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

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:

Read more in the User Guide.

Parameters:	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 overridesn_nonzero_coefs. copy_cov : boolean, optional Whether to copy the precomputed covariance matrix; if False, it may be overwritten. init : array of shape (n_samples, n_components) Initialization value of the sparse codes. Only used ifalgorithm=’lasso_cd’. max_iter : int, 1000 by default Maximum number of iterations to perform if algorithm=’lasso_cd’. 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. 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. positive : boolean, optional Whether to enforce positivity when finding the encoding. New in version 0.20.
Returns:	code : array of shape (n_samples, n_components) The sparse codes

Parameters:

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 overridesn_nonzero_coefs. copy_cov : boolean, optional Whether to copy the precomputed covariance matrix; if False, it may be overwritten. init : array of shape (n_samples, n_components) Initialization value of the sparse codes. Only used ifalgorithm=’lasso_cd’. max_iter : int, 1000 by default Maximum number of iterations to perform if algorithm=’lasso_cd’. 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. 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. positive : boolean, optional Whether to enforce positivity when finding the encoding. New in version 0.20.

Returns:

code : array of shape (n_samples, n_components) The sparse codes