Note
This page is a reference documentation. It only explains the class signature, and not how to use it. Please refer to the user guide for the big picture.
Sparse inverse covariance w/ crossvalidated choice of the parameter.
A crossvalidated value for the regularization parameter is first determined using several calls to group_sparse_covariance. Then a final optimization is run to get a value for the precision matrices, using the selected value of the parameter. Different values of tolerance and of maximum iteration number can be used in these two phases (see the tol and tol_cv keyword below for example).
Parameters:  alphas : integer
n_refinements : integer
cv : integer
tol_cv : float
max_iter_cv : integer
tol : float
max_iter : integer
verbose : integer
n_jobs : integer
debug : bool
early_stopping : bool


Notes
The search for the optimal penalization parameter (alpha) is done on an iteratively refined grid: first the crossvalidated scores on a grid are computed, then a new refined grid is centered around the maximum, and so on.
Attributes
covariances_  (numpy.ndarray, shape (n_features, n_features, n_subjects)) covariance matrices, one per subject. 
precisions_  (numpy.ndarray, shape (n_features, n_features, n_subjects)) precision matrices, one per subject. All matrices have the same sparsity pattern (if a coefficient is zero for a given matrix, it is also zero for every other.) 
alpha_  (float) penalization parameter value selected. 
cv_alphas_  (list of floats) all values of the penalization parameter explored. 
cv_scores_  (numpy.ndarray, shape (n_alphas, n_folds)) scores obtained on test set for each value of the penalization parameter explored. 
Compute crossvalidated groupsparse precisions.
Parameters:  subjects : list of numpy.ndarray with shapes (n_samples, n_features)


Returns:  self: GroupSparseCovarianceCV

Get parameters for this estimator.
Parameters:  deep: boolean, optional


Returns:  params : mapping of string to any

Set the parameters of this estimator.
The method works on simple estimators as well as on nested objects (such as pipelines). The former have parameters of the form <component>__<parameter> so that it’s possible to update each component of a nested object.
Returns:  self 
