R/getCartesianCoordinates.R

In DatabionicSwarm: Swarm Intelligence for Self-Organized Clustering

getCartesianCoordinates = function(DataBotsPos,GridRadius,GridAngle,QuadOrHexa=T){
  # BMUs = getCartesianCoordinates(DataBotsPos,GridRadius,GridAngle)
  # Transform DataBot Indizes two exac cartesian coordinates on an toroid two dimensional grid.
  #
  # INPUT
  # DataBotsPos[1:AnzData]               complex vector Two Indizes per Databot describing its positions in an two dimensional grid
  # GridRadius[Lines,Columns]              Radii Matrix of all possible Positions of DataBots in Grid, see als Doku of setPolarGrid()
  # GridAngle[Lines,Columns]               Angle Matrix of all possible Positions of DataBots in Grid, see als Doku of setPolarGrid()
  # Lines,Columns                          Size of planar toroid two dimensional grid
  # QuadOrHexa                           FALSE=If DataPos on hexadiagonal grid, runde auf 2 nachkommestellen, Default=TRUE
  #
  # OUTPUT
  # BestMatchingUnits[1:AnzData,2]         coordinates on an two dimensional grid for each databot excluding unique key, sucht that
  #                                        with gUmatrix(inputs=Data, ,projectionPoints=BestMatchingUnits,cls=cls,toroid=TRUE), see Doku there
  #                                        a visualization of the pswarm projection is possible
  # author: MT 01/2015  
  # Example:
  # If Classification cls of Data is availible
  #
  # bmu=getCartesianCoordinates(DataBotsPos,GridRadius,GridAngle)
  # ClassPlot(bmu[1,],bmu[2,],cls)
  DataBotsPosInd = cbind(Re(DataBotsPos),Im(DataBotsPos))
  bmR   = GridRadius[DataBotsPosInd]
  bmPhi = GridAngle[DataBotsPosInd]*pi/180    
  if(!QuadOrHexa){
    bmX=round(bmR*cos(bmPhi),2)
    bmY=round(bmR*sin(bmPhi),2)
  }else{
    bmX=bmR*cos(bmPhi)
    bmY=bmR*sin(bmPhi)
  }
  return(BestMatchingUnits=cbind(bmX,bmY))
}