library(mda)
library(e1071)

source("data")

fdapred <- rep(0, length(data$Class))
for(iter in 1:length(data$Class)) 
{
	set.seed(iter)
	fdamod <- fda(Class ~ ., data = data[-iter,], method=gen.ridge)
 	fdapred[iter] <- predict(fdamod, data[iter,])
}
cM <- table(true = data$Class, predicted = fdapred)
cMstats <- classAgreement(cM)

# cat("\n Penalized Discriminant Analysis \n")
# cat(" =============== ")
# cat("\n Accuracy: ") ; print(cMstats$diag)
# cat("\n Kappa: ") ; print(cMstats$kappa)
# cat("\n Confusion matrix: \n") ; print(cM)
# cat("\n")

write(cM, 'confusionMatrix')
write(cMstats$diag, 'accuracy')
write(cMstats$kappa, 'kappa')
write(fdapred, file="pred_pda", ncolumns = 1)