package sklearn
Module Manifold.LocallyLinearEmbeddingSource
Source
val create :
?n_neighbors:int ->
?n_components:int ->
?reg:float ->
?eigen_solver:[ `Auto | `Arpack | `Dense ] ->
?tol:float ->
?max_iter:int ->
?method_:Py.Object.t ->
?hessian_tol:float ->
?modified_tol:float ->
?neighbors_algorithm:[ `Auto | `Brute | `Kd_tree | `Ball_tree ] ->
?random_state:int ->
?n_jobs:int ->
unit ->
tLocally Linear Embedding
Read more in the :ref:`User Guide <locally_linear_embedding>`.
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) / 2`` see 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, default=None Determines the random number generator when ``eigen_solver`` == 'arpack'. Pass an int for reproducible results across multiple function calls. See :term: `Glossary <random_state>`.
n_jobs : int or None, optional (default=None) The number of parallel jobs to run. ``None`` means 1 unless in a :obj:`joblib.parallel_backend` context. ``-1`` means using all processors. See :term:`Glossary <n_jobs>` for 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.
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)
References ----------
.. 1 Roweis, S. & Saul, L. Nonlinear dimensionality reduction by locally linear embedding. Science 290:2323 (2000). .. 2 Donoho, D. & Grimes, C. Hessian eigenmaps: Locally linear embedding techniques for high-dimensional data. Proc Natl Acad Sci U S A. 100:5591 (2003). .. 3 Zhang, Z. & Wang, J. MLLE: Modified Locally Linear Embedding Using Multiple Weights. http://citeseerx.ist.psu.edu/viewdoc/summary?doi=10.1.1.70.382 .. 4 Zhang, Z. & Zha, H. Principal manifolds and nonlinear dimensionality reduction via tangent space alignment. Journal of Shanghai Univ. 8:406 (2004)
Compute the embedding vectors for data X
Parameters ---------- X : array-like of shape n_samples, n_features training set.
y : Ignored
Returns ------- self : returns an instance of self.
Source
val fit_transform :
?y:Py.Object.t ->
x:[> `ArrayLike ] Np.Obj.t ->
[> tag ] Obj.t ->
[> `ArrayLike ] Np.Obj.tCompute the embedding vectors for data X and transform X.
Parameters ---------- X : array-like of shape n_samples, n_features training set.
y : Ignored
Returns ------- X_new : array-like, shape (n_samples, n_components)
Get parameters for this estimator.
Parameters ---------- deep : bool, default=True 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 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.
Parameters ---------- **params : dict Estimator parameters.
Returns ------- self : object Estimator instance.
Transform new points into embedding space.
Parameters ---------- X : array-like of shape (n_samples, n_features)
Returns ------- X_new : array, shape = n_samples, n_components
Notes ----- Because of scaling performed by this method, it is discouraged to use it together with methods that are not scale-invariant (like SVMs)
Attribute embedding_: get value or raise Not_found if None.
Attribute embedding_: get value as an option.
Attribute reconstruction_error_: get value or raise Not_found if None.
Attribute reconstruction_error_: get value as an option.
Attribute nbrs_: get value or raise Not_found if None.
Attribute nbrs_: get value as an option.
Pretty-print the object to a formatter.