cross.val <- function(

  exp.df, class.vec, segments, performance, class.algo, quiet = TRUE

) {

  # Validation

  if (!(class.algo %in% c("J48", "rpart"))) {

    stop("Unknown classification algorithm")

  }

  # Start cross validation loop

  class1 <- levels(class.vec)[1]

  for (fold in seq_len(length(segments))) {

    if (!quiet) message("Fold ", fold, " of ", length(segments))

    # Define training and test set

    test.ind <- segments[[fold]]

    training.set <- exp.df[-test.ind, ]

    test.set <- exp.df[test.ind, , drop = FALSE]

    test.set$training.class <- class.vec[-test.ind]

    test.class <- class.vec[test.ind]

    # Train J48 on training set

    if (class.algo == "J48") {

      cv.model <- J48(training.class ~ ., training.set)

      pred.class <- predict(cv.model, test.set)

    } else {

      cv.model <- rpart(training.class ~ ., training.set, method = "class")

      pred.class <- predict(cv.model, test.set, type = "class")

    }

    # Evaluate model on test set

    performance <- eval.pred(

      pred.class, test.class, class1, performance

    )

  }

  return(performance)

}