val of_pyobject : Py.Object.t -> tval to_pyobject : t -> Py.Object.tval create :
?n_neighbors:int ->
?n_components:int ->
?eigen_solver:[ `Auto | `Arpack | `Dense ] ->
?tol:float ->
?max_iter:int ->
?path_method:[ `Auto | `FW | `D ] ->
?neighbors_algorithm:[ `Auto | `Brute | `Kd_tree | `Ball_tree ] ->
?n_jobs:[ `Int of int | `None ] ->
?metric:[ `String of string | `Callable of Py.Object.t ] ->
?p:int ->
?metric_params:Py.Object.t ->
unit ->
tIsomap Embedding
Non-linear dimensionality reduction through Isometric Mapping
Read more in the :ref:`User Guide <isomap>`.
Parameters ---------- n_neighbors : integer number of neighbors to consider for each point.
n_components : integer number of coordinates for the manifold
eigen_solver : 'auto'|'arpack'|'dense' 'auto' : Attempt to choose the most efficient solver for the given problem.
'arpack' : Use Arnoldi decomposition to find the eigenvalues and eigenvectors.
'dense' : Use a direct solver (i.e. LAPACK) for the eigenvalue decomposition.
tol : float Convergence tolerance passed to arpack or lobpcg. not used if eigen_solver == 'dense'.
max_iter : integer Maximum number of iterations for the arpack solver. not used if eigen_solver == 'dense'.
path_method : string 'auto'|'FW'|'D' Method to use in finding shortest path.
'auto' : attempt to choose the best algorithm automatically.
'FW' : Floyd-Warshall algorithm.
'D' : Dijkstra's algorithm.
neighbors_algorithm : string 'auto'|'brute'|'kd_tree'|'ball_tree' Algorithm to use for nearest neighbors search, passed to neighbors.NearestNeighbors instance.
n_jobs : int or None, 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.
metric : string, or callable, default="minkowski" The metric to use when calculating distance between instances in a feature array. If metric is a string or callable, it must be one of the options allowed by :func:`sklearn.metrics.pairwise_distances` for its metric parameter. If metric is "precomputed", X is assumed to be a distance matrix and must be square. X may be a :term:`Glossary <sparse graph>`.
.. versionadded:: 0.22
p : int, default=2 Parameter for the Minkowski metric from sklearn.metrics.pairwise.pairwise_distances. When p = 1, this is equivalent to using manhattan_distance (l1), and euclidean_distance (l2) for p = 2. For arbitrary p, minkowski_distance (l_p) is used.
.. versionadded:: 0.22
metric_params : dict, default=None Additional keyword arguments for the metric function.
.. versionadded:: 0.22
Attributes ---------- embedding_ : array-like, shape (n_samples, n_components) Stores the embedding vectors.
kernel_pca_ : object :class:`~sklearn.decomposition.KernelPCA` object used to implement the embedding.
nbrs_ : sklearn.neighbors.NearestNeighbors instance Stores nearest neighbors instance, including BallTree or KDtree if applicable.
dist_matrix_ : array-like, shape (n_samples, n_samples) Stores the geodesic distance matrix of training data.
Examples -------- >>> from sklearn.datasets import load_digits >>> from sklearn.manifold import Isomap >>> X, _ = load_digits(return_X_y=True) >>> X.shape (1797, 64) >>> embedding = Isomap(n_components=2) >>> X_transformed = embedding.fit_transform(X:100) >>> X_transformed.shape (100, 2)
References ----------
.. 1 Tenenbaum, J.B.; De Silva, V.; & Langford, J.C. A global geometric framework for nonlinear dimensionality reduction. Science 290 (5500)
val fit :
?y:Py.Object.t ->
x:[ `Ndarray of Ndarray.t | `PyObject of Py.Object.t ] ->
t ->
tCompute the embedding vectors for data X
Parameters ---------- X : array-like, sparse graph, BallTree, KDTree, NearestNeighbors Sample data, shape = (n_samples, n_features), in the form of a numpy array, sparse graph, precomputed tree, or NearestNeighbors object.
y : Ignored
Returns ------- self : returns an instance of self.
val fit_transform :
?y:Py.Object.t ->
x:[ `Ndarray of Ndarray.t | `PyObject of Py.Object.t ] ->
t ->
Ndarray.tFit the model from data in X and transform X.
Parameters ---------- X : array-like, sparse graph, BallTree, KDTree Training vector, where n_samples in the number of samples and n_features is the number of features.
y : Ignored
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 reconstruction_error : t -> floatCompute the reconstruction error for the embedding.
Returns ------- reconstruction_error : float
Notes ----- The cost function of an isomap embedding is
``E = frobenius_normK(D) - K(D_fit) / n_samples``
Where D is the matrix of distances for the input data X, D_fit is the matrix of distances for the output embedding X_fit, and K is the isomap kernel:
``K(D) = -0.5 * (I - 1/n_samples) * D^2 * (I - 1/n_samples)``
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.
Transform X.
This is implemented by linking the points X into the graph of geodesic distances of the training data. First the `n_neighbors` nearest neighbors of X are found in the training data, and from these the shortest geodesic distances from each point in X to each point in the training data are computed in order to construct the kernel. The embedding of X is the projection of this kernel onto the embedding vectors of the training set.
Parameters ---------- X : array-like, shape (n_queries, n_features) If neighbors_algorithm='precomputed', X is assumed to be a distance matrix or a sparse graph of shape (n_queries, n_samples_fit).
Returns ------- X_new : array-like, shape (n_queries, n_components)
val kernel_pca_ : t -> Py.Object.tAttribute kernel_pca_: see constructor for documentation
val nbrs_ : t -> Py.Object.tAttribute nbrs_: 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.