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=1, dict_init=None, code_init=None, callback=None, verbose=False, random_state=None, return_n_iter=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,
Number of parallel jobs to run, or -1 to autodetect.
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.
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.