sklearn.manifold.SpectralEmbedding  
class sklearn.manifold.SpectralEmbedding(n_components=2, *, affinity='nearest_neighbors', gamma=None, random_state=None, eigen_solver=None, n_neighbors=None, n_jobs=None) [source]
 
Spectral embedding for non-linear dimensionality reduction. Forms an affinity matrix given by the specified function and applies spectral decomposition to the corresponding graph laplacian. The resulting transformation is given by the value of the eigenvectors for each data point. Note : Laplacian Eigenmaps is the actual algorithm implemented here. Read more in the User Guide.  Parameters 
 
n_componentsint, default=2 

The dimension of the projected subspace.  
affinity{‘nearest_neighbors’, ‘rbf’, ‘precomputed’, ‘precomputed_nearest_neighbors’} or callable, default=’nearest_neighbors’ 

 How to construct the affinity matrix.

 ‘nearest_neighbors’ : construct the affinity matrix by computing a graph of nearest neighbors. ‘rbf’ : construct the affinity matrix by computing a radial basis function (RBF) kernel. ‘precomputed’ : interpret X as a precomputed affinity matrix. ‘precomputed_nearest_neighbors’ : interpret X as a sparse graph of precomputed nearest neighbors, and constructs the affinity matrix by selecting the n_neighbors nearest neighbors. callable : use passed in function as affinity the function takes in data matrix (n_samples, n_features) and return affinity matrix (n_samples, n_samples).     
gammafloat, default=None 

Kernel coefficient for rbf kernel. If None, gamma will be set to 1/n_features.  
random_stateint, RandomState instance or None, default=None 

Determines the random number generator used for the initialization of the lobpcg eigenvectors when solver == ‘amg’. Pass an int for reproducible results across multiple function calls. See :term: Glossary <random_state>.  
eigen_solver{‘arpack’, ‘lobpcg’, ‘amg’}, default=None 

The eigenvalue decomposition strategy to use. AMG requires pyamg to be installed. It can be faster on very large, sparse problems. If None, then 'arpack' is used.  
n_neighborsint, default=None 

Number of nearest neighbors for nearest_neighbors graph building. If None, n_neighbors will be set to max(n_samples/10, 1).  
n_jobsint, default=None 

The number of parallel jobs to run. None means 1 unless in a joblib.parallel_backend context. -1 means using all processors. See Glossary for more details.    Attributes 
 
embedding_ndarray of shape (n_samples, n_components) 

Spectral embedding of the training matrix.  
affinity_matrix_ndarray of shape (n_samples, n_samples) 

Affinity_matrix constructed from samples or precomputed.  
n_neighbors_int 

Number of nearest neighbors effectively used.     References  A Tutorial on Spectral Clustering, 2007 Ulrike von Luxburg http://citeseerx.ist.psu.edu/viewdoc/summary?doi=10.1.1.165.9323
 On Spectral Clustering: Analysis and an algorithm, 2001 Andrew Y. Ng, Michael I. Jordan, Yair Weiss http://citeseerx.ist.psu.edu/viewdoc/summary?doi=10.1.1.19.8100
 Normalized cuts and image segmentation, 2000 Jianbo Shi, Jitendra Malik http://citeseer.ist.psu.edu/viewdoc/summary?doi=10.1.1.160.2324
  Examples >>> from sklearn.datasets import load_digits
>>> from sklearn.manifold import SpectralEmbedding
>>> X, _ = load_digits(return_X_y=True)
>>> X.shape
(1797, 64)
>>> embedding = SpectralEmbedding(n_components=2)
>>> X_transformed = embedding.fit_transform(X[:100])
>>> X_transformed.shape
(100, 2)
 Methods  
fit(X[, y]) Fit the model from data in X.  
fit_transform(X[, y]) Fit the model from data in X and transform X.  
get_params([deep]) Get parameters for this estimator.  
set_params(**params) Set the parameters of this estimator.    
fit(X, y=None) [source]
 
Fit the model from data in X.  Parameters 
 
X{array-like, sparse matrix} of shape (n_samples, n_features) 

Training vector, where n_samples is the number of samples and n_features is the number of features. If affinity is “precomputed” X : {array-like, sparse matrix}, shape (n_samples, n_samples), Interpret X as precomputed adjacency graph computed from samples.  
yIgnored 
  Returns 
 
selfobject 

Returns the instance itself.     
  
fit_transform(X, y=None) [source]
 
Fit the model from data in X and transform X.  Parameters 
 
X{array-like, sparse matrix} of shape (n_samples, n_features) 

Training vector, where n_samples is the number of samples and n_features is the number of features. If affinity is “precomputed” X : {array-like, sparse matrix} of shape (n_samples, n_samples), Interpret X as precomputed adjacency graph computed from samples.  
yIgnored 
  Returns 
 
X_newarray-like of shape (n_samples, n_components) 
   
  
get_params(deep=True) [source]
 
Get parameters for this estimator.  Parameters 
 
deepbool, default=True 

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

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 Pipeline). The latter have parameters of the form <component>__<parameter> so that it’s possible to update each component of a nested object.  Parameters 
 
**paramsdict 

Estimator parameters.    Returns 
 
selfestimator instance 

Estimator instance.     
 
 Examples using sklearn.manifold.SpectralEmbedding
 
  Various Agglomerative Clustering on a 2D embedding of digits  

  Comparison of Manifold Learning methods  

  Manifold Learning methods on a severed sphere  

  Manifold learning on handwritten digits: Locally Linear Embedding, Isomap…