sklearn.decomposition.dict_learning_online¶
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sklearn.decomposition.dict_learning_online(X, n_components=2, alpha=1, n_iter=100, return_code=True, dict_init=None, callback=None, batch_size=3, verbose=False, shuffle=True, n_jobs=None, method='lars', iter_offset=0, random_state=None, return_inner_stats=False, inner_stats=None, return_n_iter=False, positive_dict=False, positive_code=False, method_max_iter=1000)[source]¶ Solves a dictionary learning matrix factorization problem online.
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. This is accomplished by repeatedly iterating over mini-batches by slicing the input data.
Read more in the User Guide.
- Parameters
- Xarray of shape (n_samples, n_features)
Data matrix.
- n_componentsint,
Number of dictionary atoms to extract.
- alphafloat,
Sparsity controlling parameter.
- n_iterint,
Number of mini-batch iterations to perform.
- return_codeboolean,
Whether to also return the code U or just the dictionary V.
- dict_initarray of shape (n_components, n_features),
Initial value for the dictionary for warm restart scenarios.
- callbackcallable or None, optional (default: None)
callable that gets invoked every five iterations
- batch_sizeint,
The number of samples to take in each batch.
- verbosebool, optional (default: False)
To control the verbosity of the procedure.
- shuffleboolean,
Whether to shuffle the data before splitting it in batches.
- 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.- 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.
- iter_offsetint, default 0
Number of previous iterations completed on the dictionary used for initialization.
- 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_inner_statsboolean, optional
Return the inner statistics A (dictionary covariance) and B (data approximation). Useful to restart the algorithm in an online setting. If return_inner_stats is True, return_code is ignored
- inner_statstuple of (A, B) ndarrays
Inner sufficient statistics that are kept by the algorithm. Passing them at initialization is useful in online settings, to avoid loosing the history of the evolution. A (n_components, n_components) is the dictionary covariance matrix. B (n_features, n_components) is the data approximation matrix
- 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 when solving the lasso problem.
New in version 0.22.
- Returns
- codearray of shape (n_samples, n_components),
the sparse code (only returned if
return_code=True)- dictionaryarray of shape (n_components, n_features),
the solutions to the dictionary learning problem
- n_iterint
Number of iterations run. Returned only if
return_n_iteris set toTrue.