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sklearn.decomposition.dict_learning

sklearn.decomposition.dict_learning(X, n_components, alpha, max_iter=100, tol=1e-08, method=’lars’, n_jobs=None, dict_init=None, code_init=None, callback=None, verbose=False, random_state=None, return_n_iter=False, positive_dict=False, positive_code=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 or None, optional (default=None)` Number of parallel jobs to run. `None` means 1 unless in a `joblib.parallel_backend` context. `-1` means using all processors. See Glossary for more details. `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. `positive_dict : bool` Whether to enforce positivity when finding the dictionary. New in version 0.20. `positive_code : bool` Whether to enforce positivity when finding the code. New in version 0.20.
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 or None, optional (default=None): Number of parallel jobs to run. None means 1 unless in a joblib.parallel_backend context. -1 means using all processors. See Glossary for more details.
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.
positive_dict : bool: Whether to enforce positivity when finding the dictionary.

New in version 0.20.
positive_code : bool: Whether to enforce positivity when finding the code.

New in version 0.20.

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.