brainspace.gradient.embedding.PCAMaps

class brainspace.gradient.embedding.PCAMaps(n_components=10, random_state=None)[source]

Principal component analysis.

Parameters:
  • n_components (int or None, optional) – Number of principal components. Default is 10.
  • random_state (int, RandomState instance or None, optional) – Random state. Default is None.
Variables:
  • lambdas (ndarray, shape (n_components,)) – Explained variance for first principal components in descending order.
  • maps (ndarray, shape (n_samples, n_components)) – Projection of input data onto the principal components.

See also

DiffusionMaps, LaplacianEigenmaps

__init__(n_components=10, random_state=None)[source]

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

Methods

__init__([n_components, random_state]) Initialize self.
fit(x) Compute PCA.
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(x)[source]

Compute PCA.

Parameters:x (ndarray, shape(n_samples, n_feat)) – Input 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.