brainspace.gradient.embedding.DiffusionMaps

class brainspace.gradient.embedding.DiffusionMaps(n_components=10, alpha=0.5, diffusion_time=0, random_state=None)[source]

Diffusion maps.

Parameters:
  • n_components (int or None, optional) – Number of eigenvectors. Default is 10.
  • alpha (float, optional) – Anisotropic diffusion parameter, 0 <= alpha <= 1. Default is 0.5.
  • diffusion_time (int, optional) – Diffusion time or scale. If diffusion_time == 0 use multi-scale diffusion maps. Default is 0.
  • random_state (int or None, optional) – Random state. Default is None.
Variables:
  • lambdas (1D ndarray, shape (n_components,)) – Eigenvalues of the affinity matrix in descending order.
  • maps (2D ndarray, shape (n, n_components)) – Eigenvectors of the affinity matrix in same order. Where n is the number of rows of the affinity matrix.

See also

LaplacianEigenmaps, PCAMaps

References

  • Coifman, R.R.; S. Lafon. (2006). “Diffusion maps”. Applied and Computational Harmonic Analysis 21: 5-30. doi:10.1016/j.acha.2006.04.006
  • Joseph W.R., Peter E.F., Ann B.L., Chad M.S. Accurate parameter estimation for star formation history in galaxies using SDSS spectra.
__init__(n_components=10, alpha=0.5, diffusion_time=0, random_state=None)[source]

Initialize self. See help(type(self)) for accurate signature.

Methods

__init__([n_components, alpha, …]) Initialize self.
fit(affinity) Compute the diffusion maps.
fit_transform(x) Compute embedding for x.
get_params([deep]) Get parameters for this estimator.
set_params(**params) Set the parameters of this estimator.
fit(affinity)[source]

Compute the diffusion maps.

Parameters:affinity (ndarray or sparse matrix, shape = (n, n)) – Affinity matrix.
Returns:self (object) – Returns self.
fit_transform(x)

Compute embedding for x.

Parameters:x (ndarray, shape = (n, n)) – Input matrix.
Returns:embedding (ndarray, shape(n, n_components)) – Embedded data.