9.10.15. sklearn.decomposition.sparse_encode¶
- sklearn.decomposition.sparse_encode(X, Y, gram=None, cov=None, algorithm='lasso_lars', n_nonzero_coefs=None, alpha=None, overwrite_gram=False, overwrite_cov=False, init=None)¶
Generic sparse coding
Each column of the result is the solution to a Lasso problem.
Parameters : X: array of shape (n_samples, n_components) :
Dictionary against which to optimize the sparse code.
Y: array of shape (n_samples, n_features) :
Data matrix.
gram: array, shape=(n_components, n_components) :
Precomputed Gram matrix, X^T * X
cov: array, shape=(n_components, n_features) :
Precomputed covariance, X^T * Y
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 X.T * Y
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=’threhold’, 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_components, n_features) :
Initialization value of the sparse codes. Only used if algorithm=’lasso_cd’.
overwrite_gram: boolean, :
Whether to overwrite the precomputed Gram matrix.
overwrite_cov: boolean, :
Whether to overwrite the precomputed covariance matrix.
Returns : code: array of shape (n_components, n_features) :
The sparse codes
See also
linear_model.lars_path, linear_model.orthogonal_mp, linear_model.Lasso