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')
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Multidimensional scaling
Read more in the User Guide.
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’. |
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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) |
“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)
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)
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Computes the position of the points in the embedding space
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. |
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fit_transform(X, y=None, init=None)
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Fit the data from X, and returns the embedded coordinates
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. |
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get_params(deep=True)
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Get parameters for this estimator.
Parameters: |
deep: boolean, optional : If True, will return the parameters for this estimator and contained subobjects that are estimators. |
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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.
Returns: | self : |
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sklearn.manifold.MDS
© 2007–2016 The scikit-learn developers
Licensed under the 3-clause BSD License.
http://scikit-learn.org/stable/modules/generated/sklearn.manifold.MDS.html