R/xBayesDecisionBoundaries.R

In Umatrix: Visualization of Structures in High-Dimensional Data

BayesDecisionBoundaries <- function(Means,SDs,Weights,IsLogDistribution,MinData,MaxData,Ycoor=F){
# DecisionBoundaries = BayesDecisionBoundaries(Means,SDs,Weights,IsLogDistribution);
# find the intersections of Gaussians or LogNormals
# 
# INPUT
# Means(1:L),SDs(1:L),Weights(1:L)      parameters of the Gaussians/LogNormals
# OPTIONAL
# IsLogDistribution(1:L)   ==1 if distribution(i) is a LogNormal, default zeros
# MinData
# MaxData
# Ycoor                   Bool, if TRUE instead of vector of DecisionBoundaries
#                         list of DecisionBoundaries and DBY is returned
# OUTPUT
# DecisionBoundaries(1:L-1)  Bayes decision boundaries 

# Author:  05/2015 RG
# 1.Editor: MT 08/2015
  
if(missing(IsLogDistribution)){
  IsLogDistribution = rep(FALSE,length(Means))
}  
if(missing(MinData)){
  MinData=(Means-3*SDs)[which.min(Means-3*SDs)]
}
if(missing(MaxData)){
  MaxData=(Means+3*SDs)[which.max(Means+3*SDs)]
}
L <- length(Means) # number of Gaussians

if(length(IsLogDistribution)!=L){
  warning(paste('Length of Means',L,'does not equal length of IsLogDistribution',length(IsLogDistribution),'Generating new IsLogDistribution'))  
  IsLogDistribution = rep(FALSE,L)
}
# sortieren nach Means
Sind = order(Means)
Means = sort(na.last=T,Means)
SDs = SDs[Sind]
Weights = Weights[Sind]
IsLogDistribution <- IsLogDistribution[Sind]

L1 = L-1

DecisionBoundaries <- Means[1:L1] # init
DBY = DecisionBoundaries*0 # init

# now intersect 2 Gauss
for(i in 1:L1){
	i1 = i+1
  Decision = Intersect2Mixtures(Means[i],SDs[i],Weights[i],Means[i1],SDs[i1],Weights[i1],IsLogDistribution[i:i1])
  DecisionBoundaries[i] = Decision$CutX
  DBY[i] = Decision$CutY
  DecisionBoundaries[i] = min(MaxData,DecisionBoundaries[i]);
  DecisionBoundaries[i] = max(MinData,DecisionBoundaries[i]);
}

if(Ycoor){
  return(list(DecisionBoundaries = DecisionBoundaries,DBY=DBY))
}else{
  return(DecisionBoundaries = DecisionBoundaries) 
}
}