sklearn.manifold.LocallyLinearEmbedding — scikit-learn 0.20.4 documentation (original) (raw)

class sklearn.manifold. LocallyLinearEmbedding(n_neighbors=5, n_components=2, reg=0.001, eigen_solver='auto', tol=1e-06, max_iter=100, method='standard', hessian_tol=0.0001, modified_tol=1e-12, neighbors_algorithm='auto', random_state=None, n_jobs=None)[source]¶

Locally Linear Embedding

Read more in the User Guide.

| Parameters: | n_neighbors : integer number of neighbors to consider for each point. n_components : integer number of coordinates for the manifold reg : float regularization constant, multiplies the trace of the local covariance matrix of the distances. eigen_solver : string, {‘auto’, ‘arpack’, ‘dense’} auto : algorithm will attempt to choose the best method for input data arpack : use arnoldi iteration in shift-invert mode. For this method, M may be a dense matrix, sparse matrix, or general linear operator. Warning: ARPACK can be unstable for some problems. It is best to try several random seeds in order to check results. dense : use standard dense matrix operations for the eigenvalue decomposition. For this method, M must be an array or matrix type. This method should be avoided for large problems. tol : float, optional Tolerance for ‘arpack’ method Not used if eigen_solver==’dense’. max_iter : integer maximum number of iterations for the arpack solver. Not used if eigen_solver==’dense’. method : string (‘standard’, ‘hessian’, ‘modified’ or ‘ltsa’) standard : use the standard locally linear embedding algorithm. see reference [1] hessian : use the Hessian eigenmap method. This method requires n_neighbors > n_components * (1 + (n_components + 1) / 2see reference [2] modified : use the modified locally linear embedding algorithm. see reference [3] ltsa : use local tangent space alignment algorithm see reference [4] hessian_tol : float, optional Tolerance for Hessian eigenmapping method. Only used if method == 'hessian' modified_tol : float, optional Tolerance for modified LLE method. Only used if method == 'modified' neighbors_algorithm : string [‘auto’|’brute’|’kd_tree’|’ball_tree’] algorithm to use for nearest neighbors search, passed to neighbors.NearestNeighbors instance random_state : int, RandomState instance or None, optional (default=None) If int, random_state is the seed used by the random number generator; If RandomState instance, random_state is the random number generator; If None, the random number generator is the RandomState instance used by np.random. Used when eigen_solver == ‘arpack’. n_jobs : int or None, optional (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 Glossaryfor more details. | | ----------- | --------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------- | | Attributes: | embedding_ : array-like, shape [n_samples, n_components] Stores the embedding vectors reconstruction_error_ : float Reconstruction error associated with embedding_ nbrs_ : NearestNeighbors object Stores nearest neighbors instance, including BallTree or KDtree if applicable. |

References

Examples

from sklearn.datasets import load_digits from sklearn.manifold import LocallyLinearEmbedding X, _ = load_digits(return_X_y=True) X.shape (1797, 64) embedding = LocallyLinearEmbedding(n_components=2) X_transformed = embedding.fit_transform(X[:100]) X_transformed.shape (100, 2)

Methods

__init__(n_neighbors=5, n_components=2, reg=0.001, eigen_solver='auto', tol=1e-06, max_iter=100, method='standard', hessian_tol=0.0001, modified_tol=1e-12, neighbors_algorithm='auto', random_state=None, n_jobs=None)[source]¶

fit(X, y=None)[source]¶

Compute the embedding vectors for data X

fit_transform(X, y=None)[source]¶

Compute the embedding vectors for data X and transform X.

get_params(deep=True)[source]¶

Get parameters for this estimator.

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.

transform(X)[source]¶

Transform new points into embedding space.

Notes

Because of scaling performed by this method, it is discouraged to use it together with methods that are not scale-invariant (like SVMs)

Examples using sklearn.manifold.LocallyLinearEmbedding¶