Class IsomapScikitsLearnNode

Isomap Embedding

This node has been automatically generated by wrapping the ``sklearn.manifold.isomap.Isomap`` class
from the ``sklearn`` library.  The wrapped instance can be accessed
through the ``scikits_alg`` attribute.

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.

**Attributes**

``embedding_`` : array-like, shape (n_samples, n_components)
    Stores the embedding vectors.

``kernel_pca_`` : object
    `KernelPCA` object used to implement the embedding.

``training_data_`` : array-like, shape (n_samples, n_features)
    Stores the training data.

``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.

**References**


.. [1] Tenenbaum, J.B.; De Silva, V.; & Langford, J.C. A global geometric
       framework for nonlinear dimensionality reduction. Science 290 (5500)

Overrides: object.__init__

Transform X.

This node has been automatically generated by wrapping the sklearn.manifold.isomap.Isomap class from the sklearn library. The wrapped instance can be accessed through the scikits_alg attribute.

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_samples, n_features)

Returns

X_new: array-like, shape (n_samples, n_components)

Overrides: Node.execute

Overrides: Node.is_invertible

(inherited documentation)

Overrides: Node.is_trainable

Compute the embedding vectors for data X

This node has been automatically generated by wrapping the sklearn.manifold.isomap.Isomap class from the sklearn library. The wrapped instance can be accessed through the scikits_alg attribute.

Parameters

X : {array-like, sparse matrix, BallTree, KDTree, NearestNeighbors}

Sample data, shape = (n_samples, n_features), in the form of a numpy array, precomputed tree, or NearestNeighbors object.

Returns

self : returns an instance of self.

Overrides: Node.stop_training