library(mda)
library(e1071)

source("data")

x <- subset(data, select = -Class)
y <- as.factor(data$Class)

fdapred <- rep(0, length(y))

rand <- sample(10, length(t(y)), replace = T)
for(iter in sort(unique(rand)))
{
fdamod <- fda(y[rand != iter] ~ ., data = x[rand != iter,], method=gen.ridge)
fdapred[rand == iter] <- predict(fdamod, x[rand == iter,])
 }

cM <- table(true = data$Class, predicted = fdapred)
cMstats <- classAgreement(cM)

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