{
# Generate a random vector, and permute it:
# we expect the algorithm to retrieve the permutation
- v = runif(n, min=-1, max=1)
- permutation = sample(1:n)
- v_p = v[permutation]
+ v <- runif(n, min=-1, max=1)
+ permutation <- sample(1:n)
+ v_p <- v[permutation]
# v is reference, v_p is v after permutation
- # distances[i,j] = distance between i-th component of v and j-th component of v_p
+ # distances[i,j] = distance between i-th component of v
+ # and j-th component of v_p
# "in rows : reference; in columns: after permutation"
# "permutation" contains order on v to match v_p as closely as possible
- distances = sapply(v_p, function(elt) ( abs(elt - v) ) )
- assignment = .hungarianAlgorithm(distances)
+ distances <- sapply(v_p, function(elt) ( abs(elt - v) ) )
+ assignment <- .hungarianAlgorithm(distances)
expect_equal(assignment, permutation)
}
}