sklearn.decomposition.DictionaryLearning — scikit-learn 0.20.4 documentation (original) (raw)

Parameters:

n_components : int,

number of dictionary elements to extract

alpha : float,

sparsity controlling parameter

max_iter : int,

maximum number of iterations to perform

tol : float,

tolerance for numerical error

fit_algorithm : {‘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.

New in version 0.17: cd coordinate descent method to improve speed.

transform_algorithm : {‘lasso_lars’, ‘lasso_cd’, ‘lars’, ‘omp’, ‘threshold’}

Algorithm used to transform the data lars: uses the least angle regression method (linear_model.lars_path) lasso_lars: uses Lars to compute the Lasso solution lasso_cd: uses the coordinate descent method to compute the Lasso solution (linear_model.Lasso). lasso_lars will be faster if the estimated components are sparse. omp: uses orthogonal matching pursuit to estimate the sparse solution threshold: squashes to zero all coefficients less than alpha from the projection dictionary * X'

New in version 0.17: lasso_cd coordinate descent method to improve speed.

transform_n_nonzero_coefs : int, 0.1 * n_features by default

Number of nonzero coefficients to target in each column of the solution. This is only used by algorithm=’lars’ and algorithm=’omp’and is overridden by alpha in the omp case.

transform_alpha : float, 1. by default

If algorithm=’lasso_lars’ or algorithm=’lasso_cd’, alpha is the penalty applied to the L1 norm. If algorithm=’threshold’, alpha is the absolute value of the threshold below which coefficients will be squashed to zero. If algorithm=’omp’, alpha is the tolerance parameter: the value of the reconstruction error targeted. In this case, it overridesn_nonzero_coefs.

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 Glossaryfor more details.

code_init : array of shape (n_samples, n_components),

initial value for the code, for warm restart

dict_init : array of shape (n_components, n_features),

initial values for the dictionary, for warm restart

verbose : bool, optional (default: False)

To control the verbosity of the procedure.

split_sign : bool, False by default

Whether to split the sparse feature vector into the concatenation of its negative part and its positive part. This can improve the performance of downstream classifiers.

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.

positive_code : bool

Whether to enforce positivity when finding the code.

New in version 0.20.

positive_dict : bool

Whether to enforce positivity when finding the dictionary

New in version 0.20.