Y, grid=somgrid(), rlen = 100, alpha = c(0.05, 0.01),
    radius = quantile(nhbrdist, 0.67) * c(1, -1), xweight = 0.75,
    contin, toroidal = FALSE, n.hood, keep.data = TRUE)

Arguments

`data`	a matrix, with each row representing an object.
`Y`	property that is to be modelled. In case of classification, Y is a matrix with exactly one '1' in each row indicating the class, and zeros elsewhere. For prediction of continuous properties, Y is a vector. A combination is possible, too, but one then should take care of appropriate scaling.
`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.
`xweight`	the initial weight given to the X map in the calculation of distances for updating Y, and to the Y map for updating X. This will linearly go to 0.5 during training. Defaults to 0.75.
`contin`	parameter indicating whether Y is continuous or categorical. The default is to check whether all row sums of Y equal 1: in that case `contin` is `FALSE`.
`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 value.

Value

an object of class "kohonen" with components

`data`	data matrix, only returned if `keep.data == TRUE`.
`Y`	Y, only returned if `keep.data == TRUE`.
`contin`	parameter indicating whether Y is continuous or categorical.
`grid`	the grid, an object of class "somgrid".
`codes`	list of two matrices, containing codebook vectors for X and Y, respectively.
`changes`	matrix containing two columns of mean average deviations from code vectors. Column 1 contains deviations used for updating Y; column 2 for updating X.
`toroidal`	whether a toroidal map is used.
`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`.
`method`	the type of som, here "bdk"

Author(s)

Ron Wehrens

References

W.J. Melssen, R. Wehrens, and L.M.C. Buydens. Chemom. Intell. Lab. Syst., 83, 99-113 (2006).

Examples

### Wine example
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"))

bdk.wines <- bdk(Xtraining,
                 factor(wine.classes[training]),
                 grid = somgrid(5, 5, "hexagonal"))

bdk.prediction <- predict(bdk.wines, newdata=Xtest)
table(wine.classes[-training], bdk.prediction$prediction)