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% file class/man/batchSOM.Rd
% copyright (C) 2002 W. N. Venables and B. D. Ripley
%
\name{batchSOM}
\alias{batchSOM}
\title{
Self-Organizing Maps: Batch Algorithm
}
\description{
Kohonen's Self-Organizing Maps are a crude form of multidimensional scaling.
}
\usage{
batchSOM(data, grid = somgrid(), radii, init)
}
\arguments{
\item{data}{
a matrix or data frame of observations, scaled so that Euclidean
distance is appropriate.
}
\item{grid}{
A grid for the representatives: see \code{\link{somgrid}}.
}
\item{radii}{
the radii of the neighbourhood to be used for each pass: one pass is
run for each element of \code{radii}.
}
\item{init}{
the initial representatives. If missing, chosen (without replacement)
randomly from \code{data}.
}
}
\value{
An object of class \code{"SOM"} with components
\item{grid}{the grid, an object of class \code{"somgrid"}.}
\item{codes}{a matrix of representatives.}
}
\details{
The batch SOM algorithm of Kohonen(1995, section 3.14) is used.
}
\seealso{
\code{\link{somgrid}}, \code{\link{SOM}}
}
\references{
Kohonen, T. (1995) \emph{Self-Organizing Maps.} Springer-Verlag.
Ripley, B. D. (1996)
\emph{Pattern Recognition and Neural Networks.} Cambridge.
Venables, W. N. and Ripley, B. D. (2002)
\emph{Modern Applied Statistics with S.} Fourth edition. Springer.
}
\examples{
require(graphics)
data(crabs, package = "MASS")
lcrabs <- log(crabs[, 4:8])
crabs.grp <- factor(c("B", "b", "O", "o")[rep(1:4, rep(50,4))])
gr <- somgrid(topo = "hexagonal")
crabs.som <- batchSOM(lcrabs, gr, c(4, 4, 2, 2, 1, 1, 1, 0, 0))
plot(crabs.som)
bins <- as.numeric(knn1(crabs.som$code, lcrabs, 0:47))
plot(crabs.som$grid, type = "n")
symbols(crabs.som$grid$pts[, 1], crabs.som$grid$pts[, 2],
circles = rep(0.4, 48), inches = FALSE, add = TRUE)
text(crabs.som$grid$pts[bins, ] + rnorm(400, 0, 0.1),
as.character(crabs.grp))
}
\keyword{classif}
|
