sklearn.decomposition.dict_learning_online¶

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=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_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.

Parameters :

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. 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_components, n_features), Initial value for the dictionary for warm restart scenarios. callback : : Callable that gets invoked every five iterations. batch_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_components), the sparse code (only returned if return_code=True) dictionary : array of shape (n_components, n_features), the solutions to the dictionary learning problem

X: array of shape (n_samples, n_features) :

Data matrix.

n_components : 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_components, n_features),

Initial value for the dictionary for warm restart scenarios.

callback : :

Callable that gets invoked every five iterations.

batch_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_components),

the sparse code (only returned if return_code=True)

dictionary : array of shape (n_components, n_features),

the solutions to the dictionary learning problem