R/PlotMixturesAndBoundaries.R

In Mthrun/AdaptGauss: Gaussian Mixture Models (GMM)

PlotMixturesAndBoundaries <-function(Data, Means, SDs, Weights,
                                     IsLogDistribution = rep(FALSE,length(Means)),
                                     Plotter="native",
                                     SingleColor = 'blue',
                                     MixtureColor = 'red',
                                     DataColor='black',
                                     BoundaryColor = 'magenta',
                                     xlab, ylab,
                                     SingleGausses = TRUE, ...){
  #PlotGaussMixesAndBoundaries(Data,Means,SDs,Weights,SingleColor,MixtureColor)
  # Plot a Mixture of Gaussian/LogNormal and Bayesian decision boundaries
  
  # INPUT
  # Data[1:n]                   data column for which the distribution was modelled
  # Means[1:L]                  Means of Gaussians,  L ==  Number of Gaussians
  # SDs[1:L]                  estimated Gaussian Kernels = standard deviations
  # Weights[1:L]                relative number of points in Gaussians (prior #															probabilities): sum(Weights) ==1
  # OPTIONAL
  # IsLogDistribution(1:L) 			gibt an ob die jeweilige Verteilung eine 
  #															Lognormaverteilung ist,(default ==0*(1:L))
  # SingleColor              		PlotSymbol of all the single gaussians, default 
  #															magenta
  # MixtureColor             		PlotSymbol of the mixture, default black
  # BoundaryColor               PlotSymbol of the boundaries default green
  # RoundNpower                 Decision Boundaries are rounded by 
  #															roundn(DecisionBoundariesRoundNpower)
  # ...													other plot arguments, like xlim = c(1,10)
  
  # OUTPUT
  # DecisonBoundaries(1:L-1)    Bayes decision boundaries 
  # DBY(1:L-1)                  y values at the cross points of the Gaussians
         
  # author MT 08/2015
  # Editor QS 10/2022
  
  if(!(Plotter %in% c("native", "plotly"))){
    warning("PlotMixtures: Incorrect plotter selected. Doing nothing.")
  }
  
  # Labels
  if(missing(xlab)){
  	xlab = '' # no label for x axis
  }
  if(missing(ylab)){
  	ylab = '' # no label for y axis
  }
  
  # Calculate intersections
  dec = BayesDecisionBoundaries(Means, SDs, Weights, IsLogDistribution, Ycoor = T)
  
  #MT: Bugifx
  #if(is.list(dec)){
  DecisionBoundaries = as.vector(dec$DecisionBoundaries)
  #print('dec was a list, assuming usage of BayesDecisionBoundaries()')
  #}
  # Plot Gaussians
  VPM = PlotMixtures(Data = Data, Means = Means, SDs = SDs, Weights = Weights,
                     Plotter = Plotter, IsLogDistribution = IsLogDistribution,
                     SingleColor = SingleColor, MixtureColor = MixtureColor,
                     DataColor = DataColor, xlab = xlab, ylab = ylab,
                     SingleGausses = SingleGausses, ...)
  MyPlot = VPM$MyPlot
  if(Plotter == "plotly"){
    for(i in 1:length(DecisionBoundaries)){
      MyPlot = plotly::add_segments(p    = MyPlot,
                                    x    = DecisionBoundaries[i],
                                    xend = DecisionBoundaries[i],
                                    y    = 0,
                                    yend = VPM$ylim[2],
                                    line = list("color" = BoundaryColor),
                                    showlegend = FALSE)
    }
  }else if(Plotter == "native"){
    # Intersecions
    for(i in 1:length(DecisionBoundaries)){
      abline(v = DecisionBoundaries[i], col = BoundaryColor)
    }
  }
  return (invisible(list("MyPlot"             = MyPlot,
                         "DecisionBoundaries" = DecisionBoundaries,
                         "DBY"                = dec$DBY))) 
}