W3cubDocs

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, 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:

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.

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.