#===============================================
# Show the classes as coloured text
#===============================================
plot.class <- function(train.data, train.class, test.data, test.class, lim = {},
                       main = "",  ...) {
           # Display classes as text
   if (length(lim) == 4) {
      plot(train.data[,1:2], type = "n",
            xlim = c(lim[1], lim[2]), ylim = c(lim[3], lim[4]), main = main)
   } else {
      plot(train.data[,1:2], type = "n", main = main)
   }
   text(train.data[,1], train.data[,2], as.character(train.class), col = train.class+2)
   if (length(test.class) > 0) {
      text(test.data[,1], test.data[,2], "*", col = test.class+2, cex = 2)
   }
}

plot.class.boundary <- function(train.data, train.class, test.data, test.class,
                                model.exp, predict.exp, numb.xy.pts, lim = {}, extra = 0) {
   numb.classes <- length(unique(train.class))
   model <- eval(model.exp)
   res <- f.create.grid(train.data, numb.xy.pts, size = lim)
   xp <- res$xp
   yp <- res$yp
   points <- res$zp
   dimnames(points)[[2]]<- dimnames(train.data)[[2]]
   Z <- eval(predict.exp)
   points(points[,1], points[,2], pch = ".", col = (matrix(Z, length(xp), length(yp))+2))

   for (c in 1:numb.classes) {
      contour(xp, yp, matrix(as.numeric(Z == (c - 1)), length(xp), length(yp)),
                 add = T, levels = 0.5, labex = 0, col = 1)
   }
   return (model)
}

example.display <- function(train.data, train.class, test.data, test.class,
                        numb.xy.pts, title, model.exp, predict.exp, lim = {}, extra = {}){
     # Plot the data (coloured numbers)
   plot.class(train.data, train.class, test.data, test.class, lim, main = title)
   numb.classes <- length(unique(train.class))
   model <- plot.class.boundary(train.data, train.class, test.data, test.class,
                                model.exp, predict.exp, numb.xy.pts, lim, extra = extra)
   cat("===== The model is =====\n")
   print(model)
   cat("\n===== Training =====\n")
   points <- train.data
   pred.train.class <- eval(predict.exp)
   print(confusion.expand(pred.train.class, train.class))
           # Print the total number of incorrect classifications
   misclass.train.ind <- which(pred.train.class!=train.class)
   cat("\n===== Misclassified training cases =====\n")
   if (length(misclass.train.ind) > 0) {
      tmp <- rbind(train.data[misclass.train.ind,])
      rownames(tmp) <- misclass.train.ind
           # Print the incorrect classes
      print(tmp)
           # Display the incorrect classes in blue
      points(tmp, col = "blue", pch = 24)
   } else {
      cat("No misclassified cases\n")
   }
   if (length(test.data) > 0) {
      cat("\n===== Test =====\n")
      points <- test.data
      pred.test.class <- eval(predict.exp)
      ind.tmp <- ((points-1) != test.class)*(1:length(test.class))
      print(confusion.expand(pred.test.class, test.class))
      misclass.test.ind <- which(pred.test.class != test.class)
      cat("\n===== Misclassified test cases =====\n")
      if (length(misclass.test.ind) > 0) {
         tmp <- rbind(test.data[misclass.test.ind,])
         rownames(tmp) <- misclass.test.ind
              # Print the incorrect classes
         print(tmp)
              # Display the incorrect classes in blue
         points(tmp, col = "blue", pch = 25)
      } else {
         cat("No misclassified cases\n")
      }
   }
   cat("===================================\n\n")
   bringToTop(which = dev.cur(), stay = FALSE)
   model
}