11.7.1. astroML.dimensionality.iterative_pca¶

astroML.dimensionality.
iterative_pca
(X, M, n_ev=5, n_iter=15, norm=None, full_output=False)[source]¶  Parameters
 X: ndarray, shape = (n_samples, n_features)
input data
 M: ndarray, bool, shape = (n_samples, n_features)
mask for input data. where mask == True, the spectrum is unconstrained
 n_ev: int
number of eigenvectors to use in reconstructing masked regions
 n_iter: int
number of iterations to find eigenvectors
 norm: string
what type of normalization to use on the data. Options are  None : no normalization  ‘L1’ : L1norm  ‘L2’ : L2norm
 full_output: boolean (optional)
if False (default) return only the reconstructed data X_recons if True, return the full information (see below)
 Returns
 X_recons: ndarray, shape = (n_samples, n_features)
data with masked regions reconstructed
 mu: ndarray, shape = (n_features,)
mean of data
 evecs: ndarray, shape = (min(n_samples, n_features), n_features)
eigenvectors of the reconstructed data
 evals: ndarray, size = min(n_samples, n_features)
eigenvalues of the reconstructed data
 norms: ndarray, size = n_samples
normalization of each input
 coeffs: ndarray, size = (n_samples, n_ev)
coefficients used to reconstruct X