SVM classifier in R

library('quadprog')

# load the dataset and shuffles it
datafilename <- "data/iris.data"
dataset      <- read.table(datafilename,header=FALSE,sep=",")

dataset <- data.frame(dataset[,1:(length(dataset)-1)],unclass(dataset[,length(dataset)]))

#convert to two class classification problem
#keep the selected class and merge all the others to a single one
selectedClass <- 1
dataset[dataset[,length(dataset)]==selectedClass,][length(dataset)]  <- -1
dataset[dataset[,length(dataset)]!=-1,][length(dataset)]  <- 1

#attributes to use for training/testing and plotting
xIndex <- 3
yIndex <- 4

#select only two attributes to use for learning,
#so that the results can be easily visualized
train.data <- data.frame(dataset[,xIndex],dataset[,yIndex],dataset[,dim(dataset)[2]])

# Z is a diagonal matrix created from the class column vector z
Z <- as.matrix(diag(dataset[,dim(dataset)[2]]))
z <- diag(Z)

# matrix Y = (1, Y_x)
ones <- rep(1,dim(dataset)[1])
Y_x  <- as.matrix(train.data[,-dim(train.data)[2]])
Y    <- cbind(ones,Y_x)

# preparation of the variables for quadprog library
D <- Z %*% Y_x %*% t(Y_x) %*% Z
d <- ones

I  <- diag(ones) # Identity matrix
At <- rbind(I,t(z))
A  <- t(At)

# solve the optimization problem and get the alpha values
alpha <- solve.QP(D,d,A, factorized=TRUE)$solution

print(alpha)