sklearn.decomposition.dict_learning¶
- 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)¶
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_componentswhere V is the dictionary and U is the sparse code.
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 that gets invoked every five iterations.
verbose: :
Degree of output the procedure will print.
random_state: int or RandomState :
Pseudo number generator state used for random sampling.
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.