network_loocv <- function(data_matrix,feature_type,p,corr,nf,powerS,nc,L,classifier,kern){
if(missing(p)) p=0;
if(missing(corr)) corr=0;
if(missing(L)){
L='label'
}
if(missing(nf)){
nf=0
}
if(missing(powerS)){
powerS=1
}
if(missing(nc)){
nc=1
}
if(missing(classifier)){
classifier = "SVM"
}
if(missing(kern)){
kern = "linear"
}
names(data_matrix)[colnames(data_matrix)==L] <- paste("label")
pred <- NULL
classes <- unique(data_matrix$label)
for(i in 1:nrow(data_matrix)){
data_train <- data_matrix[-i,] # training data
data_test <- data_matrix[i,] # test data
result <- network_classify(data_train,data_test,feature_type,p,corr,nf,powerS,nc,L,classifier,kern)
pred[[i]] <- result$pred
}
predx <- as.numeric(as.factor(pred))
testx <- as.numeric(as.factor(data_matrix$label))
accuracy = sum(predx==testx)/nrow(data_matrix)
return(accuracy)
}
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