Gradient Maps¶
Gradients¶
GradientMaps([n_components, approach, …]) |
Gradient maps. |
Embedding¶
Embedding([n_components]) |
Base class for embedding approaches. |
DiffusionMaps([n_components, alpha, …]) |
Diffusion maps. |
LaplacianEigenmaps([n_components, …]) |
Laplacian eigenmaps. |
PCAMaps([n_components, random_state]) |
Principal component analysis. |
diffusion_mapping(adj[, n_components, …]) |
Compute diffusion map of affinity matrix. |
laplacian_eigenmaps(adj[, n_components, …]) |
Compute embedding using Laplacian eigenmaps. |
Alignment¶
ProcrustesAlignment([n_iter, tol, verbose]) |
Iterative alignment using generalized procrustes analysis. |
procrustes_alignment(data[, reference, …]) |
Iterative alignment using generalized procrustes analysis. |
procrustes(source, target[, center, scale]) |
Align source to target using procrustes analysis. |
Kernels¶
compute_affinity(x[, kernel, sparsity, …]) |
Compute affinity matrix. |
Utility functions¶
dominant_set(s, k[, is_thresh, norm, copy, …]) |
Keep the largest elements for each row. |
is_symmetric(x[, tol]) |
Check if input is symmetric. |
make_symmetric(x[, check, tol, copy, …]) |
Make array symmetric. |