\HeaderA{mlbench.threenorm}{Threenorm Benchmark Problem}{mlbench.threenorm}
\keyword{datagen}{mlbench.threenorm}
\begin{Description}\relax
The inputs of the threenorm problem are points from two Gaussian
distributions with unit covariance matrix. Class 1 is drawn with
equal probability from a unit multivariate normal with mean
\eqn{(a,a,\ldots,a)}{} and from a unit multivariate normal with mean 
\eqn{(-a,-a,\ldots,-a)}{}. Class 2 is drawn from a multivariate normal
with mean at \eqn{(a,-a,a, \ldots,-a)}{}, \eqn{a=2/d^{-0.5}}{}.
\end{Description}
\begin{Usage}
\begin{verbatim}
mlbench.threenorm(n, d=20)
\end{verbatim}
\end{Usage}
\begin{Arguments}
\begin{ldescription}
\item[\code{n}] number of patterns to create
\item[\code{d}] dimension of the threenorm problem
\end{ldescription}
\end{Arguments}
\begin{Value}
Returns an object of class \code{"mlbench.threenorm"} with components
\begin{ldescription}
\item[\code{x}] input values
\item[\code{classes}] factor vector of length \code{n} with target classes
\end{ldescription}
\end{Value}
\begin{References}\relax
Breiman, L. (1996). Bias, variance, and arcing classifiers.
Tech. Rep. 460, Statistics Department, University of California,
Berkeley, CA, USA.
\end{References}
\begin{Examples}
\begin{ExampleCode}
p<-mlbench.threenorm(1000, d=2)
plot(p)
\end{ExampleCode}
\end{Examples}

