Function kmedoids::fastmsc

source ·
pub fn fastmsc<M, N, L>(
    mat: &M,
    med: &mut Vec<usize>,
    maxiter: usize
) -> (L, Vec<usize>, usize, usize)where
    N: Zero + PartialOrd + Copy,
    L: Float + Signed + AddAssign + From<N> + From<u32>,
    M: ArrayAdapter<N>,
Expand description

Run the FastMSC algorithm, which yields the same results as the original PAMMEDSIL.

This is faster than PAMMEDSIL, but slower than FasterMSC, and mostly of interest for academic reasons. Quality-wise, FasterMSC is not worse on average, but much faster.

This is the improved version, which costs O(n²) per iteration to find the best swap.

  • type M - matrix data type such as ndarray::Array2 or kmedoids::arrayadapter::LowerTriangle
  • type N - number data type such as u32 or f64
  • type L - number data type such as i64 or f64 for the loss (must be signed)
  • mat - a pairwise distance matrix
  • med - the list of medoids
  • maxiter - the maximum number of iterations allowed

returns a tuple containing:

  • the final loss
  • the final cluster assignment
  • the number of iterations needed
  • the number of swaps performed


  • panics when the dissimilarity matrix is not square
  • panics when k is 0 or larger than N


Given a dissimilarity matrix of size 4 x 4, use:

let data = ndarray::arr2(&[[0,1,2,3],[1,0,4,5],[2,4,0,6],[3,5,6,0]]);
let mut meds = kmedoids::random_initialization(4, 2, &mut rand::thread_rng());
let (loss, assi, n_iter, n_swap): (f64, _, _, _) = kmedoids::fastpam1(&data, &mut meds, 100);
println!("Loss is: {}", loss);