# Isomap¶

class ibex.sklearn.manifold.Isomap(n_neighbors=5, n_components=2, eigen_solver='auto', tol=0, max_iter=None, path_method='auto', neighbors_algorithm='auto', n_jobs=1)

Bases: sklearn.manifold.isomap.Isomap, ibex._base.FrameMixin

Note

The documentation following is of the class wrapped by this class. There are some changes, in particular:

Isomap Embedding

Non-linear dimensionality reduction through Isometric Mapping

Read more in the User Guide.

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, optional (default = 1)
The number of parallel jobs to run. If -1, then the number of jobs is set to the number of CPU cores.
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.
 [1] Tenenbaum, J.B.; De Silva, V.; & Langford, J.C. A global geometric framework for nonlinear dimensionality reduction. Science 290 (5500)
fit(X, y=None)[source]

Note

The documentation following is of the class wrapped by this class. There are some changes, in particular:

Compute the embedding vectors for data X

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.

y: Ignored.

self : returns an instance of self.

fit_transform(X, y=None)[source]

Note

The documentation following is of the class wrapped by this class. There are some changes, in particular:

Fit the model from data in X and transform X.

X : {array-like, sparse matrix, BallTree, KDTree}
Training vector, where n_samples in the number of samples and n_features is the number of features.

y: Ignored.

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

transform(X)[source]

Note

The documentation following is of the class wrapped by this class. There are some changes, in particular:

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.

X : array-like, shape (n_samples, n_features)

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