create.gaussians <- function(numb.clust=5, varx=1, vary=1) {
   g.1 <- seq(-10, 10, by=4)
   g.2 <- seq(-10, 10, by=4)
   possible.c <- expand.grid(g.1,g.2)
   possible.counts <- c(100, 150, 200,250, 300, 350, 400, 450, 500)

   clust.mean <- possible.c[sample(dim(possible.c)[1], numb.clust),]
   clust.count <- possible.counts[sample(length(possible.counts), numb.clust, replace=T)]
   Data <- {}
   for (c in 1:numb.clust) {
      X.1 <- rnorm(clust.count[c], clust.mean[c,1], varx)
      Y.1 <- rnorm(clust.count[c], clust.mean[c,2], vary)
      Data <- rbind(Data, cbind(X.1, Y.1, c))
   }
   Data
}

#====================================================
# Create a Gaussian cloud mean (x,y) variance (varx,vary)
#   which is rotated by the angle rot
#====================================================

#====================================================
# Create a Gaussian cloud mean (x,y) variance (varx,vary)
#   which is rotated by the angle rot
#====================================================

Make.Gaussian <- function(n, x, y, varx = 1, vary = 1, rot) {
   X.1 <- rnorm(n, 0, varx)
   Y.1 <- rnorm(n, 0, vary)
   Z.1 <- R.2D(cbind(X.1, Y.1), rot)
   sweep(Z.1, 2, c(x, y), '+')
}