package sklearn

  1. Overview
  2. Docs
package sklearn
Legend:
Library
Module
Module type
Parameter
Class
Class type
type tag = [
  1. | `LocallyLinearEmbedding
]
type t = [ `BaseEstimator | `LocallyLinearEmbedding | `Object | `TransformerMixin ] Obj.t
val of_pyobject : Py.Object.t -> t
val to_pyobject : [> tag ] Obj.t -> Py.Object.t
val as_transformer : t -> [ `TransformerMixin ] Obj.t
val as_estimator : t -> [ `BaseEstimator ] Obj.t
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 -> t

Locally 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)

val fit : ?y:Py.Object.t -> x:[> `ArrayLike ] Np.Obj.t -> [> tag ] Obj.t -> t

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.

val fit_transform : ?y:Py.Object.t -> x:[> `ArrayLike ] Np.Obj.t -> [> tag ] Obj.t -> [> `ArrayLike ] Np.Obj.t

Compute 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)

val get_params : ?deep:bool -> [> tag ] Obj.t -> Dict.t

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.

val set_params : ?params:(string * Py.Object.t) list -> [> tag ] Obj.t -> t

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.

val transform : x:[> `ArrayLike ] Np.Obj.t -> [> tag ] Obj.t -> [> `ArrayLike ] Np.Obj.t

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)

val embedding_ : t -> [> `ArrayLike ] Np.Obj.t

Attribute embedding_: get value or raise Not_found if None.

val embedding_opt : t -> [> `ArrayLike ] Np.Obj.t option

Attribute embedding_: get value as an option.

val reconstruction_error_ : t -> float

Attribute reconstruction_error_: get value or raise Not_found if None.

val reconstruction_error_opt : t -> float option

Attribute reconstruction_error_: get value as an option.

val nbrs_ : t -> Py.Object.t

Attribute nbrs_: get value or raise Not_found if None.

val nbrs_opt : t -> Py.Object.t option

Attribute nbrs_: get value as an option.

val to_string : t -> string

Print the object to a human-readable representation.

val show : t -> string

Print the object to a human-readable representation.

val pp : Stdlib.Format.formatter -> t -> unit

Pretty-print the object to a formatter.