data(iris)
dim(iris)[[1]]
##split the data based on its class
NewData <- split(iris, iris$Species)
## divide the data based on their class
NewDatSetosa <- NewData$setosa[,1:4]
NewDatVersicolor <- NewData$versicolor[,1:4]
NewDatVirginica <- NewData$virginica[,1:4]

##divide the data into train and test datasets
set.seed(1234)
datadivision <- sample(2, nrow(iris), replace=TRUE, prob=c(0.67, 0.33))
iris.training <- iris[datadivision==1, 1:4]
iris.test <- iris[datadivision==2, 1:4]
##Compute the posterior for each class

posterior <- function(data,prior,newDat){
  post <- list()
##compute the density for each point in the training data set
  den <- lapply(1:4, function(i) dnorm(data[[i]], mean(newDat[[i]]), sd(newDat[[i]])))
## compute the posterior for each point. den[[1]] is the density of the
Sepal.Length. den[2] is the density of Sepal.Width. den[3] and den[4] are the density for the resining variables.
     post <- prior*den[[1]]*den[[2]]*den[[3]]*den[[4]]
  return(post)
}
post.Setosa <- posterior(iris.training, prior=0.3, NewDatSetosa)
post.Versicolor <- posterior(iris.training, prior=0.3, NewDatVersicolor)
post.Virginica <- posterior(iris.training, prior=0.3, NewDatVirginica)