# R/minDistance.details.R In LearnClust: Learning Hierarchical Clustering Algorithms

#### Documented in minDistance.details

```#' @title Minimal distance
#' @description To explain how to get the matrix minimal value.
#' @param matrix is a numeric matrix. It could be a numeric vector.
#' @details This function is part of the hierarchical clusterization method. The function uses the numeric
#' vector or matrix \code{matrix} given and return the minimal value.
#' The function avoids distances equal 0, and initialize minimum value with an auxiliar function \code{initMin},
#' which gets the first matrix element with a valid distance.
#'
#' @author Roberto Alcántara \email{roberto.alcantara@@edu.uah.es}
#' @author Universidad de Alcalá de Henares
#' @return Numeric value. Min value from a matrix. Explanation.
#' @examples
#'
#' matrixExample <- matrix(c(1:10), nrow=2)
#'
#' minDistance(1:10)
#'
#' minDistance.details(matrixExample)
#'
#' @export

minDistance.details <-
function(matrix){
message("\n 'minDistance' function gets the minimal value from a matrix. \n")
message("\n It returns the minimal value avoiding 0 values. \n\n")
min <- initMin(matrix)
for (i in (1:length(matrix))){
value <- matrix[i]
if ((value < min) & (value != 0)){
min <- value
}
}
print(matrix)
message("\n\n ",min, " is the minimal value of the matrix. \n")
min
}
```

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LearnClust documentation built on Nov. 30, 2020, 1:09 a.m.