dot-al_s2: Implementation of Shortest Shortest Path (S2) algorithm
In e-sensing/activelearning: Active Learning Satellite Image Time Series

The S2 algorithm uses a undirected graph of the samples and iteratively removes edges, trying to identify the boundaries of each class in the graph.

1	.al_s2(s_labelled_tb, s_unlabelled_tb, sim_method, closest_n, mode)

`s_labelled_tb`	A sits tibble with labelled samples.
`s_unlabelled_tb`	A sits tibble with unlabelled samples.
`sim_method`	A character. A method for computing the similarity among samples. See proxy::simil for details.
`closest_n`	An integer. The number of most similar samples to keep while building the graph.
`mode`	A character telling if the functin runs on either the "active_learning" or "semi_supervised_learning" mode.