11.7.1. astroML.dimensionality.iterative_pca¶
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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’ : L1-norm - ‘L2’ : L2-norm
- 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