sklearn.decomposition.dict_learning — scikit-learn 0.20.4 documentation (original) (raw)

sklearn.decomposition. dict_learning(X, n_components, alpha, max_iter=100, tol=1e-08, method='lars', n_jobs=None, dict_init=None, code_init=None, callback=None, verbose=False, random_state=None, return_n_iter=False, positive_dict=False, positive_code=False)[source]¶

Solves a dictionary learning matrix factorization problem.

Finds the best dictionary and the corresponding sparse code for approximating the data matrix X by solving:

(U^*, V^*) = argmin 0.5 || X - U V ||_2^2 + alpha * || U ||_1 (U,V) with || V_k ||_2 = 1 for all 0 <= k < n_components

where V is the dictionary and U is the sparse code.

Read more in the User Guide.

Parameters:	X : array of shape (n_samples, n_features) Data matrix. n_components : int, Number of dictionary atoms to extract. alpha : int, Sparsity controlling parameter. max_iter : int, Maximum number of iterations to perform. tol : float, Tolerance for the stopping condition. method : {‘lars’, ‘cd’} lars: uses the least angle regression method to solve the lasso problem (linear_model.lars_path) cd: uses the coordinate descent method to compute the Lasso solution (linear_model.Lasso). Lars will be faster if the estimated components are sparse. n_jobs : int or None, optional (default=None) Number of parallel jobs to run.None means 1 unless in a joblib.parallel_backend context.-1 means using all processors. See Glossaryfor more details. dict_init : array of shape (n_components, n_features), Initial value for the dictionary for warm restart scenarios. code_init : array of shape (n_samples, n_components), Initial value for the sparse code for warm restart scenarios. callback : callable or None, optional (default: None) Callable that gets invoked every five iterations verbose : bool, optional (default: False) To control the verbosity of the procedure. random_state : int, RandomState instance or None, optional (default=None) If int, random_state is the seed used by the random number generator; If RandomState instance, random_state is the random number generator; If None, the random number generator is the RandomState instance used by np.random. return_n_iter : bool Whether or not to return the number of iterations. positive_dict : bool Whether to enforce positivity when finding the dictionary. New in version 0.20. positive_code : bool Whether to enforce positivity when finding the code. New in version 0.20.
Returns:	code : array of shape (n_samples, n_components) The sparse code factor in the matrix factorization. dictionary : array of shape (n_components, n_features), The dictionary factor in the matrix factorization. errors : array Vector of errors at each iteration. n_iter : int Number of iterations run. Returned only if return_n_iter is set to True.

Parameters:

X : array of shape (n_samples, n_features) Data matrix. n_components : int, Number of dictionary atoms to extract. alpha : int, Sparsity controlling parameter. max_iter : int, Maximum number of iterations to perform. tol : float, Tolerance for the stopping condition. method : {‘lars’, ‘cd’} lars: uses the least angle regression method to solve the lasso problem (linear_model.lars_path) cd: uses the coordinate descent method to compute the Lasso solution (linear_model.Lasso). Lars will be faster if the estimated components are sparse. n_jobs : int or None, optional (default=None) Number of parallel jobs to run.None means 1 unless in a joblib.parallel_backend context.-1 means using all processors. See Glossaryfor more details. dict_init : array of shape (n_components, n_features), Initial value for the dictionary for warm restart scenarios. code_init : array of shape (n_samples, n_components), Initial value for the sparse code for warm restart scenarios. callback : callable or None, optional (default: None) Callable that gets invoked every five iterations verbose : bool, optional (default: False) To control the verbosity of the procedure. random_state : int, RandomState instance or None, optional (default=None) If int, random_state is the seed used by the random number generator; If RandomState instance, random_state is the random number generator; If None, the random number generator is the RandomState instance used by np.random. return_n_iter : bool Whether or not to return the number of iterations. positive_dict : bool Whether to enforce positivity when finding the dictionary. New in version 0.20. positive_code : bool Whether to enforce positivity when finding the code. New in version 0.20.

Returns:

code : array of shape (n_samples, n_components) The sparse code factor in the matrix factorization. dictionary : array of shape (n_components, n_features), The dictionary factor in the matrix factorization. errors : array Vector of errors at each iteration. n_iter : int Number of iterations run. Returned only if return_n_iter is set to True.