sklearn.decomposition.sparse_encode  
sklearn.decomposition.sparse_encode(X, dictionary, *, gram=None, cov=None, algorithm='lasso_lars', n_nonzero_coefs=None, alpha=None, copy_cov=True, init=None, max_iter=1000, n_jobs=None, check_input=True, verbose=0, positive=False) [source]
 
Sparse coding Each row of the result is the solution to a sparse coding problem. The goal is to find a sparse array code such that: X ~= code * dictionary
 Read more in the User Guide.  Parameters 
 
Xndarray of shape (n_samples, n_features) 

Data matrix.  
dictionaryndarray 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.  
gramndarray of shape (n_components, n_components), default=None 

Precomputed Gram matrix, dictionary * dictionary'.  
covndarray of shape (n_components, n_samples), default=None 

Precomputed covariance, dictionary' * X.  
algorithm{‘lasso_lars’, ‘lasso_cd’, ‘lars’, ‘omp’, ‘threshold’}, default=’lasso_lars’ 

The algorithm used:  
'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 regularization from the projection dictionary * data'.   
n_nonzero_coefsint, default=None 

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. If None, then n_nonzero_coefs=int(n_features / 10).  
alphafloat, default=None 

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. If None, default to 1.  
copy_covbool, default=True 

Whether to copy the precomputed covariance matrix; if False, it may be overwritten.  
initndarray of shape (n_samples, n_components), default=None 

Initialization value of the sparse codes. Only used if algorithm='lasso_cd'.  
max_iterint, default=1000 

Maximum number of iterations to perform if algorithm='lasso_cd' or 'lasso_lars'.  
n_jobsint, 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.  
check_inputbool, default=True 

If False, the input arrays X and dictionary will not be checked.  
verboseint, default=0 

Controls the verbosity; the higher, the more messages.  
positivebool, default=False 

Whether to enforce positivity when finding the encoding.  New in version 0.20.     Returns 
 
codendarray of shape (n_samples, n_components) 

The sparse codes      See also  
sklearn.linear_model.lars_path


sklearn.linear_model.orthogonal_mp


sklearn.linear_model.Lasso


SparseCoder