sklearn.decomposition.sparse_encode()

X: array of shape (n_samples, n_features) :

Data matrix

dictionary: array of shape (n_components, n_features) :

The dictionary matrix against which to solve the sparse coding of the data. Some of the algorithms assume normalized rows for meaningful output.

gram: array, shape=(n_components, n_components) :

Precomputed Gram matrix, dictionary * dictionary?

cov: array, shape=(n_components, n_samples) :

Precomputed covariance, dictionary? * X

algorithm: {?lasso_lars?, ?lasso_cd?, ?lars?, ?omp?, ?threshold?} :

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?

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.

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 overrides n_nonzero_coefs.

init: array of shape (n_samples, n_components) :

Initialization value of the sparse codes. Only used if algorithm=?lasso_cd?.

max_iter: int, 1000 by default :

Maximum number of iterations to perform if algorithm=?lasso_cd?.

copy_cov: boolean, optional :

Whether to copy the precomputed covariance matrix; if False, it may be overwritten.

n_jobs: int, optional :

Number of parallel jobs to run.

check_input: boolean, optional :

If False, the input arrays X and dictionary will not be checked.

verbose : int, optional

Controls the verbosity; the higher, the more messages. Defaults to 0.