8.5.15. sklearn.decomposition.dict_learning_online¶
- sklearn.decomposition.dict_learning_online(X, n_atoms, alpha, n_iter=100, return_code=True, dict_init=None, callback=None, chunk_size=3, verbose=False, shuffle=True, n_jobs=1, method='lars', iter_offset=0, random_state=None)¶
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_atomswhere V is the dictionary and U is the sparse code. This is accomplished by repeatedly iterating over mini-batches by slicing the input data.
Parameters : X: array of shape (n_samples, n_features) :
data matrix
n_atoms: int, :
number of dictionary atoms to extract
alpha: int, :
sparsity controlling parameter
n_iter: int, :
number of iterations to perform
return_code: boolean, :
whether to also return the code U or just the dictionary V
dict_init: array of shape (n_atoms, n_features), :
initial value for the dictionary for warm restart scenarios
callback: :
callable that gets invoked every five iterations
chunk_size: int, :
the number of samples to take in each batch
verbose: :
degree of output the procedure will print
shuffle: boolean, :
whether to shuffle the data before splitting it in batches
n_jobs: int, :
number of parallel jobs to run, or -1 to autodetect.
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_offset: int, default 0 :
number of previous iterations completed on the dictionary used for initialization
random_state: int or RandomState :
Pseudo number generator state used for random sampling.
Returns : code: array of shape (n_samples, n_atoms), :
the sparse code (only returned if return_code=True)
dictionary: array of shape (n_atoms, n_features), :
the solutions to the dictionary learning problem