library(e1071)
source("data")
data.r <- data

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

pred <- rep(0, length(data$Class))
rand <- sample(10, length(t(y)), replace = T)

for(iter in sort(unique(rand)))
{
	set.seed(iter)
	mod <- svm(y[rand != iter] ~ ., data = x[rand != iter,], scale=TRUE, cache=512, kernel="sigmoid") 
    pred[rand == iter] <- predict(mod, x[rand == iter,])
}
cM <- table(true = data$Class, pred = pred)
cMstats <- classAgreement(cM)

write(cM, 'confusionMatrix', ncolumns=1)
write(cMstats$diag, 'accuracy')
write(cMstats$kappa, 'kappa')