\name{Pswarm}
\alias{pswarmCpp}
\alias{Pswarm}
\alias{pswarm}
\title{
A Swarm of Databots based on polar coordinates (Polar Swarm).
}
\description{
This projetion method is a part of the databionic swarm which uses the nash
equlibrium [Thrun/Ultsch, 2021]. Using polar coordinates for agents (here
Databots) in two dimensions has many advantages, for further details see
[Thrun, 2018] and [Thrun/Ultsch, 2021].}
\usage{
Pswarm(DataOrDistance,PlotIt=FALSE,Cls=NULL,Silent=TRUE,
Debug=FALSE,LC=c(NULL,NULL),method= "euclidean",Parallel=FALSE,...)
}
\arguments{
\item{DataOrDistance}{Numeric matrix [1:n,1:n]: symmetric matrix of
dissimilarities, if variable unsymmetric (Numeric matrix [1:d,1:n]) it is
assumed as a dataset and the euclidean distances are calculated of d variables
and n cases.}
\item{PlotIt}{Optional, bool, default=FALSE, If =TRUE, Plots the projection
during the computation prozess after every nash equlibirum.}
\item{Cls}{Optional, numeric vector [1:n], given Classification in numbers, only
for plotting if PlotIt=TRUE, irrelevant for computations.}
\item{Silent}{Optional, bool, default=FALSE, If =TRUE results in various console
messages}
\item{Debug}{Optional, Debug, default=FALSE, =TRUE results in various console
messages, depricated for CRAN, because cout is not allowed.}
\item{LC}{Optional, grid size c(Lines, Columns), sometimes it is better to call
\code{\link{setGridSize}} separately.}
\item{method}{Optional, one of 39 distance methods of \code{parDist} of package
parallelDist,  if Data matrix is chosen above}
\item{Parallel}{Optional, =TRUE: Parallel C++ implementation, =FALSE C++ implementation}
\item{\dots}{Further arguments passed on to the \code{parDist} function, e.g.
user-defined distance functions}
}
\details{
DBS is a flexible and robust clustering framework that consists of three
independent modules. The first module is the parameter-free projection method
Pswarm \code{\link{Pswarm}}, which exploits the concepts of self-organization
and emergence, game theory, swarm intelligence and symmetry considerations. The
second module is a parameter-free high-dimensional data visualization technique,
which generates projected points on a topographic map with hypsometric colors
\code{\link{GeneratePswarmVisualization}}, called the generalized U-matrix. The
third module is a clustering method with no sensitive parameters
\code{\link{DBSclustering}}. The clustering can be verified by the visualization
and vice versa. The term DBS refers to the method as a whole. }
\value{
List with
\item{ProjectedPoints}{[1:n,1:2] xy cartesian coordinates of projection}
\item{LC}{number of Lines and Columns in c(Lines,Columns). Lines is a value slightly above the maximum of the x-coordinates and Columns is a value slightly above the maximum of the y-coordinates  of ProjectedPoints}
\item{Control}{List, only for intern debugging}
}
\references{
[Thrun, 2018]  Thrun, M. C.: Projection Based Clustering through Self-Organization and Swarm Intelligence, doctoral dissertation 2017, Springer, Heidelberg, ISBN: 978-3-658-20539-3, \doi{10.1007/978-3-658-20540-9}, 2018. 

[Thrun/Ultsch, 2021]  Thrun, M. C., and Ultsch, A.: Swarm Intelligence for Self-Organized Clustering, Artificial Intelligence, Vol. 290, pp. 103237, \doi{10.1016/j.artint.2020.103237}, 2021.
}
\note{
LC is now automatically estimated; LC is the size of the grid c(Lines,Columns), number of Lines and Columns, default c(NULL,NULL) and automatic calculation by \code{\link{setGridSize}}
}
\author{
Michael Thrun
}
\examples{
data("Lsun3D")
Data=Lsun3D$Data
Cls=Lsun3D$Cls
InputDistances=as.matrix(dist(Data))
#If not called separately setGridSize() is called in Pswarm
LC=setGridSize(InputDistances)
\donttest{res=Pswarm(InputDistances,LC=LC,Cls=Cls,PlotIt=TRUE)}

\dontshow{
data=matrix(runif(n = 100),10,10)
distance=as.matrix(dist(data))
res=Pswarm(distance,LC = c(10,12))
}
}
\keyword{DBS}
\keyword{swarm}
\keyword{swarms}
\concept{Databionic swarm}
\concept{Polar Swarm}
\keyword{pswarm}
\keyword{pSwarm}
\keyword{PSwarm}
\concept{swarm intelligence}
\concept{game theory}
\concept{cluster analysis}
\keyword{projection}
\keyword{projection method}
\concept{dimensionality reduction}
\keyword{visualization}
\keyword{DR}