metric : boolean, optional, default: True
compute metric or nonmetric SMACOF (Scaling by Majorizing a Complicated Function) algorithm
n_components : int, optional, default: 2
number of dimension in which to immerse the similarities overridden if initial array is provided.
n_init : int, optional, default: 4
Number of time the smacof algorithm will be run with different initialisation. The final results will be the best output of the n_init consecutive runs in terms of stress.
max_iter : int, optional, default: 300
Maximum number of iterations of the SMACOF algorithm for a single run
verbose : int, optional, default: 0
level of verbosity
eps : float, optional, default: 1e-6
relative tolerance w.r.t stress to declare converge
n_jobs : int, optional, default: 1
The number of jobs to use for the computation. This works by breaking down the pairwise matrix into n_jobs even slices and computing them in parallel.
If -1 all CPUs are used. If 1 is given, no parallel computing code is used at all, which is useful for debugging. For n_jobs below -1, (n_cpus + 1 + n_jobs) are used. Thus for n_jobs = -2, all CPUs but one are used.
random_state : integer or numpy.RandomState, optional
The generator used to initialize the centers. If an integer is given, it fixes the seed. Defaults to the global numpy random number generator.
dissimilarity : string
Which dissimilarity measure to use. Supported are ‘euclidean’ and ‘precomputed’.
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