 sklearn.linear_model.lars_path_gram   sklearn.linear_model.lars_path_gram(Xy, Gram, *, n_samples, max_iter=500, alpha_min=0, method='lar', copy_X=True, eps=2.220446049250313e-16, copy_Gram=True, verbose=0, return_path=True, return_n_iter=False, positive=False)[source]
 
lars_path in the sufficient stats mode [1] The optimization objective for the case method=’lasso’ is: (1 / (2 * n_samples)) * ||y - Xw||^2_2 + alpha * ||w||_1
 in the case of method=’lars’, the objective function is only known in the form of an implicit equation (see discussion in [1]) Read more in the User Guide.  Parameters:
 
 
Xyarray-like of shape (n_samples,) or (n_samples, n_targets)


Xy = np.dot(X.T, y).  
Gramarray-like of shape (n_features, n_features)


Gram = np.dot(X.T * X).  
n_samplesint or float


Equivalent size of sample.  
max_iterint, default=500


Maximum number of iterations to perform, set to infinity for no limit.  
alpha_minfloat, default=0


Minimum correlation along the path. It corresponds to the regularization parameter alpha parameter in the Lasso.  
method{‘lar’, ‘lasso’}, default=’lar’


Specifies the returned model. Select 'lar' for Least Angle Regression, 'lasso' for the Lasso.  
copy_Xbool, default=True


If False, X is overwritten.  
epsfloat, default=np.finfo(float).eps


The machine-precision regularization in the computation of the Cholesky diagonal factors. Increase this for very ill-conditioned systems. Unlike the tol parameter in some iterative optimization-based algorithms, this parameter does not control the tolerance of the optimization.  
copy_Grambool, default=True


If False, Gram is overwritten.  
verboseint, default=0


Controls output verbosity.  
return_pathbool, default=True


If return_path==True returns the entire path, else returns only the last point of the path.  
return_n_iterbool, default=False


Whether to return the number of iterations.  
positivebool, default=False


Restrict coefficients to be >= 0. This option is only allowed with method ‘lasso’. Note that the model coefficients will not converge to the ordinary-least-squares solution for small values of alpha. Only coefficients up to the smallest alpha value (alphas_[alphas_ > 0.].min() when fit_path=True) reached by the stepwise Lars-Lasso algorithm are typically in congruence with the solution of the coordinate descent lasso_path function.    Returns:
 
 
alphasarray-like of shape (n_alphas + 1,)


Maximum of covariances (in absolute value) at each iteration. n_alphas is either max_iter, n_features or the number of nodes in the path with alpha >= alpha_min, whichever is smaller.  
activearray-like of shape (n_alphas,)


Indices of active variables at the end of the path.  
coefsarray-like of shape (n_features, n_alphas + 1)


Coefficients along the path  
n_iterint


Number of iterations run. Returned only if return_n_iter is set to True.      See also  lars_path
lasso_path
lasso_path_gram
LassoLars
Lars
LassoLarsCV
LarsCV
sklearn.decomposition.sparse_encode
  References   [1] “Least Angle Regression”, Efron et al. http://statweb.stanford.edu/~tibs/ftp/lars.pdf   [2] Wikipedia entry on the Least-angle regression   [3] Wikipedia entry on the Lasso   
 

    
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