val of_pyobject : Py.Object.t -> tval to_pyobject : t -> Py.Object.tval create :
?n_components:int ->
?affinity:[ `String of string | `Callable of Py.Object.t ] ->
?gamma:float ->
?random_state:[ `Int of int | `RandomState of Py.Object.t | `None ] ->
?eigen_solver:[ `Arpack | `Lobpcg | `Amg | `None ] ->
?n_neighbors:int ->
?n_jobs:[ `Int of int | `None ] ->
unit ->
tSpectral 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 :ref:`User Guide <spectral_embedding>`.
Parameters ---------- n_components : integer, default: 2 The dimension of the projected subspace.
affinity : string or callable, default : "nearest_neighbors" How to construct the affinity matrix.
gamma : float, optional, default : 1/n_features Kernel coefficient for rbf kernel.
random_state : int, RandomState instance or None, optional, default: None A pseudo random number generator used for the initialization of the lobpcg eigenvectors. 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 ``solver`` == 'amg'.
eigen_solver : None, 'arpack', 'lobpcg', or 'amg' The eigenvalue decomposition strategy to use. AMG requires pyamg to be installed. It can be faster on very large, sparse problems.
n_neighbors : int, default : max(n_samples/10 , 1) Number of nearest neighbors for nearest_neighbors graph building.
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, shape = (n_samples, n_components) Spectral embedding of the training matrix.
affinity_matrix_ : array, shape = (n_samples, n_samples) Affinity_matrix constructed from samples or precomputed.
n_neighbors_ : int Number of nearest neighbors effectively used.
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)
References ----------
val fit :
?y:Py.Object.t ->
x:[ `Ndarray of Ndarray.t | `SparseMatrix of Csr_matrix.t ] ->
t ->
tFit the model from data in X.
Parameters ---------- X : array-like, sparse matrix, 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.
Returns ------- self : object Returns the instance itself.
val fit_transform :
?y:Py.Object.t ->
x:[ `Ndarray of Ndarray.t | `SparseMatrix of Csr_matrix.t ] ->
t ->
Ndarray.tFit the model from data in X and transform X.
Parameters ---------- X : array-like, sparse matrix, 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.
Returns ------- X_new : array-like, shape (n_samples, n_components)
val get_params : ?deep:bool -> t -> Py.Object.tGet 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 -> t -> tSet 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 n_neighbors_ : t -> intAttribute n_neighbors_: see constructor for documentation
val to_string : t -> stringPrint the object to a human-readable representation.
val show : t -> stringPrint the object to a human-readable representation.
val pp : Format.formatter -> t -> unitPretty-print the object to a formatter.