get.mipp.svm.linear <- function(x.train, y.train, x.test, y.test){
 

       if(is.data.frame(x.train)) x.train <- as.matrix(x.train)
       if(is.data.frame(x.test))  x.test  <- as.matrix(x.test)

 	y.train <- factor(y.train)
 	y.test <- factor(y.test)
 
 	fit <- svm(x.train, y.train, kernel="linear")
 
 	True.class <- y.test
 	Pred.class <- predict(fit, x.test)
 
 	fofx <- numeric(length(y.test))
 	for(i in 1:length(y.test)){

 		xin <- x.test[i,,drop=FALSE]

 		fofx[i] <- linearkernel.decision.function(xin, x.train, fit)
 	}
 
       c <-1 #optimal parameter
 	prob <- 1/(1+c*exp(-fofx))
 	postdf <- data.frame(prob, True.class)
 	post.prob <- ifelse(postdf$True.class==Pred.class, 1-postdf$prob, postdf$prob)
 
 	N <- length(y.test)
 	nMiss <- N - sum(True.class==Pred.class)
 	Er <- nMiss/N
 	MiPP <- sum(post.prob)-nMiss
 	sMiPP <- MiPP/N
 	
 	return(list(N.Miss=nMiss, ErrorRate=Er, MiPP=MiPP, sMiPP=sMiPP))
 }
 

 
 # Linear Kernel Decision Function
 linearkernel.decision.function <-function(newx, oldx, svmobj) {
     # oldx is the original training data matrix
     # svmobj is the name of the svm object
 
     # Extract y*alpha:
     	svcoefs <- svmobj$coefs
     # Extract b:
     	svconstant <- -1*svmobj$rho   
     # Get the support vectors

     	svdata <- oldx[svmobj$index,,drop=FALSE]

     # Reformat the new x
     	xt <- newx

     	nrowxt <- length(oldx[1,,drop=FALSE])

     	dim(xt) <- c(nrowxt,1)   
     # linear kernel:
     	prods <- svdata %*% xt   
     # compute h(x):
     	h <- t(prods) %*% svcoefs 
     # compute f(x):
     	#h + svconstant    
     	return(h + svconstant)    
 }