Nothing

```
#' Nearest neighbor
#'
#' An auxiliary function to find the nearest neighbors from the distance matrix
#'
#'
#' @param model nearest neighbor ranking model
#' @param k number of the nearest neighbors to consider
#' @return a vector of length of \code{model}
#' @details This function is applied to find the nearest neighbors in the distance matrix.
kNN <- function(model, k) {
w <- rep(0, length(model))
w[order(model)[1:k]] <- 1
colnames(w) <- colnames(model)
w
}
#' Predicting rankings using the nearest neighbor algorithm
#'
#' This function makes prediction of rankings based on the nearest neighbor
#' @param train.x is matrix of numeric attributes in training sample
#' @param test.x is a vector of new numeric attributes for which to predict rankings
#' @param y is matrix of training rankings
#' @param k is the number of the nearest neighbors to consider (default k=3)
#' @param n is a parameter of 'memory' of how fast the past rankings gets forgotten. (see details of \link{time_weights}). By default, \code{n=1} which means that a label ranking problem does not have timing effect.
#' @return a vector of predicted ranking for attribute \code{test.x}
#' @details A function predicts the rankings based on the euclidean distance between train and test attributes.
#'
#' @importFrom pdist pdist
#'
#' @examples
#' train.x <- lr.num[1:16,]
#' test.x <- lr.num[17,]
#' ranking <- nn_rank(train.x, y, test.x, n=1,k=3)
#' @export
nn_rank <- function(train.x, y, test.x, n=1, k=3) {
w <- time_weights(nrow(as.matrix(train.x)),n)
model <- w * as.matrix(pdist(test.x, train.x))
nn <- kNN(model,k)
rank(apply(y[which(nn == 1), drop = F, ], 2, mean))
}
```

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