MDS

embed_MDS

 embed_MDS (X, ndim=2, seed=2, solver='sgd', how='metric',
            input_is_dist=True, distance_metric='euclidean',
            mds_weights=None)

source

smacof

 smacof (D, n_components=2, metric=True, init=None, random_state=None,
         verbose=0, max_iter=3000, eps=1e-06, n_jobs=1)

Metric and non-metric MDS using SMACOF Parameters ———- D : array-like, shape=[n_samples, n_samples] pairwise distances n_components : int, optional (default: 2) number of dimensions in which to embed D metric : bool, optional (default: True) Use metric MDS. If False, uses non-metric MDS init : array-like or None, optional (default: None) Initialization state random_state : int, RandomState or None, optional (default: None) numpy random state verbose : int or bool, optional (default: 0) verbosity max_iter : int, optional (default: 3000) maximum iterations eps : float, optional (default: 1e-6) stopping criterion Returns ——- Y : array-like, shape=[n_samples, n_components] embedded data

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sgd

 sgd (D, w=None, n_components=2, random_state=None, init=None)

Metric MDS using stochastic gradient descent Parameters ———- D : array-like, shape=[n_samples, n_samples] pairwise distances n_components : int, optional (default: 2) number of dimensions in which to embed D random_state : int or None, optional (default: None) numpy random state init : array-like or None Initialization algorithm or state to use for MMDS Returns ——- Y : array-like, embedded data [n_sample, ndim]

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classic

 classic (D, n_components=2, random_state=None)

Fast CMDS using random SVD Parameters ———- D : array-like, shape=[n_samples, n_samples] pairwise distances n_components : int, optional (default: 2) number of dimensions in which to embed D random_state : int, RandomState or None, optional (default: None) numpy random state Returns ——- Y : array-like, embedded data [n_sample, ndim]