# preparing pseudo.random scores and target-labels for examples: 100 examples
# and 10 classes
Scores <- matrix(runif(1000),nrow=100);
Targets <- matrix(integer(1000),nrow=100);
Targets[Scores>0.5] <- 1;
# adding noise to scores
Scores <- Scores + matrix(rnorm(1000, sd=0.3),nrow=100);
colnames(Scores) <-colnames(Targets) <- LETTERS[1:10];
# getting scores and labels of class "A"
scores <- Scores[,"A"];
labels <- Targets[,"A"];
# AUC for a single class
AUC.single(scores,labels);
# AUC for the 10 classes
AUC.single.over.classes(Targets, Scores);
# AUCn for a single class considering only the first top scored negatives
AUC.n.single(scores,labels, n=20);
# AUCn for the 10 classes considering only the first top scored negatives
AUC.n.single.over.classes(Targets, Scores, n=20);
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