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# Min-Max Distance (aka Ruzicka Distance)
# Function for computing a cosine similarity of a matrix of values,
# e.g. a table of word frequencies.
#
# Argument: a matrix or data table containing at least 2 rows and 2 cols
dist.minmax = function(x){
# test if the input dataset is acceptable
if(is.matrix(x) == FALSE & is.data.frame(x) == FALSE) {
stop("cannot apply a distance measure: wrong data format!")
}
# then, test whether the number of rows and cols is >1
if(length(x[1,]) < 2 | length(x[,1]) < 2) {
stop("at least 2 cols and 2 rows are needed to compute a distance!")
}
# getting the size of the input table
rows = length(x[,1])
# starting a new matrix
y = matrix(nrow = rows, ncol = rows)
rownames(y) = rownames(x)
colnames(y) = rownames(x)
# iterating over rows and columns
for(i in 1:rows) {
for(j in i:rows ) {
y[j,i] = 1 - sum(apply(x[c(i,j),], 2, min)) / sum(apply(x[c(i,j),], 2, max))
}
}
# converting the matrix to the class 'dist'
y = as.dist(y)
return(y)
}
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