manifold.MDS()

class sklearn.manifold.MDS(n_components=2, metric=True, n_init=4, max_iter=300, verbose=0, eps=0.001, n_jobs=1, random_state=None, dissimilarity='euclidean') [source]

Multidimensional scaling

Parameters:	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?.
Attributes:	embedding_ : array-like, shape [n_components, n_samples] Stores the position of the dataset in the embedding space stress_ : float The final value of the stress (sum of squared distance of the disparities and the distances for all constrained points)

References

?Modern Multidimensional Scaling - Theory and Applications? Borg, I.; Groenen P. Springer Series in Statistics (1997)

?Nonmetric multidimensional scaling: a numerical method? Kruskal, J. Psychometrika, 29 (1964)

?Multidimensional scaling by optimizing goodness of fit to a nonmetric hypothesis? Kruskal, J. Psychometrika, 29, (1964)

Methods

`fit`(X[, y, init])	Computes the position of the points in the embedding space
`fit_transform`(X[, y, init])	Fit the data from X, and returns the embedded coordinates
`get_params`([deep])	Get parameters for this estimator.
`set_params`(\\params)	Set the parameters of this estimator.

__init__(n_components=2, metric=True, n_init=4, max_iter=300, verbose=0, eps=0.001, n_jobs=1, random_state=None, dissimilarity='euclidean') [source]

fit(X, y=None, init=None) [source]

Computes the position of the points in the embedding space

Parameters:

Parameters:	X : array, shape=[n_samples, n_features], or [n_samples, n_samples] if dissimilarity=?precomputed? Input data. init : {None or ndarray, shape (n_samples,)}, optional If None, randomly chooses the initial configuration if ndarray, initialize the SMACOF algorithm with this array.

X : array, shape=[n_samples, n_features], or [n_samples, n_samples] if dissimilarity=?precomputed?

Input data.

init : {None or ndarray, shape (n_samples,)}, optional

If None, randomly chooses the initial configuration if ndarray, initialize the SMACOF algorithm with this array.

fit_transform(X, y=None, init=None) [source]

Fit the data from X, and returns the embedded coordinates

Parameters:

Parameters:	X : array, shape=[n_samples, n_features], or [n_samples, n_samples] if dissimilarity=?precomputed? Input data. init : {None or ndarray, shape (n_samples,)}, optional If None, randomly chooses the initial configuration if ndarray, initialize the SMACOF algorithm with this array.

X : array, shape=[n_samples, n_features], or [n_samples, n_samples] if dissimilarity=?precomputed?

Input data.

init : {None or ndarray, shape (n_samples,)}, optional

If None, randomly chooses the initial configuration if ndarray, initialize the SMACOF algorithm with this array.

get_params(deep=True) [source]

Get parameters for this estimator.

Parameters:

Parameters:	deep : boolean, optional If True, will return the parameters for this estimator and contained subobjects that are estimators.
Returns:	params : mapping of string to any Parameter names mapped to their values.

deep : boolean, optional

If True, will return the parameters for this estimator and contained subobjects that are estimators.

Returns:

params : mapping of string to any

Parameter names mapped to their values.

set_params(**params) [source]

Set the parameters of this estimator.

The method works on simple estimators as well as on nested objects (such as pipelines). The latter have parameters of the form <component>__<parameter> so that it?s possible to update each component of a nested object.