sklearn.decomposition.dict_learning¶
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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 ||_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
- Xarray of shape (n_samples, n_features)
Data matrix.
- n_componentsint,
Number of dictionary atoms to extract.
- alphaint,
Sparsity controlling parameter.
- max_iterint,
Maximum number of iterations to perform.
- tolfloat,
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_jobsint or None, optional (default=None)
Number of parallel jobs to run.
Nonemeans 1 unless in ajoblib.parallel_backendcontext.-1means using all processors. See Glossary for more details.- dict_initarray of shape (n_components, n_features),
Initial value for the dictionary for warm restart scenarios.
- code_initarray of shape (n_samples, n_components),
Initial value for the sparse code for warm restart scenarios.
- callbackcallable or None, optional (default: None)
Callable that gets invoked every five iterations
- verbosebool, optional (default: False)
To control the verbosity of the procedure.
- random_stateint, 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_iterbool
Whether or not to return the number of iterations.
- positive_dictbool
Whether to enforce positivity when finding the dictionary.
New in version 0.20.
- positive_codebool
Whether to enforce positivity when finding the code.
New in version 0.20.
- method_max_iterint, optional (default=1000)
Maximum number of iterations to perform.
New in version 0.22.
- Returns
- codearray of shape (n_samples, n_components)
The sparse code factor in the matrix factorization.
- dictionaryarray 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_iteris set to True.