sklearn.decomposition
.dict_learning¶
- 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, method_max_iter=1000)[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 ||_Fro^2 + alpha * || U ||_1,1 (U,V) with || V_k ||_2 = 1 for all 0 <= k < n_components
where V is the dictionary and U is the sparse code. ||.||_Fro stands for the Frobenius norm and ||.||_1,1 stands for the entry-wise matrix norm which is the sum of the absolute values of all the entries in the matrix.
Read more in the User Guide.
- Parameters
- Xndarray of shape (n_samples, n_features)
Data matrix.
- n_componentsint
Number of dictionary atoms to extract.
- alphaint
Sparsity controlling parameter.
- max_iterint, default=100
Maximum number of iterations to perform.
- tolfloat, default=1e-8
Tolerance for the stopping condition.
- method{‘lars’, ‘cd’}, default=’lars’
The method used:
'lars'
: uses the least angle regression method to solve the lassoproblem (
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_jobsint, default=None
Number of parallel jobs to run.
None
means 1 unless in ajoblib.parallel_backend
context.-1
means using all processors. See Glossary for more details.- dict_initndarray of shape (n_components, n_features), default=None
Initial value for the dictionary for warm restart scenarios. Only used if
code_init
anddict_init
are not None.- code_initndarray of shape (n_samples, n_components), default=None
Initial value for the sparse code for warm restart scenarios. Only used if
code_init
anddict_init
are not None.- callbackcallable, default=None
Callable that gets invoked every five iterations
- verbosebool, default=False
To control the verbosity of the procedure.
- random_stateint, RandomState instance or None, default=None
Used for randomly initializing the dictionary. Pass an int for reproducible results across multiple function calls. See Glossary.
- return_n_iterbool, default=False
Whether or not to return the number of iterations.
- positive_dictbool, default=False
Whether to enforce positivity when finding the dictionary.
New in version 0.20.
- positive_codebool, default=False
Whether to enforce positivity when finding the code.
New in version 0.20.
- method_max_iterint, default=1000
Maximum number of iterations to perform.
New in version 0.22.
- Returns
- codendarray of shape (n_samples, n_components)
The sparse code factor in the matrix factorization.
- dictionaryndarray of shape (n_components, n_features),
The dictionary factor in the matrix factorization.
- errorsarray
Vector of errors at each iteration.
- n_iterint
Number of iterations run. Returned only if
return_n_iter
is set to True.